key: cord-0319527-jm9prbl7
authors: Hupfeld, KE; McGregor, HR; Hass, CJ; Pasternak, O; Seidler, RD
title: Sensory system-specific associations between brain structure and balance
date: 2022-01-20
journal: bioRxiv
DOI: 10.1101/2022.01.17.476654
sha: 71a8aed1207b7343ef8041a1d833d37d6dd152cb
doc_id: 319527
cord_uid: jm9prbl7

Nearly 75% of older adults in the United States report balance problems. Balance difficulties are more pronounced during sensory feedback perturbation (e.g., standing with the eyes closed or on foam). Although it is known that aging results in widespread brain atrophy, less is known about how brain structure relates to balance performance under varied sensory conditions in older age. We measured postural sway of 36 young (18-34 years) and 22 older (66-84 years) adults during four conditions: eyes open, eyes closed, eyes open on foam, and eyes closed on foam. We calculated three summary measures indicating visual, proprioceptive, and vestibular contributions to balance. We also collected T 1-weighted and diffusion-weighted anatomical MRI scans. We aimed to: 1) test for age group differences in brain structure-balance relationships across a range of structural brain measures (i.e., volumetric, surface, and white matter microstructure); and 2) assess how brain structure measures relate to balance, regardless of age. 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 for balance. Poorer vestibular function was correlated with thinner vestibular cortex, greater gyrification within sensorimotor, parietal, and frontal cortices, and lower free water-corrected axial diffusivity in the superior-posterior corona radiata and across the corpus callosum. These results contribute to our scientific understanding of how individual differences in brain structure relate to balance. This has implications for developing brain stimulation interventions to improve balance. Significance Statement Older age is associated with greater postural sway, particularly when sensory information is perturbed (e.g., by closing one’s eyes). Our work contributes to the field by identifying how individual differences in regional brain structure relate to balance under varying sensory conditions in young and older adults. Across both age groups, lower cortical thickness in sensory integration and vestibular regions, greater gyrification within sensorimotor, parietal, and temporal regions, and lower free water-corrected axial diffusivity in the corpus callosum and corona radiata were related to individual differences in balance scores. We identified brain structures that are associated with specific sensory balance scores; therefore, these results have implications for which brain regions to target in future interventions for different populations.

lated 25 spatiotemporal features of postural sway using the iSway algorithm (Mancini et al., 159 2012). We then calculated three summary scores using the 95% ellipse sway area (m 2 /s 4 ) vari-160 able (i.e., the area of an ellipse covering 95% of the sway trajectory in the coronal and sagittal 161 planes) from each of the four conditions (Fig. 1) . Greater postural sway is interpreted as "worse" 162 standing balance performance (Dewey et al., 2020), as greater postural sway is typically higher 163 for older compared with young adults (e.g., Abrahamova and Hlavačka, 2008; Colledge et al., 164 1994; Røgind et al., 2003) and is linked to higher risk of falls (e.g., Laughton et al., 2003; Maki 165 et al., 1994) . 166 The visual reliance score represents the percent change in postural sway between the eyes closed and the eyes open conditions (considering the foam and firm surface conditions independently and taking the minimum score of the two). Higher scores indicate more difficulty standing still in the absence of visual input. A higher visual reliance score is the result of poorer performance (i.e., more postural sway) during the eyes closed conditions and/or better performance on the eyes open conditions (i.e., less postural sway). Thus, a higher score suggests that the individual is more "reliant" on visual input for balance. The vestibular function score represents the percent change between the ECF and EO conditions. Higher scores indicate more difficulty standing still when only vestibular input is appropriate and visual / proprioceptive inputs are compromised. Contrary to the scores described above (which represent reliance on visual and proprioceptive inputs, respectively), higher scores here indicate poorer vestibular function (Dewey et al., 2020).

