ArticlePsychological medicine2020
Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics.
Article in Psychological medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 107 citations in OpenAlex.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders.Translational psychiatry · 2020Pooled it
- Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial.Journal of affective disorders · 2025Trial
- Emotion-Related Network Reorganization Following Fish Oil Supplementation in Depressed Bipolar Offspring: An fMRI Graph-Based Connectome Analysis.Journal of affective disorders · 2021Trial
- Changes in the brain structural connectome after a prospective randomized clinical trial of lithium and quetiapine treatment in youth with bipolar disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Trial
- Evaluating topological and graph-theoretical approaches to extract complex multimodal brain connectivity patterns in multiple sclerosis.Health information science and systems · 2025Article
- Altered Brain-Behavior Association During Resting State is a Potential Psychosis Risk Marker.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
- Article
- Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.BMC neuroscience · 2024Article
- Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients.Frontiers in psychiatry · 2024Article
- Evidence for embracing normative modeling.eLife · 2023Article
- Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.CNS neuroscience & therapeutics · 2023Article
- Deep multimodal predictome for studying mental disorders.Human brain mapping · 2023Article
- Acquired Pedophilia: international Delphi-method-based consensus guidelines.Translational psychiatry · 2023Review
- Article
- Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach.Network neuroscience (Cambridge, Mass.) · 2023Article
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Authors and funding
18 authors at 9 institutions in 8 countries.
Funding
Abstract
backgroundPrevious studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient.
methodsHere we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology.
resultsConnectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions.
conclusionThese results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the 'dysconnectivity hypothesis' of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.
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