Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study

Valence-dependent dopaminergic modulation during reversal learning in Parkinson's disease: A neurocomputational approach

Reinforcement learning, crucial for behavior in dynamic environments, is driven by rewards and punishments, modulated by dopamine (DA) changes. This study explores the dopaminergic system's influence on learning, particularly in Parkinson's Disease (PD), where medication leads to impaired adaptability. Highlighting the role of tonic DA in signaling the valence of actions, this research investigates how DA affects response vigor and decision-making in PD. DA not only influences reward and punishment learning but also indicates the cognitive effort level and risk propensity in actions, which are essential for understanding and managing PD symptoms. In this work, we adapt our existing neurocomputational model of basal ganglia (BG) to simulate two reversal learning tasks proposed by Cools et al. We first optimized a Hebb rule for both probabilistic and deterministic reversal learning, conducted a sensitivity analysis (SA) on parameters related to DA effect, and compared performances between three groups: PD-ON, PD-OFF, and control subjects. In our deterministic task simulation, we explored switch error rates after unexpected task switches and found a U-shaped relationship between tonic DA levels and switch error frequency. Through SA, we classify these three groups. Then, assuming that the valence of the stimulus affects the tonic levels of DA, we were able to reproduce the results by Cools et al. As for the probabilistic task simulation, our results are in line with clinical data, showing similar trends with PD-ON, characterized by higher tonic DA levels that are correlated with increased difficulty in both acquisition and reversal tasks. Our study proposes a new hypothesis: valence, signaled by tonic DA levels, influences learning in PD, confirming the uncorrelation between phasic and tonic DA changes. This hypothesis challenges existing paradigms and opens new avenues for understanding cognitive processes in PD, particularly in reversal learning tasks.

CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort.

Basal ganglia network dynamics and function: Role of direct, indirect and hyper-direct pathways in action selection

Basal ganglia (BG) are a widely recognized neural basis for action selection, but its decision-making mechanism is still a difficult problem for researchers. Therefore, we constructed a spiking neural network inspired by the BG anatomical data. Simulation experiments were based on the principle of dis-inhibition and our functional hypothesis within the BG: the direct pathway, the indirect pathway, and the hyper-direct pathway of the BG jointly implement the initiation execution and termination of motor programs. Firstly, we studied the dynamic process of action selection with the network, which contained intra-group competition and inter-group competition. Secondly, we focused on the effects of the stimulus intensity and the proportion of excitation and inhibition on the GPi/SNr. The results suggested that inhibition and excitation shape action selection. They also explained why the firing rate of GPi/SNr did not continue to increase in the action-selection experiment. Finally, we discussed the experimental results with the functional hypothesis. Uniquely, this paper summarized the decision-making neural mechanism of action selection based on the direct pathway, the indirect pathway, and the hyper-direct pathway within BG.

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Cortical Serotonergic and Catecholaminergic Denervation in MPTP-Treated Parkinsonian Monkeys

Decreased cortical serotonergic and catecholaminergic innervation of the frontal cortex has been reported at early stages of Parkinson's disease (PD). However, the limited availability of animal models that exhibit these pathological features has hampered our understanding of the functional significance of these changes during the course of the disease. In the present study, we assessed longitudinal changes in cortical serotonin and catecholamine innervation in motor-symptomatic and asymptomatic monkeys chronically treated with low doses of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Densitometry and unbiased stereological techniques were used to quantify changes in serotonin and tyrosine hydroxylase (TH) immunoreactivity in frontal cortices of 3 control monkeys and 3 groups of MPTP-treated monkeys (motor-asymptomatic [N = 2], mild parkinsonian [N = 3], and moderate parkinsonian [N = 3]). Our findings revealed a significant decrease (P < 0.001) in serotonin innervation of motor (Areas 4 and 6), dorsolateral prefrontal (Areas 9 and 46), and limbic (Areas 24 and 25) cortical areas in motor-asymptomatic MPTP-treated monkeys. Both groups of symptomatic MPTP-treated animals displayed further serotonin denervation in these cortical regions (P < 0.0001). A significant loss of serotonin-positive dorsal raphe neurons was found in the moderate parkinsonian group. On the other hand, the intensity of cortical TH immunostaining was not significantly affected in motor asymptomatic MPTP-treated monkeys, but underwent a significant reduction in the moderate symptomatic group (P < 0.05). Our results indicate that chronic intoxication with MPTP induces early pathology in the corticopetal serotonergic system, which may contribute to early non-motor symptoms in PD.

