07. A Fast Scalable Spatiotemporal Modeling of task-fMRI Data

Accurate modeling of the blood-oxygen-level-dependent (BOLD) signal in task-based fMRI is crucial for interpreting neural activation, yet it remains challenging due to spatiotemporal variability in the hemodynamic response function (HRF). Traditional general linear models (GLMs) assume a fixed HRF across brain regions, limiting their ability to capture localized variations in amplitude, latency, and shape. While spatial smoothing and Bayesian methods improve estimation by borrowing strength across voxels, they often introduce bias, computational overhead, or depend on strict assumptions. We propose SPLASH (Spline-based Processing for Localized Adaptive Spatial Hemodynamics), a novel spatiotemporal modeling framework that integrates adaptive spatial smoothing with efficient temporal modeling of the HRF. SPLASH embeds HRF estimation within a unified spline-based regression model that flexibly adapts to both the spatial domain and local signal characteristics. By preserving cortical topology and allowing region-specific adaptiveness, SPLASH improves the accuracy of HRF amplitude estimation and enhances statistical power in detecting task-related activation. Through simulations and application to Human Connectome Project data, we demonstrate that SPLASH outperforms conventional approaches in estimation accuracy, robustness, and computational efficiency, offering a scalable solution for high-dimensional fMRI analyses.

Adaptive Spatial Modeling

High-dimensional Inference

Hemodynamic Response Function

Task-based fMRI

BOLD Signal 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025