Task switching reveals abnormal brain-heart electrophysiological signatures in cognitively healthy individuals with abnormal CSF amyloid/tau, a pilot study

Fig. 1.. Task switching testing paradigms. Each trial includes two sequential stimuli. Each stimulus is incongruent colored word. Participants were requested to respond to the word itself (no-underline), or to the color of the ink (underlined), by press a button (‘1’ for red, ‘2’ for green). Tasks include random mixture of low load repeat trials (A) or high load switch trials (B).

Fig. 2.. Behavioral responses of task switching. A) The accuracy (ACC) was not different between CH-NATs and CH-PATs. ACC during high load switch trials was lower than during low load repeat trials, in both groups. B) The response time (RT) was not different between CH-NATs and CH-PATs. RT during high load switch trials was longer than during low load repeat trials, in both groups.

Fig. 3.. Summary of effect sizes by Cohen’s d. Effect sizes for all comparisons were ranked including all combinations of: trial types (wW (3-ww), cC (4-cc), cW (5-cw), or wC (6-wc)), alpha power (low alpha (2-Low) or high alpha (1-High)), regions (F (f), C (c), P (p), LT (l), RT (r), or O (o)). Cohen’s d values were indicated at the top of each combination.

Fig. 4.. HRV comparisons between resting state and task switching in CH-NATs and CH-PATs. A) RR interval were reduced during the task than resting with medium effect size in CH-PATs, but not in CH-NATs; B) LF were reduced only in CH-PATs.

Fig. 5.. Machine learning analysis for the task switching challenge suggests HRV improves qEEG classification of CH-NATs and CH-PATs. A) Low alpha ERD on frontal (F), central (C), parietal (P), left temporal (LT), right temporal (RT), and occipital (O) regions were listed during repeat trials (r) and switch trials (s), and six low alpha features (Cr, Pr, LTr, Or, LTs, RTs) were considered brain features; B) HRV measures were listed during resting state (R) and task switching (TS) and nine HRV features (MeanRR(R), SDNN(R), MeanHR(R), LF (R), MeanRR(TS), SDNN(TS), MeanHR(TS), LF/HF(TS), HR(R)) were considered heart features; C) using two-layer fully-connected neural network, HRV can improve brain features’ test accuracy from 0.64 ± 0.04 to 0.71 ± 0.04. On the other hand, the heart feature can also achieve 0.62 ± 0.04 accuracy. The experiment is conducted 50 times with random split of training (80%) and testing (20%) data.