On fingerprinting and functional reconfiguration of functional connectomes

Welcome to our ABC Seminars! This seminar series is open for everyone. The talk will take place in Ekonominaukio 1, Room R038/V001. After the talks, coffee and pulla will be served.

The event will be also streamed via Zoom at: https://aalto.zoom.us/j/67444945844

Title: 

On fingerprinting and functional reconfiguration of functional connectomes.

Abstract: 

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.   

I will also introduce our ongoing work on task-to-rest and rest-to-task functional reconfiguration based on tangent space projections of functional connectivity. 

Bio: 

Joaquín Goñi, Ph.D: I earned my Ph.D at University of Navarra in 2008 (co-supervised by Dr. Sergio Ardanza Trevijano and Dr. Pablo Villoslada Díaz. I did my first postdoc at the Center for Applied Medical Research (2008-2011; supervised by Dr. María Asunción Pastor) and a second postdoc at Indiana University (2011-2014; supervised by Dr. Olaf Sporns). After one year at the IU Network Science Institute, I joint Purdue as an Assistant Professor in 2015. Since 2022,  I am an Associate Professor with tenure at the School of Industrial Engineering and the Weldon School of Biomedical Engineering at Purdue University. I am the head of the CONNplexity Lab. I am also a member of the Purdue Institute for Integrative Neuroscience and the Purdue Systems Collaboratory.  

I keep an Adjunct position as Associate Research Professor of Radiology and Imaging Sciences at the Indiana University School of Medicine, where I have several ongoing collaborations. The most important topics of my research are: mapping behavior and cognition into brain connectivity, measuring and understanding functional reconfiguration task-to-rest and rest-to-task, developing data driven methods to uncover functional connectivity fingerprinting. MIT press.