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
Affiliations
Kausar Abbas
Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Mintao Liu
Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Michael Wang
Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Duy Duong-Tran
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
Uttara Tipnis
Lawrence Livermore National Laboratory, Livermore, CA, USA
Enrico Amico
Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
Alan D. Kaplan
Lawrence Livermore National Laboratory, Livermore, CA, USA
Mario Dzemidzic
Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
David Kareken
Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
Beau M. Ances
Department of Neurology, Washington University in Saint Louis, School of Medicine, St Louis, MO, USA
Jaroslaw Harezlak
Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
Joaquín Goñi
Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA; Corresponding author
Summary: 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).
