fMRI数据多元回归模型线性和非线性区域的推广
On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data
摘要：
与传统的单变量分析相比，各种类型的多变量分析已被应用于功能磁共振成像（fMRI）数据。在本文中，我们比较了两个当代的多元回归基于任务的功能磁共振成像数据：线性回归与岭正则化和非线性符号回归遗传编程。该项目的数据是当代视觉刺激的功能磁共振成像试验设计的代表。为10名受试者生成线性和非线性模型，另外4名受试者保留用于验证。通过比较各种情况下的$R$分数（Pearson积差相关）来评估模型质量，包括单次运行自拟合、受试者内泛化和受试者间泛化。使用单独的静息状态扫描估计建模策略过度拟合的倾向。结果表明，没有一种方法是客观或内在优于其他。
英文摘要：
In contrast to conventional, univariate analysis, various types of multivariate analysis have been applied to functional magnetic resonance imaging (fMRI) data. In this paper, we compare two contemporary approaches for multivariate regression on task-based fMRI data: linear regression with ridge regularization and non-linear symbolic regression using genetic programming. The data for this project is representative of a contemporary fMRI experimental design for visual stimuli. Linear and non-linear models were generated for 10 subjects, with another 4 withheld for validation. Model quality is evaluated by comparing $R$ scores (Pearson product-moment correlation) in various contexts, including single run self-fit, within-subject generalization, and between-subject generalization. Propensity for modelling strategies to overfit is estimated using a separate resting state scan. Results suggest that neither method is objectively or inherently better than the other.
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