Molecular connectivity studies in neurotransmission: a scoping review

Molecular connectivity studies in neurotransmission: a scoping review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Molecular connectivity studies in neurotransmission: a scoping review Mario Severino, Débora Elisa Peretti, Marjorie Bardiau, Carlo Cavaliere, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5498198/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-called molecular connectivity approach that captures relationships of molecular imaging measures in different brain regions. Targeting these inter-regional interactions within a neuroreceptor system may allow to better understand complex brain functions. In this article, we provide a comprehensive review of molecular connectivity studies in the field of neurotransmission. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge. Methods: A systematic search in three bibliographic databases MEDLINE, EMBASE and Scopus on July 14, 2023, was conducted. A second search was rerun on April 4, 2024. Molecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria. Results: Thirty-nine studies were included in the scoping review. Studies were categorised based on the primary neurotransmitter system being targeted: dopamine, serotonin, opioid, muscarinic, glutamate and synaptic density. The most investigated system was the dopaminergic and the most investigated disease was Parkinson’s disease (PD). Conclusions: This review highlighted the diverse applications and methodologies in molecular connectivity research, particularly for neurodegenerative diseases and psychiatric disorders. Molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings. Molecular Connectivity Molecular Imaging Neuroimaging Neurotransmission positron emission tomography Figures Figure 1 Figure 2 Figure 3 Introduction PET and SPECT to study Neurotransmission Neurotransmission is the primary process by which neurons communicate and represents a biological pillar to all functions of the central and peripheral nervous system, including sensation, movement, cognition, and, ultimately, individual behaviour [ 1 ]. Neural communication can occur through two main modalities of synaptic transmission: chemical and electrical. At chemical synapses, information is transferred via the release of neurotransmitters from one cell, which are detected by an adjacent cell [ 2 ]. In contrast, at electrical synapses, the cytoplasm of adjacent cells is directly connected by clusters of intercellular channels called gap junctions [ 3 ]. Chemical synapses are more common in the human brain [ 4 ]. Neurotransmission at these synapses depends on the interaction between molecular and electrical signals (Fig. 1 ), starting with the action potential - an electrical signal generated in the neuron's cell body - which travels down the axon towards the synapse. When the action potential reaches the axon terminal or presynaptic terminal, it triggers the opening of voltage-gated calcium channels. Calcium ions (Ca²⁺) enter the presynaptic terminal through these channels, causing synaptic vesicles to fuse with the presynaptic membrane. As a result, the neurotransmitters contained in the vesicles are released into the synaptic cleft - the space between the presynaptic and postsynaptic neurons - where they transmit the signal by binding to the receptors of the postsynaptic neuron. This binding activates or inhibits the postsynaptic neuron, affecting numerous other neurons within specific pathways that are essential for maintaining the homeostatic balance of neuronal activity and overall healthy brain function [ 5 , 6 ]. Dysregulation and alterations of neurotransmitter and neuroreceptor levels and functions, whether due to deficiency or excess, are implicated in the pathophysiology of numerous neurodegenerative conditions such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) [ 7 ], as well as psychiatric disorders like Schizophrenia and Major Depressive Disorder (MDD) [ 8 , 9 ]. Therefore, elucidating the mechanisms of neurotransmission in vivo is paramount for advancing our understanding of brain function in both health and disease states and for the development of novel pharmacological treatment. Neurotransmission can be studied in vivo using molecular imaging tools including Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) [ 10 ]. These imaging modalities are powerful tools for measuring the local concentration of diverse molecular targets with remarkable sensitivity and specificity in a non-invasive manner [ 11 ]. Molecular imaging can visualise different aspects of neurotransmission (Fig. 2 ). According to the type of radiotracer used, it is possible to quantify neurotransmitter synthesis (e.g., dopamine synthesis [ 12 ]), the concentration of synaptic vesicle density (e.g., SV2A [ 13 ]), specific neurotransmitter types (such as the vesicular monoamine transporter 2 (VMAT 2 ) [ 14 ]), the density and distribution receptors (e.g. D 2 /D 3 receptors for the dopamine system [ 15 ], or 5HT 1A /5HT 2A receptors for the serotonin system [ 16 ]), or neurotransmission transporters (e.g., DAT for dopamine [ 17 ] and SERT for the serotonin system [ 18 ]). Finally, molecular imaging can be used to measure endogenous neurotransmitter levels and their release by detecting the competitive binding between endogenous neurotransmitters and radioligands to the same neuroreceptor sites [ 19 ]. In medicine, molecular imaging has repeatedly proved to be an invaluable tool for probing neurotransmission alterations underlying many brain disorders and describing the spatiotemporal evolution of neurotransmission abnormalities throughout different stages of these diseases [ 20 ]. Some of the neurotransmission imaging methods have also translated into clinical work-up as diagnostic and monitoring biomarkers recommended by international clinical guidelines [ 21 – 23 ]. The most well-known example is 123 I-FP-CIT SPECT imaging, used to identify and stage the degeneration of dopaminergic neurons in PD [ 24 ]. Finally, molecular imaging permits the temporal modelling of disease-related neurotransmission alterations, facilitating the evaluation of disease progression and the assessment of therapeutic interventions [ 25 ]. Univariate vs Network and multivariate approaches Over the past two decades, the concept of the brain as a network has become central to neuroscience. This network-based perspective emphasizes that brain functions emerge from the interaction of distributed regions within large-scale networks [ 26 ]. While blood oxygenation level-dependent (BOLD) functional MRI (fMRI) has become the most widely used tool to study functional connectivity, due to its accessibility, cost-effectiveness, and lack of ionising radiation, it relies heavily on hemodynamic signals. Thus, this method alone cannot fully capture the complexity of brain activity, which involves both biochemical and electrical processes. In contrast, molecular imaging, which uses radiotracers to detect molecular targets with high sensitivity and specificity [ 27 ], offers more accurate biological insights that complement fMRI. Given the limitations of single-modality approaches, there is a growing recognition of the need for an integrative, multimodal framework to comprehensively understand the brain's connectome. Molecular imaging, with its unique capability to probe biochemical pathways, can play a crucial role in this integrative approach. Nonetheless, traditional analysis methods in brain PET and SPECT research mainly focus on quantifying absolute tracer binding or uptake within specific brain regions [ 28 ]. While these approaches provide valuable insights into in vivo brain structure and activity, they possess limitations that warrant consideration. Region-wise analyses rely on a priori definition of anatomical or functional areas of the brain, potentially overlooking subtle or distributed effects across the brain [ 29 ]. Parametric voxel-wise analyses, on the other hand, depend on the image resolution of the scanner, reconstruction methods, partial volume correction (PVC) and reference regions used for normalisation. Moreover, regardless of the spatial resolution of the analysis, each volume of interest is typically treated as independent, ignoring brain spatial covariance. This often necessitates strict multiple comparison corrections to prevent inflated Type I error rates, which can result in overcorrection and increased Type II errors [ 30 ]. These limitations, alongside the spread of network science into neuroimaging field, motivated the development and validation of complementary multivariate and network methodologies in molecular neuroimaging, able to capture the complex interactions among brain regions and provide a more comprehensive understanding of brain functioning and disease pathology. Multivariate methods are specifically used to address the complexities and interdependencies of neuroimaging data by simultaneously considering the interrelationships among multiple sources of information and possibly multiple volumes of interest [ 31 ]. These methods facilitate the identification of intricate patterns of brain activity and structure that remain undetectable when analysing individual variables in isolation. For example, rather than examining the activity of a single brain region, multivariate methods allow for assessing the combined activity of several regions to determine their collective contribution to a specific task or condition [ 32 ]. These methods can also identify combinations of brain activities that correspond to distinct cognitive states or differentiate between healthy and diseased brains, and between different diseases and stages. Furthermore, multivariate techniques can be employed to reduce the dimensionality of the data, extract a smaller set of key features, or integrate data from diverse sources [ 33 ]. Parallel with multivariate approaches, network-based approaches, usually constructed using pair-wise correlation between regions or voxels, have been largely employed to study brain connectivity. A common feature of these network methods is the ability to construct a mathematical representation of the brain in the form of an adjacency matrix [ 34 ]. In this representation, brain regions are modelled as nodes, while the edges between them represent the biological or statistical interactions between these regions [ 35 , 36 ]. Although most of the recent multivariate and network approach findings in neuroimaging are derived from structural and fMRI studies, in recent years these methodological advances have increasingly been applied to PET/SPECT data [ 37 ]. This paradigm shift follows a decade of evidence suggesting that neurotransmission and molecular pathological alterations underlying brain diseases invariably pass through large-scale brain networks [ 38 , 39 ]. In this context, molecular connectivity refers to an approach that leverages molecular imaging to explore brain connectivity. This umbrella term is commonly used in the literature to describe the statistical interdependencies between regional measurements obtained from molecular imaging techniques [ 40 ]. In the past few years, the term ‘molecular connectivity’ has been used to describe various methodologies aimed at constructing maps or matrices that reflect the statistical relationships between brain regions based on their molecular properties (as derived from PET or SPECT). These maps are generated through different statistical analyses, depending on the type of modality, tracer used, and the computational method chosen. Consequently, the biological interpretation and insights derived from the results can vary [ 41 ]. One example is the computation of covariance matrices of regional PET signals across subjects. Up to today, this represents the most common approach used as a proxy for molecular connectivity. This method is favoured for its simplicity and the fact that it can be applied to static PET data, offering a broader perspective on shared connectivity patterns across populations [ 42 ]. However, the limitation of estimating connectivity at the group level, rather than at the individual level, poses challenges for biological interpretation [ 41 , 43 ]. An alternative approach involves using dynamic data to construct molecular connectivity maps at the individual level. This method leverages temporal information from the radiotracer kinetics to compute connectivity through various computational techniques [ 44 ]. Other approaches to study brain connectivity that do not rely on the construction of an adjacency matrix are the scaled subprofile model (SSM), a multivariate principal component analysis (PCA)-based algorithm applied directly to voxel-by-voxel covariance data. In this case, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores [ 45 ]. Ultimately, another source-based multivariate method is independent component analysis (ICA), a data-driven computational procedure that decomposes or ‘un-mixes’ a measured signal into its maximal spatially independent ‘sources’ [ 46 ]. These approaches allow researchers to simultaneously explore variations in the relationships between multiple brain regions or patterns of activation, offering valuable insights into covarying patterns of tracer binding across the entire brain. Purpose of the study This paper provides a comprehensive, state-of-the-art overview of brain connectivity analysis in the study of neurotransmission using molecular neuroimaging, presented through a scoping review. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge. Through an in-depth review, we provide researchers and clinicians with a clear understanding of the current landscape, highlighting key successes while outlining challenges and potential strategies to address them, with the goal of advancing future research and translating these approaches into clinical applications. Materials and methods The PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-analyses, guidelines extension for scoping reviews (PRISMA-ScR) were adhered to in conducting this scoping review [ 47 ]. The PRISMA-ScR checklist was used to perform the analysis. A study protocol was prepared in OSF prior to the initiation of data collection to ensure methodological rigour and transparency. Search strategy Original articles were searched for in three bibliographic databases (MEDLINE (via Ovid), EMBASE (via Elsevier), and Scopus (via Elsevier) on July 14, 2023. A second search was rerun on April 4, 2024. The search strategy consisted of two key concepts: (1) Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) and (2) connectivity. The complete search strategy is listed in Annex I. Eligibility criteria Molecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria. The eligibility criteria (inclusion and exclusion criteria) were defined as explained below. These criteria encompassed original studies that: a) employed brain PET or SPECT as imaging modalities, b) measured parameters such as blood flow, metabolism, neuroreceptor systems, protein/molecule synthesis, or protein/molecule density. Exclusion criteria comprised preclinical investigations, studies focusing on regions outside the brain, post-mortem analyses, animal studies, and those utilising monoclonal antibody imaging techniques. Finally, letters, commentaries, review papers, and conference abstracts were also excluded. Table 1 summarises the inclusion and exclusion criteria used in this study. Table 1 Eligibility criteria (inclusion and exclusion criteria) of references to be included in the scoping review Inclusion criteria Exclusion criteria Population Humans Animals Concept Connectivity, covariance, network Context PET and SPECT Measure: blood flow, metabolism, neuroreceptor systems, protein/molecule synthesis, or protein/molecule density Regions outside the brain Post-mortem analyses Monoclonal antibody imaging techniques Preclinical investigations Sources Peer-reviewed original studies Short commentaries Conference abstracts Reviews Letters to editors Studies selection and data extraction First, titles and abstracts were screened independently by two authors (DEP and MS) to exclude irrelevant records based on the eligibility criteria. A third author (MV) played the role of the third peer to arbitrate in case of disagreements. Then, the full text of each selected article was independently screened by 2 authors (DEP and MS). Additional studies were included a posteriori at the authors' discretion. Notably, we included studies published prior to 1993 (formal definition of brain functional connectivity [ 48 ]), where the terminology deviates from connectivity, networks, or connectomics (term introduced in 2005 by [ 49 ]). These studies were deemed relevant as they shared the overarching objective of investigating structural and functional interactions across brain regions. This inclusive approach ensured a comprehensive review of relevant literature, capturing both contemporary studies and earlier works contributing to the understanding of brain multi-scale architecture. Comprehensive information was extracted and organised into a pre-defined data sheet developed by the authors for each of these final articles. This information encompassed various aspects, including the names of the authors, year of publication, characteristics of the study population (both healthy control and patient groups), PET/SPECT tracer utilised, putative marker type and specification, protocol and analysis type, methods employed for connectivity analysis, software utilised, main findings, validation type (if applicable), multimodality type (if applicable), and any reported measures of multimodality similarity and performance. This systematic extraction process ensured thorough documentation of relevant details from each included study, facilitating comprehensive analysis and synthesis of findings. Results Search results After removing duplicates, a total of 3,568 references were retrieved from database searches (3,213 in July 2023 and 355 in April 2024). Following title and abstract screening, 722 references were selected for full-text review. Ultimately, 488 of these met the eligibility criteria and underwent data extraction. Full texts were excluded primarily due to incorrect study type or unsuitable analysis (i.e., lacking molecular connectivity). From the eligible studies, a subset of 32 articles specifically addressing neurotransmission systems was included in this review. Additionally, 7 more studies recommended by experts were screened and added to the scoping review, resulting in a final total of 39 articles. The identification of these articles was based mainly on the molecular probe used and its main binding targets. Figure 3 shows the PRISMA flow chart describing articles selection process. Studies characteristics Studies were categorised based on the primary neurotransmitter system being targeted (Table 2 ). In instances where a study investigated multiple neurotransmitters, it was assigned to a single category based on the predominant target. The resulting sample comprised 23 studies targeting the dopamine system (radiotracers used: 11 C-FLB457, 18 F-FDOPA, 11 C-FeCIT, 18 F-Fallypride, 11 C-MP, 11 C-FLB457, 11 C-Raclopride, 18 F-FEOBV, 18 F-CFT, 11 C-CFT, 11 C-SCH23390, 18 F-FPCIT, 123 I-FP-CIT, 11 C-DTBZ, 11 C(+)-PHNO), 9 studies focusing on the serotonin system (radiotracers used: 11 C-SB217045, 11 C-WAY-100635, 11 C-DASB, 11 C-MADAM), 2 studies examining µ-opioid receptors (radiotracer used: 11 C-Carfentanil), 2 studies assessing synaptic density (radiotracer used: 11 C-UCB-J), 2 studies targeting muscarinic receptors (radiotracer used: 123 I-QNB), and 1 study investigating glutamate receptors (radiotracer used: 11 C-ABP688). The most frequently utilised outcome measure across these studies was the tracer binding potential ( \(\:{BP}_{\text{N}\text{D}}\) , [ 50 ]) employed by 20 studies. This metric represents the equilibrium ratio of the concentration of specifically bound radioligand to the combined concentration of free and non-specifically bound radioligand. Other studies looked at additional parameters as proxies for tracer-specific activity, such as, Specific Binding Ratio ( SBR ), used in 4 studies, Standardized Uptake Value ratio ( \(\:{SUV}_{r}\) ), also used in 4 studies, the distribution volume ratio ( DVR ), in 3 studies, and total distribution volume ( \(\:{V}_{\text{T}}\) ) , used in 2 studies. In terms of study populations, 34 studies included healthy control (HC) subjects, 15 focused on PD, 4 on mild cognitive impairment (MCI), 3 on Schizophrenia, 3 on Dementia with Lewy bodies (DLB), 3 on MDD, and 2 on AD. Only 1 study investigated anxiety, epilepsy, cocaine-use disorder (CUD) and attention-deficit/hyperactivity disorder (ADHD). Pairwise correlation was the most frequently applied methodology to investigate molecular connectivity (14 studies), followed by inter-regional correlation (10 studies), PCA-base approaches (6 studies), and ICA (4 studies). Partial least squares (PLS) was employed in 2 studies, while the remaining studies used combinations of these approaches, or “non-conventional” approaches usually not employed in this field. Finally, in terms of the type of analysis conducted, 34 studies performed inter-subject analyses, where comparisons are made between different individuals or groups to identify variations across subjects. 