Timothy D. Hanks - US grants

DESCRIPTION (provided by applicant): A central goal of cognitive neuroscience is to elucidate the neural processes that underlie decision making. Even simple decisions often rely on deliberation that extends in time beyond the immediate sensory environment. Thus, the brain requires mechanisms that allow information to combine over time during the deliberative process. This project aims to exploit a recently developed rat-based model of deliberative decision-making to study the neural process that underlies accumulation of evidence and decision commitment. The Brody lab has been able to train rats to perform the Poisson Clicks Accumulation task, where rats make a decision based on comparing the number of auditory clicks presented on two speakers, one to the left of the rat, the other to its right. It has been shown that rats temporally integrate the sensory evidence provided by the clicks to perform this task. We plan to test the extent to which two candidate areas of the rat brain -- the frontal orienting fields (FOF) and posterior parietal cortex (PPC) -- play a role in sensory evidence-based decision making. To do this, we will reversibly inactivate each area separately while rats perform the Poisson Clicks Accumulation task and measure the effect on performance. We will then test whether neurons in these areas represent quantities related to the accumulation of evidence and decision commitment in their responses by recording neural activity during performance of the task. Finally, we will explore the extent to which these areas play similar or different roles using a network model of the decision process. Fulfillment of the aims of this project would further our understanding of the neural processes that underlie decision making and also develop the rat as a model organism for future studies of these questions. Understanding these brain processes is a key step towards developing targeted, principled treatments for disorders of higher brain function.

Project Summary Impaired decision making is observed in almost all mental disorders. Many types of decisions rely on the gradual accumulation of evidence for the alternatives under consideration. In order to understand the neural circuit mechanisms that underlie evidence accumulation and develop targeted treatments when this process is impaired, we must first identify the neural circuit elements involved. An extensive body of research in primates and rodents has posited that the posterior parietal cortex (PPC) plays an important role in this process, but recent work has called into question the nature of this role. The long-term goal of this research is to first clarify the specific role of PPC for evidence accumulation as a foundation to understand the neural mechanisms that control the natural adaptability of decision policies. The proposed experiments will use a rat model system to examine the primary hypotheses for PPC's role that are consistent with the existing literature. The first aim will test whether PPC plays a direct but non-obligatory role in accumulation-based perceptual decision making. This will be made possible with the use of high temporal resolution optogenetic manipulations to perturb neural activity in PPC during evidence accumulation on randomly interleaved trials. The second aim will test whether PPC plays a role in decision commitment during perceptual decision making rather than a direct role in evidence accumulation. This will be achieved using a free response (reaction time) perceptual decision task where rats control the timing of decision commitment. This work is significant for elucidating the role of PPC, a brain region thought to be critical in decision making. The approach is innovative because it develops a conceptual framework for understanding decision processes at a single trial level in situations where subjects control when they respond in combination with optogenetic techniques in rodentsp to answer a previously inaccessible question. In the long term, we expect this research to harness both primate and rodent model systems to produce a detailed understanding of the neural circuit mechanisms that underlie evidence accumulation, decision commitment, and flexible control of both in decision making. The objective is to develop principled treatments targeted to specific neural circuits for the impairments to decision making associated with mental disorders.

Project Summary Impaired decision making is observed in almost all mental disorders. Neural circuits mechanisms responsible for the flexible control of decision making therefore hold particular promise as targets for treatments to improve decision making impairments. Much progress has been made in elucidating the neural mechanisms supporting many aspects of cognitive flexibility that influence decision processes, but relevantly little is known about the mechanisms that control the form and timescale of evidence evaluation for decision making. In contrast to the dominant paradigms used for the study of perceptual decisions that focus on situations involving linear integration of repeated samples of evidence, many decisions instead benefit from weighting evidence differentially as a function of time. The proposed experiments will use a rat model system to probe the neural circuits underlying flexible control of evidence evaluation in these circumstances. The first aim will develop a new auditory change detection paradigm to study neural contributions to decisions involving non-integrative forms of evidence evaluation. Specifically, we will examine how a network of brain regions known to encode decision variables in tasks involving linear integration of evidence here encodes decision variables based on dynamic weighting with time in evidence evaluation. The second aim will test how altered decision bounds affect this encoding in the change detection paradigm. Decision bounds determine the amount of evidence needed for choice commitment, so they play a key role in control over the timescale of evidence evaluation. We will simultaneously record from three brain regions previously implicated separately in the control of decision bounds to examine how associated neural changes are coordinated between regions. The third aim will test how altered decision kernels induce changes in the same network of brain regions. Decision kernels determine how evidence is weighted as a function of time, so they play a key role in control over the form of evidence evaluation. We will first examine the degree to which each of the studied brain regions alters it response dynamics to external evidence based on altered decision kernels. Next, we will probe the direct involvement of those regions in circuits responsible for the altered response dynamics versus the inheriting of altered response dynamics from upstream neural processing. The objective of this work is to expose targets for principled treatments at the level of specific neural circuit mechanisms to improve decision making impairments associated with mental disorders, including ongoing work in our lab. In the long term, we expect this research to harness a combination of human, non-human primate, and rodent model systems to produce a detailed understanding of the neural circuit mechanisms that underlie flexible control over evidence evaluation for decision making, paving the way for treatment development.

