Abstract
Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivity of individual voxels, neural variability is correlated across voxels, and such noise correlations may contribute importantly to accurate decoding. Indeed, a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations. Here we extend this theory from the scale of neurons to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogeneous populations of voxels (i.e., voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations.
Highlights
The development of functional magnetic resonance imaging (fMRI) has made it possible to observe the human brain noninvasively as it responds to stimuli or engages in cognitive tasks
Across several analyses of an fMRI dataset, we demonstrate a positive relationship between the magnitude of noise correlations and decoding performance, and we show that as expected with such classifier algorithms [46, 49], multivoxel pattern analysis (MVPA) exploits noise correlations by assigning higher weights to voxels with higher noise correlations
We focused on voxels with the highest vs. lowest 1% of noise correlations (Fig 2A) and found that classification was better for voxels with the highest noise correlations (t16 = 7.24, p < 0.0001)
Summary
The development of fMRI has made it possible to observe the human brain noninvasively as it responds to stimuli or engages in cognitive tasks. Different events in the experiment can be linked to changes in BOLD activity, permitting inferences about the neural basis of cognition (in the example above, about category-selective object perception). This is a challenging endeavor because both the physiological processes underlying BOLD activity and the measurement of BOLD activity with fMRI are noisy, and because the resulting datasets can be large and statistically complex [1, 2]. FMRI analyses have focused on the information contained in the timecourse of individual voxels or regions Such methods are “univariate” because they seek to relate experimental events to single dimensions of BOLD variability, such as the activity averaged across voxels in a region of interest (ROI). We used an FIR model because it avoids a priori assumptions about the
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