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

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.

Download Full-text

Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

10.1101/344473 ◽

2018 ◽

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Temporal Structure ◽

Structural Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Network ◽

Dynamical Analysis ◽

Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

Download Full-text

Resting-state fMRI correlations: from link-wise unreliability to whole brain stability

10.1101/081976 ◽

2016 ◽

Cited By ~ 1

Author(s):

Mario Pannunzi ◽

Rikkert Hindriks ◽

Ruggero G. Bettinardi ◽

Elisabeth Wenger ◽

Nina Lisofsky ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Systematic Investigation ◽

Resting State Functional Connectivity ◽

Data Set ◽

Whole Brain ◽

Statistical Framework ◽

Subject Variability ◽

Test Retest Reliability ◽

Different Sources

AbstractThe functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze both the within- and between-subject variability as well as the test-retest reliability of resting-state functional connectivity (FC) estimates in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. To this aim we adopted a statistical framework enabling us to disentangle the contribution of different sources of variability and their dependence on scan duration, and showed that the low reliability of single links can be largely improved using multiple scans per subject. Moreover, we show that practically all observed inter-region variability (at the link-level) is not significant and due to the statistical uncertainty of the estimator itself rather than to genuine variability among areas. Finally, we use the proposed statistical framework to demonstrate that, despite the poor consistency of single links, the information carried by the whole-brain spontaneous correlation structure is indeed robust, and can in fact be used as a functional fingerprint.

Download Full-text

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

10.1101/2020.01.16.909580 ◽

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.

Download Full-text

A Comparison of PTSD and Normal Adults Using Whole Brain Network Science Based Analyses of Resting State fMRI

PsycEXTRA Dataset ◽

10.1037/e502412013-419 ◽

2012 ◽

Author(s):

Paige L. Roseman ◽

Jennifer Stapleton ◽

Jared A. Rowland ◽

Dwayne Godwin ◽

Katherine Taber ◽

...

Keyword(s):

Resting State ◽

Network Science ◽

Resting State Fmri ◽

Brain Network ◽

Whole Brain ◽

Normal Adults

Download Full-text

Whole-Brain Network Connectivity Underlying the Human Speech Articulation as Emerged Integrating Direct Electric Stimulation, Resting State fMRI and Tractography

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2018.00405 ◽

2018 ◽

Vol 12 ◽

Cited By ~ 10

Author(s):

Domenico Zacà ◽

Francesco Corsini ◽

Umberto Rozzanigo ◽

Monica Dallabona ◽

Paolo Avesani ◽

...

Keyword(s):

Resting State ◽

Electric Stimulation ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Whole Brain ◽

Human Speech ◽

Speech Articulation ◽

Brain Network Connectivity

Download Full-text

Dynamic properties of simulated brain network models and empirical resting-state data

Network Neuroscience ◽

10.1162/netn_a_00070 ◽

2019 ◽

Vol 3 (2) ◽

pp. 405-426 ◽

Cited By ~ 3

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Structural Connectivity ◽

Simulated Data ◽

Network Models ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Whole Brain

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.

Download Full-text

Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

10.1101/2020.02.17.953547 ◽

2020 ◽

Cited By ~ 1

Author(s):

Norio Takata ◽

Nobuhiko Sato ◽

Yuji Komaki ◽

Hideyuki Okano ◽

Kenji F. Tanaka

Keyword(s):

Functional Connectivity ◽

Mouse Brain ◽

Resting State ◽

Brain Structures ◽

Resting State Fmri ◽

Brain Atlas ◽

List Type ◽

Resting State Functional Connectivity ◽

Whole Brain ◽

Link Type

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1,381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.Highlights–A flexible annotation atlas (FAA) for the mouse brain is proposed.–FAA is expected to improve whole brain ROI-definition consistency among laboratories.–The ROI can be combined or divided objectively while maintaining anatomical hierarchy.–FAA realizes functional connectivity analysis across the anatomical hierarchy.–Codes for FAA reconstruction is available at https://github.com/ntakata/flexible-annotation-atlas–Datasets for resting-state fMRI in awake mice are available at https://openneuro.org/datasets/ds002551

Download Full-text

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

European Psychiatry ◽

10.1016/j.eurpsy.2016.01.099 ◽

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.

Download Full-text

GP.04 Network connectivity following a single unprovoked seizure using 7 Tesla resting-state fMRI

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2017.63 ◽

2017 ◽

Vol 44 (S2) ◽

pp. S8-S8

Author(s):

KM Ikeda ◽

SM Mirsattari ◽

AR Khan ◽

I Johnsrude ◽

JG Burneo ◽

...

Keyword(s):

Functional Mri ◽

Resting State ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Clustering Coefficient ◽

7 Tesla ◽

Unprovoked Seizure ◽

Whole Brain ◽

Network Function

Background: Predicting epilepsy following a first seizure is difficult. Network abnormalities are observed in patients with epilepsy using resting-state functional MRI (rs-fMRI), which worsen with duration of epilepsy. We use rs-fMRI to identify network abnormalities in patients after a first seizure that can be used as a biomarker to predict development of epilepsy. Methods: Patients after a single, unprovoked seizure and age/sex matched healthy controls underwent 7 Tesla structural and resting-state functional MRI. Data were analyzed using graph theory measures. Patients were followed for development of epilepsy. Results: Nine patients and nine control subjects were analyzed. There were no differences in baseline characteristics. No patients developed epilepsy (average follow-up 3 months). No differences between groups occurred on a whole-brain network level. At a 20% threshold, significant differences occurred in the default mode network (DMN). Patients demonstrated an increased local efficiency (p=0.02) and clustering coefficient (p=0.04), and decreased path length (p=0.02) and betweenness centrality (p=0.02). Conclusions: No whole-brain network changes occur after a single unprovoked seizure. No patient has developed epilepsy suggesting this group does not have network alterations after a single seizure. In the DMN, the alterations noted indicate increased segregation of network function.

Download Full-text