Title: Structural Connectome Validation Using Pairwise Classification,
Abstract: In this work, we study the extent to which structural connectomes and
topological derivative measures are unique to individual changes within human
brains. To do so, we classify structural connectome pairs from two large
longitudinal datasets as either belonging to the same individual or not. Our
data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative
(PPMI). We achieve 0.99 area under the ROC curve score for features which
represent either weights or network structure of the connectomes (node degrees,
PageRank and local efficiency). Our approach may be useful for eliminating
noisy features as a preprocessing step in brain aging studies and early
diagnosis classification problems.