ArticleHuman brain mapping2019
Classification of schizophrenia by intersubject correlation in functional connectome.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission.Schizophrenia bulletin · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.Human brain mapping · 2023Article
- Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis.NeuroImage. Clinical · 2023Article
- Brain functional specialization and cooperation in Parkinson's disease.Brain imaging and behavior · 2022Article
- Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants.European psychiatry : the journal of the Association of European Psychiatrists · 2021Article
- Searching for Imaging Biomarkers of Psychotic Dysconnectivity.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Article
- Dynamic brain connectome and high risk of mental problem in clinical nurses.Human brain mapping · 2021Article
- Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study.Scientific reports · 2021Article
- Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity.Frontiers in neuroscience · 2021Article
- Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder.Frontiers in neuroscience · 2020Article
- Magnetic Resonance Imaging-Based Connectomics in First-Episode Schizophrenia: From Preclinical Study to Clinical Translation.Frontiers in psychiatry · 2020Article
- Classification of schizophrenia by intersubject correlation in functional connectome.Human brain mapping · 2019Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.
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