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Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification

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Abstract
Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.
Author(s)
이중선Amanda E LyallAnil K MalhotraAnthony JamesAristotle N VoineskosBrett A ClementzCarol A TammingaDavid J SchretlenDoron EladFan ZhangGodfrey D PearlsonJohanna Seitz-HollandJohn A SweeneyKang Ik K ChoKatharina StegmayerMarek R KubickiMartha E ShentonMartha E ShentonMatcheri S Ke
Issued Date
2021
Type
Article
Keyword
Anisotropydiffusion magnetic resonance imagingMachine learningMagnetic resonance imagingModelsprecision medicineSchizophreniawhite matter
DOI
10.1002/hbm.25574
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8643
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2568595548&amp;context=PC&amp;vid=ULSAN&amp;lang=ko_KR&amp;search_scope=default_scope&amp;adaptor=primo_central_multiple_fe&amp;tab=default_tab&amp;query=any,contains,Improving%20the%20predictive%20potential%20of%20diffusion%20MRI%20in%20schizophrenia%20using%20normative%20models-Towards%20subject-level%20classification&amp;offset=0&amp;pcAvailability=true
Publisher
HUMAN BRAIN MAPPING
Location
영국
Language
영어
ISSN
1065-9471
Citation Volume
42
Citation Number
14
Citation Start Page
4658
Citation End Page
4670
Appears in Collections:
Medicine > Medicine
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