Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons

Abstract

Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.

Loss of the APP regulator RHBDL4 preserves memory in an Alzheimer’s disease mouse model

“…The clinical diagnosis of Alzheimer’s dementia was uniform and structured and followed the NINCDS-ADRD criteria as reported (Bennett et al, 2006). Nineteen of the tests were z-scored and averaged for a global measure of cognition (Tasaki et al, 2023). The pathologic diagnosis of AD was made by NIA-Reagan criteria as previously described (Hyman et al, 2012).…”