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Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.

Abstract：

:Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within-site and between-site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using two independent resting-state functional magnetic resonance imaging data sets collected from different sites and scanners. We established within-site generalizability of the classification framework in the main data set using cross-validation. Then, we trained a model in the main data set and investigated between-site generalization in the validated data set using external validation. Finally, recognizing the poor between-site generalization performance, we updated the unsupervised algorithm to investigate if transfer learning using additional unlabeled data were able to improve between-site classification performance. Cross-validation showed that the published classification procedure achieved an accuracy of 0.73 using majority voting across all selected components. External validation found a classification accuracy of 0.55 (not significant) and 0.70 (significant) using the direct and transfer learning procedures, respectively. The failure of direct generalization from one site to another demonstrates the limitation of within-site cross-validation and points toward the need to incorporate efforts to facilitate application of machine learning across multiple data sets. The improvement in performance with transfer learning highlights the importance of taking into account the properties of data when constructing predictive models across samples and sites. Our findings suggest that machine learning classification result based on a single study should be interpreted cautiously.

journal_name

Hum Brain Mapp

journal_title

Human brain mapping

authors

Cai XL,Xie DJ,Madsen KH,Wang YM,Bögemann SA,Cheung EFC,Møller A,Chan RCK

doi

10.1002/hbm.24797

subject

Has Abstract

pub_date

2020-01-01 00:00:00

pages

172-184

issue

eissn

1065-9471

issn

1097-0193

journal_volume

pub_type

杂志文章

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Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.