Action selection and refinement in subcortical loops through basal ganglia and cerebellum

Subcortical Aphasia: An Update

PURPOSE OF REVIEW

This review aims to rediscuss the leading theories concerning the role of basal ganglia and the thalamus in the genesis of aphasic symptoms in the absence of gross anatomical lesions in cortical language areas as assessed by conventional neuroimaging studies.

RECENT FINDINGS

New concepts in language processing and modern neuroimaging techniques have enabled some progress in resolving the impasse between the current dominant theories: (a) direct and specific linguistic processing and (b) subcortical structures as processing relays in domain-general functions. Of particular interest are studies of connectivity based on functional magnetic resonance imaging (MRI) and tractography that highlight the impact of white matter pathway lesions on aphasia development and recovery. Connectivity studies have put into evidence the central role of the arcuate fasciculus (AF), inferior frontal occipital fasciculus (IFOF), and uncinate fasciculus (UF) in the genesis of aphasia. Regarding the thalamus, its involvement in lexical-semantic processing through modulation of the frontal cortex is becoming increasingly apparent.

The cerebellum and the Mirror Neuron System: A matter of inhibition? From neurophysiological evidence to neuromodulatory implications. A narrative review

Mirror neurons show activity during both the execution (AE) and observation of actions (AO). The Mirror Neuron System (MNS) could be involved during motor imagery (MI) as well. Extensive research suggests that the cerebellum is interconnected with the MNS and may be critically involved in its activities. We gathered evidence on the cerebellum's role in MNS functions, both theoretically and experimentally. Evidence shows that the cerebellum plays a major role during AO and MI and that its lesions impair MNS functions likely because, by modulating the activity of cortical inhibitory interneurons with mirror properties, the cerebellum may contribute to visuomotor matching, which is fundamental for shaping mirror properties. Indeed, the cerebellum may strengthen sensory-motor patterns that minimise the discrepancy between predicted and actual outcome, both during AE and AO. Furthermore, through its connections with the hippocampus, the cerebellum might be involved in internal simulations of motor programs during MI. Finally, as cerebellar neuromodulation might improve its impact on MNS activity, we explored its potential neurophysiological and neurorehabilitation implications.

Probabilistic Tractography-Based Tremor Network Connectivity in Tremor Dominant Parkinson's Disease and Essential Tremor plus

Background: The basal ganglia-thalamocortical (BGTC) and cerebello-thalamocortical (CTC) networks are implicated in tremor genesis; however, exact contributions across disorders have not been studied. Objective: Evaluate the structural connectivity of BGTC and CTC in tremor-dominant Parkinson's disease (TDPD) and essential tremor plus (ETP) with the aid of probabilistic tractography and graph theory analysis. Methods: Structural connectomes of the BGTC and CTC were generated by probabilistic tractography for TDPD (n = 25), ETP (ET with rest tremor, n = 25), and healthy control (HC, n = 22). The Brain Connectivity Toolbox was used for computing standard topological graph measures of segregation, integration, and centrality. Tremor severity was ascertained using the Fahn-Tolosa-Marin tremor rating scale (FTMRS). Results: There was no difference in total FTMRS scores. Compared with HC, TDPD had a lower global efficiency and characteristic path length. Abnormality in segregation, integration, and centrality of bilateral putamen, globus pallidus externa (GPe), and GP interna (GPi), with reduction of centrality of right caudate and cerebellar lobule 8, was observed. ETP showed reduction in segregation and integration of right GPe and GPi, ventrolateral posterior nucleus, and centrality of right putamen, compared with HC. Differences between TDPD and ETP were a reduction of strength of the right putamen, and lower clustering coefficient, local efficiency, and strength of the left GPi in TDPD. Conclusions: Contrary to expectations, TDPD and ETP may not be significantly different with regard to tremor pathogenesis, with definite overlaps. There may be fundamental similarities in network disruption across different tremor disorders with the same tremor activation patterns, along with disease-specific changes.

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

Motor cortex is required for flexible but not automatic motor sequences

How motor cortex contributes to motor sequence execution is much debated, with studies supporting disparate views. Here we probe the degree to which motor cortex's engagement depends on task demands, specifically whether its role differs for highly practiced, or 'automatic', sequences versus flexible sequences informed by external events. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex revealed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex-dependent, suggesting that subcortical consolidation of an automatic motor sequence is delayed or prevented when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and explained the underlying circuit mechanisms. Our results critically delineate the role of motor cortex in motor sequence execution, describing the condition under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex's role in motor sequence generation.

Initial study on an expert system for spine diseases screening using inertial measurement unit

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach

Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.

