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During childhood, neural systems supporting high-level cognitive processes undergo periods of rapid growth and refinement, which rely on the successful coordination of activation across the brain. Some coordination occurs via cortical hubs-brain regions that coactivate with functional networks other than their own. Adult cortical hubs map into three distinct profiles, but less is known about hub categories during development, when critical improvement in cognition occurs. We identify four distinct hub categories in a large youth sample (n = 567, ages 8.5-17.2), each exhibiting more diverse connectivity profiles than adults. Youth hubs integrating control-sensory processing split into two distinct categories (visual control and auditory/motor control), whereas adult hubs unite under one. This split suggests a need for segregating sensory stimuli while functional networks are experiencing rapid development. Functional coactivation strength for youth control-processing hubs are associated with task performance, suggesting a specialized role in routing sensory information to and from the brain's control system., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress., Competing Interests: Declaration of interests Anisha Keshavan is an employee of Octave Bioscience., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)