ArticleMolecular neurobiology2020
Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder.
Article in Molecular neurobiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 41 citations in OpenAlex.
- Dynamic neural network modulation associated with rumination in major depressive disorder: a prospective observational comparative analysis of cognitive behavioral therapy and pharmacotherapy.Translational psychiatry · 2025Observational
- Exploratory GABAa-informed control network modulates hyperarousal brain dynamics in chronic insomnia.Communications biology · 2025Article
- Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Symptom-based depression subtypes: brain dynamic specificity and its association with gene expression profiles.Translational psychiatry · 2025Article
- Dynamic reconfiguration and transition of whole-brain networks in patients with MELAS revealed by a hidden Markov model.Frontiers in neurology · 2025Article
- Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach.Translational psychiatry · 2024Article
- Deep-learning-optimized microstate network analysis for early Parkinson's disease with mild cognitive impairment.Cognitive neurodynamics · 2024Article
- Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models.Journal of translational medicine · 2024Article
- Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models.CNS neuroscience & therapeutics · 2024Article
- Aberrant resting-state co-activation network dynamics in major depressive disorder.Translational psychiatry · 2024Article
- Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study.Frontiers in neuroscience · 2024Article
- Improved dynamic functional connectivity estimation with an alternating hidden Markov model.Cognitive neurodynamics · 2023Article
- Trait- and state-like co-activation pattern dynamics in current and remitted major depressive disorder.Journal of affective disorders · 2023Article
- Altered brain dynamic in major depressive disorder: state and trait features.Translational psychiatry · 2023Article
- Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model.Frontiers in human neuroscience · 2023Article
- Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.European archives of psychiatry and clinical neuroscience · 2022Article
- Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning.Translational psychiatry · 2022Article
- Surface-based map plasticity of brain regions related to sensory motor and pain information processing after osteonecrosis of the femoral head.Neural regeneration research · 2022Article
- Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model.Frontiers in human neuroscience · 2022Article
- Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis.Frontiers in integrative neuroscience · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) has been characterized by abnormal brain activity and interactions across the whole-brain functional networks. However, the underlying alteration of brain dynamics remains unclear. Here, we aim to investigate in detail the temporal dynamics of brain activity for MDD, and to characterize the spatiotemporal specificity of whole-brain networks and transitions across them. We developed a hidden Markov model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from two independent cohorts with MDD. In particular, one cohort included 127 MDD patients and 117 gender- and age-matched healthy controls, and the other included 44 MDD patients and 33 controls. We identified brain states characterized by the engagement of distinct functional networks that recurred over time and assessed the dynamical configuration of whole-brain networks and the patterns of activation of states that characterized the MDD groups. Furthermore, we analyzed the community structure of transitions across states to investigate the specificity and abnormality of transitions for MDD. Based on our identification of 12 HMM states, we found that the temporal reconfiguration of states in MDD was associated with the high-order cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found that the specific module of transitions was closely related to MDD, which were characterized by two HMM states with opposite activations in DMN, SMN, and subcortical areas. Notably, our results provide novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggest that brain dynamics should remain a prime target for further MDD research.
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