Improving the predictive potential of diffusion MRI in schizophrenia using normative models—Towards subject‐level classification. Issue 14 (29th July 2021)

• Record Type: Journal Article
• Authors: 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.; Pasternak, Ofer
• Is Part Of: Human brain mapping. Volume 42:Issue 14(2021)
• Page Start: 4658
• Page End: 4670
• Publication Date: 2021-07-29
• Subjects: diffusion magnetic resonance imaging -- machine learning -- precision medicine -- schizophrenia -- white matter; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25574
• Languages: English
• ISSNs: 1065-9471

Abstract

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. Abstract : We use a large ( n = 1113) harmonized diffusion MRI dataset of individuals diagnosed with schizophrenia and healthy controls, in order to study the predictive performance of a normative modeling approach. This approach compares each individual with distribution derived from healthy controls to derive measures of deviation from the normal distribution. Our results show that combining deviation information from different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance, and could therefore aid in subject‐level classification.