Dataset Information

Increased Cerebellar Functional Connectivity With the Default-Mode Network in Unaffected Siblings of Schizophrenia Patients at Rest.

ABSTRACT: The default-mode network (DMN) is vital in the neurobiology of schizophrenia, and the cerebellum participates in the high-order cognitive network such as the DMN. However, the specific contribution of the cerebellum to the DMN abnormalities remains unclear in unaffected siblings of schizophrenia patients. Forty-six unaffected siblings of schizophrenia patients and 46 healthy controls were recruited for a resting-state scan. The images were analyzed using the functional connectivity (FC) method. The siblings showed significantly increased FCs between the left Crus I and the left superior medial prefrontal cortex (MPFC), as well as between the lobule IX and the bilateral MPFC (orbital part) and right superior MPFC compared with the controls. No significantly decreased FC was observed in the siblings relative to the controls. The analyses were replicated in 49 first-episode, drug-naive patients with schizophrenia, and the results showed that the siblings and the patients shared increased FCs between the left Crus I and the left superior MPFC, as well as between the lobule IX and the left MPFC (orbital part) compared with the controls. These findings suggest that increased cerebellar-DMN connectivities emerge earlier than illness onset, which highlight the contribution of the cerebellum to the DMN alterations in unaffected siblings. The shared increased cerebellar-DMN connectivities between the patients and the siblings may be used as candidate endophenotypes for schizophrenia.

SUBMITTER: Guo W

PROVIDER: S-EPMC4601712 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Increased Cerebellar Functional Connectivity With the Default-Mode Network in Unaffected Siblings of Schizophrenia Patients at Rest.

Guo Wenbin W Liu Feng F Zhang Zhikun Z Liu Guiying G Liu Jianrong J Yu Liuyu L Xiao Changqing C Zhao Jingping J

Schizophrenia bulletin 20150507 6

The default-mode network (DMN) is vital in the neurobiology of schizophrenia, and the cerebellum participates in the high-order cognitive network such as the DMN. However, the specific contribution of the cerebellum to the DMN abnormalities remains unclear in unaffected siblings of schizophrenia patients. Forty-six unaffected siblings of schizophrenia patients and 46 healthy controls were recruited for a resting-state scan. The images were analyzed using the functional connectivity (FC) method. ...[more]

PMID: 25956897

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Project description:Women are more likely to have Alzheimer's disease (AD) and decline more rapidly once diagnosed despite greater verbal memory early in the disease compared to men-an advantage that has been termed "memory reserve." Resting state functional MRI (fMRI) investigations demonstrate interactions between sex and AD risk factors in default mode network (DMN) connectivity, a network of brain regions showing progressive dysfunction in AD. Separate work suggests connectivity of left prefrontal cortex (PFC) may correlate with more general cognitive reserve in healthy aging. It is unknown whether left prefrontal functional connectivity with anterior and posterior default mode network (aDMN, pDMN) might differ by sex in AD. This study employed group independent component analysis (ICA) to analyze resting state fMRI data from 158 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with baseline diagnoses of normal cognition or early mild cognitive impairment (eMCI). pDMN and aDMN were defined on a subject-specific basis; prefrontal areas were selected from the Brodmann atlas (BA 6, 44, 8, and 9). Moderation regression analyses examined whether sex and amyloid PET positivity (A+/-) moderated effects of apolipoprotein ε4 (APOE ε4) on connectivity between left PFC, aDMN, and pDMN; and between aDMN and pDMN. Significant analyses were followed up with partial correlations assessing relationship of connectivity to verbal memory on the Rey Auditory Verbal Learning Test (RAVLT), and with preliminary analyses within NC and eMCI groups separately. Results showed no sex moderation of effects of A+ and APOE ε4 on left prefrontal/DMN connectivity in the full sample. However, sex significantly moderated impact of A+ and APOE ε4 on connectivity between aDMN and pDMN (p < 0.01). Women with an APOE allele (ε4+) and A+ showed greater aDMN/pDMN connectivity than their ε4- counterparts. No significant results were observed in men. Subgroup analyses suggested the aDMN/pDMN finding was true for those with NC, not eMCI. Partial correlations controlling for age and education showed increased aDMN/pDMN connectivity related to better verbal learning in women (p < 0.01) and not men (p = 0.18). In women at risk for AD or in early symptomatic stages who also have evidence of amyloid burden, stronger aDMN/pDMN connectivity may support verbal learning.

Project description:IntroductionDynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features.MethodsWe apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity.ResultsImportantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states.ConclusionWe present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.

Project description:Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6 ± 1.8 years old) and 31 healthy controls (15.4 ± 1.6 years old). Data from 22 participants with ASD (13.1 ± 3.1 years old) and 21 healthy controls (12.9 ± 2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy = 83%; ASD dataset: accuracy = 80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p = 0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776-1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.Lay summaryAutism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand the pathology of these two neurodevelopmental disorders.

Dataset Information

Increased Cerebellar Functional Connectivity With the Default-Mode Network in Unaffected Siblings of Schizophrenia Patients at Rest.

Publications

Increased Cerebellar Functional Connectivity With the Default-Mode Network in Unaffected Siblings of Schizophrenia Patients at Rest.

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