In the last year, my laboratory has published three papers related to the neural basis of the spontaneous fMRI signal (Leopold and Maier, NeuroImage, 2012;Hutchison et al. NeuroImage, 2013;Scholvinck et al, NeuroImage, 2013). The gist of these papers is that the spontaneous fMRI correlations that are commonly used to evaluate the connectional structure of the human brain are well-behaved and link to electrophysiogical signals, but their origins remain mysterious. We also have made experimental headway on this topic with two recent studies. In Spaak et al, (Current Biology, 2012), we show that resting state electrophysiological processes exhibit a layer-specific coupling, including over time scales relevant for fMRI. Likewise, in a collaborative study (Fukushima et al, Neuron, 2012), we demonstrate that the spatial patterns of spontaneous activity observed in the auditory cortex during rest closely resemble those found during normal acoustic stimulation. These findings are present evidence of a link between spontaneous electrophysiological correlations and fMRI resting state correlations. In a new project, we have begun to investigate whether correlated spontaneous fMRI fluctuations may have their origins not only in the direct connections between two areas, but also in the shared innervation of areas based on input from the basal forebrain. While it is clear that anatomical connections will constrain patterns of activity correlations, their existence does not explain why slow fluctuations in spontaneous activity emerges in the first place. One possibility is that the far-reaching modulatory cholinergic and GABAergic inputs emanating from the several basal forebrain structures drives periods of high and low cortical excitability in concert, which would result in a pattern of functional correlations commonly measured in human fMRI. Our planned first steps to test this hypothesis involves combining inactivation of several basal forebrain structures with fMRI measurements during rest, and later during natural viewing. The null hypothesis of this experiment is clear: inactivation of these projection neurons should have no effect on the pattern of correlated fluctuations over the cortical surface. In another project, we asked to what extent responses observed in high-level visual cortex using fMRI would resemble neural responses observed in the same piece of cortex using microelectrode recordings. We approached this project using natural viewing of social videos, recording both fMRI and electrophysiological responses while monkeys repeatedly watched a number of five-minute video clips. Using this method, we measured a consistent time course evoked for each movie in the microelectrode spiking data and the hemodynamic fMRI data. Given that each voxel in the fMRI signal contains hundreds of thousands of neurons, these results raise the question: to what extent does the activity time course of the fMRI signal in a voxel reflect the time course of spiking within that voxel? As a first step, we have assessed the correlation between adjacent neurons within our microwire array. Neurons were separated by no more than 400 microns, and were thus part of the same cortical column. We found that, even though individual cells are very reliably driven by multiple repetitions of the same movie, nearby cells are remarkably uncorrelated in their responses. Our planned extension of this work is to examine whether some aspect of collective neural activity, such as the mean activity of all neurons in a voxel, or the activity of special, identified neurons, most closely reflects the hemodynamic time course. The hope is that this approach can not only allow us to understand about the coding principles in this part of the brain, but also about how this coding is reflected in the fMRI signal. Finally, we have begun another experiment in which we measure single unit activity from implanted, MR-compatible electrodes, in monkeys that are undergoing fMRI. As described above, adjacent neurons show very different time courses during natural viewing. The same is true during spontaneous activity. Thus one planned experiment is to determine whether two neurons showing different time courses are functionally linked with different fMRI networks in the brain. Thus using spiking fluctuations of two different neurons to reveal two different networks may elucidate the relative contributions of different brain networks to the local neural machinery in a confined cortical area.
|Liu, Xiao; Yanagawa, Toru; Leopold, David A et al. (2014) Robust Long-Range Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia. Cereb Cortex :|
|McMahon, David B T; Bondar, Igor V; Afuwape, Olusoji A T et al. (2014) One month in the life of a neuron: longitudinal single-unit electrophysiology in the monkey visual system. J Neurophysiol 112:1748-62|
|Liu, Junjie V; Hirano, Yoshiyuki; Nascimento, George C et al. (2013) fMRI in the awake marmoset: somatosensory-evoked responses, functional connectivity, and comparison with propofol anesthesia. Neuroimage 78:186-95|
|Leopold, David A (2009) Neuroscience: Pre-emptive blood flow. Nature 457:387-8|
|Shmuel, Amir; Leopold, David A (2008) Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest. Hum Brain Mapp 29:751-61|
|Maier, Alexander; Wilke, Melanie; Aura, Christopher et al. (2008) Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey. Nat Neurosci 11:1193-200|