A data-driven approach to the automated study of cross-species homologies

Behavioral neuroscience has made great strides in developing animal models of human behavior and psychiatric disorders. Animal models allow for the formulation of hypotheses regarding the mechanisms underlying psychiatric disorders, and the opportunity to test these hypotheses using procedures that are too invasive for human participants. However, recent scientific reviews have highlighted the low success rate of translating results from animal models into clinical interventions in humans. A potential roadblock is that bidirectional functional mappings between the human and rodent brain are incomplete. To narrow this gap, we created a framework, Neurobabel, for performing large-scale automated synthesis of human neuroimaging data and behavioral neuroscience data. By leveraging the semantics of how researchers within each field describe their studies, this framework enables region to region mapping of brain regions across species, as well as cross-species mapping of psychological functions. As a proof of concept, we utilize the framework to create a functional cross-species mapping between the amygdala and hippocampus for fear-related and spatial memories, respectively. We then proceed to address two open questions in the field: (1) Do rodents have a dorsolateral prefrontal cortex? (2) Which human brain region corresponds to the rodent prelimbic cortex

Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference

Adaptive behavior in even the simplest decision-making tasks requires predicting future events in an environment that is generally nonstationary. As an inductive problem, this prediction requires a commitment to the statistical process underlying environmental change. This challenge can be formalized in a Bayesian framework as a question of choosing a generative model for the task dynamics. Previous learning models assume, implicitly or explicitly, that nonstationarity follows either a continuous diffusion process or a discrete changepoint process. Each approach is slow to adapt when its assumptions are violated. A new mixture of Bayesian experts framework proposes separable brain systems approximating inference under different assumptions regarding the statistical structure of the environment. This model explains data from a laboratory foraging task, in which rats experienced a change in reward contingencies after pharmacological disruption of dorsolateral (DLS) or dorsomedial striatum (DMS). The data and model suggest DLS learns under a diffusion prior whereas DMS learns under a changepoint prior. The combination of these two systems offers a new explanation for how the brain handles inference in an uncertain environment

Long-term memories in dorsomedial and dorsolateral striatal areas differentially affect behavioral flexibility

Actions can be performed because they are expected to be rewarded, or simply because they have been expressed so many times that contextual cues elicit them automatically. Existing data suggest that the dorsomedial striatum (DMS) supports expectancy-driven behaviors, and that the dorsolateral striatum (DLS) allows contextual cues to elicit habitual behavior. However, it is still an open question whether or not the separable contributions of these neural substrates depend on a long-term memory of task-relevant information. Alternatively, this information may be stored in other areas that are interacting with these striatal regions. In order to address this question, rats were trained to press two levers associated with concurrent variable interval schedules of reinforcement. We first demonstrated that injecting the NMDA antagonist DL-AP5 in the DMS during testing renders rats insensitive to contingency changes, but when injected into the DLS, it enhanced their sensitivity to contingency changes, thereby implicating their differential in expectancy-driven and habitual responding. We then used the protein kinase zeta (PKMζ) inhibitor zeta pseudosubstrate inhibitory peptide (ZIP) to determine if these regions provide storage support for the information needed to respond to contingency changes. Injecting ZIP into the DLS enhanced the rats\u27 ability to adapt to the contingency shift, whereas injecting ZIP into the DMS had the opposite effect. It was possible to rescue the sensitivity to contingency changes by preventing the effect of ZIP with infusions of GluR23Y into the DMS one hour before ZIP microinfusions. This indicates that ZIP was preventing the ability of PKMζ to maintain established memories in the dorsal striatum by regulating GluR2-dependent AMPA receptor trafficking. These results indicate that the two dorsal striatal areas actively maintain long-term memories that affect the sensitivity to contingency changes

A data-driven approach to the automated study of cross-species homologies

Behavioral neuroscience has made great strides in developing animal models of human behavior and psychiatric disorders. Animal models allow for the formulation of hypotheses regarding the mechanisms underlying psychiatric disorders, and the opportunity to test these hypotheses using procedures that are too invasive for human participants. However, recent scientific reviews have highlighted the low success rate of translating results from animal models into clinical interventions in humans. A potential roadblock is that bidirectional functional mappings between the human and rodent brain are incomplete. To narrow this gap, we created a framework, Neurobabel, for performing large-scale automated synthesis of human neuroimaging data and behavioral neuroscience data. By leveraging the semantics of how researchers within each field describe their studies, this framework enables region to region mapping of brain regions across species, as well as cross-species mapping of psychological functions. As a proof of concept, we utilize the framework to create a functional cross-species mapping between the amygdala and hippocampus for fear-related and spatial memories, respectively. We then proceed to address two open questions in the field: (1) Do rodents have a dorsolateral prefrontal cortex? (2) Which human brain region corresponds to the rodent prelimbic cortex

