ArticleFrontiers in human neuroscience2018
Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression.
Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Estimating Mutual Information and Pearson Correlation on Neural Evoked Responses.Neuroinformatics · 2026Article
- Brain causality alterations in major depressive disorder treatment.Frontiers in psychiatry · 2025Article
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
- Functional Brain Network Disruptions in Parkinson's Disease: Insights from Information Theory and Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Systems-level analysis of local field potentials reveals differential effects of lysergic acid diethylamide and ketamine on neuronal activity and functional connectivity.Frontiers in neuroscience · 2023Article
- Article
- Filtering ASVs/OTUs via mutual information-based microbiome network analysis.BMC bioinformatics · 2022Article
- Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke.Scientific reports · 2022Article
- Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy.Neuroinformatics · 2022Article
- A novel nonlinear analysis of blood flow dynamics applied to the human lung.Journal of applied physiology (Bethesda, Md. : 1985) · 2022Article
- EEG functional connectivity is sensitive for nitrogen narcosis at 608 kPa.Scientific reports · 2022Article
- Yijinjing Qigong intervention shows strong evidence on clinical effectiveness and electroencephalography signal features for early poststroke depression: A randomized, controlled trial.Frontiers in aging neuroscience · 2022Article
- Neural Substrates of Poststroke Depression: Current Opinions and Methodology Trends.Frontiers in neuroscience · 2022Review
- Characterization of kinesthetic motor imagery compared with visual motor imageries.Scientific reports · 2021Article
- Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients.Brain sciences · 2020Article
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Authors and funding
7 authors.
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Abstract
Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10-30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke.
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