Characterizing Functional Connectivity Gradients for the Hippocampus-Amygdala Complex in Healthy and Psychiatric Cohorts

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N. Vrijsen, Linlin Yan, Koen Haak, Rose Collard, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7032613/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 The hippocampus and amygdala play essential roles in human cognition and emotion, through their extensive connectivity with other brain regions and close interaction between them. Uncovering the functional organization of the hippocampus–amygdala complex and how it is modulated by neurotransmitters can enhance our understanding of their biological functionality, and provide a basis for further exploration of the clinical relevance. An emerging functional connectivity analysis method, “connectopic mapping”, may offer a novel approach to characterize this functional organization. In this study, we applied "connectopic mapping" to the hippocampus-amygdala complex, testing its utility with resting-state functional magnetic resonance imaging (fMRI) scans of two independent datasets: one comprising healthy individuals (N = 410) and another comprising a psychiatric cohort (N = 367). The spatial organization of derived gradient maps was compared to 18 positron emission tomography (PET) or single photon emission computed tomography (SPECT) scan templates for different neurotransmitter systems. Individual gradient–neurotransmitter similarity indices were correlated with mental health outcomes. Our analyses identified six distinct gradient maps in both datasets. The third-order gradients showed stable similarity with 5-HT1A receptor maps across various resting-state scans. Similarities were also observed between gradient maps and the distribution patterns of neurotransmitters within the dopaminergic system. Individual gradient-to-5-HT1A similarity was positively correlated with depression severity and anxiety sensitivity, highlighting the psychopathological relevance. These findings demonstrate that across the psychiatric continuum, "connectopic mapping" is a powerful tool for exploring the relationship between functional connectivity and neurotransmitter modulation, showing potential as a comprehensive transdiagnostic biomarker. Biological sciences/Neuroscience Health sciences/Biomarkers Figures Figure 1 Figure 2 Figure 3 Introduction The hippocampus and amygdala, as two adjacent medial temporal lobe (MTL) structures, have consistently been investigated as key brain regions in cognitive affective neuroscience 1 , 2 . The hippocampus, shaped like a seahorse, is essential for episodic memory. It processes and connects the sensory inputs, enabling our memory for daily events built up with spatial, temporal and other contextual information 3 , 4 . The anterior segment of hippocampus is closely adjacent to the amygdala -- a grey matter complex located in the dorsal sector of MTL. The amygdala is the central region for emotional responses to environmental stimuli, participating in circuits that process heightened arousal and survival-related emotions, such as fear, threat, and reward 5 – 8 . Effective functioning of these two anatomically adjacent regions is inseparable from their close interactions with each other: processes such as fear learning 9 , as well as the encoding, retrieval, and consolidation of emotional memories 1 , 10 , 11 critically depend on hippocampus–amygdala interactions. As pivotal hubs for cognition and emotion, the hippocampus and amygdala also maintain widespread functional connections with other brain regions. Connections with the medial prefrontal cortex (mPFC), anterior temporal lobe, insula and posterior cortical areas are shown to support episodic encoding and retrieval, aversive learning, as well as stress and emotion regulation 12 – 18 . Maladaptive variations in these neural circuits lead to corresponding functional disruptions, which are commonly observed in mood disorders such as depression 19 , 20 , anxiety disorders 21 , 22 and post-traumatic stress disorder (PTSD) 18 , 23 , and may also account for certain comorbidity across these. Notably, studies demonstrated that functional domains within the hippocampus vary along its long, curved posterior-to-anterior axis, with gradients observed in gene expression, place cell field sizes, and the representation of gist versus detailed information 16 , 24 . Anatomically, dorsoventral (corresponding to anterior-posterior in human) topographical gradients in hippocampal–cortical and subcortical connectivity were revealed in animal studies 25 , 26 , which may support its functional gradient organization. Building on above findings, it’s important to examine if these gradient organization could be replicated in living humans (e.g., using noninvasive imaging techniques such as fMRI) for both healthy and clinical populations, and how it further matches with cognitive performance and psychiatric outcomes. These investigations will help us build a clear overview of hippocampal function and dysfunction. Similarly, projections between the amygdala and various cortical regions (e.g., mPFC, inferior temporal cortex), as well as subcortical structures (e.g., the striatum), have also been found to exhibit a topographically organized pattern 27 – 29 . As a compact, deep-lying nucleus, however, the amygdala’s small size may limit the suitability of investigating this topography in humans using neuroimaging techniques, due to imaging resolution constraints. The hippocampus and amygdala are not only spatially adjacent structures, but both play closely interconnected roles within cognitive and affective functional networks; the amygdala is often regarded as a unit with the hippocampus when examining its functional role or psychiatric aberrance 30 , 31 . Considering both practical advantages and biological relevance, we would like to integrate the hippocampus and amygdala into a single complex, and capture the functional connectivity organization of this complex, through a data-driven technique "connectopic mapping". This method is designed to identify multiple overlapping connectivity patterns (gradient maps) within a predefined region of interest (ROI), with each gradient representing a distinct topographic mode of connectivity variation within the ROI relative to the rest of the brain 32 . A pioneering study focused on the striatum, demonstrating that connectopic gradients replicate the topographical organization of cortical projections to the striatum observed in animal anatomical studies 33 . In the current study, with "connectopic mapping", we aimed to provide an overview of functional connectivity for the hippocampus-amygdala complex, by identifying its topographic connectivity gradients, then further explore its psychiatric relevance. The topographic gradients obtained by "connectopic mapping" are data-derived, and may result from combined influences of multiple biological mechanisms, including anatomical projections and neurotransmitter modulation. As for the hippocampus and amygdala, they are structures functioning under the support of various neurotransmitter systems, which extensively regulate their communication with other regions of functional networks 34 , 35 . For example, as a fundamental process for long-term memory, hippocampal long-term potentiation depends on N-methyl-D-aspartate (NMDA) glutamatergic receptors and is further regulated by beta-noradrenergic receptors 36 . The beta-noradrenergic pathway in the amygdala is crucial for both the acquisition and consolidation of fear conditioning 37 , 38 . Other neurotransmitter systems, such as serotonin and dopamine, are also extensively distributed, prominently modulating the fear circuitry, emotional processing and learning 39 , 40 . Given the essential role of neurotransmitters in functional networks of the hippocampus-amygdala complex, we hypothesized that topographic gradient modes should, to some extent, reflect the underlying influence of neurotransmitter systems. To test this hypothesis, we compared the spatial topologies of hippocampus-amygdala gradient maps with multiple neurotransmitter maps. The similarity in spatial layouts offers a proxy for estimating the extent of a neurotransmitter's influence and aids in interpreting the biological meanings of these data-driven gradient modes 41 , 42 . Neurotransmitter modulations in the hippocampus-amygdala complex are closely associated with mental health and the development of various psychiatric disorders. For example, altered function of serotonin receptors and transporters in the hippocampus and amygdala is commonly observed in mood disorders 43 , 44 . Such alterations influence individuals' coping mechanisms following stress 45 and interact with childhood trauma to further shape depressive symptomatology 46 . Therefore, it is worthwhile to investigate how individual differences in neurotransmitter modulations of hippocampus-amygdala complex functional connectivity, depicted by gradient maps, relate to mental health outcomes. To achieve this goal, we characterized the gradient-neurotransmitter similarity in spatial topologies for each participant, and tested if this similarity could predict mental health-related factors (e.g., depressive and anxiety symptoms, as well as childhood trauma). By applying "connectopic mapping" to the hippocampus-amygdala complex, we aim to 1). create an overview of its functional connectivity organization by identifying several topographical gradient modes; 2). assess whether these gradient modes align with specific neurotransmitter functions; and 3). explore if individual differences in the correspondence between neurotransmitter distribution and gradient modes are related to mental health factors. For reproducibility validation, two independent databases were included: the Healthy Brain Study (HBS) comprising healthy adults 47 , and ‘Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-related Mental Disorders’ (MIND-Set) study including a highly comorbid psychiatric cohort 48 . HBS dataset enables us to characterize hippocampus-amygdala gradient modes in healthy population, and MIND-Set provides the opportunity to explore its translational value for clinical samples. Methods and Materials Participants In this study, two databases were utilized and analyzed independently. One dataset was from the Healthy Brain Study, jointly conducted by Radboud University, Radboud University Medical Center, and the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands. The analysis included all participants from the first data release (N = 410; 169 males; mean age = 33.8 ± 2.8 years; see Table S1 for characteristic information of the two samples). Participants had no history of psychiatric illness or current diseases affecting the brain, and none were taking brain-targeted medication at the time of participation. The study protocol and more details can be found in Healthy Brain Study Consortium et al 47 . Another dataset was from the MIND-Set cohort, collected by the Department of Psychiatry at Radboud University Medical Center and the Donders Institute in Nijmegen, the Netherlands. All participants with available resting-state neuroimaging data were included (N = 367; 202 males; age 37.6 ± 14.0 years). In this sample, 286 participants were psychiatric patients (164 males; age 37.6 ± 13.4 years; see Supplementary for n per diagnosis), and 81 individuals did not have a current or past psychiatric disorder (37 males; age 37.67 ± 15.80 years). 