ArticleFrontiers in neuroinformatics2018
Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 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
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Influence of Functional Magnetic Resonance Imaging Data Preprocessing Pipelines on the Accuracy of Schizophrenia Classification Using Machine Learning Methods.Sovremennye tekhnologii v meditsine · 2026Article
- Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach.Frontiers in neuroinformatics · 2023Article
- Evaluation of functional MRI-based human brain parcellation: a review.Journal of neurophysiology · 2022Review
- Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity.Frontiers in neuroscience · 2021Article
- ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia.AIMS neuroscience · 2021Article
- Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features.Frontiers in neuroscience · 2020Article
- Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks.Frontiers in bioengineering and biotechnology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance.
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