Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5‐years apart. Issue 15 (11th July 2020) - Jalbrzikowski, Maria; Liu, Fuchen; Foran, William; Klei, Lambertus; Calabro, Finnegan J.; Roeder, Kathryn; Devlin, Bernie; Luna, Beatriz
Abstract: Pioneering studies have shown that individual correlation measures from resting‐state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as "connectotyping" or functional connectome "fingerprinting." We analyzed a unique dataset of 12–30 years old ( N = 140) individuals who had two distinct resting state scans on the same day and again 12–18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify one's own scan was high (average AUC = 0.94), although it was significantly higher in the same day (average AUC = 0.97) than 1.5‐years later (average AUC = 0.91). Accuracy in youths (average AUC = 0.93) was not significantly different from adults (average AUC = 0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set ( N = 208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of one's "fingerprint" contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of one's connectome. Abstract : The ability to accurately identify an individual's resting state scan is similar in youths and adults. Identification accuracy of resting‐state functional magnetic resonance scans in both youths and adults is more accurate when scans are closer together in time. Predictive edges generalize across data sets and methods, providing a sparse feature set for identifying individual scans.