242 participants were taking one or more medications during their participation. More details on the sample were introduced in van Eijndhoven et al 48 . fMRI data acquisition and preprocessing The HBS study includes three resting-state scans, conducted within one year, each separated by a four-month interval. T2*-weighted resting-state BOLD data were acquired using a multiband-accelerated gradient echo EPI sequence (66 slices; TR = 1000 ms; TE = 34 ms; flip angle = 60°; voxel size = 2.0 × 2.0 × 2.0 mm; FOV = 210 mm), with the duration of 10 minutes. Preprocessing steps comprised motion correction, distortion correction with field maps, and non-linear registration to MNI152 space. FSL FIX’s ICA and Gradient Distortion Correction were used for further denoising and distortion correction 47 ( https://osf.io/jzwrg/ ). In the MIND-Set study, three resting-state scans were conducted as well. Resting-state scans 1 and 2 each lasted 8.5 minutes and were separated by a neutral movie clip. Following resting-state scan 2, an aversive movie clip was shown to induce acute stress. Resting-state scan 3, lasting 12.6 minutes, was then acquired. All resting-state scans used a multi-band 6 protocol with an interleaved slice acquisition sequence to capture T2*-weighted EPI BOLD-fMRI images (66 slices; TR = 1000 ms; TE = 34 ms; flip angle = 60°; voxel size = 2.0 × 2.0 × 2.0 mm; FOV = 210 mm). Preprocessing was performed using FSL version 5.0.11 (FMRIB, Oxford, UK), including brain extraction, motion correction, bias field correction, high-pass temporal filtering (100s cut-off), spatial smoothing with a 4 mm FWHM Gaussian kernel, boundary-based registration to T1-weighted images, and nonlinear registration to standard space (MNI152). Motion-related artifacts were removed using ICA-based Automatic Removal of Motion Artifacts (ICA-AROMA). Further details on fMRI acquisition and preprocessing can be found in previous studies 48 , 49 . Characterizing hippocampus-amygdala connectivity gradients For both datasets, we applied ConGrads 32 to the preprocessed resting-state data (three scans per dataset; see Supplementary Methods), using the left and right hippocampus-amygdala complexes as ROIs. The masks were derived from the Harvard-Oxford atlas with a threshold of 20% probability. To better visualize gradient changes along the hippocampal long axis, the functional image data and masks were rotated around the X-axis in MNI152 space by an angle of 37°. ConGrads identified gradient modes of functional connectivity changes based on a similarity matrix computed within the hippocampus-amygdala complex, estimated from the functional connectivity between voxels in the complex and the rest of the brain. For each resting-state scan of the two datasets, we derived group-average gradient maps by combining functional image data from all available participants as inputs, separately for the left and right hippocampus-amygdala complexes. To enable statistical comparisons across participants, the order of individual gradient maps was adjusted by swapping gradients based on their spatial correlation with group-average maps (i.e., swapping within the participant when the other gradient map showed a higher correlation coefficient with the group average than the original one). After swapping, as a quality control measure, individual maps were excluded if their spatial correlation with the group-average map was below 0.50 41 . We monitored the proportion of participants retained at this threshold and determined the number of gradient modes based on the point at which a sharp decline in this proportion was observed. To characterize the spatial organization of these gradient modes, a trend surface model (TSM) was applied to both group-average and individual gradient maps 32 . Following previous work on hippocampal gradients 50 , a trend surface regression model with nine coefficients (three parameters along X, Y, and Z axes) was chosen. Mapping gradients with neurotransmitters To better understand the data-driven gradient maps, we examined their relationships with multiple neurotransmitter systems. We utilized PET or SPECT scans for various neurotransmitters from the publicly available JuSpace toolbox ( https://github.com/juryxy/JuSpace ). Following prior research exploring striatal connectivity gradients and neurotransmitters 41 , a total of 18 neurotransmitter templates were included (see Table S2 for a full list). The same trend surface regression model, with nine coefficients, was applied to these PET/SPECT scans, with the left and right hippocampus-amygdala complexes defined as ROIs. Correlation coefficients were then calculated between the TSM coefficients obtained from these neurotransmitter maps and the TSM coefficients characterizing the group average hippocampus-amygdala gradient maps. To standardize comparisons, the absolute correlation coefficients were transformed using Fisher's r-to-z transformation. To examine the statistical significance of these gradient-neurotransmitter correlations, permutation testing was performed (N = 10,000, p < 0.05, Bonferroni corrected). A null distribution was generated by permuting the PET/SPECT TSM coefficients (separately for each coefficient) and calculating correlations between gradient TSM coefficients and permuted PET/SPECT TSM coefficients (Fisher r-to-z transformed, absolute values). The observed correlations were then compared to this null distribution. Linking individual gradient-neurotransmitter similarity with behavioral outcomes Neurotransmitter modulations in the hippocampus-amygdala complex are relevant for psychopathology. Therefore, we also examined whether individual differences in gradient-to-neurotransmitter similarity could be associated with variations in behavioral outcomes for mental health. For neurotransmitters that showed similarity in spatial layouts with the group-average connectivity gradients, we calculated the correlation coefficients between the TSM coefficients of individual gradient maps per participant and TSM coefficients of PET/SPECT data in the JuSpace toolbox. Subsequently, Spearman correlation analysis was conducted between the Fisher r-to-z transformed absolute correlation coefficients and behavioral outcomes ( p < 0.05, FDR corrected). For the behavioral outcomes, we included depression and anxiety symptom levels as psychiatric measures, and childhood trauma as a pathogenic environmental factor. In both datasets, depressive symptoms were measured using the Inventory of Depressive Symptomatology-Self Report (IDS-SR) 51 , and anxiety sensitivity was assessed with the Anxiety Sensitivity Index (ASI) 52 . In the HBS dataset, childhood trauma was measured using the Childhood Trauma Questionnaire-Short Form (CTQ-SF) 53 . Both the total score and subscale scores (emotional neglect, physical neglect, emotional abuse, physical abuse) were included. In MIND-Set, childhood trauma frequency and diversity was measured using the questionnaire from the Netherlands Mental Health Survey and Incidence Study (NEMESIS) 54 . Both the overall index and subscales (representing the frequency of occurrence of emotional neglect, psychological abuse, and physical abuse before the age of 16 years) were included in the analysis. Additionally, for this psychiatric cohort, we accounted for comorbidity levels by summing the number of diagnosed psychiatric disorder clusters (i.e. Mood Disorder, Anxiety Disorder including PTSD and Obsessive Compulsive Disorder, Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, and Addiction) for each participant. Results The hippocampus-amygdala functional connectivity gradient maps Across both datasets, we identified six functional connectivity gradient modes in total (as a sharp decline in the proportion of participants retained after quality assurance was observed at the sixth mode; Table S3). Figure 1 illustrates the hippocampus-amygdala gradient maps for both datasets. Because gradient maps across different resting states were highly similar, we visualized the maps derived from resting-state 1 as a representative example. The dominant (zeroth-order) gradient shows a gradual change along the coordinate space, starting in the amygdala and extending along the hippocampal long axis. The first-order gradient is organized from the middle of the hippocampus towards the anterior and posterior ends of the complex. We observed that the second- and third-order gradients appear in reversed order between the HBS and MIND-set samples, although their overall spatial patterns remain consistent. In the following results, for the convenience of comparison, we used the order in HBS to name gradient maps. (Insert Fig. 1 around here) For quality assurance, we excluded participants whose spatial correlation with the group-average gradient maps was lower than 0.5. Table S3 shows the proportion of participants retained based on this threshold. In the HBS dataset, all six connectopic gradient modes had inclusion rates of 50% or higher. In the MIND-Set dataset, while the fourth-order gradient on the right side during resting-state 2 and the fifth-order gradient bilaterally across all three resting states exhibited lower stability, the first four gradient modes consistently showed inclusion rates exceeding 50%. The results indicate the robustness of these data-driven connectopic maps, especially for the first four gradient modes. Similarity in spatial layouts between gradient and neurotransmitter maps The correlation analysis with permutation testing identified several significant similarities between hippocampus-amygdala gradient and neurotransmitter maps (see Table 1 , Figure S1 ; Table S4 for the full list), with findings highlighting the serotonin and dopaminergic systems. The most stable similarity is between the third-order gradients and 5-HT1A receptor maps: significant correlations were observed bilaterally across all resting states in both datasets (Fig. 2 A), except for the right side in resting-state 2 of MIND-Set. In HBS, the left-side correlation was not Bonferroni-significant but survived when we applied FDR correction. Additionally, the second-order gradients exhibited similarity with dopamine type 1 (D1) receptor maps on the left side across all resting states in both datasets. In MIND-Set only, the third-order gradients demonstrated similarity to dopamine type 2 (D2) receptor maps on the left side. In HBS only, the fourth-order gradients displayed similarity with dopamine transporter (DAT) maps on the left side (Fig. 2 B). Table 1 Significant correlations between gradient and neurotransmitter layouts Left_ Second-order & D1 Left_ Third-order &5HT1A Right_ Third -order &5HT1A Left_ Third -order & D2 Left_ Fourth-order & DAT HBS Resting-State1 z = 1.948; p < 0.01 z = 1.444; p < 0.05 (FDR) z = 1.514; p < 0.05 no sig. z = 1.459; p < 0.05 Resting-State2 z = 1.799; p < 0.05 z = 1.545; p < 0.05 (FDR) z = 1.558; p < 0.05 no sig. z = 1.459; p < 0.05 Resting-State3 z = 1.813; p < 0.05 z = 1.543; p < 0.05 (FDR) z = 1.615; p < 0.05 no sig. z = 1.456; p < 0.05 MIND-Set Resting-State1 z = 1.586; p < 0.05 z = 1.955; p < 0.01 z = 1.838; p < 0.01 z = 1.708; p < 0.05 no sig. Resting-State2 z = 1.568; p < 0.05 z = 1.955; p < 0.01 no sig. z = 1.703; p < 0.05 no sig. Resting-State3 z = 1.417; p < 0.05 z = 1.948; p < 0.01 z = 1.507; p < 0.05 z = 1.732; p < 0.05 no sig. D1: dopamine type 1; D2: dopamine type 2; DAT: dopamine transporter (Insert Table 1 around here) (Insert Fig. 2 around here) To determine whether the observed similarities in the MIND-Set sample were specific to psychiatric patients, we further separated the sample into patient and healthy control subgroups. Results showed that both subgroups exhibited the same patterns with those observed in the full sample (Table S5). Individual gradient-neurotransmitter similarity predicts mental health outcomes For neurotransmitter maps that showed significant spatial similarity with group-average gradients (bilateral 5-HT1A receptors; left D2 receptors in MIND-Set; left D1 receptors; and left DAT in HBS), we computed correlation coefficients between the TSM coefficients of JuSpace PET/SPECT images and individual gradient maps for each participant. Taking Fisher r-to-z transformed absolute correlation coefficients, Spearman correlation analysis revealed several significant associations with anxiety sensitivity and depressive levels. For HBS resting-state 1, the similarity between 5-HT1A receptors and the third-order gradients on the left side of the hippocampus-amygdala complex was positively correlated with depressive levels ( r s = 0.19, p fdr = 0.021), and approaching significance for anxiety sensitivity ( r s = 0.16, p fdr = 0.072). In the MIND-Set dataset, these correlations were observed for the right side in resting-state 1 (depressive levels: r s = 0.13, p fdr = 0.057; anxiety sensitivity: r s = 0.15, p fdr = 0.038; Fig. 3 ). Given that the two datasets span distinct ranges of anxiety sensitivity and depressive symptoms (Table S1 ) —with HBS falling into the relatively lower end and MIND-Set the higher—these replicated correlations may indicate both sensitivity and stability across the symptom spectrum. As for the dopaminergic system, in MIND-Set resting-state 2, the similarity between D1 receptor and second-order gradient maps showed a positive correlation with anxiety sensitivity ( r s = 0.13, p fdr = 0.046). This association was not significant in the HBS sample ( r s = 0.06, p fdr = 0.50), although Fisher’s z test comparing the two correlations (e.g., in MIND-Set and HBS) revealed no significant difference ( z = 0.682, p = 0.495). In HBS resting-state 3, the similarity between DAT and fourth-order gradient maps was negatively associated with anxiety sensitivity ( r s = -0.18, p fdr = 0.048). No other significant correlations were observed ( p s > 0.10). Full lists of correlations are presented in Table S6 and S7. We also divided the MIND-Set sample into patient and healthy controls, and ran the correlation analyses separately (Table S8). Fisher’s z tests revealed no significant differences in the observed correlations above between two subgroups ( p s > 0.16). (Insert Fig. 3 around here) Discussion Widespread functional connections between the hippocampus-amygdala complex and other brain regions play important roles in human cognition and emotion, thereby contributing to the most common psychiatric disorders. In this study, with both healthy and psychiatric cohorts, we 1). identified six distinct connectopic gradient modes for the hippocampus-amygdala complex, with the first two exhibiting gradual changes along the hippocampal long axis; 2). revealed that neurotransmitters from the serotonin and dopamine systems displayed spatial similarities with connectopic gradients of the hippocampus-amygdala complex; and 3). found that individual variability in these gradient-neurotransmitter similarities was associated with depression severity and anxiety sensitivity. Our findings highlight the potential of “connectopic mapping” to bridge functional neuroimaging, biomolecular markers and behavioral measures, offering a novel perspective for understanding brain function and dysfunctions in living human beings. The first two connectopic gradient maps of the hippocampus–amygdala complex did show gradient changes following the hippocampal long axis in both samples. This demonstrates that “connectopic mapping” can capture the connectivity organization previously identified in animal studies, using fMRI scans of living humans. The spatial organization of our gradient maps also aligns with hippocampal gradients reported in earlier studies 42 , 50 . The amygdala exhibited connectivity modes similar to those of the anterior hippocampus, partly because of the spatial proximity, also indicating their close functional coupling. While previous research focused on lower-order connectopic gradients, our study extended it by extracting higher-order gradient modes and replicating the findings in both healthy and psychiatric samples. Various neurotransmitters contribute to regulating the functionality of the hippocampus and amygdala. "connectopic mapping" offers an approach to better understand how neurotransmitters influence hippocampus-amygdala related functional connectivity, by breaking down overall connectivity patterns into specific topographical gradient modes. For each identified gradient, we evaluated its similarity in spatial layouts with various neurotransmitter maps, which serves as an indirect measure of how much influence a particular neurotransmitter has on the functional connectivity. The most robust gradient-neurotransmitter similarity we found is between the third-order gradients and 5-HT1A receptor maps. 5-HT is a monoamine neurotransmitter synthesized in both the central nervous system and gastrointestinal cells 55 . The 5-HT1A receptor is one of the most abundant receptor subtypes in the mammalian brain 56 . In the hippocampus and amygdala, most 5-HT1A receptors function as postsynaptic receptors suppressing pyramidal cell firing 57 . Our third-order gradient generally captured the organizational patterns of 5-HT1A receptor distribution within the hippocampus-amygdala complex, which may suggest that functional communication between the complex and other brain regions is heavily influenced by serotonin modulation. In addition to previous reports from animal and PET studies 58 , 59 , our findings provide evidence for the feasibility of receiving serotonin receptor related readouts from resting-state fMRI scans on an individual basis. The activity of 5-HT1A receptors is implicated in emotion processing and stress coping 45 , 60 , with alterations shown in mood disorders. Studies have reported increased hippocampal 5-HT1A receptor binding potential in individuals with depressive episodes and childhood adversity 46 , and elevated 5-HT1A receptor density in people who committed suicide 61 . Higher 5-HT1A receptor binding potential was also associated with poor responses to antidepressant treatment 62 . In this study, we found that greater similarity between the third-order gradients and 5-HT1A receptor maps was related to more severe depressive symptoms and heightened anxiety sensitivity. This supports previous findings in elevated 5-HT1A receptor involvement and internalizing psychiatric symptoms. These results may suggest that increased involvement of 5-HT1A receptor activity in hippocampus-amygdala related functional connectivity could serve as a potential risk factor underlying mood disorder symptoms. Notably, we observed this relationship in both the healthy sample (with a narrower symptom range) and the clinical sample (with a broader range), suggesting its sensitivity to transdiagnostic symptom variation. Compared to anatomical or PET studies, “connectopic mapping” could be a more efficient and cost-effective approach for depicting this alteration in 5-HT1A receptor functionality. Beyond the serotonin system, our findings also identified similarities between hippocampus-amygdala gradients and the spatial organization of dopamine-related neurotransmitters: D1 receptors with the second-order gradient, D2 receptors with the third-order gradient, DAT with the fourth-order gradient. D1 and D2 receptors are the most abundant dopamine receptor subtypes 63 . D1 receptors have an excitatory role in signaling pathways, whereas D2 receptors exhibit more complex effects, generally with inhibitory properties 64 , 65 . Together with other dopamine receptors, D1 and D2 receptors coordinate hippocampal plasticity and influence learning and affective behaviors 66 . DAT regulates dopamine reuptake and maintains homeostasis 67 . Our finding in D1 receptors is consistent with the mirrored distribution of D1 receptors and the second-order hippocampal gradients reported by Nordin et al 42 . These results highlight the role of dopamine system in hippocampus-amygdala related functional communication. Moreover, we observed that greater gradient-D1 map similarity was associated with higher anxiety sensitivity, while gradient-DAT map similarity showed an inverse relationship. Studies showed the anxiogenic effects of D1 receptor activation; for instance, infusing D1 agonists into the amygdala was proved to induce heightened anxiety behaviors 68 , 69 . Reduced DAT availability has been linked to greater anxiety severity in Parkinson’s disease patients 70 . The correlations between D1, DAT and anxiety sensitivity did align with the previous literatures, indicating alterations in the dopamine system may contribute to anxiety symptoms partly through exerting influence on the hippocampus-amygdala related functional connectivity. However, these findings should be interpreted with caution. Although Fisher’s z-tests did not reveal significant differences in correlation strengths between the two datasets, these correlations only reached significance in one dataset (D1 in MIND-Set, DAT in HBS). Future studies are needed to determine whether limited replicability reflects meaningful differences between clinical and non-clinical populations or is driven by spurious factors. Our study has several limitations. First, the neurotransmitter maps used were templates derived from previous studies, rather than data measured directly within HBS and MIND-Set. This may explain the small effect sizes for correlations between individual gradient-neurotransmitter similarities and mental health outcomes. Datasets that include both resting-state fMRI and PET scans could provide a more sensitive approach, while also serving to validate our findings regarding the serotonin and dopamine systems. Second, the relationships between gradient and neurotransmitters remain indirect. Further research is needed to better understand hippocampus-amygdala gradients, particularly how they are influenced by neurochemical modulation, gene expression variability, or changes in mental states. Such investigations will contribute to a deeper interpretation of the gradient–neurotransmitter relationship. In conclusion, across both healthy and psychiatric cohorts, our study provides an overview of the functional connectivity topography of the hippocampus–amygdala complex. Certain connectopic gradients reflect the influence of serotonin and dopaminergic system on hippocampus-amygdala related functional connectivity, with individual differences in this influence associated with depression and anxiety symptoms. As an emerging analytical method, “connectopic mapping” demonstrates promise as a biomarker for assessing neurotransmitter related psychiatric symptomatology with resting- state fMRI. Declarations Acknowledgments and Disclosures This work was supported by the Ph.D. fellowship of the Chinese Scholarship Council (XSL; 202106010068). The funder had no role in study design, data collection and analysis, the decision to publish, or preparation of the article. XSL, JNV, KVH, CFB, GF, IT and NK were responsible for conceptualization. XSL, LY, NK were responsible for formal analysis and visualization. XSL, JNV, GF, IT and NK were responsible for validation. RMC and PFPvE were responsible for project administration. 