Gait control by the frontal lobe

The frontal lobe is crucial and contributes to controlling truncal motion, postural responses, and maintaining equilibrium and locomotion. The rich repertoire of frontal gait disorders gives some indication of this complexity. For human walking, it is necessary to simultaneously achieve at least two tasks, such as maintaining a bipedal upright posture and locomotion. Particularly, postural control plays an extremely significant role in enabling the subject to maintain stable gait behaviors to adapt to the environment. To achieve these requirements, the frontal cortex (1) uses cognitive information from the parietal, temporal, and occipital cortices, (2) creates plans and programs of gait behaviors, and (3) acts on the brainstem and spinal cord, where the core posture-gait mechanisms exist. Moreover, the frontal cortex enables one to achieve a variety of gait patterns in response to environmental changes by switching gait patterns from automatic routine to intentionally controlled and learning the new paradigms of gait strategy via networks with the basal ganglia, cerebellum, and limbic structures. This chapter discusses the role of each area of the frontal cortex in behavioral control and attempts to explain how frontal lobe controls walking with special reference to postural control.

The mouse claustrum synaptically connects cortical network motifs

Spatially distant areas of the cerebral cortex coordinate their activity into networks that are integral to cognitive processing. A common structural motif of cortical networks is co-activation of frontal and posterior cortical regions. The neural circuit mechanisms underlying such widespread inter-areal cortical coordination are unclear. Using a discovery based functional magnetic resonance imaging (fMRI) approach in mouse, we observe frontal and posterior cortical regions that demonstrate significant functional connectivity with the subcortical nucleus, the claustrum. Examining whether the claustrum synaptically supports such frontoposterior cortical network architecture, we observe cortico-claustro-cortical circuits reflecting the fMRI data: significant trans-claustral synaptic connectivity from frontal cortices to posteriorly lying sensory and sensory association cortices contralaterally. These data reveal discrete cortical pathways through the claustrum that are positioned to support cortical network motifs central to cognitive control functions and add to the canon of major extended cortico-subcortico-cortical systems in the mammalian brain.

Integrated world modeling theory expanded: Implications for the future of consciousness

Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT's integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the "Bayesian blur problem," or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated "unlimited associative learning." While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.

What is the contribution of voluntary and reflex processes to sensorimotor control of balance?

The contribution to balance of spinal and transcortical processes including the long-latency reflex is well known. The control of balance has been modelled previously as a continuous, state feedback controller representing, long-latency reflexes. However, the contribution of slower, variable delay processes has not been quantified. Compared with fixed delay processes (spinal, transcortical), we hypothesize that variable delay processes provide the largest contribution to balance and are sensitive to historical context as well as current states. Twenty-two healthy participants used a myoelectric control signal from their leg muscles to maintain balance of their own body while strapped to an actuated, inverted pendulum. We study the myoelectric control signal (u) in relation to the independent disturbance (d) comprising paired, discrete perturbations of varying inter-stimulus-interval (ISI). We fit the closed loop response, u from d, using one linear and two non-linear non-parametric (many parameter) models. Model M1 (ARX) is a generalized, high-order linear-time-invariant (LTI) process with fixed delay. Model M1 is equivalent to any parametric, closed-loop, continuous, linear-time-invariant (LTI), state feedback model. Model M2, a single non-linear process (fixed delay, time-varying amplitude), adds an optimized response amplitude to each stimulus. Model M3, two non-linear processes (one fixed delay, one variable delay, each of time-varying amplitude), add a second process of optimized delay and optimized response amplitude to each stimulus. At short ISI, the myoelectric control signals deviated systematically both from the fixed delay LTI process (M1), and also from the fixed delay, time-varying amplitude process (M2) and not from the two-process model (M3). Analysis of M3 (all fixed delay and variable delay response amplitudes) showed the variable (compared with fixed) delay process 1) made the largest contribution to the response, 2) exhibited refractoriness (increased delay related to short ISI) and 3) was sensitive to stimulus history (stimulus direction 2 relative to stimulus 1). For this whole-body balance task and for these impulsive stimuli, non-linear processes at variable delay are central to control of balance. Compared with fixed delay processes (spinal, transcortical), variable delay processes provided the largest contribution to balance and were sensitive to historical context as well as current states.

Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data-Driven Test of the Common Model of Cognition Using Granger Causality

Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known as the "Common Model of Cognition," to task-based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top-down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we propose a bottom-up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task-based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top-down and bottom-up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition.

