Biology subjects

Frank, M. J.

Publications and source records attributed to Frank, M. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Acute stress reorganizes pregenual cingulate and subthalamic coupling to approach-avoidance decisions in remitted depression

Major depressive disorder (MDD) is highly recurrent, and psychosocial stress is a central precipitant of relapses. Daily choices require weighing potential rewards against costs, a process disrupted in MDD that may contribute to relapse. Here, we tested whether a psychosocial stressor affects neural and computational mechanisms underlying approach-avoidance decision making in individuals with remitted MDD (rMDD, N=50) and healthy controls (N=74), prioritizing pregenual anterior cingulate cortex (pgACC) and subthalamic nucleus (STN) due to their roles in value integration and decision caution, respectively. Participants performed a dynamic approach-avoidance conflict task with fMRI before and after psychosocial stress. Hierarchical Bayesian drift-diffusion modeling evaluated whether stress modulated computational and neural mechanisms, with trial-wise pgACC and STN activity entered as neural regressors on evidence accumulation and decision threshold, respectively. Before stress, groups did not differ in any decision parameter, but pgACC activity contributed to evidence accumulation toward avoidance in controls only. After stress, rMDD showed larger increases than controls in the impact of both reward and aversiveness on accumulation toward approach and avoidance, respectively, predominantly for net-aversive offers. Furthermore, in rMDD, stress rendered both approach and avoidance decisions more sensitive to pgACC activity, and STN activity was more strongly coupled to the decision threshold than in controls. Finally, stress-induced increases in reward sensitivity in rMDD predicted one-year course of depressive symptoms. Thus, MDD remission is not characterized by a generalized blunting of decision-making, but by a latent circuit difference that acute stress reorganizes and predicts future symptoms.

neuroscience↗

bioRxiv2026

HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neuro-cognitive Modeling

Computational models are central to cognitive neuroscience, but their rigorous application to experimental datasets is often constrained to a narrow set of canonical models that afford tractable analytical computations. We introduce the HSSM (Hierarchical Sequential Sampling Model) ecosystem, a Python toolbox that democratizes access to a broad, extensible array of neurocognitive process models through hierarchical Bayesian inference. Naturally leveraging simulation-based inference via likelihood surrogates, HSSM enables fast parameter estimation for models lacking closed-form likelihoods. Built atop PyMC and Bambi, HSSM provides a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters, incorporating trial-by-trial neural or physiological covariates. The ecosystem allows fast model simulation and training data generation, as well as the neural network training utilities to deploy surrogate likelihood networks via HuggingFace. Contributions are designed to benefit not only the single researcher working on a problem, but organically, the entire research community. Together, the tools in the HSSM ecosystem bridge the interests of computational theorists as well as experimentalists, accelerating the cycle from model development to rigorous empirical testing.

neuroscience↗

bioRxiv2025

Behavioral Opportunism and Altered Dopamine Dynamics in Mice Exposed to Early Life Adversity

Early life adversity (ELA) confers risk for reward-related psychopathologies. These risks may stem from adaptations optimizing reward pursuit in anticipation of unreliable, resource poor environments. One rational adaptation to poor, unreliable environments is Behavioral Opportunism: updating expectations more slowly and acting vigorously only when reward is immediately available. To systematically test the impact of ELA on behavioral strategies and underlying reward processing mechanisms, we exposed mice to resource restriction (limited bedding and nesting materials for 7 days) to manipulate the reliability and quality of early life care. Subsequently, we tested adults reward learning and decision making in a two-arm bandit task and recorded dopamine signaling using dLight1.2 fiber photometry in the nucleus accumbens core. Exposure to ELA led to poorer choice discrimination, impaired learning, and decreased adaptation to changes in reward availability. Furthermore, ELA mice were slower to choose between levers but were faster to retrieve immediately available rewards when delivered, consistent with a strategy of behavioral opportunism. Dopamine signaling predicted behavior in both rearing conditions, and its fluctuations were strongly predictive of faster retrieval in ELA mice and an increased likelihood of choice repetition, implying that aberrant dopamine signals underlie slowed learning and vigorous action for immediately available rewards. To understand key features of maternal interactions driving these effects, we used home cage video monitoring to quantify maternal behaviors, continuously, across early life. We found that specific experiential outcomes, such as maternal kicking, intensified behavioral opportunism in adults, predicting poorer bandit task performance beyond the group effect of ELA. Behavioral opportunism provides an explanatory framework for interpreting altered reward processing and reward pursuit in adulthood for individuals exposed to ELA.

