Abstract #4987

Brain-behavior prediction using functional connectivity from older adults with mild cognitive impairment

Michelle Karker^1,2, Douglas Noll^1,2,3, Benjamin M. Hampstead^4,5, and Scott Peltier^1,2

¹Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ³Radiology, University of Michigan, Ann Arbor, MI, United States, ⁴Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States, ⁵Research Program on Cognition and Neuromodulation Based Interventions, Psychiatry, University of Michigan, Ann Arbor, MI, United States

Synopsis

Partial least squares regression with feature selection (PLS-BETA) was applied to task-based and resting-state connectivity data. Leveraging a MCI-relevant object-location or face-name task illuminates relationships with measures of total cognition (RBANS_total) and memory (RBANS_delayed). This provides support for the use of clinically-relevant tasks in cases where a “driven” connectivity network may elucidate pathological changes.

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