Elimination of the Cortico-Subthalamic Hyperdirect Pathway Induces Motor Hyperactivity in Mice

The substantia nigra pars reticulata (SNr) is the output station of the basal ganglia and receives cortical inputs by way of the following three basal ganglia pathways: the cortico-subthalamo (STN)-SNr hyperdirect, the cortico-striato-SNr direct, and the cortico-striato-external pallido-STN-SNr indirect pathways. Compared with the classical direct and indirect pathways via the striatum, the functions of the hyperdirect pathway remain to be fully elucidated. Here we used a photodynamic technique to selectively eliminate the cortico-STN projection in male mice and observed neuronal activity and motor behaviors in awake conditions. After cortico-STN elimination, cortically evoked early excitation in the SNr was diminished, while the cortically evoked inhibition and late excitation, which are delivered through the direct and indirect pathways, respectively, were unchanged. In addition, locomotor activity was significantly increased after bilateral cortico-STN elimination, and apomorphine-induced ipsilateral rotations were observed after unilateral cortico-STN elimination, suggesting that cortical activity was increased. These results are compatible with the notion that the cortico-STN-SNr hyperdirect pathway quickly conveys cortical excitation to the output station of the basal ganglia, resets or suppresses the cortical activity related to ongoing movements, and prepares for the forthcoming movement.SIGNIFICANCE STATEMENT The basal ganglia play a pivotal role in the control of voluntary movements, and their malfunctions lead to movement disorders, such as Parkinson's disease and dystonia. Understanding their functions is important to find better treatments for such diseases. Here we used a photodynamic technique to selectively eliminate the projection from the motor cortex to the subthalamic nucleus, the input station of the basal ganglia, and found greatly reduced early excitatory signals from the cortex to the output station of the basal ganglia and motor hyperactivity. These results suggest that the neuronal signals through the cortico-subthalamic hyperdirect pathway reset or suppress ongoing movements and that blockade of this pathway may be beneficial for Parkinson's disease, which is characterized by oversuppression of movements.

The basal ganglia corticostriatal loops and conditional learning

Brief maneuvering of the literature as to the various roles attributed to the basal ganglia corticostriatal circuits in a variety of cognitive processes such as working memory, selective attention, and category learning has inspired us to investigate the interplay of the two major basal ganglia open-recurrent loops, namely, visual and executive loops specifically the possible involvement of their overlap in conditional learning. We propose that the interaction of the visual and executive loops reflected through their cortical overlap in the dorsolateral prefrontal cortex (DL-PFC), lateral orbitofrontal cortex (LO-PFC), and presupplementary motor area (SMA) plays an instrumental role preliminary first in forming associations between a series of correct responses following similar stimuli and then in shifting, abstracting, and generalizing conditioned responses. The premotor and supplementary motor areas have been shown essential to producing a sequence of movements while the SMA is engaged in monitoring complex movements. In light of the recent studies, we will suggest that the interaction of visual and executive loops could strengthen or weaken learned associations following different reward values. Furthermore, we speculate that the overlap of the visual and executive loops can account for the switching between the associative vs. rule-based category learning systems.

Differential early predictive factors for upper and lower extremity motor recovery after ischaemic stroke

BACKGROUND AND PURPOSE

Various clinical and neuroimaging predictive factors have been identified for the recovery of upper extremity (UE) motor function after stroke. However, few studies have addressed factors related to the recovery of lower extremity (LE) motor function after stroke or performed direct comparisons of UE and LE motor recovery in the same set of patients. In this study, predictive factors for UE and LE motor recovery after stroke were investigated using clinical and neuroimaging characteristics.

METHODS

Forty-two subacute ischaemic stroke patients underwent structural and functional magnetic resonance imaging data acquisition and cognitive/behavioral assessments using the Fugl-Meyer assessment, the National Institutes of Health Stroke Scale (NIHSS) and the Mini-Mental State Examination (MMSE) 2 weeks after stroke onset. Neuroimaging factors, including corticospinal tract (CST) fractional anisotropy, lesion volume, CST lesion load and interhemispheric homotopic functional connectivity, were extracted. The outcome of motor function was assessed by Fugl-Meyer assessment scores 3 months after onset.

