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首页> 外文期刊>Nonlinear dynamics, psychology and life sciences >Understanding Neuromotor Strategy During Functional Upper Extremity Tasks Using Symbolic Dynamics
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Understanding Neuromotor Strategy During Functional Upper Extremity Tasks Using Symbolic Dynamics

机译：使用符号动力学了解上肢功能性任务中的神经运动策略

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The ability to model and quantify brain activation patterns that pertain to natural neuromotor strategy of the upper extremities during functional task performance is critical to the development of therapeutic interventions such as neuroprosthetic devices. The mechanisms of information flow, activation sequence and patterns, and the interaction between anatomical regions of the brain that are specific to movement planning, intention and execution of voluntary upper extremity motor tasks were investigated here. This paper presents a novel method using symbolic dynamics (orbital decomposition) and nonlinear dynamic tools of entropy, self-organization and chaos to describe the underlying structure of activation shifts in regions of the brain that are involved with the cognitive aspects of functional upper extremity task performance. Several questions were addressed: (a) How is it possible to distinguish deterministic or causal patterns of activity in brain fMRI from those that are really random or non-contributory to the neuromotor control process? (b) Can the complexity of activation patterns over time be quantified? (c) What are the optimal ways of organizing fMRI data to preserve patterns of activation, activation levels, and extract meaningful temporal patterns as they evolve over time? Analysis was performed using data from a custom developed time resolved fMRI paradigm involving human subjects (N=18) who performed functional upper extremity motor tasks with varying time delays between the onset of intention and onset of actual movements. The results indicate that there is structure in the data that can be quantified through entropy and dimensional complexity metrics and statistical inference, and furthermore, orbital decomposition is sensitive in capturing the transition of states that correlate with the cognitive aspects of functional task performance.
机译：在执行功能性任务期间，建模和量化与上肢的自然神经运动策略有关的大脑激活模式的能力对于开发诸如神经修复设备等干预措施至关重要。在这里研究了信息流，激活序列和模式的机制，以及大脑解剖区域之间的相互作用，这些运动特定于运动计划，意图和自愿性上肢运动任务的执行。本文提出了一种新方法，该方法使用符号动力学（轨道分解）和熵，自组织和混沌的非线性动力学工具来描述与功能性上肢任务的认知方面有关的大脑区域激活转移的潜在结构。性能。解决了几个问题：（a）如何将脑功能磁共振成像活动的确定性或因果活动模式与真正随机的或对神经运动控制过程无贡献的活动模式区分开？ （b）是否可以量化激活模式随时间的复杂性？ （c）组织fMRI数据以保留激活模式，激活水平并随着时间的推移提取有意义的时间模式的最佳方法是什么？使用来自定制开发的时间分辨fMRI范例的数据进行分析，该范例涉及人类受试者（N = 18），这些受试者执行了功能性上肢运动任务，并且在意图发作和实际运动发作之间的时间延迟有所变化。结果表明，数据中存在可以通过熵和维度复杂性度量以及统计推断进行量化的结构，此外，轨道分解在捕获与功能性任务执行的认知方面相关的状态转变方面非常敏感。

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