摘要：SummaryDistinguishing 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.Graphical AbstractDisplay OmittedHighlights•High-order functional brain networks play key role in functional fingerprints•Functional fingerprints are stable, even identifying pairs scanned months apart•Features of functional fingerprints are highly stable between children and adults•Classifier accuracy decreases as genetic similarity decreasesBiological Sciences; Neuroscience; Computational Bioinformatics
