Functional magnetic resonance imaging (fMRI) is undeniably the neuroimaging methodology that has become the workhorse for neuroscience and psychology researchers who want access to localized measurements of physiological changes in the brain that correlate with human behavior. The high value of fMRI measurements is based on the fact that they have been shown, time and again, to exhibit a linear correlation with the local neural population response. There are, however, a recent smattering of articles in the literature indicating a mismatch between the fMRI response and the measured or presumed neural activity. These mismatches appear limited to experiments in which only a small neural population is stimulated;they also seem most likely to occur when the balance between local neural excitation and inhibition is tipped in favor of inhibition. These reports of fMRI responses that fail to correlate with neural responses are puzzling at best, and potentially troublesome for scientists who want to draw quantitative conclusions about neural population activity from fMRI data. Our first series of proposed experiments will characterize the effects of sampling resolution on the interpretability of the fMRI response to small stimuli. Not only do flanking negative responses confound accurate interpretation of the fMRI response to small patches of neural activity, because the boundary regions are large compared to the total neural response, but size-dependent intrinsic inhibition shapes the neural response yet has an unknown representation in the hemodynamic response. The result of the first series of experiments will be a computational model characterizing (1) neuro-hemodynamic coupling at the edges of isolated patches of neural activity, and (2) the contribution of inhibitory neural activity to the fMRI response. Our second series of experiments will characterize fMRI response evoked by neural networks with different balances between excitation and inhibition. All local neural codes contain a balance between input and output;local computations use a balance of excitation and inhibition to shape the input and define output spiking rates. In this series of experiments, we will investigate the implications of our recent study showing that localized fMRI of individual image patches cannot be predicted simply from the responses of the neurons that respond best to those stimuli. Working with a computational model that demonstrates how the entire local neural population response can be used to predict fMRI responses, this second series of experiments will seek to identify signature hemodynamic response characteristics that are present when heterogeneous neural responses mask key information encoded in a sub-population of neurons. Together, these experiments will improve our ability to use high-resolution fMRI to characterize patterned neural activity, improving the utility of fMRI for clinicl applications such as neurosurgical planning and seizure locus detection.
Scientists who study the brain need high-resolution imaging tools in order to understand how different patterns of neural activity correlate with different aspects of behavior;functional magnetic resonance imaging (fMRI) is one of the tools that can provide the highest imaging resolution. However, we still need to answer some fundamental questions about the relationship between the fMRI signal and the underlying neural activity in the brain. The work funded by this grant will develop more accurate models for linking neural activity patterns to fMRI responses when (1) the neural response occupies only a small portion of cortex, and (2) sub- populations of neurons right next to each other have different responses. This ability to detect activity or dysregulation of activity in a subpopulation of neurons is key fr high-resolution localization of neural function, for neurosurgical planning or seizure locus detection, as well as for quantifying biomarkers of diseases such as schizophrenia, which differentially affects inhibitory neurons in visual cortex.