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Bartlett EA, Yttredahl AA, Boldrini M, Tyrer AE, Hill KR, Ananth MR et al. In vivo serotonin 1A receptor hippocampal binding potential in depression and reported childhood adversity. Eur Psychiatry 2023; 66: e17. Healthy Brain Study consortium, Aarts E, Akkerman A, Altgassen M, Bartels R, Beckers D et al. Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context. Plos One 2021; 16: e0260952. van Eijndhoven P, Collard R, Vrijsen J, Geurts DE, Vasquez AA, Schellekens A et al. Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders (MIND-SET): protocol for a cross-sectional comorbidity study from a Research Domain Criteria perspective. JMIRx Med 2022; 3: e31269. van Oort J, Kohn N, Vrijsen J, Collard R, Duyser F, Brolsma S et al. Absence of default mode downregulation in response to a mild psychological stressor marks stress-vulnerability across diverse psychiatric disorders. NeuroImage Clin 2020; 25: 102176. Przeździk I, Faber M, Fernández G, Beckmann CF, Haak KV. The functional organisation of the hippocampus along its long axis is gradual and predicts recollection. Cortex 2019; 119: 324–335. Rush AJ, Carmody T, Reimitz P. The Inventory of Depressive Symptomatology (IDS): clinician (IDS‐C) and self‐report (IDS‐SR) ratings of depressive symptoms. Int J Methods Psychiatr Res 2000; 9: 45–59. Rodriguez BF, Bruce SE, Pagano ME, Spencer MA, Keller MB. Factor structure and stability of the Anxiety Sensitivity Index in a longitudinal study of anxiety disorder patients. Behav Res Ther 2004; 42: 79–91. Hagborg JM, Kalin T, Gerdner A. The Childhood Trauma Questionnaire—Short Form (CTQ-SF) used with adolescents–methodological report from clinical and community samples. J Child Adolesc Trauma 2022; 15: 1199–1213. Bergman MA, Schene AH, Vissers CTW, Vrijsen JN, Kan CC, van Oostrom I. Systematic review of cognitive biases in autism spectrum disorders: A neuropsychological framework towards an understanding of the high prevalence of co-occurring depression. Res Autism Spectr Disord 2020; 69: 101455. Jonnakuty C, Gragnoli C. What do we know about serotonin? J Cell Physiol 2008; 217: 301–306. Popova NK, Naumenko VS. 5-HT1A receptor as a key player in the brain 5-HT system. Rev Neurosci 2013; 24: 191–204. Ögren SO, Eriksson TM, Elvander-Tottie E, D’Addario C, Ekström JC, Svenningsson P et al. The role of 5-HT1A receptors in learning and memory. Behav Brain Res 2008; 195: 54–77. Gener T, Campo AT, Alemany-González M, Nebot P, Delgado-Sallent C, Chanovas J et al. Serotonin 5-HT1A, 5-HT2A and dopamine D2 receptors strongly influence prefronto-hippocampal neural networks in alert mice: Contribution to the actions of risperidone. Neuropharmacology 2019; 158: 107743. Jovanovic H, Perski A, Berglund H, Savic I. Chronic stress is linked to 5-HT1A receptor changes and functional disintegration of the limbic networks. Neuroimage 2011; 55: 1178–1188. Celada P, Bortolozzi A, Artigas F. Serotonin 5-HT1A receptors as targets for agents to treat psychiatric disorders: rationale and current status of research. CNS Drugs 2013; 27: 703–716. Underwood MD, Kassir SA, Bakalian MJ, Galfalvy H, Dwork AJ, Mann JJ et al. Serotonin receptors and suicide, major depression, alcohol use disorder and reported early life adversity. Transl Psychiatry 2018; 8: 279. Parsey RV, Olvet DM, Oquendo MA, Huang Y, Ogden RT, Mann JJ. Higher 5-HT1A receptor binding potential during a major depressive episode predicts poor treatment response: preliminary data from a naturalistic study. Neuropsychopharmacology 2006; 31: 1745–1749. Ayano G. Dopamine: receptors, functions, synthesis, pathways, locations and mental disorders: review of literatures. J Ment Disord Treat 2016; 2: 2. Beaulieu J-M, Gainetdinov RR. The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol Rev 2011; 63: 182–217. Grilli M, Nisoli E, Memo M, Missale C, Spano P. Pharmacological characterization of D1 and D2 dopamine receptors in rat limbocortical areas. II. Dorsal hippocampus. Neurosci Lett 1988; 87: 253–258. Edelmann E, Lessmann V. Dopaminergic innervation and modulation of hippocampal networks. Cell Tissue Res 2018; 373: 711–727. Madras BK, Miller GM, Fischman AJ. The dopamine transporter and attention-deficit/hyperactivity disorder. Biol Psychiatry 2005; 57: 1397–1409. Guarraci FA, Frohardt RJ, Kapp BS. Amygdaloid D1 dopamine receptor involvement in Pavlovian fear conditioning. Brain Res 1999; 827: 28–40. de la Mora MP, Gallegos-Cari A, Arizmendi-García Y, Marcellino D, Fuxe K. Role of dopamine receptor mechanisms in the amygdaloid modulation of fear and anxiety: Structural and functional analysis. Prog Neurobiol 2010; 90: 198–216. Erro R, Pappatà S, Amboni M, Vicidomini C, Longo K, Santangelo G et al. Anxiety is associated with striatal dopamine transporter availability in newly diagnosed untreated Parkinson’s disease patients. Parkinsonism Relat Disord 2012; 18: 1034–1038. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryMaterialTP.docx Characterizing_hippocampus-amygdala_gradient_Supplementary_Material 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7032613","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496190272,"identity":"80b32de0-cbf0-4028-99f0-ea8fa25a7b2a","order_by":0,"name":"Xiang-Shen Liu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-6041-9914","institution":"Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Medical Neuroscience, Nijmegen, The Netherlands","correspondingAuthor":true,"prefix":"","firstName":"Xiang-Shen","middleName":"","lastName":"Liu","suffix":""},{"id":496190273,"identity":"0b8bb023-9514-48da-b569-baab32924fb7","order_by":1,"name":"J. N. 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Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Koen","middleName":"","lastName":"Haak","suffix":""},{"id":496190276,"identity":"ddafd906-08a6-4723-9d9e-abd992bd9ee5","order_by":4,"name":"Rose Collard","email":"","orcid":"","institution":"Radboud University Medical Centre, Department of Psychiatry, Nijmegen, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Rose","middleName":"","lastName":"Collard","suffix":""},{"id":496190277,"identity":"b0ca6e26-4519-4c55-87da-76672d17f182","order_by":5,"name":"Philip van Eijndhoven","email":"","orcid":"","institution":"Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Psychiatry, Nijmegen, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"van","lastName":"Eijndhoven","suffix":""},{"id":496190278,"identity":"2aa8af1d-dbe8-4751-9b6f-fe45a57c8812","order_by":6,"name":"Christian Beckmann","email":"","orcid":"","institution":"Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Medical Neuroscience, Nijmegen, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Beckmann","suffix":""},{"id":496190279,"identity":"984a4f0e-ef9d-4138-b025-82ff8cbb1e71","order_by":7,"name":"Guillén Fernández","email":"","orcid":"","institution":"Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Medical Neuroscience, Nijmegen, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Guillén","middleName":"","lastName":"Fernández","suffix":""},{"id":496190280,"identity":"1e590235-7e32-453a-aa70-f9621404780b","order_by":8,"name":"Indira Tendolkar","email":"","orcid":"https://orcid.org/0000-0003-3171-3671","institution":"Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Psychiatry, Nijmegen, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Indira","middleName":"","lastName":"Tendolkar","suffix":""},{"id":496190281,"identity":"8f59f126-2fa8-4fe3-9be6-dc1633f4a2ac","order_by":9,"name":"Nils Kohn","email":"","orcid":"","institution":"Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Medical Neuroscience, Nijmegen, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Nils","middleName":"","lastName":"Kohn","suffix":""}],"badges":[],"createdAt":"2025-07-02 21:25:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7032613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7032613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90507681,"identity":"2832276d-09f1-4d9e-96d6-1723788a6092","added_by":"auto","created_at":"2025-09-03 13:01:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1212234,"visible":true,"origin":"","legend":"\u003cp\u003eHippocampus-amygdala gradient maps in the HBS and MIND-Set datasets (the second- and third-order gradients in MIND-Set were swapped for the convenience of comparison to HBS).\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7032613/v1/34e163359fdb005ab3e6bcf3.jpg"},{"id":90507685,"identity":"a2ea0c5c-bccb-4c51-9237-a733b0a19600","added_by":"auto","created_at":"2025-09-03 13:01:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":652110,"visible":true,"origin":"","legend":"\u003cp\u003eSimilar spatial layouts between gradients and 5-HT1A receptor maps (A), several neurotransmitters of the dopamine system (B) within the hippocampus-amygdala complex.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7032613/v1/37d8506489dfebba657b7b20.jpg"},{"id":90509011,"identity":"e89bd33e-72d0-4749-8780-a62514748210","added_by":"auto","created_at":"2025-09-03 13:09:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":696924,"visible":true,"origin":"","legend":"\u003cp\u003eThe higher level of spatial similarity between the third-order gradients and 5-HT1A receptor maps (Fisher r-to-z transformed absolute correlation coefficients; the horizontal axis) was associated with higher levels of depressive symptoms and anxiety sensitivity (the vertical axis).