animal behavior and cognition↗

bioRxiv2025

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

BackgroundMajor depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Understanding both the similarities and differences in these mechanisms is important to guide the development of new, targeted interventions. Methods255 participants diagnosed with MDD (n=54), BP (n=47), SCZ (n=67) or without any diagnoses (CTRL; n=87) performed an associative learning task. Computational modeling quantified the mechanistic interplay between working memory (WM) and reinforcement learning (RL). The latent RL and WM signatures in the EEG dynamics showed shared and distinct neurocognitive mechanisms underlying learning. ResultsAll clinical groups showed learning impairments at the behavioral level. Model-based EEG analyses linked these impairments to distinct patterns in the dynamic interplay between latent RL and WM mechanisms, contrasting with the typical patterns observed in CTRL. SCZ was characterized by reduced neural markers of WM, weakening the cooperative influence of WM onto RL (reduced WM recruitment), and reduced integration of negative feedback. Conversely, MDD was characterized by reduced reciprocal influence of RL onto WM, reducing the tendency to upregulate WM contribution with reward history (impaired WM management). Finally, BP was characterized by deficits in both WM and RL recruitment, along with higher WM decay. ConclusionsBehavioral learning impairments that appear similar across clinical groups can be linked to distinct neurocognitive mechanisms via integrative neurocomputational modeling. Our approach provides insights into the interplay of underlying learning mechanisms and how they manifest differently across psychopathologies. CitationThis manuscript is a preprint version of the later manuscript accepted for publication in Biological Psychiatry: Global Open Science. The content may differ from the final published version following peer review and editorial revisions. Ging-Jehli, N.R., Rac-Lubashevsky, R., Bera, K., Roberts, A., Loder, A., Boudewyn, M.A., Carter, C.S., Erickson, M., Gold, J., Luck, S.J., Ragland, J.D., Yonelinas, A.P., MacDonald III, A.W., Barch, D.M., & Frank, M.J. (2025). Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies. Preprint at bioRxiv.

pathology↗

bioRxiv2025

Ventral striatal astrocytes contribute to reinforcement learning

Astrocytes influence synaptic plasticity and neuronal function through astrocytic calcium dynamics (ACD). However, astrocytes contribution to cognitive operations like reinforcement learning (RL) remains unclear. To examine this, we trained mice on a RL dependent probabilistic decision-making task. We attenuated ACD across distinct striatal regions, and found ACD attenuation specifically in ventral striatum (VS) increased decision noisiness and impaired reward-guided choice performance. This effect was largely due to a reduction in "win-stay" behavior. Using in-vivo calcium imaging, we found that VS ACD correlated with reward prediction errors (RPEs). Furthermore, these trial-by-trial ACD fluctuations predicted trial-by-trial choice variability. In-silico lesions of a biologically constrained circuit model suggest that astrocytes could regulate behavioral variability by sharing RPE signals across populations of striatal neurons. Together, these results suggest that VS astrocytes contribute to cortico-striatal functions to mediate decision noisiness.

neuroscience↗

bioRxiv2025

A Novel Approach-Avoidance Task to Study Decision Making Under Outcome Uncertainty

To behave adaptively, people need to integrate information about probabilistic outcomes and balance drives to approach positive outcomes and avoid negative outcomes. However, questions remain about how uncertainty in positive and negative outcomes influence approach-avoid decision-making dynamics. To fill this gap, we developed a novel Probabilistic Approach Avoidance Task (PAAT) and characterized behavior in this task using sequential sampling models In this task, participants (Study 1: blinded mixed clinical sample N=34; Study 2: online nonpsychiatric sample N = 58) made a series of choices between pairs of options, each consisting of variable probabilities of reaching a positive outcome (monetary reward) and of reaching a negative outcome (aversive image). Participants tended to choose options that maximized the likelihood of reward and minimized the likelihood of aversive outcomes. Moreover, the weights they placed on each of these differed for choices where these likelihoods were in opposition (i.e., the riskier option was also more rewarding; incongruent trials) relative to when these were aligned (congruent trials). Computational modeling revealed that the relative influence of rewarding and aversive outcomes on choice was captured by differences in the rate of decision-relevant information accumulation. These modeling results were validated with a series of model comparisons and posterior predictive checks, demonstrating that our sequential sampling models reliably captured our behavioral data. Together, these findings improve our understanding of the influence of motivational conflict, outcome type, and levels of uncertainty on approach-avoid decision-making.