RESULTS

Early clinical and neuroimaging factors for predicting motor recovery were noticeably different for UE and LE. UE motor function recovery was related to age, NIHSS, MMSE, CST lesion load, lesion volume, ipsilesional CST integrity and interhemispheric homotopic functional connectivity. In contrast, LE motor recovery was related to ipsilesional and contralesional CST integrity and MMSE. Specifically, LE recovery showed a strong relationship to the preservation of cognitive function compared with motor impairment.

CONCLUSIONS

Our results indicate that different mechanisms underlie UE and LE motor recovery after stroke. LE motor recovery seems to be more intensively modulated by cognitive functions than UE.

Network topology of symbolic and nonsymbolic number comparison

Studies of brain activity during number processing suggest symbolic and nonsymbolic numerical stimuli (e.g., Arabic digits and dot arrays) engage both shared and distinct neural mechanisms. However, the extent to which number format influences large-scale functional network organization is unknown. In this study, using 7 Tesla MRI, we adopted a network neuroscience approach to characterize the whole-brain functional architecture supporting symbolic and nonsymbolic number comparison in 33 adults. Results showed the degree of global modularity was similar for both formats. The symbolic format, however, elicited stronger community membership among auditory regions, whereas for nonsymbolic, stronger membership was observed within and between cingulo-opercular/salience network and basal ganglia communities. The right posterior inferior temporal gyrus, left intraparietal sulcus, and two regions in the right ventromedial occipital cortex demonstrated robust differences between formats in terms of their community membership, supporting prior findings that these areas are differentially engaged based on number format. Furthermore, a unified fronto-parietal/dorsal attention community in the nonsymbolic condition was fractionated into two components in the symbolic condition. Taken together, these results reveal a pattern of overlapping and distinct network architectures for symbolic and nonsymbolic number processing.

Complex Movement Control in a Rat Model of Parkinsonian Falls: Bidirectional Control by Striatal Cholinergic Interneurons

Older persons and, more severely, persons with Parkinson's disease (PD) exhibit gait dysfunction, postural instability and a propensity for falls. These dopamine (DA) replacement-resistant symptoms are associated with losses of basal forebrain and striatal cholinergic neurons, suggesting that falls reflect disruption of the corticostriatal transfer of movement-related cues and their striatal integration with movement sequencing. To advance a rodent model of the complex movement deficits of Parkinsonian fallers, here we first demonstrated that male and female rats with dual cortical cholinergic and striatal DA losses (DL rats) exhibit cued turning deficits, modeling the turning deficits seen in these patients. As striatal cholinergic interneurons (ChIs) are positioned to integrate movement cues with gait, and as ChI loss has been associated with falls in PD, we next used this task, as well as a previously established task used to reveal heightened fall rates in DL rats, to broadly test the role of ChIs. Chemogenetic inhibition of ChIs in otherwise intact male and female rats caused cued turning deficits and elevated fall rates. Spontaneous turning was unaffected. Furthermore, chemogenetic stimulation of ChIs in DL rats reduced fall rates and restored cued turning performance. Stimulation of ChIs was relatively more effective in rats with viral transfection spaces situated lateral to the DA depletion areas in the dorsomedial striatum. These results indicate that striatal ChIs are essential for the control of complex movements, and they suggest a therapeutic potential of stimulation of ChIs to restore gait and balance, and to prevent falls in PD.SIGNIFICANCE STATEMENT In persons with Parkinson's disease, gait dysfunction and the associated risk for falls do not benefit from dopamine replacement therapy and often result in long-term hospitalization and nursing home placement. Here, we first validated a new task to demonstrate impairments in cued turning behavior in rodents modeling the cholinergic-dopaminergic losses observed in Parkinsonian fallers. We then demonstrated the essential role of striatal cholinergic interneurons for turning behavior as well as for traversing dynamic surfaces and avoiding falls. Stimulation of these interneurons in the rat model rescued turning performance and reduced fall rates. Our findings indicate the feasibility of investigating the neuronal circuitry underling complex movement control in rodents, and that striatal cholinergic interneurons are an essential node of such circuitry.

