Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI

Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI Mahdi Mohammadkhanloo, Mohammad Pooyan, Hamid Sharini, Mitra Yousefpour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5176630/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Background Cognitive impairments are common in neuropsychiatric disorders like Attention Deficit Hyperactivity Disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ). While previous research has focused on specific brain regions, the role of the procedural memory as a type of long-term memory to cognitive function in these disorders remains unclear. This study investigates the association between cognitive impairments and alterations in resting-state functional connectivity (rs-FC) within procedural memory network in patients with these disorders. Methods This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ADHD, 49 with BD, 50 with SZ, and 50 healthy controls (HCs). A procedural memory network was defined based on the selection of 34 regions of interest (ROIs) associated with the network in the Harvard-Oxford Cortical Structural Atlas (default atlas). Multivariate ROI-to-ROI connectivity (mRRC) was used to analyze the rs-FC between the defined network regions. Significant differences in rs-FC between patients and HCs were identified (P < 0.001). Results ADHD patients showed increased Cereb45 l - Cereb3 r rs-FC (p = 0.000067) and decreased Cereb1 l - Cereb6 l rs-FC (p = 0.00092). BD patients exhibited increased rs-FC between multiple regions, including Claustrum r - Caudate r (p = 0.00058), subthalamic nucleus r - Pallidum l (p = 0.00060), substantia nigra l - Cereb2 l (p = 0.00082), Cereb10 r - SMA r (p = 0.00086), and Cereb9 r - SMA l (p = 0.00093) as well as decreased rs-FC in subthalamic nucleus r - Cereb6 l (p = 0.00013) and Cereb9 r - Cereb9 l (p = 0.00033). SZ patients indicated increased Caudate r– putamen l rs-FC (p = 0.00057) and decreased rs-FC in subthalamic nucleus r – Cereb6 l (p = 0.000063), and Cereb1 r – subthalamic nucleus r (p = 0.00063). Conclusions This study found significant alterations in rs-FC within the procedural memory network in patients with ADHD, BD, and SZ compared to HCs. These findings suggest that disrupted rs-FC within this network may contribute to cognitive impairments observed in these disorders. Procedural memory Cognitive impairments Functional connectivity Resting-state functional magnetic resonance imaging Brain mapping Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Neurological and mental disorders afflict nearly a billion people worldwide, underscoring their significant global health burden [ 1 ]. ADHD [ 2 ], BD [ 3 ], and SZ [ 4 ] affecting 5%, 2.5%, and 0.5% of the population respectively, collectively impact closer to 8% of the population. These disorders are characterized by cognitive dysfunction and impaired motor control [ 5 , 6 ]. A common deficit in these disorders is impaired long-term memory, impacting cognitive and mental functions [ 7 – 9 ]. Long-term memory is typically divided into implicit and explicit memory. [ 10 ]. Implicit memory operates below conscious awareness, storing and retrieving information without intention. Brain regions like the cerebellum, subcortical motor areas, and basal ganglia are crucial for this type of memory, facilitating the acquisition and consolidation of skills, behaviors, and habits [ 11 ]. Unlike implicit memory, explicit memory requires conscious effort for storage, retrieval, and sharing [ 12 ]. The cerebral cortex, particularly the neocortex, is the neural basis for explicit memory [ 13 ]. Accessing explicit memories requires intentional effort and conscious focus [ 14 ]. Given the challenges neuropsychiatric patients, especially ADHD, BD, and SZ, face with task cooperation [ 15 ], investigating implicit memory during rest may offer insights into their cognitive impairments [ 13 ]. Procedural memory, a subconscious form of long-term memory for motor skills and habits, is interconnected with various cognitive functions [ 16 ]. Procedural memory allows tasks to be recalled and executed effortlessly, without conscious deliberation [ 17 ]. The cerebellum, supplementary motor area (SMA), and basal ganglia form the procedural memory network [ 18 ]. These regions are functionally connected, meaning that their neural activities are statistically dependent on each other [ 19 ]. Because these areas are active in the resting state, the intrinsic patterns of activity and neural connections that occur spontaneously in the brain demonstrate a special type of functional connectivity: rs-FC [ 20 ]. Given the functional connectivity within the procedural memory network and its resting-state activity, exploring rs-FC patterns can provide valuable insights into the neural mechanisms underlying its organization [ 21 ]. Neuroimaging is crucial for studying brain functional connections, the patterns of interaction between different brain regions [ 22 ]. fMRI is a prominent neuroimaging method due to its high spatial resolution, non-invasive nature, and strong signal-to-noise ratio [ 23 ]. rs-fMRI is a specialized fMRI technique that measures brain activity at rest, focusing on the spontaneous fluctuations of the BOLD signal [ 24 ]. BOLD signals exhibit high temporal coherence between functionally related brain regions [ 25 ]. Various methods can be used to analyze functional connections between brain regions based on these fluctuations. Some of these methods include seed-based connectivity, mRRC, network measures, dynamic connectivity, graph theory and etc. [ 26 ]. MRRC approach offers several advantages over other connectivity methods: 1) unlike seed-based connectivity, which focuses on the connectivity of a single seed region [ 27 ], mRRC allows for the simultaneous examination of the interrelationships between multiple ROIs. This provides a more holistic understanding of the network's dynamics [ 28 ]. 2) While network measures can provide global properties of the network, mRRC focuses on the specific interactions between individual ROIs [ 29 ]. This allows for a more detailed investigation of the network's structure and function. 3) While dynamic connectivity explores how connectivity patterns change over time, mRRC can capture the static or relatively stable patterns within the network [ 30 ]. This is particularly useful for understanding the underlying architecture of the procedural memory network. 4) Graph theory provides a framework for analyzing networks as graphs, but it often focuses on global properties rather than specific ROI-to-ROI interactions [ 31 ]. According to the mentioned advantages, mRRC method was chosen for this study due to its ability to comprehensively analyze the interactions between multiple ROIs within the procedural memory network. Studies investigating cognitive impairments using alterations in the rs-FC within the procedural memory network in neuropsychiatric patients, particularly those with ADHD, BD, and SZ using rs-fMRI imaging have not been reported. In this study, we investigate whether rs-FC changes in the procedural memory network exist in patients with ADHD, BD, and SZ during rest, and whether these alterations are related to their cognitive impairments. By employing rs-fMRI and focusing on the mRRC method, we assesse rs-FC alterations in the procedural memory network of studied patients and discussed the resulting cognitive impairments. Material and methods Participants This study leveraged the UCLA Consortium for Neuropsychiatric Phenomics (CNP) dataset [ 32 ]. Demographic details of the 189 participants (104 male, 85 female) are provided in Table 1 . The dataset included individuals aged 21–50 years (mean: 33.72, median: 31.0). Participants were selected based on the following criteria: right-handedness, absence of metal implants, non-pregnancy, lack of MRI fear, and no history of head trauma or loss of consciousness. All participants underwent 314-second fMRI scans. [ 32 ]. Table 1 Demographic characteristics of the UCLA CNP database Groups ADHD (n = 40) BD (n = 49) SZ (n = 50) HCs (n = 50) Age (M ± SD) Male 32.90 ± 10.60 35.89 ± 9.22 35.61 ± 8.91 31.96 ± 8.78 Female 33.27 ± 11.16 34.48 ± 8.93 39.17 ± 8.59 30.50 ± 8.69 Gender (%) Male 7.7 10.3 14 25 Female 8.1 7.7 4.4 22.8 Image acquisition This study utilized rs-fMRI data from a publicly available resource: the UCLA CNP dataset on OpenNeuro ( https://doi.org/10.18112/openneuro.ds000030.v1.0.0 ) [ 32 ]. The CNP database employed a 3 Tesla (3T) Siemens Trio scanner to acquire both fMRI and structural MRI (sMRI) images from participants. The fMRI images were acquired using a T2-weighted echo planar imaging (EPI) sequence with the following parameters: images slice thickness = 4mm, 34 slices, TR = 2s, TE = 30ms, FOV = 192mm, flip angle = 90°, matrix size = 64 × 64. Additionally, T1-weighted high-resolution anatomical scans (MPRAGE) were obtained with the following parameters: images slice thickness = 1mm, 176 slices, TR = 1.9s, TE = 2.26ms, FOV = 250mm, matrix size = 256 × 256. Image preprocessing The CONN v21.a, functional connectivity toolbox [ 28 ], built upon SPM12, was employed to preprocess both sMRI and fMRI data. For sMRI preprocessing, the following steps were executed: 1) the image center was translated to the origin coordinates (0,0,0) to establish a consistent reference point. 2) Unified segmentation and MNI (Montreal Neurological Institute) normalization were applied using a comprehensive model that integrates segmentation, registration, and normalization into a single process [ 33 ]. The fMRI data underwent a rigorous preprocessing pipeline to ensure data quality and reliability. Key steps included: 1) Motion Correction : Head motion was corrected using realignment and unwarp techniques. The image center was translated to the origin coordinates (0,0,0) to establish a consistent frame of reference. 2) Temporal Correction : Slice-time correction was applied to account for temporal variations. 3) Outlier Detection : ART-based outlier scan detection and scrubbing methods were used to identify and remove volumes with excessive head motion, ensuring data integrity [ 34 ]. 4) Spatial Normalization : fMRI images were normalized to the MNI space to facilitate inter-subject comparisons. 5 ) Nuisance Regression : To account for noise variables, nuisance regression was performed using the 6 realignment parameters, their derivatives, scrubbing vectors, and the first 5 principal components derived from white matter (WM) and cerebrospinal fluid (CSF) time series using CompCor [ 28 ]. 