In Cerebral cortex (New York, N.Y. : 1991)
Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.
Horien Corey, Greene Abigail S, Shen Xilin, Fortes Diogo, Brennan-Wydra Emma, Banarjee Chitra, Foster Rachel, Donthireddy Veda, Butler Maureen, Powell Kelly, Vernetti Angelina, Mandino Francesca, O’Connor David, Lake Evelyn M R, McPartland James C, Volkmar Fred R, Chun Marvin, Chawarska Katarzyna, Rosenberg Monica D, Scheinost Dustin, Constable R Todd
2022-Dec-27
fingerprinting, functional connectivity, individual differences, machine learning, predictive modeling
