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 Verena; Stegmayer, Katharina; Walther, Sebastian; Lee, Jungsun; Crow, Tim J.; James, Anthony; Voineskos, Aristotle N.; Buchanan, Robert W.; Szeszko, Philip R.; Malhotra, Anil K.; Keshavan, Matcheri S.; Shenton, Martha E.; Rathi, Yogesh; Bouix, Sylvain; Sochen, Nir; Kubicki, Marek R. und Pasternak, Ofer (2021): Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification. In: Human Brain Mapping, Bd. 42, Nr. 14: S. 4658-4670

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1065-9471
• Sprache: Englisch