Neural computations underlying inverse reinforcement learning in the human brain

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself

A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

Recent advances in magnetic resonance imaging methods, including data acquisition, pre-processing and analysis, have benefited research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in vivo anatomical atlas of subcortical nuclei. In order to address this need, we constructed high spatial resolution, three-dimensional templates, using high-accuracy diffeomorphic registration of T_1- and T_2- weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineations of subcortical nuclei complement current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in vivoprobabilistic anatomical atlas of the human brain

Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference

Adaptive behavior in even the simplest decision-making tasks requires predicting future events in an environment that is generally nonstationary. As an inductive problem, this prediction requires a commitment to the statistical process underlying environmental change. This challenge can be formalized in a Bayesian framework as a question of choosing a generative model for the task dynamics. Previous learning models assume, implicitly or explicitly, that nonstationarity follows either a continuous diffusion process or a discrete changepoint process. Each approach is slow to adapt when its assumptions are violated. A new mixture of Bayesian experts framework proposes separable brain systems approximating inference under different assumptions regarding the statistical structure of the environment. This model explains data from a laboratory foraging task, in which rats experienced a change in reward contingencies after pharmacological disruption of dorsolateral (DLS) or dorsomedial striatum (DMS). The data and model suggest DLS learns under a diffusion prior whereas DMS learns under a changepoint prior. The combination of these two systems offers a new explanation for how the brain handles inference in an uncertain environment

Regional specialization within the human striatum for diverse psychological functions

Decades of animal and human neuroimaging research have identified distinct, but overlapping, striatal zones, which are interconnected with separable corticostriatal circuits, and are crucial for the organization of functional systems. Despite continuous efforts to subdivide the human striatum based on anatomical and resting-state functional connectivity, characterizing the different psychological processes related to each zone remains a work in progress. Using an unbiased, data-driven approach, we analyzed large-scale coactivation data from 5,809 human imaging studies. We (i) identified five distinct striatal zones that exhibited discrete patterns of coactivation with cortical brain regions across distinct psychological processes and (ii) identified the different psychological processes associated with each zone. We found that the reported pattern of cortical activation reliably predicted which striatal zone was most strongly activated. Critically, activation in each functional zone could be associated with distinct psychological processes directly, rather than inferred indirectly from psychological functions attributed to associated cortices. Consistent with well-established findings, we found an association of the ventral striatum (VS) with reward processing. Confirming less well-established findings, the VS and adjacent anterior caudate were associated with evaluating the value of rewards and actions, respectively. Furthermore, our results confirmed a sometimes overlooked specialization of the posterior caudate nucleus for executive functions, often considered the exclusive domain of frontoparietal cortical circuits. Our findings provide a precise functional map of regional specialization within the human striatum, both in terms of the differential cortical regions and psychological functions associated with each striatal zone

Behavioural evidence for parallel outcome-sensitive and outcome-insensitive Pavlovian learning systems in humans

There is a dichotomy in instrumental conditioning between goal-directed actions and habits that are distinguishable on the basis of their relative sensitivity to changes in outcome value. It is less clear whether a similar distinction applies in Pavlovian conditioning, where responses have been found to be predominantly outcome-sensitive. To test for both devaluation-insensitive and devaluation-sensitive Pavlovian conditioning in humans, we conducted four experiments combining Pavlovian conditioning and outcome-devaluation procedures while measuring multiple conditioned responses. Our results suggest that Pavlovian conditioning involves two distinct types of learning: one that learns the current value of the outcome, which is sensitive to devaluation, and one that learns about the spatial localization of the outcome, which is insensitive to devaluation. Our findings have implications for the mechanistic understanding of Pavlovian conditioning and provide a more nuanced understanding of Pavlovian mechanisms that might contribute to a number of psychiatric disorders

Neural computations underlying inverse reinforcement learning in the human brain

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself