Biology subjects

Verstynen, T.

Publications and source records attributed to Verstynen, T..

15 recordsLinked to original sources

bioRxiv2026

Learning reweights the decision dynamics of cortico-basal ganglia-thalamic pathways from deliberation to commitment

Mammals flexibly adjust their decision strategies in dynamic environments based on prior experience. The cortico-basal ganglia-thalamic (CBGT) circuit is recognized as a critical driver of this adaptability, yet how plasticity-induced modifications to CBGT dynamics translate into modifications of decision policies remains poorly understood. Here we simulate learning in a biologically-grounded spiking CBGT model that learns to select a rewarded action via dopamine-dependent plasticity at corticostriatal synapses. Relying on three previously identified control ensembles (responsiveness, pliancy, and choice) within CBGT circuits, we disentangle the distinct roles these subnetworks have in reshaping decision trajectories across learning. Control ensemble dynamics were mapped onto the evolution of evidence accumulation, revealing within-trial parameter adjustments that shape decision dynamics and outcomes. Our results emphasize that learning optimizes not only what choice is favored, but also how the phases within the decision unfold. Early in the decision, learning accelerates evidence accumulation by driving the activity of corticothalamic and direct pathways. During later deliberation, this drive is temporarily restrained, and the indirect and pallidostriatal pathways become more critical for maintaining decision thresholds, preventing premature commitment despite the increased choice bias. As the system approaches the decision point, the direct pathway regains dominance, triggering boundary collapse to facilitate action selection. This mechanism effectively shifts decisions from deliberative to committed reward-directed choices, improving both speed and accuracy while preserving system stability and control throughout the process. Author summaryWe investigate how specific subnetworks within cortico-basal ganglia-thalamic (CBGT) circuits reshape decision-making through dopamine-dependent plasticity at corticostriatal synapses. Using simulations of a simple two-choice task with a single rewarded target, we identify phase-specific reconfigurations of subnetwork activity that emerge over the course of learning. We then link these circuit-level changes to an algorithmic description of behavior by mapping them onto decision policies within a dynamic evidence accumulation framework. This analysis reveals a learning-driven shift from deliberative to more committed decision strategies, achieved through flexible coordination of decision boundaries and evidence accumulation rates. Together, these adjustments optimize decision speed and accuracy while preserving caution during ongoing choices. Our findings provide mechanistic insight into how plasticity within CBGT circuits supports adaptive decision-making across different levels of uncertainty and task demands.

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bioRxiv2025

How the dynamic interplay of cortico-basal ganglia-thalamic pathways shapes the time course of deliberation and commitment

Although the cortico-basal ganglia-thalamic (CBGT) network is identified as a central circuit for decision-making, the dynamic interplay of multiple control pathways within this network in shaping decision trajectories remains poorly understood. Here we develop and apply a novel computational framework -- CLAW (Circuit Logic Assessed via Walks) -- for tracing the instantaneous flow of neural activity as it progresses through CBGT networks engaged in a virtual decision-making task. Our CLAW analysis reveals that the complex dynamics of network activity is functionally dissectible into two critical phases: deliberation and commitment. These two phases are governed by distinct contributions of underlying CBGT pathways, with indirect and pallidostriatal pathways influencing deliberation, while the direct pathway drives action commitment. We translate CBGT dynamics into the evolution of decision-related policies, based on three previously identified control ensembles (responsiveness, pliancy, and choice) that encapsulate the relationship between CBGT activity and the evidence accumulation process. Our results demonstrate two contrasting strategies for decision-making. Fast decisions, with direct pathway dominance, feature an early response in both boundary height and drift rate, leading to a rapid collapse of decision boundaries and a clear directional bias. In contrast, slow decisions, driven by indirect and pallidostriatal pathway dominance, involve delayed changes in both decision policy parameters, allowing for an extended period of deliberation before commitment to an action. These analyses provide important insights into how the CBGT circuitry can be tuned to adopt various decision strategies and how the decision-making process unfolds within each regime. Author summaryWe investigate how the cortico-basal ganglia-thalamic (CBGT) network coordinates decision-making through its interconnected pathways. Using a novel Circuit Logic Assessed via Walks (CLAW) framework, we trace instantaneous neural activity through virtual CBGT networks as they engage in forced choice decisions. This analysis uncovers two key phases of a decision: deliberation, shaped by the indirect and pallidostriatal pathways, and commitment, driven by the direct pathway. We also demonstrate that CBGT activity supports two distinct decision styles: fast decisions involve an early decision boundary collapse and strong directional preference, while slow decisions feature minimal changes during an extended deliberation phase. These findings reveal the dynamic mechanisms within the CBGT network that underlie the different decision processes and how these can be tuned to adapt decision-making across varying demands and contexts.

