Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

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, ped...

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Main Authors:	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
Format:	Article
Language:	English
Published:	Elsevier 2020-01-01
Series:	iScience
Online Access:	http://www.sciencedirect.com/science/article/pii/S2589004219305474

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spelling	doaj-2478cdd2223149c4bd361481e2d921902020-11-25T02:56:45ZengElsevieriScience2589-00422020-01-01231Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic SimilarityDamion V. Demeter0Laura E. Engelhardt1Remington Mallett2Evan M. Gordon3Tehila Nugiel4K. Paige Harden5Elliot M. Tucker-Drob6Jarrod A. Lewis-Peacock7Jessica A. Church8Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Corresponding authorDepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USADepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USAVISN 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, USADepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USADepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Population Research Center, The University of Texas at Austin, Austin, TX 78712, USADepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Population Research Center, The University of Texas at Austin, Austin, TX 78712, USADepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Biomedical Imaging Center, The University of Texas at Austin, Austin, TX 78712, USADepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USA; Biomedical Imaging Center, The University of Texas at Austin, Austin, TX 78712, USASummary: 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 Bioinformaticshttp://www.sciencedirect.com/science/article/pii/S2589004219305474
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	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
spellingShingle	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 Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity iScience
author_facet	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
author_sort	Damion V. Demeter
title	Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
title_short	Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
title_full	Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
title_fullStr	Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
title_full_unstemmed	Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
title_sort	functional connectivity fingerprints at rest are similar across youths and adults and vary with genetic similarity
publisher	Elsevier
series	iScience
issn	2589-0042
publishDate	2020-01-01
description	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
url	http://www.sciencedirect.com/science/article/pii/S2589004219305474
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Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

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