Competing basal ganglia pathways determine the difference between stopping and deciding not to go

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 summary

We 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.

Patterns of brain activity in choice or instructed go and no-go tasks

The goal of this study was to investigate the decision making process for choosing what movements to make. We used electroencephalography (EEG) to investigate patterns of the contingent negative variation (CNV) associated with free-choice decisions to move or abstain, comparing them to conditions where actions were commanded. Our primary hypothesis was that choice tasks would differ significantly from each other and exhibit EEG patterns akin to their command-driven counterparts after the decisions were made, at least, in the 50 ms block of time prior to movement. A secondary analysis evaluated post hoc comparisons of time, in 50 ms blocks, to understand the temporal development of the CNV for each condition. We also conducted an exploratory analysis of EEG event-related desynchronization (ERD) to identify patterns of brain activity associated with the decision-making process. This approach was taken due to the exploratory nature of our hypotheses concerning the spatial and temporal characteristics of EEG activity during these free-choice versus commanded tasks. We studied 12 right-handed healthy volunteers (7 women, mean age 53 years, range 39-73 years) with no prior history of neurological or major psychiatric illness. A CNV paradigm encompassing commanded and choice tasks was devised, with a 2500 ms interval between S1 and S2, while recording EEG and electromyography (EMG). S1 provided full information about the upcoming task, which was to be executed at the time of S2. We assessed CNV and explored whole scalp EEG activity, including both voltage as well as power in the alpha and beta frequency ranges. Clear and similar CNVs were observed for command and choice go tasks prior to the movements, contrasting with near-zero CNVs for the command and choice no-go tasks. Separation of CNVs for command go and no-go tasks occurred around 1600 ms post-S1, and choice CNVs separated about 2150 ms post-S1. Exploratory analysis revealed that beta power provided information about decision and preparation processes much earlier. The left dorsolateral prefrontal cortex (DLPFC) exhibited the initial sign of decision approximately 500 ms post-S1 for all tasks, with subsequent preparation for movement or restraint involving distinct activity in various brain regions. The localization of effects in the left DLPFC was determined by visual analysis of the informative electrode sites. The CNVs separate about 2 s after S1, and it appears that this process represents preparation for movement (or no movement). Exploration of the beta activity suggests an earlier decision process which leads eventually to subsequent task preparation and activation. Choice decisions lag slightly behind command decisions, with the CNV apparently reflecting motor implementation rather than the decision-making process. In a simple motor task with an exploratory analysis, both commanded and choice-based decisions are rapidly initiated in the left DLPFC. While the CNV distinguishes between go and no-go conditions, it primarily appears to signify preparation for implementation of the task following the earlier decision. Further controlled studies will be needed to confirm these results.

Pallidal prototypic neuron and astrocyte activities regulate flexible reward-seeking behaviors

Behavioral flexibility allows animals to adjust actions to changing environments. While the basal ganglia are critical for adaptation, the specific role of the external globus pallidus (GPe) is unclear. This study examined the contributions of two major GPe cell types-prototypic neurons projecting to the subthalamic nucleus (ProtoGPe→STN neurons) and astrocytes-to behavioral flexibility. Using longitudinal operant conditioning with context reversals, we found that ProtoGPe→STN neurons dynamically represent contextual information correlating with behavioral optimality. In contrast, GPe astrocytes exhibited gradual contextual encoding independent of performance. Deleting ProtoGPe→STN neurons impaired adaptive responses to changing action-outcome contingencies without altering initial reward-seeking acquisition, highlighting their specific role in enabling behavioral flexibility. Furthermore, we discovered that ProtoGPe→STN neurons integrate inhibitory striatal and excitatory subthalamic inputs, modulating downstream basal ganglia circuits to support flexible behavior. This research elucidates the complementary roles of ProtoGPe→STN neurons and astrocytes in cellular mechanisms of flexible reward-seeking behavior.

Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits

For decades, the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic nuclei by inhibiting GPe activity. Here, we revisit this assumption, in light of new and emerging work on the cellular complexity, connectivity and functional role of the GPe in behaviour. We show how, according to this new circuit-level logic, the GPe is ideally positioned for relaying ascending and descending control signals within the basal ganglia. Focusing on the problem of inhibitory control, we illustrate how this bidirectional flow of information allows for the integration of reactive and proactive control mechanisms during action selection. Taken together, this new evidence points to the GPe as being a central hub in the CBGT circuit, participating in bidirectional information flow and linking multifaceted control signals to regulate behaviour.

Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.

Failures to launch preclude response inhibition

Neural analyses of response inhibition rely on separating trials with and without a behavioral response. Can researchers be sure the absence of a behavioral outcome equates to the presence of inhibitory control? We emphasize advancing response inhibition research by utilizing peripheral measures of response progress to define behavioral stopping contrasts.

Action initiation and action inhibition follow the same time course when compared under matched experimental conditions

The ability to initiate an action quickly when needed and the ability to cancel an impending action are both fundamental to action control. It is often presumed that they are qualitatively distinct processes, yet they have largely been studied in isolation and little is known about how they relate to one another. Comparing previous experimental results shows a similar time course for response initiation and response inhibition. However, the exact time course varies widely depending on experimental conditions, including the frequency of different trial types and the urgency to respond. For example, in the stop-signal task, where both action initiation and action inhibition are involved and could be compared, action inhibition is typically found to be much faster. However, this apparent difference is likely due to there being much greater urgency to inhibit an action than to initiate one in order to avoid failing at the task. This asymmetry in the urgency between action initiation and action inhibition makes it impossible to compare their relative time courses in a single task. Here, we demonstrate that when action initiation and action inhibition are measured separately under conditions that are matched as closely as possible, their speeds are not distinguishable and are positively correlated across participants. Our results raise the possibility that action initiation and action inhibition may not necessarily be qualitatively distinct processes but may instead reflect complementary outcomes of a single decision process determining whether or not to act.NEW & NOTEWORTHY The time courses of initiating an action and canceling an action have largely been studied in isolation, and little is known about their relationship. Here, we show that when measured under comparable conditions the speeds of action initiation and action inhibition are the same. This finding raises the possibility that these two functions may be more closely related than previously assumed, with potentially important implications for their underlying neural basis.

Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits

For decades the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic nuclei by inhibiting GPe activity. Here we revisit this assumption, in light of new and emerging work on the cellular complexity, connectivity, and functional role of the GPe in behavior. We show how, according to this new circuit-level logic, the GPe is ideally positioned for relaying ascending and descending control signals within the basal ganglia. Focusing on the problem of inhibitory control, we illustrate how this bidirectional flow of information allows for the integration of reactive and proactive control mechanisms during action selection. Taken together, this new evidence points to the GPe as being a central hub in the CBGT circuit, participating in bidirectional information flow and linking multifaceted control signals to regulate behavior.

Competing neural representations of choice shape evidence accumulation in humans

Making adaptive choices in dynamic environments requires flexible decision policies. Previously, we showed how shifts in outcome contingency change the evidence accumulation process that determines decision policies. Using in silico experiments to generate predictions, here we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in decision policies. 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. When competition between action representations is highest, the rate of evidence accumulation is the lowest. This prediction was validated in in vivo experiments on human participants, using fMRI, which showed that (1) evoked hemodynamic responses can reliably predict trial-wise choices and (2) competition between action representations, measured using a classifier model, tracked with changes in the rate of evidence accumulation. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals.

The indirect pathway of the basal ganglia promotes transient punishment but not motor suppression

Optogenetic stimulation of Adora2a receptor-expressing spiny projection neurons (A2A-SPNs) in the striatum drives locomotor suppression and transient punishment, results attributed to activation of the indirect pathway. The sole long-range projection target of A2A-SPNs is the external globus pallidus (GPe). Unexpectedly, we found that inhibition of the GPe drove transient punishment but not suppression of movement. Within the striatum, A2A-SPNs inhibit other SPNs through a short-range inhibitory collateral network, and we found that optogenetic stimuli that drove motor suppression shared a common mechanism of recruiting this inhibitory collateral network. Our results suggest that the indirect pathway plays a more prominent role in transient punishment than in motor control and challenges the assumption that activity of A2A-SPNs is synonymous with indirect pathway activity.

Dopamine agonists in Parkinson's disease: Impact of D1-like or D2-like dopamine receptor subtype selectivity and avenues for future treatment

Dopamine agonists (DAs) have demonstrated efficacy for the treatment of Parkinson's disease (PD) but are limited by adverse effects (AEs). DAs can vary considerably in their receptor subtype selectivity and affinity, chemical composition, receptor occupancy, and intrinsic activity on the receptor. Most currently approved DAs for PD treatment primarily target D2/D3 (D2-like) dopamine receptors. However, selective activation of D1/D5 (D1-like) dopamine receptors may enable robust activation of motor function while avoiding AEs related to D2/D3 receptor agonism. Full D1/D5 receptor-selective agonists have been explored in small, early-phase clinical studies, and although their efficacy for motor symptoms was robust, challenges with pharmacokinetics, bioavailability, cardiovascular AEs, and dyskinesia rates similar to levodopa prevented clinical advancement. Generally, repeated dopaminergic stimulation with full DAs is associated with frontostriatal dysfunction and sensitization that may induce plastic changes in the motor system, and neuroadaptations that produce long-term motor and nonmotor complications, respectively. Recent preclinical and clinical studies suggest that a D1/D5 receptor-selective partial agonist may hold promise for providing sustained, predictable, and robust motor control, while reducing risk for motor complications (e.g., levodopa-induced dyskinesia) and nonmotor AEs (e.g., impulse control disorders and excessive daytime sleepiness). Clinical trials are ongoing to evaluate this hypothesis. The potential emerging availability of novel dopamine receptor agonists with selective dopamine receptor pharmacology suggests that the older terminology "dopamine agonist" may need revision to distinguish older-generation D2/D3-selective agonists from D1/D5-selective agonists with distinct efficacy and tolerability characteristics.

Neuromodulatory control of complex adaptive dynamics in the brain

How is the massive dimensionality and complexity of the microscopic constituents of the nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance is to poise neurons close to the critical point of a phase transition, at which a small change in neuronal excitability can manifest a nonlinear augmentation in neuronal activity. How the brain could mediate this critical transition is a key open question in neuroscience. Here, I propose that the different arms of the ascending arousal system provide the brain with a diverse set of heterogeneous control parameters that can be used to modulate the excitability and receptivity of target neurons-in other words, to act as control parameters for mediating critical neuronal order. Through a series of worked examples, I demonstrate how the neuromodulatory arousal system can interact with the inherent topological complexity of neuronal subsystems in the brain to mediate complex adaptive behaviour.

Multi-scale and cross-dimensional TMS mapping: A proof of principle in patients with Parkinson's disease and deep brain stimulation

Introduction

Transcranial magnetic stimulation (TMS) mapping has become a critical tool for exploratory studies of the human corticomotor (M1) organization. Here, we propose to gather existing cutting-edge TMS-EMG and TMS-EEG approaches into a combined multi-dimensional TMS mapping that considers local and whole-brain excitability changes as well as state and time-specific changes in cortical activity. We applied this multi-dimensional TMS mapping approach to patients with Parkinson's disease (PD) with Deep brain stimulation (DBS) of the sub-thalamic nucleus (STN) ON and OFF. Our goal was to identifying one or several TMS mapping-derived markers that could provide unprecedent new insights onto the mechanisms of DBS in movement disorders.

