Association of resting-state network dysfunction with their dynamics of inter-network interactions in depression

Wei, Maobin; Jiang, Qin; Yan, Rui; Bi, Ke; Chu, Liang‐Yin; Yao, Zhijian; Lü, Qing

doi:10.1016/j.jad.2014.12.020

Cited by 45 publications

(36 citation statements)

References 60 publications

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“…Intriguingly, we found that the DFC pattern derived from R‐fMRI showed a notable ability to differentiate between individuals, suggesting that the chronnectome at rest may act as a fingerprint that reflects individual intrinsic characteristics. Previous studies have explored inter‐individual variability in dynamic functional architecture in terms of associations with individual cognitive performance (Bassett, Yang, Wymbs, & Grafton, ; Braun et al, ; Davison et al, ; Gonzalez‐Castillo et al, ; Madhyastha et al, ; Nomi et al, ), individual demographics (Davison et al, ) and clinical characteristics (Damaraju et al, ; Rashid et al, ; Wei et al, ; Zhang et al, ). In a recent study, by performing a hypergraph analysis (a method based on dynamic network theory) on lifespan datasets, Davison et al () showed that one dynamic metric (i.e., hypergraph cardinality) exhibited individual differences and was significantly correlated with age.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, there has been growing interest in the “chronnectome,” a new concept that has emerged to emphasize the dynamic characteristics of functional brain connectivity (Allen et al, ; Calhoun & Adali, ; Calhoun, Miller, Pearlson, & Adali, ; Hutchison et al, ; Preti, Bolton, Van, & Ville, ). Mounting evidence has suggested that the chronnectome at rest reflects underlying temporal changes in neural activities measured by electrophysiological recording (Chang, Liu, Chen, Liu, & Duyn, ; Keilholz, ; Tagliazucchi, von Wegner, Morzelewski, Brodbeck, & Laufs, ; Zhang et al, ), is structurally constrained by white matter connectivity (Liao et al, ; Shen, Hutchison, Bezgin, Everling, & McIntosh, ; Zhang et al, ), and is able to trace alterations in normal development (Davison et al, ; Hutchison & Morton, ; Qin et al, ) and neuropsychiatric disorders, such as depression (Wei et al, ) and schizophrenia (Damaraju et al, ; Rashid, Damaraju, Pearlson, & Calhoun, ; Zhang et al, ). Notably, most research involving the dynamic functional network has primarily focused on group‐level analyses, largely ignoring individual‐specific characteristics in the chronnectome.…”

Section: Introductionmentioning

confidence: 99%

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Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Liu

Liao

Xia

et al. 2017

Human Brain Mapping

183

190

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The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Liu

Liao

Xia

et al. 2017

Human Brain Mapping

183

190

View full text Add to dashboard Cite

show abstract

“…In depressed patients, a low Hurst exponent was detected within the DMN, thus indicating uneven signal oscillation over time. Nevertheless, in this same sample the frontoparietal, ventromedial prefrontal and salience networks showed increased Hurst exponent values [Wei et al, ]. Several other studies investigated the role of dynamic FC in schizophrenia [Damaraju et al, ; Rashid et al, ; Yu et al, ], bipolar disorder [Rashid et al, ] and psychedelic experience [Tagliazucchi et al, ].…”

Section: Introductionmentioning

confidence: 87%

“…Regarding the latter, two recent studies by Wei et al [ and 2015] found alterations in the Hurst exponent of the time series in MDD patients. The Hurst exponent indicates the self‐similarity or regularity of a time series, where a greater Hurst exponent value signifies highly regular fluctuations over time, suggesting a tendency toward coordinated signal organization within a network [Wei et al, ]. In depressed patients, a low Hurst exponent was detected within the DMN, thus indicating uneven signal oscillation over time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder

Demirtaş

Tornador

Falcón

et al. 2016

Human Brain Mapping

194

148

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Resting‐state fMRI (RS‐fMRI) has become a useful tool to investigate the connectivity structure of mental health disorders. In the case of major depressive disorder (MDD), recent studies regarding the RS‐fMRI have found abnormal connectivity in several regions of the brain, particularly in the default mode network (DMN). Thus, the relevance of the DMN to self‐referential thoughts and ruminations has made the use of the resting‐state approach particularly important for MDD. The majority of such research has relied on the grand averaged functional connectivity measures based on the temporal correlations between the BOLD time series of various brain regions. We, in our study, investigated the variations in the functional connectivity over time at global and local level using RS‐fMRI BOLD time series of 27 MDD patients and 27 healthy control subjects. We found that global synchronization and temporal stability were significantly increased in the MDD patients. Furthermore, the participants with MDD showed significantly increased overall average (static) functional connectivity (sFC) but decreased variability of functional connectivity (vFC) within specific networks. Static FC increased to predominance among the regions pertaining to the default mode network (DMN), while the decreased variability of FC was observed in the connections between the DMN and the frontoparietal network. Hum Brain Mapp 37:2918–2930, 2016. © 2016 Wiley Periodicals, Inc.

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“…This renders traditional bivariate Granger causality analysis ineffective in determining direct temporal causality (Bressler & Seth, ; Granger, ; Liao et al, ; Tang, Bressler, Sylvester, Shulman, & Corbetta, ). To analyze multivariate systems, such as our resting‐state fMRI datasets, we used CGCA to account for tertiary variables that may moderate the relationship between the two variables in question (Bressler & Seth, ; Geweke, ; Liao et al, ; Tang et al, ; Wei et al, ). Here, defining significance in the causal relationship between two time courses is conditional on all other time courses in the system.…”

Section: Methodsmentioning

confidence: 99%

Cardiorespiratory fitness predicts effective connectivity between the hippocampus and default mode network nodes in young adults

et al. 2019

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Rodent and human studies examining the relationship between aerobic exercise, brain structure, and brain function indicate that the hippocampus (HC), a brain region critical for episodic memory, demonstrates striking plasticity in response to exercise.Beyond the hippocampal memory system, human studies also indicate that aerobic exercise and cardiorespiratory fitness (CRF) are associated with individual differences in large-scale brain networks responsible for broad cognitive domains. Examining network activity in large-scale resting-state brain networks may provide a link connecting the observed relationships between aerobic exercise, hippocampal plasticity, and cognitive enhancement within broad cognitive domains. Previously, CRF has been associated with increased functional connectivity of the default mode network (DMN), specifically in older adults. However, how CRF relates to the magnitude and directionality of connectivity, or effective connectivity, between the HC and other DMN nodes remains unknown. We used resting-state fMRI and conditional Granger causality analysis (CGCA) to test the hypothesis that CRF positively predicts effective connectivity between the HC and other DMN nodes in healthy young adults.Twenty-six participants (ages 18-35 years) underwent a treadmill test to determine CRF by estimating its primary determinant, maximal oxygen uptake (VO 2max ), and a 10-min resting-state fMRI scan to examine DMN effective connectivity. We identified the DMN using group independent component analysis and examined effective connectivity between nodes using CGCA. Linear regression analyses demonstrated that CRF significantly predicts causal influence from the HC to the ventromedial prefrontal cortex, posterior cingulate cortex, and lateral temporal cortex and to the HC from the dorsomedial prefrontal cortex. The observed relationship between CRF and Corey A. Kronman and Kathryn L. Kern share the first authorship.

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Association of resting-state network dysfunction with their dynamics of inter-network interactions in depression

Cited by 45 publications

References 60 publications

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder

Cardiorespiratory fitness predicts effective connectivity between the hippocampus and default mode network nodes in young adults

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