Aurreko ekintzak: Joaquin Goñi. Tangent functional connectomes uncover more unique phenotypic traits

What: Tangent functional connectomes uncover more unique phenotypic traits

Where:  BCBL Auditorium and zoom room # 2 (If you would like to attend to this meeting reserve at info@bcbl.eu)

Who: Joaquin Goñi, PhD, Associate Professor, School of Industrial Engineering, Weldon School of Biomedical Engineering, Purdue University, US

When:  Monday,  May 29th at 12:00 PM Noon

Functional connectomes (FCs) contain pairwise estimations of functional couplings based on pairs of brain regions activity derived from fMRI BOLD signals. FCs are commonly represented as correlation matrices that are symmetric positive definite (SPD) matrices lying on or inside the SPD manifold. Since the geometry on the SPD manifold is non-Euclidean, the inter-related entries of FCs undermine the use of Euclidean-based distances and its stability when using them as features in machine learning algorithms. By projecting FCs into a tangent space, we can obtain tangent functional connectomes (tangent-FCs), whose entries would not be inter-related, and thus, allow the use of Euclidean-based methods. Tangent-FCs have shown a higher predictive power of behavior and cognition, but no studies have evaluated the effect of such projections with respect to fingerprinting.  
In this work, we hypothesize that tangent-FCs have a higher fingerprint than “regular” (i.e., no tangent-projected) FCs. Fingerprinting was measured by identification rates (ID rates) using the standard test-retest approach as well as incorporating monozygotic and dizygotic twins. We assessed: (i) Choice of the Reference matrix Cref. Tangent projections require a reference point on the SPD manifold, so we explored the effect of choosing different reference matrices. (ii) Main-diagonal Regularization. We explored the effect of weighted main diagonal regularization1. (iii) Different fMRI conditions. We included resting state and seven fMRI tasks, (iv) Parcellation granularities from 100 to 900 cortical brain regions (plus subcortical), (v) Different distance metrics. Correlation and Euclidean distances were used to compare regular FCs as well as tangent-FCs. (vi) fMRI scan length on resting state and when comparing task-based versus (matching scan length) resting-state fingerprint.
Our results showed that identification rates are systematically higher when using tangent-FCs. Specifically, we found: (i) Riemann and log-Euclidean matrix references systematically led to higher ID rates for all configurations assessed. (ii) In tangent-FCs, Main-diagonal regularization prior to tangent space projection was critical for ID rate when using Euclidean distance, whereas barely affected ID rates when using correlation distance. (iii) ID rates were dependent on condition and fMRI scan length. (iv) Parcellation granularity was key for ID rates in FCs, as well as in tangent-FCs with fixed regularization, whereas optimal regularization of tangent-FCs mostly removed this effect. (v) Correlation distance in tangent-FCs outperformed any other configuration of distance on FCs or on tangent-FCs across the “fingerprint gradient” (here sampled by assessing test-retest, Monozygotic twins and Dizygotic twins). (vi) ID rates tended to be higher in task scans compared to resting-state scans when accounting for fMRI scan length.
In summary, we posit that FCs, when projected to a tangent space, display more unique phenotypic traits, and thus have greater potential for developing clinical biomarkers based on brain functional connectivity.