\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7032613/v1/0a5972b94005140225f34dcc.jpg"},{"id":90879969,"identity":"32a8aa77-11a4-46a2-9497-75f49b472ebe","added_by":"auto","created_at":"2025-09-09 09:37:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3357504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7032613/v1/6dbab698-be23-4c7d-a250-951862c8f072.pdf"},{"id":90507684,"identity":"0297b0a5-6675-41e1-8d2e-0d8d92e90a95","added_by":"auto","created_at":"2025-09-03 13:01:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1054676,"visible":true,"origin":"","legend":"Characterizing_hippocampus-amygdala_gradient_Supplementary_Material","description":"","filename":"SupplementaryMaterialTP.docx","url":"https://assets-eu.researchsquare.com/files/rs-7032613/v1/fff6ff594239c957ec6d6752.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Characterizing Functional Connectivity Gradients for the Hippocampus-Amygdala Complex in Healthy and Psychiatric Cohorts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe hippocampus and amygdala, as two adjacent medial temporal lobe (MTL) structures, have consistently been investigated as key brain regions in cognitive affective neuroscience\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The hippocampus, shaped like a seahorse, is essential for episodic memory. It processes and connects the sensory inputs, enabling our memory for daily events built up with spatial, temporal and other contextual information\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The anterior segment of hippocampus is closely adjacent to the amygdala -- a grey matter complex located in the dorsal sector of MTL. The amygdala is the central region for emotional responses to environmental stimuli, participating in circuits that process heightened arousal and survival-related emotions, such as fear, threat, and reward\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Effective functioning of these two anatomically adjacent regions is inseparable from their close interactions with each other: processes such as fear learning\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, as well as the encoding, retrieval, and consolidation of emotional memories\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e critically depend on hippocampus–amygdala interactions.\u003c/p\u003e\u003cp\u003eAs pivotal hubs for cognition and emotion, the hippocampus and amygdala also maintain widespread functional connections with other brain regions. Connections with the medial prefrontal cortex (mPFC), anterior temporal lobe, insula and posterior cortical areas are shown to support episodic encoding and retrieval, aversive learning, as well as stress and emotion regulation\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Maladaptive variations in these neural circuits lead to corresponding functional disruptions, which are commonly observed in mood disorders such as depression\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, anxiety disorders\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and post-traumatic stress disorder (PTSD)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and may also account for certain comorbidity across these.\u003c/p\u003e\u003cp\u003eNotably, studies demonstrated that functional domains within the hippocampus vary along its long, curved posterior-to-anterior axis, with gradients observed in gene expression, place cell field sizes, and the representation of gist versus detailed information\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Anatomically, dorsoventral (corresponding to anterior-posterior in human) topographical gradients in hippocampal–cortical and subcortical connectivity were revealed in animal studies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, which may support its functional gradient organization. Building on above findings, it’s important to examine if these gradient organization could be replicated in living humans (e.g., using noninvasive imaging techniques such as fMRI) for both healthy and clinical populations, and how it further matches with cognitive performance and psychiatric outcomes. These investigations will help us build a clear overview of hippocampal function and dysfunction.\u003c/p\u003e\u003cp\u003eSimilarly, projections between the amygdala and various cortical regions (e.g., mPFC, inferior temporal cortex), as well as subcortical structures (e.g., the striatum), have also been found to exhibit a topographically organized pattern\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. As a compact, deep-lying nucleus, however, the amygdala’s small size may limit the suitability of investigating this topography in humans using neuroimaging techniques, due to imaging resolution constraints. The hippocampus and amygdala are not only spatially adjacent structures, but both play closely interconnected roles within cognitive and affective functional networks; the amygdala is often regarded as a unit with the hippocampus when examining its functional role or psychiatric aberrance\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Considering both practical advantages and biological relevance, we would like to integrate the hippocampus and amygdala into a single complex, and capture the functional connectivity organization of this complex, through a data-driven technique \"connectopic mapping\". This method is designed to identify multiple overlapping connectivity patterns (gradient maps) within a predefined region of interest (ROI), with each gradient representing a distinct topographic mode of connectivity variation within the ROI relative to the rest of the brain\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. A pioneering study focused on the striatum, demonstrating that connectopic gradients replicate the topographical organization of cortical projections to the striatum observed in animal anatomical studies\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In the current study, with \"connectopic mapping\", we aimed to provide an overview of functional connectivity for the hippocampus-amygdala complex, by identifying its topographic connectivity gradients, then further explore its psychiatric relevance.\u003c/p\u003e\u003cp\u003eThe topographic gradients obtained by \"connectopic mapping\" are data-derived, and may result from combined influences of multiple biological mechanisms, including anatomical projections and neurotransmitter modulation. As for the hippocampus and amygdala, they are structures functioning under the support of various neurotransmitter systems, which extensively regulate their communication with other regions of functional networks\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. For example, as a fundamental process for long-term memory, hippocampal long-term potentiation depends on N-methyl-D-aspartate (NMDA) glutamatergic receptors and is further regulated by beta-noradrenergic receptors\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The beta-noradrenergic pathway in the amygdala is crucial for both the acquisition and consolidation of fear conditioning\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Other neurotransmitter systems, such as serotonin and dopamine, are also extensively distributed, prominently modulating the fear circuitry, emotional processing and learning\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Given the essential role of neurotransmitters in functional networks of the hippocampus-amygdala complex, we hypothesized that topographic gradient modes should, to some extent, reflect the underlying influence of neurotransmitter systems. To test this hypothesis, we compared the spatial topologies of hippocampus-amygdala gradient maps with multiple neurotransmitter maps. The similarity in spatial layouts offers a proxy for estimating the extent of a neurotransmitter's influence and aids in interpreting the biological meanings of these data-driven gradient modes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNeurotransmitter modulations in the hippocampus-amygdala complex are closely associated with mental health and the development of various psychiatric disorders. For example, altered function of serotonin receptors and transporters in the hippocampus and amygdala is commonly observed in mood disorders\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Such alterations influence individuals' coping mechanisms following stress\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and interact with childhood trauma to further shape depressive symptomatology\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, it is worthwhile to investigate how individual differences in neurotransmitter modulations of hippocampus-amygdala complex functional connectivity, depicted by gradient maps, relate to mental health outcomes. To achieve this goal, we characterized the gradient-neurotransmitter similarity in spatial topologies for each participant, and tested if this similarity could predict mental health-related factors (e.g., depressive and anxiety symptoms, as well as childhood trauma).\u003c/p\u003e\u003cp\u003eBy applying \"connectopic mapping\" to the hippocampus-amygdala complex, we aim to 1). create an overview of its functional connectivity organization by identifying several topographical gradient modes; 2). assess whether these gradient modes align with specific neurotransmitter functions; and 3). explore if individual differences in the correspondence between neurotransmitter distribution and gradient modes are related to mental health factors. For reproducibility validation, two independent databases were included: the Healthy Brain Study (HBS) comprising healthy adults\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and ‘Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-related Mental Disorders’ (MIND-Set) study including a highly comorbid psychiatric cohort\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. HBS dataset enables us to characterize hippocampus-amygdala gradient modes in healthy population, and MIND-Set provides the opportunity to explore its translational value for clinical samples.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, two databases were utilized and analyzed independently. One dataset was from the Healthy Brain Study, jointly conducted by Radboud University, Radboud University Medical Center, and the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands. The analysis included all participants from the first data release (N = 410; 169 males; mean age = 33.8 ± 2.8 years; see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for characteristic information of the two samples). Participants had no history of psychiatric illness or current diseases affecting the brain, and none were taking brain-targeted medication at the time of participation. The study protocol and more details can be found in Healthy Brain Study Consortium et al\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnother dataset was from the MIND-Set cohort, collected by the Department of Psychiatry at Radboud University Medical Center and the Donders Institute in Nijmegen, the Netherlands. All participants with available resting-state neuroimaging data were included (N = 367; 202 males; age 37.6 ± 14.0 years). In this sample, 286 participants were psychiatric patients (164 males; age 37.6 ± 13.4 years; see Supplementary for \u003cem\u003en\u003c/em\u003e per diagnosis), and 81 individuals did not have a current or past psychiatric disorder (37 males; age 37.67 ± 15.80 years). 