Deep Generative Analysis for Task-Based Functional MRI Experiments-猫眼课题宝

DOI：

10.1101/2021.04.04.438365

发表时间：

2021-04

期刊：

bioRxiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Daniela de Albuquerque;Jack Goffinet;R. Wright;John M. Pearson

虽然功能性磁共振成像（fMRI）仍然是基础和临床神经科学中最广泛和最重要的方法之一，但它产生的数据-大脑体积的时间序列-继续构成令人生畏的分析挑战。当前的标准（“质量单变量”）方法涉及构建任务回归量矩阵，在每个体积像素（“体素”）处拟合单独的一般线性模型，计算每个模型的测试统计量，以及使用自举或其他恢复方法事后校正假阳性。尽管它很简单，但这种方法在过去二十年中取得了巨大的成功，这是由于：1）它能够产生效果图，突出显示其活动与给定的感兴趣变量显著相关的大脑区域; 2）它将实验效果建模为可分离的，因此易于解释。然而，这种方法有几个众所周知的缺点，即：不准确的线性和噪声高斯性的假设;有限的能力，以捕捉个人的影响和变化;和困难，在执行适当的统计测试次要的独立拟合体素。在这项工作中，我们采用了不同的方法，直接建模整个体积的方式，增加模型的灵活性，同时保持可解释性。具体来说，我们使用广义加性模型（GAM），其中每个回归变量的影响保持可分离，由变分自动编码器产生的空间映射和由协变量特定的高斯过程建模的（潜在非线性）增益的产物。其结果是一个模型，产生组水平的效果图可比或上级的标准功能磁共振成像分析软件获得的，同时也产生单个主题的效果图捕捉个体差异。这表明，生成模型与可分解的结构可能会提供一个更灵活的替代任务为基础的功能磁共振成像数据的分析。

While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces—time series of brain volumes—continue to pose daunting analysis challenges. The current standard (“mass univariate”) approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel (“voxel”), computing test statistics for each model, and correcting for false positives post hoc using bootstrap or other resampling methods. Despite its simplicity, this approach has enjoyed great success over the last two decades due to: 1) its ability to produce effect maps highlighting brain regions whose activity significantly correlates with a given variable of interest; and 2) its modeling of experimental effects as separable and thus easily interpretable. However, this approach suffers from several well-known drawbacks, namely: inaccurate assumptions of linearity and noise Gaussianity; a limited ability to capture individual effects and variability; and difficulties in performing proper statistical testing secondary to independently fitting voxels. In this work, we adopt a different approach, modeling entire volumes directly in a manner that increases model flexibility while preserving interpretability. Specifically, we use a generalized additive model (GAM) in which the effects of each regressor remain separable, the product of a spatial map produced by a variational autoencoder and a (potentially nonlinear) gain modeled by a covariate-specific Gaussian Process. The result is a model that yields group-level effect maps comparable or superior to the ones obtained with standard fMRI analysis software while also producing single-subject effect maps capturing individual differences. This suggests that generative models with a decomposable structure might offer a more flexible alternative for the analysis of task-based fMRI data.

Deep Generative Analysis for Task-Based Functional MRI Experiments

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