There is a range of approaches to understanding how fMRI signals can be understood in terms of underlying neural data. In some laboratories, experiments are aimed at carefully determining what types of neurons and molecules serve as the actual mediators for regulating blood flow. Such experiments ask, what are the biological principles, and specific mechanisms, that determine neurovascular coupling? Our approach is somewhat coarser and asks instead more practical questions. One such question asks how does the firing of a single neuron, or a population of neurons, contribute to the measured fMRI response within a small volume of the brain, or voxel? Consider the following. When measuring brain activity with fMRI, each voxel is a smallest unit the electron of brain activity measurement. However, within one of these voxels, there typically resides hundreds of thousands of individual neurons. We know from direct recordings of such neurons that neural responses in such a volume are diverse. For example, when monkeys view natural movies, nearby neurons across a population respond at different times, to different events in the movie. But if all of the neurons respond differently at a given point in time, then how can the hemodynamic signal from the point in the brain be interpreted in terms of neural firing? Is it the mean of the population? Are there particular, special neurons that determine the fMRI signal? Another question we ask relates to the brains spontaneous activity. During rest, the brain continues to show massive fMRI fluctuations. This activity has an internal origin, but exhibits a certain amount of spatial consistency in its pattern of correlation. In this second line of research, we aim to understand the neural origins of this spontaneous activity. In the first direction, we have been combining experiments using implanted multielectrodes with those using fMRI, in each case to measure activity in the brain. Rather than showing conventional, flashed, static stimuli, we have used dynamic movies as a method for tapping into the diversity of neural responses. We have, for example, recorded neural responses within an fMRI-defined face patch (where during conventional testing responses are higher for faces than other stimulus categories). In a significantly surprising result, we found that during the viewing of socially rich videos individual neurons had two properties Elnaeim et al. SFN Abstr (2013). First, their responses were very consistent across viewings of the same movie. Second, which is the surprising part, most of the nearby neurons responded at completely different times during the movie. In trying to understand how the activity of neurons all within half a millimeter of one another could be so uncoupled with one another, we are presently attempting testing individual neurons with thousands of different static and dynamic visual stimuli to understand under what conditions they behave similarly and under what conditions they begin to diverge. At the same time, we are attempting to understand what aspects of this population activity most closely match the fMRI signal measured from the same point in visual cortex Park et al, SFN Abstr (2014). These findings may provide some insights into the complex relationship between neural responses, the relationship of neural firing within a population, and the larger relationship to hemodynamic regulation. In a rather different approach, we have been attempting to understand the principles governing spontaneous activity in the brain, including the neural mechanisms that give rise to the prominent hemodynamic fluctuations commonly observed at rest. Over the previous year, my laboratory has contributed to at four publications investigating spontaneous fMRI activity Liu et al, NeuroImage (2013);Scholvinck et al, NeuroImage (2013);Hutchison et al, NeuroImage (2013);Liu et al, Cerebral Cortex (2014). Experiments going on in the lab currently attempt to ask whether the activity in the basal forebrain may be a critical factor shaping spontaneous neural activity throughout the rest of the brain. The basal forebrain is a small region that is the origin of many long-range anatomical projections that reach virtually the entire cerebral cortex. Our initial expeiments inactivating portions of this structure in animals undergoing fMRI testing suggests that the basal forebrain is strongly involved in regulation of spontaneous signals throughout the telencephalon, particularly during over transitions of arousal gauged by eye opening and closure. This work has lead to the publication of two abstracts at the upcoming Society for Neuroscience Meeting in Washington, DC Chang et al, SFN Abstr (2014);Turchi et al, SFN Abstr (2014).
| Ghazizadeh, Ali; Griggs, Whitney; Leopold, David A et al. (2018) Temporal-prefrontal cortical network for discrimination of valuable objects in long-term memory. Proc Natl Acad Sci U S A 115:E2135-E2144 |
| Seidlitz, Jakob; Sponheim, Caleb; Glen, Daniel et al. (2018) A population MRI brain template and analysis tools for the macaque. Neuroimage 170:121-131 |
| Turchi, Janita; Chang, Catie; Ye, Frank Q et al. (2018) The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations. Neuron 97:940-952.e4 |
| Seidlitz, Jakob; Váša, František; Shinn, Maxwell et al. (2018) Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron 97:231-247.e7 |
| Liu, Xiao; de Zwart, Jacco A; Schölvinck, Marieke L et al. (2018) Subcortical evidence for a contribution of arousal to fMRI studies of brain activity. Nat Commun 9:395 |
| Takemura, Hiromasa; Pestilli, Franco; Weiner, Kevin S et al. (2017) Occipital White Matter Tracts in Human and Macaque. Cereb Cortex 27:3346-3359 |
| Park, Soo Hyun; Russ, Brian E; McMahon, David B T et al. (2017) Functional Subpopulations of Neurons in a Macaque Face Patch Revealed by Single-Unit fMRI Mapping. Neuron 95:971-981.e5 |
| Leopold, David A; Russ, Brian E (2017) Human Neurophysiology: Sampling the Perceptual World. Curr Biol 27:R71-R73 |
| Reveley, Colin; Gruslys, Audrunas; Ye, Frank Q et al. (2017) Three-Dimensional Digital Template Atlas of the Macaque Brain. Cereb Cortex 27:4463-4477 |
| Papoti, Daniel; Yen, Cecil Chern-Chyi; Hung, Chia-Chun et al. (2017) Design and implementation of embedded 8-channel receive-only arrays for whole-brain MRI and fMRI of conscious awake marmosets. Magn Reson Med 78:387-398 |
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