Functional connectivity associated with gait velocity during walking and walking-while-talking in aging: a resting-state fMRI study

Abstract

Gait decline is common among older adults and is a risk factor for adverse outcomes. Poor gait performance in dual-task conditions, such as walking while performing a secondary cognitive interference task, is associated with increased risk of frailty, disability, and death. Yet, the functional neural substrates that support locomotion are not well established. We examined the functional connectivity associated with gait velocity in single- (normal pace walking) and dual-task (walking while talking) conditions using resting-state functional Magnetic Resonance Imaging (fMRI). We acquired 6 minutes of resting-state fMRI data in 30 cognitively healthy older adults. Independent components analyses were performed to separate resting-state fMRI data into group-level statistically independent spatial components that correlated with gait velocity in single- and dual-task conditions. Gait velocity in both task conditions was associated with similar functional connectivity in sensorimotor, visual, vestibular, and left fronto-parietal cortical areas. Compared to gait velocity in the single-task condition, the networks associated with gait velocity in the dual-task condition were associated with greater functional connectivity in supplementary motor and prefrontal regions. Our findings show that there are partially overlapping functional networks associated with single- and dual-task walking conditions. These initial findings encourage the future use of resting-state fMRI as tool in developing a comprehensive understanding of age-related mobility impairments.

Keywords: aging; dual-task; fMRI; gait; resting-state.

Figure 1

Resting‐state networks associated with NW gait velocity (A) and WWT gait velocity (B). This figure shows the three most informative axial, sagittal, and coronal slices of each resting‐state network superimposed on the Montreal Neurologic Institute (MNI) template supplied by MRIcron software. The left side of the image corresponds to the left side of the brain. All ICA spatial maps were converted to z statistic images via a normalized mixture‐model fit, and then thresholded at z = 2.30.

Figure 2

A: Overlay of the sensorimotor resting‐state networks associated with NW (red) and WWT (blue) on the same template. B: Overlay of the left fronto‐parietal resting‐state networks associated with NW (red) and WWT (blue) on the same template. The purple color represents the areas in which the networks overlap. The blue color shows where the WWT networks extend beyond the NW networks.