The spatial scale dimension of speech processing in the human brain

TL;DR

This study investigates how different spatial filter sizes in fMRI analysis affect neuroanatomical localization, signal strength, and cluster size, revealing significant variability and biases in current single-filter approaches.

Contribution

It introduces a multiscale analysis method for fMRI data, highlighting the impact of filter size on results and proposing a more comprehensive approach to interpret BOLD signals.

Findings

Variability in neuroanatomical localization with different Gaussian filters.

Small filters bias results towards subcortical and cerebellar regions.

Distinct scale-dependent cluster size dynamics between cortical and cerebellar areas.

Abstract

In the past three decades, neuroimaging has provided important insights into structure-function relationships in the human brain. Recently, however, the methods for analyzing functional magnetic resonance imaging (fMRI) data have come under scrutiny, with studies questioning cross-software comparability, the validity of statistical inference and interpretation, and the influence of the spatial filter size on neuroimaging analyses. As most fMRI studies only use a single filter for analysis, much information on the size and shape of the BOLD signal in Gaussian scale space remains hidden and constrains the interpretation of fMRI studies. To investigate the influence of the spatial observation scale on fMRI analysis, we use a spatial multiscale analysis with a range of Gaussian filters from 1-20 mm (full width at half maximum) to analyze fMRI data from a speech repetition paradigm in 25 healthy subjects. We show that analyzing the fMRI data over a range of Gaussian filter kernels reveals substantial variability in the neuroanatomical localization and the average signal strength and size of suprathreshold clusters depending on the filter size. We also demonstrate how small spatial filters bias the results towards subcortical and cerebellar clusters. Furthermore, we describe substantially different scale-dependent cluster size dynamics between cortical and cerebellar clusters. We discuss how spatial multiscale analysis may substantially improve the interpretation of fMRI data. We propose to further develop a spatial multiscale analysis to fully explore the deep structure of the BOLD signal in Gaussian scale space.