Principal Component Regression predicts functional responses across individuals

A collection of 65 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

### Collection description

<p>Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.</p>

本数据集包含65张脑图谱。每一张脑图谱均由三维数值数组构成，用以表示大脑在不同位置的属性。

### 集合描述

受试者间的变异性是神经影像学组内推断的主要障碍，因为它产生了复杂图像模式，而这些模式未能被标准分析模型所捕捉，并危及统计方法的敏感性。解决这一问题的方法是通过利用多个成像对比所传达的冗余信息来建模随机受试者效应。在本文中，我们介绍了一种新颖的分析框架，其中我们估计由其他图像上学习到的随机效应子空间所拟合的方差量；我们表明，主成分回归估计量优于其他回归模型，并且它能拟合一定比例（10%至25%）的受试者间变异性。这首次证明了每个个体中对比度的累积可以为更敏感的神经影像学组分析提供基础。