Resting-state Functional Magnetic Imaging Research Articles

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

Comparative analysis of rs-fMRI markers in heat and mechanical pain sensitivity.

This study investigates the comparative analysis of resting-state functional magnetic imaging (rs-fMRI) markers in heat and mechanical pain sensitivity among healthy adults. Using quantitative sensory testing (QST) in the orofacial area and rs-fMRI, we explored the relationship between pain sensitivities and resting-state functional connectivity (rsFC) across whole brain areas. Brain regions were spatially divided using group independent component analysis (gICA), and additional masked gICA was performed for brainstem regions. Our findings revealed that a significant number of rsFCs were correlated with either heat or mechanical pain sensitivity, with a substantial portion originating from the Sensorimotor Network (SMN). Furthermore, multivariable regression models incorporating rsFC features demonstrated predictive capabilities for pain sensitivities, with the inclusion of brainstem gICA components significantly enhancing model accuracy. Finally, a composite critical rsFC value was introduced to simplify and describe overall abnormal communication in the brain network, which could also be used in univariable regression models to predict heat and mechanical pain sensitivity.

Dynamic alterations of striatal-related functional networks in juvenile absence epilepsy

PurposeTo explore the features of dynamic functional connectivity (dFC) variability of striatal-cortical/subcortical networks in juvenile absence epilepsy (JAE). MethodsWe collected resting-state functional magnetic imaging data from 18 JAE patients and 28 healthy controls. The striatum was divided into six pairs of regions: the inferior-ventral striatum (VSi), superior-ventral striatum (VSs), dorsal-caudal putamen, dorsal-rostral putamen, dorsal-caudate (DC) and ventral-rostral putamen. We assessed the dFC variability of each subdivision in the whole brain using the sliding-window method, and correlated altered circuit with clinical variables in JAE patients. ResultsWe found altered dFC variability of striatal-cortical/subcortical networks in patients with JAE. The VSs exhibited decreased dFC variability with subcortical regions, and dFC variability between VSs and thalamus was negatively correlated with epilepsy duration. For the striatal-cortical networks, the dFC variability was decreased in VSi-affective network but increased in DC-executive network. The altered dynamics of striatal-cortical networks involved crucial nodes of the default mode network (DMN). ConclusionJAE patients exhibit excessive stability in the striatal-subcortical networks. For striatal-cortical networks in JAE, the striatal-affective circuit was more stable, while the striatal-executive circuit was more variable. Furthermore, crucial nodes of DMN were changed in striatal-cortical networks in JAE.

Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study.

Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks. In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon. At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study. These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon.

Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI

Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI time-series data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction.

C9orf72 ALS mutation carriers show extensive cortical and subcortical damage compared to matched wild-type ALS patients

ObjectiveC9orf72 mutation carriers with different neurological phenotypes show cortical and subcortical atrophy in multiple different brain regions, even in pre-symptomatic phases. Despite there is a substantial amount of knowledge, small sample sizes, clinical heterogeneity, as well as different choices of image analysis may hide anatomical abnormalities that are unique to amyotrophic lateral sclerosis (ALS) patients with this genotype or that are indicative of the C9orf72-specific trait overlain in fronto-temporal dementia patients. MethodsBrain structural and resting state functional magnetic imaging was obtained in 24 C9orf72 positive (ALSC9+) ALS patients paired for burden disease with 24 C9orf72 negative (ALSC9-) ALS patients. A comprehensive structural evaluation of cortical thickness and subcortical volumes between ALSC9+ and ALSC9- patients was performed while a region of interest (ROI)-ROI analysis of functional connectivity was implemented to assess functional alterations among abnormal cortical and subcortical regions. Results were corrected for multiple comparisons. ResultsCompared to ALSC9- patients, ALSC9+ patients exhibited extensive disease-specific patterns of thalamo-cortico-striatal atrophy, supported by functional alterations of the identified abnormal regions. Cortical thinning was most pronounced in posterior areas and extended to frontal regions. Bilateral atrophy of the mediodorsal and pulvinar nuclei was observed, emphasizing a focal rather than global thalamus atrophy. Volume loss in a large portion of bilateral caudate and left putamen was reported. The marked reduction of functional connectivity observed between the left posterior thalamus and almost all the atrophic cortical regions support the central role of the thalamus in the pathogenic mechanism of C9orf72-mediated disease. ConclusionsThese findings constitute a coherent and robust picture of ALS patients with C9orf72-mediated disease, unveiling a specific structural and functional characterization of thalamo-cortico-striatal circuit alteration. Our study introduces new evidence in the characterization of the pathogenic mechanisms of C9orf72 mutation.

Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence.

Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.

Aberrant intrinsic functional brain networks in patients with functional constipation.

Patients with functional constipation (FCon) often suffer from mental and psychological problems. To explore the possible neurological interaction, we used resting-state functional magnetic imaging (RS-fMRI) to compare the alterations in intrinsic brain functional networks at multiple levels between patients with FCon and healthy controls (HC). Twenty-eight patients with FCon and twenty-nine HC were recruited for a series of examinations and RS-fMRI. Both graph theory analysis and functional connectivity (FC) analysis were used to investigate brain functional alterations between the two groups. Correlation analyses were performed among neuropsychological scores, clinical indexes, and neuroimaging data. Compared with the HC, the assortativity showed significantly increased in global level in patients with FCon. In regional level, we found obviously increased nodal degree and nodal efficiency in somatosensory network (SMN), decreased nodal degree, and increased nodal efficiency in default mode network (DMN) in the FCon group. Furthermore, FC analysis demonstrated several functional alterations within and between the networks, particularly including the SMN and visual network (VN) in sub-network and large-scale network analysis. Moreover, correlation analysis indicated that nodal metrics and aberrant FC among functional brain networks were associated with emotion and scores of constipation in patients with FCon. All these findings reflect the differences in intrinsic brain functional networks between FCon and HC. Our study highlighted SMN, DMN, and VN as critical network and may be involved in the neurophysiology of FCon, which may contribute to improve personalized treatment in patients with FCon.

Two-step clustering-based pipeline for big dynamic functional network connectivity data.

BackgroundDynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power.MethodsIn the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP).ResultsWe found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach (p < 0.001). We show that the results are replicated across four different datasets from HCP.ConclusionWe developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study.

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.

A two-step clustering-based pipeline for big dynamic functional network connectivity data.

Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporal properties of FNC among brain networks by putting them into distinct states using the clustering method. The computational cost of clustering dFNCs has become a significant practical barrier given the availability of enormous neuroimaging datasets. To this end, we developed a new dFNC pipeline to analyze large dFNC data without accessing hug processing capacity. We validated our proposed pipeline and compared it with the standard one using a publicly available dataset. We found that both standard and iSparse kmeans generate similar dFNC states while our approach is 27 times faster than the traditional method in finding the optimum number of clusters and creating better clustering quality.

A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.

Resting-state functional magnetic resonance imaging analysis of modified electroconvulsive therapy in the treatment of major depressive disorder

Objective: To explore the effect of modified electroconvulsive therapy (MECT) on resting-state functional connectivity (RSFC) in patients with major depressive disorder (MDD). Methods: Patients with MDD from Anhui Mental Health Center from October 2017 to May 2019 were included. Using bilateral nucleus accumbens (NAcc) as seed points, changes of RSFC were investigated before and after MECT through resting-state functional magnetic imaging (fMRI). Antidepressant effects were measured by 17 items of Hamilton Depressive Rating Scale (HDRS-17). Correlation analysis was performed between changed HRSD-17 scores and changes of functional connectivity. Results: A total of 40 MDD patients (10 males and 30 females), aged (38±11) years, who received MECT were included in the study. After MECT, patients showed increased RSFC in the right NAcc (rNAcc) and superior frontal gyrus (P<0.001), right supramarginal gyrus (P<0.001), right angular gyrus (rAG) (P= 0.017), right inferior parietal lobule (P= 0.017), left superior frontal gyrus (LSFG) (P<0.001), left middle temporal gyrus (P=0.017) and left angular gyrus (LAG) (P=0.012), respectively. The RSFC changes of rNAcc-LSFG (r=-0.454, P = 0.003), rNAcc-rAG (r=-0.437, P=0.005) and rNAcc-lAG (r=-0.383, P=0.015) were negatively correlated with the changes of HRSD-17 scores. Conclusions: MECT may alleviate major depression by regulating the functional connectivity between the rNAcc and bilateral angular gyrus and left superior frontal gyrus.

Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study.

