Multidimensional analysis and detection of informative features in human brain white matter
Fig 3
Predicting age with tractometry and SGL.
(top) The predicted age vs. true age of each individual from the test splits (i.e., when each subject’s data was held out in fitting the model) for the (a) WH, (b) HBN, and (c) Cam-CAN datasets; an accurate prediction falls close to the y = x line (dashed). The mean absolute error (MAE) and coefficient of determination R2 are presented in the lower right of each scatter plot. (middle) Feature importance for predicting age from tract profile in the (d) WH, (e) HBN, and (f) Cam-CAN datasets. The orientation of the brain is that same as in Fig 2b, however because the coefficients exhibit high global sparsity (as opposed to group sparsity), we plot the mean of the absolute value of for each bundle on the core fiber. The global distrubution of the
coefficients reflects the fact that aging is not confined to a single white matter bundle. (bottom) Age quintile bundle profiles for the (g) WH, (h) HBN, and (i) Cam-CAN datasets.