4 studies included both inter-subject and intra-subject comparisons, the latter involving analyses within the same individual over time or under different conditions. Only 1 study focused solely on intra-subject analysis. Dopamine system Several studies employed PET imaging to investigate imaging-derived dopamine-weighted networks across various neurological conditions. Yasuno et al. [ 51 ] applied structural equation modelling to 11 C-FLB457 brain PET imaging of HC and Schizophrenia patients. Using this method, the inter-regional correlations of D 2 receptor binding were decomposed to assign numerical weights (called path coefficients) to the anatomical connections and to evaluate the effective connectivity of regional D 2 receptor binding in Schizophrenia. The strength and signs of these path coefficients were compared between groups and used to identify disease-specific changes in the connectivity of regional D 2 receptor binding within the same anatomical networks. Kaasinen et al. [ 52 ] employed regional Pearson correlation and PCA to investigate corticostriatal profiles of glucose consumption and extrastriatal dopamine synthesis capacity covariance patterns in PD. The examination of striatal tracer binding revealed asymmetrical sex-dependent uptake of 18 F-FDOPA in the putamen, while revealing negative correlations between striatal 18 F-FDOPA uptake and PD clinical severity. None of these findings were seen with 18 F-FDG. Similarly, the network analysis using PCA revealed a specific component related to thalamus and cerebellum in 18 F-FDG uptake associated with both 18 F-FDOPA uptake and disease severity. On the contrary, univariate analysis showed poor correlations between 18 F-FDOPA and 18 F-FDG uptake in PD when using raw regional uptake. Cervenka et al. [ 53 ] examined the relationship between dopamine D 2 receptors across all brain regions in HC. 11 C-FLB457 PET was used to measure binding in extrastriatal regions, while 11 C-raclopride was employed for the measurements of D 2 distribution in the striatum. Pairwise correlations were calculated between regional \(\:{BP}_{\text{N}\text{D}}\) values of 11 C-raclopride, and a voxel-based correlation analysis was performed using parametric images of 11 C-FLB457 binding. Additionally, correlations between regional-based \(\:{BP}_{\text{N}\text{D}}\) values and parametric values were assessed for each region separately. The results showed that striatal receptor availability did not exhibit statistically significant correlations with any of the extrastriatal regions. These findings suggested that striatal dopaminergic biomarkers may not serve as a reliable index for global dopamine function, and results do not support using the striatum as an index for global D 2 receptor availability. Caminiti et al. [ 54 ] characterised presynaptic dopamine activity in early PD patients using 11 C-FeCIT PET and assessed connectivity within nigrostriatal and mesolimbic systems using partial correlation. The aim of the study was to assess – by means of univariate and multivariate approaches - if the axons of the nigrostriatal dopaminergic system are an early site for vulnerability in PD. The findings indicated greater presynaptic degeneration in dorsal putamen than substantia nigra, and more severe molecular connectivity alteration in the nigrostriatal than mesolimbic pathway. Worhunsky et al. [ 55 ] examined D 2 and D 3 receptors alterations in the midbrain, striatum and other subcortical structures in individuals with CUD and HC. The aim was to apply ICA on 11 C(+)-PHNO PET \(\:{BP}_{\text{N}\text{D}}\) data with the objective of unmix the D 2 and D 3 components of \(\:{BP}_{\text{N}\text{D}}\) and examine distinct sources of receptor availability. ICA analysis identified three distinct source-based patterns of \(\:{BP}_{\text{N}\text{D}}\) , suggesting that cocaine-related alterations in D 2 and D 3 may not be limited to the dorsal striatum and midbrain respectively but may extend into the pallidum and ventral striatum. Furthermore, these alterations sources were associated with duration of cocaine use and may indicate reciprocal and compensatory mechanisms of dopaminergic function in addiction. Klyuzhin et al. [ 56 ] applied PCA to identify voxel covariance patterns, and LASSO to optimally combine several patterns. These approaches were applied to analyse dopaminergic PET tracers ( 11 C-DTBZ and 11 C-raclopride) binding in the striatum of PD subjects. The principal component (PC) loadings obtained in different groups of subjects revealed predominant voxel-level binding patterns associated with the initial symptom onset and disease progression. The PC-LASSO estimators captured information in a non-local manner, and hence enabled data-driven visualisation and interpretation of spatial patterns manifested in the images. Kim et al. [ 57 ] investigated interregional correlations of D 2 and D 3 receptor availability in Schizophrenia patients receiving antipsychotics using 18 F-fallypride PET and resting state-fMRI, revealing altered molecular and functional connectivity between striatal and extrastriatal regions in stable outpatients with schizophrenia on antipsychotics, which is mainly characterised by increased interregional relationships. These results suggested that the spatial organisation of D 2 and D 3 receptor availability and related functional connectivity were significantly perturbed in these subjects. Fu et al. [ 58 ] introduced a joint pattern analysis approach, canonical correlation analysis and orthogonal signal correction to identify characteristic spatial and temporal distribution patterns in PD using 11 C-DTBZ (VMAT 2 marker) and 11 C-MP (DAT marker) PET data. Results showed that the proposed approach was able to capture the spatial and temporal disease patterns with higher sensitivity compared to univariate analysis. The approach provided information not only on localized alterations but also on the spatial extent of such alterations, emphasizing a network behaviour of the molecular targets under investigation. Moreover, the approach decomposed the common information between data sets into distinct orthogonal patterns of characteristic dopaminergic changes that were more sensitive either to disease discrimination or to disease progression. Veronese et al. [ 59 ] conducted a graph-based analysis across different PET tracers ( 18 F-FDG, 18 F-FDOPA, 11 C-SB217045) both in controls and in diseased groups (AD and MCI), revealing that these metrics can complement standard PET analysis to understand how biological functions are organized across brain regions in healthy and pathological conditions. The study also showcased the sensitivity of connectivity results to experimental design and variables, including group inhomogeneity and image resolution and suggested that further methodological work is required to validate the use of more complex network metrics in the context of PET covariance analysis and to understand their biological interpretability. Verger et al. [ 60 ] investigated the feasibility and potential of molecular connectivity using neurotransmission tracers ( 18 F-FDOPA and 123 I-FP-CIT) compared to metabolic connectivity ( 18 F-FDG) in dopaminergic pathways of HC. Through interregional correlation analysis to construct a brain connectivity network, the study demonstrated that specific neurotransmission tracers provide higher specificity in revealing the mesotelencephalic system (nigro-striatal, mesolimbic, and mesocortical pathways) compared to metabolic connectivity. Notably, 18 F-FDOPA was more effective than 123 I-FP-CIT in identifying the mesotelencephalic system, indicating that these dopaminergic targets are not equivalent. The findings underscore the advantages of using 18 F-FDOPA PET imaging for molecular connectivity, highlighting its superior sensitivity and specificity relative to 18 F-FDG metabolic connectivity and emphasising that the choice of imaging modality and neurotransmitter targeting is crucial. Mihaiescu et al. [ 61 ] performed a graph theory analysis of D 2 receptors measured with 11 C-FLB-457 in two brain networks: the meso-cortical dopamine network and the meso-limbic dopamine network in PD patients with cognitive decline. The findings suggested how connectivity dysregulation in extrastriatal dopamine networks may contribute to cognitive decline. Furthermore, this study wanted to highlight that multivariate network analysis captured different aspects of the dopaminergic dysfunction compared to univariate regional comparisons of localised receptor density differences. Sala et al. [ 62 ] examined the molecular connectivity alterations in AD, MCI and HC subjects’ data measured with 123 I-FP-CIT SPECT tracer using partial correlation with gender, age, and reconstruction method included as nuisance covariates. The study provided biological in vivo evidence for a significant derangement of the meso-limbic dopaminergic system in AD, already plateauing in the prodromal stages. Both in vivo dopaminergic binding density and molecular connectivity analysis, pointed to different degrees of vulnerability of the dopaminergic afferents from specific dopaminergic nuclei. Smart et al. [ 63 ] assessed the utility of four-dimensional ICA application to a competition binding PET study using 11 C(+)-PHNO PET tracer with the D 3 antagonist ABT-728, for the estimation of subtype-specific receptor occupancy. The results showed that ICA identified two distinct components of change in binding on the basis of spatiotemporally coherent variance across subjects and time points. The spatial sources of these components were highly consistent with D 2 and D 3 related 11 C(+)-PHNO binding distributions in the brain, suggesting that this analysis successfully separated each receptor subtype without any a priori assumptions. This interpretation was further supported by relative changes in the intensity of each source during blockade with the D 3 -selective antagonist ABT-728, which were closely matched to region-based occupancy estimates. Rebelo et al. [ 64 ] used covariance statistics at molecular and functional levels (measured through fMRI) to explore striato-cortical links in PD in on/off medication states using 11 C-Raclopride PET tracer. The study showed that functional and molecular forms of brain plasticity are related. These authors found a tight link between functional activation and synaptic changes at the molecular level, reflecting network reorganisation of compensatory molecular and functional mechanisms in PD. Peng et al. [ 65 ] found that PD-related pattern expression levels, calculated using SSM-PCA, and measured in early-phase 18 F-FPCIT PET scans, discriminated patients with early-stage PD from age-matched HC subjects with similar accuracy for the first 2, 5, and 10 min of the dynamic 18 F-FPCIT PET acquisitions. These results suggested that dual-phase 18 F-FPCIT PET is a viable methodology for quantitative assessment of PD-related metabolic brain networks, as an alternative to 18 F-FDG PET, and presynaptic nigrostriatal dopaminergic functioning in a single imaging session. Sanchez-Catasus et al. [ 66 ] examined the striatal acetylcholine–dopamine imbalance hypothesis in early PD patients using dual-tracer PET and dopaminergic PET–informed correlational tractography. Firstly, the authors estimated the integrity of the dopaminergic nigrostriatal white matter tracts in PD subjects by incorporating molecular information from striatal 11 C-DTBZ into the fibre-tracking process using correlational tractography (based on quantitative anisotropy (QA)). Subsequently, they used voxel-based correlation to test the association of the mean QA of the nigrostriatal tract of each cerebral hemisphere with the striatal 18 F-FEOBV DVR in PD subjects. The same analysis was performed for 11 C-DTBZ DVR in 12 striatal subregions. Taken together, results provided in vivo evidence of the imbalance between acetylcholine and dopamine signalling systems in the striatum in early PD. Boccalini et al. [ 67 ] investigated gender differences in the molecular connectivity of the dopaminergic systems using a large PPMI cohort of newly diagnosed and drug-naïve idiopathic PD patients measured with 123 I-FP-CIT SPECT. Partial correlation was used to assess regional co-variation in tracer uptake across subjects, and percentage of altered molecular connections in each network was used to quantify the severity of connectivity alterations between males and females. Results showed that nigrostriatal bindings and connectivity were more altered in males than females, providing unique evidence of gender effects in molecular connectivity of both dopaminergic systems affected by the disease. Liu et al. [ 68 ] conducted a dual-tracer PET study employing both 11 C-CFT DAT imaging and 18 F-FDG imaging to compare dopaminergic dysfunction and glucose metabolism characteristics in early-onset PD caused by different gene mutations (PRKN-EOPD and GU-EOPD) using seed-based correlation analysis. Results demonstrated differences in the symmetry and severity of dopaminergic dysfunction between the two gene mutations, suggesting potential network reorganisation due to compensatory mechanism in PRKN-EOPD which did not occur in those with GU-EOPD. Boccalini et al. [ 69 ] aimed to investigate molecular connectivity alterations in nigrostriatal and mesolimbic dopaminergic pathways focusing on sex differences by using 123 I-FP-CIT binding in striatal and extrastriatal regions in patients with probable DLB (pDLB). Assessment of molecular connectivity between targets of each dopaminergic pathway was performed via partial correlation analysis, and percentage of altered molecular connections in each network for males and females was calculated to quantify the severity of connectivity alterations. Results showed that connectivity of the nigrostriatal and mesolimbic systems was affected in both sex groups but with different patterns, with pDLB females showing more long-distance connectivity alterations between subcortical and cortical regions of the dopaminergic systems. Caminiti et al. [ 70 ] using 123 I-FP-CIT SPECT imaging adopted correlation analysis to assess the involvement of the ventral and dorsal dopaminergic circuitries in prodromal and clinical phases of DLB. Correlation analyses assessed the significant differences in connectivity between each clinical group and a subgroup of control subjects. This work provided the first evidence of widespread adaptive reconfigurations of dopaminergic networks in the continuum of Lewy body disease. The dopaminergic network showed an extensive increase of connectivity in prodromal phases, both in dorsal and ventral dopaminergic systems, supporting adaptive/compensating mechanisms, whereas a widespread loss of connectivity was prominent in overt DLB. Luo et al. [ 71 ] using 11 C-CFT and 18 F-FDG PET imaging investigated the effects of Subthalamic nucleus (STN) deep brain stimulation (DBS) on the distribution of presynaptic DAT and the pattern of cerebral glucose metabolism in PD patients before and after surgery. By applying SSM-PCA, they found that STN-DBS could modify the cerebral network without preventing striatal DAT decline. On the other hand, UPDRS-III scores, particularly resting tremor and rigidity, were significantly reduced after STN-DBS surgery, confirming that STN-DBS is an effective therapeutic approach in controlling symptoms in patients with PD. Boccalini et al. [ 72 ] investigated dopamine transporter, using semiquantitative 123 I-FP-CIT SPECT imaging, in a large cohort of idiopathic PD patients, healthy subjects and Scan Without Evidence of Dopaminergic Deficit (SWEDD) cases. Their covariance statistics analysis highlighted distinct clinical and molecular trajectories of PD and SWEDD subjects. SWEDD subjects were characterised by prominent non-motor symptoms, absence of hyposmia, and generally preserved dopaminergic binding, but prevalent mesocorticolimbic connectivity impairment, suggesting other mechanisms contributing to SWEDD pathophysiology. Finally, by using the world's largest combined 11 C-SCH23390 D 1 receptors PET and MRI dataset from the DyNAMiC study, Pedersen et al. [ 73 ] tested the hypothesis that D 1 receptors organisation is aligned with functional architecture and that inter-regional relationships in D 1 receptors co-expression modulates functional cross-talk in control subjects. They applied a nonlinear embedding approach where functional and dopaminergic organisations were characterised as a set of low-dimensional manifolds and extended this analysis also to individual participants. Results demonstrated that D 1 receptors organisation followed a unimodal–transmodal hierarchy, expressing a high spatial correspondence to the principal gradient of functional connectivity. They also demonstrated that individual differences in D 1 receptors density between unimodal and transmodal regions were associated with functional differentiation of the apices in the cortical hierarchy. Finally, they showed that spatial co-expression of D 1 receptors primarily modulates couplings within, but not between, functional networks. Together, these results showed that D 1 receptors co-expression provides a biomolecular layer to the functional organisation of the brain. Serotonin system Several studies have also investigated the brain network alterations in serotonin neurotransmission, mainly in neuropsychiatric disorders. Hahn et al. [ 74 ] explored the association of serotonin-1A receptor binding obtained with 11 C-WAY-100635 PET imaging in the dorsal raphe nucleus and the entire brain in anxiety disorder patients before and after escitalopram treatment using covariance statistics, revealing enhanced autoreceptor-to-heteroreceptor binding correlation after treatment. Results underlined the evaluation of neurotransmitter systems on a network level potentially provides important complementary information to regional receptor levels. Tuominen et al. [ 75 ] applied a seed-based voxel-wise correlation analysis method for studying internal neurotransmitter network structure and intercorrelations of different neurotransmitter systems in the human brain of HC subjects. They evaluated serotonin transporter ( 11 C-MADAM) and µ-opioid ( 11 C-Carfentanil) receptor \(\:{BP}_{\text{N}\text{D}}\) intra- and intercorrelations. The analyses revealed nonuniformity in the serotonin transporter intracorrelations and identified a highly connected local network. Regionally specific intercorrelations between the opioid and serotonin tracers were found in areas relevant to several neuropsychiatric disorders, especially affective disorders. Hahn et al. [ 76 ] investigated serotonin transporter associations using 11 C-DASB PET tracer in major depression from a network perspective, revealing disturbances in a major serotonin pathway. They identified the disturbance of a major 5-HT pathway in MDD through an interregional correlation approach. Results suggested a reduced serotonin transporter association between the midbrain dorsal raphe and the ventral striatum/nucleus accumbens complementing the biological mechanisms of anhedonia in major depression and further underlines the importance of the serotonergic system in reward processing. These results emphasised the importance of investigating neurotransmitter systems on a network level. Norgaard et al. [ 77 ] employed a multivariate PLS approach to identify a pattern of serotonin transporter (5-HTT) levels, measured with 11 C-DASB PET imaging, fluctuating with group and season in seasonal affective disorder (SAD) a subtype of MDD. The method was able to identify and map a whole-brain pattern of 5-HTT levels that distinguished the brains of females without SAD from females suffering from SAD. Vanicek et al. [ 78 ] investigated the altered interregional molecular associations of the serotonin transporter in ADHD using PET imaging. They utilised 11 C-DASB PET to assess SERT binding potential in regions rich in SERT and observed differences in SERT availability between adult patients with ADHD and healthy controls. Additionally, they conducted a correlational analysis to examine the interregional association of SERT binding, finding significant interregional differences in SERT \(\:{BP}_{\text{N}\text{D}}\) correlations. Fu et al. [ 79 ] applying SSM-PCA to 11 C-DASB PET data, identified a serotonergic spatial covariance pattern characteristic of PD, strongly correlated with disease duration and dopaminergic denervation measured with 11 C-DTBZ PET imaging. The study highlighted that compared to previously used univariate analysis approaches, the spatial covariance method was found to be more sensitive in identifying disease-related abnormalities since no correlation between DTBZ and DASB \(\:{BP}_{\text{N}\text{D}}\) values of individual regions was found, suggesting PD affects the serotonergic system on a more global network level rather than any particular region in isolation. These findings suggested that disease-induced alterations of the serotonergic system, rather than being purely local, also affect interactions between separate regions in a disease-specific fashion and are closely linked to abnormalities in the dopaminergic system. Similarly, Pillai et al. [ 80 ] investigated molecular connectivity disruptions in MDD using covariance statistics applied to 11 C-WAY-100635 PET data. Results showed compromised structural and compensatory mechanisms of post-synaptic receptor regulation in MDD men. Interestingly, the study suggested that these individual differences in molecular connectivity between HC and MDD were so large that they may serve as a biomarker for the disorder. Fazio et al. [ 81 ] examined the impairment of serotonin transporter availability measured with 11 C-MADAM PET in early non-depressed PD patients using covariance statistics and graph metrics, detecting network changes preceding overt depletion in the serotoninergic system. The findings indicated that the serotoninergic system might become involved in PD patients as the disease progresses and importantly this finding was only captured by network measures, but not by direct regional binding. Smith et al. [ 82 ] studied the association between serotonin degeneration measured with 11 C -DASB and beta-amyloid deposition in mild cognitive impairment measured with 11 C-PIB using a multi-modal PLS algorithm. This approach identified a spatial covariance pattern that distinguished MCI from healthy controls characterised by lower serotonin transporter availability and greater cortical amyloid deposition. The pattern was expressed to a significantly greater extent in the MCI relative to the control group and was correlated with impairment in memory and executive function in the MCI group. Opioid system Only two studies employed covariance analysis to investigate the modulation of µ-opioid receptor activity and its implications in disease conditions. Wager et al. [ 83 ] investigated the placebo effects on µ-opioid receptor binding potential using 11 C-carfentanil PET imaging in HC. Through interregional correlations, the authors found that placebo treatment increased functional connectivity between µ-opioid-rich limbic and paralimbic regions, suggesting a mechanism for placebo-induced pain relief mediated by the endogenous opioid system. In contrast, Ashok et al. [ 84 ] utilised 11 C-carfentanil PET imaging to examine µ-opioid receptor availability in Schizophrenia patients. Their findings revealed reduced µ-opioid receptor availability in the striatum and brain regions associated with hedonic responses compared to healthy controls. Furthermore, correlation analysis indicated a significant global increase in µ-opioid receptor connection strength in Schizophrenia patients relative to controls, highlighting aberrant µ-opioid system activity in the context of Schizophrenia