ArticleInternational journal of environmental research and public health2022
Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Utilization of EEG microstates as a prospective biomarker for assessing the impact of ketogenic diet in GLUT1-DS.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Pooled it
- Altering Temporal Dynamics of Sleepiness and Mood During Sleep Deprivation: Evidence from Resting-State EEG Microstates.Brain sciences · 2025Article
- Abnormal nonlinear features of EEG microstate sequence in obsessive-compulsive disorder.BMC psychiatry · 2024Article
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- Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome.Journal of clinical medicine · 2023Article
- Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.Computational intelligence and neuroscience · 2023Article
- The effect of acupuncture at theFrontiers in integrative neuroscience · 2023Article
- Machine learning approaches for diagnosing depression using EEG: A review.Translational neuroscience · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio-temporal dynamics of rest-state EEG signals in female college students (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.