title: Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints.
abstract: Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N = 213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns for repeat scans acquired within 3 months. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings, relative to differences between unrelated subjects. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.
authors.affiliation: Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Fang-Cheng
authors.identifier: http://orcid.org/0000-0002-7946-2173
authors.initials: FC
authors.lastname: Yeh
authors.affiliation: U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland, United States of America.|Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.|University of Pennsylvania, Department of Bioengineering, Philadelphia, PA, United States of America.
authors.forename: Jean M
authors.identifier: http://orcid.org/0000-0001-5336-2986
authors.initials: JM
authors.lastname: Vettel
authors.affiliation: Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Aarti
authors.initials: A
authors.lastname: Singh
authors.affiliation: Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Barnabas
authors.initials: B
authors.lastname: Poczos
authors.affiliation: Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.
authors.forename: Scott T
authors.identifier: http://orcid.org/0000-0003-4015-3151
authors.initials: ST
authors.lastname: Grafton
authors.affiliation: Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania United States of America.
authors.forename: Kirk I
authors.initials: KI
authors.lastname: Erickson
authors.affiliation: Institute of Medical Device and Imaging and Molecular Imaging Center, National Taiwan University, Taipei, Taiwan.
authors.forename: Wen-Yih I
authors.initials: WI
authors.lastname: Tseng
authors.affiliation: Department of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Timothy D
authors.initials: TD
authors.lastname: Verstynen
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D066127:White Matter / Q000033:anatomy & histology*; D055815:Young Adult