Biology subjects

Ballard, I. C.

Publications and source records attributed to Ballard, I. C..

7 recordsLinked to original sources

bioRxiv2024

A dopaminergic basis of behavioral control

Both goal-directed and automatic processes shape human behavior, but these processes often conflict. Behavioral control is the decision about which process guides behavior. Despite the importance of behavioral control for adaptive decision-making, its neural mechanisms remain unclear. Critically, it is unknown if there are mechanisms for behavioral control that are distinct from those supporting the formation of goal-relevant knowledge. We performed deep phenotyping of individual dopamine system function by combining multiple PET scans, fMRI, and dopaminergic drug administration in a within-subject, double-blind, placebo-controlled design. Subjects performed a rule-based response time task, with goal-directed and automatic decision-making operationalized as model-based and model-free influences on behavior. We found a double dissociation between two aspects of ventral striatal dopamine physiology: D2/3 receptor availability and dopamine synthesis capacity. Convergent and causal evidence indicated that D2/3 receptors regulate behavioral control by enhancing model-based and blunting model-free influences on behavior but do not affect model-based knowledge formation. In contrast, dopamine synthesis capacity was linked to the formation of model-based knowledge but not behavioral control. D2/3 receptors also modulated frontostriatal functional connectivity, suggesting they regulate behavioral control by gating prefrontal inputs to the striatum. These results identify central mechanisms underlying individual and state differences in behavioral control and point to striatal D2/3 receptors as targets for interventions for improving goal-directed behavior.

neuroscience↗

bioRxiv2023

In Vivo Structural Connectivity of the Reward System Along the Hippocampal Long-Axis

Recent work has identified a critical role for the hippocampus in reward-sensitive behaviors, including motivated memory, reinforcement learning, and decision-making. Animal histology and human functional neuroimaging have shown that brain regions involved in reward processing and motivation are more interconnected with the ventral/anterior hippocampus. However, direct evidence examining gradients of structural connectivity between reward regions and the hippocampus in humans is lacking. The present study used diffusion MRI and probabilistic tractography to quantify the structural connectivity of the hippocampus with key reward processing regions in vivo. Using a large sample of subjects (N=628) from the human connectome dMRI data release, we found that connectivity profiles with the hippocampus varied widely between different regions of the reward circuit. While the dopaminergic midbrain (VTA) showed stronger connectivity with the anterior versus posterior hippocampus, the vmPFC showed stronger connectivity with the posterior hippocampus. The nucleus accumbens (NAc) showed a more homogeneous connectivity profile along the hippocampal long-axis. This is the first study to generate a probabilistic atlas of the hippocampal structural connectivity with reward-related networks, which is essential to investigating how these circuits contribute to normative adaptive behavior and maladaptive behaviors in psychiatric illness. These findings describe nuanced structural connectivity that sets the foundation to better understand how the hippocampus influences reward-guided behavior in humans.

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bioRxiv2021

Emotional context sculpts action goal representations in the lateral frontal pole

Emotional states provide an ever-present source of contextual information that should inform behavioral goals. Despite the ubiquity of emotional signals in our environment, the neural mechanisms underlying their influence on goal-directed action remains unclear. Prior work suggests that the lateral frontal pole (FPl) is uniquely positioned to integrate affective information into cognitive control representations. We used pattern similarity analysis to examine the content of representations in FPl and interconnected mid-lateral prefrontal and amygdala circuitry. Healthy participants (n=37; n=21 females) were scanned while undergoing an event-related Affective Go/No-Go task, which requires goal-oriented action selection during emotional processing. We found that FPl contained conjunctive emotion-action goal representations that were related to successful cognitive control during emotional processing. These representations differed from conjunctive emotion-action goal representations found in the basolateral amygdala. While robust action goal representations were present in mid-lateral prefrontal cortex, they were not modulated by emotional valence. Finally, converging results from functional connectivity and multivoxel pattern analyses indicated that FPls emotional valence signals likely originated from interconnected subgenual ACC (BA25), which was in turn functionally coupled with the amygdala. Thus, our results identify a key pathway by which internal emotional states influence goal-directed behavior. Significance statementOptimal functioning in everyday life requires behavioral regulation that flexibly adapts to dynamically changing emotional states. However, precisely how emotional states influence goal-directed action remains unclear. Unveiling the neural architecture that supports emotion-goal integration is critical for our understanding of disorders such as psychopathy, which is characterized by deficits in incorporating emotional cues into goals, as well as mood and anxiety disorders, which are characterized by impaired goal-based emotion regulation. Our study identifies a key circuit through which emotional states influence goal-directed behavior. This circuitry comprised the lateral frontal pole (FPl), which represented integrated emotion-goal information, as well as interconnected amygdala and subgenual ACC, which conveyed emotional signals to FPl.

