Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Zhang, Jiang; Cui, Hongjie; Yang, Huadong; Li, Yuanyuan; Xu, Dundi; Zhao, Tianyu; Wu, Huawang; Du, Zhengcong; Huang, Wei; Wang, Chong; Ai, Chen; Wang, Jiaojian

doi:10.1016/j.pnpbp.2021.110369

Cited by 24 publications

(7 citation statements)

References 62 publications

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“…The copyright holder for this preprint this version posted August 22, 2022. ; https://doi.org/10.1101/2022.08.20.504632 doi: bioRxiv preprint fluctuate over the course of an MRI session, taking on distinct configurations that vary from more sparsely-connected connectivity states that anatomically overlap with well-defined networks (e.g., CEN, DNM) to highly-interconnected states that resemble interactions between these networks (Allen et al, 2014;Preti et al, 2017). Dynamic functional connectivity (dFC) enables the investigation of these time-variant FC patterns and has been increasingly employed in healthy subjects and individuals that suffer from neurologic and psychiatric conditions (Allen et al, 2014;Li et al, 2019;Long et al, 2020;Zhang et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Age-dependent changes in the dynamic functional organization of the brain at rest – a cross-cultural replication approach

Yang¹,

Zhou

Xin

et al. 2022

Preprint

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Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity (FC), accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto has received less attention. Moreover, conclusions of previous studies in this domain are limited by suboptimal replicability of resting state fMRI and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both cohorts we consistently identify two distinct connectivity states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). In both cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Moreover, older participants tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in key dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally-specific networks.

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Section: Introductionmentioning

confidence: 99%

Age-dependent changes in the dynamic functional organization of the brain at rest – a cross-cultural replication approach

Yang¹,

Zhou

Xin

et al. 2022

Preprint

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“…This is suggestive of a link between T2D and SZ. Given their shared role in emotion processing function (Wilcox et al, 2016;Pierce and Peron, 2020;Zhang et al, 2021b), it is possible that the cerebellum, which has been believed to be strongly connected with the limbic system (Schmahmann, 2000;Silvia et al, 2017;Zhang et al, 2021a), will proceed to develop GMV abnormalities after the limbic system. Poor emotion regulation has been observed in T2D patients (Rezaei et al, 2019;Pourmohammad Fahreh and Shirazi, 2020), and limbiccerebellum GMV alterations might be the structural basis for this finding.…”

Section: Discussionmentioning

confidence: 99%

Causal Structural Covariance Network Suggesting Structural Alterations Progression in Type 2 Diabetes Patients

Zhang

Liu

Guo

et al. 2022

Front. Hum. Neurosci.

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Background and PurposeAccording to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress.Materials and MethodsStructural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01).ResultsOur findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas.ConclusionOur results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.

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“…In many neuropsychiatric conditions (e.g. major depressive disorder and schizophrenia 6,[16][17][18][19][20] ) and developmental disorders (e.g. autism spectrum disorder 21 ), the brain has been shown to persist (or dwell) in certain patterns of activation, settling into potentially pathological low-energy states.…”

Section: Introductionmentioning

confidence: 99%

Pathological brain states in Alzheimer’s disease

Adams,

Kark,

Chappel-Farley

et al. 2023

Preprint

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Dynamic and rapid reconfigurations of neural activation patterns, known as brain states, support cognition. Recent analytic advances applied to functional magnetic resonance imaging now enable the quantification of brain states, which offers a substantial methodological improvement in characterizing spatiotemporal dynamics of activation over previous functional connectivity methods. Dysfunction to the persistence and temporal transitions between discrete brain states may be proximal factors reflecting neurophysiological disruptions in Alzheimer's disease, although this has not yet been established. Here, we identified six distinct brain states, representing spatiotemporal trajectories of coactivation at single time points, in older adults across the Alzheimer's disease continuum. Critically, we identified a pathological brain state that reflects coactivation within limbic regions. Higher persistence within and transitions to this limbic state, at the expense of other brain states, is associated with an increased likelihood of a clinically impaired diagnosis, worse cognitive performance, greater Alzheimer's pathology, and neurodegeneration. Together, our results provide compelling evidence that neural activity settling into a pathological limbic state reflects the progression to Alzheimer's disease. As brain states have recently been shown to be modifiable targets, this work may inform the development of novel neuromodulation techniques to reduce limbic state persistence. This application would be an innovative clinical approach to rescue cognitive decline in the early stages of Alzheimer's disease.

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Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Cited by 24 publications

References 62 publications

Age-dependent changes in the dynamic functional organization of the brain at rest – a cross-cultural replication approach

Age-dependent changes in the dynamic functional organization of the brain at rest – a cross-cultural replication approach

Causal Structural Covariance Network Suggesting Structural Alterations Progression in Type 2 Diabetes Patients

Pathological brain states in Alzheimer’s disease

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