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

Doron Elad, Suheyla Cetin-Karayumak, Fan Zhang, Kang Ik K. Cho, Amanda E. Lyall, Johanna Seitz-Holland, Rami Ben-Ari, Godfrey D. Pearlson, Carol A. Tamminga, John A. Sweeney, Brett A. Clementz, David J. Schretlen, Petra Verena Viher, Katharina Stegmayer, Sebastian Walther, Jungsun Lee, Tim J. Crow, Anthony James, Aristotle N. Voineskos, Robert W. BuchananPhilip R. Szeszko, Anil K. Malhotra, Matcheri S. Keshavan, Martha E. Shenton, Yogesh Rathi, Sylvain Bouix, Nir Sochen, Marek R. Kubicki, Ofer Pasternak*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)4658-4670
Number of pages13
JournalHuman Brain Mapping
Issue number14
StatePublished - 1 Oct 2021


FundersFunder number
ERA‐NET Neuron
National Institutes of HealthMH077945, MH081928, MH108574, MH077852, MH076995, MH102318, MH078113, MH115247, MH096957, MH077862, 152619
National Institute of Mental HealthR01MH077851
Medical Research CouncilG0500092
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Ministry of Health, State of Israel3‐13898


    • diffusion magnetic resonance imaging
    • machine learning
    • precision medicine
    • schizophrenia
    • white matter


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