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bioRxiv2020

Executive modulation of brain reward systems endows goals with value

People often learn from the outcomes of their actions, even when these outcomes do not involve material rewards or punishments. How does our brain provide this flexibility? We combined behavior, computational modeling, and functional neuroimaging to probe whether learning from transient goals harnesses the same circuitry that supports learning from secondary reinforcers. Behavior and neuroimaging revealed that "one-shot" transient goals (abstract fractal images seen once) can act as a substitute for rewards during instrumental learning, and produce reliable reward-like signals in dopaminergic reward circuits. Moreover, we found evidence that prefrontal correlates of executive control may play a role in shaping these responses in reward circuits. These results suggest that learning from abstract goal outcomes is supported by an interplay between high-level representations in prefrontal cortex and low-level responses in subcortical reward circuits. This interaction may allow humans to perform reinforcement learning over flexible, arbitrarily abstract reward functions.

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bioRxiv2018

Joint Modeling of Reaction Times and Choice Improves Parameter Identifiability in Reinforcement Learning Models

BackgroundReinforcement learning models provide excellent descriptions of learning in multiple species across a variety of tasks. Many researchers are interested in relating parameters of reinforcement learning models to neural measures, psychological variables or experimental manipulations. We demonstrate that parameter identification is difficult because a range of parameter values provide approximately equal quality fits to data. This identification problem has a large impact on power: we show that a researcher who wants to detect a medium sized correlation (r = .3) with 80% power between a variable and learning rate must collect 60% more subjects than specified by a typical power analysis in order to account for the noise introduced by model fitting.\n\nNew MethodWe derive a Bayesian optimal model fitting technique that takes advantage of information contained in choices and reaction times to constrain parameter estimates.\n\nResultsWe show using simulation and empirical data that this method substantially improves the ability to recover learning rates.\n\nComparison with Existing MethodsWe compare this method against the use of Bayesian priors. We show in simulations that the combined use of Bayesian priors and reaction times confers the highest parameter identifiability. However, in real data where the priors may have been misspecified, the use of Bayesian priors interferes with the ability of reaction time data to improve parameter identifiability.\n\nConclusionsWe present a simple technique that takes advantage of readily available data to substantially improve the quality of inferences that can be drawn from parameters of reinforcement learning models.\n\nHighlights- Parameters of reinforcement learning models are particularly difficult to estimate\n- Incorporating reaction times into model fitting improves parameter identifiability\n- Bayesian weighting of choice and reaction times improves the power of analyses assessing learning rate

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bioRxiv2018

Causal Evidence for the Dependence of the Magnitude Effect on Dorsolateral Prefrontal Cortex

Impulsivity refers to the tendency to insufficiently consider alternatives or to overvalue rewards that are available immediately. Impulsivity is a hallmark of human decision making with well documented health and financial ramifications. Numerous contextual changes and framing manipulations powerfully influence impulsivity. One of the most robust such phenomenon is the finding that people are more patient as the values of choice options are increased. This magnitude effect has been related to cognitive control mechanisms in the dorsal lateral prefrontal cortex (dlPFC). We used repetitive transcranial magnetic stimulation (rTMS) to transiently disrupt dlPFC neural activity. This manipulation dramatically reduced the magnitude effect, establishing causal evidence that the magnitude effect depends on dlPFC.

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bioRxiv2018

Hippocampal Pattern Separation Supports Reinforcement Learning

1Animals rely on learned associations to make decisions. Associations can be based on relationships between object features (e.g., the three-leaflets of poison ivy leaves) and outcomes (e.g., rash). More often, outcomes are linked to multidimensional states (e.g., poison ivy is green in summer but red in spring). Feature-based reinforcement learning fails when the values of individual features depend on the other features present. One solution is to assign value to multifeatural conjunctive representations. We tested if the hippocampus formed separable conjunctive representations that enabled learning of response contingencies for stimuli of the form: AB+, B-, AC-, C+. Pattern analyses on functional MRI data showed the hippocampus formed conjunctive representations that were dissociable from feature components and that these representations influenced striatal PEs. Our results establish a novel role for hippocampal pattern separation and conjunctive representation in reinforcement learning.

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