Neural markers of impending task performance

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PROJECT ABSTRACT The neural mechanisms underlying successful cognitive performance are not well understood. In this R03 project, we will thoroughly investigate the ability to predict behavioral improvements through changes in shared underlying neural mechanisms. Identifying neural metrics that predict individual performance gains will ensure more reliable behavioral outcomes across diverse populations with varying cognitive capabilities and age ranges. Additionally, understanding the neural mechanisms underlying successful cognition will allow for rescuing of lost cognitive abilities and prophylaxis of cognitive decline in aging and other vulnerable populations such as in mild cognitive impairment (MCI). We recently identified several signals in the electroencephalogram (EEG) that correlated with cognitive performance improvements following and intervention with cognitive training and/or noninvasive neurostimulation. Specifically, frontal midline power in the theta band (4-8 Hz) correlates with improved divided attention ability and transfer to improved sustained attention ability in older adults (Anguera et al., 2013), and frontoparietal connectivity in the theta band is linked to improved attention (Anguera et al., 2013) and working memory (Jones et al., 2017). In addition to band- limited theta, we found that cross-frequency coupling between frontal theta oscillations and temporo-parietal gamma (>30 Hz) activity tracked individual performance gains in working memory following intervention with training and neurostimulation (Jones et al., 2020). These findings suggest that theta oscillations, as measured by power, connectivity, and cross-frequency coupling in task-relevant regions, underlie individual differences in cognitive performance. Indeed, the application of noninvasive neurostimulation confirmed the critical role of theta oscillations in divided attention by showing that direct entrainment of frontal theta oscillations further enhanced performance (Hsu et al., 2017, 2018). The proposed research will determine the mechanisms by which theta oscillations, assessed at baseline, predict subsequent cognitive performance outcomes in aging and vulnerable populations. Critically, we identified preliminary evidence that an individual's intrinsic peak theta frequency predicted the efficacy of theta entrainment with neurostimulation on divided attention ability. This important preliminary result demonstrates that a nuanced approach to tailoring an intervention to the individual is feasible. Here, our analyses seek to predict subsequent performance during divided attention prior to intervention (Aim 1) and before trial onset in a range of cognitive tasks (Aim 2). We will conduct comprehensive regularized multiple regression and deep learning analyses on 13 existing EEG datasets, several of which share the same divided attention task, yet vary in participant demographics, including age and cognitive capability (healthy older and younger adults, multi-domain amnestic MCI patients), to develop a predictive model for an individually-tailored cognitive intervention. The results will inform future research that seeks to maximize the reliability of intervention protocols across demographics at any age or cognitive capability.