Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

  1. Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Multi-dimensional predictions of psychotic symptoms via machine learning

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Common HTR2A variants and 5-HTTLPR are not associated with human in vivo serotonin 2A receptor levels

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Widespread higher fractional anisotropy associates to better cognitive functions in individuals at ultra-high risk for psychosis

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Can topical application of numbing cream improve the efficacy of sham TDCS?

    Publikation: KonferencebidragPosterForskning

  2. Data-driven separation of MRI signal components for tissue characterization

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. New tools for computational modeling of non-invasive brain stimulation in SimNIBS

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Ergodicity-breaking reveals time optimal decision making in humans

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  5. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement

    Publikation: Bidrag til tidsskriftReviewForskningpeer review

  • Xin-Lu Cai
  • Dong-Jie Xie
  • Kristoffer H Madsen
  • Yong-Ming Wang
  • Sophie Alida Bögemann
  • Eric F C Cheung
  • Arne Møller
  • Raymond C K Chan
Vis graf over relationer

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.

OriginalsprogEngelsk
TidsskriftHuman Brain Mapping
Vol/bind41
Udgave nummer1
Sider (fra-til)172-184
Antal sider13
ISSN1065-9471
DOI
StatusUdgivet - 1 jan. 2020

Bibliografisk note

© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

ID: 58060515