Structural connectome validation using pairwise classification

Dmitry Petrov, Boris Gutman, Alexander Ivanov, Joshua Faskowitz, Neda Jahanshad, Mikhail Belyaev, Paul Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 Alzheimers 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.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509011711
Publication statusPublished - 15 Jun 2017
Externally publishedYes
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017


  • DWI
  • Machine learning
  • Structural connectomes


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