Objective: We aimed to determine whether a novel, white matter (WM)
network-based approach (i.e. structural connectomes; SC) outperforms
traditional tract-based measures and hippocampal volume (HCV) for discriminating
temporal lobe epilepsy (TLE) patients with memory-impairment from those with normal
Methods: T1-and diffusion-weighted imaging (DWI), clinical, and
neuropsychological data were available for 81 patients with TLE and 61 healthy
controls. Five medial temporal lobe WM tracts were extracted from the DWI and
fractional anisotropy (FA) was calculated for each tract. Left and right HCVs were
derived using FreeSurfer. SCs were derived by performing probabilistic tractography
and measuring the number of connections among cortical regions. SCs were fed into
XGBoost, a robust tree-based classifier. The model was trained on 48 patients
from UCSD and tested on 33 patients from UCSF.
Results: Both SCs (76% accuracy) and tracts (73% accuracy) were better
classifiers of verbal memory performance than HCV (66\% accuracy) or
clinical variables (61% accuracy). Within the WM-based models, the SC
performed similar to or better than the tract-based models, with slightly
higher accuracy and positive predictive value (PPV; 0.81 for SC, 0.77 for
tracts). The SC appeared to provide more specific information regarding
abnormal connectivity contributing to verbal memory performance. Multivariate
models seemed to be most robust classifiers, providing the highest sensitivity
(0.95 for Tract+HCV) and specificity (0.67 for Connectome+HCV).
Conclusion: SCs and tract-based measures appear to outperform HCVs and clinical
variables for predicting memory impairment in TLE. However, network-based
models combined with HCVs may provide the best prediction accuracy.