Striatal Cholinergic Interneurons: How to Elucidate Their Function in Health and Disease

Striatal cholinergic interneurons (CINs) are the main source of acetylcholine in the striatum and are believed to play an important role in basal ganglia physiology and pathophysiology. The role of CINs in striatal function is known mostly from extracellular recordings of tonically active striatal neurons in monkeys, which are believed to correspond to CINs. Because these neurons transiently respond to motivationally cues with brief pauses, flanked by bursts of increased activity, they are classically viewed as key players in reward-related learning. However, CIN modulatory function within the striatal network has been mainly inferred from the action of acetylcholine agonists/antagonists or through CIN activation. These manipulations are far from recapitulating CIN activity in response to behaviorally-relevant stimuli. New technical tools such as optogenetics allow researchers to specifically manipulate this sparse neuronal population and to mimic their typical pause response. For example, it is now possible to investigate how short inhibition of CIN activity shapes striatal properties. Here, we review the most recent literature and show how these new techniques have brought considerable insights into the functional role of CINs in normal and pathological states, raising several interesting and novel questions. To continue moving forward, it is crucial to determine in detail CIN activity changes during behavior, particularly in rodents. We will also discuss how computational approaches combined with optogenetics will contribute to further our understanding of the CIN role in striatal circuits.

Oculomotor effects of medical and surgical treatments of Parkinson's disease

Oculomotor abnormalities are fast becoming a proxy for disease diagnosis and progression. Saccades-ballistic eye movements-are known to be affected by dopaminergic cell loss in the basal ganglia, caused by Parkinson's disease. Pharmaceutical and neurosurgical interventions such as deep brain stimulation and functional neurosurgery have both been noted to have an effect on saccades. Comparing and contrasting these effects may yield insights into Parkinson's disease pathophysiology, and the mechanisms of pharmacological and neurosurgical treatments. Computational models of saccadic control, such as the LATER model, can help to interpret the distribution of saccadic latencies, providing a framework for objectively comparing the effects of pharmaceutical interventions and deep brain stimulation.

A Computational Model of Dual Competition between the Basal Ganglia and the Cortex

We propose a model that includes interactions between the cortex, the basal ganglia (BG), and the thalamus based on a dual competition. We hypothesize that the striatum, the subthalamic nucleus (STN), the internal globus pallidus (GPi), the thalamus, and the cortex are involved in closed feedback loops through the hyperdirect and direct pathways. These loops support a competition process that results in the ability of BG to make a cognitive decision followed by a motor one. Considering lateral cortical interactions, another competition takes place inside the cortex allowing the latter to make a cognitive and a motor decision. We show how this dual competition endows the model with two regimes. One is driven by reinforcement learning and the other by Hebbian learning. The final decision is made according to a combination of these two mechanisms with a gradual transfer from the former to the latter. We confirmed these theoretical results on primates (Macaca mulatta) using a novel paradigm predicted by the model.

A natural history of skills

The dorsal pallium (a.k.a. cortex in mammals) makes a loop circuit with the basal ganglia and the thalamus known to control and adapt behavior but the who's who of the functional roles of these structures is still debated. Influenced by the Triune brain theory that was proposed in the early sixties, many current theories propose a hierarchical organization on the top of which stands the cortex to which the subcortical structures are subordinated. In particular, habits formation has been proposed to reflect a switch from conscious on-line control of behavior by the cortex, to a fully automated subcortical control. In this review, we propose to revalue the function of the network in light of the current experimental evidence concerning the anatomy and physiology of the basal ganglia-cortical circuits in vertebrates. We briefly review the current theories and show that they could be encompassed in a broader framework of skill learning and performance. Then, after reminding the state of the art concerning the anatomical architecture of the network and the underlying dynamic processes, we summarize the evolution of the anatomical and physiological substrate of skill learning and performance among vertebrates. We then review experimental evidence supporting for the hypothesis that the development of automatized skills relies on the BG teaching cortical circuits and is actually a late feature linked with the development of a specialized cortex or pallium that evolved in parallel in different taxa. We finally propose a minimal computational framework where this hypothesis can be explicitly implemented and tested.

