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Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer’s diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

The entorhinal–hippocampal circuit is one of the earliest sites of cortical pathology in Alzheimer's disease (AD). Visuospatial memory paradigms that are mediated by the entorhinal–hippocampal circuit may offer a means to detect memory impairment during the early stages of AD. In this study, we developed a 4-min visuospatial memory paradigm called VisMET (Visuospatial Memory Eye-Tracking Task) that passively assesses memory using eye movements rather than explicit memory judgements. We had 296 control or memory-impaired participants view a set of images followed by a modified version of the images with either an object removed, or a new object added. Healthy controls spent significantly more time viewing these manipulations compared to subjects with mild cognitive impairment and AD. Using a logistic regression model, the amount of time that individuals viewed these manipulations could predict cognitive impairment and disease status with an out of sample area under the receiver–operator characteristic curve of 0.85. Based on these results, VisMET offers a passive, sensitive, and efficient memory paradigm capable of detecting objective memory impairment and predicting cognitive and disease status.

Neural activity preceding an event can influence subsequent memory formation, yet the precise cortical dynamics underlying this activity and the associated cognitive states remain unknown. We investigate these questions here by examining intracranial EEG recordings as 28 participants with electrodes placed for seizure monitoring participated in a verbal paired-associates memory task. We found that, preceding successfully remembered word pairs, an orientation cue triggered a low-frequency 2–4 Hz phase reset in the right temporoparietal junction with concurrent increases in low-frequency power across cortical regions that included the prefrontal cortex and left temporal lobe. Regions that exhibited a significant increase in 2–4 Hz power were functionally bound together through progressive low-frequency 2–4 Hz phase synchrony. Our data suggest that the interaction between power and phase synchrony reflects the engagement of attentional networks that in large part determine the extent to which memories are successfully encoded.SIGNIFICANCE STATEMENTHere we investigate the spatiotemporal cortical dynamics that precede successful memory encoding. Using intracranial EEG, we observed significant changes in oscillatory power, intertrial phase consistency, and pairwise phase synchrony that predict successful encoding. Our data suggest that the interaction between power and phase synchrony reflects the engagement of attentional networks that in large part determine the extent to which memories are successfully encoded.

Episodic memory encoding refers to the cognitive process by which items and their associated contexts are stored in memory. To investigate changes directly attributed to the formation of explicit associations, we examined oscillatory power captured through intracranial electroencephalography (iEEG) as 27 neurosurgical patients receiving subdural and depth electrodes for seizure monitoring participated in a paired associates memory task. We examined low (3–8 Hz) and high (45–95 Hz) frequency activity, and found that the successful formation of new associations was accompanied by broad decreases in low frequency activity and a posterior to anterior progression of increases in high frequency activity in the left hemisphere. These data suggest that the observed patterns of activity may reflect the neural mechanisms underlying the formation of novel item-item associations.