健康成人和多发性硬化症患者的障碍回避:fNIRS 初步研究

IF 4.8 2区 医学 Q2 ENGINEERING, BIOMEDICAL IEEE Transactions on Neural Systems and Rehabilitation Engineering Pub Date : 2024-10-28 DOI:10.1109/TNSRE.2024.3487526
Fares Al-Shargie;Michael Glassen;John DeLuca;Soha Saleh
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引用次数: 0

摘要

本研究探讨了在可预测和不可预测的避障过程中,步态适应如何影响健康对照组(HC)和多发性硬化症患者(pwMS)的感觉运动网络。我们利用 fNIRS 测量 HbO2 和 HHb 来估计皮质激活和连接网络,然后使用功率谱密度 (PSD) 和部分定向相干 (PDC) 对其进行分析。研究结果表明,在每个任务条件下,两组人的大脑皮层激活和连通性都有不同的模式。在不可预测的障碍回避过程中,健康人双侧运动皮层(MC)的皮层激活较低,这表明神经处理效率较高。另一方面,在不可预测的任务中,pwMS 在大多数脑区表现出较低的皮质激活,这表明神经资源分配可能存在限制。当合并任务时,与 HC 相比,pwMS 在所有记录到的脑区中都表现出更高的皮质激活,这表明存在一种保持步态稳定的补偿机制。功能连通性分析表明,与正常人相比,pwMS 需要更多的双侧躯体感觉联结皮层(SAC),而健康人需要更多的双侧运动前皮层(PMC)。这些发现表明,pwMS患者的感觉运动整合和运动规划发生了改变。四种机器学习模型(KNN、SVM、DT 和 DA)在区分各组任务条件时达到了很高的分类准确率(92-99%)。这些结果凸显了将基于 fNIRS 的皮层激活和连接测量与机器学习相结合作为多发性硬化症相关认知运动交互障碍的生物标志物的潜力。这种生物标志物有助于预测未来的行动能力下降、跌倒风险和疾病进展。
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Obstacle Avoidance in Healthy Adults and People With Multiple Sclerosis: Preliminary fNIRS Study
This study examined how gait adaptation during predictable and non-predictable obstacle avoidance affects the sensorimotor network in both healthy controls (HC) and persons with multiple sclerosis (pwMS). We utilized fNIRS measurements of HbO2 and HHb to estimate cortical activations and connectivity networks, which were then analyzed using power spectral density (PSD) and partial directed coherence (PDC). The findings revealed distinct patterns of cortical activation and connectivity for each task condition in both groups. Healthy individuals displayed lower cortical activations in the bilateral motor cortex (MC) during non-predictable obstacle avoidance, indicating efficient neural processing. On the other hand, pwMS exhibited lower cortical activations across most brain areas during non-predictable tasks, suggesting potential limitations in neural resource allocation. When tasks were combined, pwMS demonstrated higher cortical activation across all recorded brain areas compared to HC, indicating a compensatory mechanism to maintain gait stability. Functional connectivity analysis revealed that pwMS recruited higher bilateral somatosensory association cortex (SAC) than HC, whereas healthy individuals engaged more bilateral premotor cortices (PMC). These findings suggest alterations in sensorimotor integration and motor planning in pwMS. Four machine learning models (KNN, SVM, DT, and DA) achieved high classification accuracies (92-99%) in differentiating between task conditions within each group. These results highlight the potential of integrating fNIRS-based cortical activation and connectivity measures with machine learning as biomarkers for MS-related impairments in cognitive-motor interaction. Such biomarkers could aid in predicting future mobility decline, fall risk, and disease progression.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
期刊最新文献
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