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

Elad, Doron; Cetin-Karayumak, Suheyla; Zhang, Fan; Cho, Kang Ik K; Lyall, Amanda E; Seitz-Holland, Johanna; Ben-Ari, Rami; Pearlson, Godfrey D; Tamminga, Carol A; Sweeney, John A; Clementz, Brett A; Schretlen, David J; Viher, Petra; Stegmayer, Katharina; Walther, Sebastian; Lee, Jungsun; Crow, Tim J; James, Anthony; Voineskos, Aristotle N; Buchanan, Robert W; ... (2021). Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification. Human brain mapping, 42(14), pp. 4658-4670. Wiley-Blackwell 10.1002/hbm.25574

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy > Translational Research Center

UniBE Contributor:

Viher, Petra; Stegmayer, Katharina Deborah Lena and Walther, Sebastian

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1065-9471

Publisher:

Wiley-Blackwell

Language:

English

Submitter:

Sebastian Walther

Date Deposited:

17 Sep 2021 15:37

Last Modified:

17 Sep 2021 15:54

Publisher DOI:

10.1002/hbm.25574

PubMed ID:

34322947

Uncontrolled Keywords:

diffusion magnetic resonance imaging machine learning precision medicine schizophrenia white matter

BORIS DOI:

10.48350/159040

URI:

https://boris.unibe.ch/id/eprint/159040

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