Neuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, Argentina.
ETSIDI-Center for Automation and Robotics, Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
Sensors (Basel). 2024 Feb 7;24(4):1089. doi: 10.3390/s24041089.
Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject's available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in n=17 healthy volunteers (9 males, 8 females, age 28.66±8.8 years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease.
神经退行性疾病(NDs),如阿尔茨海默病、帕金森病、肌萎缩性侧索硬化症和额颞叶痴呆等,在全球人口中越来越普遍。这些 NDs 的临床诊断基于运动和非运动症状的检测和特征描述。然而,当做出这些诊断时,受试者通常处于神经肌肉改变经常不可逆转的晚期。在这种情况下,我们提出了一种评估涉及决策过程的运动任务认知工作量(CWL)的方法。CWL 是一个广泛用于解决任务需求与主体完成该任务可用资源之间平衡的概念。在这项研究中,通过记录 EEG 和 EMG 信号,我们为 17 名健康志愿者(9 名男性,8 名女性,年龄 28.66±8.8 岁)开发了多个用于运动决策任务中运动计划的模型。在提出的测试中,志愿者必须根据视觉刺激的出现做出关于哪只手应该移动的决定。我们使用皮质肌和肌间相干性计算了皮质和肌肉之间以及肌肉之间的功能连接。尽管生成了三个模型,但只有一个具有很强的性能。结果表明,取决于要移动的手,存在两种类型的运动决策过程。此外,通过结合行为措施(如规划时间)和外周记录(如 EMG 信号),可以准确估计左手运动的决策的中央处理。本研究提供的模型可以被视为一种方法学基础,用于检测无症状患者的神经肌肉改变,以及监测退行性疾病的过程。