Methods

Six PD patients (1 female, mean age: 62.5 yo [59-65]) implanted with DBS-STN for 1 year, underwent a robotized sulcus-shaped TMS motor mapping to measure changes in muscle-specific corticomotor representations and a movement initiation task to probe state-dependent modulations of corticospinal excitability in the ON (using clinically relevant DBS parameters) and OFF DBS states. Cortical excitability and evoked dynamics of three cortical areas involved in the neural control of voluntary movements (M1, pre-supplementary motor area - preSMA and inferior frontal gyrus - IFG) were then mapped using TMS-EEG coupling in the ON and OFF state. Lastly, we investigated the timing and nature of the STN-to-M1 inputs using a paired pulse DBS-TMS-EEG protocol.

Results

In our sample of patients, DBS appeared to induce fast within-area somatotopic re-arrangements of motor finger representations in M1, as revealed by mediolateral shifts of corticomuscle representations. STN-DBS improved reaction times while up-regulating corticospinal excitability, especially during endogenous motor preparation. Evoked dynamics revealed marked increases in inhibitory circuits in the IFG and M1 with DBS ON. Finally, inhibitory conditioning effects of STN single pulses on corticomotor activity were found at timings relevant for the activation of inhibitory GABAergic receptors (4 and 20 ms).

Conclusion

Taken together, these results suggest a predominant role of some markers in explaining beneficial DBS effects, such as a context-dependent modulation of corticospinal excitability and the recruitment of distinct inhibitory circuits, involving long-range projections from higher level motor centers and local GABAergic neuronal populations. These combined measures might help to identify discriminative features of DBS mechanisms towards deep clinical phenotyping of DBS effects in Parkinson's Disease and in other pathological conditions.

Exploring stop signal reaction time over two sessions of the anticipatory response inhibition task

Various behavioural tasks measure response inhibition encompassing the ability to cancel unwanted actions, evaluated via stop signal reaction time (SSRT). It is unclear whether SSRT is an unchangeable inherent measure of inhibitory network integrity or whether it can improve with repetition. The current study explored if and how SSRT changed over two sessions for the Anticipatory Response Inhibition Task (ARIT), and how this compared with the Stop Signal Task (SST). Forty-four participants repeated the ARIT and SST over two sessions. SSRT and its constituent measures (Go trial reaction time, stop signal delay) were calculated. SSRT reflecting non-selective response inhibition was consistent between sessions in the ARIT and SST (both p > 0.293). Reaction time and stop signal delay also remained stable across sessions in the ARIT (all p > 0.063), whereas in the SST, reaction time (p = 0.013) and stop signal delay (p = 0.009) increased. SSRT reflecting behaviourally selective stopping on the ARIT improved (p < 0.001) over two sessions, which was underpinned by changes to reaction time (p < 0.001) and stop signal delay (p < 0.001). Overall, the maximal efficiency of non-selective inhibition remained stable across two sessions in the ARIT. Results of the SST confirmed that non-selective inhibition can, however, be affected by more than inhibitory network integrity. Behaviourally selective stopping on the ARIT changed across sessions, suggesting the sequential neural process captured by the SSRT occurred more quickly in session two. These findings have implications for future studies that necessitate behavioural measures over multiple sessions.

Identifying control ensembles for information processing within the cortico-basal ganglia-thalamic circuit

In situations featuring uncertainty about action-reward contingencies, mammals can flexibly adopt strategies for decision-making that are tuned in response to environmental changes. Although the cortico-basal ganglia thalamic (CBGT) network has been identified as contributing to the decision-making process, 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 by which evidence is accumulated and influences behavior. 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 (a measure of how quickly evidence evaluation gets underway, associated with overall activity in corticothalamic and direct pathways), pliancy (a measure of the standard of evidence needed to commit to a decision, associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (a measure of commitment toward one available option, associated with differences in direct and indirect pathways across action channels). These analyses provide mechanistic predictions about the roles of specific CBGT network elements in tuning the way that information is accumulated and translated into decision-related behavior.

Evidence for non-selective response inhibition in uncertain contexts revealed by combined meta-analysis and Bayesian analysis of fMRI data

Response inhibition is typically considered a brain mechanism selectively triggered by particular “inhibitory” stimuli or events. Based on recent research, an alternative non-selective mechanism was proposed by several authors. Presumably, the inhibitory brain activity may be triggered not only by the presentation of “inhibitory” stimuli but also by any imperative stimuli, including Go stimuli, when the context is uncertain. Earlier support for this notion was mainly based on the absence of a significant difference between neural activity evoked by equiprobable Go and NoGo stimuli. Equiprobable Go/NoGo design with a simple response time task limits potential confounds between response inhibition and accompanying cognitive processes while not preventing prepotent automaticity. However, previous neuroimaging studies used classical null hypothesis significance testing, making it impossible to accept the null hypothesis. Therefore, the current research aimed to provide evidence for the practical equivalence of neuronal activity in the Go and NoGo trials using Bayesian analysis of functional magnetic resonance imaging (fMRI) data. Thirty-four healthy participants performed a cued Go/NoGo task with an equiprobable presentation of Go and NoGo stimuli. To independently localize brain areas associated with response inhibition in similar experimental conditions, we performed a meta-analysis of fMRI studies using equal-probability Go/NoGo tasks. As a result, we observed overlap between response inhibition areas and areas that demonstrate the practical equivalence of neuronal activity located in the right dorsolateral prefrontal cortex, parietal cortex, premotor cortex, and left inferior frontal gyrus. Thus, obtained results favour the existence of non-selective response inhibition, which can act in settings of contextual uncertainty induced by the equal probability of Go and NoGo stimuli.