neuroscience↗

bioRxiv2025

Adaptive learning via BG-thalamo-cortical circuitry

People adjust their use of feedback over time through a process referred to as adaptive learning. We have recently proposed that the underlying mechanisms of adaptive learning are rooted in how the brain organizes time into similarly credited units, which we refer to as latent states. Here we develop a basal ganglia-thalamo-cortical circuit model of this process and show that it captures both the commonalities and heterogeneity in human adaptive learning behavior. Our model learns incrementally through synaptic plasticity in prefrontal-basal ganglia (PFC-BG) connections, but upon observing discordant information, produces thalamocortical reset signals that alter PFC connectivity, driving attractor state transitions that facilitate rapid updating of behavioral policy. We demonstrate that this mechanism can give rise to optimized learning dynamics in the context of either change-points or reversals, and that under reasonable biological assumptions the model is able to generalize efficiently across these conditions, adjusting behavior in a context-appropriate manner. Taken together, our results provide a biologically plausible mechanistic model for adaptive learning that explains existing behavioral data and makes testable predictions about the computational roles of different brain regions in complex learning behaviors.

neuroscience↗

bioRxiv2024

Distinct basal ganglia decision dynamics under conflict and uncertainty

The basal ganglia (BG) play a key role in decision-making, preventing impulsive actions in some contexts while facilitating fast adaptations in others. The specific contributions of different BG structures to this nuanced behavior remain unclear, particularly under varying situations of noisy and conflicting information that necessitate ongoing adjustments in the balance between speed and accuracy. Theoretical accounts suggest that dynamic regulation of the amount of evidence required to commit to a decision (a dynamic "decision boundary") may be necessary to meet these competing demands. Through the application of novel computational modeling tools in tandem with direct neural recordings from human BG areas, we find that neural dynamics in the theta band manifest as variations in a collapsing decision boundary as a function of conflict and uncertainty. We collected intracranial recordings from patients diagnosed with either Parkinsons disease (n=14) or dystonia (n=3) in the subthalamic nucleus (STN), globus pallidus internus (GPi), and externus (GPe) during their performance of a novel perceptual discrimination task in which we independently manipulated uncertainty and conflict. To formally characterize whether these task and neural components influenced decision dynamics, we leveraged modified diffusion decision models (DDMs). Behavioral choices and response time distributions were best characterized by a modified DDM in which the decision boundary collapsed over time, but where the onset and shape of this collapse varied with conflict. Moreover, theta dynamics in BG structures predicted the onset and shape of this collapse but differentially across task conditions. In STN, theta activity was related to a prolonged decision boundary (indexed by slower collapse and therefore more deliberate choices) during high-conflict situations. Conversely, rapid declines in GPe theta during low conflict conditions were related to rapidly collapsing boundaries and expedited choices, with additional complementary decision bound adjustments during high uncertainty situations. Finally, GPi theta effects were uniform across conditions, with increases in theta prolonging the collapse of decision bounds. Together, these findings provide a nuanced understanding of how our brain thwarts impulsive actions while nonetheless enabling behavioral adaptation amidst noisy and conflicting information.

neuroscience↗

bioRxiv2024

Adaptive chunking improves effective working memory capacity in a prefrontal cortex and basal ganglia circuit

How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such "chunking" strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinsons disease, ADHD and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.

neuroscience↗

bioRxiv2024

Striatal Dopamine Can Enhance Learning, Both Fast and Slow, and Also Make it Cheaper

Associations can be learned incrementally, via reinforcement learning (RL), or stored instantly in working memory (WM). While WM is fast, it is also capacity-limited and effortful. Striatal dopamine may promote RL plasticity, and WM, by facilitating updating and effort exertion. Yet, prior studies have failed to distinguish between dopamines effects on RL versus WM. N = 100 participants completed a paradigm isolating these systems in a double-blind study measuring dopamine synthesis with [18F]-FDOPA imaging and manipulating dopamine with methylphenidate and sulpiride. Learning is enhanced among high synthesis capacity individuals and by methylphenidate, but impaired by sulpiride. Methylphenidate also blunts effort cost learning. Computational modeling reveals that individuals with high dopamine synthesis rely more on WM, while methylphenidate boosts their RL rates. The D2 antagonist sulpiride reduces accuracy due to diminished WM involvement and faster WM decay. We conclude that dopamine enhances both slow RL, and fast WM, by promoting plasticity and reducing effort sensitivity. These results also highlight the need to control for dopamines effects on WM when testing its effects on RL.

animal behavior and cognition↗

bioRxiv2022

Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM

Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision making intended to capture processes jointly giving rise to reaction time distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of sequential sampling models. In addition, recent work has motivated the combination of SSMs with reinforcement learning (RL) models, which had historically been considered in separate literatures. Here we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user extensible) wide variety of SSMs, and how they can be combined with RL models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.

bioinformatics↗

bioRxiv2022

On the normative advantages of dopamine and striatal opponency for learning and choice

The basal ganglia (BG) contribute to reward-driven behavior, but it is unclear why such behavior should rely on notoriously complex circuitry involving opponent striatal pathways. Dopamine (DA) nonlinearly modulates activity and plasticity in the BG and is itself dynamically modulated across tasks. We develop the OpAL* model to assess the normative advantages of such circuitry. In OpAL*, dynamic DA modulation differentially amplifies the striatal pathway most tuned to the accumulated history of positive or negative outcomes. This efficient coding mechanism avoids a vexing explore-exploit tradeoff that plagues traditional RL models in sparse reward environments. OpAL* exhibits robust advantages over alternative models across a range of environments, particularly with sparse reward. These advantages depend on opponent and nonlinear Hebbian plasticity mechanisms previously thought to be pathological. Finally, OpAL* captures risky choice patterns arising from DA and environmental manipulations across species, suggesting that they result from a normative biological mechanism. Everybody wants the most they can possibly get For the least they can possibly do - Todd Snider, "Easy Money"

neuroscience↗

bioRxiv2021

Beyond gradients: Noise correlations control Hebbian plasticity to shape credit assignment

Interference and generalization, which refer to counter-productive and useful interactions between learning episodes, respectively, are poorly understood in biological neural networks. Whereas much previous work has addressed these topics in terms of specialized brain systems, here we investigated how learning rules should impact them. We found that plasticity between groups of neurons can be decomposed into biologically meaningful factors, with factor geometry controlling interference and generalization. We introduce a "coordinated eligibility theory" in which plasticity is determined according to products of these factors, and is subject to surprise-based metaplasticity. This model computes directional derivatives of loss functions, which need not align with task gradients, allowing it to protect networks against catastrophic interference and facilitate generalization. Because the models factor structure is closely related to other plasticity rules, and is independent of how feedback is transmitted, it introduces a widely-applicable framework for interpreting supervised, reinforcement-based, and unsupervised plasticity in nervous systems.

neuroscience↗

bioRxiv2021

Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict

The cortico-basal ganglia circuit is needed to suppress prepotent actions and to facilitate controlled behavior. Under conditions of response conflict, the frontal cortex and subthalamic nucleus [STN] exhibit increased spiking and theta band power, which are linked to adaptive regulation of behavioral output. The electrophysiological mechanisms underlying these neural signatures of impulse control remain poorly understood. To address this lacuna, we constructed a novel large-scale, biophysically principled model of the subthalamopallidal [STN-Globus Pallidus externus (GPe)] network, and examined the mechanisms that modulate theta power and spiking in response to cortical input. Simulations confirmed that theta power does not emerge from intrinsic network dynamics but is robustly elicited in response to cortical input as burst events representing action selection dynamics. Rhythmic burst events of multiple cortical populations, representing a state of conflict where cortical motor plans vacillate in the theta range, led to prolonged STN theta and increased spiking, consistent with empirical literature. Notably, theta band signaling required NMDA, but not AMPA, currents, which were in turn related to a triphasic STN response characterized by spiking, silence and bursting periods. Finally, theta band resonance was also strongly modulated by architectural connectivity, with maximal theta arising when multiple cortical populations project to individual STN "conflict detector" units, due to an NMDA-dependent supralinear response. Our results provide insights into the biophysical principles and architectural constraints that give rise to STN dynamics during response conflict, and how their disruption can lead to impulsivity and compulsivity.

