Abstract #3989

Behavioral performance prediction in aging with advanced resting-state imaging acquisitions

Scott James Peltier^1,2, Michelle Karker², Bruno Giordani³, Henry Paulson³, and Benjamin M Hampstead^4,5

¹Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ²Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ³Neurology, University of Michigan, Ann Arbor, MI, United States, ⁴Psychiatry, University of Michigan, Ann Arbor, MI, United States, ⁵Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States

Multi-band resting state data using two different protocols was collected in older subjects. Connectome-based predictive modeling yielded significant fits for measures of learning, memory, and language; with higher spatiotemporal sampling being more sensitive.

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