Iterative free-energy optimization for recurrent neural networks (INFERNO)

The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

A Neurocomputational Model of the Effect of Cognitive Load on Freezing of Gait in Parkinson's Disease

Experimental data show that perceptual cues can either exacerbate or ameliorate freezing of gait (FOG) in Parkinson's Disease (PD). For example, simple visual stimuli like stripes on the floor can alleviate freezing whereas complex stimuli like narrow doorways can trigger it. We present a computational model of the cognitive and motor cortico-basal ganglia loops that explains the effects of sensory and cognitive processes on FOG. The model simulates strong causative factors of FOG including decision conflict (a disagreement of various sensory stimuli in their association with a response) and cognitive load (complexity of coupling a stimulus with downstream mechanisms that control gait execution). Specifically, the model simulates gait of PD patients (freezers and non-freezers) as they navigate a series of doorways while simultaneously responding to several Stroop word cues in a virtual reality setup. The model is based on an actor-critic architecture of Reinforcement Learning involving Utility-based decision making, where Utility is a weighted sum of Value and Risk functions. The model accounts for the following experimental data: (a) the increased foot-step latency seen in relation to high conflict cues, (b) the high number of motor arrests seen in PD freezers when faced with a complex cue compared to the simple cue, and (c) the effect of dopamine medication on these motor arrests. The freezing behavior arises as a result of addition of task parameters (doorways and cues) and not due to inherent differences in the subject group. The model predicts a differential role of risk sensitivity in PD freezers and non-freezers in the cognitive and motor loops. Additionally this first-of-its-kind model provides a plausible framework for understanding the influence of cognition on automatic motor actions in controls and Parkinson's Disease.

Decision making under uncertainty in a spiking neural network model of the basal ganglia

The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used these properties to develop and extend a connectionist model at a spiking neuron level based on a previous rate model approach. This model is demonstrated on decision-making tasks that have been studied in primates and the electrophysiology interpreted to show that the decision is made in two steps. To model this, we have used two parallel loops, each of which performs decision-making based on interactions between positive and negative feedback pathways. This model is able to perform two-level decision-making as in primates. We show here that, before learning, synaptic noise is sufficient to drive the decision-making process and that, after learning, the decision is based on the choice that has proven most likely to be rewarded. The model is then submitted to lesion tests, reversal learning and extinction protocols. We show that, under these conditions, it behaves in a consistent manner and provides predictions in accordance with observed experimental data.

The globus pallidus pars interna in goal-oriented and routine behaviors: Resolving a long-standing paradox

BACKGROUND

There is an apparent contradiction between experimental data showing that the basal ganglia are involved in goal-oriented and routine behaviors and clinical observations. Lesion or disruption by deep brain stimulation of the globus pallidus interna has been used for various therapeutic purposes ranging from the improvement of dystonia to the treatment of Tourette's syndrome. None of these approaches has reported any severe impairment in goal-oriented or automatic movement.

METHOD

To solve this conundrum, we trained 2 monkeys to perform a variant of a 2-armed bandit-task (with different reward contingencies). In the latter we alternated blocks of trials with choices between familiar rewarded targets that elicit routine behavior and blocks with novel pairs of targets that require an intentional learning process.

RESULTS

Bilateral inactivation of the globus pallidus interna, by injection of muscimol, prevents animals from learning new contingencies while performance remains intact, although slower for the familiar stimuli. We replicate in silico these data by adding lateral competition and Hebbian learning in the cortical layer of the theoretical model of the cortex-basal ganglia loop that provided the framework of our experimental approach.

CONCLUSION

The basal ganglia play a critical role in the deliberative process that underlies learning but are not necessary for the expression of routine movements. Our approach predicts that after pallidotomy or during stimulation, patients should have difficulty with complex decision-making processes or learning new goal-oriented behaviors. © 2016 Movement Disorder Society.

Pallidal Deep Brain Stimulation Improves Higher Control of the Oculomotor System in Parkinson's Disease

The frontal cortex and basal ganglia form a set of parallel but mostly segregated circuits called cortico-basal ganglia loops. The oculomotor loop controls eye movements and can direct relatively simple movements, such as reflexive prosaccades, without external help but needs input from "higher" loops for more complex behaviors. The antisaccade task requires the dorsolateral prefrontal cortex, which is part of the prefrontal loop. Information flows from prefrontal to oculomotor circuits in the striatum, and directional errors in this task can be considered a measure of failure of prefrontal control over the oculomotor loop. The antisaccadic error rate (AER) is increased in Parkinson's disease (PD). Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has no effect on the AER, but a previous case suggested that DBS of the globus pallidus interna (GPi) might. Our aim was to compare the effects of STN DBS and GPi DBS on the AER. We tested eye movements in 14 human DBS patients and 10 controls. GPi DBS substantially reduced the AER, restoring lost higher control over oculomotor function. Interloop information flow involves striatal neurons that receive cortical input and project to pallidum. They are normally silent when quiescent, but in PD they fire randomly, creating noise that may account for the degradation in interloop control. The reduced AER with GPi DBS could be explained by retrograde stimulation of striatopallidal axons with consequent activation of inhibitory collaterals and reduction in background striatal firing rates. This study may help explain aspects of PD pathophysiology and the mechanism of action of GPi DBS. Significance statement: Parkinson's disease causes symptoms including stiffness, slowness of movement, and tremor. Electrical stimulation of specific areas deep in the brain can effectively treat these symptoms, but exactly how is not fully understood. Part of the cause of such symptoms may be impairments in the way information flows from one circuit within the brain to another, as a result of overactivity of certain nerve cells. By demonstrating that stimulation of an area called the globus pallidus interna partially reverses deficits in voluntary control of eye movements, this study shows that stimulation can improve information flow between circuits, probably by calming down the overactive cells.