6) Temporal Filtering : Temporal band-pass filtering (0.008–0.09 Hz) and linear detrending were applied to reduce noise and drift, enhancing the quality of the fMRI data [ 33 ]. Procedural memory network Procedural memory, a fundamental component of the memory system, facilitates the automatic execution and retrieval of motor and cognitive skills necessary for various tasks. Operating primarily at a subconscious level, it seamlessly guides activities. When required, procedural memories are automatically retrieved and applied in executing complex procedures involving motor and cognitive functions [ 35 ]. The procedural memory network is comprised of key anatomical brain structures: the basal ganglia, cerebellum, and SMA. These regions, interconnected through neural pathways, collectively facilitate the automatic execution and retrieval of motor and cognitive skills [ 18 ]. The basal ganglia play a pivotal role in selecting and initiating motor actions, as well as learning and refining motor and cognitive skills through practice [ 36 ]. The cerebellum contributes to motor coordination, precision, and timing, aiding in fine-tuning movements and error correction during skill acquisition [ 37 ]. Moreover, the SMA is responsible for planning and coordinating complex movements, particularly sequences or well-learned motor patterns. It plays a crucial role in initiating and executing motor programs [ 38 ]. In this study, we defined a procedural memory network comprising 34 ROIs using the CONN toolbox. These ROIs were distributed across the basal ganglia (12), cerebellum (lobules I-X; 20), and SMA (2) [ 18 ] (Table 2 ). The ROIs were defined using MNI coordinates from the default atlas in the CONN toolbox [ 28 ] and the study by Song X, et al [ 39 ]. This network was established to examine the functional connectivity between ROIs within the procedural memory network in individuals with ADHD, BD, and SZ. Table 2 MNI coordinates of the 34 ROIs relevant to procedural memory network ROI MNI coordinate Abbreviation Left caudate nucleus (-13, 9, 10) Caudate l Right caudate nucleus (13, 10, 10) Caudate r Left cerebellar lobule I (-36, -66, -30) Cereb1 l Right cerebellar lobule I (38, -67, -30) Cereb1 r Left cerebellar lobule II (-29, -73, -38) Cereb2 l Right cerebellar lobule II (32, -69, -40) Cereb2 r Left cerebellar lobule III (-9, -37, -19) Cereb3 l Right cerebellar lobule III (12, -35, -19) Cereb3 r Left cerebellar lobules I V &V (-14, -44, -17) Cereb45 l Right cerebellar lobules IV&V (16, -44, -19) Cereb45 r Left cerebellar lobule VI (-23, -58, -24) Cereb6 l Right cerebellar lobule VI (24, -58, -25) Cereb6 r Left cerebellar lobule VII (-32, -60, -45) Cereb7 l Right cerebellar lobule VII (33, -63, -48) Cereb7 r Left cerebellar lobule VIII (-26, -55, -48) Cereb8 l Right cerebellar lobule VIII (25, -56, -49) Cereb8 r Left cerebellar lobule IX (-11, -49, -46) Cereb9 l Right cerebellar lobule IX (9, -49, -46) Cereb9 r Left cerebellar lobule X (-23, -34, -42) Cereb10 l Right cerebellar lobule X (26, -34, -41) Cereb10 r Left Claustrum (-33, -20, 12) Claustrum l Right Claustrum (33, -19, 10) Claustrum r Left Substantia Nigra (-12, -15, -18) Substantia nigra l Right Substantia Nigra (13, -17, -16) Substantia nigra r Left Subthalamic nucleus (-18, -18, -8) Subthalamic nucleus l Right Subthalamic nucleus (12, -18, -7) Subthalamic nucleus r Left Supplementary motor area (-5, -3, 56) SMA l Right Supplementary motor area (6, -3, 58) SMA r Left Putamen (-25, 0, 0) Putamen l Right Putamen (25, 2, 0) Putamen r Left Pallidum (-19, -5, -1) Pallidum l Right Pallidum (20, -4, -1) Pallidum r Rs-FC analysis The human brain is a complex network of interconnected regions, both functionally and structurally. Effective functional communication between these regions is crucial for complex cognitive processes, as it enables the seamless integration of information across different brain areas. Studying functional connectivity in the human brain is essential for gaining deeper insights into its fundamental organization [ 40 ]. rs-FC examines the statistical dependencies between spatially distributed neuronal units while the brain is at rest, revealing the intrinsic functional organization of the brain [ 41 ]. We employed the mRRC method within the CONN toolbox to conduct correlation analyses and estimate functional connectivity in the procedural memory network. Pre-defined ROIs associated with the procedural memory network (Table 2 ) were used to calculate correlations between brain regions within this network. The mRRC approach enabled us to spatially map correlation patterns in the brain during rest, identifying abnormal functional connections among studied neuropsychiatric disorders. Statistical analysis To analyze the data, we utilized the CONN v21.a toolbox [ 28 ] within MATLAB R2019b software. Prior to analysis, we verified the normality of the data distribution. Independent t-tests were conducted to examine significant differences in the rs-FC within the procedural memory network between neuropsychiatric patients (ADHD, BD, and SZ) and HCs. A significance level of p < 0.001 was considered to identify statistically significant differences in rs-FC between the two groups. Results Results indicated there were significant differences in some rs-FC within the procedural memory network between individuals with ADHD, BD, SZ, and HCs. In the following, detailed analysis of rs-FC between regions associated with the procedural memory network, focusing on significant differences (p-value < 0.001) observed between the studied patients and HCs are explained in detail with accompanying figures. rs-FC analysis in ADHD vs. HCs Individuals with ADHD exhibited significantly elevated rs-FC between the Cereb3 r and Cereb45 l (p = 0.000067) compared to HCs. Conversely, they demonstrated significantly decreased rs-FC between the Cereb1 l and the Cereb6 l (p = 0.0009182). Table 3 provided a detailed statistical analysis to offer a more comprehensive understanding of the statistical significance and magnitude of these differences. The table outlines the significance level (p-value) and the rs-FC strength (t-test), categorizing results into instances where ADHD patients exhibited heightened (ADHD > HCs) or diminished (ADHD < HCs) rs-FC compared to HCs. Figures 1 and 2 visually depict the regions within the procedural memory network of ADHD patients that exhibited significantly elevated and reduced rs-FC between them, respectively, compared to HCs. Table 3 Significant differences in rs-FC within the procedural memory network of ADHD patients compared to HCs (p HCs Cereb45 l – Cereb3 r 4.00 0.000067 ADHD < HCs Cereb1 l – Cereb6 l -3.13 0.00091 rs-FC analysis in BD vs. HCs BD patients illustrated significantly increased rs-FC between the Claustrum r and Caudate r (p = 0.000584), subthalamic nucleus r and Pallidum l (p = 0.000604), substantia nigra l and Cereb2 l (p = 0.000818), Cereb10 r and SMA r (p = 0.000862), Cereb9 r and SMA l (p = 0.000933) compared to HCs. Whilst, they displayed significantly reduced rs-FC between the subthalamic nucleus r and the Cereb6 l (p = 0.000126), Cereb9 r and Cereb9 l (p = 0.000329) compared to HCs. Table 4 presented a detailed statistical analysis of these differences. The table outlines the significance level (p-value) and the rs-FC strength (t-test), categorizing results into cases where BD patients exhibited higher (BD > HCs) or lower (BD < HCs) rs-FC compared to HCs. Figures 3 and 4 visually showed the regions within the procedural memory network of BD patients that exhibited significantly increased and reduced rs-FC between them, respectively, compared to HCs. Table 4 Significant differences in rs-FC within the procedural memory network of BD patients compared to HCs. (p HCs Claustrum r – Caudate r 3.35 0.00058 subthalamic nucleus r – Pallidum l 3.34 0.00060 substantia nigra l – Cereb2 l 3.24 0.00082 Cereb10 r – SMA r 3.22 0.00086 Cereb9 r – SMA l 3.20 0.00093 BD < HCs subthalamic nucleus r – Cereb6 l -3.80 0.00013 Cereb9 r – Cereb9 l -3.52 0.00033 rs-FC analysis in SZ vs. HCs SZ patients illustrated significantly increased rs-FC between the Caudate r and putamen l (p = 0.000567). Whiles, they demonstrated significantly reduced rs-FC between the subthalamic nucleus r and the Cereb6 l (p = 0.000063), Cereb1 r and subthalamic nucleus r (p = 0.000628) compared to HCs. Table 5 presented a detailed statistical analysis of these differences. The table outlines the significance level (p-value) and the rs-FC strength (t-test), categorizing results into cases where SZ patients exhibited higher (SZ > HCs) or lower (SZ < HCs) rs-FC compared to HCs. Figures 5 and 6 visually indicated the regions within the procedural memory network of SZ patients that exhibited significantly increased and reduced rs-FC between them, respectively, compared to HCs. Table 5 Significant differences in rs-FC within the procedural memory network of SZ patients compared to HCs (p HCs Caudate r– putamen l 3.35 0.00057 SZ < HCs subthalamic nucleus r – Cereb6 l -3.99 0.000063 Cereb1 r – subthalamic nucleus r -3.32 0.00063 Discussion To examine cognitive impairment in patients with ADHD, BD, and SZ, We identified distinct patterns of altered rs-FC within procedural memory network in each patient group using rs-fMRI and mRRC method, independent of any specific task. Given the roles of the brain's procedural memory network regions, disruptions in their functional connectivity, such as increased or decreased connectivity, were associated with cognitive and behavioral impairments in ADHD, BD, and SZ [ 42 ]. In the following, we will elucidate the role of each ROI within the procedural memory network in cognitive function. Subsequently, we will delve into rs-FC alterations between these ROIs and their potential implications for cognition in individuals with ADHD, BD, and SZ (Sections 4.1, 4.2, and 4.3). The cerebellum, traditionally associated with motor control, is increasingly recognized for its pivotal role in various cognitive functions, including working memory and executive function, particularly in the visual-spatial domain [ 43 ]. The SMA, a part of the premotor cortex, plays a critical role in planning and executing complex movements, as well as in cognitive functions such as working memory and decision-making [ 44 , 45 ]. The Subthalamic nucleus and pallidum are key components of the basal ganglia, a group of brain structures involved in motor control, reward processing, and learning [ 46 ]. The Subthalamic nucleus is thought to play a role in inhibiting unwanted movements [ 47 ], while the pallidum is involved in regulating motor output [ 48 ]. The caudate and putamen are both components of the striatum [ 49 ], a key brain region involved in motor control, reward processing, and learning [ 50 ]. The caudate is thought to play a role in planning and initiating movements [ 51 ], while the putamen is involved in executing movements and regulating motor output [ 52 ]. The substantia nigra plays a multifaceted role in cognitive function, regulating reward, motivation, movement, learning, and executive functions through its production of dopamine [ 53 ]. ADHD Patients with ADHD demonstrated decreased functional connectivity between Cereb1l and Cereb6l regions. This finding suggests potential disruptions in information transfer between these regions, which are implicated in cognitive processes [ 54 ]. These disruptions could contribute to the observed cognitive deficits in ADHD patients, such as impaired motor-cognitive integration, attentional problems, and executive function difficulties, particularly in visual-spatial tasks [ 55 ]. The rs-FC analysis performed in the ADHD sample closely aligned with previously published rs-FC analyses conducted by Jiang K and colleagues [ 56 ]. Furthermore, our analysis indicated increased functional connectivity between regions Cereb3 r and Cereb45 l, potentially reflecting hyperactive communication within the procedural memory network [ 57 ]. This aberrant connectivity pattern may contribute to the cognitive impairments experienced by ADHD patients, potentially leading to difficulties in task