Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.
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DOI：
10.1088/1741-2552/ab9064
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发表时间：
2020-07-24
影响因子：
4
通讯作者：
Bassett DS
中科院分区：
文献类型：
--
作者：
Stiso J;Corsi MC;Vettel JM;Garcia J;Pasqualetti F;De Vico Fallani F;Lucas TH;Bassett DS
基于运动图像的脑机接口（BCI）利用个体的能力来自愿调节局部大脑活动，通常作为运动功能障碍的治疗或探索大脑活动与行为之间的因果关系。然而，许多人无法学会成功地调节他们的大脑活动，这极大地限制了BCI在治疗和基础科学研究中的功效。旨在探索BCI学习本质的正式实验提供了初步证据，表明空间分布和功能多样的认知系统之间的连贯活动是能够成功学习控制BCI的个体的标志。然而，很少有人知道这些分布式网络如何通过时间来支持学习。在这里，我们通过构建和应用多模态网络方法来解决这一知识差距，以利用脑磁图来破译基于运动图像的脑机接口学习中的脑行为关系。具体来说，我们采用了最小约束矩阵分解方法-非负矩阵分解-同时识别正则化，功能连接的协变子图，评估其相似性的任务性能，并检测其随时间变化的表达。我们发现，学习的特点是分散的大脑行为关系：好的学习者显示了许多子图的时间表达跟踪性能。个体在子图的空间属性（如额叶与大脑其他部分的连接）和子图的时间属性（如达到最大表达的学习阶段）方面也表现出明显的差异。根据这些观察，我们建立了一个概念模型，在这个模型中，某些子图通过调节对维持注意力很重要的区域附近的传感器的大脑活动来支持学习。为了测试这个模型，我们使用的工具，规定区域动态网络系统（网络控制理论），并发现良好的学习者显示一个单一的子图，其时间表达跟踪性能和其架构支持容易调制的传感器位于附近的大脑区域的重要注意。因此，我们对脑机接口学习的神经科学的贡献的性质是计算和理论的;我们首先使用最小约束的、个体特定的方法来识别动态脑活动中的中尺度结构，以显示分布式网络之间的全局连接和交互如何支持BCI学习，然后，我们使用一个正式的网络模型的控制借给理论支持的假设，这些确定的子图是非常适合调节注意。
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.
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影响因子：
16.2
作者：
Bastos AM;Usrey WM;Adams RA;Mangun GR;Fries P;Friston KJ
通讯作者：
Friston KJ
影响因子：
19.9
作者：
Bassett DS;Mattar MG
通讯作者：
Mattar MG
影响因子：
5.7
作者：
Colclough GL;Woolrich MW;Tewarie PK;Brookes MJ;Quinn AJ;Smith SM
通讯作者：
Smith SM
影响因子：
4.7
作者：
Chai, Lucy R.;Khambhati, Ankit N.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.
影响因子：
5.4
作者：
Dion, Michelle L.;Sumner, Jane Lawrence;Mitchell, Sara McLaughlin
通讯作者：
Mitchell, Sara McLaughlin