Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, U.S.A.
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A.
Neural Comput. 2019 Sep;31(9):1751-1788. doi: 10.1162/neco_a_01196. Epub 2019 Jul 23.
Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable-called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants () performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.
认知过程，如学习和认知灵活性，既难以使用客观工具进行测量和连续采样，因为认知过程源于分布式的高维神经活动。对于研究和临床应用来说，这种维度必须降低。为了降低维度并测量潜在的认知过程，我们提出了一个建模框架，其中认知过程被定义为一个低维动态潜在变量，称为认知状态，它将高维神经记录和多维行为输出联系起来。该框架允许我们将建模神经和行为数据之间关系的难题分解为可分离的编码-解码方法。我们首先使用状态空间建模框架，即行为解码器，来阐明客观行为输出（例如，反应时间）与认知状态之间的关系。第二步，神经编码器涉及使用广义线性模型（GLM）来识别认知状态与神经信号（如局部场电位（LFP））之间的关系。然后，我们使用神经编码器模型和贝叶斯滤波器来使用神经数据（LFP 功率）估计认知状态，以生成神经解码器。我们提供拟合度分析和模型选择标准，以支持编码-解码结果。我们将该框架应用于人类参与者执行认知冲突任务时从神经数据中估计潜在的认知状态。我们成功地以行为输出为参照，在 95%的置信区间内估计了认知状态，平均每个参与者的任务试验中有 90%的认知状态可以被估计。与之前的编码器-解码器模型不同，我们提出的建模框架将 LFP 光谱功率纳入到编码和解码认知状态中。该框架使我们能够捕捉潜在认知过程的时间演变，这可能是开发闭环实验和治疗的关键。
