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Abstract

Neural activity in the brain is correlated with the blood-oxygen level dependent (BOLD) contrast
which can be measured non-invasively by functional magnetic resonance imaging (fMRI).
Up to date, many fMRI analysis methods are based on simplifying assumptions about the BOLD
signal. Two popular assumptions are spatial independence and homogeneity of the haemodynamic
response function (HRF) across voxels. As single voxels usually are not independent
and moreover also exhibit different haemodynamic response characteristics, these assumptions
might lead astray interpretations of fMRI data.
In this study we present an analysis framework that reveals the spatio-temporal correlation
structure between simultaneously measured intracortical neurophysiological activity in primary
visual cortex of the non-human primate and BOLD response. Given the spectrograms of
neurophysiological activity and the simultaneously recorded BOLD data we compute a spatiotemporal
convolution that links the activity measured at the electrode to the multivariate
BOLD response. The convolution can be interpreted as the pattern in time-voxel space that
reflects best the neural activity as it maximises the canonical correlation [1] between neural
and haemodynamic activity.
Inspection of the estimated time-voxel patterns yields new insights in the spatio-temporal dependency
structure of neurovascular coupling mechanisms. This study thereby extends previous
results reported in [2,3], where the convolution was a time-frequency convolution estimated
for the neurophysiological activity.
We show results from data collected during spontaneous activity and during visual stimulation.
The analysis resulted in robust spatio-temporal coupling patterns across different experimental
conditions. We compared the multivariate patterns with univariate coupling measures and
spatial principal component analysis (SPCA), a popular method for connectivity analysis on
fMRI data. Our findings suggest that neither univariate methods nor unimodal methods such
as SPCA, which are based on autocorrelations of fMRI data only, can recover the multivariate
spatio-temporal coupling structure in primary visual cortex.