Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.
Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.
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利用功能连通性确定卒中康复脑机接口治疗阶段的机器学习分类。
DOI：
10.3389/fnins.2018.00353
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发表时间：
2018
影响因子：
4.3
通讯作者：
Prabhakaran V
中科院分区：
文献类型：
--
作者：
Mohanty R;Sinha AM;Remsik AB;Dodd KC;Young BM;Jacobson T;McMillan M;Thoma J;Advani H;Nair VA;Kang TJ;Caldera K;Edwards DF;Williams JC;Prabhakaran V
使用脑机接口（BCI）技术的介入治疗在促进中风幸存者的运动恢复方面显示出了希望;然而，这种形式的干预对运动网络之外的功能网络的影响还没有得到很好的理解。在这里，我们研究了在不同阶段（即干预前和干预后）接受BCI治疗的中风参与者的静息状态功能连接（rs-FC），以使用机器学习分类器识别有区别的功能变化，目标是将参与者分类为两个治疗阶段之一。20名患有持续性上肢运动障碍的慢性卒中参与者使用闭环神经反馈BCI设备接受神经调节训练，并在四个时间点收集rs-功能性MRI（rs-fMRI）扫描：治疗前，中期，治疗后和1个月。为了评估这种干预的峰值效应，从两个特定阶段（即治疗前和治疗后）分析rs-FC。在每个阶段，总共计算了236个种子，这些种子跨越大脑的运动和非运动区域。应用单变量特征选择以减少特征的数量，然后由线性二进制支持向量机（SVM）分类器使用基于主成分的数据转换将每个参与者分类到治疗阶段。SVM分类器使用留一法实现了92.5%的交叉验证准确率。在运动网络之外，来自额顶叶任务控制、默认模式、皮层下和视觉网络的种子成为分类的重要贡献者。此外，从治疗前到治疗后阶段，观察到更多的功能变化是加强的，而不是减弱的，这两种变化都涉及大脑的运动和非运动区域。这些发现可能提供新的证据来支持BCI疗法作为中风康复的一种形式的潜在临床实用性，不仅有利于运动恢复，而且有助于其他脑网络的恢复。此外，更强和更弱的变化的描绘可以为BCI介入治疗的更优化设计提供信息，以便在恢复过程中促进加强和抑制减弱的变化。
Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.
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影响因子：
5.7
作者：
Dai, Zhengjia;Yan, Chaogan;He, Yong
通讯作者：
He, Yong
影响因子：
3.2
作者：
Brumberg, Jonathan S.;Nieto-Castanon, Alfonso;Kennedy, Philip R.;Guenther, Frank H.
通讯作者：
Guenther, Frank H.
影响因子：
3
作者：
de Kroon, JR;van der Lee, JH;Lankhorst, GJ
通讯作者：
Lankhorst, GJ
DOI：
10.1016/0021-9681(65)90030-5
发表时间：
1965-01-01
期刊：
JOURNAL OF CHRONIC DISEASES
影响因子：
--
作者：
CARROLL, D
通讯作者：
CARROLL, D
DOI：
10.1109/tpami.2010.125
发表时间：
2011-03-01
影响因子：
23.6
作者：
Cecotti, Hubert;Graeser, Axel
通讯作者：
Graeser, Axel