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Neural Correlates of Gait Adaptation in Younger and Older Adults

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Journal Sci Rep
Specialty Science
Date 2023 Mar 8
PMID 36890163
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Abstract

Mobility decline is a major concern for older adults. A key component of maintaining mobility with advancing age is the ability to learn and adapt to the environment. The split-belt treadmill paradigm is an experimental protocol that tests the ability to adapt to a dynamic environment. Here we examined the magnetic resonance imaging (MRI) derived structural neural correlates of individual differences in adaptation to split-belt walking for younger and older adults. We have previously shown that younger adults adopt an asymmetric walking pattern during split-belt walking, particularly in the medial-lateral (ML) direction, but older adults do not. We collected T[Formula: see text]-weighted and diffusion-weighted MRI scans to quantify brain morphological characteristics (i.e. in the gray matter and white matter) on these same participants. We investigated two distinct questions: (1) Are there structural brain metrics that are associated with the ability to adopt asymmetry during split-belt walking; and (2) Are there different brain-behavior relationships for younger and older adults? Given the growing evidence that indicates the brain has a critical role in the maintenance of gait and balance, we hypothesized that brain areas commonly associated with locomotion (i.e. basal ganglia, sensorimotor cortex, cerebellum) would be associated with ML asymmetry and that older adults would show more associations between split-belt walking and prefrontal brain areas. We identified multiple brain-behavior associations. More gray matter volume in the superior frontal gyrus and cerebellar lobules VIIB and VIII, more sulcal depth in the insula, more gyrification in the pre/post central gyri, and more fractional anisotropy in the corticospinal tract and inferior longitudinal fasciculus corresponded to more gait asymmetry. These associations did not differ between younger and older adults. This work progresses our understanding of how brain structure is associated with balance during walking, particularly during adaptation.

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References
1.
Seidler R, Noll D . Neuroanatomical correlates of motor acquisition and motor transfer. J Neurophysiol. 2008; 99(4):1836-45. DOI: 10.1152/jn.01187.2007. View

2.
Yang F, Pai Y . Can sacral marker approximate center of mass during gait and slip-fall recovery among community-dwelling older adults?. J Biomech. 2014; 47(16):3807-12. PMC: 4469384. DOI: 10.1016/j.jbiomech.2014.10.027. View

3.
Yamamoto K, Kawato M, Kotosaka S, Kitazawa S . Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields. J Neurophysiol. 2006; 97(2):1588-99. DOI: 10.1152/jn.00206.2006. View

4.
Du Boisgueheneuc F, Levy R, Volle E, Seassau M, Duffau H, Kinkingnehun S . Functions of the left superior frontal gyrus in humans: a lesion study. Brain. 2006; 129(Pt 12):3315-28. DOI: 10.1093/brain/awl244. View

5.
Albizu A, Fang R, Indahlastari A, OShea A, Stolte S, See K . Machine learning and individual variability in electric field characteristics predict tDCS treatment response. Brain Stimul. 2020; 13(6):1753-1764. PMC: 7731513. DOI: 10.1016/j.brs.2020.10.001. View