Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America. Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
J Neural Eng. 2020 Jul 24;17(4):046018. doi: 10.1088/1741-2552/ab9064.
Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning.
Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression.
We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention.
The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.
基于运动想象的脑机接口 (BCI) 利用个体有能力主动调节局部脑活动，通常作为治疗运动功能障碍的一种方法，或用于探究脑活动与行为之间的因果关系。然而，许多个体无法成功学习调节其脑活动，这极大地限制了 BCI 在治疗和基础科学研究中的疗效。旨在探究 BCI 学习本质的正式实验已提供初步证据表明，跨空间分布和功能多样化的认知系统的相干活动是能够成功学习控制 BCI 的个体的标志。然而，对于这些分布式网络如何随时间相互作用以支持学习，人们知之甚少。
为了弥补这一知识空白，我们构建并应用了一种多模态网络方法，通过脑磁图来解码基于运动想象的脑机接口学习中的脑-行为关系。具体来说，我们采用一种最小约束矩阵分解方法——非负矩阵分解——同时识别功能连接的正则、协变子图，以评估它们与任务表现的相似性，并检测它们的时变表达。
我们发现，学习的标志是弥散的脑-行为关系：表现好的学习者显示出许多子图，其时间表达与表现相关。个体在子图的空间特性（如额叶与大脑其余部分的连接）和子图的时间特性（如达到最大表达的学习阶段）方面也表现出明显的变化。从这些观察结果中，我们提出了一个概念模型，即某些子图通过调节对注意力至关重要的区域附近的脑活动来支持学习。为了测试这个模型，我们使用了规定网络系统中区域动态的工具（网络控制理论），并发现表现好的学习者显示出一个单一的子图，其时间表达与表现相关，其结构支持对注意力重要的大脑区域附近的传感器进行轻松调节。
因此，我们对 BCI 学习神经科学的贡献既是计算性的，也是理论性的；我们首先使用一种最小约束、个体特定的方法来识别动态脑活动中的中尺度结构，以展示全局连通性和分布式网络之间的相互作用如何支持 BCI 学习，然后我们使用正式的网络控制模型来为假设提供理论支持，即这些识别出的子图非常适合调节注意力。
