Dataset Information

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

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.

SUBMITTER: Elad D

PROVIDER: S-EPMC8410550 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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

Elad Doron D Cetin-Karayumak Suheyla S Zhang Fan F Cho Kang Ik K KIK Lyall Amanda E AE Seitz-Holland Johanna J Ben-Ari Rami R Pearlson Godfrey D GD Tamminga Carol A CA Sweeney John A JA Clementz Brett A BA Schretlen David J DJ Viher Petra Verena PV Stegmayer Katharina K Walther Sebastian S Lee Jungsun J Crow Tim J TJ James Anthony A Voineskos Aristotle N AN Buchanan Robert W RW Szeszko Philip R PR Malhotra Anil K AK Keshavan Matcheri S MS Shenton Martha E ME Rathi Yogesh Y Bouix Sylvain S Sochen Nir N Kubicki Marek R MR Pasternak Ofer O

Human brain mapping 20210729 14

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 extre ...[more]

PMID: 34322947

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Dataset Information

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

Publications

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

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