Brain networks for exploration decisions utilizing distinct modeled information types during contextual learning

Exploration permits acquisition of the most relevant information during learning. However, the specific information needed, the influences of this information on decision making, and the relevant neural mechanisms remain poorly understood. We modeled distinct information types available during contextual association learning and used model-based fMRI in conjunction with manipulation of exploratory decision making to identify neural activity associated with information-based decisions. We identified hippocampal-prefrontal contributions to advantageous decisions based on immediately available novel information, distinct from striatal contributions to advantageous decisions based on the sum total available (accumulated) information. Furthermore, network-level interactions among these regions during exploratory decision making were related to learning success. These findings link strategic exploration decisions during learning to quantifiable information and advance understanding of adaptive behavior by identifying the distinct and interactive nature of brain-network contributions to decisions based on distinct information types.

Saccade learning with concurrent cortical and subcortical basal ganglia loops

The Basal Ganglia (BG) is a central structure involved in multiple cortical and subcortical loops. Some of these loops are believed to be responsible for saccade target selection. We study here how the very specific structural relationships of these saccadic loops can affect the ability of learning spatial and feature-based tasks. We propose a model of saccade generation with reinforcement learning capabilities based on our previous BG and superior colliculus models. It is structured around the interactions of two parallel cortico-basal loops and one tecto-basal loop. The two cortical loops separately deal with spatial and non-spatial information to select targets in a concurrent way. The subcortical loop is used to make the final target selection leading to the production of the saccade. These different loops may work in concert or disturb each other regarding reward maximization. Interactions between these loops and their learning capabilities are tested on different saccade tasks. The results show the ability of this model to correctly learn basic target selection based on different criteria (spatial or not). Moreover the model reproduces and explains training dependent express saccades toward targets based on a spatial criterion. Finally, the model predicts that in absence of prefrontal control, the spatial loop should dominate.

Where attention falls: Increased risk of falls from the converging impact of cortical cholinergic and midbrain dopamine loss on striatal function

Falls are a major source of hospitalization, long-term institutionalization, and death in older adults and patients with Parkinson's disease (PD). Limited attentional resources are a major risk factor for falls. In this review, we specify cognitive-behavioral mechanisms that produce falls and map these mechanisms onto a model of multi-system degeneration. Results from PET studies in PD fallers and findings from a recently developed animal model support the hypothesis that falls result from interactions between loss of basal forebrain cholinergic projections to the cortex and striatal dopamine loss. Striatal dopamine loss produces inefficient, low-vigor gait, posture control, and movement. Cortical cholinergic deafferentation impairs a wide range of attentional processes, including monitoring of gait, posture and complex movements. Cholinergic cell loss reveals the full impact of striatal dopamine loss on motor performance, reflecting loss of compensatory attentional supervision of movement. Dysregulation of dorsomedial striatal circuitry is an essential, albeit not exclusive, mediator of falls in this dual-system model. Because cholinergic neuromodulatory activity influences cortical circuitry primarily via stimulation of α4β2* nicotinic acetylcholine receptors, and because agonists at these receptors are known to benefit attentional processes in animals and humans, treating PD fallers with such agonists, as an adjunct to dopaminergic treatment, is predicted to reduce falls. Falls are an informative behavioral endpoint to study attentional-motor integration by striatal circuitry.

An attractor-based complexity measurement for Boolean recurrent neural networks

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

The Michelin red guide of the brain: role of dopamine in goal-oriented navigation

Spatial learning has been recognized over the years to be under the control of the hippocampus and related temporal lobe structures. Hippocampal damage often causes severe impairments in the ability to learn and remember a location in space defined by distal visual cues. Such cognitive disabilities are found in Parkinsonian patients. We recently investigated the role of dopamine in navigation in the 6-Hydroxy-dopamine (6-OHDA) rat, a model of Parkinson’s disease (PD) commonly used to investigate the pathophysiology of dopamine depletion (Retailleau et al., 2013). We demonstrated that dopamine (DA) is essential to spatial learning as its depletion results in spatial impairments. Our results showed that the behavioral effect of DA depletion is correlated with modification of the neural encoding of spatial features and decision making processes in hippocampus. However, the origin of these alterations in the neural processing of the spatial information needs to be clarified. It could result from a local effect: dopamine depletion disturbs directly the processing of relevant spatial information at hippocampal level. Alternatively, it could result from a more distributed network effect: dopamine depletion elsewhere in the brain (entorhinal cortex, striatum, etc.) modifies the way hippocampus processes spatial information. Recent experimental evidence in rodents, demonstrated indeed, that other brain areas are involved in the acquisition of spatial information. Amongst these, the cortex—basal ganglia (BG) loop is known to be involved in reinforcement learning and has been identified as an important contributor to spatial learning. In particular, it has been shown that altered activity of the BG striatal complex can impair the ability to perform spatial learning tasks. The present review provides a glimpse of the findings obtained over the past decade that support a dialog between these two structures during spatial learning under DA control.

Exploring the cognitive and motor functions of the basal ganglia: an integrative review of computational cognitive neuroscience models

Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.

Leakage of decision uncertainty into movement execution in Parkinson's disease?

The concept of segregated basal ganglia-cortical loops entails that functional disturbances may result from abnormal processing within loops, but also from abnormal communication between loops. Cognitive and motor processes subserved by different basal ganglia-frontal loops may interfere with one another as a result of such abnormal communication, leakage, between loops. In Parkinson's disease, movement execution has been found susceptible to decision uncertainty, attributed to this mechanism. Here, we evaluate whether this mechanism of abnormal coupling or leakage extends to perceptual decision-making with trial-by-trial control of decision uncertainty. We examined 10 Parkinson's disease (PD) patients and healthy control subjects in a random-dot motion direction discrimination task with concurrent EEG recording. Random-dot motion was manipulated to make direction discrimination easy or difficult. Reaction times (RT) and movement times (MT) were recorded, and EEG was analysed to extract movement-related potentials. Easy versus difficult direction discrimination produced robust, equally large RT differences in patients and controls (>400 ms), along with a marked difference in error rates, confirming the efficacy of the task. Effects of easy versus difficult discrimination on MT were comparatively small (<50 ms) and did not differ between groups, despite robustly slower MT in patients. Lateralised movement-related EEG potentials reproduced the MT difference between patients and controls. Together, the results do not demonstrate an enhanced effect of decision uncertainty onto movement execution in PD. We surmise that leakage of decision uncertainty into movement execution is probably task-dependent, consistent with the view that the degree to which partial information is allowed to influence the motor system is under strategic control.

Why am I lost without dopamine? Effects of 6-OHDA lesion on the encoding of reward and decision process in CA3

There is growing evidence that Parkinson's disease, generally characterized by motor symptoms, also causes cognitive impairment such as spatial disorientation. The hippocampus is a critical structure for spatial navigation and receives sparse but comprehensive dopamine (DA) innervation. DA loss is known to be the cause of Parkinson's disease and therefore it has been hypothesized that the associated spatial disorientation could result from hippocampal dysfunction. Because DA is involved in the prediction of reward expectation, it is possible to infer that spatial disorientation in DA depleted subjects results from the loss of the ability to detect the rewarding features within the environment. Amongst hippocampal formation subdivisions, CA3 properties such as the high liability of its place fields make it a serious candidate for interfacing DA reward system and spatial information encoding. We addressed this issue using multiple electrode recordings of CA3 in normal and dopamine depleted rats performing a spatial learning in a Y-maze. Our data confirm that DA is essential to spatial learning as its depletion results in spatial impairments. The present work also shows that CA3 involvement in the detection of spatial feature contextual significance is under DA control. Finally, it also shows that CA3 contributes to the decision making processes of navigation tasks. The data also reveal a lateralization effect of DA depletion underlined by neural correlates.