Topological inference for EEG and MEG

TL;DR

This paper discusses how random field theory enables topological inference in neuroimaging data like EEG and MEG, accounting for data continuity and invariance to manifold geometry, improving statistical analysis of brain signals.

Contribution

It demonstrates the application of random field theory to time, space, and frequency data in neuroimaging, highlighting its advantages over classical methods.

Findings

Random field theory provides invariant topological inference across different geometries.

It effectively handles smooth data on scalp or cortical meshes.

The approach simplifies statistical analysis in electromagnetic neuroimaging.

Abstract

Neuroimaging produces data that are continuous in one or more dimensions. This calls for an inference framework that can handle data that approximate functions of space, for example, anatomical images, time--frequency maps and distributed source reconstructions of electromagnetic recordings over time. Statistical parametric mapping (SPM) is the standard framework for whole-brain inference in neuroimaging: SPM uses random field theory to furnish $p$-values that are adjusted to control family-wise error or false discovery rates, when making topological inferences over large volumes of space. Random field theory regards data as realizations of a continuous process in one or more dimensions. This contrasts with classical approaches like the Bonferroni correction, which consider images as collections of discrete samples with no continuity properties (i.e., the probabilistic behavior at one point in the image does not depend on other points). Here, we illustrate how random field theory can be applied to data that vary as a function of time, space or frequency. We emphasize how topological inference of this sort is invariant to the geometry of the manifolds on which data are sampled. This is particularly useful in electromagnetic studies that often deal with very smooth data on scalp or cortical meshes. This application illustrates the versatility and simplicity of random field theory and the seminal contributions of Keith Worsley (1951--2009), a key architect of topological inference.