Abstract
The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.
Highlights
Different layers in the neocortex support different types of neural computations
Selectivity estimates using Deming regression, Voxel ratio and regions of interest (ROI) ratio were plotted on the same axis as they generate commensurate estimates; the remaining three metrics were plotted on individual axes and scaled to best match the ground truth profile
The “ground truth” attentional modulation (Figure 3A) is obtained from the attentional modulation [a(c,l(v))] that is used as a model input while the raw contrast estimates (Figure 3B) is given by RD+ (v) in Eq 4 and reflects the response obtained by fitting a general linear model (GLM) to the simulated data and contrasting the blocks, identical to what is done to the real fMRI data in section “Laminar Analysis of Real 7T Data.”
Summary
Different layers in the neocortex support different types of neural computations. Different layers of the visual cortex are preferentially involved in feedforward vs feedback connectivity (Rockland and Pandya, 1979; Rockland, 2017), suggesting that they encode distinct “bottom-up” and “top-down” processes. With higher field strengths (e.g., 7T), and in turn higher signal-to-noise ratios, human scanners are able to acquire data at submillimeter resolution, and thereby offer layer-specific or “laminar fMRI.”. Recent 7T fMRI studies (Polimeni et al, 2010; Muckli et al, 2015; Kok et al, 2016; Lawrence et al, 2019) have suggested that top-down modulation of neural activity (for example, by attention or expectation) With higher field strengths (e.g., 7T), and in turn higher signal-to-noise ratios, human scanners are able to acquire data at submillimeter resolution, and thereby offer layer-specific or “laminar fMRI.” Recent 7T fMRI studies (Polimeni et al, 2010; Muckli et al, 2015; Kok et al, 2016; Lawrence et al, 2019) have suggested that top-down modulation of neural activity (for example, by attention or expectation)
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