switching, attention, and working memory [ 49 , 50 ]. This finding is consistent with previous research indicating increased functional connectivity between cerebellar regions, which has been associated with cognitive deficits, including working memory, in ADHD [ 60 ]. BD This study's findings, examining rs-FC between the claustrum and caudate in BD patients, suggest potential mechanisms underlying several BD symptoms. The claustrum's role in attention and emotion regulation [ 61 ] might be impaired due to its hyperconnectivity with the caudate, a brain region implicated in reward processing. [ 62 ]. These findings align with the "default mode network" (DMN) hypothesis of BD, which suggests that individuals with BD exhibit aberrant activity in the DMN [ 63 ], a network of brain regions involved in introspection and self-referential thought [ 64 ]. Niccolò Zovetti et al.'s study [ 65 ] demonstrated that BD is linked to alterations in the frontal and posterior DMN structures, primarily in the prefrontal, posterior, and inferior cingulate cortices. Given that the claustrum and caudate are both situated within the frontal region of the DMN, the findings of this study corroborate the potential involvement of these structures in BD. Moreover, the findings of this study, which examined rs-FC between the subthalamic nucleus and pallidum in BD patients, provide further support for the "reward circuit" hypothesis of BD. Increased rs-FC between these regions may contribute to several symptoms associated with the disorder, including motor disturbances and cognitive impairments [ 66 ]. These results are consistent with previous research [ 67 ] suggesting that disruptions in the basal ganglia, a key component of the reward circuit, can lead to cognitive deficits in neuropsychiatric conditions. The hyperconnectivity observed in BD patients could potentially disrupt the balance of excitatory and inhibitory signals within the reward circuit, leading to difficulties in regulating emotions, motivation, and behavior [ 68 , 69 ]. Furthermore, the increased rs-FC between the cerebellum and SMA in BD patients could potentially contribute to several symptoms associated with the disorder. For example, the cerebellum's involvement in emotion regulation and social cognition [ 70 , 71 ] might be affected by its hyper connectivity with the SMA, which is implicated in planning and executing movements [ 72 ]. These findings align with the "motor network" hypothesis of BD [ 73 ], which suggests that abnormalities in brain regions involved in motor control contribute to the development and maintenance of the disorder. Arshaq Saleem et al.'s study [ 74 ] found that increased functional connectivity between sensory-motor areas is correlated with the intensity of both motor control and emotional experiences. This suggests that heightened connectivity in these regions may be a specific marker of mood state or a general indicator of disease severity. Also, the decreased rs-FC between the Subthalamic nucleus and cerebellum in BD patients could potentially contribute to several symptoms associated with the disorder. For example, the Subthalamic nucleus's involvement in motor control and reward processing [ 66 ] might be affected by its reduced connectivity with the cerebellum, which is implicated in motor coordination and cognitive functions. This could lead to difficulties in regulating movements and emotional responses, which are common features of BD [ 70 , 71 ]. These findings align with the "motor network" and "reward circuit" hypotheses of BD, which suggest that abnormalities in brain regions involved in motor control and reward processing contribute to the development and maintenance of the disorder. Our findings of decreased rs-FC between the subthalamic nucleus and cerebellum in BD patients are consistent with previous research by Tao Wu et al., [ 75 ] which implicated disruptions in the motor network and reduced connectivity between these brain regions in Parkinson's disease. These results suggest that similar mechanisms may underlie motor coordination and cognitive deficits in both conditions. In addition, Interhemispheric communication within the cerebellum is essential for coordinating movements and maintaining balance [ 76 ]. The decreased rs-FC between the cerebellar hemispheres in BD patients might be related to several symptoms associated with the disorder such as difficulties in motor coordination, balance, and cognitive functions [ 77 ]. These findings align with the "motor network" hypothesis of BD, which suggests that abnormalities in brain regions involved in motor control contribute to the development and maintenance of the disorder. Our findings of decreased rs-FC between the cerebellar hemispheres in BD patients are consistent with previous research by Ying Wang et al., [ 78 ] which identified interhemispheric coordination deficits in individuals with BD. These results suggest that impaired communication between the two cerebellar hemispheres may contribute to the motor coordination, balance, and cognitive difficulties often observed in BD patients. This aligns with the "motor network" hypothesis, which posits that abnormalities in brain regions involved in motor control play a role in the development and maintenance of BD. SZ Increased rs-FC between the caudate and putamen in SZ patients may contribute to hyperconnectivity within the striatum, potentially leading to difficulties in controlling motor behavior, such as motor tics or abnormal movements [ 79 ]. Moreover, given the striatum's role in reward processing [ 80 ], altered rs-FC in this region could contribute to motivational deficits and anhedonia, which are common symptoms of SZ [ 81 ]. In a study by Mingjun Duan et al., [ 82 ] functional connectivity changes within the basal ganglia network of individuals with SZ were examined. They found that increased functional connections within this network were associated with symptoms such as impaired motor processing, cognitive deficits, motivational difficulties, and emotional control issues. These findings align with our results. On the other hand, decreased rs-FC between the subthalamic nucleus and cerebellum in patients with SZ could contribute to symptoms such as impaired motor control, difficulties with reward processing, and cognitive deficits [ 83 ]. These disruptions might lead to challenges in regulating movements and emotional responses, which are common characteristics of SZ [ 84 ]. Our findings of decreased rs-FC between the subthalamic nucleus and cerebellum in patients with SZ align with previous research by Hugo C. Baggio et al., [ 83 ] who demonstrated that deficits in motor, cognitive, and emotional functions in Parkinson's and multiple system atrophy patients arise from impaired connectivity between these brain regions. This suggests that disruptions in the subthalamic nucleus-cerebellum circuit may underlie similar symptoms in SZ, such as impaired motor control, difficulties with reward processing, and cognitive deficits. Limitations While this study provides valuable insights, it is essential to acknowledge its limitations, including sample size and the focus on three specific disorders. Future research should expand on these findings by incorporating larger cohorts, exploring additional psychiatric conditions, and employing more advanced analysis techniques such as dynamic causal modeling [ 85 ] and asymmetrical functional connectivity [ 86 ]. Understanding the neural basis of cognitive impairments in psychiatric disorders is crucial for developing targeted interventions. Our findings contribute to this knowledge by identifying specific brain regions and network abnormalities associated with ADHD, BD, and SZ. By targeting these neural substrates, future therapeutic approaches may aim to improve cognitive function and overall quality of life for affected individuals. Conclusions This study highlights the importance of the procedural memory network in cognitive function and provides evidence for its involvement in the pathophysiology of ADHD, BD, and SZ. By examining rs-FC within the procedural memory network, we identified distinct patterns of altered connectivity in each patient group. These findings suggest that disruptions in the functional communication between key brain regions within this network play a significant role in the cognitive and behavioral deficits observed in these disorders. Future research can build upon these findings to develop targeted interventions aimed at improving cognitive function in these disorders. Abbreviations ADHD attention deficit hyperactivity disorder BD bipolar disorder SZ schizophrenia HCs healthy controls rs-FC resting-state functional connectivity fMRI functional magnetic resonance imaging rs-fMRI resting-state functional magnetic resonance imaging ROI regions of interest mRRC, multivariate ROI-to-ROI connectivity DMN default mode network SMA supplementary motor area MNI Montreal Neurological Institute SPM Statistical Parametric Mapping WM white matter CSF cerebrospinal fluid CNP Consortium for Neuropsychiatric Phenomics M Mean SD Standard Deviation Declarations Clinical trial number Not applicable Ethics approval and consent to participate The UCLA Consortium for Neuropsychiatric Phenomics (CNP) dataset [32] was utilized in this study. This dataset is publicly accessible from the OpenNeuro repository. Ethical approval for this study was obtained from the Kermanshah University of Medical Sciences Ethics Committee (reference number IR.KUMS.REC.1402.036). Consent for publication Not applicable Availability of data and materials The data is publicly available, which means that anyone can access and use it. https://doi.org/10.18112/openneuro.ds000030.v1.0.0 Competing interests The authors declare no competing interests. Funding Not applicable Authors' contributions M.M. and H.S. designed the study, wrote the initial draft, and performed data preprocessing. M.M. developed the analysis code. M.Y. contributed to the discussion section. M.P. reviewed, revised, and provided critical feedback on the manuscript. All authors participated in reviewing and approving the final version of the manuscript. Acknowledgements The original dataset used in this study was generously provided by the Consortium for Neuropsychiatric Phenomics (CNP). Their dedication to advancing neuroscience research through data sharing is invaluable. This dataset was supported by NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410. We extend our sincere gratitude to the researchers who contributed to the data collection and preparation, making this research possible. References Collaborators GBD 2019 MD. Song P, Zha M, Yang Q, Zhang Y, Li X, Rudan I. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health 2021;11:1–9. https://doi.org/10.7189/jogh.11.04009. The Lancet Psychiatry. 2022;9:137–50. Song P, Zha M, Yang Q, Zhang Y, Li X, Rudan I. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health. 2021;11:1–9. Zhong Y, Chen Y, Su X, Wang M, Li Q, Shao Z, et al. Global, regional and national burdens of bipolar disorders in adolescents and young adults: a trend analysis from 1990 to 2019. 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Strata P, Scelfo B, Sacchetti B. Involvement of cerebellum in emotional behavior. Physiol Res. 2011;60 SUPPL.1. Picard N, Strick PL. Activation of the supplementary motor area (SMA) during performance of visually guided movements. Cereb Cortex. 