Tangent functional connectomes uncover more unique phenotypic traits

Kausar Abbas, Mintao Liu, Michael Wang, Duy Duong-Tran, Uttara Tipnis, Enrico Amico, Alan D. Kaplan, Mario Dzemidzic, David Kareken, Beau M. Ances, Jaroslaw Harezlak, Joaquín Goñi

Research output: Contribution to journalArticlepeer-review

Abstract

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

Original languageEnglish
Article number107624
JournaliScience
Volume26
Issue number9
DOIs
StatePublished - Sep 15 2023

Keywords

  • Biological sciences
  • Phenotyping

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