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bioRxiv2024

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here we investigate this suggestion by harnessing a biologically-constrained spiking model of the corticobasal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect path-way spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

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bioRxiv2022

Competition between action plans tracks with evidence accumulation during flexible decision-making

Changing your mind requires shifting the way streams of information lead to a decision. Using in silico experiments we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in the evidence accumulation process. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. This finding predicts that when competition between action representations is highest, the rate of evidence accumulation is lowest. We then validate this prediction in a sample of homo sapiens as they perform an adaptive decision-making task while whole-brain hemodynamic responses are recorded. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals. One-sentence SummaryInteractions between cortical and subcortical circuits in the mammalian brain flexibly control the flow of information streams that drive decisions by shifting the balance of power both within and between action representations.

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bioRxiv2022

D2 dopamine receptor expression, sensitivity to rewards, and reinforcement learning in a complex value-based decision-making task

In the basal ganglia, different dopamine subtypes have opposing dynamics at post-synaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to reward feedback interacts with phasic dopamine levels to determine the effectiveness of learning, particularly in dynamic feedback situations where frequency and magnitude of rewards need to be integrated over time to make optimal decisions. Using both simulations and behavioral data in humans, we evaluated how reduced sensitivity to losses, relative to gains, leads to suboptimal learning in the Iowa Gambling Task (IGT), a complex value-learning task. In the behavioral data, we tested individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N=119) and compared their performance to non-carrier controls (N=319). The magnitude of the reward response was measured by looking at ventral striatal (VS) reactivity to rewards in the Cards task using fMRI. DRD2 variant carriers had generally lower performance in the IGT than non-carriers, consistent with reduced sensitivity to losses. There was also a positive association between VS reactivity and performance in the IGT, however, we found no statistically significant difference in this effect between DRD2 carriers and non-carriers. Thus, while reduced D2 receptor expression was associated with less efficient learning in the IGT, we did not find evidence for the moderation of this effect by the magnitude of the reward response.

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bioRxiv2021

Identifying control ensembles for decision-making within the cortico-basal ganglia-thalamic circuit

In situations featuring uncertainty about action-reward contingencies, mammals exhibit a high degree of flexibility in adopting strategies for decision-making that are tuned in response to the conditions that they encounter. Although the cortico-basal ganglia thalamic (CBGT) network is implicated in information processing during decisions, it features a complex synaptic architecture, comprised of multiple feed-forward, reciprocal, and feedback pathways, that complicate efforts to elucidate the roles of specific CBGT populations in the process of evidence accumulation. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how variations in CBGT network properties, including subpopulation firing rates and synaptic weights, map to variability of parameters in a normative drift diffusion model (DDM), representing algorithmic aspects of information processing during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles within the CBGT network that impact specific qualities of the emergent decision policy: responsiveness (associated with overall activity in corticothalamic and direct pathways), pliancy (associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (associated with differences in direct and indirect pathways across action channels). These analyses provide key mechanistic predictions about the roles of specific CBGT network elements in tuning information processing dynamics during decisions. Author summaryMammals are continuously subjected to uncertain situations in which they have to choose among behavioral options. The cortico-basal ganglia-thalamic (CBGT) circuit is a complicated collection of interconnected nuclei believed to strongly influence the ability to adapt to environmental changes. The roles of specific CBGT components in controlling information during decisions remains unclear. At a more phenomenological, algorithmic level, drift-diffusion models have been shown to be able to reproduce behavioral data (action selection probabilities and the time needed to make a decision) obtained experimentally from mammals and to provide an abstract representation of a decision policy. In this work, we use simulated decision-making to establish a mapping from neural activity in the CBGT circuit to behavioral outcomes. This mapping illuminates the importance of three core sets of CBGT subnetworks in the action selection process and how they are involved in adapting decision policies across exploitative and exploratory situations.

