Structural Connectome Validation Using Pairwise Classification
This work addresses the need for reliable connectome validation in brain aging studies and early diagnosis, though it is incremental as it applies existing classification methods to new data.
The study tackled the problem of validating structural connectomes by classifying pairs from longitudinal datasets as belonging to the same individual or not, achieving a 0.99 area under the ROC curve score using features like weights and network structure.
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.