242 participants were taking one or more medications during their participation. More details on the sample were introduced in van Eijndhoven et al\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003efMRI data acquisition and preprocessing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe HBS study includes three resting-state scans, conducted within one year, each separated by a four-month interval. T2*-weighted resting-state BOLD data were acquired using a multiband-accelerated gradient echo EPI sequence (66 slices; TR = 1000 ms; TE = 34 ms; flip angle = 60°; voxel size = 2.0 × 2.0 × 2.0 mm; FOV = 210 mm), with the duration of 10 minutes. Preprocessing steps comprised motion correction, distortion correction with field maps, and non-linear registration to MNI152 space. FSL FIX’s ICA and Gradient Distortion Correction were used for further denoising and distortion correction\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/jzwrg/\u003c/span\u003e\u003cspan address=\"https://osf.io/jzwrg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the MIND-Set study, three resting-state scans were conducted as well. Resting-state scans 1 and 2 each lasted 8.5 minutes and were separated by a neutral movie clip. Following resting-state scan 2, an aversive movie clip was shown to induce acute stress. Resting-state scan 3, lasting 12.6 minutes, was then acquired. All resting-state scans used a multi-band 6 protocol with an interleaved slice acquisition sequence to capture T2*-weighted EPI BOLD-fMRI images (66 slices; TR = 1000 ms; TE = 34 ms; flip angle = 60°; voxel size = 2.0 × 2.0 × 2.0 mm; FOV = 210 mm). Preprocessing was performed using FSL version 5.0.11 (FMRIB, Oxford, UK), including brain extraction, motion correction, bias field correction, high-pass temporal filtering (100s cut-off), spatial smoothing with a 4 mm FWHM Gaussian kernel, boundary-based registration to T1-weighted images, and nonlinear registration to standard space (MNI152). Motion-related artifacts were removed using ICA-based Automatic Removal of Motion Artifacts (ICA-AROMA). Further details on fMRI acquisition and preprocessing can be found in previous studies\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterizing hippocampus-amygdala connectivity gradients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor both datasets, we applied ConGrads\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e to the preprocessed resting-state data (three scans per dataset; see Supplementary Methods), using the left and right hippocampus-amygdala complexes as ROIs. The masks were derived from the Harvard-Oxford atlas with a threshold of 20% probability. To better visualize gradient changes along the hippocampal long axis, the functional image data and masks were rotated around the X-axis in MNI152 space by an angle of 37°. ConGrads identified gradient modes of functional connectivity changes based on a similarity matrix computed within the hippocampus-amygdala complex, estimated from the functional connectivity between voxels in the complex and the rest of the brain.\u003c/p\u003e\u003cp\u003eFor each resting-state scan of the two datasets, we derived group-average gradient maps by combining functional image data from all available participants as inputs, separately for the left and right hippocampus-amygdala complexes. To enable statistical comparisons across participants, the order of individual gradient maps was adjusted by swapping gradients based on their spatial correlation with group-average maps (i.e., swapping within the participant when the other gradient map showed a higher correlation coefficient with the group average than the original one). After swapping, as a quality control measure, individual maps were excluded if their spatial correlation with the group-average map was below 0.50\u003csup\u003e41\u003c/sup\u003e. We monitored the proportion of participants retained at this threshold and determined the number of gradient modes based on the point at which a sharp decline in this proportion was observed.\u003c/p\u003e\u003cp\u003eTo characterize the spatial organization of these gradient modes, a trend surface model (TSM) was applied to both group-average and individual gradient maps \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Following previous work on hippocampal gradients\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, a trend surface regression model with nine coefficients (three parameters along X, Y, and Z axes) was chosen.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMapping gradients with neurotransmitters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better understand the data-driven gradient maps, we examined their relationships with multiple neurotransmitter systems. We utilized PET or SPECT scans for various neurotransmitters from the publicly available JuSpace toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/juryxy/JuSpace\u003c/span\u003e\u003cspan address=\"https://github.com/juryxy/JuSpace\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Following prior research exploring striatal connectivity gradients and neurotransmitters\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, a total of 18 neurotransmitter templates were included (see Table S2 for a full list).\u003c/p\u003e\u003cp\u003eThe same trend surface regression model, with nine coefficients, was applied to these PET/SPECT scans, with the left and right hippocampus-amygdala complexes defined as ROIs. Correlation coefficients were then calculated between the TSM coefficients obtained from these neurotransmitter maps and the TSM coefficients characterizing the group average hippocampus-amygdala gradient maps. To standardize comparisons, the absolute correlation coefficients were transformed using Fisher's r-to-z transformation. To examine the statistical significance of these gradient-neurotransmitter correlations, permutation testing was performed (N = 10,000, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Bonferroni corrected). A null distribution was generated by permuting the PET/SPECT TSM coefficients (separately for each coefficient) and calculating correlations between gradient TSM coefficients and permuted PET/SPECT TSM coefficients (Fisher r-to-z transformed, absolute values). The observed correlations were then compared to this null distribution.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLinking individual gradient-neurotransmitter similarity with behavioral outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNeurotransmitter modulations in the hippocampus-amygdala complex are relevant for psychopathology. Therefore, we also examined whether individual differences in gradient-to-neurotransmitter similarity could be associated with variations in behavioral outcomes for mental health. For neurotransmitters that showed similarity in spatial layouts with the group-average connectivity gradients, we calculated the correlation coefficients between the TSM coefficients of individual gradient maps per participant and TSM coefficients of PET/SPECT data in the JuSpace toolbox. Subsequently, Spearman correlation analysis was conducted between the Fisher r-to-z transformed absolute correlation coefficients and behavioral outcomes (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, FDR corrected).\u003c/p\u003e\u003cp\u003eFor the behavioral outcomes, we included depression and anxiety symptom levels as psychiatric measures, and childhood trauma as a pathogenic environmental factor. In both datasets, depressive symptoms were measured using the Inventory of Depressive Symptomatology-Self Report (IDS-SR)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and anxiety sensitivity was assessed with the Anxiety Sensitivity Index (ASI)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In the HBS dataset, childhood trauma was measured using the Childhood Trauma Questionnaire-Short Form (CTQ-SF)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Both the total score and subscale scores (emotional neglect, physical neglect, emotional abuse, physical abuse) were included. In MIND-Set, childhood trauma frequency and diversity was measured using the questionnaire from the Netherlands Mental Health Survey and Incidence Study (NEMESIS)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Both the overall index and subscales (representing the frequency of occurrence of emotional neglect, psychological abuse, and physical abuse before the age of 16 years) were included in the analysis. Additionally, for this psychiatric cohort, we accounted for comorbidity levels by summing the number of diagnosed psychiatric disorder clusters (i.e. Mood Disorder, Anxiety Disorder including PTSD and Obsessive Compulsive Disorder, Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, and Addiction) for each participant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eThe hippocampus-amygdala functional connectivity gradient maps\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAcross both datasets, we identified six functional connectivity gradient modes in total (as a sharp decline in the proportion of participants retained after quality assurance was observed at the sixth mode; Table S3). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the hippocampus-amygdala gradient maps for both datasets. Because gradient maps across different resting states were highly similar, we visualized the maps derived from resting-state 1 as a representative example. The dominant (zeroth-order) gradient shows a gradual change along the coordinate space, starting in the amygdala and extending along the hippocampal long axis. The first-order gradient is organized from the middle of the hippocampus towards the anterior and posterior ends of the complex. We observed that the second- and third-order gradients appear in reversed order between the HBS and MIND-set samples, although their overall spatial patterns remain consistent. In the following results, for the convenience of comparison, we used the order in HBS to name gradient maps.