A Bayesian network analysis of gait speed change upon transition to uneven surfaces in older adults.

Yixiao Song, Caterina Rosano, Lana M Chahine, Andrea L Rosso, Fabrisia Ambrosio, Nicolaas Bohnen, Zhihao Chen, Seyoung Kim
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Abstract

Background: Gait adaptability, defined as the ability to adjust walking performance to environmental challenges, likely reflects complex interactions among the central nervous system (CNS) and other physiological systems. However, the drivers of lower gait adaptability in older adults are poorly understood.

Methods: We applied a Bayesian network framework to understand multisystem interactions contributing to percent change in gait speed (%GSC) on transition from even to uneven surface in 159 older adults. Neuroimaging measures include gray matter volume, white matter hyperintensities, striatal dopaminergic (DA) neurotransmission, and functional connectivity. Other measures were obtained for domains important for locomotor control: health history, lifestyle, psychological well-being, cognition, and musculoskeletal and peripheral nervous systems. The Bayesian network captured relationships among these variables as a network, quantifying their direct and indirect dependencies. The predictive accuracy of %GSC from the Bayesian network was compared with that of multivariate linear regression using 10-fold cross-validation.

Results: Participants exhibited slower gait on uneven compared to even surfaces (mean %GSC = -6.32%). The Bayesian network outperformed linear regression in predicting %GSC and identified four direct paths to %GSC from body mass index, muscle strength, striato-cortical sensorimotor connectivity, and purpose in life. Indirect paths to %GSC showed interrelations among CNS and non-CNS variables, including striatal DA neurotransmission, total grey matter volume, medications, proprioception, and sex.

Conclusions: Gait adaptability in older adults is influenced by interactions among functional connectivity, body composition, muscle strength, and psychological well-being. Strengthening both neural and physical systems through targeted interventions may mitigate declines in gait instability and preserve mobility with aging.

老年人过渡到不平整表面时步态速度变化的贝叶斯网络分析。
背景:步态适应性被定义为根据环境挑战调整步行行为的能力,可能反映了中枢神经系统(CNS)和其他生理系统之间复杂的相互作用。然而,老年人步态适应性较低的驱动因素尚不清楚。方法:我们应用贝叶斯网络框架来理解多系统相互作用对159名老年人从平坦表面过渡到不平坦表面时步态速度百分比变化的影响。神经影像学测量包括灰质体积、白质高信号、纹状体多巴胺能神经传递和功能连通性。对运动控制的重要领域,如健康史、生活方式、心理健康、认知、肌肉骨骼和周围神经系统,也进行了其他测量。贝叶斯网络将这些变量之间的关系作为一个网络,量化它们的直接和间接依赖关系。采用10倍交叉验证,比较贝叶斯网络对%GSC的预测精度与多元线性回归的预测精度。结果:参与者在不平整的地面上表现出较慢的步态(平均%GSC = -6.32%)。贝叶斯网络在预测%GSC方面优于线性回归,并从BMI、肌肉力量、纹状体-皮层感觉运动连通性和生活目标确定了四条直接途径。间接通往%GSC的路径显示了中枢神经系统和非中枢神经系统变量之间的相互关系,包括纹状体多巴胺能神经传递、灰质总体积、药物、本体感觉和性别。结论:老年人的步态适应性受功能连通性、身体组成、肌肉力量和心理健康的相互作用影响。通过有针对性的干预措施加强神经和身体系统可以减轻步态不稳定的下降,并随着年龄的增长保持活动能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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