Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, United States of America.
J Neural Eng. 2020 Feb 5;17(1):016045. doi: 10.1088/1741-2552/ab6040.
Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions.
We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions.
We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization.
Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC = 0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.
范畴感知（CP）是言语感知的固有属性。听众感知言语识别的反应时间（RT）对个体差异高度敏感。虽然 CP 的神经相关性在大脑对行为的区域贡献方面得到了很好的研究，但标志着听众言语分类速度（RT）个体差异的功能连接模式则不太清楚。在这项研究中，我们引入了一种新的方法来解决这些问题。
我们将几种计算方法应用于 EEG，包括图挖掘、机器学习（即支持向量机）和稳定性选择，以研究预测听众行为决策速度的独特大脑状态（功能神经连接）。
我们推断（i）听众的感知速度与大脑连接组学的动态变化直接相关，（ii）全局网络聚类系数和效率区分了快、中、慢 RT，（iii）加速决策的功能网络在较慢 RT 时的负聚类系数（即变得不聚类）增加，（iv）较慢的范畴性言语决策导致过度使用神经资源和 CP 电路内的信息异常流动，（v）较慢的反应者倾向于过度（或不恰当地）利用功能大脑网络，而快速反应者（具有较低的全局效率）则利用相同的神经通路，但组织更受限。
研究结果表明，神经分类器（SVM）与稳定性选择相结合，可以仅从功能连接中正确分类行为 RT，准确率超过 92%（AUC = 0.9）。我们的结果通过支持使用完全数据驱动的方法在 CP 中涉及相似的颞叶（STG）、顶叶、运动和前额叶区域，与之前的研究结果相吻合。