The Structured Mind at Rest: Low-Frequency Oscillations Reflect Interactive Dynamics Between Spontaneous Brain Activity and a Common Architecture for Task Control

The Common Model of Cognition (CMC) has been proposed as a high level framework through which functional neuroimaging data can be predicted and interpreted. Previous work has found the CMC is capable of predicting brain activity across a variety of tasks, but it has not been tested on resting state data. This paper adapts a previously used method for comparing theoretical models of brain structure, Dynamic Causal Modeling, for the task-free environment of resting state, and compares the CMC against six alternate architectural frameworks while also separately modeling spontaneous low-frequency oscillations. For a large sample of subjects from the Human Connectome Project, the CMC provides the best account of resting state brain activity, suggesting the presence of a general purpose structure of connections in the brain that drives activity when at rest and when performing directed task behavior. At the same time, spontaneous brain activity was found to be present and significant across all frequencies and in all regions. Together, these results suggest that, at rest, spontaneous low-frequency oscillations interact with the general cognitive architecture for task-based activity. The possible functional implications of these findings are discussed.

Gait phenotype in Batten disease: A marker of disease progression

BACKGROUND

Gait impairment and its etiologic correlate has not previously been subject of special attention in Batten disease.

METHODS

In the present review, the clinical picture of gait phenotype during Batten disease course accompanied by descriptions of the known concomitant patho-anatomical changes is presented.

RESULTS

In CLN1 a non-rhythmic gait is seen around 1-1½ years of age. Shortly after, postural hypotonia and exaggerated tendon reflexes develop. The disease reaches a burnt-out stage during the third year of age and subsequently the children are almost without voluntary movements. The existing literature indicates that gait phenotype in CLN1 is caused by early involvement of the spinal interneurons followed by impact of the cortex and the cortico-spinal tracts. The earliest walking abnormality in children with CLN2 is a clumsy, ataxic, and spastic gait, which is in accordance with the existing imaging and histologic studies showing early involvement of the cerebellum and the cortico-spinal pathways. In CLN3, a reduction in walking speed is present at the age of 7-8 years. It occurs simultaneously with a reduction in the white matter microstructure and brain connectivity networks. Functional impairment of the basal ganglia contributing to a parkinsonian gait phenotype occurs in the mid-teens. In the late teens and early twenties involvement of the peripheral nerves, neurogenic musculoskeletal atrophy, loss of tendon reflexes and postural control are seen.

CONCLUSION

The progressively impaired gait function in Batten disease is related to timing of damage of distinct areas of the nervous system depending on subtype and is a powerful marker of disease progression.

Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task

Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant’s sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences.

Corticostriatal Regulation of Language Functions

The role of corticostriatal circuits in language functions is unclear. In this review, we consider evidence from language learning, syntax, and controlled language production and comprehension tasks that implicate various corticostriatal circuits. Converging evidence from neuroimaging in healthy individuals, studies in populations with subcortical dysfunction, pharmacological studies, and brain stimulation suggests a domain-general regulatory role of corticostriatal systems in language operations. The role of corticostriatal systems in language operations identified in this review is likely to reflect a broader function of the striatum in responding to uncertainty and conflict which demands selection, sequencing, and cognitive control. We argue that this role is dynamic and varies depending on the degree and form of cognitive control required, which in turn will recruit particular corticostriatal circuits and components organised in a cognitive hierarchy.

Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.

Predicting visual working memory with multimodal magnetic resonance imaging

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.

Variability of Voluntary Cough Airflow in Healthy Adults and Parkinson's Disease

Cough is an important airway protective behavior responsible for ejecting material from the airway to prevent pneumonia, a leading cause of death in older adults and individuals with Parkinson's disease (PD). Variability of motor performance for both spinal and bulbar functions has been documented; however, there are no studies examining variability of cough motor control in PD and healthy controls. The present study examined the effects of age and PD on variability of voluntary cough performance. Twenty-five healthy younger adults (HYA), 26 healthy older adults (HOA), and 16 participants with PD completed three trials of sequential voluntary cough with spirometry. Coefficients of variation were used to examine variability between groups. Increased variability of cough expired volume (p = 0.012) and inspiratory volume (p = 0.006) was appreciated in HOAs compared to HYAs. Participants with PD demonstrated increased variability of cough expired volume (p = 0.029), peak expiratory flow rise time (p = 0.016), and cough volume acceleration (p = 0.034) compared to HOAs. Though participants with PD descriptively demonstrated increased peak expiratory flow rate compared to HOAs, this finding was statistically nonsignificant after adjusting for multiple comparisons (p = 0.072). This study identified that variability in cough airflow increases in healthy aging and Parkinson's disease. These motor control impairments may be attributed to age and disease-related sensorimotor changes in the peripheral and central nervous system. Future research will be necessary to examine the relationship between inconsistent cough motor output, airway invasion, and aspiration pneumonia in PD.