pathology. Muscarinic receptor system Colloby et al. conducted two studies investigating cholinergic muscarinic M1/M4 receptor networks in DLB and PD, respectively. In the study on DLB [ 85 ], they utilized spatial covariance analysis on 123 I-QNB SPECT scans to explore muscarinic M1/M4 connectivity in Cholinesterase Inhibitor (ChEI) naive patients. They identified baseline spatial covariance patterns of M1/M4 receptors that distinguished DLB from healthy individuals and were associated with positive changes in global cognition and neuropsychiatric symptoms after ChEI treatment. These findings suggested that specific brain regions play a crucial role in the neuropsychiatric profile of DLB. In the PD study [ 86 ], they employed a similar approach using 123 I-QNB SPECT scans to derive patterns distinguishing PD from healthy individuals and correlating with global cognition, motor severity, and cognitive decline in PD patients. They identified multiple cholinergic muscarinic receptor networks in PD, with cognition and motor severity showing similar topography, suggesting related cholinergic mechanisms underlying both phenotypes. The relative decrease in M1/M4 receptor expression within default mode network and frontal executive hubs could potentially serve as an indicator of future cognitive decline in PD. Glutamate receptor system DuBois et al. [ 87 ] conducted a study aiming to characterise the mGluR5 network in patients with focal cortical dysplasia (FCD) using 11 C-ABP688 PET imaging. Through graph theoretical analysis based on the comparison of probability density function of each regional \(\:{BP}_{\text{N}\text{D}}\) , at the individual subject level, calculated by Jensen-Shannon divergence, they revealed abnormalities in large-scale mGluR5 networks linked to the duration of epilepsy in FCD patients. Their findings indicated decreased resilience and global efficiency, suggesting a less integrated network in FCD patients. These results support the notion that FCD may be better understood as a system-wide disorder rather than a focal abnormality from a glutamatergic neuroreceptor perspective. The graph approach employed in this study allows for the comparison of neuroreceptor systems imaged with PET to other measures of functional and structural connectivity, offering insights into the broader neurological implications of FCD. Synaptic density The studies by Fang et al. delve into the exploration of synaptic density networks. In the first study [ 88 ], the authors employed ICA on 11 C-UCB-J PET data to identify coherent patterns of synaptic density variability in healthy individuals. The analysis revealed sample-independent networks consistently extracted across different model orders, suggesting that these networks contain both complementary and unique information compared to 18 F-FDG PET and resting state-fMRI. In their second study, Fang and colleagues [ 89 ] expanded on this by using ICA to examine associations between resting-state network (RSN) fluctuations and synaptic density using multimodal fMRI and 11 C-UCB-J PET in healthy controls. They found potential links between RSN activity and 11 C-UCB-J source networks, indicating that synaptic density networks may be intricately connected to the functioning of large-scale intrinsic brain networks. These findings shed light on the relationship between synaptic physiology and brain network organization that can be captured through the application of connectivity/multivariate approaches, providing valuable insights into the underlying mechanisms of brain function. Table 2 Table summarizing the findings of the reviewed studies Authors Title PET tracer Putative marker Population Method Yasuno et al. 2005 Abnormal effective connectivity of dopamine D2 receptor binding in schizophrenia. 11 C-FLB457 Dopaminergic D 2 Receptors HC (n = 19) and Schizophrenia (n = 10) Inter-regional correlation Kaasinen et al. 2006 Corticostriatal covariance patterns of 6-[18F]fluoro-L-dopa and [18F]fluorodeoxyglucose PET in Parkinson's disease. 18 F-FDG and 18 F-FDOPA Glucose Metabolism and Dopa-Decarboxylase PD (n = 25) Inter-regional correlation Wager et al. 2007 Placebo effects on human mu-opioid activity during pain. 11 C-Carfentanil µ-Opioid Receptors HC (n = 15) Pairwise correlation Hahn et al. 2010 Escitalopram enhances the association of serotonin-1A autoreceptors to heteroreceptors in anxiety disorders. 11 C-WAY-100635 Serotonin 5HT 1A Receptors HC (n = 36) and Anxiety (n = 21) Inter-regional correlation Cervenka et al. 2010 PET Studies of D2-Receptor Binding in Striatal and Extrastriatal Brain Regions: Biochemical Support In Vivo for Separate Dopaminergic Systems in Humans 11 C-FLB457 and 11 C-raclopride Dopaminergic D 2 Receptors HC (n = 16) Inter-regional correlation Tuominen et al. 2014 Mapping neurotransmitter networks with PET: an example on serotonin and opioid systems 11 C-MADAM and 11 C-Carfentanil Serotonin Transporter and µ-Opioid Receptors HC (n = 21) Inter-regional correlation Hahn et al. 2014 Attenuated Serotonin Transporter Association Between Dorsal Raphe and Ventral Striatum in Major Depression 11 C-DASB Serotonin Transporter HC (n = 20) and MDD (n = 20) Inter-regional correlation Ashok et al. 2014 Reduced mu opioid receptor availability in schizophrenia revealed with [11C]-carfentanil positron emission tomographic Imaging 11 C-Carfentanil µ-opioid Receptors HC (n = 20) and Schizophrenia (n = 19) Pairwise correlation Nørgaard et al. 2017 Brain Networks Implicated in Seasonal Affective Disorder: A Neuroimaging PET Study of the Serotonin Transporter. 11 C-DASB Serotonin Transporter Non-SAD (n = 13) and SAD (n = 6) Multivariate PLS Vanicek et al. 2017 Altered interregional molecular associations of the serotonin transporter in attention deficit/hyperactivity disorder assessed with PET. 11 C-DASB Serotonin Transporter HC (n = 25) and ADHD (n = 25) Pairwise correlation Worhunsky et al. 2017 Regional and source-based patterns of [11C]-(+)-PHNO binding potential reveal concurrent alterations in dopamine D2 and D3 receptor availability in cocaine-use disorder 11 C-(+)-PHNO Dopaminergic D 2 and D 3 Receptors HC (n = 26) and CUD (n = 26) ICA Caminiti et al. 2017 Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease 11 C-FeCIT Dopamine Transporter HC (n = 14) and PD (n = 26) Pairwise correlation Kim et al. 2018 Altered connectivity between striatal and extrastriatal regions in patients with schizophrenia on maintenance antipsychotics: an [(18) F]fallypride PET and functional MRI study. 18 F-Fallypride Dopaminergic D 2 Receptors HC (n = 14) and Schizophrenia (n = 11) Pairwise correlation Klyuzhin et al. 2018 Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration. 11 C-DTBZ and 11 C-raclopride Dopamine Transporter HC (n = 10) and PD (n = 41) PCA - LASSO Pillai et al. 2019 Molecular connectivity disruptions in males with major depressive disorder 11 C-WAY-100635 Serotonin 5HT 1A Receptors HC (n = 20) and MDD (n = 16) Inter-regional correlation Fu et al. 2019 Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations. 18 F-DBTZ and 11 C-MP Dopamine Transporter and VMAT 2 PD (n = 15) Canonical correlation analysis Veronese et al. 2019 Covariance statistics and network analysis of brain PET imaging studies. 18 F-FDG, 18 F-FDOPA and 11 C-SB217045 Glucose Metabolism, Dopa-Decarboxylase and Serotonin 5HT 4 Receptors HC (n = 80), AD (n = 76) and MCI (n = 137) Pairwise correlation Verger et al. 2020 From metabolic connectivity to molecular connectivity: application to dopaminergic pathways 18 F-FDG and 18 F-FDOPA Glucose Metabolism and Dopa-Decarboxylase HC (n = 47) Inter-regional correlation Fazio et al. 2020 High-resolution PET imaging reveals subtle impairment of the serotonin transporter in an early non-depressed Parkinson's disease cohort. 11 C-MADAM Serotonin Transporter HC (n = 20) and PD (n = 20) Pairwise correlation Colloby et al. 2020 Cholinergic muscarinic M1/M4 receptor networks in dementia with Lewi bodies 123 I-QNB and 99mTc-exametyzime Muscarinic Receptors M1 and M4 and Cerebral blood flow Elderly controls (n = 24) and DLB (n = 14) PCA spatial covariance Mihaescu et al. 2021 Graph theory analysis of the dopamine D2 receptor network in Parkinson's disease patients with cognitive decline 11 C-FLB457 Dopaminergic D 2 Receptors HC (n = 13), MCI (n = 17) and PD (n = 13) Pairwise correlation Sala et al. 2021 In vivo human molecular neuroimaging of dopaminergic vulnerability along the Alzheimer’s disease phases 123 I-FP-CIT Dopamine HC (n = 74), MCI (n = 16) and AD (n = 22) Pairwise correlation Smart et al. 2021 Separating dopamine D2 and D3 receptor sources of [11C]-(+) PHNO binding potential: independent component analysis of competitive binding 11 C-(+) PHNO Dopaminergic D 2 and D 3 Receptors HC (n = 8) ICA Fang et al. 2021 Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis. 11 C-UCB-J Synaptic Density HC (n = 80) ICA DuBois et al. 2021 Large-scale mGluR5 network abnormalities linked to epilepsy duration in focal cortical dysplasia 11 C-ABP688 Metabotropic Glutamate Receptor Type 5 HC (n = 33) and Epilepsy (n = 17) Jensen- Shannon divergence Rebelo et al. 2021 A link between synaptic plasticity and reorganization of brain activity in Parkinson's disease 11 C-Raclopride Dopaminergic D 2 Receptors HC (n = 9) and PD (n = 14) Pairwise correlation Fu et al. 2021 Investigation of serotonergic Parkinson's disease-related covariance pattern using [11C]-DASB/PET 11 C-DASB Serotonin Transporter HC (n = 9) and PD (n = 30) SSM-PCA Peng et al. 2021 Dynamic 18F-FPCIT PET: Quantification of Parkinson Disease Metabolic Networks and Nigrostriatal Dopaminergic Dysfunction in a Single Imaging Session 18 F-FPCIT and 18 F-FDG Dopamine Transporter and Glucose metabolism HC (n = 16) and PD (n = 25) SSM-PCA Colloby et al. 2021 Spatial Covariance of Cholinergic Muscarinic M1/M4 Receptors in Parkinson's Disease 123 I-QNB and 99mTc-exametyzime Muscarinic Receptors M1 and M4 and Cerebral blood flow Elderly controls (n = 24) and PD (n = 19) PCA spatial covariance Sanchez-Catasus et al. 2022 Striatal Acetylcholine–Dopamine Imbalance in Parkinson Disease: In Vivo Neuroimaging Study with Dual-Tracer PET and Dopaminergic PET–Informed Correlational Tractography 18 F-FEOBV and 11 C-DTBZ Acetylcholine– Dopamine Transporters HC (n = 15) and PD (n = 45) Inter-regional correlation Boccalini et al. 2022 Gender differences in dopaminergic system dysfunction in de novo Parkinson’s disease clinical subtypes 123 I-FP-CIT Dopamine HC (n = 73) and PD (n = 286) Pairwise correlation Boccalini et al. 2023 Sex differences in dementia with Lewy bodies: an imaging study of neurotransmission pathways 123 I-FP-CIT Dopamine Controls (n = 78) and pDLB (n = 123) Pairwise correlation Caminiti et al. 2023 Dopaminergic connectivity reconfiguration in the dementia with Lewy bodies continuum 123 I-FP-CIT Dopamine Controls (n = 52), pDLB (n = 20) and DLB (n = 29) Pairwise correlation Fang et al. 2023 Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study. 11 C-UCB-J Synaptic Density HC (n = 34) ICA Liu et al. 2023 Dopaminergic Dysfunction and Glucose Metabolism Characteristics in Parkin-Induced Early-Onset Parkinson’s Disease Compared to Genetically Undetermined Early-Onset Parkinson’s Disease 18 F-CFT and 18 F-FDG Dopamine Transporter and Glucose metabolism PD (n = 28) Inter-regional correlation Smith et al. 2023 Molecular imaging of the association between serotonin degeneration and beta-amyloid deposition in mild cognitive impairment 11 C-DASB and 11 C-PIB Serotonin Transporter and Amyloid burden HC (n = 27) and MCI (n = 22) PLS Luo et al. 2023 Effects of STN-DBS surgery on cerebral glucose metabolism and distribution of DAT in Parkinson's disease. 18 F-FDG and 11 C-CFT Glucose Metabolism and Dopamine Transporter PD (n = 12) SSM-PCA Pedersen et al. 2024 Dopamine D1-Receptor Organization Contributes to Functional Brain Architecture. 11 C-SCH23390 Dopamine HC (n = 176) Low dimensional manifold representation Boccalini et al. 2024 Distinctive clinical and imaging trajectories in SWEDD and Parkinson's disease patients. 123 I-FP-CIT Dopamine HC (n = 49), SWEED (n = 36) and idiopathic PD (n = 49) Pairwise correlation Discussion In this paper, we explored the use of molecular imaging to study neurotransmission through a connectivity lens. We reviewed studies involving healthy volunteers, neurological diseases, psychiatric disorders, and other conditions, aiming to encompass all relevant applications discussed in the literature. We examined studies employing various methods, such as covariance statistics and network analyses, which complement traditional univariate approaches. Collectively, these findings reveal patterns of molecular connectivity that are essential for understanding disease mechanisms. The value of molecular connectivity for studying neurotransmission The reviewed studies highlight how molecular connectivity is frequently employed alongside traditional univariate analyses to strengthen research findings. Often, multivariate and network-based methods are used to complement and validate results from conventional univariate approaches, enhancing the robustness and reproducibility of conclusions by offering a broader contextual perspective and reinforcing initial insights approaches [ 54 , 62 , 72 , 74 , 78 , 83 , 84 ]. Beyond simply confirming results, molecular connectivity has also provided novel insights and uncovered patterns otherwise undetectable through univariate methods alone [ 52 , 55 , 63 , 68 , 72 , 85 ]. This integrated approach is capable of merging localized insights with broader network-level relationships, increasing the robustness of findings and opening avenues for further applications. Notably, several studies have positioned molecular connectivity as the primary analytical method [ 58 , 59 , 61 , 66 , 73 , 81 , 87 ]. Although this is less common, it highlights a growing focus on network-level hypotheses in PET research, where molecular connectivity serves as the main investigative tool rather than secondary support. This shift underscores the view that the understanding of brain physiology and disease mechanisms requires a global, interconnected perspective beyond isolated regional analyses [ 74 , 81 , 90 ]. The literature reviewed highlights that the primary advantage of multivariate and network-based approaches is their capacity to assess molecular interactions at a systemic level. A particularly notable observation was the involvement of the dopaminergic system at a broader level, extending beyond the dopaminergic regions typically targeted by radiotracers (e.g., extrastriatal regions). These methods reveal coordinated, disease-related changes across multiple regions and modular network alterations that affect overall system function, underscoring the necessity of a comprehensive approach to detect such patterns. Through these methodologies, researchers have identified spatial patterns of alterations and pathway disruptions linked to disease origins, reinforcing the value of network-level analysis in understanding disease etiology [ 51 , 52 , 57 , 68 , 79 ]. Similarly, "disease-specific brain networks" have been associated with stage-dependent disease changes and compensatory processes [ 55 , 56 , 58 , 64 ]. Such approaches effectively distinguish healthy from diseased individuals and differentiate among disease subtypes [ 67 , 68 , 70 ]. Furthermore, they reveal statistically significant correlations with disease duration and cognitive measures, underscoring their clinical relevance [ 82 , 85 , 86 ]. Molecular connectivity has proven particularly insightful in studying neurodegenerative diseases, where widespread and progressive pathology is better characterised by group-level molecular covariance, as opposed to focal pathologies where individual-level analysis is required [ 91 ]. PD is the most extensively studied condition, with researchers systematically uncovering multivariate and network-level dysregulation patterns within dopaminergic pathways, [ 54 , 72 ] and their associations with symptom onset and disease progression [ 56 , 58 ]. Further research suggested that as PD advances, the serotonergic system may become involved, likely exhibiting a broader impact compared to the localised effects typically observed within the dopaminergic system [ 81 ]. This finding implies that the long-recognized association between dopamine and PD may not be as robust as that between serotonin and PD. Supporting this, Fu et al. [ 79 ] reported that disease-induced alterations in the serotonergic system affect interactions between distinct brain regions in a manner specific to PD, closely tied to abnormalities within dopaminergic pathways. Ultimately, cognitive decline in PD has been linked to changes in extrastriatal dopaminergic patterns [ 61 ]. Several studies have also investigated DLB, revealing notable findings. Boccalini et al. [ 69 ] observed affected connectivity within both the nigrostriatal and mesolimbic systems in DLB, with notable sex-based connectivity variations. Caminiti et al. [ 70 ] identified a marked increase in dopaminergic connectivity during DLB’s prodromal stages, suggesting adaptive mechanisms at work. Additionally, Colloby et al. [ 85 ] found spatial covariance patterns in M1/M4 receptors that distinguish DLB from healthy individuals and are associated with cognitive and neuropsychiatric improvements following ChEI. A portion of reviewed studies have explored network patterns across different radiotracers, offering insights into complementary and regulatory neurotransmitter dynamics that may contribute to specific disorders. For example, Sanchez et al. [ 66 ] demonstrated an imbalance between acetylcholine and dopamine signalling in the striatum during early PD. Tuominen et al. [ 75 ] noted significant opioid-serotonin tracer intercorrelations in regions linked to neuropsychiatric conditions, especially affective disorders, while Smith et al. [ 82 ] distinguished MCI from healthy controls through a serotonin and amyloid covariance pattern. Notable findings have also emerged from studies comparing or integrating molecular connectivity with other imaging modalities. Kim et al. [ 57 ] noted spatial alterations in D 2 and D 3 receptor availability post-antipsychotic treatment, aligning with functional connectivity changes, while Rebelo et al. [ 64 ] demonstrated connections between functional activation and molecular-level synaptic changes in PD, suggesting compensatory reorganization mechanisms. Pedersen et al. [ 73 ] mapped D 1 receptor organisation along a unimodal-transmodal gradient closely aligned with the brain's primary functional gradient. Fang et al. [ 89 ] reported potential ties between resting-state network activity and synaptic density, suggesting that synaptic density may significantly influence large-scale brain network function. Major neurotransmitter systems—including dopaminergic, serotonergic, and opioidergic systems—demonstrate strong alignment with structural and functional connectivity patterns in the brain [ 92 ]. Consequently, mapping the brain-wide distribution of radiotracers provides valuable insights into the connectivity dynamics of specific neurotransmitter systems [ 27 ], paving the way for future multi-tracer and multi-modality studies in molecular connectivity research. Methodological advances and considerations The reviewed papers highlight several innovative methodologies for studying neurotransmission through molecular connectivity, extending beyond conventional intercorrelation techniques and covariance statistics. For instance, the method proposed by Fu et al. [ 58 ] employed joint pattern analysis canonical correlation to integrate information from multiple PET tracers allowing the evaluation of the interplay between two different receptor systems. This method demonstrated the potential of such approaches to integrate different tracers and go beyond the investigation of one receptor system at a time, which is often challenging with traditional correlation analyses. Furthermore, Sanchez et al. [ 66 ] proposed a dopaminergic PET-Informed Correlational Tractography which integrated the information of striatal dopamine into the white matter fibre reconstruction process to optimally guide tract reconstruction with dopaminergic specificity. The resulting metrics were then correlated to striatal dopamine and cholinergic signals showing stronger and more robust associations with respect to non-informed tractography. Through this approach, this study showed that merging information from multiple modalities allows obtaining results with higher biological specificity. Similarly, Worhunsky et al. [ 55 ] applied ICA to 11 C(+)-PHNO PET BPND data to separate D 2 and D 3 receptors distribution, effectively isolating distinct sources of radiotracer signal. This approach enabled a nuanced analysis of receptor subtypes, previously unattainable through conventional D 2 /D 3 radioligand analyses, underscoring the advantage of 11 C(+)-PHNO PET in investigating concurrent changes in D 2 and D 3 receptors. Finally, Pedersen et al. [ 73 ] utilised a nonlinear embedding approach to decompose group representative covariance maps into low-dimensional manifolds, elucidating transitions in covariance patterns of D 1 receptors across the cortical surface and linking dopamine expression to functional brain organisation. The proposed approach could be a valuable tool to decompose and integrate the information provided by different imaging modalities or different molecular targets allowing to uncover possible interactions that would be difficult to establish with “common” connectivity approaches. These methodological advancements deepen the understanding of regional relationships and facilitate the integration of molecular data among different tracers and complementary imaging modalities. This integration provides a holistic understanding of connectivity across multiple hierarchical levels and paves the way for numerous future applications. Overall, these findings underscore the advantages of network-level approaches over traditional quantitative analyses. They offer novel insights not discernible through conventional methods, frequently extending the results obtained with univariate approaches, and provide a robust framework for integrating multiple sources of information, thereby broadening the potential scope of future applications. Nonetheless, the adoption of such techniques should be contingent upon rigorous statistical and mechanistic validation, ensuring that analyses are as complex as necessary to achieve reliable outcomes, but not unduly so. Limitations and future