ArticleBiomedical engineering online2018
Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application ofSchizophrenia (Heidelberg, Germany) · 2024Article
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Beta-informativeness-diffusion multilayer graph embedding for brain network analysis.Frontiers in neuroscience · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression.Brain imaging and behavior · 2022Article
- Decreased resting-state neural signal in the left angular gyrus as a potential neuroimaging biomarker of schizophrenia: An amplitude of low-frequency fluctuation and support vector machine analysis.Frontiers in psychiatry · 2022Article
- Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals.Frontiers in neuroinformatics · 2021Article
- Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy.Frontiers in neuroscience · 2021Article
- Hybrid Functional Brain Network With First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia.Frontiers in neuroscience · 2019Article
- Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy.Frontiers in computational neuroscience · 2019Article
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3 authors.
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Abstract
backgroundSchizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level.
methods24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features.
resultsThe proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia.
conclusionsThis study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.
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