Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
Damion V. Demeter,
Laura E. Engelhardt,
Remington Mallett,
Evan M. Gordon,
Tehila Nugiel,
K. Paige Harden,
Elliot M. Tucker-Drob,
Jarrod A. Lewis-Peacock,
Jessica A. Church
Affiliations
Damion V. Demeter
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Corresponding author
Laura E. Engelhardt
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
Remington Mallett
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
Evan M. Gordon
VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX 76711, USA; Center for Vital Longevity, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX 75235, USA; Department of Psychology and Neuroscience, Baylor University, Waco, TX 76789, USA
Tehila Nugiel
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
K. Paige Harden
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Population Research Center, The University of Texas at Austin, Austin, TX 78712, USA
Elliot M. Tucker-Drob
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Population Research Center, The University of Texas at Austin, Austin, TX 78712, USA
Jarrod A. Lewis-Peacock
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Biomedical Imaging Center, The University of Texas at Austin, Austin, TX 78712, USA
Jessica A. Church
Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Biomedical Imaging Center, The University of Texas at Austin, Austin, TX 78712, USA
Summary: Distinguishing individuals from brain connectivity, and studying the genetic influences on that identification across different ages, improves our basic understanding of functional brain network organization. We applied support vector machine classifiers to two datasets of twins (adult, pediatric) and two datasets of repeat-scan individuals (adult, pediatric). Classifiers were trained on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) data and used to predict individuals and co-twin pairs from independent data. The classifiers successfully identified individuals from a previous scan with 100% accuracy, even when scans were separated by months. In twin samples, classifier accuracy decreased as genetic similarity decreased. Our results demonstrate that classification is stable within individuals, similar within families, and contains similar representations of functional connections over a few decades of life. Moreover, the degree to which these patterns of connections predict siblings' data varied by genetic relatedness, suggesting that genetic influences on rs-fcMRI connectivity are established early in life. : Biological Sciences; Neuroscience; Computational Bioinformatics Subject Areas: Biological Sciences, Neuroscience, Computational Bioinformatics