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e around here)\u003c/p\u003e\u003cp\u003eFor quality assurance, we excluded participants whose spatial correlation with the group-average gradient maps was lower than 0.5. Table S3 shows the proportion of participants retained based on this threshold. In the HBS dataset, all six connectopic gradient modes had inclusion rates of 50% or higher. In the MIND-Set dataset, while the fourth-order gradient on the right side during resting-state 2 and the fifth-order gradient bilaterally across all three resting states exhibited lower stability, the first four gradient modes consistently showed inclusion rates exceeding 50%. The results indicate the robustness of these data-driven connectopic maps, especially for the first four gradient modes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSimilarity in spatial layouts between gradient and neurotransmitter maps\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe correlation analysis with permutation testing identified several significant similarities between hippocampus-amygdala gradient and neurotransmitter maps (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Table S4 for the full list), with findings highlighting the serotonin and dopaminergic systems. The most stable similarity is between the third-order gradients and 5-HT1A receptor maps: significant correlations were observed bilaterally across all resting states in both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), except for the right side in resting-state 2 of MIND-Set. In HBS, the left-side correlation was not Bonferroni-significant but survived when we applied FDR correction. Additionally, the second-order gradients exhibited similarity with dopamine type 1 (D1) receptor maps on the left side across all resting states in both datasets. In MIND-Set only, the third-order gradients demonstrated similarity to dopamine type 2 (D2) receptor maps on the left side. In HBS only, the fourth-order gradients displayed similarity with dopamine transporter (DAT) maps on the left side (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\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\u003eSignificant correlations between gradient and neurotransmitter layouts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeft_ Second-order \u0026amp; D1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeft_ Third-order \u0026amp;5HT1A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRight_ Third -order \u0026amp;5HT1A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLeft_ Third -order \u0026amp; D2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLeft_ Fourth-order \u0026amp; DAT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eHBS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eResting-State1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.948;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.444;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (FDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.514;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.459;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eResting-State2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.799;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.545;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (FDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.558;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.459;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eResting-State3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.813;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.543;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (FDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.615;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.456;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eMIND-Set\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eResting-State1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.586;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.955;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.838;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.708;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eResting-State2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.568;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.955;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.703;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eResting-State3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.417;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.948;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.507;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u0026thinsp;=\u0026thinsp;1.732;\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eno sig.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eD1: dopamine type 1; D2: dopamine type 2; DAT: dopamine transporter\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e around here)\u003c/p\u003e\u003cp\u003e(Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e around here)\u003c/p\u003e\u003cp\u003eTo determine whether the observed similarities in the MIND-Set sample were specific to psychiatric patients, we further separated the sample into patient and healthy control subgroups. Results showed that both subgroups exhibited the same patterns with those observed in the full sample (Table S5).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndividual gradient-neurotransmitter similarity predicts mental health outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor neurotransmitter maps that showed significant spatial similarity with group-average gradients (bilateral 5-HT1A receptors; left D2 receptors in MIND-Set; left D1 receptors; and left DAT in HBS), we computed correlation coefficients between the TSM coefficients of JuSpace PET/SPECT images and individual gradient maps for each participant. Taking Fisher r-to-z transformed absolute correlation coefficients, Spearman correlation analysis revealed several significant associations with anxiety sensitivity and depressive levels. For HBS resting-state 1, the similarity between 5-HT1A receptors and the third-order gradients on the left side of the hippocampus-amygdala complex was positively correlated with depressive levels (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.19, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.021), and approaching significance for anxiety sensitivity (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.16, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.072). In the MIND-Set dataset, these correlations were observed for the right side in resting-state 1 (depressive levels: \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.13, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.057; anxiety sensitivity: \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.15, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.038; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Given that the two datasets span distinct ranges of anxiety sensitivity and depressive symptoms (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) \u0026mdash;with HBS falling into the relatively lower end and MIND-Set the higher\u0026mdash;these replicated correlations may indicate both sensitivity and stability across the symptom spectrum.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs for the dopaminergic system, in MIND-Set resting-state 2, the similarity between D1 receptor and second-order gradient maps showed a positive correlation with anxiety sensitivity (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.13, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.046). This association was not significant in the HBS sample (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.06, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.50), although Fisher\u0026rsquo;s z test comparing the two correlations (e.g., in MIND-Set and HBS) revealed no significant difference (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.682, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.495). In HBS resting-state 3, the similarity between DAT and fourth-order gradient maps was negatively associated with anxiety sensitivity (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = -0.18, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e = 0.048). No other significant correlations were observed (\u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;0.10). Full lists of correlations are presented in Table S6 and S7.\u003c/p\u003e\u003cp\u003eWe also divided the MIND-Set sample into patient and healthy controls, and ran the correlation analyses separately (Table S8). Fisher\u0026rsquo;s z tests revealed no significant differences in the observed correlations above between two subgroups (\u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;0.16).\u003c/p\u003e\u003cp\u003e(Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e around here)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWidespread functional connections between the hippocampus-amygdala complex and other brain regions play important roles in human cognition and emotion, thereby contributing to the most common psychiatric disorders. In this study, with both healthy and psychiatric cohorts, we 1). identified six distinct connectopic gradient modes for the hippocampus-amygdala complex, with the first two exhibiting gradual changes along the hippocampal long axis; 2). revealed that neurotransmitters from the serotonin and dopamine systems displayed spatial similarities with connectopic gradients of the hippocampus-amygdala complex; and 3). found that individual variability in these gradient-neurotransmitter similarities was associated with depression severity and anxiety sensitivity. Our findings highlight the potential of “connectopic mapping” to bridge functional neuroimaging, biomolecular markers and behavioral measures, offering a novel perspective for understanding brain function and dysfunctions in living human beings.\u003c/p\u003e\u003cp\u003eThe first two connectopic gradient maps of the hippocampus–amygdala complex did show gradient changes following the hippocampal long axis in both samples. This demonstrates that “connectopic mapping” can capture the connectivity organization previously identified in animal studies, using fMRI scans of living humans. The spatial organization of our gradient maps also aligns with hippocampal gradients reported in earlier studies\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The amygdala exhibited connectivity modes similar to those of the anterior hippocampus, partly because of the spatial proximity, also indicating their close functional coupling. While previous research focused on lower-order connectopic gradients, our study extended it by extracting higher-order gradient modes and replicating the findings in both healthy and psychiatric samples.\u003c/p\u003e\u003cp\u003eVarious neurotransmitters contribute to regulating the functionality of the hippocampus and amygdala. \"connectopic mapping\" offers an approach to better understand how neurotransmitters influence hippocampus-amygdala related functional connectivity, by breaking down overall connectivity patterns into specific topographical gradient modes. For each identified gradient, we evaluated its similarity in spatial layouts with various neurotransmitter maps, which serves as an indirect measure of how much influence a particular neurotransmitter has on the functional connectivity.