Effects of beta-band and gamma-band rhythmic stimulation on motor inhibition

To investigate whether beta oscillations are causally related to motor inhibition, thirty-six participants underwent two concurrent transcranial alternating current stimulation (tACS) and electroencephalography (EEG) sessions during which either beta (20 Hz) or gamma (70 Hz) stimulation was applied while participants performed a stop-signal task. In addition, we acquired magnetic resonance images to simulate the electric field during tACS. 20 Hz stimulation targeted at the pre-supplementary motor area enhanced inhibition and increased beta oscillatory power around the time of the stop-signal in trials directly following stimulation. The increase in inhibition on stop trials followed a dose-response relationship with the strength of the individually simulated electric field. Computational modeling revealed that 20 and 70 Hz stimulation had opposite effects on the braking process. These results highlight that the effects of tACS are state-dependent and demonstrate that fronto-central beta activity is causally related to successful motor inhibition, supporting its use as a functional biomarker.

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Beta tACS over preSMA improved motor inhibition

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Gamma tACS slowed down the stop process but primarily affected movement execution

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Beta tACS resulted in higher beta spectral power around the time of the stop-signal

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Effects of tACS showed a dose-response relationship with electric field strength

Compromised reactive but intact proactive inhibitory motor control in Tourette disorder

Tourette disorder (TD) is characterized by tics, which are sudden repetitive involuntary movements or vocalizations. Deficits in inhibitory control in TD patients remain inconclusive from the traditional method of estimating the ability to stop an impending action, which requires careful interpretation of a metric derived from race model. One possible explanation for these inconsistencies is that race model's assumptions of independent and stochastic rise of GO and STOP process to a fixed threshold are often violated, making the classical metric to assess inhibitory control less robust. Here, we used a pair of metrics derived from a recent alternative model to address why stopping performance in TD is unaffected despite atypical neural circuitry. These new metrics distinguish between proactive and reactive inhibitory control and estimate them separately. When these metrics in adult TD group were contrasted with healthy controls (HC), we identified robust deficits in reactive control, but not in proactive control in TD. The TD group exhibited difficulty in slowing down the speed of movement preparation, which they rectified by their intact ability to postpone the movement.

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning

OBJECTIVE

The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.

BACKGROUND

Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD.

METHODS

Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation.

RESULTS

The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task).

CONCLUSIONS

This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

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.

Neurochemical substrates linked to impulsive and compulsive phenotypes in addiction: A preclinical perspective

Drug compulsion manifests in some but not all individuals and implicates multifaceted processes including failures in top-down cognitive control as drivers for the hazardous pursuit of drug use in some individuals. As a closely related construct, impulsivity encompasses rash or risky behaviour without foresight and underlies most forms of drug taking behaviour, including drug use during adverse emotional states (i.e., negative urgency). While impulsive behavioural dimensions emerge from drug-induced brain plasticity, burgeoning evidence suggests that impulsivity also predates the emergence of compulsive drug use. Although the neural substrates underlying the apparently causal relationship between trait impulsivity and drug compulsion are poorly understood, significant advances have come from the interrogation of defined limbic cortico-striatal circuits involved in motivated behaviour and response inhibition, together with chemical neuromodulatory influences from the ascending neurotransmitter systems. We review what is presently known about the neurochemical mediation of impulsivity, in its various forms, and ask whether commonalities exist in the neurochemistry of compulsive drug-motivated behaviours that might explain individual risk for addiction.

The Human Basal Ganglia Mediate the Interplay between Reactive and Proactive Control of Response through Both Motor Inhibition and Sensory Modulation

The basal ganglia (BG) have long been known for contributing to the regulation of motor behaviour by means of a complex interplay between tonic and phasic inhibitory mechanisms. However, after having focused for a long time on phasic reactive mechanisms, it is only recently that psychological research in healthy humans has modelled tonic proactive mechanisms of control. Mutual calibration between anatomo-functional and psychological models is still needed to better understand the unclear role of the BG in the interplay between proactive and reactive mechanisms of control. Here, we implemented an event-related fMRI design allowing proper analysis of both the brain activity preceding the target-stimulus and the brain activity induced by the target-stimulus during a simple go/nogo task, with a particular interest in the ambiguous role of the basal ganglia. Post-stimulus activity was evoked in the left dorsal striatum, the subthalamus nucleus and internal globus pallidus by any stimulus when the situation was unpredictable, pinpointing its involvement in reactive, non-selective inhibitory mechanisms when action restraint is required. Pre-stimulus activity was detected in the ventral, not the dorsal, striatum, when the situation was unpredictable, and was associated with changes in functional connectivity with the early visual, not the motor, cortex. This suggests that the ventral striatum supports modulatory influence over sensory processing during proactive control.

The credit assignment problem in cortico-basal ganglia-thalamic networks: A review, a problem and a possible solution

The question of how cortico-basal ganglia-thalamic (CBGT) pathways use dopaminergic feedback signals to modify future decisions has challenged computational neuroscientists for decades. Reviewing the literature on computational representations of dopaminergic corticostriatal plasticity, we show how the field is converging on a normative, synaptic-level learning algorithm that elegantly captures both neurophysiological properties of CBGT circuits and behavioral dynamics during reinforcement learning. Unfortunately, the computational studies that have led to this normative algorithmic model have all relied on simplified circuits that use abstracted action-selection rules. As a result, the application of this corticostriatal plasticity algorithm to a full model of the CBGT pathways immediately fails because the spatiotemporal distance between integration (corticostriatal circuits), action selection (thalamocortical loops) and learning (nigrostriatal circuits) means that the network does not know which synapses should be reinforced to favor previously rewarding actions. We show how observations from neurophysiology, in particular the sustained activation of selected action representations, can provide a simple means of resolving this credit assignment problem in models of CBGT learning. Using a biologically realistic spiking model of the full CBGT circuit, we demonstrate how this solution can allow a network to learn to select optimal targets and to relearn action-outcome contingencies when the environment changes. This simple illustration highlights how the normative framework for corticostriatal plasticity can be expanded to capture macroscopic network dynamics during learning and decision-making.