prospectives Despite the promise of molecular connectivity, the field faces several challenges. There is a lack of standard terminology and consistent methods, making it difficult to compare results across studies. For instance, not all studies used the terms "connectivity," "network," or "covariance" consistently when employing these approaches. This variability in nomenclature is also influenced by the studies’ year of publication, since throughout the years the definition of brain connectivity has changed. Only recently the terminology used in functional connectivity with fMRI has become aligned to the PET field [ 38 , 43 ]. Another limitation is the methodological fragmentation across studies, which hinders direct comparisons between methodologies and complicates the replication and validation of results. There is a notable lack of best practices for data processing, network construction, and network statistics, as well as inadequate validation of key factors such as statistical thresholds, appropriate sample sizes, and the inclusion of normal control groups for comparison. As a result, variations in methodological approaches, including the radiotracer/target chosen, the construction of the connectivity matrix, data acquisition, scanner type and resolution, radiotracer specificity to neurotransmitter systems, off-target binding/kinetic parameter outcome of interest, processing techniques, and standardization protocols, can lead to discrepancies in results across studies [ 59 ]. Furthermore, the absence of robust validation mechanisms for the obtained results exacerbates these challenges. While statistical validation is common, the lack of validation through pharmacological agents or ex vivo data undermines the credibility and interpretability of the findings. The limited validation efforts reported in the literature highlight the urgent need for more rigorous validation protocols to enhance the reliability and value of biological insights obtained with molecular connectivity analyses. One example is represented by the use of several dopamine-based PET imaging to explore cortical connections. While it is possible to investigate dopamine extrastriatal signals (see for example [ 93 ]), there are brain regions where the PET signal for dopamine-target tracers is driven by perfusion and non-specific binding rather than dopamine. Therefore, even if a strong statistical effect can be found for these regions, the interpretation via dopamine-mediated mechanisms remains questionable and warrants further investigations. Additionally, the scarcity of longitudinal studies prevents the assessment of these molecular connectivity approaches in tracking network alterations and changes over time throughout the course of a disease. Finally, most molecular connectivity results are based on group-level analyses due to the inherently "static" nature of PET images [ 37 ], which contain either tracer uptake values averaged over a certain time window or parametric values derived from the tracer's dynamics. This limitation makes within-subject "fMRI-like" analysis of PET images challenging, resulting in molecular connectivity analysis typically being performed at the group level. Recently, new studies have conducted single-subject level analyses using static PET data through specific methodologies like Jensen-Shannon entropy [ 94 ], Kullback-Liebler divergence [ 95 ], and perturbation approaches [ 96 ]. However, these applications are still limited and not validated. Another line of research applies network-based approaches to dynamic bolus injection and infusion data, often called functional PET (fPET), attempting to mimic the approaches used for functional connectivity in fMRI, opening new possibilities for assessing molecular connectivity at the individual level. Until now, this has only been applied to 18 F-FDG PET data [ 44 , 97 ]. The only study in the literature using fPET data with a neurotransmitter tracer ( 18 F-DOPA) is the one by Hahn et al. [ 98 ], although it employed only univariate analysis methods. Future studies could extend these results by applying connectivity-based approaches. Addressing these methodological limitations requires a united effort to establish standardised protocols, enhance validation procedures, and overcome logistical hurdles associated with data integration. By overcoming these challenges, we can push molecular connectivity research towards greater robustness and applicability in clinical settings, realising its potential as a transformative tool in neuroscience and clinical practice. Finally, the existing literature predominantly focuses on the dopamine and serotonin systems, as these neurotransmitters are among the most frequently studied and utilised tracers. Consequently, there is a notable gap in research concerning other tracers. Further investigation into these less-explored tracers is necessary to enhance our understanding of their complementary roles in neural connectivity and to elucidate how their inclusion may advance the field’s comprehension of behaviour and disease states. Conclusions In summary, this review has explored how connectivity-based methods are changing the way we study neurotransmission using molecular imaging. Our comprehensive search highlighted the diverse applications and methodologies employed in this field, with a strong emphasis on their utility in studying neurodegenerative diseases and psychiatric disorders. The integration of approaches like covariance statistics and network analyses, alongside traditional methods, has helped us gain a better understanding of how neurotransmission systems work. These methods not only reinforce results from conventional analyses but also reveal new patterns of molecular activity that are critical for understanding disease mechanisms. However, despite the promising potential of these molecular connectivity methodologies, the field still faces several challenges. The lack of consistent nomenclature, varied methods, and a lack of strong validation highlight the need to establish best practices, robust validation protocols, and validation through application in drug studies, to push this research forward and bring it into clinical use. In conclusion, molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author contributions Mario Severino and Débora Elisa Peretti screened and analysed all the studies. Mattia Veronese served as the third reviewer to resolve any disagreements during the screening process and acted as the project administrator. Mario Severino drafted the manuscript. Débora Elisa Peretti and Mattia Veronese made substantial contributions to the study's conception, participated in drafting or critically revising the article for significant intellectual content, and approved the final version of the manuscript. All authors discussed the results and provided feedback and contributions on the manuscript. Acknowledgments This work was supported by the molecular connectivity working group ( https://molecularconnectivity.com/ ), an initiative of an international and interdisciplinary group of neuroimaging experts with background in biomedicine, health sciences, neuroscience, and physics with the ultimate goal to establish molecular imaging as tool to study brain connectivity. Silvia Paola Caminiti is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). Mattia Veronese is supported by EU funding within the MUR PNRR “National Center for HPC, BIG DATA AND QUANTUM COMPUTING (Project no. CN00000013 CN1), the Ministry of University and Research within the Complementary National Plan PNC DIGITAL LIFELONG PREVENTION - DARE (Project no PNC0000002_DARE), and by Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN), (Project no 2022RXM3H7). Arianna Sala is a postdoctoral researcher at FRS-FNRS. References Jessell TM, Kandel ER (1993) Synaptic transmission: A bidirectional and self-modifiable form of cell-cell communication. 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J Cereb Blood Flow Metabolism 41:2973–2985 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Calhoun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vince","middleName":"D.","lastName":"Calhoun","suffix":""},{"id":380969238,"identity":"a572361b-7354-422e-9c60-f8ba7570779a","order_by":18,"name":"Silvia Paola Caminiti","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"Paola","lastName":"Caminiti","suffix":""},{"id":380969239,"identity":"d38b7df4-036f-4e6e-83d3-63a4b1f8f950","order_by":19,"name":"Xin Di","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Di","suffix":""},{"id":380969240,"identity":"65649483-b2f1-4957-845b-f9a2606016d6","order_by":20,"name":"Christian Habeck","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Habeck","suffix":""},{"id":380969241,"identity":"efa2f612-11d9-4699-9e65-3e6a16cf244b","order_by":21,"name":"Sharna Jamadar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sharna","middleName":"","lastName":"Jamadar","suffix":""},{"id":380969242,"identity":"47a016da-37ef-4a71-aa05-d2215840b1b0","order_by":22,"name":"Daniela Perani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Perani","suffix":""},{"id":380969243,"identity":"2b73381b-e014-4756-985e-cbc189ce9f72","order_by":23,"name":"Arianna Sala","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Arianna","middleName":"","lastName":"Sala","suffix":""},{"id":380969244,"identity":"0e3af857-769b-456b-b84e-ce7098106848","order_by":24,"name":"Vesna Sossi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vesna","middleName":"","lastName":"Sossi","suffix":""},{"id":380969245,"identity":"fbee5310-1df4-433c-b17a-9962f82ab350","order_by":25,"name":"Igor Yakushev","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Igor","middleName":"","lastName":"Yakushev","suffix":""},{"id":380969246,"identity":"d2efefe3-5574-4b75-9927-a38f39b623ad","order_by":26,"name":"Joana B. Pereira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Joana","middleName":"B.","lastName":"Pereira","suffix":""},{"id":380969247,"identity":"a199a704-c1a3-43c2-8b4b-e984fa3efaa5","order_by":27,"name":"Mattia Veronese","email":"","orcid":"","institution":"Università degli Studi di Padova","correspondingAuthor":false,"prefix":"","firstName":"Mattia","middleName":"","lastName":"Veronese","suffix":""}],"badges":[],"createdAt":"2024-11-21 13:31:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5498198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5498198/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69832417,"identity":"d800379e-0011-4b27-b7a7-f82b2b512488","added_by":"auto","created_at":"2024-11-25 15:45:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259353,"visible":true,"origin":"","legend":"\u003cp\u003eNeurotransmission: 1) Neurotransmitters are synthesised 2) Neurotransmitters are stored in vesicles 3) Action potential arrives at the presynaptic terminal 4) The action potential causes the opening of voltage-gated Ca²⁺ channels allowing the influx of calcium ions 5) Ca²⁺allows vesicles docking and neurotransmitters release in the synaptic cleft 6) Neurotransmitter binds to receptor causing the opening or closing of channels 7) Postsynaptic potential is generated 8) Neurotransmitters are removed back to the presynaptic cleft 9) Vesicular membranes are retrieved from the plasma membrane (Figure created in \u003ca href=\"https://www.biorender.com/\"\u003ehttps://www.biorender.com/\u003c/a\u003e)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5498198/v1/4005e3bd40aa50b25ab6fde7.png"},{"id":69830917,"identity":"973b8a81-bb01-4255-98ae-6f03bdec84aa","added_by":"auto","created_at":"2024-11-25 15:37:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104569,"visible":true,"origin":"","legend":"\u003cp\u003eTargets of neurotransmission measurable with PET and SPECT imaging (Figure created in \u003ca href=\"https://www.biorender.com/\"\u003ehttps://www.biorender.com/\u003c/a\u003e)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5498198/v1/e93370d5b6a3ce760a586ca8.png"},{"id":69830921,"identity":"8abe0745-b21b-473c-9478-2f320509a52e","added_by":"auto","created_at":"2024-11-25 15:37:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":555375,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flowchart for studies ‘selection\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5498198/v1/3dcc2b471f1da0d54f7ce4fb.jpeg"},{"id":69834531,"identity":"3ebd8dc5-007d-4f99-b00b-74dce08ec338","added_by":"auto","created_at":"2024-11-25 16:06:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2226181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5498198/v1/8e5d364a-620e-4f15-b38b-af7bdfcdf345.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMolecular connectivity studies in neurotransmission: a scoping review\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003ePET and SPECT to study Neurotransmission\u003c/h2\u003e \u003cp\u003eNeurotransmission is the primary process by which neurons communicate and represents a biological pillar to all functions of the central and peripheral nervous system, including sensation, movement, cognition, and, ultimately, individual behaviour [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Neural communication can occur through two main modalities of synaptic transmission: chemical and electrical. At chemical synapses, information is transferred via the release of neurotransmitters from one cell, which are detected by an adjacent cell [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast, at electrical synapses, the cytoplasm of adjacent cells is directly connected by clusters of intercellular channels called gap junctions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Chemical synapses are more common in the human brain [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Neurotransmission at these synapses depends on the interaction between molecular and electrical signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), starting with the action potential - an electrical signal generated in the neuron's cell body - which travels down the axon towards the synapse. When the action potential reaches the axon terminal or presynaptic terminal, it triggers the opening of voltage-gated calcium channels. Calcium ions (Ca\u0026sup2;⁺) enter the presynaptic terminal through these channels, causing synaptic vesicles to fuse with the presynaptic membrane. As a result, the neurotransmitters contained in the vesicles are released into the synaptic cleft - the space between the presynaptic and postsynaptic neurons - where they transmit the signal by binding to the receptors of the postsynaptic neuron. This binding activates or inhibits the postsynaptic neuron, affecting numerous other neurons within specific pathways that are essential for maintaining the homeostatic balance of neuronal activity and overall healthy brain function [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDysregulation and alterations of neurotransmitter and neuroreceptor levels and functions, whether due to deficiency or excess, are implicated in the pathophysiology of numerous neurodegenerative conditions such as Alzheimer\u0026rsquo;s disease (AD) and Parkinson\u0026rsquo;s disease (PD) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], as well as psychiatric disorders like Schizophrenia and Major Depressive Disorder (MDD) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, elucidating the mechanisms of neurotransmission in vivo is paramount for advancing our understanding of brain function in both health and disease states and for the development of novel pharmacological treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNeurotransmission can be studied in vivo using molecular imaging tools including Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These imaging modalities are powerful tools for measuring the local concentration of diverse molecular targets with remarkable sensitivity and specificity in a non-invasive manner [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Molecular imaging can visualise different aspects of neurotransmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the type of radiotracer used, it is possible to quantify neurotransmitter synthesis (e.g., dopamine synthesis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]), the concentration of synaptic vesicle density (e.g., SV2A [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]), specific neurotransmitter types (such as the vesicular monoamine transporter 2 (VMAT\u003csub\u003e2\u003c/sub\u003e) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]), the density and distribution receptors (e.g. D\u003csub\u003e2\u003c/sub\u003e/D\u003csub\u003e3\u003c/sub\u003e receptors for the dopamine system [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], or 5HT\u003csub\u003e1A\u003c/sub\u003e/5HT\u003csub\u003e2A\u003c/sub\u003e receptors for the serotonin system [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]), or neurotransmission transporters (e.g., DAT for dopamine [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and SERT for the serotonin system [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]). Finally, molecular imaging can be used to measure endogenous neurotransmitter levels and their release by detecting the competitive binding between endogenous neurotransmitters and radioligands to the same neuroreceptor sites [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn medicine, molecular imaging has repeatedly proved to be an invaluable tool for probing neurotransmission alterations underlying many brain disorders and describing the spatiotemporal evolution of neurotransmission abnormalities throughout different stages of these diseases [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Some of the neurotransmission imaging methods have also translated into clinical work-up as diagnostic and monitoring biomarkers recommended by international clinical guidelines [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The most well-known example is \u003csup\u003e123\u003c/sup\u003eI-FP-CIT SPECT imaging, used to identify and stage the degeneration of dopaminergic neurons in PD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, molecular imaging permits the temporal modelling of disease-related neurotransmission alterations, facilitating the evaluation of disease progression and the assessment of therapeutic interventions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate vs Network and multivariate approaches\u003c/h2\u003e \u003cp\u003eOver the past two decades, the concept of the brain as a network has become central to neuroscience. This network-based perspective emphasizes that brain functions emerge from the interaction of distributed regions within large-scale networks [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While blood oxygenation level-dependent (BOLD) functional MRI (fMRI) has become the most widely used tool to study functional connectivity, due to its accessibility, cost-effectiveness, and lack of ionising radiation, it relies heavily on hemodynamic signals. Thus, this method alone cannot fully capture the complexity of brain activity, which involves both biochemical and electrical processes. In contrast, molecular imaging, which uses radiotracers to detect molecular targets with high sensitivity and specificity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], offers more accurate biological insights that complement fMRI. Given the limitations of single-modality approaches, there is a growing recognition of the need for an integrative, multimodal framework to comprehensively understand the brain's connectome. Molecular imaging, with its unique capability to probe biochemical pathways, can play a crucial role in this integrative approach.\u003c/p\u003e \u003cp\u003eNonetheless, traditional analysis methods in brain PET and SPECT research mainly focus on quantifying absolute tracer binding or uptake within specific brain regions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. While these approaches provide valuable insights into \u003cem\u003ein vivo\u003c/em\u003e brain structure and activity, they possess limitations that warrant consideration. Region-wise analyses rely on a priori definition of anatomical or functional areas of the brain, potentially overlooking subtle or distributed effects across the brain [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Parametric voxel-wise analyses, on the other hand, depend on the image resolution of the scanner, reconstruction methods, partial volume correction (PVC) and reference regions used for normalisation. Moreover, regardless of the spatial resolution of the analysis, each volume of interest is typically treated as independent, ignoring brain spatial covariance. This often necessitates strict multiple comparison corrections to prevent inflated Type I error rates, which can result in overcorrection and increased Type II errors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These limitations, alongside the spread of network science into neuroimaging field, motivated the development and validation of complementary multivariate and network methodologies in molecular neuroimaging, able to capture the complex interactions among brain regions and provide a more comprehensive understanding of brain functioning and disease pathology.