2003;13:977–86. Bellani M, Bontempi P, Zovetti N, Gloria Rossetti M, Perlini C, Dusi N, et al. Resting state networks activity in euthymic bipolar disorder. Bipolar Disord. 2020;22:593–601. Saleem A, Harmata G, Jain S, Voss MW, Fiedorowicz JG, Williams AJ, et al. Functional connectivity of the cerebellar vermis in bipolar disorder and associations with mood. Front Psychiatry. 2023;14:1147540. Wu T, Hallett M. The cerebellum in Parkinson’s disease. Brain. 2013;136:696–709. Serrien DJ, O’Regan L. Attention and Interhemispheric Communication: Implications for Language Dominance. Neuroscience. 2023;510:21–31. Huang Y, Zhang Z, Lin S, Zhou H, Xu G. Cognitive Impairment Mechanism in Patients with Bipolar Disorder. Neuropsychiatr Dis Treat. 2023;19:361–6. Wang Y, Zhong S, Jia Y, Zhou Z, Wang B, Pan J, et al. Interhemispheric resting state functional connectivity abnormalities in unipolar depression and bipolar depression. Bipolar Disord. 2015;17:486–95. Wang HLS, Rau CL, Li YM, Chen YP, Yu R. Disrupted thalamic resting-state functional networks in schizophrenia. Front Behav Neurosci. 2015;9 FEB:45. Delgado MR, Tricomi E. Reward processing and decision making in the human striatum. In: Neuroscience of Decision Making. Psychology Press; 2011. p. 145–72. Lee J, Jung S, Park I, Kim J-J. Neural Basis of Anhedonia and Amotivation in Patients with Schizophrenia: The Role of Reward System. Curr Neuropharmacol. 2015;13:750–9. Duan M, Chen X, He H, Jiang Y, Jiang S, Xie Q, et al. Altered basal ganglia network integration in schizophrenia. Front Hum Neurosci. 2015;9 OCT:561. Baggio HC, Abos A, Segura B, Campabadal A, Uribe C, Giraldo DM, et al. Cerebellar resting-state functional connectivity in Parkinson’s disease and multiple system atrophy: characterization of abnormalities and potential for differential diagnosis at the single-patient level. NeuroImage Clin. 2019;22:101720. Horan WP, Hajcak G, Wynn JK, Green MF. Impaired emotion regulation in schizophrenia:Evidence from event-related potentials. Psychol Med. 2013;43:2377–91. Snyder AD, Ma L, Steinberg JL, Woisard K, Moeller FG. Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature. Front Neurosci. 2021;15:636273. Williams LZJ, Fitzgibbon SP, Bozek J, Winkler AM, Dimitrova R, Poppe T, et al. Structural and functional asymmetry of the neonatal cerebral cortex. Nat Hum Behav. 2023;7:942–55. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviews received at journal 09 Nov, 2024 Reviews received at journal 08 Nov, 2024 Reviewers agreed at journal 20 Oct, 2024 Reviewers agreed at journal 12 Oct, 2024 Reviewers invited by journal 10 Oct, 2024 Editor invited by journal 10 Oct, 2024 Editor assigned by journal 05 Oct, 2024 Submission checks completed at journal 05 Oct, 2024 First submitted to journal 29 Sep, 2024 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. 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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-5176630","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376971682,"identity":"a76775d0-c33f-40e4-a441-42a52e6794ef","order_by":0,"name":"Mahdi Mohammadkhanloo","email":"","orcid":"","institution":"Qazvin Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"","lastName":"Mohammadkhanloo","suffix":""},{"id":376971683,"identity":"98e70579-afef-4271-9dc4-d5fb77649d31","order_by":1,"name":"Mohammad Pooyan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYPACGwYDBh4GEGJgYCZOSxrpWg4jaSEEzNmbj0n83HHe3pz97MEHbxjs5BnYeR/g1WLZcyxNsvfM7cSdPXnJhnMYkg0bmNkN8GoxuJFjJsHbdjvB4ECOmTQPA3MCAzMbfoeBtEj+bTtnb3D+jflvHoZ64rRI87YdYNwAZDDzMBwmrAXol2Rr2bbkxA033hhLzjE4bthGSAswxA7efNtmB3RYjuGHNxXV8vz8xwg4jIGBRQKFS8AOsBrmD4QUjYJRMApGwQgHADm3PPPk452qAAAAAElFTkSuQmCC","orcid":"","institution":"Shahed University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Pooyan","suffix":""},{"id":376971684,"identity":"61c55055-faf3-4da9-8d4d-d8f1321e47f2","order_by":2,"name":"Hamid Sharini","email":"","orcid":"","institution":"Kermanshah University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Sharini","suffix":""},{"id":376971685,"identity":"096de3dd-98f0-4c46-b4c1-f3c29138e5db","order_by":3,"name":"Mitra Yousefpour","email":"","orcid":"","institution":"AJA University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Mitra","middleName":"","lastName":"Yousefpour","suffix":""}],"badges":[],"createdAt":"2024-09-29 20:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5176630/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5176630/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-024-01527-7","type":"published","date":"2025-01-13T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70208084,"identity":"0273e910-7fea-40e9-a932-f6ba075f05b0","added_by":"auto","created_at":"2024-11-29 14:00:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":252465,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b, and c) 3D views of coronal, sagittal, and axial planes of the brain's procedural memory network with increased rs-FC in ADHD patients compared to HCs. (d) Ring view of regions with increased rs-FC in ADHD patients compared to HCs. (ADHD \u0026gt;HCs).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5176630/v1/9c8b51cbecbc97c6c5423c5a.jpg"},{"id":70208090,"identity":"89565dde-aba3-4564-9443-5df99378e37e","added_by":"auto","created_at":"2024-11-29 14:00:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250900,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b, and c) 3D views of coronal, sagittal, and axial planes of the brain's procedural memory network with decreased rs-FC in ADHD patients compared to HCs. (d) Ring view of regions with decreased rs-FC in ADHD patients compared to HCs (ADHD \u0026lt; HCs).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5176630/v1/470f7774246ed1207cea1545.jpg"},{"id":70208085,"identity":"83aac661-b6bb-4490-a033-026ae81e52d9","added_by":"auto","created_at":"2024-11-29 14:00:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":296975,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b, and c) 3D views of coronal, sagittal, and axial planes of the brain's procedural memory network with increased rs-FC in BD patients compared to HCs. (d) Ring view of regions with increased rs-FC in BD patients compared to HCs. (BD \u0026gt;HCs).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5176630/v1/b47fe3c38bd171ec7c411231.jpg"},{"id":70208519,"identity":"128b9a84-1ee4-41d3-b147-f82eb45363e0","added_by":"auto","created_at":"2024-11-29 14:08:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":260185,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b, and c) 3D views of coronal, sagittal, and axial planes of the brain's procedural memory network with decreased rs-FC in BD patients compared to HCs. (d) Ring view of regions with decreased rs-FC in BD patients compared to HCs (BD \u0026lt; HCs).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5176630/v1/32f2ce9d7ee089cbe918e8a0.jpg"},{"id":70208517,"identity":"7798606f-b5fc-429f-903d-3041632f0329","added_by":"auto","created_at":"2024-11-29 14:08:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":246213,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b, and c) 3D views of coronal, sagittal, and axial planes of the brain's procedural memory network with increased rs-FC in SZ patients compared to HCs. (d) Ring view of regions with increased rs-FC in SZ patients compared to HCs. (SZ \u0026gt;HCs).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5176630/v1/4534e6ec2c1a0a2f57531877.jpg"},{"id":70208516,"identity":"3b2962cc-cb94-4a4f-a258-44885157b42c","added_by":"auto","created_at":"2024-11-29 14:08:38","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":253941,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b, and c) 3D views of coronal, sagittal, and axial planes of the brain's procedural memory network with decreased rs-FC in SZ patients compared to HCs. 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ADHD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], BD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and SZ [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] affecting 5%, 2.5%, and 0.5% of the population respectively, collectively impact closer to 8% of the population. These disorders are characterized by cognitive dysfunction and impaired motor control [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A common deficit in these disorders is impaired long-term memory, impacting cognitive and mental functions [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Long-term memory is typically divided into implicit and explicit memory. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Implicit memory operates below conscious awareness, storing and retrieving information without intention. Brain regions like the cerebellum, subcortical motor areas, and basal ganglia are crucial for this type of memory, facilitating the acquisition and consolidation of skills, behaviors, and habits [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Unlike implicit memory, explicit memory requires conscious effort for storage, retrieval, and sharing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The cerebral cortex, particularly the neocortex, is the neural basis for explicit memory [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Accessing explicit memories requires intentional effort and conscious focus [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given the challenges neuropsychiatric patients, especially ADHD, BD, and SZ, face with task cooperation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], investigating implicit memory during rest may offer insights into their cognitive impairments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Procedural memory, a subconscious form of long-term memory for motor skills and habits, is interconnected with various cognitive functions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Procedural memory allows tasks to be recalled and executed effortlessly, without conscious deliberation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The cerebellum, supplementary motor area (SMA), and basal ganglia form the procedural memory network [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These regions are functionally connected, meaning that their neural activities are statistically dependent on each other [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Because these areas are active in the resting state, the intrinsic patterns of activity and neural connections that occur spontaneously in the brain demonstrate a special type of functional connectivity: rs-FC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given the functional connectivity within the procedural memory network and its resting-state activity, exploring rs-FC patterns can provide valuable insights into the neural mechanisms underlying its organization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Neuroimaging is crucial for studying brain functional connections, the patterns of interaction between different brain regions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. fMRI is a prominent neuroimaging method due to its high spatial resolution, non-invasive nature, and strong signal-to-noise ratio [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. rs-fMRI is a specialized fMRI technique that measures brain activity at rest, focusing on the spontaneous fluctuations of the BOLD signal [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. BOLD signals exhibit high temporal coherence between functionally related brain regions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Various methods can be used to analyze functional connections between brain regions based on these fluctuations. Some of these methods include seed-based connectivity, mRRC, network measures, dynamic connectivity, graph theory and etc. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. MRRC approach offers several advantages over other connectivity methods: 1) unlike seed-based connectivity, which focuses on the connectivity of a single seed region [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], mRRC allows for the simultaneous examination of the interrelationships between multiple ROIs. This provides a more holistic understanding of the network's dynamics [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 2) While network measures can provide global properties of the network, mRRC focuses on the specific interactions between individual ROIs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This allows for a more detailed investigation of the network's structure and function. 3) While dynamic connectivity explores how connectivity patterns change over time, mRRC can capture the static or relatively stable patterns within the network [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is particularly useful for understanding the underlying architecture of the procedural memory network. 4) Graph theory provides a framework for analyzing networks as graphs, but it often focuses on global properties rather than specific ROI-to-ROI interactions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. According to the mentioned advantages, mRRC method was chosen for this study due to its ability to comprehensively analyze the interactions between multiple ROIs within the procedural memory network. Studies investigating cognitive impairments using alterations in the rs-FC within the procedural memory network in neuropsychiatric patients, particularly those with ADHD, BD, and SZ using rs-fMRI imaging have not been reported. In this study, we investigate whether rs-FC changes in the procedural memory network exist in patients with ADHD, BD, and SZ during rest, and whether these alterations are related to their cognitive impairments. By employing rs-fMRI and focusing on the mRRC method, we assesse rs-FC alterations in the procedural memory network of studied patients and discussed the resulting cognitive impairments.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study leveraged the UCLA Consortium for Neuropsychiatric Phenomics (CNP) dataset [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Demographic details of the 189 participants (104 male, 85 female) are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The dataset included individuals aged 21\u0026ndash;50 years (mean: 33.72, median: 31.0). Participants were selected based on the following criteria: right-handedness, absence of metal implants, non-pregnancy, lack of MRI fear, and no history of head trauma or loss of consciousness. All participants underwent 314-second fMRI scans. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\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\u003eDemographic characteristics of the UCLA CNP database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADHD (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSZ (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHCs (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.90\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.61\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.96\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.27\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.48\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.17\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImage acquisition\u003c/h3\u003e\n\u003cp\u003eThis study utilized rs-fMRI data from a publicly available resource: the UCLA CNP dataset on OpenNeuro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18112/openneuro.ds000030.v1.0.0\u003c/span\u003e\u003cspan address=\"10.18112/openneuro.ds000030.v1.0.0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The CNP database employed a 3 Tesla (3T) Siemens Trio scanner to acquire both fMRI and structural MRI (sMRI) images from participants. The fMRI images were acquired using a T2-weighted echo planar imaging (EPI) sequence with the following parameters: images slice thickness\u0026thinsp;=\u0026thinsp;4mm, 34 slices, TR\u0026thinsp;=\u0026thinsp;2s, TE\u0026thinsp;=\u0026thinsp;30ms, FOV\u0026thinsp;=\u0026thinsp;192mm, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, matrix size\u0026thinsp;=\u0026thinsp;64 \u0026times; 64. Additionally, T1-weighted high-resolution anatomical scans (MPRAGE) were obtained with the following parameters: images slice thickness\u0026thinsp;=\u0026thinsp;1mm, 176 slices, TR\u0026thinsp;=\u0026thinsp;1.9s, TE\u0026thinsp;=\u0026thinsp;2.26ms, FOV\u0026thinsp;=\u0026thinsp;250mm, matrix size\u0026thinsp;=\u0026thinsp;256 \u0026times; 256.\u003c/p\u003e\n\u003ch3\u003eImage preprocessing\u003c/h3\u003e\n\u003cp\u003eThe CONN v21.a, functional connectivity toolbox [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], built upon SPM12, was employed to preprocess both sMRI and fMRI data. For sMRI preprocessing, the following steps were executed: \u003cb\u003e1)\u003c/b\u003e the image center was translated to the origin coordinates (0,0,0) to establish a consistent reference point. \u003cb\u003e2)\u003c/b\u003e Unified segmentation and MNI (Montreal Neurological Institute) normalization were applied using a comprehensive model that integrates segmentation, registration, and normalization into a single process [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The fMRI data underwent a rigorous preprocessing pipeline to ensure data quality and reliability. Key steps included: \u003cb\u003e1) Motion Correction\u003c/b\u003e: Head motion was corrected using realignment and unwarp techniques. The image center was translated to the origin coordinates (0,0,0) to establish a consistent frame of reference. \u003cb\u003e2) Temporal Correction\u003c/b\u003e: Slice-time correction was applied to account for temporal variations. \u003cb\u003e3) Outlier Detection\u003c/b\u003e: ART-based outlier scan detection and scrubbing methods were used to identify and remove volumes with excessive head motion, ensuring data integrity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. \u003cb\u003e4) Spatial Normalization\u003c/b\u003e: fMRI images were normalized to the MNI space to facilitate inter-subject comparisons. 5\u003cb\u003e) Nuisance Regression\u003c/b\u003e: To account for noise variables, nuisance regression was performed using the 6 realignment parameters, their derivatives, scrubbing vectors, and the first 5 principal components derived from white matter (WM) and cerebrospinal fluid (CSF) time series using CompCor [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cb\u003e6) Temporal Filtering\u003c/b\u003e: Temporal band-pass filtering (0.008\u0026ndash;0.09 Hz) and linear detrending were applied to reduce noise and drift, enhancing the quality of the fMRI data [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eProcedural memory network\u003c/h3\u003e\n\u003cp\u003eProcedural memory, a fundamental component of the memory system, facilitates the automatic execution and retrieval of motor and cognitive skills necessary for various tasks. Operating primarily at a subconscious level, it seamlessly guides activities. When required, procedural memories are automatically retrieved and applied in executing complex procedures involving motor and cognitive functions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The procedural memory network is comprised of key anatomical brain structures: the basal ganglia, cerebellum, and SMA. These regions, interconnected through neural pathways, collectively facilitate the automatic execution and retrieval of motor and cognitive skills [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The basal ganglia play a pivotal role in selecting and initiating motor actions, as well as learning and refining motor and cognitive skills through practice [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The cerebellum contributes to motor coordination, precision, and timing, aiding in fine-tuning movements and error correction during skill acquisition [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, the SMA is responsible for planning and coordinating complex movements, particularly sequences or well-learned motor patterns. It plays a crucial role in initiating and executing motor programs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, we defined a procedural memory network comprising 34 ROIs using the CONN toolbox. These ROIs were distributed across the basal ganglia (12), cerebellum (lobules I-X; 20), and SMA (2) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ROIs were defined using MNI coordinates from the default atlas in the CONN toolbox [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and the study by Song X, et al [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This network was established to examine the functional connectivity between ROIs within the procedural memory network in individuals with ADHD, BD, and SZ.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMNI coordinates of the 34 ROIs relevant to procedural memory network\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMNI coordinate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft caudate nucleus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-13, 9, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudate l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight caudate nucleus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(13, 10, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudate r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-36, -66, -30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb1 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(38, -67, -30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb1 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-29, -73, -38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb2 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(32, -69, -40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb2 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-9, -37, -19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb3 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(12, -35, -19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb3 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobules I\u003c/b\u003eV\u003cb\u003e\u0026amp;V\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-14, -44, -17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb45 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobules IV\u0026amp;V\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(16, -44, -19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb45 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule