白质的变化预测了重复性跨脑磁性刺激在帕金森病中的有效性
Jinying Han1, Lingling Lv2, Xin Chen1
1Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, PR China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, PR China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230032, PR China.
NeuroImage
|July 4, 2025
概括
白质 (WM) 活动模式可以预测帕金森病 (PD) 连续达爆发刺激 (cTBS) 的有效性. 在特定路径中较低的基线WM活性表明对cTBS治疗的运动反应更好.
科学领域:
- 神经科学是一个神经科学.
- 神经学 神经学
- 医疗成像医学成像
背景情况:
- 帕金森病 (PD) 治疗疗效,特别是在持续的甲基突发刺激 (cTBS) 中,显示出显著的变化.
- 改变大脑白质 (WM) 活动越来越多地与PD发病和进展有关.
研究的目的:
- 调查WM活动模式,以预测帕金森病患者对cTBS的治疗反应.
- 确定特定的WM区域,其基线功能与cTBS后的运动改善相关.
主要方法:
- 对68名帕金森病患者进行了追溯分析,他们接受了针对补充运动区域的14天cTBS治疗.
- 使用fMRI分析低频波动的干预前白质幅度 (ALFF) 和分数ALFF.
- 开发和验证支持矢量机 (SVM) 模型,以预测cTBS的有效性.
主要成果:
- 与非响应者相比,对cTBS的响应者表现出较低的基线WM ALFF的刚性和轴性症状的改善.
- SVM确定了较高的基线UPDRS III,刚性得分,以及在特定的WM通道 (左皮质脊髓管,右ILF,左前丘脑辐射) 中降低ALFF作为运动结果的预测因素.
- 在独立队列中的验证显示,预测和实际的UPDRS III改善之间有很好的一致性 (CCC = 0.630),组合模型在刚性改善方面取得了中度的准确性 (CCC = 0.725).
结论:
- 基线白质 (WM) 功能作为预测帕金森病中对cTBS的运动反应的潜在生物标志物.
- 肌肉运动活动模式,特别是在特定的区域,可以帮助个性化PD的cTBS治疗策略.
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Long-term Potentiation
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Long-term Potentiation
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
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Hebbian LTP
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