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bioRxiv2021

Similarity in evoked responses does not imply similarity in macroscopic network states across tasks

There is an ongoing debate as to whether cognitive processes arise from a group of functionally specialized brain modules (modularism) or as the result of distributed nonlinear processes (dynamical systems theory). The former predicts that tasks recruiting similar local brain areas should be equally similar in their network profiles. The latter allows for differential connectivity, even when the areas recruited are largely the same. Here we evaluated both views at the macroscopic level by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N=242) that performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. At the group level, the two tasks were much more different in their network profiles than in their evoked activity patterns. This is found even when matching the degrees of freedom of both activation patterns and functional correlation profiles, when considering subject-level differences, after changing brain parcellations, and if employing alternative methods for defining task-related network profiles. Our results are consistent with the perspective of the brain as a dynamical system, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels. Significant StatementIf the brain is strictly modular at the macroscopic scale, then recruiting the same brain regions should result in the same functional interactions between regions. However, if the brain is a dynamical system, with information represented at both the node and edge levels, then two tasks could have the same pattern of activation, but largely different functional correlation profiles. Here we tested this contrastive prediction using two tasks with overlapping cognitive demands, but different sensory signals. Despite being nearly identical in their activation patterns, we found that the tasks produced largely different functional correlation profiles. These findings reinforce the view of the brain as a dynamical system, with task states represented both within and across regions.

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bioRxiv2020

Dynamic decision policy reconfiguration under outcome uncertainty

In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height), respectively. At the trialwise level, following a switch in the optimal choice, the drift rate plummets and the boundary height weakly spikes, leading to a slow exploratory state. We find that the drift rate drives most of this response, with an unreliable contribution of boundary height across experiments. Surprisingly, we find no evidence that pupillary responses associated with decision policy changes. We conclude that humans show a stereotypical shift in their decision policies in response to environmental changes.

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bioRxiv2019

How structural brain network topologies associate with cognitive abilities in a value-based decision-making task

Value-based decision-making relies on effective communication across disparate brain networks. Given the scale of the networks involved in adaptive decision-making, variability in how they communicate should impact behavior; however, precisely how the topological pattern of structural connectivity of individual brain networks influences individual differences in value-based decision-making remains unclear. Using diffusion MRI, we measured structural connectivity networks in a sample of community dwelling adults (N=124). We used standard graph theoretic measures to characterize the topology of the networks in each individual and correlated individual differences in these topology measures with differences in the Iowa Gambling Task. A principal components regression approach revealed that individual differences in brain network topology associate with differences in optimal decision-making, as well as associate with differences in each participants sensitivity to high frequency rewards. These findings show that aspects of structural brain network organization can constrain how information is used in value-based decision-making.\n\nAbbreviationsMRI - Magnetic Resonance Imaging; IGT - Iowa Gambling Task; DWI - Diffusion Weighted Imaging; QSDR - Q-Space Diffeomorphic Reconstruction; PCA - Principal Components Analysis; GLM - Generalized Linear Models

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bioRxiv2018

Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity. Author summaryCognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an "upwards mapping" approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping "up" the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.

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bioRxiv2018

Binding during sequence learning does not alter cortical representations of individual actions

As a movement sequence is learned, serially ordered actions get bound together into sets in order to reduce computational complexity during planning and execution. Here we examined how the binding of serial actions alters the cortical representations of individual movements. Across five weeks of practice, healthy human subjects learned either a complex 32-item sequence of finger movements (Trained group, N=9) or randomly ordered actions (Control group, N=9). After five weeks of training, responses during sequence production in the Trained group were correlated, consistent with being bound together under a common command. These behavioral changes, however, did not coincide with plasticity in the multivariate representations of individual finger movements, assessed using fMRI, at any level of the cortical motor hierarchy. This suggests that the representations of individual actions remain stable, even as the execution of those same actions become bound together in the context of producing a well learned sequence.

