Characterizing biopsychological mismatch during cognitive training in mild cognitive impairment as a means of improving transfer

Characterizing biopsychological mismatch during cognitive training in mild cognitive impairment as a means of improving transfer ABSTRACT Cognitive training is a scalable, well-tolerated intervention for slowing cognitive decline and preventing Alzheimer’s Disease and Alzheimer’s Disease related dementias (AD/ADRD) with minimal side effects. Despite much promise, there have been mixed findings on strong, reliable transfer of cognitive training to non-trained domains in older adults at risk for AD/ADRD, including those with mild cognitive impairment (MCI), a critical pre-clinical stage for intervention. Transfer is hypothesized to occur when a mismatch between cognitive resources and task demands leads to increases in the efficiency with which existing cognitive resources can be applied to untrained tasks. However, few studies have attempted to quantify mismatch. The overall objective of this K01 award is to develop a new conceptual and operational framework for biopsychological mismatch in cognitive training in MCI, quantified as high mental energy and task absorption (using experience sampling) and autonomic adaptation (from electrocardiogram/ECG) both within and across training sessions. Using an existing speed of processing training (SOPT) dataset (Aim 1) with weekly measures of mental energy and autonomic adaptation we hypothesize that higher biopsychological mismatch across sessions will be associated with greater far transfer to executive function and episodic memory in MCI. We will also compare biopsychological mismatch in MCI with healthy controls. In a small pilot experiment (Aim 2) we will collect measures of mental energy, autonomic adaptation, and task absorption during SOPT sessions in a local community representative sample of Asian, Hispanic/Latino, and Non-Hispanic White older adults with MCI, and hypothesize that higher biopsychological mismatch will be associated with increased near transfer from pre- to post-session. We will also explore differences in biopsychological mismatch across racial/ethnic groups. This K01 award application will enhance the career of Dr. Adam Turnbull, a cognitive neuroscientist and young investigator with a strong research record in experience sampling (method) and cognitive aging (content), allowing him to become a lead investigator in slowing and preventing AD/ADRD by developing personalized, scalable non-pharmacological interventions (NPIs: e.g., cognitive training). The candidate will gain research skills in: 1) behavioral intervention trial design and analysis applying principles from the Science of Behavior Change and the NIH Stage Model, 2) novel biobehavioral measures and relevant signal processing, data harmonization, and computational modeling; and 3) intersections on biobehavioral and sociocultural research using a multi-dimensional framework. Dr. Vankee Lin, who was the candidate’s postdoc mentor with a strong track record and lab infrastructure for NPIs in AD/ADRD, will guide the candidate in establishing his independent research program at Stanford University. The candidate will also be mentored by Dr. Booil Jo (clinical trial design), Dr. Ehsan Adeli (machine learning), Dr. James Gross (psychophysiology/affect science), and Dr. Michelle Odden (aging and population health research).