Nicholas A. Steinmetz, Ph.D. - US grants

Summary/Abstract Understanding the neural mechanisms underpinning cognition and behavior requires the ability to measure the dynamics and interactions of populations of neurons spread across many brain regions. Electrophysiological techniques provide the ability to measure this activity across superficial and deep structures at the speed of thought. Recent advances in electrophysiology have massively increased data quantity, quality, and ease of acquisition, thereby meaningfully reducing barriers to understanding the global brain circuits underlying behavior. A significant remaining challenge is to optimize device characteristics in order to further broaden utility, improve data quality, and accelerate the pace of research. In particular, state of the art site density is spatially too coarse to detect some cell types and neuronal processes; it remains challenging to record neurons stably in the face of brain motion; and data preprocessing is still a major limiting factor in the pace of experiments. This proposal will address these limitations by producing and evaluating a new device with >10x the number of recording sites than state-of-the-art, corresponding to an order of magnitude higher density. This device thus functions like a high-resolution electrical camera in the brain, able to image tiny electrical fields and capable of capitalizing on techniques from optics such as image registration for recording stability. We will validate and develop the new probe's characteristics by quantifying their increased ability to detect a large range of neuron types; by testing and developing their ability to track neurons across brain motion using controlled conditions; by improving algorithms towards automation of data preprocessing; and by conducting multi-modal ground-truth experiments. These probes will go beyond solving technical limitations, additionally providing new types of data: electrical imaging of `electro-morphological' shapes will enable enhanced cell-type identification and validation of neuronal biophysical models in vivo. We will disseminate the new probes, along with user-friendly software to take advantage of their improved characteristics, to `beta-tester' labs specifically interested in studying key areas of scientific opportunity. These areas include dendritic computation, freely-moving behavior, and cerebellar function, and this direct dissemination will rapidly accelerate their impact on scientific advancement.

PROJECT SUMMARY When searching for resources such as food animals accumulate information over time. How this is accomplished by the olfactory system is largely unknown due to two constraints. First, the sensory cues used for odor-guided searches (odor plumes) are notoriously complex and unable to be completely predicted by computational models. Second, the current technology for detecting odor plumes is too large to use with freely moving animals. These limitations mean that our understanding of how animals search with odors, an ability seen across numerous diverse species, is still in its infancy. This is especially true for mice, animals that are one of the major biomedical model species and that rely on odors to find food. This proposal will use new head-mounted odor sensors to accurately detect odor plume encounters by mice while they are using these sensory cues to search. We will combine these sensors with Neuropixels probes to record from hundreds of neurons simultaneously and chart the flow of information through the olfactory system and to cortical decision- making structures. Specifically, we will test the relationship between neural signatures of odor encounters in the olfactory cortex and the guidance of search behavior by the orbitofrontal cortex. We will assess how information is transmitted between these two connected structures as well as how the orbitofrontal cortex accumulates odor evidence. These goals will be accomplished by training mice to find the source of a volatile organic compound, ethanol, which will be detected by miniature sensors that we have altered to become fast response and head-mountable. While animals search for the source of this odor, the sensor will transmit any contact that they make with the odor plume. We will then reconstruct the information obtained by the animal during its search to ascertain how this information guides decisions. Using Neuropixels probes we will extend this analysis into the large-scale neural circuits that support this complex behavior. By recording neural activity simultaneously in the olfactory and orbitofrontal cortices we will test how odor information is routed from sensory to decision-making areas under multiple odor-guided search conditions. These conditions will include searches in complex environments with background odors. We will functionally test this circuit by targeted optogenetic inactivation of the feedback pathway from the oribitofrontal cortex to the olfactory cortex. We will measure the impact of this inactivation both behaviorally and neurophysiologically and quantify changes in odor information in both structures. We postulate that orbitofrontal cortex will accumulate information during odor-guided search and that feedback from the orbitofrontal to the olfactory cortex will suppress background odor input, enhancing search effectiveness. The successful development of this paradigm can serve as an innovative new model for studying the interactions between sensory and decision-making systems of the brain, enhancing understanding of how the brain accumulates information from complex sensory signals.

While the brain's computational abilities are in many respects unrivaled, its workings appear to be far noisier than those of almost any engineered computational system. Even in carefully controlled experiments in which the same conditions are presented over multiple trials, neural activity is strikingly variable from one trial to the next. This project aims to resolve the apparent contradiction between the brain's computational proficiency and its apparently high levels of noise. The core hypothesis is that much of the observed neural variability is driven not by noise but by internal brain modes -- that is, by coordinated patterns of activity across the brain. Thus, variability may be a signature of dynamic and uniquely biological computations rather than noisy fluctuations. If this hypothesis is correct, then observation of these global modes should explain choices that subjects make in behavioral tasks, and perturbation of the modes should alter their patterns of choices in systematic ways. To test this hypothesis, the project employs a novel joint experimental and theoretical approach to measure the variability in brain-wide neural activity across scales, to define its relationship to behavior, and to dynamically perturb these modes to impact behavioral performance both within and across individuals.

To build a transformative understanding of the link between neural and behavioral variability, the project will use multi-probe Neuropixels technology that enables simultaneous recording at submillisecond resolution from thousands of individual neurons distributed across the brain, coupled with advanced data analytic and dynamical modeling tools to extract activity modes from these data. These analyses will be performed together with behavioral assays that probe multiple aspects of behavioral performance, including engagement, perceptual sensitivity, and vigor. Statistical modeling will then be used to identify the functional role of these modes in regulating behavioral performance, and how their activity drives behavioral differences both across trials and across individuals. Beyond correlative analysis, control theory tools will design patterned optogenetic perturbations to provide direct causal tests of this novel functional role for brain-wide activity modes. If the project succeeds, the result will be a new understanding of the nature of the ongoing fluctuations in brain-wide activity patterns trial by trial and individual by individual in terms of behavior rather than noise -- a key step in deciphering the logic of distributed computation underlying perception and cognition. The project will develop and disseminate novel open data and code to impact research and training nationwide. It will also build a dynamic, team-mentored environment led by investigators from very distinct disciplines -- from neuroscience to applied mathematics to control engineering -- which will prepare both undergraduate and graduate trainees to bridge disciplines and scientific cultures.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.