\u003c/p\u003e \u003cp\u003eMultivariate methods are specifically used to address the complexities and interdependencies of neuroimaging data by simultaneously considering the interrelationships among multiple sources of information and possibly multiple volumes of interest [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These methods facilitate the identification of intricate patterns of brain activity and structure that remain undetectable when analysing individual variables in isolation. For example, rather than examining the activity of a single brain region, multivariate methods allow for assessing the combined activity of several regions to determine their collective contribution to a specific task or condition [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These methods can also identify combinations of brain activities that correspond to distinct cognitive states or differentiate between healthy and diseased brains, and between different diseases and stages. Furthermore, multivariate techniques can be employed to reduce the dimensionality of the data, extract a smaller set of key features, or integrate data from diverse sources [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParallel with multivariate approaches, network-based approaches, usually constructed using pair-wise correlation between regions or voxels, have been largely employed to study brain connectivity. A common feature of these network methods is the ability to construct a mathematical representation of the brain in the form of an adjacency matrix [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this representation, brain regions are modelled as nodes, while the edges between them represent the biological or statistical interactions between these regions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough most of the recent multivariate and network approach findings in neuroimaging are derived from structural and fMRI studies, in recent years these methodological advances have increasingly been applied to PET/SPECT data [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This paradigm shift follows a decade of evidence suggesting that neurotransmission and molecular pathological alterations underlying brain diseases invariably pass through large-scale brain networks [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this context, \u003cem\u003emolecular connectivity\u003c/em\u003e refers to an approach that leverages molecular imaging to explore brain connectivity. This umbrella term is commonly used in the literature to describe the statistical interdependencies between regional measurements obtained from molecular imaging techniques [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the past few years, the term \u0026lsquo;molecular connectivity\u0026rsquo; has been used to describe various methodologies aimed at constructing maps or matrices that reflect the statistical relationships between brain regions based on their molecular properties (as derived from PET or SPECT). These maps are generated through different statistical analyses, depending on the type of modality, tracer used, and the computational method chosen. Consequently, the biological interpretation and insights derived from the results can vary [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. One example is the computation of covariance matrices of regional PET signals across subjects. Up to today, this represents the most common approach used as a proxy for molecular connectivity. This method is favoured for its simplicity and the fact that it can be applied to static PET data, offering a broader perspective on shared connectivity patterns across populations [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, the limitation of estimating connectivity at the group level, rather than at the individual level, poses challenges for biological interpretation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. An alternative approach involves using dynamic data to construct molecular connectivity maps at the individual level. This method leverages temporal information from the radiotracer kinetics to compute connectivity through various computational techniques [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOther approaches to study brain connectivity that do not rely on the construction of an adjacency matrix are the scaled subprofile model (SSM), a multivariate principal component analysis (PCA)-based algorithm applied directly to voxel-by-voxel covariance data. In this case, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Ultimately, another source-based multivariate method is independent component analysis (ICA), a data-driven computational procedure that decomposes or \u0026lsquo;un-mixes\u0026rsquo; a measured signal into its maximal spatially independent \u0026lsquo;sources\u0026rsquo; [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese approaches allow researchers to simultaneously explore variations in the relationships between multiple brain regions or patterns of activation, offering valuable insights into covarying patterns of tracer binding across the entire brain.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePurpose of the study\u003c/h3\u003e\n\u003cp\u003eThis paper provides a comprehensive, state-of-the-art overview of brain connectivity analysis in the study of neurotransmission using molecular neuroimaging, presented through a scoping review. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge. Through an in-depth review, we provide researchers and clinicians with a clear understanding of the current landscape, highlighting key successes while outlining challenges and potential strategies to address them, with the goal of advancing future research and translating these approaches into clinical applications.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-analyses, guidelines extension for scoping reviews (PRISMA-ScR) were adhered to in conducting this scoping review [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The PRISMA-ScR checklist was used to perform the analysis. A study protocol was prepared in OSF prior to the initiation of data collection to ensure methodological rigour and transparency.\u003c/p\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eOriginal articles were searched for in three bibliographic databases (MEDLINE (via Ovid), EMBASE (via Elsevier), and Scopus (via Elsevier) on July 14, 2023. A second search was rerun on April 4, 2024. The search strategy consisted of two key concepts: (1) Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) and (2) connectivity. The complete search strategy is listed in Annex I.\u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eMolecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria. The eligibility criteria (inclusion and exclusion criteria) were defined as explained below. These criteria encompassed original studies that: a) employed brain PET or SPECT as imaging modalities, b) measured parameters such as blood flow, metabolism, neuroreceptor systems, protein/molecule synthesis, or protein/molecule density. Exclusion criteria comprised preclinical investigations, studies focusing on regions outside the brain, post-mortem analyses, animal studies, and those utilising monoclonal antibody imaging techniques. Finally, letters, commentaries, review papers, and conference abstracts were also excluded. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the inclusion and exclusion criteria used in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEligibility criteria (inclusion and exclusion criteria) of references to be included in the scoping review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnimals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConcept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConnectivity, covariance, network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContext\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET and SPECT\u003c/p\u003e \u003cp\u003eMeasure: blood flow, metabolism, neuroreceptor systems, protein/molecule synthesis, or protein/molecule density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegions outside the brain\u003c/p\u003e \u003cp\u003ePost-mortem analyses\u003c/p\u003e \u003cp\u003eMonoclonal antibody imaging techniques\u003c/p\u003e \u003cp\u003ePreclinical investigations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeer-reviewed original studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShort commentaries\u003c/p\u003e \u003cp\u003eConference abstracts\u003c/p\u003e \u003cp\u003eReviews\u003c/p\u003e \u003cp\u003eLetters to editors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudies selection and data extraction\u003c/h2\u003e \u003cp\u003eFirst, titles and abstracts were screened independently by two authors (DEP and MS) to exclude irrelevant records based on the eligibility criteria. A third author (MV) played the role of the third peer to arbitrate in case of disagreements. Then, the full text of each selected article was independently screened by 2 authors (DEP and MS). Additional studies were included a posteriori at the authors' discretion. Notably, we included studies published prior to 1993 (formal definition of brain functional connectivity [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]), where the terminology deviates from connectivity, networks, or connectomics (term introduced in 2005 by [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]). These studies were deemed relevant as they shared the overarching objective of investigating structural and functional interactions across brain regions. This inclusive approach ensured a comprehensive review of relevant literature, capturing both contemporary studies and earlier works contributing to the understanding of brain multi-scale architecture. Comprehensive information was extracted and organised into a pre-defined data sheet developed by the authors for each of these final articles. This information encompassed various aspects, including the names of the authors, year of publication, characteristics of the study population (both healthy control and patient groups), PET/SPECT tracer utilised, putative marker type and specification, protocol and analysis type, methods employed for connectivity analysis, software utilised, main findings, validation type (if applicable), multimodality type (if applicable), and any reported measures of multimodality similarity and performance. This systematic extraction process ensured thorough documentation of relevant details from each included study, facilitating comprehensive analysis and synthesis of findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSearch results\u003c/h2\u003e \u003cp\u003eAfter removing duplicates, a total of 3,568 references were retrieved from database searches (3,213 in July 2023 and 355 in April 2024). Following title and abstract screening, 722 references were selected for full-text review. Ultimately, 488 of these met the eligibility criteria and underwent data extraction. Full texts were excluded primarily due to incorrect study type or unsuitable analysis (i.e., lacking molecular connectivity). From the eligible studies, a subset of 32 articles specifically addressing neurotransmission systems was included in this review. Additionally, 7 more studies recommended by experts were screened and added to the scoping review, resulting in a final total of 39 articles. The identification of these articles was based mainly on the molecular probe used and its main binding targets. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the PRISMA flow chart describing articles selection process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudies characteristics\u003c/h2\u003e \u003cp\u003eStudies were categorised based on the primary neurotransmitter system being targeted (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In instances where a study investigated multiple neurotransmitters, it was assigned to a single category based on the predominant target. The resulting sample comprised 23 studies targeting the dopamine system (radiotracers used: \u003csup\u003e11\u003c/sup\u003eC-FLB457, \u003csup\u003e18\u003c/sup\u003eF-FDOPA, \u003csup\u003e11\u003c/sup\u003eC-FeCIT, \u003csup\u003e18\u003c/sup\u003eF-Fallypride, \u003csup\u003e11\u003c/sup\u003eC-MP, \u003csup\u003e11\u003c/sup\u003eC-FLB457, \u003csup\u003e11\u003c/sup\u003eC-Raclopride, \u003csup\u003e18\u003c/sup\u003eF-FEOBV, \u003csup\u003e18\u003c/sup\u003eF-CFT, \u003csup\u003e11\u003c/sup\u003eC-CFT, \u003csup\u003e11\u003c/sup\u003eC-SCH23390, \u003csup\u003e18\u003c/sup\u003eF-FPCIT, \u003csup\u003e123\u003c/sup\u003eI-FP-CIT, \u003csup\u003e11\u003c/sup\u003eC-DTBZ, \u003csup\u003e11\u003c/sup\u003eC(+)-PHNO), 9 studies focusing on the serotonin system (radiotracers used: \u003csup\u003e11\u003c/sup\u003eC-SB217045, \u003csup\u003e11\u003c/sup\u003eC-WAY-100635, \u003csup\u003e11\u003c/sup\u003eC-DASB, \u003csup\u003e11\u003c/sup\u003eC-MADAM), 2 studies examining \u0026micro;-opioid receptors (radiotracer used: \u003csup\u003e11\u003c/sup\u003eC-Carfentanil), 2 studies assessing synaptic density (radiotracer used: \u003csup\u003e11\u003c/sup\u003eC-UCB-J), 2 studies targeting muscarinic receptors (radiotracer used: \u003csup\u003e123\u003c/sup\u003eI-QNB), and 1 study investigating glutamate receptors (radiotracer used: \u003csup\u003e11\u003c/sup\u003eC-ABP688). The most frequently utilised outcome measure across these studies was the tracer binding potential (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e, [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]) employed by 20 studies. This metric represents the equilibrium ratio of the concentration of specifically bound radioligand to the combined concentration of free and non-specifically bound radioligand. Other studies looked at additional parameters as proxies for tracer-specific activity, such as, Specific Binding Ratio (\u003cem\u003eSBR\u003c/em\u003e), used in 4 studies, Standardized Uptake Value ratio (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SUV}_{r}\\)\u003c/span\u003e\u003c/span\u003e), also used in 4 studies, the distribution volume ratio (\u003cem\u003eDVR\u003c/em\u003e), in 3 studies, and total distribution volume (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, used in 2 studies.\u003c/p\u003e \u003cp\u003eIn terms of study populations, 34 studies included healthy control (HC) subjects, 15 focused on PD, 4 on mild cognitive impairment (MCI), 3 on Schizophrenia, 3 on Dementia with Lewy bodies (DLB), 3 on MDD, and 2 on AD. Only 1 study investigated anxiety, epilepsy, cocaine-use disorder (CUD) and attention-deficit/hyperactivity disorder (ADHD). Pairwise correlation was the most frequently applied methodology to investigate molecular connectivity (14 studies), followed by inter-regional correlation (10 studies), PCA-base approaches (6 studies), and ICA (4 studies). Partial least squares (PLS) was employed in 2 studies, while the remaining studies used combinations of these approaches, or \u0026ldquo;non-conventional\u0026rdquo; approaches usually not employed in this field.\u003c/p\u003e \u003cp\u003eFinally, in terms of the type of analysis conducted, 34 studies performed inter-subject analyses, where comparisons are made between different individuals or groups to identify variations across subjects. 4 studies included both inter-subject and intra-subject comparisons, the latter involving analyses within the same individual over time or under different conditions. Only 1 study focused solely on intra-subject analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDopamine system\u003c/h2\u003e \u003cp\u003eSeveral studies employed PET imaging to investigate imaging-derived dopamine-weighted networks across various neurological conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eYasuno et al.\u003c/b\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] applied structural equation modelling to \u003csup\u003e11\u003c/sup\u003eC-FLB457 brain PET imaging of HC and Schizophrenia patients. Using this method, the inter-regional correlations of D\u003csub\u003e2\u003c/sub\u003e receptor binding were decomposed to assign numerical weights (called path coefficients) to the anatomical connections and to evaluate the effective connectivity of regional D\u003csub\u003e2\u003c/sub\u003e receptor binding in Schizophrenia. The strength and signs of these path coefficients were compared between groups and used to identify disease-specific changes in the connectivity of regional D\u003csub\u003e2\u003c/sub\u003e receptor binding within the same anatomical networks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKaasinen et al.\u003c/b\u003e [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] employed regional Pearson correlation and PCA to investigate corticostriatal profiles of glucose consumption and extrastriatal dopamine synthesis capacity covariance patterns in PD. The examination of striatal tracer binding revealed asymmetrical sex-dependent uptake of \u003csup\u003e18\u003c/sup\u003eF-FDOPA in the putamen, while revealing negative correlations between striatal \u003csup\u003e18\u003c/sup\u003eF-FDOPA uptake and PD clinical severity. None of these findings were seen with \u003csup\u003e18\u003c/sup\u003eF-FDG. Similarly, the network analysis using PCA revealed a specific component related to thalamus and cerebellum in \u003csup\u003e18\u003c/sup\u003eF-FDG uptake associated with both \u003csup\u003e18\u003c/sup\u003eF-FDOPA uptake and disease severity. On the contrary, univariate analysis showed poor correlations between \u003csup\u003e18\u003c/sup\u003eF-FDOPA and \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in PD when using raw regional uptake.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCervenka et al.\u003c/b\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] examined the relationship between dopamine D\u003csub\u003e2\u003c/sub\u003e receptors across all brain regions in HC. \u003csup\u003e11\u003c/sup\u003eC-FLB457 PET was used to measure binding in extrastriatal regions, while \u003csup\u003e11\u003c/sup\u003eC-raclopride was employed for the measurements of D\u003csub\u003e2\u003c/sub\u003e distribution in the striatum. Pairwise correlations were calculated between regional \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e values of \u003csup\u003e11\u003c/sup\u003eC-raclopride, and a voxel-based correlation analysis was performed using parametric images of \u003csup\u003e11\u003c/sup\u003eC-FLB457 binding. Additionally, correlations between regional-based \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e values and parametric values were assessed for each region separately. The results showed that striatal receptor availability did not exhibit statistically significant correlations with any of the extrastriatal regions. These findings suggested that striatal dopaminergic biomarkers may not serve as a reliable index for global dopamine function, and results do not support using the striatum as an index for global D\u003csub\u003e2\u003c/sub\u003e receptor availability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCaminiti et al.\u003c/b\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] characterised presynaptic dopamine activity in early PD patients using \u003csup\u003e11\u003c/sup\u003eC-FeCIT PET and assessed connectivity within nigrostriatal and mesolimbic systems using partial correlation. The aim of the study was to assess \u0026ndash; by means of univariate and multivariate approaches - if the axons of the nigrostriatal dopaminergic system are an early site for vulnerability in PD. The findings indicated greater presynaptic degeneration in dorsal putamen than substantia nigra, and more severe molecular connectivity alteration in the nigrostriatal than mesolimbic pathway.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWorhunsky et al.\u003c/b\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] examined D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e receptors alterations in the midbrain, striatum and other subcortical structures in individuals with CUD and HC. The aim was to apply ICA on \u003csup\u003e11\u003c/sup\u003eC(+)-PHNO PET \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e data with the objective of unmix the D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e components of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e and examine distinct sources of receptor availability. ICA analysis identified three distinct source-based patterns of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e, suggesting that cocaine-related alterations in D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e may not be limited to the dorsal striatum and midbrain respectively but may extend into the pallidum and ventral striatum. Furthermore, these alterations sources were associated with duration of cocaine use and may indicate reciprocal and compensatory mechanisms of dopaminergic function in addiction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKlyuzhin et al.\u003c/b\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] applied PCA to identify voxel covariance patterns, and LASSO to optimally combine several patterns. These approaches were applied to analyse dopaminergic PET tracers (\u003csup\u003e11\u003c/sup\u003eC-DTBZ and \u003csup\u003e11\u003c/sup\u003eC-raclopride) binding in the striatum of PD subjects. The principal component (PC) loadings obtained in different groups of subjects revealed predominant voxel-level binding patterns associated with the initial symptom onset and disease progression. The PC-LASSO estimators captured information in a non-local manner, and hence enabled data-driven visualisation and interpretation of spatial patterns manifested in the images.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKim et al.\u003c/b\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] investigated interregional correlations of D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e receptor availability in Schizophrenia patients receiving antipsychotics using \u003csup\u003e18\u003c/sup\u003eF-fallypride PET and resting state-fMRI, revealing altered molecular and functional connectivity between striatal and extrastriatal regions in stable outpatients with schizophrenia on antipsychotics, which is mainly characterised by increased interregional relationships. These results suggested that the spatial organisation of D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e receptor availability and related functional connectivity were significantly perturbed in these subjects.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFu et al.\u003c/b\u003e [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] introduced a joint pattern analysis approach, canonical correlation analysis and orthogonal signal correction to identify characteristic spatial and temporal distribution patterns in PD using \u003csup\u003e11\u003c/sup\u003eC-DTBZ (VMAT\u003csub\u003e2\u003c/sub\u003e marker) and \u003csup\u003e11\u003c/sup\u003eC-MP (DAT marker) PET data. Results showed that the proposed approach was able to capture the spatial and temporal disease patterns with higher sensitivity compared to univariate analysis. The approach provided information not only on localized alterations but also on the spatial extent of such alterations, emphasizing a network behaviour of the molecular targets under investigation. Moreover, the approach decomposed the common information between data sets into distinct orthogonal patterns of characteristic dopaminergic changes that were more sensitive either to disease discrimination or to disease progression.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVeronese et al.