Decreased interhemispheric resting state functional connection in schizophrenic patients with auditory hallucinations

2016 ◽  
Vol 33 (S1) ◽  
pp. S109-S109
Author(s):  
H. Wang ◽  
G. Wang
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Auditory Hallucination ◽  
Superior Temporal Gyrus ◽  
Resting State Fmri ◽  
Functional Connection ◽  
Whole Brain ◽  
Schizophrenic Patients ◽  
Competing Interest ◽  
Normal Controls

IntroductionAuditory hallucination (AH) has been always concerned as a main core symptom of schizophrenia. However, the mechanisms of AH are still unclear.ObjectivesThe aim of this study is to further explore the complicated neuroimaging mechanism of AHs from a new insight by using voxel-mirrored homotopic connectivity (VMHC).MethodsForty-two patients with AH (APG), 26 without AHs (NPG) and 82 normal controls (NC) participated in resting state fMRI scan. Correlation analyses were used to assess the relationships between VMHC and Hoffman scores. Additionally, ROI analysis was used to further know about the functional connectivity between the brain areas with changed interhemispheric FC and the whole brain.ResultsAPG showed reduced VMHC in the parahippocapus, fusiform gyrus, rolandic operculum, insula, heschl's gyrus and superior temporal gyrus (STG). Hoffman score of APG group had negative correlation with VMHC in these regions. Besides, ROI analysis supported decreased interhemispheric FC in schizophrenia with AH and verified functional connectivity abnormalities in schizophrenia.ConclusionsThese findings suggest impairment of interhemispheric coordination and whole brain FC in schizophrenia with AH, which may be implicated to the neuroimaging mechanism of auditory hallucination. Furthermore, this research highly support dysconnectivity hypothesis that schizophrenia related to abnormalities in neuronal connectivity.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Reduced Default Mode Network Functional Connectivity in Patients with Recurrent Major Depressive Disorder

2018 ◽  
Author(s):  
Chao-Gan Yan ◽  
Xiao Chen ◽  
Le Li ◽  
Francisco Xavier Castellanos ◽  
Tong-Jian Bai ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Default Mode Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Research Groups ◽  
Major Depressive ◽  
Default Mode ◽  
Normal Controls

ABSTRACTMajor Depressive Disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol prior to aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. Finally, all resting-state fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.SIGNIFICANCE STATEMENTFunctional connectivity within the default mode network in major depressive disorder patients has been frequently reported abnormal but with contradicting directions in previous small sample size studies. In creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, we found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration. These findings suggest that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments.

scholarly journals Whole-brain dynamics in aging: disruptions in functional connectivity and the role of the rich club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

AbstractNormal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state fMRI studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent (BOLD) signals to analyze resting-state fMRI data from 620 subjects divided into two groups (‘middle-age group’ (n=310); age range, 50-65 years vs. ‘older group’ (n=310); age range, 66-91 years). Applying the Intrinsic-Ignition Framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

scholarly journals COVID-19 and the Brain: A Psychological and Resting-state fMRI Study of the Whole-brain Functional Connectivity

2021 ◽  
pp. 1-29
Author(s):  
Mohammad Niroumand Sarvandani ◽  
◽  
Javad Sheikhi Koohsar ◽  
Raheleh Rafaiee ◽  
Maryam Saeedi ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Cognitive Assessment ◽  
Neural Circuits ◽  
Resting State Fmri ◽  
Montreal Cognitive Assessment ◽  
Whole Brain ◽  
Neuropsychological Status ◽  
The Mean ◽  
The Relationship

Background: Coronavirus 2019 (COVID-19) spreads rapidly worldwide and causes severe acute respiratory syndrome. The current study aimed at evaluating the relationship between the whole-brain functional connections in resting state and cognitive impairments in patients with COVID-19 compared with that of a healthy control group. Methods: Resting-state fMRI and Montreal cognitive assessment (MoCA) data were obtained from 29 patients of the acute stage of COVID-19 on the third day of admission and 20 healthy controls. Cross-correlation of the mean resting-state signals was determined in the voxels of 23 IC (Independent Components) of brain neural circuits. To assess cognitive function and neuropsychological status, MoCA was performed on all participants. The relationship between rs-fMRI information, neuropsychological status, and paraclinical data were analyzed. Results: The COVID-19 group got a lower mean MoCA score and showed a significant reduction in the functional connectivity of the IC14 (P <0.001) and IC38 (P <0.001) regions compared with controls. The increase of functional connectivity was observed in the COVID-19 group compared with controls at baseline in the default mode network (DMN) IC00 (P <0.001) and dorsal attention network (DAN) IC08 (P <0.001) regions. Furthermore, alternation of functional connectivity in the mentioned ICs was significantly correlated with the mean Montreal Cognitive Assessment (MoCA) scores and inflammatory parameters-ie, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Conclusions: Functional connectivity abnormalities in four brain neural circuits associated with cognitive impairment and increased inflammatory markers in patients with COVID-19.

scholarly journals Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

2020 ◽  
Author(s):  
Behnaz Yousefi ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Whole Brain ◽  
Functional Connections ◽  
Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

scholarly journals Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

2018 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Leonie Klock ◽  
Johanna Baechle ◽  
Laura Stoll ◽  
Patrick Giemsa ◽  
...  
Keyword(s):  
Resting State ◽  
Local Level ◽  
Resting State Fmri ◽  
Brain Network ◽  
Graph Analysis ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Schizophrenic Patients ◽  
Brain Circuitry ◽  
Behavioral Impairments

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology. In this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41 controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology. Using NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain. Our results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

scholarly journals A Whole-Brain Regression Method to Identify Individual and Group Variations in Functional Connectivity

2020 ◽  
Author(s):  
Yi Zhao ◽  
Brian S. Caffo ◽  
Bingkai Wang ◽  
Chiang-shan R. Li ◽  
Xi Luo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Statistical Power ◽  
Resting State Fmri ◽  
Regression Method ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Group Ica ◽  
Brain Functional Connectivity

AbstractResting-state functional connectivity is an important and widely used measure of individual and group differences. These differences are typically attributed to various demographic and/or clinical factors. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a generalized linear model method that regresses whole-brain functional connectivity on covariates. Our approach builds on two methodological components. We first employ whole-brain group ICA to reduce the dimensionality of functional connectivity matrices, and then search for matrix variations associated with covariates using covariate assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results show that the approach enjoys improved statistical power in detecting interaction effects of sex and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.

scholarly journals Characterizations of resting-state modulatory interactions in the human brain

2015 ◽  
Vol 114 (5) ◽  
pp. 2785-2796 ◽  
Author(s):  
Xin Di (邸新) ◽  
Bharat B. Biswal
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Interaction Analysis ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Processing Efficiency ◽  
Whole Brain ◽  
Positive Effects

Functional connectivity between two brain regions, measured using functional MRI (fMRI), has been shown to be modulated by other regions even in a resting state, i.e., without performing specific tasks. We aimed to characterize large-scale modulatory interactions by performing region-of-interest (ROI)-based physiophysiological interaction analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all of the significant modulatory interactions, there were considerably more negative than positive effects; i.e., in more cases, an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as to whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases in which the three ROIs were from a single module, suggesting an increase within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases in which the three ROIs were from two modules, suggesting a tendency of between-module segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole-brain characteristics of modulatory interactions and may provide guidance for future studies of connectivity dynamics in both resting state and task state.

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