The thalamus integrates the macrosystems of the brain to facilitate complex, adaptive brain network dynamics

The human brain is a complex, adaptive system comprised of billions of cells with trillions of connections. The interactions between the elements of the system oppose this seemingly limitless capacity by constraining the system's dynamic repertoire, enforcing distributed neural states that balance integration and differentiation. How this trade-off is mediated by the brain, and how the emergent, distributed neural patterns give rise to cognition and awareness, remains poorly understood. Here, I argue that the thalamus is well-placed to arbitrate the interactions between distributed neural assemblies in the cerebral cortex. Different classes of thalamocortical connections are hypothesized to promote either feed-forward or feedback processing modes in the cerebral cortex. This activity can be conceptualized as emerging dynamically from an evolving attractor landscape, with the relative engagement of distinct distributed circuits providing differing constraints over the manner in which brain state trajectories change over time. In addition, inputs to the distinct thalamic populations from the cerebellum and basal ganglia, respectively, are proposed to differentially shape the attractor landscape, and hence, the temporal evolution of cortical assemblies. The coordinated engagement of these neural macrosystems is then shown to share key characteristics with prominent models of cognition, attention and conscious awareness. In this way, the crucial role of the thalamus in mediating the distributed, multi-scale network organization of the central nervous system can be related to higher brain function.

Adiposity covaries with signatures of asymmetric feedback learning during adaptive decisions

Unhealthy weight gain relates, in part, to how people make decisions based on prior experience. Here we conducted post hoc analysis on an archival data set to evaluate whether individual differences in adiposity, an anthropometric construct encompassing a spectrum of body types, from lean to obese, associate with signatures of asymmetric feedback learning during value-based decision-making. In a sample of neurologically healthy adults (N = 433), ventral striatal responses to rewards, measured using fMRI, were not directly associated with adiposity, but rather moderated its relationship with feedback-driven learning in the Iowa gambling task, tested outside the scanner. Using a biologically inspired model of basal ganglia-dependent decision processes, we found this moderating effect of reward reactivity to be explained by an asymmetrical use of feedback to drive learning; that is, with more plasticity for gains than for losses, stronger reward reactivity leads to decisions that minimize exploration for maximizing long-term outcomes. Follow-up analysis confirmed that individual differences in adiposity correlated with signatures of asymmetric use of feedback cues during learning, suggesting that reward reactivity may especially relate to adiposity, and possibly obesity risk, when gains impact future decisions more than losses.

Detecting brain network communities: Considering the role of information flow and its different temporal scales

The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a parameter-dependent modularity function that often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. Next, it was applied to real and simulated anatomical and/or functional connectomes in the macaque and human brains. We found a clear hierarchical repertoire of community structures in both the anatomical and the functional networks. The observed partitions range from the evident division in two hemispheres -in which all processes are managed globally- to specialized communities seemingly shaped by physical proximity and shared function. Additionally, the spatial scales of a network's community structure (characterized by a measure we term within-communities path length) appear inversely proportional to the oscillatory modes' average frequencies. The proportionality constant may constitute a network-specific propagation velocity for the information flow. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow in the brain network.

A non-linear deterministic model of action selection in the basal ganglia to simulate motor fluctuations in Parkinson's disease

Motor fluctuations and dyskinesias are severe complications of Parkinson's disease (PD), especially evident at its advanced stage, under long-term levodopa therapy. Despite their strong clinical prevalence, the neural origin of these motor symptoms is still a subject of intense debate. In this work, a non-linear deterministic neurocomputational model of the basal ganglia (BG), inspired by biology, is used to provide more insights into possible neural mechanisms at the basis of motor complications in PD. In particular, the model is used to simulate the finger tapping task. The model describes the main neural pathways involved in the BG to select actions [the direct or Go, the indirect or NoGo, and the hyperdirect pathways via the action of the sub-thalamic nucleus (STN)]. A sensitivity analysis is performed on some crucial model parameters (the dopamine level, the strength of the STN mechanism, and the strength of competition among different actions in the motor cortex) at different levels of synapses, reflecting major or minor motor training. Depending on model parameters, results show that the model can reproduce a variety of clinically relevant motor patterns, including normokinesia, bradykinesia, several attempts before movement, freezing, repetition, and also irregular fluctuations. Motor symptoms are, especially, evident at low or high dopamine levels, with excessive strength of the STN and with weak competition among alternative actions. Moreover, these symptoms worsen if the synapses are subject to insufficient learning. The model may help improve the comprehension of motor complications in PD and, ultimately, may contribute to the treatment design.

Globus pallidus dynamics reveal covert strategies for behavioral inhibition

Flexible behavior requires restraint of actions that are no longer appropriate. This behavioral inhibition critically relies on frontal cortex - basal ganglia circuits. Within the basal ganglia, the globus pallidus pars externa (GPe) has been hypothesized to mediate selective proactive inhibition: being prepared to stop a specific action, if needed. Here we investigate population dynamics of rat GPe neurons during preparation-to-stop, stopping, and going. Rats selectively engaged proactive inhibition towards specific actions, as shown by slowed reaction times (RTs). Under proactive inhibition, GPe population activity occupied state-space locations farther from the trajectory followed during normal movement initiation. Furthermore, the state-space locations were predictive of distinct types of errors: failures-to-stop, failures-to-go, and incorrect choices. Slowed RTs on correct proactive trials reflected starting bias towards the alternative action, which was overcome before progressing towards action initiation. Our results demonstrate that rats can exert cognitive control via strategic adjustments to their GPe network state.

Alcohol effects on globus pallidus connectivity: Role of impulsivity and binge drinking

Despite the harm caused by binge drinking, the neural mechanisms leading to risky and disinhibited intoxication-related behaviors are not well understood. Evidence suggests that the globus pallidus externus (GPe), a substructure within the basal ganglia, participates in inhibitory control processes, as examined in stop-signaling tasks. In fact, studies in rodents have revealed that alcohol can change GPe activity by decreasing neuronal firing rates, suggesting that the GPe may have a central role in explaining impulsive behaviors and failures of inhibition that occur during binge drinking. In this study, twenty-five healthy volunteers underwent intravenous alcohol infusion to achieve a blood alcohol level of 0.08 g/dl, which is equivalent to a binge drinking episode. A resting state functional magnetic resonance imaging scan was collected prior to the infusion and at binge-level exposure. Functional connectivity analysis was used to investigate the association between alcohol-induced changes in GPe connectivity, drinking behaviors, and impulsivity traits. We found that individuals with greater number of drinks or heavy drinking days in the recent past had greater alcohol-induced deficits in GPe connectivity, particularly to the striatum. Our data also indicated an association between impulsivity and alcohol-induced deficits in GPe-frontal/precentral connectivity. Moreover, alcohol induced changes in GPe-amygdala circuitry suggested greater vulnerabilities to stress-related drinking in some individuals. Taken together, these findings suggest that alcohol may interact with impulsive personality traits and drinking patterns to drive alterations in GPe circuitry associated with behavioral inhibition, possibly indicating a neural mechanism by which binge drinking could lead to impulsive behaviors.

Tuning the Corticospinal System: How Distributed Brain Circuits Shape Human Actions

Interactive behaviors rely on the operation of several processes allowing the control of actions, including their selection, withholding, and cancellation. The corticospinal system provides a unique route through which multiple brain circuits can exert control over bodily motor acts. In humans, the influence of these modulatory circuits on the corticospinal system can be probed using various transcranial magnetic stimulation (TMS) protocols. Here, we review neural data from TMS studies at the basis of our current understanding of how diverse pathways—including intra-cortical, trans-cortical, and subcortico-cortical circuits—contribute to action control by tuning the activity of the corticospinal system. Critically, when doing so, we point out important caveats in the field that arise from the fact that these circuits, and their impact on the corticospinal system, have not been considered equivalently for action selection, withholding, and cancellation. This has led to the misleading view that some circuits or regions are specialized in specific control processes and that they produce particular modulatory changes in corticospinal excitability (e.g., generic vs. specific modulation of corticospinal excitability). Hence, we point to the need for more transversal research approaches in the field of action control.

Individual Differences in the Neural Dynamics of Response Inhibition

Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

Common Neural Network for Different Functions: An Investigation of Proactive and Reactive Inhibition

Successful behavioral inhibition involves both proactive and reactive inhibition, allowing people to prepare for restraining actions, and cancel their actions if the response becomes inappropriate. In the present study, we utilized the stop-signal paradigm to examine whole-brain contrasts and functional connectivity for proactive and reactive inhibition. The results of our functional magnetic resonance imaging (fMRI) data analysis show that the inferior frontal gyrus (IFG), the supplementary motor area (SMA), the subthalamic nucleus (STN), and the primary motor cortex (M1) were activated by both proactive and reactive inhibition. We then created 70 dynamic causal models (DCMs) representing the alternative hypotheses of modulatory effects from proactive and reactive inhibition in the IFG-SMA-STN-M1 network. Bayesian model selection (BMS) showed that causal connectivity from the IFG to the SMA was modulated by both proactive and reactive inhibition. To further investigate the possible brain circuits involved in behavioral control, including proactive inhibitory processes, we compared 13 DCMs representing the alternative hypotheses of proactive modulation in the dorsolateral prefrontal cortex (DLPFC)-caudate-IFG-SMA neural circuits. BMS revealed that the effective connectivity from the caudate to the IFG is modulated only in the proactive inhibition condition but not in the reactive inhibition. Together, our results demonstrate how fronto-basal ganglia pathways are commonly involved in proactive and reactive inhibitory control, with a "longer" pathway (DLPFC-caudate-IFG-SMA-STN-M1) playing a modulatory role in proactive inhibitory control, and a "shorter" pathway (IFG-SMA-STN-M1) involved in reactive inhibition. These results provide causal evidence for the roles of indirect and hyperdirect pathways in mediating proactive and reactive inhibitory control.

Motivation and cognitive control in depression

Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression.

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.

Cognitive 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.

Errors in Action Timing and Inhibition Facilitate Learning by Tuning Distinct Mechanisms in the Underlying Decision Process

Goal-directed behavior requires integrating action selection processes with learning systems that adapt control using environmental feedback. These functions are known to intersect at a common neural substrate with multiple known targets of plasticity (the cortico-basal ganglia-thalamic network), suggesting that feedback signals have a multifaceted impact on future 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: 37 males, 38 females) 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.SIGNIFICANCE STATEMENT Many complex behavioral goals rely on the 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 fundamental components of behavior become tuned to the control demands of the environment by combining principles of reinforcement learning with accumulation-to-bound models. Model fits to behavioral data in an adaptive stop signal task revealed two adaptive mechanisms: (1) timing error-related changes in the rate of the execution signal; and (2) an increase in the execution boundary after failed stops. These findings demonstrate unique effects of timing and control errors on the underlying mechanisms of control, the rate and threshold of accumulating action signals.