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bioRxiv2017

Abdominal adiposity negatively associates with the rate of long term sequential skill learning

Obesity is associated with functional and structural differences in the corticostriatal systems of the brain. These pathways are known to be critical for the acquisition of complex sensorimotor skills, such as the ability to learn a coordinated sequence of actions. Thus, individual differences in obesity should be associated with reduced efficiency of learning sequential skills. Here we measured long-term sequence learning across five days of training on the serial reaction time task in a cohort of neurologically healthy adults (N=30) with body types ranging from lean to obese. As expected, individuals with a greater degree of central adiposity, measured as central waist circumference, exhibited slower rates of learning, across all training days, than their leaner counterparts. This association between learning and central adiposity was restricted to response speeds, but not accuracy. These findings show that obesity is negatively associated with the efficiency of learning a long-term sequential skill, possibly due to previously observed associations between obesity and general basal ganglia function.

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bioRxiv2017

Dual learning mechanisms drive adaptive inhibitory control

Goal-directed behavior requires integrating action selection processes with learning systems that adapt control using environmental feedback. These functions intersect in the basal ganglia (BG), which has at least two targets of plasticity: a dopaminergic modulation of striatal pathways and cortical modulation of the subthalamic nucleus (STN). Dual learning mechanisms suggests that feedback signals have a multifaceted impact on BG-dependent decisions. Using a hybrid of accumulation-to-bound decision models and reinforcement learning, we modeled the performance of humans in a stop-signal task where participants (N=75) learned the prior distribution of the timing of a stop signal through trial-and-error feedback. Changes in the drift-rate of the action execution process were driven by errors in action timing, whereas adaptation in the boundary height served to increase caution following failed stops. These findings highlight two interactive learning mechanisms for adapting the control of goal-directed actions based on dissociable dimensions of feedback error.\n\nAuthor SummaryMany complex behavioral goals rely on ones ability to regulate the timing of action execution while also maintaining enough control to cancel actions in response to \"Stop\" cues in the environment. Here we examined how these two fundamental components of behavior become tuned to the control demands of the environment by combining principles of reinforcement learning with accumulator models of decision making. The synthesis of these two theoretical frameworks is motivated by previous work showing that reinforcement learning and control rely on overlapping circuitry in the basal ganglia. Leveraging knowledge about the interaction of learning and control signals in this network, we formulated a computational model in which performance feedback is used to modulate key mechanisms of the decision process to facilitate goal acquisition. Model-based analysis of behavioral data collected on an adaptive stop-signal task revealed two critical learning mechanisms: one that adjusts the accumulation rate of the \"Go\" signal to errors in action timing and another that exercises caution by raising the height of the execution boundary after a failed Stop trial. We show how these independent learning mechanisms interact over the course of learning, shedding light on the behavioral effects plasticity in different pathways of the basal ganglia.

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bioRxiv2017

A Population-Based Atlas Of The Macroscale Structural Connectome In The Human Brain

A comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N=842). This was achieved by creating a high-resolution template of diffusion patterns averaged across individual subjects and using tractography to generate 550,000 trajectories of representative white matter fascicles annotated by 80 anatomical labels. The trajectories were subsequently clustered and labeled by a team of experienced neuroanatomists in order to conform to prior neuroanatomical knowledge. A multi-level network topology was then described using whole-brain connectograms, with subdivisions of the association pathways showing small-worldness in intra-hemisphere connections, projection pathways showing hub structures at thalamus, putamen, and brainstem, and commissural pathways showing bridges connecting cerebral hemispheres to provide global efficiency. This atlas of the structural connectome provides representative organization of human brain white matter, complementary to traditional histologically-derived and voxel-based white matter atlases, allowing for better modeling and simulation of brain connectivity for future connectome studies.

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bioRxiv2017

Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N=841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.\n\nAuthor SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel and reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This inter-subject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how shared variability across individuals is reflected in the local connectome.

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