斯特罗普效应
任务正网络
高强度
注意力网络
心理学
认知
背
病变
医学
物理医学与康复
默认模式网络
神经科学
病理
磁共振成像
解剖
人工智能
放射科
计算机科学
作者
Rachel A. Crockett,Chun Liang Hsu,Elizabeth Dao,Roger Tam,Janice J. Eng,Todd C. Handy,Teresa Liu‐Ambrose
摘要
Background: White matter hyperintensities (WMH) are associated with impaired cognition and increased falls risk. Resistance training (RT) is a promising intervention to reduce WMH progression, improve executive functions, and reduce falls. However, the underlying neurobiological process by which RT improves executive functions and falls risk remain unclear. We hypothesized that: 1) RT reduces the level of WMH-related disruption to functional networks; and 2) reduced disruption to the sensorimotor and attention networks will be associated with improved executive function and reduced falls risk. Objective: Investigate the impact of 52 weeks of RT on WMH-related disruption to functional networks. Methods: Thirty-two older females (65–75 years) were included in this exploratory analysis of a 52-week randomized controlled trial. Participants received either twice-weekly RT or balance and tone training (control). We used lesion network mapping to assess changes in WMH-related disruption to the sensorimotor, dorsal attention, and ventral attention networks. Executive function was measured using the Stroop Colour-Word Test. Falls risk was assessed using the Physiological Profile Assessment (PPA) and the foam sway test. Results: RT significantly reduced the level of WMH-related disruption to the sensorimotor network (p = 0.012). Reduced disruption to the dorsal attention network was associated with improvements in Stroop performance (r = 0.527, p = 0.030). Reduced disruption to the ventral attention network was associated with reduced PPA score (r = 0.485, p = 0.049) Conclusion: RT may be a promising intervention to mitigate WMH-related disruption to the sensorimotor network. Additionally, reducing disruption to the dorsal and ventral attention networks may contribute to improved executive function and reduced falls risk respectively.
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