Robust FWER control in Neuroimaging using Random Field Theory: Riding the SuRF to Continuous Land Part 2

TL;DR

This paper introduces a transformation to improve Random Field Theory-based inference in fMRI, effectively controlling false positives even with non-Gaussian data by leveraging a Gaussianization approach and extensive validation.

Contribution

It proposes a Gaussianization transformation that accelerates CLT convergence, enabling RFT to accurately control FWER in non-Gaussian fMRI data, validated on large-scale real data.

Findings

Accurately controls false positive rate in non-Gaussian fMRI data

Enables RFT to be used without Gaussianity assumptions

Validated on 7000-subject UK BioBank data

Abstract

Historically, applications of RFT in fMRI have relied on assumptions of smoothness, stationarity and Gaussianity. The first two assumptions have been addressed in Part 1 of this article series. Here we address the severe non-Gaussianity of (real) fMRI data to greatly improve the performance of voxelwise RFT in fMRI group analysis. In particular, we introduce a transformation which accelerates the convergence of the Central Limit Theorem allowing us to rely on limiting Gaussianity of the test-statistic. We shall show that, when the GKF is combined with the Gaussianization transformation, we are able to accurately estimate the EEC of the excursion set of the transformed test-statistic even when the data is non-Gaussian. This allows us to drop the key assumptions of RFT inference and enables us to provide a fast approach which correctly controls the voxelwise false positive rate in fMRI. We employ a big data \cite{Eklund2016} style validation in which we process resting state data from 7000 subjects from the UK BioBank with fake task designs. We resample from this data to create realistic noise and use this to demonstrate that the error rate is correctly controlled.