Fuglsang Søren A, Madsen Kristoffer H, Puonti Oula, Siebner Hartwig R, Hjortkjær Jens
Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Copenhagen, Denmark.
Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
Imaging Neurosci (Camb). 2024 May 20;2. doi: 10.1162/imag_a_00155. eCollection 2024.
Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in decoding models. When fitting such models, it can be relevant to allow differential shrinkage of the different groups of regression weights. Here, we explore a framework that allows for straightforward definition and estimation of such models. We present an expectation-maximization algorithm for tuning hyperparameters that control shrinkage of groups of weights. We highlight properties, limitations, and potential use-cases of the model using simulated data. Next, we explore the model in the context of a BOLD fMRI encoding analysis and an EEG decoding analysis. Finally, we discuss cases where the model can be useful and scenarios where regularization procedures complicate model interpretation.
在计算神经科学中，回归是将大脑反应与刺激或任务相关联的主要工具。这通常涉及使用可分为不同组的预测变量来拟合线性模型，例如编码模型中的不同刺激特征子集或解码模型中不同神经反应通道的特征。在拟合此类模型时，允许不同组的回归权重有不同程度的收缩可能是有意义的。在这里，我们探索了一个框架，该框架允许对这类模型进行直接定义和估计。我们提出了一种期望最大化算法，用于调整控制权重组收缩的超参数。我们使用模拟数据突出了该模型的特性、局限性和潜在用例。接下来，我们在BOLD功能磁共振成像编码分析和脑电图解码分析的背景下探索该模型。最后，我们讨论了该模型可能有用的情况以及正则化程序使模型解释复杂化的场景。