V estibular F unction Score = ( ECF − EO EO ) * 100

These formulas represent those recommended by APDM (the IMU company) for calculat- 167 ing mCTSIB summary scores (for further details, see: https://support.apdm.com/hc/en-168 us/articles/217035886-How-are-the-ICTSIB-composite-scores-computed-). For simplicity 169 and to keep with prior literature (Goble et al., 2019 , 2020), we will use the interpretation of 170 higher visual and proprioceptive scores indicating more "reliance" on these two sensory sys-171 tems for balance. However, it is worth noting that this interpretation might be oversimplified. 172 These scores may also index sensory reweighting and integration more so than reliance on a 173 single sensory modality (Kalron, 2017) . We expand on this in the Discussion. 175 We used a Siemens MAGNETOM Prisma 3 T scanner (Siemens Healthcare, Erlangen, 176 Germany) with a 64-channel head coil to collect T 1 -weighted and diffusion-weighted scans for 177 each participant. We collected the 3D T 1 -weighted anatomical image using a magnetization- T 1 -Weighted Image Processing for Voxelwise Analyses 188 We used the same T 1 -weighted processing steps as described in our previous work ( mutations. This toolbox provides non-parametric estimation using TFCE for models previously 303 estimated using SPM parametric designs. Statistical significance was determined at p < 0.05 304 (two-tailed) and family-wise error (FWE) corrected for multiple comparisons. In each of the be-305 low models, we set the brain structure map as the outcome variable. In the gray matter volume 306 models only, we set the absolute masking threshold to 0.1 (Gaser and Kurth, 2017) and used 307 an explicit gray matter mask that excluded the cerebellum (because we analyzed cerebellar 308 volume separately from "whole brain" gray matter volume).

Age group differences in brain structure 310 We previously reported the results of two-sample t-tests for age group differences in brain 311 structure (Hupfeld et al., 2021a).

Interaction of age group and balance scores 313 First, we tested for regions in which the relationship between brain structure and balance 314 performance differed between young and older adults. We ran independent samples t-tests 315 and included the balance scores for young and older adults as covariates of interest. We tested 316 for regions in which the correlation between brain structure and balance performance differed 317 between young and older adults (i.e., for statistical significance in the interaction term). We con-318 trolled for sex in all models and also for head size (i.e., total intracranial volume, as calculated 319 by CAT12) in the gray matter and cerebellar volume models. 320 Next, we conducted a linear regression omitting the age group*balance score interaction 322 term, to test for regions of association between brain structure and balance performance, re-323 gardless of age or sex. That is, in each of these models, we included the whole cohort and 324 controlled for age and sex (but did not include an age group predictor or interaction term). In 325 the gray matter and cerebellar volume models, we also controlled for head size.

326 Ventricular volume statistical models 327 We carried out linear models in R to test for relationships between ventricular volume and 328 balance, controlling for age and sex. We then ran linear models testing for an interaction be-329 tween age group and balance scores, controlling for sex. In each case, we FDR-corrected the 330 p values for the predictor of interest (i.e., balance score or the interaction term, respectively; 331 Benjamini and Hochberg, 1995). 333 We used a stepwise multivariate linear regression to directly compare the predictive strength 334 of the brain structure correlates of balance scores identified by the analyses described above. 335 We ran one model for the vestibular function scores (as the visual and proprioceptive reliance 336 scores did not produce more than one resulting brain structure measure). We included as pre-337 dictors age, sex and values from the peak result coordinate for each model that indicated a 338 statistically significant relationship between brain structure and vestibular function scores. We allowed us to fit the best model using brain structure to predict vestibular function scores.

There were no significant differences between the age groups for most demographic vari-346 ables, including sex, handedness, footedness, and alcohol use ( Table 1) 