Freezing of gait (FOG) in Parkinson's disease (PD) leads to devastating consequences; however, little is known about its functional brain network. We explored the differences in degree centrality (DC) of functional networks among PD with FOG (PD FOG+), PD without FOG (PD FOG–), and healthy control (HC) groups. In all, 24 PD FOG+, 37 PD FOG–, and 22 HCs were recruited and their resting-state functional magnetic imaging images were acquired. The whole brain network was analyzed using graph theory analysis. DC was compared among groups using the two-sample t-test. The DC values of disrupted brain regions were correlated with the FOG Questionnaire (FOGQ) scores. Receiver operating characteristic curve analysis was performed. We found significant differences in DC among groups. Compared with HCs, PD FOG+ patients showed decreased DC in the middle frontal gyrus (MFG), superior temporal gyrus (STG), parahippocampal gyrus (PhG), inferior temporal gyrus (ITG), and middle temporal gyrus (MTG). Compared with HC, PD FOG– presented with decreased DC in the MFG, STG, PhG, and ITG. Compared with PD FOG–, PD FOG+ showed decreased DC in the MFG and ITG. A negative correlation existed between the DC of ITG and FOGQ scores; the DC in ITG could distinguish PD FOG+ from PD FOG– and HC. The calculated AUCs were 81.3, 89.5, and 77.7% for PD FOG+ vs. HC, PD FOG– vs. HC, and PD FOG+ vs. PD FOG–, respectively. In conclusion, decreased DC of ITG in PD FOG+ patients compared to PD FOG– patients and HCs may be a unique feature for PD FOG+ and can likely distinguish PD FOG+ from PD FOG– and HC groups.

Altered functional connectivity of the dentate nuclei in patients with schizophrenia

Dentate nuclei (DN) are vital structures in the anatomical circuits that link the cerebellum to the cerebrum. However, the characteristics of DN functional connectivity (FC) in schizophrenia remain largely unknown. In this study, we investigated the FC of the DN in patients with schizophrenia and examined their possible clinical correlates using resting-state functional magnetic imaging data. We found that the patient group had greater DN FC with the parietal lobe (e.g., postcentral gyrus and superior parietal lobule) and less DN FC with the prefrontal cortex (e.g., superior frontal gyrus), posterior cingulate cortex, and regional cerebellum (e.g., vermis 4–5 and crus I) than did the control group. Furthermore, some abnormal connectivities of the DN with these regions significantly correlated with psychiatric symptoms. These results suggest that the DN circuits are disturbed and may participate in the pathophysiology of schizophrenia.

Disrupted communication of the temporoparietal junction in patients with major depressive disorder.

Patients with major depressive disorder (MDD) suffer impairment in the transmission and integration of internal and external information sources. Accumulating evidence suggests that the temporoparietal junction (TPJ) is important for multiple cognitive and social functions and may act as a key node for the integration of internal and external information. Therefore, the TPJ's aberrant interaction mechanism may underpin MDD psychopathology. To answer this question, we conducted a comprehensive study using resting-state functional magnetic imaging data recorded from 74 patients with MDD and 69 normal controls. First, we examined whether TPJ was the most prominent region with altered functional/effective connectivity with multiple depression-related regions/networks, based on either zero-lag correlations or temporal mutual information (total interdependence and Granger causality) measurements. Accordingly, we derived a network model that depicts alterations of TPJ-connectivity in patients with MDD. Lastly, we performed a cross-approach comparison demonstrating more conducive indicators in delineating the network alteration model. Functional/effective connectivity between the TPJ and major functional networks that govern internal and external-driven information resources was attenuated in patients with MDD. TPJ acts like a key node for information-inflow and integration of multiple information streams. Therefore, dysfunctional connectivity indicators may serve as effective biomarkers for MDD. MDD is associated with the breakdown of the TPJ interaction model and its connections with the default mode network and the task-positive network.

Enhanced Anti-correlation Between the Dorsal Attention and Default-mode Networks: A Resting-state fMRI Study of Acute Insomnia

With a greatly changed environment, sleep problem becomes a common phenomenon among college freshmen. However, this type of acute insomnia usually recovers after adapting to the circumstances, which can be defined as adaptive sleep problem (ASP). Few studies deal with this type of sleep problems. In this study, 991 first-year college freshmen were recruited on different days of the first semester to investigate their sleep status. We found that the sleep problem of college freshmen at the beginning of the semester was the most severe, compared with the other two-thirds of semester. Next, behavioral and resting-state functional magnetic imaging (rs-fMRI) data from 30 freshmen with ASP and 28 matched healthy controls (HC) were used to explore the neural basis of acute insomnia. Results showed that ASP group performed worse on many behavioral indices, such as fatigue, depression, and trait anxiety. Interestingly, students with ASP also showed significantly more negative functional connectivity between the anterior default mode network (aDMN) and the dorsal attentional network (DAN). Furthermore, a significant negative correlation was observed between Insomnia Severity Index (ISI) score and aDMN-DAN functional connectivity in the HC group, which was not observed in the case of ASP. In conclusion, the study explored the neural biomarker of adaptive sleep problem (ASP) in freshmen, and found its potentiating antagonism within the DMN-DAN. This enhanced anticorrelation may corroborate that students with ASP are in a hyperarousal state. Our current study may deepen our understanding of sleep disorders, and the enhanced anticorrelation may corroborate that ASP in due to a hyperarousal state.