VI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-23, -58, -24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb6 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule VI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(24, -58, -25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb6 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule VII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-32, -60, -45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb7 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule VII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(33, -63, -48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb7 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule VIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-26, -55, -48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb8 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule VIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(25, -56, -49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb8 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule IX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-11, -49, -46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb9 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule IX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9, -49, -46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb9 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft cerebellar lobule X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-23, -34, -42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb10 l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight cerebellar lobule X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(26, -34, -41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCereb10 r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft Claustrum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-33, -20, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClaustrum l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight Claustrum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(33, -19, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClaustrum r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft Substantia Nigra\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-12, -15, -18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubstantia nigra l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight Substantia Nigra\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(13, -17, -16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubstantia nigra r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft Subthalamic nucleus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-18, -18, -8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubthalamic nucleus l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight Subthalamic nucleus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(12, -18, -7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubthalamic nucleus r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft Supplementary motor area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-5, -3, 56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMA l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight Supplementary motor area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6, -3, 58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMA r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft Putamen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-25, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutamen l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight Putamen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(25, 2, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutamen r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft Pallidum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-19, -5, -1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePallidum l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight Pallidum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(20, -4, -1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePallidum r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRs-FC analysis\u003c/h3\u003e\n\u003cp\u003eThe human brain is a complex network of interconnected regions, both functionally and structurally. Effective functional communication between these regions is crucial for complex cognitive processes, as it enables the seamless integration of information across different brain areas. Studying functional connectivity in the human brain is essential for gaining deeper insights into its fundamental organization [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. rs-FC examines the statistical dependencies between spatially distributed neuronal units while the brain is at rest, revealing the intrinsic functional organization of the brain [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. We employed the mRRC method within the CONN toolbox to conduct correlation analyses and estimate functional connectivity in the procedural memory network. Pre-defined ROIs associated with the procedural memory network (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were used to calculate correlations between brain regions within this network. The mRRC approach enabled us to spatially map correlation patterns in the brain during rest, identifying abnormal functional connections among studied neuropsychiatric disorders.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo analyze the data, we utilized the CONN v21.a toolbox [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] within MATLAB R2019b software. Prior to analysis, we verified the normality of the data distribution. Independent t-tests were conducted to examine significant differences in the rs-FC within the procedural memory network between neuropsychiatric patients (ADHD, BD, and SZ) and HCs. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 was considered to identify statistically significant differences in rs-FC between the two groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eResults indicated there were significant differences in some rs-FC within the procedural memory network between individuals with ADHD, BD, SZ, and HCs. In the following, detailed analysis of rs-FC between regions associated with the procedural memory network, focusing on significant differences (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) observed between the studied patients and HCs are explained in detail with accompanying figures.\u003c/p\u003e\n\u003ch3\u003ers-FC analysis in ADHD vs. HCs\u003c/h3\u003e\n\u003cp\u003eIndividuals with ADHD exhibited significantly elevated rs-FC between the Cereb3 r and Cereb45 l (p\u0026thinsp;=\u0026thinsp;0.000067) compared to HCs. Conversely, they demonstrated significantly decreased rs-FC between the Cereb1 l and the Cereb6 l (p\u0026thinsp;=\u0026thinsp;0.0009182). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provided a detailed statistical analysis to offer a more comprehensive understanding of the statistical significance and magnitude of these differences. The table outlines the significance level (p-value) and the rs-FC strength (t-test), categorizing results into instances where ADHD patients exhibited heightened (ADHD\u0026thinsp;\u0026gt;\u0026thinsp;HCs) or diminished (ADHD\u0026thinsp;\u0026lt;\u0026thinsp;HCs) rs-FC compared to HCs. Figures\u0026nbsp;1 and 2 visually depict the regions within the procedural memory network of ADHD patients that exhibited significantly elevated and reduced rs-FC between them, respectively, compared to HCs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant differences in rs-FC within the procedural memory network of ADHD patients compared to HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConnectivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic (T test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADHD\u0026thinsp;\u0026gt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCereb45 l \u0026ndash; Cereb3 r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADHD\u0026thinsp;\u0026lt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCereb1 l \u0026ndash; Cereb6 l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ers-FC analysis in BD vs. HCs\u003c/h2\u003e \u003cp\u003eBD patients illustrated significantly increased rs-FC between the Claustrum r and Caudate r (p\u0026thinsp;=\u0026thinsp;0.000584), subthalamic nucleus r and Pallidum l (p\u0026thinsp;=\u0026thinsp;0.000604), substantia nigra l and Cereb2 l (p\u0026thinsp;=\u0026thinsp;0.000818), Cereb10 r and SMA r (p\u0026thinsp;=\u0026thinsp;0.000862), Cereb9 r and SMA l (p\u0026thinsp;=\u0026thinsp;0.000933) compared to HCs. Whilst, they displayed significantly reduced rs-FC between the subthalamic nucleus r and the Cereb6 l (p\u0026thinsp;=\u0026thinsp;0.000126), Cereb9 r and Cereb9 l (p\u0026thinsp;=\u0026thinsp;0.000329) compared to HCs. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presented a detailed statistical analysis of these differences. The table outlines the significance level (p-value) and the rs-FC strength (t-test), categorizing results into cases where BD patients exhibited higher (BD\u0026thinsp;\u0026gt;\u0026thinsp;HCs) or lower (BD\u0026thinsp;\u0026lt;\u0026thinsp;HCs) rs-FC compared to HCs. Figures\u0026nbsp;3 and 4 visually showed the regions within the procedural memory network of BD patients that exhibited significantly increased and reduced rs-FC between them, respectively, compared to HCs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant differences in rs-FC within the procedural memory network of BD patients compared to HCs. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConnectivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic (T test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBD\u0026thinsp;\u0026gt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClaustrum r \u0026ndash; Caudate r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esubthalamic nucleus r \u0026ndash; Pallidum l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esubstantia nigra l \u0026ndash; Cereb2 l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCereb10 r \u0026ndash; SMA r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCereb9 r \u0026ndash; SMA l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBD\u0026thinsp;\u0026lt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esubthalamic nucleus r \u0026ndash; Cereb6 l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCereb9 r \u0026ndash; Cereb9 l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ers-FC analysis in SZ vs. HCs\u003c/h2\u003e \u003cp\u003eSZ patients illustrated significantly increased rs-FC between the Caudate r and putamen l (p\u0026thinsp;=\u0026thinsp;0.000567). Whiles, they demonstrated significantly reduced rs-FC between the subthalamic nucleus r and the Cereb6 l (p\u0026thinsp;=\u0026thinsp;0.000063), Cereb1 r and subthalamic nucleus r (p\u0026thinsp;=\u0026thinsp;0.000628) compared to HCs. