Abstract
Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a non-parametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.
Highlights
The advent of functional magnetic resonance imaging at ultra-high-field strengths allows an impressively fine-grained insight into human cortical function
We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution functional magnetic resonance imaging (fMRI) data
We investigate the quality of the searchlight method as a tool for the analysis of ultra-high-field fMRI data
Summary
The advent of functional magnetic resonance imaging (fMRI) at ultra-high-field strengths allows an impressively fine-grained insight into human cortical function. Modern scanners at 7T or higher allow researchers to resolve functional data at ever finer spatial scales, even to the point of resolving individual gray matter layers (Polimeni et al, 2010; Trampel et al, 2011). Classical activation-based approaches such as the general linear model (Poline and Brett, 2012) generally rely on spatial smoothing for statistical correction for multiple comparisons, and are unable to make appropriate use of the high resolutions. A more promising means of exploiting higher resolution is multivariate pattern recognition analysis (MVPA), which enables fine-grained components of the brain activity signal to contribute relevantly (Norman et al, 2006). It is often desirable to map the spatial location of discriminating patterns, or in other words, where in the brain information about the experimental condition is present
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