Brain function complexity during dual-tasking is associated with cognitive impairment and age

Abstract

Background and purpose: Early diagnosis of cognitive impairment is important because symptoms can be delayed through therapies. Synaptic disconnections are the key characteristics of dementia, and through nonlinear complexity analysis of brain function, it is possible to identify long-range synaptic disconnections in the brain.

Methods: We investigated the capability of a novel upper-extremity function (UEF) dual-task paradigm in the functional MRI (fMRI) setting, where the participant flexes and extends their arm while counting, to differentiate between cognitively normal (CN) and those with mild cognitive impairment (MCI). We used multiscale entropy (MSE) complexity analysis of the blood oxygen-level dependent time-series across neural networks and brain regions. Outside of the fMRI, we used the UEF dual-task test, while the elbow kinematics were measured using motion sensors, to record the motor function score.

Results: Results showed 34% lower MSE values in MCI compared to CN (p<.04 for all regions and networks except cerebellum when counting down by one; effect size = 1.35±0.15) and a negative correlation between MSE values and age (average r² of 0.30 for counting down by one and 0.36 for counting backward by three). Results also showed an improvement in the logistic regression model sensitivity by 14-24% in predicting the presence of MCI when brain function measure was added to the motor function score (kinematics data).

Conclusions: Current findings suggest that combining measures of neural network and motor function, in addition to neuropsychological testing, may provide an accurate tool for assessing early-stage cognitive impairment and age-related decline in cognition.

Keywords: Alzheimer's disease; aging; dual-task function; entropy; fMRI; repeatability.

Figure 1:

Flow chart demonstrating pre-processing steps in SPM12 and the customized MATLAB program for processing fMRI data. Our dual-task paradigm consisted of 30 s (N=15 data points) blocks for rest, counting (cognitive task), flexion (motor task), and counting + flexion (dual-task) conditions. Two trials resulted in two whole time series (N = number of data points, 180 data points each, for a time (t) of 260 second (s)), which underwent complexity analysis. Cog 1: counting down by ones; and Cog 2: counting down by threes; CN: cognitively normal; MCI: mild cognitive impairment; SMP: statistical parametric mapping; DICOM: digital imaging and communications in medicine; NIfTI: neuroimaging informatics technology initiative; MNI: Montreal Neurological Institute; SampEn: sample entropy; MSE: multiscale entropy; Hz: Hertz

Figure 2:

Spearman correlation between age (in years) and multiscale entropy (MSE scale 6) values across brain regions for Cog 1 of counting by ones (A-D) and Cog 2 of counting backward by threes (E-H). p-value cutoff is 0.05.

Figure 3:

Results from univariate analysis of variance models for differences in multiscale entropy (MSE Scale 6) between cognitive groups across brain regions (top) and neural networks (bottom). A significant between-group (CN versus MCI) difference is indicated with the asterisk (* for p<0.05 and ** for p<0.01). Cog 1: counting down by ones; and Cog 2: counting down by threes; CN: cognitively normal; and MCI: mild cognitive impairment.

Figure 4:

Spearman correlation between age (in years) and multiscale entropy (MSE scale 6) values across neural networks for Cog 1 of counting by ones (A-D) and Cog 2 of counting backward by threes (E-H). p-value cutoff is 0.05.