\u003c/p\u003e\u003cp\u003eThe most robust gradient-neurotransmitter similarity we found is between the third-order gradients and 5-HT1A receptor maps. 5-HT is a monoamine neurotransmitter synthesized in both the central nervous system and gastrointestinal cells\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The 5-HT1A receptor is one of the most abundant receptor subtypes in the mammalian brain\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. In the hippocampus and amygdala, most 5-HT1A receptors function as postsynaptic receptors suppressing pyramidal cell firing\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Our third-order gradient generally captured the organizational patterns of 5-HT1A receptor distribution within the hippocampus-amygdala complex, which may suggest that functional communication between the complex and other brain regions is heavily influenced by serotonin modulation. In addition to previous reports from animal and PET studies\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, our findings provide evidence for the feasibility of receiving serotonin receptor related readouts from resting-state fMRI scans on an individual basis.\u003c/p\u003e\u003cp\u003eThe activity of 5-HT1A receptors is implicated in emotion processing and stress coping\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, with alterations shown in mood disorders. Studies have reported increased hippocampal 5-HT1A receptor binding potential in individuals with depressive episodes and childhood adversity\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and elevated 5-HT1A receptor density in people who committed suicide\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Higher 5-HT1A receptor binding potential was also associated with poor responses to antidepressant treatment\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In this study, we found that greater similarity between the third-order gradients and 5-HT1A receptor maps was related to more severe depressive symptoms and heightened anxiety sensitivity. This supports previous findings in elevated 5-HT1A receptor involvement and internalizing psychiatric symptoms. These results may suggest that increased involvement of 5-HT1A receptor activity in hippocampus-amygdala related functional connectivity could serve as a potential risk factor underlying mood disorder symptoms. Notably, we observed this relationship in both the healthy sample (with a narrower symptom range) and the clinical sample (with a broader range), suggesting its sensitivity to transdiagnostic symptom variation. Compared to anatomical or PET studies, “connectopic mapping” could be a more efficient and cost-effective approach for depicting this alteration in 5-HT1A receptor functionality.\u003c/p\u003e\u003cp\u003eBeyond the serotonin system, our findings also identified similarities between hippocampus-amygdala gradients and the spatial organization of dopamine-related neurotransmitters: D1 receptors with the second-order gradient, D2 receptors with the third-order gradient, DAT with the fourth-order gradient. D1 and D2 receptors are the most abundant dopamine receptor subtypes\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. D1 receptors have an excitatory role in signaling pathways, whereas D2 receptors exhibit more complex effects, generally with inhibitory properties\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Together with other dopamine receptors, D1 and D2 receptors coordinate hippocampal plasticity and influence learning and affective behaviors\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. DAT regulates dopamine reuptake and maintains homeostasis\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Our finding in D1 receptors is consistent with the mirrored distribution of D1 receptors and the second-order hippocampal gradients reported by Nordin et al\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These results highlight the role of dopamine system in hippocampus-amygdala related functional communication. Moreover, we observed that greater gradient-D1 map similarity was associated with higher anxiety sensitivity, while gradient-DAT map similarity showed an inverse relationship. Studies showed the anxiogenic effects of D1 receptor activation; for instance, infusing D1 agonists into the amygdala was proved to induce heightened anxiety behaviors\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Reduced DAT availability has been linked to greater anxiety severity in Parkinson’s disease patients\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The correlations between D1, DAT and anxiety sensitivity did align with the previous literatures, indicating alterations in the dopamine system may contribute to anxiety symptoms partly through exerting influence on the hippocampus-amygdala related functional connectivity. However, these findings should be interpreted with caution. Although Fisher’s z-tests did not reveal significant differences in correlation strengths between the two datasets, these correlations only reached significance in one dataset (D1 in MIND-Set, DAT in HBS). Future studies are needed to determine whether limited replicability reflects meaningful differences between clinical and non-clinical populations or is driven by spurious factors.\u003c/p\u003e\u003cp\u003eOur study has several limitations. First, the neurotransmitter maps used were templates derived from previous studies, rather than data measured directly within HBS and MIND-Set. This may explain the small effect sizes for correlations between individual gradient-neurotransmitter similarities and mental health outcomes. Datasets that include both resting-state fMRI and PET scans could provide a more sensitive approach, while also serving to validate our findings regarding the serotonin and dopamine systems. Second, the relationships between gradient and neurotransmitters remain indirect. Further research is needed to better understand hippocampus-amygdala gradients, particularly how they are influenced by neurochemical modulation, gene expression variability, or changes in mental states. Such investigations will contribute to a deeper interpretation of the gradient–neurotransmitter relationship.\u003c/p\u003e\u003cp\u003eIn conclusion, across both healthy and psychiatric cohorts, our study provides an overview of the functional connectivity topography of the hippocampus–amygdala complex. Certain connectopic gradients reflect the influence of serotonin and dopaminergic system on hippocampus-amygdala related functional connectivity, with individual differences in this influence associated with depression and anxiety symptoms. As an emerging analytical method, “connectopic mapping” demonstrates promise as a biomarker for assessing neurotransmitter related psychiatric symptomatology with resting- state fMRI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments and Disclosures\u003c/p\u003e\u003cp\u003eThis work was supported by the Ph.D. fellowship of the Chinese Scholarship Council (XSL; 202106010068). The funder had no role in study design, data collection and analysis, the decision to publish, or preparation of the article.\u003c/p\u003e\u003cp\u003eXSL, JNV, KVH, CFB, GF, IT and NK were responsible for conceptualization. XSL, LY, NK were responsible for formal analysis and visualization. XSL, JNV, GF, IT and NK were responsible for validation. RMC and PFPvE were responsible for project administration. JNV, GF, IT and NK were responsible for supervision. XSL, JNV and NK wrote the original draft, and all authors contributed to reviewing and editing on the final manuscript.\u003c/p\u003e\u003cp\u003eThe authors report no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLaBar KS, Cabeza R. Cognitive neuroscience of emotional memory. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e 2006; 7: 54\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003ePanksepp J, Lane RD, Solms M, Smith R. 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Role of dopamine receptor mechanisms in the amygdaloid modulation of fear and anxiety: Structural and functional analysis. \u003cem\u003eProg Neurobiol\u003c/em\u003e 2010; 90: 198\u0026ndash;216.\u003c/li\u003e\n\u003cli\u003eErro R, Pappat\u0026agrave; S, Amboni M, Vicidomini C, Longo K, Santangelo G \u003cem\u003eet al.\u003c/em\u003e Anxiety is associated with striatal dopamine transporter availability in newly diagnosed untreated Parkinson\u0026rsquo;s disease patients. \u003cem\u003eParkinsonism Relat Disord\u003c/em\u003e 2012; 18: 1034\u0026ndash;1038.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7032613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7032613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hippocampus and amygdala play essential roles in human cognition and emotion, through their extensive connectivity with other brain regions and close interaction between them. Uncovering the functional organization of the hippocampus\u0026ndash;amygdala complex and how it is modulated by neurotransmitters can enhance our understanding of their biological functionality, and provide a basis for further exploration of the clinical relevance. An emerging functional connectivity analysis method, \u0026ldquo;connectopic mapping\u0026rdquo;, may offer a novel approach to characterize this functional organization. In this study, we applied \"connectopic mapping\" to the hippocampus-amygdala complex, testing its utility with resting-state functional magnetic resonance imaging (fMRI) scans of two independent datasets: one comprising healthy individuals (N\u0026thinsp;=\u0026thinsp;410) and another comprising a psychiatric cohort (N\u0026thinsp;=\u0026thinsp;367). The spatial organization of derived gradient maps was compared to 18 positron emission tomography (PET) or single photon emission computed tomography (SPECT) scan templates for different neurotransmitter systems. Individual gradient\u0026ndash;neurotransmitter similarity indices were correlated with mental health outcomes. Our analyses identified six distinct gradient maps in both datasets. The third-order gradients showed stable similarity with 5-HT1A receptor maps across various resting-state scans. Similarities were also observed between gradient maps and the distribution patterns of neurotransmitters within the dopaminergic system. Individual gradient-to-5-HT1A similarity was positively correlated with depression severity and anxiety sensitivity, highlighting the psychopathological relevance. These findings demonstrate that across the psychiatric continuum, \"connectopic mapping\" is a powerful tool for exploring the relationship between functional connectivity and neurotransmitter modulation, showing potential as a comprehensive transdiagnostic biomarker.\u003c/p\u003e","manuscriptTitle":"Characterizing Functional Connectivity Gradients for the Hippocampus-Amygdala Complex in Healthy and Psychiatric Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 13:01:23","doi":"10.21203/rs.3.rs-7032613/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":"bfbe1740-acef-499c-b428-41da89c143aa","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52700681,"name":"Biological sciences/Neuroscience"},{"id":52700682,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-09-09T09:29:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 13:01:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7032613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7032613","identity":"rs-7032613","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}