An Augmented Aging Process in Brain White Matter in HIV

T. Kuhn; T. Kaufmann; N.T. Doan; L.T. Westlye; J. Jones; R.A. Nunez; S.Y. Bookheimer; E.J. Singer; C.H. Hinkin; A.D. Thames

AGE PREDICTION AND BRAIN AGE GAP: UiO and Cam-CAN data were used to train a support vector regression model (SVR) to predict participant age using FA, L1, RD and MD from atlas-derived ROIs (the exact same regions described above 29, 30, 31 ) as features. Similar methods have been employed using imaging data previously 9, 10, 32, including using DTI to assess participant age in a healthy cohort 33 . SVR was conducted in Matlab (https://mathworks.com/help/stats/fitrsvm.html) using the implementation "fitrsvm" with a linear kernel, automatic hyperparameter tuning and Sequential Minimal Optimization. Given that multiple MR scanners were used to collect the HIV and training data, scanner was used as a regressor on the features to control for interscanner variability. The model accuracy was validated using 10-fold cross-validation on the training set. After successful validation, the trained SVR was used to predict age of participants in the independent UCLA sample (HIV+ / HIV-). For each individual, BAG was computed by subtracting the participant's predicted brain age by their chronologic . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/265199 doi: bioRxiv preprint 11 Kuhn, T., Ph.