Sensory system-specific associations between brain structure and balance

Abstract

Nearly 75% of older adults in the US report balance problems. Although it is known that aging results in widespread brain atrophy, less is known about how brain structure relates to balance in aging. We collected T₁- and diffusion-weighted MRI scans and measured postural sway of 36 young (18-34 years) and 22 older (66-84 years) adults during eyes open, eyes closed, eyes open-foam, and eyes closed-foam conditions. We calculated summary measures indicating visual, proprioceptive, and vestibular contributions to balance. Across both age groups, thinner cortex in multisensory integration regions was associated with greater reliance on visual inputs for balance. Greater gyrification within sensorimotor and parietal cortices was associated with greater reliance on proprioceptive inputs. Poorer vestibular function was correlated with thinner vestibular cortex, greater gyrification within sensorimotor, parietal, and frontal cortices, and lower free water-corrected axial diffusivity across the corona radiata and corpus callosum. These results expand scientific understanding of how individual differences in brain structure relate to balance and have implications for developing brain stimulation interventions to improve balance.

Keywords: Aging; Axial diffusivity; Balance; Cortical thickness; Free-water; Gray matter volume; Gyrification.

Fig. 1.

mCTSIB balance conditions. Participants completed 4 30-second trials: eyes open (EO), eyes closed (EC), eyes open-foam (EOF), and eyes closed-foam (ECF). Postural sway from each condition was used to calculate the 3 balance outcome variables, that is, the visual reliance, proprioceptive reliance, and vestibular function scores. The middle and bottom rows depict the sway path (black line) and area (blue oval) for each condition for representative young and older adult participants. These individuals generally showed greater postural sway as the conditions progressed.

Fig. 2.

Age group differences in balance composite scores. Balance scores are shown for the younger (orange) and older (blue) adults. The red arrows point in the direction of higher scores. Higher scores indicate a greater reliance on visual (A) and proprioceptive (B) inputs for maintaining standing balance, or poorer vestibular function (C).

Fig. 3.

Regions of correlation between cortical thickness and balance scores. Top. Regions showing statistically significant (p_FWE-corr < 0.05) relationships between cortical thickness and vision (left) and vestibular (right) balance scores across the whole cohort. These group-level results are overlaid onto CAT12 standard space tem-plates. Warmer colors indicate regions of stronger correlation. To aid visualization, the 2 vestibular function results clusters are magnified in the inset box and outlined in pink and yellow. L, left hemisphere; R, right hemisphere. Bottom. Mean cortical thickness values across one exemplar cluster are plotted against balance scores for each participant to illustrate examples of the relationships identified by the voxelwise statistical tests. The visual reliance model (A) yielded one significant cluster (k = 344 voxels) that overlapped with the right cingulate gyrus, precuneus, and lingual gyrus (Table 3), which is plotted here. The vestibular function model (B) yielded 2 significant clusters (Table 3). Here, mean cortical thickness is plotted for the cluster with the smallest p value, that is, the k = 188 voxels cluster, which overlapped both the left supramarginal gyrus (SMG) and left postcentral gyrus. For completeness, mean cortical thickness for the second (k = 55 voxels) left superior temporal sulcus cluster is plotted in Supplemental Fig. 1. The fit line and confidence interval shading are included only to aid visualization of these relationships. We plotted the residuals instead of the raw values here to adjust for the effects of the age and sex covariates included in each model.

Fig. 4.

Regions of correlation between gyrification index and balance scores. Top. Regions showing statistically significant (p_FWE-corr < 0.05) relationships between gyrification index and proprioceptive (A) and vestibular (B) balance scores across the whole cohort. These group-level results are overlaid onto CAT12 standard space templates. Warmer colors indicate regions of stronger correlation. L, left hemisphere; R, right hemisphere. Bottom. Mean gyrification index values across one exemplar cluster are plotted against balance scores for each participant to illustrate examples of the relationships identified by the voxelwise statistical tests. The proprioceptive reliance model (A) yielded 2 significant clusters (Table 4). Here, mean gyrification index is plotted for the cluster with the smallest p value, that is, the k = 2555 voxels cluster, which overlapped the left postcentral gyrus and parietal cortex, among other regions (Table 4). For completeness, mean gyrification index for the second (k = 800 voxels) cluster is plotted in Supplemental Fig. 2. The vestibular function model (B) yielded 3 significant clusters (Table 4). Here, mean gyrification index is plotted for the cluster with the smallest p value, that is, the k = 13,292 voxels cluster, which overlapped the left superior frontal gyrus (SFG) and parietal cortex, among other regions (Table 4). For completeness, mean gyrification index for the other 2 clusters is plotted in Supplemental Fig. 2. The fit line and confidence interval shading are included only to aid visualization of these relationships. We plotted the residuals instead of the raw values here to adjust for the effects of the age and sex covariates included in each model.

Fig. 5.

Regions of correlation between ADt and vestibular function scores. Left. Regions showing statistically significant (p_FWE-corr < 0.05) relationships between ADt and vestibular function scores across the whole cohort. These group-level results are shown on the FMRIB58 FA template with the group mean white matter skeleton (green) overlaid. Warmer colors indicate regions of stronger correlation. Right. To illustrate an example of the relationship identified by the voxelwise statistical test, mean ADt is plotted against vestibular function score for each participant for the cluster with the smallest p value, that is, the k = 630 voxels cluster, which overlapped the genu of the left corpus callosum (CC), among other regions (Table 5). As the model yielded 5 significant clusters (Table 5), for completeness, mean ADt for the remaining 4 clusters is plotted against vestibular function score in Supplemental Fig. 3. The fit line and confidence interval shading are included only to aid visualization of this relationship. We plotted the residuals instead of the raw values here to adjust for the effects of the age and sex covariates included in each model.