ArticleMolecular psychiatry2024
Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia.
Article in Molecular psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- What Static Connectivity Misses: Dynamic Alpha-Band Brain Network States in First-Episode Psychosis.Brain topography · 2026Article
- Sex-stratified associations between depressive symptoms and cognitive impairment in treatment-resistant vs. non-resistant schizophrenia.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Dynamic Global and Local Enhancement of Theta-Band Functional Networks as Neural Markers of Spinal Cord Stimulation Efficacy in Disorders of Consciousness.CNS neuroscience & therapeutics · 2026Article
- Next-generation precision medicine for mood disorders: reproducible blood biomarkers enable objective diagnostics, subtyping, and targeted therapeutics.Molecular psychiatry · 2026Article
- Unraveling the Individualized Shared and Distinct Dynamic Functional Connectivity in Idiopathic Autism Spectrum Disorder and Fragile X Syndrome.CNS neuroscience & therapeutics · 2026Article
- A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.Scientific reports · 2026Article
- Distinct Biotypes of Visual Perception in Major Depressive Disorder.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MambaKAN: An Interpretable Framework for Alzheimer's Disease Diagnosis via Selective State Space Modeling of Dynamic Functional Connectivity.Brain sciences · 2026Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE for Schizophrenia Recognition.Brain and behavior · 2026Article
- Characteristics of resting-state EEG after deep brain stimulation in nucleus accumbens and anterior limb of internal capsule: a pilot study.BMC psychiatry · 2026Article
- Prevalence and associated factors of psychotic symptoms in young first-episode, drug-naïve major depressive disorder patients with abnormal lipid metabolism.European journal of medical research · 2025Article
- Enhanced hybrid deep neural network for EEG-based schizophrenia diagnosis using functional and temporal features.Scientific reports · 2025Article
- DCM-ML: An Electroencephalography-based Classifier for Early Diagnosis of Schizophrenia Based on Dynamic Connectivity Matrices and Machine Learning Algorithms.Basic and clinical neuroscience · 2025Article
- Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Integrating Cortical Source Reconstruction and Adversarial Learning for EEG Classification.Sensors (Basel, Switzerland) · 2025Article
- Development of brain metastable dynamics during the equivalent of the third gestational trimester.Developmental cognitive neuroscience · 2025Review
- Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Abnormal brain network reconfiguration in neuropsychiatric disorders across cognitive decline, Depression, and Schizophrenia.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.
Indexed as
Identifiers
38267620What OpenQuestion holds
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.