Aberrant Resting-State Cerebellar-Cerebral Functional Connectivity in Unmedicated Patients With Obsessive-Compulsive Disorder.

Background: Although abnormality of cerebellar-cerebral functional connectivity at rest in obsessive-compulsive disorder (OCD) has been hypothesized, only a few studies have investigated the neural mechanism. To verify the findings of previous studies, a large sample of patients with OCD was studied because OCD shows possible heterogeneity.Methods: Forty-seven medication-free patients with OCD and 62 healthy controls (HCs) underwent resting-state functional magnetic imaging scans. Seed-based connectivity was examined to investigate differences in cerebellar-cerebral functional connectivity in OCD patients compared with HCs. Correlations between functional connectivity and the severity of obsessive-compulsive symptoms were analyzed.Results: In OCD, we found significantly increased functional connectivity between the right lobule VI and the left precuneus, which is a component of the default mode network (DMN), compared to HCs. However, there was no correlation between the connectivity of the right lobule VI-left precuneus and obsessive-compulsive severity.Conclusions: These findings suggest that altered functional connectivity between the cerebellum and DMN might cause changes in intrinsic large-scale brain networks related to the traits of OCD.

Atypical Resting-State Functional Connectivity of Intra/Inter-Sensory Networks Is Related to Symptom Severity in Young Boys With Autism Spectrum Disorder.

Autism spectrum disorder (ASD) has been reported to have altered brain connectivity patterns in sensory networks, assessed using resting-state functional magnetic imaging (rs-fMRI). However, the results have been inconsistent. Herein, we aimed to systematically explore the interaction between brain sensory networks in 3–7-year-old boys with ASD (N = 29) using independent component analysis (ICA). Participants were matched for age, head motion, and handedness in the MRI scanner. We estimated the between-group differences in spatial patterns of the sensory resting-state networks (RSNs). Subsequently, the time series of each RSN were extracted from each participant’s preprocessed data and associated estimates of interaction strength between intra- and internetwork functional connectivity (FC) and symptom severity in children with ASD. The auditory network (AN), higher visual network (HVN), primary visual network (PVN), and sensorimotor network (SMN) were identified. Relative to TDs, individuals with ASD showed increased FC in the AN and SMN, respectively. Higher positive connectivity between the PVN and HVN in the ASD group was shown. The strength of such connections was associated with symptom severity. The current study might suggest that the abnormal connectivity patterns of the sensory network regions may underlie impaired higher-order multisensory integration in ASD children, and be associated with social impairments.

Disruption of functional and structural networks in first-episode, drug-naïve adolescents with generalized anxiety disorder

BackgroundIndividuals with generalized anxiety disorder (GAD) tend to worry exaggeratedly and uncontrollably about various daily routines. Previous studies have demonstrated that the GAD patients exhibited widespread alternations in both functional networks (FN) and structural networks (SN). However, the simultaneous alternations of the topological organization of FN, SN, as well as their couplings in GAD still remain unknown. MethodsUsing multimodal approach, we constructed FN from resting-state functional magnetic imaging (R-fMRI) data and SN from diffusion magnetic resonance imaging (dMRI) data of 32 adolescent GAD patients and 25 healthy controls (HC). Graph theory analysis was employed to investigate the topological properties of FN, SN, and FN-SN coupling. ResultsCompared to HC, the GAD patients showed disruptions in global (i.e., decreased clustering coefficient, global, and local efficiency) and subnetwork (i.e., reduced intermodular connections, rich club, and feeder connections) levels in FN. Abnormal global level properties (i.e., increased characteristic path length and reduced global efficiency) were also observed in SN. Altered FN-SN couplings in normalized characteristic path length and feeder connections were identified in the GAD patients. The identified network measures were correlated with anxiety severity in the GAD patients. LimitationsThe sample size of the current study is small and the cross-sectional nature can not infer causal relationship. ConclusionsOur findings identified GAD-related topological alternations in both FN and SN, together with the couplings between FN and SN, providing us with a novel perspective for understanding the pathophysiological mechanisms of GAD.