Our recent publication provides a detailed report of age group differences in brain structure Note: In the second and third columns, we report the median ± interquartile range (IQR) for each age group in all cases except for sex. For sex, we report the number of males and females in each age group. In the fourth and fifth columns, for all variables except sex, we report the result of a nonparametric two-sample, two-sided Wilcoxon rank-sum test. For sex, we report the result of a Pearson's chi-square test for differences in the sex distribution within each age group. All participants with T 1 -weighted scans are included in the comparisons in this f Here we report the days between the testing sessions and the hours between the start time of the testing sessions. Across all brain structure metrics, there were no age differences in the relationship between 371 the balance scores and brain structure. That is, there was no interaction of age group and 372 balance scores; therefore, our second set of statistical analyses did not include an interaction 373 term and instead aimed to identify relationships between brain structure and balance scores 374 across the whole cohort (regardless of age). 375 There were no relationships between gray matter volume, cortical complexity, sulcal depth, 377 or cerebellar volume and balance performance across the whole cohort. Thinner cortex (i.e., 378 "worse" brain structure) within a region encompassing portions of the right cingulate gyrus (isth-379 mus), precuneus, and lingual gyrus was associated with higher visual reliance scores ( Fig. 3; 380 Table 3 ). That is, those individuals who had the thinnest cortex in these regions also showed and frontal cortices and precuneus was associated with higher proprioceptive reliance scores 391 ( Fig. 4; Table 4 ). That is, those individuals with the highest gyrification index in these regions 392 also showed the greatest increase in postural sway for conditions using the foam compared to Table 4 ). That is, those individuals who had the highest gyrification index in these regions also 397 exhibited the most postural sway during the ECF relative to the EO condition (indicating poorer 398 vestibular function). This relationship between "better" brain structure and worse vestibular 399 function is seemingly contradictory, though these resulting regions did not include the so-called Poorer vestibular function scores were also associated with lower ADt (i.e., typically inter- dictors except for sex. That is, the combination of these brain metrics and age (rather than any 418 given metric on its own) best predicted the vestibular function scores (i.e., produced the model 419 Note: Here we report the results of the stepwise multiple linear regression testing for the best model of vestibular balance scores. As diffusion-weighted results were included in this model, n = 35 young and 20 older adults. *p < 0.05, **p < 0.01, ***p < 0.001. Significant p values are bolded.

We identified age group differences for two of the three balance scores, i.e., higher pro-423 prioceptive reliance and poorer vestibular function scores for older adults. This indicates that, 424 compared with young adults, older adults rely more heavily on proprioceptive inputs for main-425 taining balance, and have poorer vestibular function. We also observed multiple significant 426 relationships between brain structure and balance scores. Thinner cortex (i.e., "worse" brain 427 structure) in regions related to multisensory integration correlated with greater reliance on vi-428 sual inputs for balance. Higher gyrification index (i.e., more "youth-like" brain structure) within and Kensinger, 2018); lower gyrification indices may indicate poorer regional brain structure, 490 i.e., less cortex buried within the sulcal folds (Luders et al., 2006) . Thus, it follows that lower 491 gyrification index in a region specifically related to processing lower limb somatosensory in-492 formation would be associated with less reliance on proprioceptive inputs for balance. As de-493 scribed above, it could be that poorer structure in the brain regions primarily associated with 494 processing one type of sensory information (e.g., proprioceptive) correlates with less reliance 495 on that system and more reliance on other systems (e.g., visual) for maintaining balance. with the poorest brain structure (i.e., the thinnest cortex) in these brain regions specifically re-510 lated to vestibular and multisensory processing also encounter the most difficulty standing with 511 minimal postural sway during a balance condition that specifically tasks the vestibular system. It is somewhat surprising that we did not identify age differences in the relationship between 539 brain structure and balance. One previous study reported relationships between brain structure 540 and balance for older but not younger adults (Van Impe et al., 2012). In our prior work on 541 this dataset (Hupfeld et al., 2021a) , we identified multiple relationships between brain structure 542 and dual task walking for older but not young adults. It is worth noting that this is a group of 543 high functioning older adults in relatively good health, thus, the balance tasks used here may not 544 have been sufficiently biomechanically challenging or cognitively-demanding for age differences 545 in brain-behavior relationships to emerge. If we had incorporated a secondary cognitive task, 546 perhaps we would have found age group differences. Performing a secondary cognitive task has 547 been found to disproportionately affect older adults (e.g., increasing sway variability by 5% for conditions. We did not examine other balance outcome variables, such as sway range or veloc-566 ity. Lastly, in the current acquisition protocol we had a single-shell diffusion sequence. Future 567 studies should consider a multi-shell sequence for a more robust estimation of the free water 568 fraction.

Conclusions and Future Directions 570 We identified relationships between regional brain structure (cortical thickness, gyrification 

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The authors wish to thank Aakash Anandjiwala, Justin Geraghty, Pilar Alvarez Jerez, and 603 Alexis Jennings-Coulibaly for their assistance in subject recruitment and data collection, as well