\u003c/b\u003e [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] conducted a graph-based analysis across different PET tracers (\u003csup\u003e18\u003c/sup\u003eF-FDG, \u003csup\u003e18\u003c/sup\u003eF-FDOPA, \u003csup\u003e11\u003c/sup\u003eC-SB217045) both in controls and in diseased groups (AD and MCI), revealing that these metrics can complement standard PET analysis to understand how biological functions are organized across brain regions in healthy and pathological conditions. The study also showcased the sensitivity of connectivity results to experimental design and variables, including group inhomogeneity and image resolution and suggested that further methodological work is required to validate the use of more complex network metrics in the context of PET covariance analysis and to understand their biological interpretability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVerger et al.\u003c/b\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] investigated the feasibility and potential of molecular connectivity using neurotransmission tracers (\u003csup\u003e18\u003c/sup\u003eF-FDOPA and \u003csup\u003e123\u003c/sup\u003eI-FP-CIT) compared to metabolic connectivity (\u003csup\u003e18\u003c/sup\u003eF-FDG) in dopaminergic pathways of HC. Through interregional correlation analysis to construct a brain connectivity network, the study demonstrated that specific neurotransmission tracers provide higher specificity in revealing the mesotelencephalic system (nigro-striatal, mesolimbic, and mesocortical pathways) compared to metabolic connectivity. Notably, \u003csup\u003e18\u003c/sup\u003eF-FDOPA was more effective than \u003csup\u003e123\u003c/sup\u003eI-FP-CIT in identifying the mesotelencephalic system, indicating that these dopaminergic targets are not equivalent. The findings underscore the advantages of using \u003csup\u003e18\u003c/sup\u003eF-FDOPA PET imaging for molecular connectivity, highlighting its superior sensitivity and specificity relative to \u003csup\u003e18\u003c/sup\u003eF-FDG metabolic connectivity and emphasising that the choice of imaging modality and neurotransmitter targeting is crucial.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMihaiescu et al.\u003c/b\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] performed a graph theory analysis of D\u003csub\u003e2\u003c/sub\u003e receptors measured with \u003csup\u003e11\u003c/sup\u003eC-FLB-457 in two brain networks: the meso-cortical dopamine network and the meso-limbic dopamine network in PD patients with cognitive decline. The findings suggested how connectivity dysregulation in extrastriatal dopamine networks may contribute to cognitive decline. Furthermore, this study wanted to highlight that multivariate network analysis captured different aspects of the dopaminergic dysfunction compared to univariate regional comparisons of localised receptor density differences.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSala et al.\u003c/b\u003e [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] examined the molecular connectivity alterations in AD, MCI and HC subjects\u0026rsquo; data measured with \u003csup\u003e123\u003c/sup\u003eI-FP-CIT SPECT tracer using partial correlation with gender, age, and reconstruction method included as nuisance covariates. The study provided biological in vivo evidence for a significant derangement of the meso-limbic dopaminergic system in AD, already plateauing in the prodromal stages. Both in vivo dopaminergic binding density and molecular connectivity analysis, pointed to different degrees of vulnerability of the dopaminergic afferents from specific dopaminergic nuclei.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSmart et al.\u003c/b\u003e [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] assessed the utility of four-dimensional ICA application to a competition binding PET study using \u003csup\u003e11\u003c/sup\u003eC(+)-PHNO PET tracer with the D\u003csub\u003e3\u003c/sub\u003e antagonist ABT-728, for the estimation of subtype-specific receptor occupancy. The results showed that ICA identified two distinct components of change in binding on the basis of spatiotemporally coherent variance across subjects and time points. The spatial sources of these components were highly consistent with D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e related \u003csup\u003e11\u003c/sup\u003eC(+)-PHNO binding distributions in the brain, suggesting that this analysis successfully separated each receptor subtype without any a priori assumptions. This interpretation was further supported by relative changes in the intensity of each source during blockade with the D\u003csub\u003e3\u003c/sub\u003e-selective antagonist ABT-728, which were closely matched to region-based occupancy estimates.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRebelo et al.\u003c/b\u003e [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] used covariance statistics at molecular and functional levels (measured through fMRI) to explore striato-cortical links in PD in on/off medication states using \u003csup\u003e11\u003c/sup\u003eC-Raclopride PET tracer. The study showed that functional and molecular forms of brain plasticity are related. These authors found a tight link between functional activation and synaptic changes at the molecular level, reflecting network reorganisation of compensatory molecular and functional mechanisms in PD.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePeng et al.\u003c/b\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] found that PD-related pattern expression levels, calculated using SSM-PCA, and measured in early-phase \u003csup\u003e18\u003c/sup\u003eF-FPCIT PET scans, discriminated patients with early-stage PD from age-matched HC subjects with similar accuracy for the first 2, 5, and 10 min of the dynamic \u003csup\u003e18\u003c/sup\u003eF-FPCIT PET acquisitions. These results suggested that dual-phase \u003csup\u003e18\u003c/sup\u003eF-FPCIT PET is a viable methodology for quantitative assessment of PD-related metabolic brain networks, as an alternative to \u003csup\u003e18\u003c/sup\u003eF-FDG PET, and presynaptic nigrostriatal dopaminergic functioning in a single imaging session.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSanchez-Catasus et al.\u003c/b\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] examined the striatal acetylcholine\u0026ndash;dopamine imbalance hypothesis in early PD patients using dual-tracer PET and dopaminergic PET\u0026ndash;informed correlational tractography. Firstly, the authors estimated the integrity of the dopaminergic nigrostriatal white matter tracts in PD subjects by incorporating molecular information from striatal \u003csup\u003e11\u003c/sup\u003eC-DTBZ into the fibre-tracking process using correlational tractography (based on quantitative anisotropy (QA)). Subsequently, they used voxel-based correlation to test the association of the mean QA of the nigrostriatal tract of each cerebral hemisphere with the striatal \u003csup\u003e18\u003c/sup\u003eF-FEOBV \u003cem\u003eDVR\u003c/em\u003e in PD subjects. The same analysis was performed for \u003csup\u003e11\u003c/sup\u003eC-DTBZ \u003cem\u003eDVR\u003c/em\u003e in 12 striatal subregions. Taken together, results provided in vivo evidence of the imbalance between acetylcholine and dopamine signalling systems in the striatum in early PD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBoccalini et al.\u003c/b\u003e [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] investigated gender differences in the molecular connectivity of the dopaminergic systems using a large PPMI cohort of newly diagnosed and drug-na\u0026iuml;ve idiopathic PD patients measured with \u003csup\u003e123\u003c/sup\u003eI-FP-CIT SPECT. Partial correlation was used to assess regional co-variation in tracer uptake across subjects, and percentage of altered molecular connections in each network was used to quantify the severity of connectivity alterations between males and females. Results showed that nigrostriatal bindings and connectivity were more altered in males than females, providing unique evidence of gender effects in molecular connectivity of both dopaminergic systems affected by the disease.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLiu et al.\u003c/b\u003e [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] conducted a dual-tracer PET study employing both \u003csup\u003e11\u003c/sup\u003eC-CFT DAT imaging and \u003csup\u003e18\u003c/sup\u003eF-FDG imaging to compare dopaminergic dysfunction and glucose metabolism characteristics in early-onset PD caused by different gene mutations (PRKN-EOPD and GU-EOPD) using seed-based correlation analysis. Results demonstrated differences in the symmetry and severity of dopaminergic dysfunction between the two gene mutations, suggesting potential network reorganisation due to compensatory mechanism in PRKN-EOPD which did not occur in those with GU-EOPD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBoccalini et al.\u003c/b\u003e [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] aimed to investigate molecular connectivity alterations in nigrostriatal and mesolimbic dopaminergic pathways focusing on sex differences by using \u003csup\u003e123\u003c/sup\u003eI-FP-CIT binding in striatal and extrastriatal regions in patients with probable DLB (pDLB). Assessment of molecular connectivity between targets of each dopaminergic pathway was performed via partial correlation analysis, and percentage of altered molecular connections in each network for males and females was calculated to quantify the severity of connectivity alterations. Results showed that connectivity of the nigrostriatal and mesolimbic systems was affected in both sex groups but with different patterns, with pDLB females showing more long-distance connectivity alterations between subcortical and cortical regions of the dopaminergic systems.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCaminiti et al.\u003c/b\u003e [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] using \u003csup\u003e123\u003c/sup\u003eI-FP-CIT SPECT imaging adopted correlation analysis to assess the involvement of the ventral and dorsal dopaminergic circuitries in prodromal and clinical phases of DLB. Correlation analyses assessed the significant differences in connectivity between each clinical group and a subgroup of control subjects. This work provided the first evidence of widespread adaptive reconfigurations of dopaminergic networks in the continuum of Lewy body disease. The dopaminergic network showed an extensive increase of connectivity in prodromal phases, both in dorsal and ventral dopaminergic systems, supporting adaptive/compensating mechanisms, whereas a widespread loss of connectivity was prominent in overt DLB.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLuo et al.\u003c/b\u003e [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] using \u003csup\u003e11\u003c/sup\u003eC-CFT and \u003csup\u003e18\u003c/sup\u003eF-FDG PET imaging investigated the effects of Subthalamic nucleus (STN) deep brain stimulation (DBS) on the distribution of presynaptic DAT and the pattern of cerebral glucose metabolism in PD patients before and after surgery. By applying SSM-PCA, they found that STN-DBS could modify the cerebral network without preventing striatal DAT decline. On the other hand, UPDRS-III scores, particularly resting tremor and rigidity, were significantly reduced after STN-DBS surgery, confirming that STN-DBS is an effective therapeutic approach in controlling symptoms in patients with PD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBoccalini et al.\u003c/b\u003e [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] investigated dopamine transporter, using semiquantitative \u003csup\u003e123\u003c/sup\u003eI-FP-CIT SPECT imaging, in a large cohort of idiopathic PD patients, healthy subjects and Scan Without Evidence of Dopaminergic Deficit (SWEDD) cases. Their covariance statistics analysis highlighted distinct clinical and molecular trajectories of PD and SWEDD subjects. SWEDD subjects were characterised by prominent non-motor symptoms, absence of hyposmia, and generally preserved dopaminergic binding, but prevalent mesocorticolimbic connectivity impairment, suggesting other mechanisms contributing to SWEDD pathophysiology.\u003c/p\u003e \u003cp\u003eFinally, by using the world's largest combined \u003csup\u003e11\u003c/sup\u003eC-SCH23390 D\u003csub\u003e1\u003c/sub\u003e receptors PET and MRI dataset from the DyNAMiC study, \u003cb\u003ePedersen et al.\u003c/b\u003e [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] tested the hypothesis that D\u003csub\u003e1\u003c/sub\u003e receptors organisation is aligned with functional architecture and that inter-regional relationships in D\u003csub\u003e1\u003c/sub\u003e receptors co-expression modulates functional cross-talk in control subjects. They applied a nonlinear embedding approach where functional and dopaminergic organisations were characterised as a set of low-dimensional manifolds and extended this analysis also to individual participants. Results demonstrated that D\u003csub\u003e1\u003c/sub\u003e receptors organisation followed a unimodal\u0026ndash;transmodal hierarchy, expressing a high spatial correspondence to the principal gradient of functional connectivity. They also demonstrated that individual differences in D\u003csub\u003e1\u003c/sub\u003e receptors density between unimodal and transmodal regions were associated with functional differentiation of the apices in the cortical hierarchy. Finally, they showed that spatial co-expression of D\u003csub\u003e1\u003c/sub\u003e receptors primarily modulates couplings within, but not between, functional networks. Together, these results showed that D\u003csub\u003e1\u003c/sub\u003e receptors co-expression provides a biomolecular layer to the functional organisation of the brain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSerotonin system\u003c/h2\u003e \u003cp\u003eSeveral studies have also investigated the brain network alterations in serotonin neurotransmission, mainly in neuropsychiatric disorders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHahn et al.\u003c/b\u003e [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] explored the association of serotonin-1A receptor binding obtained with \u003csup\u003e11\u003c/sup\u003eC-WAY-100635 PET imaging in the dorsal raphe nucleus and the entire brain in anxiety disorder patients before and after escitalopram treatment using covariance statistics, revealing enhanced autoreceptor-to-heteroreceptor binding correlation after treatment. Results underlined the evaluation of neurotransmitter systems on a network level potentially provides important complementary information to regional receptor levels.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTuominen et al.\u003c/b\u003e [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] applied a seed-based voxel-wise correlation analysis method for studying internal neurotransmitter network structure and intercorrelations of different neurotransmitter systems in the human brain of HC subjects. They evaluated serotonin transporter (\u003csup\u003e11\u003c/sup\u003eC-MADAM) and \u0026micro;-opioid (\u003csup\u003e11\u003c/sup\u003eC-Carfentanil) receptor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e intra- and intercorrelations. The analyses revealed nonuniformity in the serotonin transporter intracorrelations and identified a highly connected local network. Regionally specific intercorrelations between the opioid and serotonin tracers were found in areas relevant to several neuropsychiatric disorders, especially affective disorders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHahn et al.\u003c/b\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] investigated serotonin transporter associations using \u003csup\u003e11\u003c/sup\u003eC-DASB PET tracer in major depression from a network perspective, revealing disturbances in a major serotonin pathway. They identified the disturbance of a major 5-HT pathway in MDD through an interregional correlation approach. Results suggested a reduced serotonin transporter association between the midbrain dorsal raphe and the ventral striatum/nucleus accumbens complementing the biological mechanisms of anhedonia in major depression and further underlines the importance of the serotonergic system in reward processing. These results emphasised the importance of investigating neurotransmitter systems on a network level.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNorgaard et al.\u003c/b\u003e [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] employed a multivariate PLS approach to identify a pattern of serotonin transporter (5-HTT) levels, measured with \u003csup\u003e11\u003c/sup\u003eC-DASB PET imaging, fluctuating with group and season in seasonal affective disorder (SAD) a subtype of MDD. The method was able to identify and map a whole-brain pattern of 5-HTT levels that distinguished the brains of females without SAD from females suffering from SAD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVanicek et al.\u003c/b\u003e [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] investigated the altered interregional molecular associations of the serotonin transporter in ADHD using PET imaging. They utilised \u003csup\u003e11\u003c/sup\u003eC-DASB PET to assess SERT binding potential in regions rich in SERT and observed differences in SERT availability between adult patients with ADHD and healthy controls. Additionally, they conducted a correlational analysis to examine the interregional association of SERT binding, finding significant interregional differences in SERT \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e correlations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFu et al.\u003c/b\u003e [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] applying SSM-PCA to \u003csup\u003e11\u003c/sup\u003eC-DASB PET data, identified a serotonergic spatial covariance pattern characteristic of PD, strongly correlated with disease duration and dopaminergic denervation measured with \u003csup\u003e11\u003c/sup\u003eC-DTBZ PET imaging. The study highlighted that compared to previously used univariate analysis approaches, the spatial covariance method was found to be more sensitive in identifying disease-related abnormalities since no correlation between DTBZ and DASB \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e values of individual regions was found, suggesting PD affects the serotonergic system on a more global network level rather than any particular region in isolation. These findings suggested that disease-induced alterations of the serotonergic system, rather than being purely local, also affect interactions between separate regions in a disease-specific fashion and are closely linked to abnormalities in the dopaminergic system.\u003c/p\u003e \u003cp\u003eSimilarly, \u003cb\u003ePillai et al.\u003c/b\u003e [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] investigated molecular connectivity disruptions in MDD using covariance statistics applied to \u003csup\u003e11\u003c/sup\u003eC-WAY-100635 PET data. Results showed compromised structural and compensatory mechanisms of post-synaptic receptor regulation in MDD men. Interestingly, the study suggested that these individual differences in molecular connectivity between HC and MDD were so large that they may serve as a biomarker for the disorder.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFazio et al.\u003c/b\u003e [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] examined the impairment of serotonin transporter availability measured with \u003csup\u003e11\u003c/sup\u003eC-MADAM PET in early non-depressed PD patients using covariance statistics and graph metrics, detecting network changes preceding overt depletion in the serotoninergic system. The findings indicated that the serotoninergic system might become involved in PD patients as the disease progresses and importantly this finding was only captured by network measures, but not by direct regional binding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSmith et al.\u003c/b\u003e [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] studied the association between serotonin degeneration measured with \u003csup\u003e11\u003c/sup\u003eC -DASB and beta-amyloid deposition in mild cognitive impairment measured with \u003csup\u003e11\u003c/sup\u003eC-PIB using a multi-modal PLS algorithm. This approach identified a spatial covariance pattern that distinguished MCI from healthy controls characterised by lower serotonin transporter availability and greater cortical amyloid deposition. The pattern was expressed to a significantly greater extent in the MCI relative to the control group and was correlated with impairment in memory and executive function in the MCI group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOpioid system\u003c/h2\u003e \u003cp\u003eOnly two studies employed covariance analysis to investigate the modulation of \u0026micro;-opioid receptor activity and its implications in disease conditions. \u003cb\u003eWager et al.\u003c/b\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] investigated the placebo effects on \u0026micro;-opioid receptor binding potential using \u003csup\u003e11\u003c/sup\u003eC-carfentanil PET imaging in HC. Through interregional correlations, the authors found that placebo treatment increased functional connectivity between \u0026micro;-opioid-rich limbic and paralimbic regions, suggesting a mechanism for placebo-induced pain relief mediated by the endogenous opioid system.\u003c/p\u003e \u003cp\u003eIn contrast, \u003cb\u003eAshok et al.\u003c/b\u003e [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] utilised \u003csup\u003e11\u003c/sup\u003eC-carfentanil PET imaging to examine \u0026micro;-opioid receptor availability in Schizophrenia patients. Their findings revealed reduced \u0026micro;-opioid receptor availability in the striatum and brain regions associated with hedonic responses compared to healthy controls. Furthermore, correlation analysis indicated a significant global increase in \u0026micro;-opioid receptor connection strength in Schizophrenia patients relative to controls, highlighting aberrant \u0026micro;-opioid system activity in the context of Schizophrenia pathology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMuscarinic receptor system\u003c/h2\u003e \u003cp\u003e \u003cb\u003eColloby et al.