Distinct neural circuits support incentivized inhibition

The ability to inhibit responses under high stakes, or "incentivized inhibition," is critical for adaptive impulse control. While previous research indicates that right ventrolateral prefrontal cortical (VLPFC) activity plays a key role in response inhibition, less research has addressed how incentives might influence this circuit. By combining a novel behavioral task, functional magnetic resonance imaging (FMRI), and diffusion-weighted imaging (DWI), we targeted and characterized specific neural circuits that support incentivized inhibition. Behaviorally, large incentives enhanced responses to obtain money, but also reduced response inhibition. Functionally, activity in both right VLPFC and right anterior insula (AIns) predicted successful inhibition for high incentives. Structurally, characterization of a novel white-matter tract connecting the right AIns and VLPFC revealed an association of tract coherence with incentivized inhibition performance. Finally, individual differences in right VLPFC activity statistically mediated the association of right AIns-VLPFC tract coherence with incentivized inhibition performance. These multimodal findings bridge brain structure, brain function, and behavior to clarify how individuals can inhibit impulses, even in the face of high stakes.

Neuro-Cognitive Effects of Acute Tyrosine Administration on Reactive and Proactive Response Inhibition in Healthy Older Adults

The aging brain is characterized by altered dopamine signaling. The amino acid tyrosine, a catecholamine precursor, is known to improve cognitive performance in young adults, especially during high environmental demands. Tyrosine administration might also affect catecholamine transmission in the aging brain, thereby improving cognitive functioning. In healthy older adults, impairments have been demonstrated in two forms of response inhibition: reactive inhibition (outright stopping) and proactive inhibition (anticipatory response slowing) under high information load. However, no study has directly compared the effects of a catecholamine precursor on reactive and load-dependent proactive inhibition. In this study we explored the effects of tyrosine on reactive and proactive response inhibition and signal in dopaminergically innervated fronto-striatal regions. Depending on age, tyrosine might lead to beneficial or detrimental neurocognitive effects. We aimed to address these hypotheses in 24 healthy older human adults (aged 61-72 years) using fMRI in a double blind, counterbalanced, placebo-controlled, within-subject design. Across the group, tyrosine did not alter reactive or proactive inhibition behaviorally but did increase fronto-parietal proactive inhibition-related activation. When taking age into account, tyrosine affected proactive inhibition both behaviorally and neurally. Specifically, increasing age was associated with a greater detrimental effect of tyrosine compared with placebo on proactive slowing. Moreover, with increasing age, tyrosine decreased fronto-striatal and parietal proactive signal, which correlated positively with tyrosine's effects on proactive slowing. Concluding, tyrosine negatively affected proactive response slowing and associated fronto-striatal activation in an age-dependent manner, highlighting the importance of catecholamines, perhaps particularly dopamine, for proactive response inhibition in older adults.

Response inhibition activates distinct motor cortical inhibitory processes

We routinely cancel preplanned movements that are no longer required. If stopping is forewarned, proactive processes are engaged to selectively decrease motor cortex excitability. However, without advance information there is a nonselective reduction in motor cortical excitability. In this study we examined modulation of human primary motor cortex inhibitory networks during response inhibition tasks with informative and uninformative cues using paired-pulse transcranial magnetic stimulation. Long- (LICI) and short-interval intracortical inhibition (SICI), indicative of GABAB- and GABAA-receptor mediated inhibition, respectively, were examined from motor evoked potentials obtained in task-relevant and task-irrelevant hand muscles when response inhibition was preceded by informative and uninformative cues. When the participants (10 men and 8 women) were cued to stop only a subcomponent of the bimanual response, the remaining response was delayed, and the extent of delay was greatest in the more reactive context, when cues were uninformative. For LICI, inhibition was reduced in both muscles during all types of response inhibition trials compared with the pre-task resting baseline. When cues were uninformative and left-hand responses were suddenly canceled, task-relevant LICI positively correlated with response times of the responding right hand. In trials where left-hand responding was highly probable or known (informative cues), task-relevant SICI was reduced compared with that when cued to rest, revealing a motor set indicative of responding. These novel findings indicate that the GABAB-receptor-mediated pathway may set a default inhibitory tone according to task context, whereas the GABAA-receptor-mediated pathways are recruited proactively with response certainty.

NEW & NOTEWORTHY We examined how informative and uninformative cues that trigger both proactive and reactive processes modulate GABAergic inhibitory networks within human primary motor cortex. We show that GABAB inhibition was released during the task regardless of cue type, whereas GABAA inhibition was reduced when responding was highly probable or known compared with rest. GABAB-receptor-mediated inhibition may set a default inhibitory tone, whereas GABAA circuits may be modulated proactively according to response certainty.

Neural substrates of norm compliance in perceptual decisions

Societal norms exert a powerful influence on our decisions. Behaviours motivated by norms, however, do not always concur with the responses mandated by decision relevant information potentially generating a conflict. To probe the interplay between normative and informational influences, we examined how prosocial norms impact on perceptual decisions subjects made in the context of a simultaneous presentation of social information. Participants displayed a bias in their perceptual decisions towards that mandated by social information. However, normative prescriptions modulated this bias bi-directionally depending on whether norms mandated a decision in accord or contrary to the contextual social information. At a neural level, the addition of a norms increased activity in prefrontal cortex and modulated functional connectivity between prefrontal and parietal areas. The bi-directional effect of our norms was captured by differential activations when participants decided against the social information. When norms indicated a decision in line with social information, non-compliance modulated lateral prefrontal cortex activity. By contrast, when norms mandated a decision against social information norm compliance increased activity in the anterior cingulate cortex. Hence, social norms changed the balance between a reliance on perceptual and social information by modulating brain activity in regions associated with response inhibition and conflict monitoring.