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presented a detailed statistical analysis of these differences. The table outlines the significance level (p-value) and the rs-FC strength (t-test), categorizing results into cases where SZ patients exhibited higher (SZ\u0026thinsp;\u0026gt;\u0026thinsp;HCs) or lower (SZ\u0026thinsp;\u0026lt;\u0026thinsp;HCs) rs-FC compared to HCs. Figures\u0026nbsp;5 and 6 visually indicated the regions within the procedural memory network of SZ patients that exhibited significantly increased and reduced rs-FC between them, respectively, compared to HCs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant differences in rs-FC within the procedural memory network of SZ patients compared to HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConnectivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic (T test)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSZ\u0026thinsp;\u0026gt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaudate r\u0026ndash; putamen l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSZ\u0026thinsp;\u0026lt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esubthalamic nucleus r \u0026ndash; Cereb6 l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCereb1 r \u0026ndash; subthalamic nucleus r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo examine cognitive impairment in patients with ADHD, BD, and SZ, We identified distinct patterns of altered rs-FC within procedural memory network in each patient group using rs-fMRI and mRRC method, independent of any specific task. Given the roles of the brain's procedural memory network regions, disruptions in their functional connectivity, such as increased or decreased connectivity, were associated with cognitive and behavioral impairments in ADHD, BD, and SZ [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the following, we will elucidate the role of each ROI within the procedural memory network in cognitive function. Subsequently, we will delve into rs-FC alterations between these ROIs and their potential implications for cognition in individuals with ADHD, BD, and SZ (Sections 4.1, 4.2, and 4.3). The cerebellum, traditionally associated with motor control, is increasingly recognized for its pivotal role in various cognitive functions, including working memory and executive function, particularly in the visual-spatial domain [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The SMA, a part of the premotor cortex, plays a critical role in planning and executing complex movements, as well as in cognitive functions such as working memory and decision-making [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The Subthalamic nucleus and pallidum are key components of the basal ganglia, a group of brain structures involved in motor control, reward processing, and learning [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The Subthalamic nucleus is thought to play a role in inhibiting unwanted movements [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], while the pallidum is involved in regulating motor output [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The caudate and putamen are both components of the striatum [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], a key brain region involved in motor control, reward processing, and learning [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The caudate is thought to play a role in planning and initiating movements [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], while the putamen is involved in executing movements and regulating motor output [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The substantia nigra plays a multifaceted role in cognitive function, regulating reward, motivation, movement, learning, and executive functions through its production of dopamine [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eADHD\u003c/h2\u003e \u003cp\u003ePatients with ADHD demonstrated decreased functional connectivity between Cereb1l and Cereb6l regions. This finding suggests potential disruptions in information transfer between these regions, which are implicated in cognitive processes [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These disruptions could contribute to the observed cognitive deficits in ADHD patients, such as impaired motor-cognitive integration, attentional problems, and executive function difficulties, particularly in visual-spatial tasks [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The rs-FC analysis performed in the ADHD sample closely aligned with previously published rs-FC analyses conducted by Jiang K and colleagues [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, our analysis indicated increased functional connectivity between regions Cereb3 r and Cereb45 l, potentially reflecting hyperactive communication within the procedural memory network [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This aberrant connectivity pattern may contribute to the cognitive impairments experienced by ADHD patients, potentially leading to difficulties in task switching, attention, and working memory [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This finding is consistent with previous research indicating increased functional connectivity between cerebellar regions, which has been associated with cognitive deficits, including working memory, in ADHD [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBD\u003c/h2\u003e \u003cp\u003eThis study's findings, examining rs-FC between the claustrum and caudate in BD patients, suggest potential mechanisms underlying several BD symptoms. The claustrum's role in attention and emotion regulation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] might be impaired due to its hyperconnectivity with the caudate, a brain region implicated in reward processing. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These findings align with the \"default mode network\" (DMN) hypothesis of BD, which suggests that individuals with BD exhibit aberrant activity in the DMN [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], a network of brain regions involved in introspection and self-referential thought [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Niccol\u0026ograve; Zovetti et al.'s study [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] demonstrated that BD is linked to alterations in the frontal and posterior DMN structures, primarily in the prefrontal, posterior, and inferior cingulate cortices. Given that the claustrum and caudate are both situated within the frontal region of the DMN, the findings of this study corroborate the potential involvement of these structures in BD. Moreover, the findings of this study, which examined rs-FC between the subthalamic nucleus and pallidum in BD patients, provide further support for the \"reward circuit\" hypothesis of BD. Increased rs-FC between these regions may contribute to several symptoms associated with the disorder, including motor disturbances and cognitive impairments [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These results are consistent with previous research [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] suggesting that disruptions in the basal ganglia, a key component of the reward circuit, can lead to cognitive deficits in neuropsychiatric conditions. The hyperconnectivity observed in BD patients could potentially disrupt the balance of excitatory and inhibitory signals within the reward circuit, leading to difficulties in regulating emotions, motivation, and behavior [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Furthermore, the increased rs-FC between the cerebellum and SMA in BD patients could potentially contribute to several symptoms associated with the disorder. For example, the cerebellum's involvement in emotion regulation and social cognition [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] might be affected by its hyper connectivity with the SMA, which is implicated in planning and executing movements [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. These findings align with the \"motor network\" hypothesis of BD [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], which suggests that abnormalities in brain regions involved in motor control contribute to the development and maintenance of the disorder. Arshaq Saleem et al.'s study [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] found that increased functional connectivity between sensory-motor areas is correlated with the intensity of both motor control and emotional experiences. This suggests that heightened connectivity in these regions may be a specific marker of mood state or a general indicator of disease severity. Also, the decreased rs-FC between the Subthalamic nucleus and cerebellum in BD patients could potentially contribute to several symptoms associated with the disorder. For example, the Subthalamic nucleus's involvement in motor control and reward processing [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] might be affected by its reduced connectivity with the cerebellum, which is implicated in motor coordination and cognitive functions. This could lead to difficulties in regulating movements and emotional responses, which are common features of BD [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. These findings align with the \"motor network\" and \"reward circuit\" hypotheses of BD, which suggest that abnormalities in brain regions involved in motor control and reward processing contribute to the development and maintenance of the disorder. Our findings of decreased rs-FC between the subthalamic nucleus and cerebellum in BD patients are consistent with previous research by Tao Wu et al., [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] which implicated disruptions in the motor network and reduced connectivity between these brain regions in Parkinson's disease. These results suggest that similar mechanisms may underlie motor coordination and cognitive deficits in both conditions. In addition, Interhemispheric communication within the cerebellum is essential for coordinating movements and maintaining balance [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. The decreased rs-FC between the cerebellar hemispheres in BD patients might be related to several symptoms associated with the disorder such as difficulties in motor coordination, balance, and cognitive functions [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. These findings align with the \"motor network\" hypothesis of BD, which suggests that abnormalities in brain regions involved in motor control contribute to the development and maintenance of the disorder. Our findings of decreased rs-FC between the cerebellar hemispheres in BD patients are consistent with previous research by Ying Wang et al., [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] which identified interhemispheric coordination deficits in individuals with BD. These results suggest that impaired communication between the two cerebellar hemispheres may contribute to the motor coordination, balance, and cognitive difficulties often observed in BD patients. This aligns with the \"motor network\" hypothesis, which posits that abnormalities in brain regions involved in motor control play a role in the development and maintenance of BD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSZ\u003c/h2\u003e \u003cp\u003eIncreased rs-FC between the caudate and putamen in SZ patients may contribute to hyperconnectivity within the striatum, potentially leading to difficulties in controlling motor behavior, such as motor tics or abnormal movements [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Moreover, given the striatum's role in reward processing [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], altered rs-FC in this region could contribute to motivational deficits and anhedonia, which are common symptoms of SZ [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. In a study by Mingjun Duan et al., [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] functional connectivity changes within the basal ganglia network of individuals with SZ were examined. They found that increased functional connections within this network were associated with symptoms such as impaired motor processing, cognitive deficits, motivational difficulties, and emotional control issues. These findings align with our results. On the other hand, decreased rs-FC between the subthalamic nucleus and cerebellum in patients with SZ could contribute to symptoms such as impaired motor control, difficulties with reward processing, and cognitive deficits [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. These disruptions might lead to challenges in regulating movements and emotional responses, which are common characteristics of SZ [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Our findings of decreased rs-FC between the subthalamic nucleus and cerebellum in patients with SZ align with previous research by Hugo C. Baggio et al., [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] who demonstrated that deficits in motor, cognitive, and emotional functions in Parkinson's and multiple system atrophy patients arise from impaired connectivity between these brain regions. This suggests that disruptions in the subthalamic nucleus-cerebellum circuit may underlie similar symptoms in SZ, such as impaired motor control, difficulties with reward processing, and cognitive deficits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights, it is essential to acknowledge its limitations, including sample size and the focus on three specific disorders. Future research should expand on these findings by incorporating larger cohorts, exploring additional psychiatric conditions, and employing more advanced analysis techniques such as dynamic causal modeling [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] and asymmetrical functional connectivity [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the neural basis of cognitive impairments in psychiatric disorders is crucial for developing targeted interventions. Our findings contribute to this knowledge by identifying specific brain regions and network abnormalities associated with ADHD, BD, and SZ. By targeting these neural substrates, future therapeutic approaches may aim to improve cognitive function and overall quality of life for affected individuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the importance of the procedural memory network in cognitive function and provides evidence for its involvement in the pathophysiology of ADHD, BD, and SZ. By examining rs-FC within the procedural memory network, we identified distinct patterns of altered connectivity in each patient group. These findings suggest that disruptions in the functional communication between key brain regions within this network play a significant role in the cognitive and behavioral deficits observed in these disorders. Future research can build upon these findings to develop targeted interventions aimed at improving cognitive function in these disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; attention deficit hyperactivity disorder\u003c/p\u003e\n\u003cp\u003eBD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; bipolar disorder\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSZ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;schizophrenia\u003c/p\u003e\n\u003cp\u003eHCs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;healthy controls\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ers-FC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;resting-state functional connectivity\u003c/p\u003e\n\u003cp\u003efMRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;functional magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003ers-fMRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;resting-state functional magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eROI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;regions of interest\u003c/p\u003e\n\u003cp\u003emRRC, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; multivariate ROI-to-ROI connectivity\u003c/p\u003e\n\u003cp\u003eDMN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;default mode network\u003c/p\u003e\n\u003cp\u003eSMA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; supplementary motor area\u003c/p\u003e\n\u003cp\u003eMNI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Montreal Neurological Institute\u003c/p\u003e\n\u003cp\u003eSPM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical Parametric Mapping\u003c/p\u003e\n\u003cp\u003eWM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; white matter\u003c/p\u003e\n\u003cp\u003eCSF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003eCNP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Consortium for Neuropsychiatric Phenomics\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;M \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mean\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Standard Deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UCLA Consortium for Neuropsychiatric Phenomics (CNP) dataset\u0026nbsp;[32]\u0026nbsp;was utilized in this study. This dataset is publicly accessible from the OpenNeuro repository. Ethical approval for this study was obtained from the Kermanshah University of Medical Sciences Ethics Committee (reference number IR.KUMS.REC.1402.036).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is publicly available, which means that anyone can access and use it.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.18112/openneuro.ds000030.v1.0.0\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.M. and H.S. designed the study, wrote the initial draft, and performed data preprocessing. M.M. developed the analysis code. M.Y. contributed to the discussion section. M.P. reviewed, revised, and provided critical feedback on the manuscript. All authors participated in reviewing and approving the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original dataset used in this study was generously provided by the Consortium for Neuropsychiatric Phenomics (CNP). Their dedication to advancing neuroscience research through data sharing is invaluable. This dataset was supported by NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410. We extend our sincere gratitude to the researchers who contributed to the data collection and preparation, making this research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators GBD 2019 MD. 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Nat Hum Behav. 2023;7:942\u0026ndash;55.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Procedural memory, Cognitive impairments, Functional connectivity, Resting-state functional magnetic resonance imaging, Brain mapping","lastPublishedDoi":"10.21203/rs.3.rs-5176630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5176630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCognitive impairments are common in neuropsychiatric disorders like Attention Deficit Hyperactivity Disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ). While previous research has focused on specific brain regions, the role of the procedural memory as a type of long-term memory to cognitive function in these disorders remains unclear. This study investigates the association between cognitive impairments and alterations in resting-state functional connectivity (rs-FC) within procedural memory network in patients with these disorders.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ADHD, 49 with BD, 50 with SZ, and 50 healthy controls (HCs). A procedural memory network was defined based on the selection of 34 regions of interest (ROIs) associated with the network in the Harvard-Oxford Cortical Structural Atlas (default atlas). Multivariate ROI-to-ROI connectivity (mRRC) was used to analyze the rs-FC between the defined network regions. Significant differences in rs-FC between patients and HCs were identified (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eADHD patients showed increased Cereb45 l - Cereb3 r rs-FC (p\u0026thinsp;=\u0026thinsp;0.000067) and decreased Cereb1 l - Cereb6 l rs-FC (p\u0026thinsp;=\u0026thinsp;0.00092). BD patients exhibited increased rs-FC between multiple regions, including Claustrum r - Caudate r (p\u0026thinsp;=\u0026thinsp;0.00058), subthalamic nucleus r - Pallidum l (p\u0026thinsp;=\u0026thinsp;0.00060), substantia nigra l - Cereb2 l (p\u0026thinsp;=\u0026thinsp;0.00082), Cereb10 r - SMA r (p\u0026thinsp;=\u0026thinsp;0.00086), and Cereb9 r - SMA l (p\u0026thinsp;=\u0026thinsp;0.00093) as well as decreased rs-FC in subthalamic nucleus r - Cereb6 l (p\u0026thinsp;=\u0026thinsp;0.00013) and Cereb9 r - Cereb9 l (p\u0026thinsp;=\u0026thinsp;0.00033). SZ patients indicated increased Caudate r\u0026ndash; putamen l rs-FC (p\u0026thinsp;=\u0026thinsp;0.00057) and decreased rs-FC in subthalamic nucleus r \u0026ndash; Cereb6 l (p\u0026thinsp;=\u0026thinsp;0.000063), and Cereb1 r \u0026ndash; subthalamic nucleus r (p\u0026thinsp;=\u0026thinsp;0.00063).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study found significant alterations in rs-FC within the procedural memory network in patients with ADHD, BD, and SZ compared to HCs. These findings suggest that disrupted rs-FC within this network may contribute to cognitive impairments observed in these disorders.\u003c/p\u003e","manuscriptTitle":"Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 14:00:33","doi":"10.21203/rs.3.rs-5176630/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-12T05:42:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-10T03:20:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-09T02:30:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314978246463083915591281123356909497580","date":"2024-10-20T15:47:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235445672252701444036480688777356860697","date":"2024-10-12T19:53:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-10T19:52:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-10T18:15:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-05T06:01:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-05T05:59:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2024-09-29T20:40:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09fadae3-077c-4d42-95dc-c531de2f62bf","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:06:16+00:00","versionOfRecord":{"articleIdentity":"rs-5176630","link":"https://doi.org/10.1186/s12880-024-01527-7","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2025-01-13 15:57:33","publishedOnDateReadable":"January 13th, 2025"},"versionCreatedAt":"2024-11-29 14:00:33","video":"","vorDoi":"10.1186/s12880-024-01527-7","vorDoiUrl":"https://doi.org/10.1186/s12880-024-01527-7","workflowStages":[]},"version":"v1","identity":"rs-5176630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5176630","identity":"rs-5176630","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}