Can motor function uncertainty and local instability within upper-extremity dual-tasking predict amnestic mild cognitive impairment and early-stage Alzheimer's disease?. (May 2020)

• Record Type: Journal Article
• Authors: Ehsani, Hossein; Parvaneh, Saman; Mohler, Jane; Wendel, Christopher; Zamrini, Edward; O'Connor, Kathy; Toosizadeh, Nima
• Is Part Of: Computers in biology and medicine. Volume 120(2020)
• Publication Date: 2020-05
• Subjects: Wearable motion sensors -- Largest Lyapunov exponent -- Shannon entropy -- MCI -- Early detection -- Executive function -- Motor control -- Computer modeling -- Nonlinear dynamical systems -- Biomechanics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103705
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this study, we examined the uncertainty and local instability of motor function for cognitive impairment screening using a previously validated upper-extremity function (UEF). This approach was established based upon the fact that elders with an impaired executive function have trouble in the simultaneous execution of a motor and a cognitive task (dual-tasking). Older adults aged 65 years and older were recruited and stratified into 1) cognitive normal (CN), 2) amnestic MCI of the Alzheimer's type (aMCI), and 3) early-stage Alzheimer's Disease (AD). Participants performed normal-paced repetitive elbow flexion without counting and while counting backward by ones and threes. The influence of cognitive task on motor function was measured using uncertainty (measured by Shannon entropy), and local instability (measured by the largest Lyapunov exponent) of elbow flexion and compared between cognitive groups using ANOVAs, while adjusting for age, sex, and BMI. We developed logistic ordinal regression models for predicting cognitive groups based on these nonlinear measures. A total of 81 participants were recruited, including 35 CN (age = 83.8 ± 6.9), 30 aMCI (age = 83.9 ± 6.9), and 16 early AD (age = 83.2 ± 6.6). Uncertainty of motor function demonstrated the strongest associations with cognitive impairment, with an effect size of 0.52, 0.88, and 0.51 for CN vs. aMCI, CN vs. AD, and aMCI vs. AD comparisons, respectively. Ordinal logistic models predicted cognitive impairment (aMCI and AD combined) with a sensitivity and specificity of 0.82. The findings accentuate the potential of employing nonlinear dynamical features of motor functions during dual-tasking, especially uncertainty, in detecting cognitive impairment. Highlights: A quick and objective memory screening tool is essential for the early detection of mild cognitive impairment. Dual-task assessment within upper-extremity differentiates between cognitive aging groups of normal, MCI, and early AD. Motor function uncertainty, as measured by Shannon entropy, increases with the level of cognitive impairment. Motor function instability, as measured by the largest Lyapunov exponent, increases with the level of cognitive impairment. We use motor function uncertainty and conventional variability measures to detect cognitive impairment with high accuracy.