Project Summary A defining feature of cognitive flexibility is the ability to exert control over how information is treated when acting upon it. This is important for decision making, which often involves evaluating information from one's surroundings to select appropriate choices. Corticostriatal circuits have been suggested to play critical roles in these decision processes, but their exact contributions remain unresolved. Our long-term goal is to understand how corticostriatal circuits contribute to flexible control of evidence evaluation and decision selection. Studies in rodents have identified the frontal orienting field (FOF) in the cortex and the anterior dorsal striatum (ADS) to which it projects as playing important roles in evidence evaluation and decision selection. However, little is known about how this circuit controls the timescale of evidence evaluation that guides decision selection, which is important for any situation where evidence is acquired sequentially in time. Building on previous work, our overarching hypothesis is that the ADS plays a role in controlling the period of influence of evidence on choices and that the FOF plays a role in decision selection that is guided by information routed via the ADS. Here, we train rats to perform a novel change detection task that we have developed to address these questions. In Aim 1, we will identify contributions of the ADS to control the timescales of evidence evaluation. We will use a combination of neural recordings and optogenetic perturbation to study neural representations and associated circuit mechanisms. In Aim 2, we will identify contributions of the FOF to free response decision selection with a parallel approach as Aim 1, again combining neural recordings and optogenetic perturbations. In Aim 3, we will measure the influence of the ADS and FOF on each other with simultaneous neural recordings to inform mechanistic models of corticostriatal circuit contributions to evidence evaluation and decision selection. The approach we take is innovative because the lab has developed novel techniques, important refinements of cutting edge techniques, and extensions of established techniques to previously unexplored questions. The contribution of this work is significant because it will fill multiple major gaps in our knowledge about this critically important function and clinically relevant circuit. Furthermore, because corticostriatal circuits and the functions studied here are impacted by multiple brain disorders, an improved understanding of the connection between the two will be useful for developing new mental health treatments and avoiding side effects of treatments targeted to these brain circuits.

Decisions in the brain are collectively made by a network of interacting brain regions that each play different roles in the decision-making process. These distributed networks are flexible, able to respond effectively to changing circumstances, while also highly robust, and able to preserve functionality even when partially disrupted or damaged. This project seeks to understand how these brain regions interact with each other during decision making and how these interactions confer their remarkable flexibility and robustness. To do this, the research team will combine cutting edge technologies for recording and modifying the activity of neurons while rats make decisions, with machine learning techniques for modeling the data generated. The project will improve understanding of brain systems that support decision making and cognition more generally, while also providing critical insight for the next generation of brain-inspired artificial intelligence systems.<br/><br/>The proposal combines large-scale multi-region recordings in awake, behaving rats and data-driven recurrent neural network modeling to investigate the role of association cortex during decision-making through its impact on interacting subcortical areas. The first part of the project will use Neuropixels recordings along with multi-area recurrent neural network modeling to identify whether and how association cortex plays a role in controlling interconnected subcortical dynamics during decision making. The recordings will be targeted to two areas of association cortex and two subcortical regions while rats perform decision tasks that require flexible integration of noisy sensory information over time. The network modeling will be used to disambiguate competing hypotheses for the specific roles of each brain region in shaping neural dynamics during decision formation. The second part of the project will combine similar network modeling with experimental perturbation of brain activity through optogenetics to identify mechanisms that underlie the robustness of neural decision making. The investigators will identify mechanisms of structural robustness, which arise from the architecture of the distributed network itself, and mechanisms of dynamic robustness, which involve active compensation for perturbation. In this manner, the project will synergize recent exciting advances in both machine learning and tools for systems neuroscience to create a tight experiment-theory loop to address questions with broad interdisciplinary importance, with implications at the core of developing principled treatments for cognitive disorders.<br/><br/>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.