\u003c/b\u003e conducted two studies investigating cholinergic muscarinic M1/M4 receptor networks in DLB and PD, respectively. In the study on DLB [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], they utilized spatial covariance analysis on \u003csup\u003e123\u003c/sup\u003eI-QNB SPECT scans to explore muscarinic M1/M4 connectivity in Cholinesterase Inhibitor (ChEI) naive patients. They identified baseline spatial covariance patterns of M1/M4 receptors that distinguished DLB from healthy individuals and were associated with positive changes in global cognition and neuropsychiatric symptoms after ChEI treatment. These findings suggested that specific brain regions play a crucial role in the neuropsychiatric profile of DLB. In the PD study [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], they employed a similar approach using \u003csup\u003e123\u003c/sup\u003eI-QNB SPECT scans to derive patterns distinguishing PD from healthy individuals and correlating with global cognition, motor severity, and cognitive decline in PD patients. They identified multiple cholinergic muscarinic receptor networks in PD, with cognition and motor severity showing similar topography, suggesting related cholinergic mechanisms underlying both phenotypes. The relative decrease in M1/M4 receptor expression within default mode network and frontal executive hubs could potentially serve as an indicator of future cognitive decline in PD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGlutamate receptor system\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDuBois et al.\u003c/b\u003e [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] conducted a study aiming to characterise the mGluR5 network in patients with focal cortical dysplasia (FCD) using \u003csup\u003e11\u003c/sup\u003eC-ABP688 PET imaging. Through graph theoretical analysis based on the comparison of probability density function of each regional \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{BP}_{\\text{N}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e, at the individual subject level, calculated by Jensen-Shannon divergence, they revealed abnormalities in large-scale mGluR5 networks linked to the duration of epilepsy in FCD patients. Their findings indicated decreased resilience and global efficiency, suggesting a less integrated network in FCD patients. These results support the notion that FCD may be better understood as a system-wide disorder rather than a focal abnormality from a glutamatergic neuroreceptor perspective. The graph approach employed in this study allows for the comparison of neuroreceptor systems imaged with PET to other measures of functional and structural connectivity, offering insights into the broader neurological implications of FCD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSynaptic density\u003c/h2\u003e \u003cp\u003eThe studies by \u003cb\u003eFang et al.\u003c/b\u003e delve into the exploration of synaptic density networks. In the first study [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], the authors employed ICA on \u003csup\u003e11\u003c/sup\u003eC-UCB-J PET data to identify coherent patterns of synaptic density variability in healthy individuals. The analysis revealed sample-independent networks consistently extracted across different model orders, suggesting that these networks contain both complementary and unique information compared to \u003csup\u003e18\u003c/sup\u003eF-FDG PET and resting state-fMRI. In their second study, \u003cb\u003eFang\u003c/b\u003e and colleagues [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] expanded on this by using ICA to examine associations between resting-state network (RSN) fluctuations and synaptic density using multimodal fMRI and \u003csup\u003e11\u003c/sup\u003eC-UCB-J PET in healthy controls. They found potential links between RSN activity and \u003csup\u003e11\u003c/sup\u003eC-UCB-J source networks, indicating that synaptic density networks may be intricately connected to the functioning of large-scale intrinsic brain networks. These findings shed light on the relationship between synaptic physiology and brain network organization that can be captured through the application of connectivity/multivariate approaches, providing valuable insights into the underlying mechanisms of brain function.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable summarizing the findings of the reviewed studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePET tracer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePutative marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYasuno et al. 2005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal effective\u0026nbsp;connectivity\u0026nbsp;of dopamine D2 receptor binding in schizophrenia.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-FLB457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;19) and Schizophrenia (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKaasinen et al. 2006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorticostriatal\u0026nbsp;covariance\u0026nbsp;patterns of 6-[18F]fluoro-L-dopa and [18F]fluorodeoxyglucose\u0026nbsp;PET\u0026nbsp;in Parkinson's disease.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG and \u003csup\u003e18\u003c/sup\u003eF-FDOPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlucose Metabolism and Dopa-Decarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWager et al. 2007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlacebo effects on\u0026nbsp;human\u0026nbsp;mu-opioid activity during pain.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-Carfentanil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026micro;-Opioid Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHahn et al. 2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEscitalopram enhances the association of serotonin-1A autoreceptors to heteroreceptors in anxiety disorders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-WAY-100635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin 5HT\u003csub\u003e1A\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;36) and Anxiety (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervenka et al. 2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET Studies of D2-Receptor Binding in Striatal and Extrastriatal Brain Regions: Biochemical Support In Vivo for Separate Dopaminergic Systems in Humans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-FLB457 and \u003csup\u003e11\u003c/sup\u003eC-raclopride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTuominen et al. 2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMapping neurotransmitter\u0026nbsp;networks\u0026nbsp;with\u0026nbsp;PET: an example on serotonin and opioid systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-MADAM and \u003csup\u003e11\u003c/sup\u003eC-Carfentanil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter and \u0026micro;-Opioid Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHahn et al. 2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttenuated Serotonin Transporter Association Between Dorsal Raphe and Ventral Striatum in Major Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-DASB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;20) and MDD (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAshok et al. 2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced mu opioid receptor availability in schizophrenia revealed with [11C]-carfentanil positron emission tomographic Imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-Carfentanil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026micro;-opioid Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;20) and Schizophrenia (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u0026oslash;rgaard et al. 2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrain\u0026nbsp;Networks\u0026nbsp;Implicated in Seasonal Affective Disorder: A Neuroimaging\u0026nbsp;PET\u0026nbsp;Study of the Serotonin Transporter.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-DASB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-SAD (n\u0026thinsp;=\u0026thinsp;13) and SAD (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariate PLS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVanicek et al. 2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltered interregional\u0026nbsp;molecular\u0026nbsp;associations of the serotonin transporter in attention deficit/hyperactivity disorder assessed with\u0026nbsp;PET.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-DASB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;25) and ADHD (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorhunsky et al. 2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional and source-based patterns of [11C]-(+)-PHNO binding potential reveal concurrent alterations in dopamine D2 and D3 receptor availability in cocaine-use disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-(+)-PHNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;26) and CUD (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaminiti et al. 2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAxonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-FeCIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;14) and PD (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKim et al. 2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltered\u0026nbsp;connectivity\u0026nbsp;between striatal and extrastriatal regions in patients with schizophrenia on maintenance antipsychotics: an [(18) F]fallypride\u0026nbsp;PET\u0026nbsp;and functional\u0026nbsp;MRI\u0026nbsp;study.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-Fallypride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;14) and Schizophrenia (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKlyuzhin et al. 2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-DTBZ and \u003csup\u003e11\u003c/sup\u003eC-raclopride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;10) and PD (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePCA - LASSO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePillai et al. 2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular connectivity disruptions in males with major depressive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-WAY-100635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin 5HT\u003csub\u003e1A\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;20) and MDD (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFu et al. 2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint pattern analysis applied to\u0026nbsp;PET\u0026nbsp;DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-DBTZ and \u003csup\u003e11\u003c/sup\u003eC-MP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine Transporter and VMAT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCanonical correlation analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVeronese et al. 2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariance\u0026nbsp;statistics and\u0026nbsp;network\u0026nbsp;analysis of brain\u0026nbsp;PET\u0026nbsp;imaging studies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG, \u003csup\u003e18\u003c/sup\u003eF-FDOPA and \u003csup\u003e11\u003c/sup\u003eC-SB217045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlucose Metabolism, Dopa-Decarboxylase and Serotonin 5HT\u003csub\u003e4\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;80), AD (n\u0026thinsp;=\u0026thinsp;76) and MCI (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVerger et al. 2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrom metabolic connectivity to molecular connectivity: application to dopaminergic pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG and \u003csup\u003e18\u003c/sup\u003eF-FDOPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlucose Metabolism and Dopa-Decarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFazio et al. 2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-resolution\u0026nbsp;PET\u0026nbsp;imaging reveals subtle impairment of the serotonin transporter in an early non-depressed Parkinson's disease cohort.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-MADAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;20) and PD (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColloby et al. 2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholinergic muscarinic M1/M4 receptor networks in dementia with Lewi bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-QNB and 99mTc-exametyzime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMuscarinic Receptors M1 and M4 and Cerebral blood flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElderly controls (n\u0026thinsp;=\u0026thinsp;24) and DLB (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePCA spatial covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMihaescu et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraph\u0026nbsp;theory\u0026nbsp;analysis of the dopamine D2 receptor\u0026nbsp;network\u0026nbsp;in Parkinson's disease patients with cognitive decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-FLB457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;13), MCI (n\u0026thinsp;=\u0026thinsp;17) and PD (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSala et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn vivo human molecular neuroimaging of\u0026nbsp;dopaminergic vulnerability along\u0026nbsp;the\u0026nbsp;Alzheimer\u0026rsquo;s disease phases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-FP-CIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;74), MCI (n\u0026thinsp;=\u0026thinsp;16) and AD (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmart et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparating dopamine D2 and D3 receptor sources of [11C]-(+) PHNO binding potential: independent component analysis of competitive binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-(+) PHNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFang et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentifying brain\u0026nbsp;networks\u0026nbsp;in synaptic density\u0026nbsp;PET\u0026nbsp;((11)C-UCB-J) with independent component analysis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-UCB-J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSynaptic Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuBois et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge-scale mGluR5\u0026nbsp;network\u0026nbsp;abnormalities linked to epilepsy duration in focal cortical dysplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-ABP688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetabotropic Glutamate Receptor Type 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;33) and Epilepsy (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJensen- Shannon divergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRebelo et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA link between synaptic plasticity and reorganization of brain activity in Parkinson's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-Raclopride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopaminergic D\u003csub\u003e2\u003c/sub\u003e Receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;9) and PD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFu et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvestigation of serotonergic Parkinson's disease-related covariance pattern using [11C]-DASB/PET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-DASB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;9) and PD (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSSM-PCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeng et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic 18F-FPCIT PET: Quantification of Parkinson Disease Metabolic Networks and Nigrostriatal Dopaminergic Dysfunction in a Single Imaging Session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FPCIT and \u003csup\u003e18\u003c/sup\u003eF-FDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine Transporter and Glucose metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;16) and PD (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSSM-PCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColloby et al. 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpatial Covariance of Cholinergic Muscarinic M1/M4 Receptors in Parkinson's Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-QNB and 99mTc-exametyzime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMuscarinic Receptors M1 and M4 and Cerebral blood flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElderly controls (n\u0026thinsp;=\u0026thinsp;24) and PD (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePCA spatial covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSanchez-Catasus et al. 2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStriatal Acetylcholine\u0026ndash;Dopamine Imbalance in Parkinson Disease: In Vivo Neuroimaging Study with Dual-Tracer PET and Dopaminergic PET\u0026ndash;Informed Correlational Tractography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FEOBV and \u003csup\u003e11\u003c/sup\u003eC-DTBZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcetylcholine\u0026ndash; Dopamine Transporters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;15) and PD (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoccalini et al. 2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender differences in dopaminergic system dysfunction in de novo Parkinson\u0026rsquo;s disease clinical subtypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-FP-CIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;73) and PD (n\u0026thinsp;=\u0026thinsp;286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoccalini et al. 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex differences in\u0026nbsp;dementia with\u0026nbsp;Lewy bodies: an\u0026nbsp;imaging study of\u0026nbsp;neurotransmission pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-FP-CIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;78) and pDLB (n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaminiti et al. 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopaminergic connectivity reconfiguration in the dementia with Lewy bodies continuum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-FP-CIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;52), pDLB (n\u0026thinsp;=\u0026thinsp;20) and DLB (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFang et al. 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-UCB-J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSynaptic Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiu et al. 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopaminergic Dysfunction and Glucose Metabolism Characteristics in Parkin-Induced Early-Onset Parkinson\u0026rsquo;s Disease Compared to Genetically Undetermined Early-Onset Parkinson\u0026rsquo;s Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-CFT and \u003csup\u003e18\u003c/sup\u003eF-FDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine Transporter and Glucose metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-regional correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmith et al. 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular imaging of the association between serotonin degeneration and beta-amyloid deposition in mild cognitive impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-DASB and \u003csup\u003e11\u003c/sup\u003eC-PIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerotonin Transporter and Amyloid burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;27) and MCI (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePLS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLuo et al. 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffects of STN-DBS surgery on cerebral glucose metabolism and distribution of DAT in Parkinson's disease.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG and \u003csup\u003e11\u003c/sup\u003eC-CFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlucose Metabolism and Dopamine Transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSSM-PCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePedersen et al. 2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopamine D1-Receptor Organization Contributes to Functional Brain Architecture.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-SCH23390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow dimensional manifold representation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoccalini et al. 2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistinctive clinical and imaging trajectories in SWEDD and Parkinson's disease patients.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e123\u003c/sup\u003eI-FP-CIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;49), SWEED (n\u0026thinsp;=\u0026thinsp;36) and idiopathic PD (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this paper, we explored the use of molecular imaging to study neurotransmission through a connectivity lens. We reviewed studies involving healthy volunteers, neurological diseases, psychiatric disorders, and other conditions, aiming to encompass all relevant applications discussed in the literature. We examined studies employing various methods, such as covariance statistics and network analyses, which complement traditional univariate approaches. Collectively, these findings reveal patterns of molecular connectivity that are essential for understanding disease mechanisms.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe value of molecular connectivity for studying neurotransmission\u003c/h2\u003e \u003cp\u003eThe reviewed studies highlight how molecular connectivity is frequently employed alongside traditional univariate analyses to strengthen research findings. Often, multivariate and network-based methods are used to complement and validate results from conventional univariate approaches, enhancing the robustness and reproducibility of conclusions by offering a broader contextual perspective and reinforcing initial insights approaches [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Beyond simply confirming results, molecular connectivity has also provided novel insights and uncovered patterns otherwise undetectable through univariate methods alone [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. This integrated approach is capable of merging localized insights with broader network-level relationships, increasing the robustness of findings and opening avenues for further applications.