What Makes You Go Faster?: The Effect of Reward on Speeded Action under Risk

Evaluating the potential reward and risk associated with a choice of action plays an important role in everyday decision making. However, the details behind how reward and risk affect the decisions for actions remain unclear. The present study investigates the influence of reward and risk on a decision to make a speeded motor response. One hundred and ten college students performed a Speed-Rewarded Go-NoGo task during which they were rewarded proportionally based on the speed and accuracy of their response. On each trial, the magnitude of potential reward and the probability of a forthcoming Go signal (Go-probability) were presented prior to the Go or NoGo signal. Personality traits, such as risk taking and impulsive tendencies, were measured to determine their contribution in explaining individual differences in task performance. The results showed that larger amount of rewards can motivate people to respond faster, and this effect was modulated by the assessed risk, suggesting that decisions for actions are based on a systematic trade-off between rewards and risks. Moreover, when the assessed risk was high, individuals with greater risk taking and impulsive tendencies did not adequately adjust their behavior across different reward levels. These findings shed light on the mechanistic understanding of the effect of reward and risk on decisions for a speeded action.

Reliable estimation of inhibitory efficiency: to anticipate, choose or simply react?

Response inhibition is an important executive process studied by clinical and experimental psychologists, neurophysiologists and cognitive neuroscientists alike. Stop-signal paradigms are popular because they are grounded in a theory that provides methods to estimate the latency of an unobservable process: the stop-signal reaction time (SSRT). Critically, SSRT estimates can be biased by skew of the response time distribution and gradual slowing over the course of the experiment. Here, we present a series of experiments that directly compare three common stop-signal paradigms that differ in the distribution of response times. The results show that the widely used choice response (CR) and simple response (SR) time versions of the stop-signal paradigm are particularly susceptible to skew of the response time distribution and response slowing, and that using the anticipated response (AR) paradigm based on the Slater-Hammel task offers a viable alternative to obtain more reliable SSRT estimates.

Go-activation endures following the presentation of a stop-signal: evidence from startle

It has been proposed that, in a stop-signal task (SST), independent go- and stop-processes "race" to control behavior. If the go-process wins, an overt response is produced, whereas, if the stop-process wins, the response is withheld. One prediction that follows from this proposal is that, if the activation associated with one process is enhanced, it is more likely to win the race. We looked to determine whether these initiation and inhibition processes (and thus response outcomes) could be manipulated by using a startling acoustic stimulus (SAS), which has been shown to provide additional response activation. In the present study, participants were to respond to a visual go-stimulus; however, if a subsequent stop-signal appeared, they were to inhibit the response. The stop-signal was presented at a delay corresponding to a probability of responding of 0.4 (determined from a baseline block of trials). On stop-trials, a SAS was presented either simultaneously with the go-signal or stop-signal or 100, 150, or 200 ms following the stop-signal. Results showed that presenting a SAS during stop-trials led to an increase in probability of responding when presented with or following the stop-signal. The latency of SAS responses at the stop-signal + 150 ms and stop-signal + 200 ms probe times suggests that they would have been voluntarily inhibited but instead were involuntarily initiated by the SAS. Thus results demonstrate that go-activation endures even 200 ms following a stop-signal and remains accessible well after the response has been inhibited, providing evidence against a winner-take-all race between independent go- and stop-processes.

NEW & NOTEWORTHY

In this study, a startling acoustic stimulus (SAS) was used to determine whether response outcome could be manipulated in a stop-signal task. Results revealed that presenting a SAS during stop-signal trials led to an increase in probability of responding even when presented 200 ms following the stop-signal. The latency of SAS responses indicates that go-activation remains accessible and modifiable well after the response is voluntarily inhibited, providing evidence against an irrevocable commitment to inhibition.

Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling

In the last few decades, non-invasive neuroimaging has revealed macro-scale brain dynamics that underlie perception, cognition and action. Advances in non-invasive neuroimaging target two capabilities; 1) increased spatial and temporal resolution of measured neural activity, and 2) innovative methodologies to extract brain-behavior relationships from evolving neuroimaging technology. We target the second. Our novel methodology integrated three neuroimaging methodologies and elucidated expertise-dependent differences in functional (fused EEG-fMRI) and structural (dMRI) brain networks for a perception-action coupling task. A set of baseball players and controls performed a Go/No-Go task designed to mimic the situation of hitting a baseball. In the functional analysis, our novel fusion methodology identifies 50ms windows with predictive EEG neural correlates of expertise and fuses these temporal windows with fMRI activity in a whole-brain 2mm voxel analysis, revealing time-localized correlations of expertise at a spatial scale of millimeters. The spatiotemporal cascade of brain activity reflecting expertise differences begins as early as 200ms after the pitch starts and lasting up to 700ms afterwards. Network differences are spatially localized to include motor and visual processing areas, providing evidence for differences in perception-action coupling between the groups. Furthermore, an analysis of structural connectivity revealed that the players have significantly more connections between cerebellar and left frontal/motor regions, and many of the functional activation differences between the groups are located within structurally defined network modules that differentiate expertise. In short, our novel method illustrates how multimodal neuroimaging can provide specific macro-scale insights into the functional and structural correlates of expertise development.

Inhibitory Control in the Cortico-Basal Ganglia-Thalamocortical Loop: Complex Regulation and Interplay with Memory and Decision Processes

We developed a circuit model of spiking neurons that includes multiple pathways in the basal ganglia (BG) and is endowed with feedback mechanisms at three levels: cortical microcircuit, corticothalamic loop, and cortico-BG-thalamocortical system. We focused on executive control in a stop signal task, which is known to depend on BG across species. The model reproduces a range of experimental observations and shows that the newly discovered feedback projection from external globus pallidus to striatum is crucial for inhibitory control. Moreover, stopping process is enhanced by the cortico-subcortical reverberatory dynamics underlying persistent activity, establishing interdependence between working memory and inhibitory control. Surprisingly, the stop signal reaction time (SSRT) can be adjusted by weights of certain connections but is insensitive to other connections in this complex circuit, suggesting novel circuit-based intervention for inhibitory control deficits associated with mental illness. Our model provides a unified framework for inhibitory control, decision making, and working memory.