Tangent functional connectomes uncover more unique phenotypic traits

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

doi:10.1016/j.isci.2023.107624

Tangent functional connectomes uncover more unique phenotypic traits

Journal Article · Sat Aug 12 00:00:00 EDT 2023 · iScience

DOI:https://doi.org/10.1016/j.isci.2023.107624· OSTI ID:2471162

^[1]; Liu, Mintao ^[1]; Wang, Michael ^[1]; Duong-Tran, Duy ^[2]; Tipnis, Uttara ^[3]; ^[4]; ^[3]; Dzemidzic, Mario ^[5]; ^[5]; ^[6]; ^[7]; Goñi, Joaquín ^[1]

Purdue University, West Lafayette, IN (United States)
University of Pennsylvania, Philadelphia, PA (United States); United States Naval Academy, Annapolis, MD (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Ecole Polytechnique Federale de Lausanne (Switzerland); University of Geneva (UNIGE) (Switzerland)
Indiana University School of Medicine, Indianapolis, IN (United States)
Washington University, St. Louis, MO (United States)
Indiana University, Bloomington, IN (United States)

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).

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; National Institutes of Health (NIH); Indiana Alcohol Research Center; Swiss National Science Foundation (SNSF)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2471162

Journal Information:: iScience, Journal Name: iScience Journal Issue: 9 Vol. 26; ISSN 2589-0042

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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