ArticleNeuroImage. Clinical2020
Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.
Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Polysomnographic Parameters in Schizoaffective Disorder: A Systematic Review and Meta-Analysis.Alpha psychiatry · 2026Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.Human brain mapping · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Schizophrenia and Neurodevelopment: Insights From Connectome Perspective.Schizophrenia bulletin · 2025Review
- Article
- Inhibition abilities and functional brain connectivity in school-aged term-born and preterm-born children.Pediatric research · 2025Article
- Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.Schizophrenia bulletin · 2024Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.Brain sciences · 2024Article
- The overlap across psychotic disorders: A functional network connectivity analysis.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2024Article
- Pons-to-Cerebellum Hypoconnectivity Along the Psychosis Spectrum and Associations With Sensory Prediction and Hallucinations in Schizophrenia.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants.Biological psychiatry · 2024Article
- The Modern Concept of Schizoaffective Disorder: A Narrative Review.Consortium psychiatricum · 2024Review
- Beta-informativeness-diffusion multilayer graph embedding for brain network analysis.Frontiers in neuroscience · 2024Article
- Functional Connectivity Biomarkers in Schizophrenia.Advances in neurobiology · 2024Review
- Aberrant brain dynamics and spectral power in children with ADHD and its subtypes.European child & adolescent psychiatry · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Dysconnectivity in Schizophrenia Revisited: Abnormal Temporal Organization of Dynamic Functional Connectivity in Patients With a First Episode of Psychosis.Schizophrenia bulletin · 2023Article
- Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives.Brain sciences · 2023Article
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5 authors.
Funding
Abstract
It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.
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