Selection of a Model of Cerebral Activity for fMRI Group Data Analysis

TL;DR

This paper introduces a Bayesian model selection method for analyzing multi-subject fMRI data, improving the detection of brain activity regions by addressing limitations of traditional threshold-based approaches.

Contribution

A novel Bayesian approach for fMRI group data analysis that incorporates prior information and spatial uncertainty, avoiding arbitrary thresholds and reducing biases.

Findings

Corrects biases of traditional methods in detecting active regions

Balances false positive and false negative risks effectively

Performs well on both simulated and real datasets

Abstract

This thesis is dedicated to the statistical analysis of multi-sub ject fMRI data, with the purpose of identifying bain structures involved in certain cognitive or sensori-motor tasks, in a reproducible way across sub jects. To overcome certain limitations of standard voxel-based testing methods, as implemented in the Statistical Parametric Mapping (SPM) software, we introduce a Bayesian model selection approach to this problem, meaning that the most probable model of cerebral activity given the data is selected from a pre-defined collection of possible models. Based on a parcellation of the brain volume into functionally homogeneous regions, each model corresponds to a partition of the regions into those involved in the task under study and those inactive. This allows to incorporate prior information, and avoids the dependence of the SPM-like approach on an arbitrary threshold, called the cluster- forming threshold, to define active regions. By controlling a Bayesian risk, our approach balances false positive and false negative risk control. Furthermore, it is based on a generative model that accounts for the spatial uncertainty on the localization of individual effects, due to spatial normalization errors. On both simulated and real fMRI datasets, we show that this new paradigm corrects several biases of the SPM-like approach, which either swells or misses the different active regions, depending on the choice of a cluster-forming threshold.