\u003c/p\u003e \u003cp\u003eNotably, several studies have positioned molecular connectivity as the primary analytical method [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Although this is less common, it highlights a growing focus on network-level hypotheses in PET research, where molecular connectivity serves as the main investigative tool rather than secondary support. This shift underscores the view that the understanding of brain physiology and disease mechanisms requires a global, interconnected perspective beyond isolated regional analyses [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature reviewed highlights that the primary advantage of multivariate and network-based approaches is their capacity to assess molecular interactions at a systemic level. A particularly notable observation was the involvement of the dopaminergic system at a broader level, extending beyond the dopaminergic regions typically targeted by radiotracers (e.g., extrastriatal regions).\u003c/p\u003e \u003cp\u003eThese methods reveal coordinated, disease-related changes across multiple regions and modular network alterations that affect overall system function, underscoring the necessity of a comprehensive approach to detect such patterns. Through these methodologies, researchers have identified spatial patterns of alterations and pathway disruptions linked to disease origins, reinforcing the value of network-level analysis in understanding disease etiology [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, \"disease-specific brain networks\" have been associated with stage-dependent disease changes and compensatory processes [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Such approaches effectively distinguish healthy from diseased individuals and differentiate among disease subtypes [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Furthermore, they reveal statistically significant correlations with disease duration and cognitive measures, underscoring their clinical relevance [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular connectivity has proven particularly insightful in studying neurodegenerative diseases, where widespread and progressive pathology is better characterised by group-level molecular covariance, as opposed to focal pathologies where individual-level analysis is required [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. PD is the most extensively studied condition, with researchers systematically uncovering multivariate and network-level dysregulation patterns within dopaminergic pathways, [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and their associations with symptom onset and disease progression [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Further research suggested that as PD advances, the serotonergic system may become involved, likely exhibiting a broader impact compared to the localised effects typically observed within the dopaminergic system [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. This finding implies that the long-recognized association between dopamine and PD may not be as robust as that between serotonin and PD. Supporting this, Fu et al. [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] reported that disease-induced alterations in the serotonergic system affect interactions between distinct brain regions in a manner specific to PD, closely tied to abnormalities within dopaminergic pathways. Ultimately, cognitive decline in PD has been linked to changes in extrastriatal dopaminergic patterns [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have also investigated DLB, revealing notable findings. Boccalini et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] observed affected connectivity within both the nigrostriatal and mesolimbic systems in DLB, with notable sex-based connectivity variations. Caminiti et al. [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] identified a marked increase in dopaminergic connectivity during DLB\u0026rsquo;s prodromal stages, suggesting adaptive mechanisms at work. Additionally, Colloby et al. [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] found spatial covariance patterns in M1/M4 receptors that distinguish DLB from healthy individuals and are associated with cognitive and neuropsychiatric improvements following ChEI.\u003c/p\u003e \u003cp\u003eA portion of reviewed studies have explored network patterns across different radiotracers, offering insights into complementary and regulatory neurotransmitter dynamics that may contribute to specific disorders. For example, Sanchez et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] demonstrated an imbalance between acetylcholine and dopamine signalling in the striatum during early PD. Tuominen et al. [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] noted significant opioid-serotonin tracer intercorrelations in regions linked to neuropsychiatric conditions, especially affective disorders, while Smith et al. [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] distinguished MCI from healthy controls through a serotonin and amyloid covariance pattern.\u003c/p\u003e \u003cp\u003eNotable findings have also emerged from studies comparing or integrating molecular connectivity with other imaging modalities. Kim et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] noted spatial alterations in D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e receptor availability post-antipsychotic treatment, aligning with functional connectivity changes, while Rebelo et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] demonstrated connections between functional activation and molecular-level synaptic changes in PD, suggesting compensatory reorganization mechanisms. Pedersen et al. [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] mapped D\u003csub\u003e1\u003c/sub\u003e receptor organisation along a unimodal-transmodal gradient closely aligned with the brain's primary functional gradient. Fang et al. [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] reported potential ties between resting-state network activity and synaptic density, suggesting that synaptic density may significantly influence large-scale brain network function.\u003c/p\u003e \u003cp\u003eMajor neurotransmitter systems\u0026mdash;including dopaminergic, serotonergic, and opioidergic systems\u0026mdash;demonstrate strong alignment with structural and functional connectivity patterns in the brain [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Consequently, mapping the brain-wide distribution of radiotracers provides valuable insights into the connectivity dynamics of specific neurotransmitter systems [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], paving the way for future multi-tracer and multi-modality studies in molecular connectivity research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMethodological advances and considerations\u003c/h2\u003e \u003cp\u003e The reviewed papers highlight several innovative methodologies for studying neurotransmission through molecular connectivity, extending beyond conventional intercorrelation techniques and covariance statistics. For instance, the method proposed by Fu et al. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] employed joint pattern analysis canonical correlation to integrate information from multiple PET tracers allowing the evaluation of the interplay between two different receptor systems. This method demonstrated the potential of such approaches to integrate different tracers and go beyond the investigation of one receptor system at a time, which is often challenging with traditional correlation analyses. Furthermore, Sanchez et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] proposed a dopaminergic PET-Informed Correlational Tractography which integrated the information of striatal dopamine into the white matter fibre reconstruction process to optimally guide tract reconstruction with dopaminergic specificity. The resulting metrics were then correlated to striatal dopamine and cholinergic signals showing stronger and more robust associations with respect to non-informed tractography. Through this approach, this study showed that merging information from multiple modalities allows obtaining results with higher biological specificity.\u003c/p\u003e \u003cp\u003eSimilarly, Worhunsky et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] applied ICA to \u003csup\u003e11\u003c/sup\u003eC(+)-PHNO PET BPND data to separate D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e receptors distribution, effectively isolating distinct sources of radiotracer signal. This approach enabled a nuanced analysis of receptor subtypes, previously unattainable through conventional D\u003csub\u003e2\u003c/sub\u003e/D\u003csub\u003e3\u003c/sub\u003e radioligand analyses, underscoring the advantage of \u003csup\u003e11\u003c/sup\u003eC(+)-PHNO PET in investigating concurrent changes in D\u003csub\u003e2\u003c/sub\u003e and D\u003csub\u003e3\u003c/sub\u003e receptors.\u003c/p\u003e \u003cp\u003eFinally, Pedersen et al. [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] utilised a nonlinear embedding approach to decompose group representative covariance maps into low-dimensional manifolds, elucidating transitions in covariance patterns of D\u003csub\u003e1\u003c/sub\u003e receptors across the cortical surface and linking dopamine expression to functional brain organisation. The proposed approach could be a valuable tool to decompose and integrate the information provided by different imaging modalities or different molecular targets allowing to uncover possible interactions that would be difficult to establish with \u0026ldquo;common\u0026rdquo; connectivity approaches.\u003c/p\u003e \u003cp\u003eThese methodological advancements deepen the understanding of regional relationships and facilitate the integration of molecular data among different tracers and complementary imaging modalities. This integration provides a holistic understanding of connectivity across multiple hierarchical levels and paves the way for numerous future applications.\u003c/p\u003e \u003cp\u003eOverall, these findings underscore the advantages of network-level approaches over traditional quantitative analyses. They offer novel insights not discernible through conventional methods, frequently extending the results obtained with univariate approaches, and provide a robust framework for integrating multiple sources of information, thereby broadening the potential scope of future applications. Nonetheless, the adoption of such techniques should be contingent upon rigorous statistical and mechanistic validation, ensuring that analyses are as complex as necessary to achieve reliable outcomes, but not unduly so.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future prospectives\u003c/h2\u003e \u003cp\u003eDespite the promise of molecular connectivity, the field faces several challenges. There is a lack of standard terminology and consistent methods, making it difficult to compare results across studies. For instance, not all studies used the terms \"connectivity,\" \"network,\" or \"covariance\" consistently when employing these approaches. This variability in nomenclature is also influenced by the studies\u0026rsquo; year of publication, since throughout the years the definition of brain connectivity has changed. Only recently the terminology used in functional connectivity with fMRI has become aligned to the PET field [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother limitation is the methodological fragmentation across studies, which hinders direct comparisons between methodologies and complicates the replication and validation of results. There is a notable lack of best practices for data processing, network construction, and network statistics, as well as inadequate validation of key factors such as statistical thresholds, appropriate sample sizes, and the inclusion of normal control groups for comparison. As a result, variations in methodological approaches, including the radiotracer/target chosen, the construction of the connectivity matrix, data acquisition, scanner type and resolution, radiotracer specificity to neurotransmitter systems, off-target binding/kinetic parameter outcome of interest, processing techniques, and standardization protocols, can lead to discrepancies in results across studies [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the absence of robust validation mechanisms for the obtained results exacerbates these challenges. While statistical validation is common, the lack of validation through pharmacological agents or ex vivo data undermines the credibility and interpretability of the findings. The limited validation efforts reported in the literature highlight the urgent need for more rigorous validation protocols to enhance the reliability and value of biological insights obtained with molecular connectivity analyses. One example is represented by the use of several dopamine-based PET imaging to explore cortical connections. While it is possible to investigate dopamine extrastriatal signals (see for example [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]), there are brain regions where the PET signal for dopamine-target tracers is driven by perfusion and non-specific binding rather than dopamine. Therefore, even if a strong statistical effect can be found for these regions, the interpretation via dopamine-mediated mechanisms remains questionable and warrants further investigations. Additionally, the scarcity of longitudinal studies prevents the assessment of these molecular connectivity approaches in tracking network alterations and changes over time throughout the course of a disease.\u003c/p\u003e \u003cp\u003eFinally, most molecular connectivity results are based on group-level analyses due to the inherently \"static\" nature of PET images [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which contain either tracer uptake values averaged over a certain time window or parametric values derived from the tracer's dynamics. This limitation makes within-subject \"fMRI-like\" analysis of PET images challenging, resulting in molecular connectivity analysis typically being performed at the group level. Recently, new studies have conducted single-subject level analyses using static PET data through specific methodologies like Jensen-Shannon entropy [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], Kullback-Liebler divergence [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], and perturbation approaches [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. However, these applications are still limited and not validated. Another line of research applies network-based approaches to dynamic bolus injection and infusion data, often called functional PET (fPET), attempting to mimic the approaches used for functional connectivity in fMRI, opening new possibilities for assessing molecular connectivity at the individual level. Until now, this has only been applied to \u003csup\u003e18\u003c/sup\u003eF-FDG PET data [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. The only study in the literature using fPET data with a neurotransmitter tracer (\u003csup\u003e18\u003c/sup\u003eF-DOPA) is the one by Hahn et al. [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], although it employed only univariate analysis methods. Future studies could extend these results by applying connectivity-based approaches.\u003c/p\u003e \u003cp\u003eAddressing these methodological limitations requires a united effort to establish standardised protocols, enhance validation procedures, and overcome logistical hurdles associated with data integration. By overcoming these challenges, we can push molecular connectivity research towards greater robustness and applicability in clinical settings, realising its potential as a transformative tool in neuroscience and clinical practice.\u003c/p\u003e \u003cp\u003eFinally, the existing literature predominantly focuses on the dopamine and serotonin systems, as these neurotransmitters are among the most frequently studied and utilised tracers. Consequently, there is a notable gap in research concerning other tracers. Further investigation into these less-explored tracers is necessary to enhance our understanding of their complementary roles in neural connectivity and to elucidate how their inclusion may advance the field\u0026rsquo;s comprehension of behaviour and disease states.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this review has explored how connectivity-based methods are changing the way we study neurotransmission using molecular imaging. Our comprehensive search highlighted the diverse applications and methodologies employed in this field, with a strong emphasis on their utility in studying neurodegenerative diseases and psychiatric disorders. The integration of approaches like covariance statistics and network analyses, alongside traditional methods, has helped us gain a better understanding of how neurotransmission systems work. These methods not only reinforce results from conventional analyses but also reveal new patterns of molecular activity that are critical for understanding disease mechanisms. However, despite the promising potential of these molecular connectivity methodologies, the field still faces several challenges. The lack of consistent nomenclature, varied methods, and a lack of strong validation highlight the need to establish best practices, robust validation protocols, and validation through application in drug studies, to push this research forward and bring it into clinical use.\u003c/p\u003e \u003cp\u003eIn conclusion, molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eMario Severino and D\u0026eacute;bora Elisa Peretti screened and analysed all the studies. Mattia Veronese served as the third reviewer to resolve any disagreements during the screening process and acted as the project administrator. Mario Severino drafted the manuscript. D\u0026eacute;bora Elisa Peretti and Mattia Veronese made substantial contributions to the study's conception, participated in drafting or critically revising the article for significant intellectual content, and approved the final version of the manuscript. All authors discussed the results and provided feedback and contributions on the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by the molecular connectivity working group (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://molecularconnectivity.com/\u003c/span\u003e\u003cspan address=\"https://molecularconnectivity.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an initiative of an international and interdisciplinary group of neuroimaging experts with background in biomedicine, health sciences, neuroscience, and physics with the ultimate goal to establish molecular imaging as tool to study brain connectivity. Silvia Paola Caminiti is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) \u0026ndash; A multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). Mattia Veronese is supported by EU funding within the MUR PNRR \u0026ldquo;National Center for HPC, BIG DATA AND QUANTUM COMPUTING (Project no. CN00000013 CN1), the Ministry of University and Research within the Complementary National Plan PNC DIGITAL LIFELONG PREVENTION - DARE (Project no PNC0000002_DARE), and by Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN), (Project no 2022RXM3H7). Arianna Sala is a postdoctoral researcher at FRS-FNRS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJessell TM, Kandel ER (1993) Synaptic transmission: A bidirectional and self-modifiable form of cell-cell communication. Cell 72:1\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026uuml;dhof TC, Malenka RC (2008) Understanding Synapses: Past, Present, and Future. Neuron 60:469\u0026ndash;476\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBennett MVL, Zukin RS (2004) Electrical Coupling and Neuronal Synchronization in the Mammalian Brain. Neuron 41:495\u0026ndash;511\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereda AE (2014) Electrical synapses and their functional interactions with chemical synapses. 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J Cereb Blood Flow Metabolism 41:2973\u0026ndash;2985\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Padua","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Molecular Connectivity, Molecular Imaging, Neuroimaging, Neurotransmission, positron emission tomography","lastPublishedDoi":"10.21203/rs.3.rs-5498198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5498198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose:\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePositron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-called \u003cem\u003emolecular connectivity\u003c/em\u003e approach that captures relationships of molecular imaging measures in different brain regions. Targeting these inter-regional interactions within a neuroreceptor system may allow to better understand complex brain functions. In this article, we provide a comprehensive review of molecular connectivity studies in the field of neurotransmission. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA systematic search in three bibliographic databases MEDLINE, EMBASE and Scopus on July 14, 2023, was conducted. A second search was rerun on April 4, 2024. Molecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003e Thirty-nine studies were included in the scoping review. Studies were categorised based on the primary neurotransmitter system being targeted: dopamine, serotonin, opioid, muscarinic, glutamate and synaptic density. The most investigated system was the dopaminergic and the most investigated disease was Parkinson\u0026rsquo;s disease (PD).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis review highlighted the diverse applications and methodologies in molecular connectivity research, particularly for neurodegenerative diseases and psychiatric disorders. Molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings.\u003c/p\u003e","manuscriptTitle":"Molecular connectivity studies in neurotransmission: a scoping review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:37:25","doi":"10.21203/rs.3.rs-5498198/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"307735c3-d556-4b51-8c73-141f5f24b240","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T15:37:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 15:37:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5498198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5498198","identity":"rs-5498198","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}