Benjamin Chiêm1,2, Kausar Abbas3,4, Enrico Amico5,6, Duy Anh Duong-Tran3,4, Frédéric Crevecoeur1,2, Joaquín Goñi3,4,7. 1. Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium. 2. Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium. 3. Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA. 4. School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA. 5. Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. 6. Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland. 7. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.
Abstract
Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine. Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks. Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space. Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected subnetworks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.
Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine. Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks. Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space. Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected subnetworks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.
Authors: Benjamin A Seitzman; Caterina Gratton; Timothy O Laumann; Evan M Gordon; Babatunde Adeyemo; Ally Dworetsky; Brian T Kraus; Adrian W Gilmore; Jeffrey J Berg; Mario Ortega; Annie Nguyen; Deanna J Greene; Kathleen B McDermott; Steven M Nelson; Christina N Lessov-Schlaggar; Bradley L Schlaggar; Nico U F Dosenbach; Steven E Petersen Journal: Proc Natl Acad Sci U S A Date: 2019-10-14 Impact factor: 11.205
Authors: James M Shine; Michael Breakspear; Peter T Bell; Kaylena A Ehgoetz Martens; Richard Shine; Oluwasanmi Koyejo; Olaf Sporns; Russell A Poldrack Journal: Nat Neurosci Date: 2019-01-21 Impact factor: 24.884