neuroscience↗

bioRxiv2021

Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures in reinforcement learning

A hallmark of human intelligence, but challenging for reinforcement learning (RL) agents, is the ability to compositionally generalise, that is, to recompose familiar knowledge components in novel ways to solve new problems. For instance, when navigating in a city, one needs to know the location of the destination and how to operate a vehicle to get there, whether it be pedalling a bike or operating a car. In RL, these correspond to the reward function and transition function, respectively. To compositionally generalize, these two components need to be transferable independently of each other: multiple modes of transport can reach the same goal, and any given mode can be used to reach multiple destinations. Yet there are also instances where it can be helpful to learn and transfer entire structures, jointly representing goals and transitions, particularly whenever these recur in natural tasks (e.g., given a suggestion to get ice cream, one might prefer to bike, even in new towns). Prior theoretical work has explored how, in model-based RL, agents can learn and generalize task components (transition and reward functions). But a satisfactory account for how a single agent can simultaneously satisfy the two competing demands is still lacking. Here, we propose a hierarchical RL agent that learns and transfers individual task components as well as entire structures (particular compositions of components) by inferring both through a non-parametric Bayesian model of the task. It maintains a factorised representation of task components through a hierarchical Dirichlet process, but it also represents different possible covariances between these components through a standard Dirichlet process. We validate our approach on a variety of navigation tasks covering a wide range of statistical correlations between task components and show that it can also improve generalisation and transfer in more complex, hierarchical tasks with goal/subgoal structures. Finally, we end with a discussion of our work including how this clustering algorithm could conceivably be implemented by cortico-striatal gating circuits in the brain.

animal behavior and cognition↗

bioRxiv2021

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This "associative cluster-dependent chain" (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous "Thunderstruck" song intro and then flexibly play it in a "bossa nova" rhythm without further training.

neuroscience↗

bioRxiv2020

Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

Adaptive cognitive-control involves a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a task that independently manipulates selective gating operations into working-memory (input gating), from working-memory (output gating), and of responses (motor gating) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. Further, categorical representations of specific maintained items and of motor responses could be decoded from EEG when the corresponding gate was switching, thereby linking gating operations to prioritization. Finally, gate switching at all levels was related to increases in the motor decision threshold as quantified by the drift diffusion model. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating. Author summaryHow do humans decide which information is relevant to attend to in memory, which cognitive operation to take, and when? Flexibly updating, maintenance and retrieval of relevant information from working memory (WM) are thought to be managed by gating computations in the frontostriatal network, supporting higher order learning and cognitive flexibility. Using the reference-back-2 task, we tested the key properties of gating. Namely that they are selective ("content-addressable") and that principles of cognitive "actions" (including input gating of WM, output gating from WM) are scaffold on top of the motor gating operations. Using trial-by-trial EEG indexing and quantitative computational modeling (the hierarchical drift-diffusion model) we showed that action selection at all three levels of gating have separable neural signatures but they operate partly in parallel, such that decisions about a response are processed to some degree even while the identity of the cognitive rule were uncertain. Furthermore, we showed analogous computations across levels of gating as selection of WM representation and of motor action lead to increase in the estimated decision threshold and to enhanced neural coding of the selected information thereby providing a novel link between WM gating and WM prioritization.

neuroscience↗

bioRxiv2019

Dopamine Promotes Cognitive Effort by Biasing the Benefits Versus Costs of Cognitive Work

Stimulants like methylphenidate are increasingly used for cognitive enhancement, but precise mechanisms are unknown. We found that methylphenidate boosts willingness to expend cognitive effort by altering the benefit-to-cost ratio of cognitive work. Willingness to expend effort was greater for participants with higher striatal dopamine synthesis capacity, while methylphenidate and sulpiride - a selective D2 receptor antagonist - increased cognitive motivation more for participants with lower synthesis capacity. A sequential sampling model informed by momentary gaze revealed that decisions to expend effort are related to amplification of benefit-versus-cost information attended early in the decision process, while the effect of benefits is strengthened with higher synthesis capacity and by methylphenidate. These findings demonstrate that methylphenidate boosts the perceived benefits-versus-costs of cognitive effort by modulating striatal dopamine signaling. One Sentence SummaryStriatal dopamine increases cognitive effort by respectively amplifying and attenuating the subjective benefits and costs of cognitive control.

neuroscience↗