TY - JOUR
T1 - Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity
AU - Demeter, Damion V.
AU - Engelhardt, Laura E.
AU - Mallett, Remington
AU - Gordon, Evan M.
AU - Nugiel, Tehila
AU - Harden, K. Paige
AU - Tucker-Drob, Elliot M.
AU - Lewis-Peacock, Jarrod A.
AU - Church, Jessica A.
N1 - Funding Information:
This research project was supported by National Institutes of Health grants P50 HD052117 (overall PI: Jack M. Fletcher, subaward to J.A.C.), R21 HD081437 (J.A.C., E.M.T.-D.). Additional datapoints were contributed from the Developmental Cognitive Neuroscience Lab through a Brain and Behavior Research Foundation NARSAD Young Investigator award (J.A.C), start-up funds to J.A.C. (University of Texas), and a Pilot grant from the Biomedical Imaging Center at the University of Texas at Austin ( 20141031a ). The authors would like to thank Jack M. Fletcher for spearheading the Texas Center for Learning Disabilities (TCLD), Jenifer Juranek for heading the Houston TCLD neuroimaging team, as well as to the broader TCLD team in Austin and Houston, TX (the TCLD contributed data to the repeat pediatric samples); the Human Connectome Project and the Midnight Scan Club for their significant contributions to open science; Mary Abbe Roe, Mackenzie Mitchell, Annie Zheng, Leonel Olmedo, Joel Martinez, and Lauren Deschner for their contribution to scan data collection; and all the participating families for their time and contribution to research.
Funding Information:
This research project was supported by National Institutes of Health grants P50 HD052117 (overall PI: Jack M. Fletcher, subaward to J.A.C.), R21 HD081437 (J.A.C. E.M.T.-D.). Additional datapoints were contributed from the Developmental Cognitive Neuroscience Lab through a Brain and Behavior Research Foundation NARSAD Young Investigator award (J.A.C), start-up funds to J.A.C. (University of Texas), and a Pilot grant from the Biomedical Imaging Center at the University of Texas at Austin (20141031a). The authors would like to thank Jack M. Fletcher for spearheading the Texas Center for Learning Disabilities (TCLD), Jenifer Juranek for heading the Houston TCLD neuroimaging team, as well as to the broader TCLD team in Austin and Houston, TX (the TCLD contributed data to the repeat pediatric samples); the Human Connectome Project and the Midnight Scan Club for their significant contributions to open science; Mary Abbe Roe, Mackenzie Mitchell, Annie Zheng, Leonel Olmedo, Joel Martinez, and Lauren Deschner for their contribution to scan data collection; and all the participating families for their time and contribution to research. Conceptualization, D.V.D. J.A.C. and J.A.L.-P.; Data Curation, L.E.E. T.N. and D.V.D.; Formal Analysis and Visualization, D.V.D.; Methodology, D.V.D. J.A.C. J.A.L.-P. L.E.E. R.M. and T.N.; Software, D.V.D. R.M. and E.M.G.; Consultation, K.P.H. and E.M.T.-D. Writing ? Original Draft, D.V.D. and J.A.C.; Writing ? Review & Editing, D.V.D. J.A.C. J.A.L.-P. L.E.E. R.M. E.M.G. T.N. K.P.H. and E.M.T.-D.; Supervision, J.A.C. and J.A.L.-P.; Funding Acquisition, J.A.C. and E.M.T.-D. The authors declare no competing interests.
Publisher Copyright:
© 2019 The Author(s)
PY - 2020/1/24
Y1 - 2020/1/24
N2 - 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.
AB - 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.
KW - Biological Sciences
KW - Computational Bioinformatics
KW - Neuroscience
UR - http://www.scopus.com/inward/record.url?scp=85077921092&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2019.100801
DO - 10.1016/j.isci.2019.100801
M3 - Article
C2 - 31958758
AN - SCOPUS:85077921092
SN - 2589-0042
VL - 23
JO - iScience
JF - iScience
IS - 1
M1 - 100801
ER -