A Revised Computational Neuroanatomy for Motor Control

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

Neuropsychiatric aspects of Parkinson disease psychopharmacology: Insights from circuit dynamics

Parkinson disease (PD) is a neurodegenerative disorder with a complex pathophysiology characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Persons with PD experience several motoric and neuropsychiatric symptoms. Neuropsychiatric features of PD include depression, anxiety, psychosis, impulse control disorders, and apathy. In this chapter, we will utilize the National Institutes of Mental Health Research Domain Criteria (RDoC) to frame and integrate observations from two prevailing disease constructions: neurotransmitter anomalies and circuit physiology. When there is available evidence, we posit how unified translational observations may have clinical relevance and postulate importance outside of PD. Finally, we review the limited evidence available for pharmacologic management of these symptoms.

Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

Bipolar disorder is characterized by mood swings-oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patient's mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms (action responses) leads to a positive outcome, while the other leads to a negative outcome. We explore the dynamics of key reward and risk related parameters in the system while the model agent receives various outcomes. Particularly, we study the system using a model that represents the fast dynamics of decision making, and a module to capture the slow dynamics that describe the variation of some meta-parameters of fast dynamics over long time scales. The model is cast at three levels of abstraction: (1) a two-dimensional dynamical system model, that is a simple two variable model capable of showing bistability for rewarding and punitive outcomes; (2) a phenomenological basal ganglia model, to extend the implications from the reduced model to a cortico-basal ganglia setup; (3) a detailed network model of basal ganglia, that incorporates detailed cellular level models for a more realistic understanding. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological 'bipolar-like' oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The network's dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.

Developmental Changes in the Integration of Affective and Cognitive Corticostriatal Pathways are Associated with Reward-Driven Behavior

The relative influence of affective and cognitive processes on behavior is increasingly understood to transform through development, from adolescence into adulthood, but the neuroanatomical mechanisms underlying this change are not well understood. We analyzed diffusion magnetic resonance imaging in 115 10- to 28-year-old participants to identify convergent corticostriatal projections from cortical systems involved in affect and cognitive control and determined the age-related differences in their relative structural integrity. Results indicate that the relative integrity of affective projections, in relation to projections from cognitive control systems, decreases with age and is positively associated with reward-driven task performance. Together, these findings provide new evidence that developmental differences in the integration of corticostriatal networks involved in affect and cognitive control underlie known developmental decreases in the propensity for reward-driven behavior into adulthood.

Hippocampal-neocortical interactions sharpen over time for predictive actions

When an action is familiar, we are able to anticipate how it will change the state of the world. These expectations can result from retrieval of action-outcome associations in the hippocampus and the reinstatement of anticipated outcomes in visual cortex. How does this role for the hippocampus in action-based prediction change over time? We use high-resolution fMRI and a dual-training behavioral paradigm to examine how the hippocampus interacts with visual cortex during predictive and nonpredictive actions learned either three days earlier or immediately before the scan. Just-learned associations led to comparable background connectivity between the hippocampus and V1/V2, regardless of whether actions predicted outcomes. However, three-day-old associations led to stronger background connectivity and greater differentiation between neural patterns for predictive vs. nonpredictive actions. Hippocampal prediction may initially reflect indiscriminate binding of co-occurring events, with action information pruning weaker associations and leading to more selective and accurate predictions over time.

In familiar environments, humans automatically anticipate the sensory consequences of their motor actions. Here, the authors show how action-based predictions arise from interactions between the hippocampus and visual cortex, and how these interactions strengthen and weaken over time.

Dystonia and dopamine: From phenomenology to pathophysiology

A line of evidence suggests that the pathophysiology of dystonia involves the striatum, whose activity is modulated among other neurotransmitters, by the dopaminergic system. However, the link between dystonia and dopamine appears complex and remains unclear. Here, we propose a physiological approach to investigate the clinical and experimental data supporting a role of the dopaminergic system in the pathophysiology of dystonic syndromes. Because dystonia is a disorder of motor routines, we first focus on the role of dopamine and striatum in procedural learning. Second, we consider the phenomenology of dystonia from every angle in order to search for features giving food for thought regarding the pathophysiology of the disorder. Then, for each dystonic phenotype, we review, when available, the experimental and imaging data supporting a connection with the dopaminergic system. Finally, we propose a putative model in which the different phenotypes could be explained by changes in the balance between the direct and indirect striato-pallidal pathways, a process critically controlled by the level of dopamine within the striatum. Search strategy and selection criteria References for this article were identified through searches in PubMed with the search terms « dystonia », « dopamine", « striatum », « basal ganglia », « imaging data », « animal model », « procedural learning », « pathophysiology », and « plasticity » from 1998 until 2018. Articles were also identified through searches of the authors' own files. Only selected papers published in English were reviewed. The final reference list was generated on the basis of originality and relevance to the broad scope of this review.

Get a grip: individual variations in grip strength are a marker of brain health

Demonstrations that grip strength has predictive power in relation to a range of health conditions-even when these are assessed decades later-has motivated claims that hand-grip dynamometry has the potential to serve as a "vital sign" for middle-aged and older adults. Central to this belief has been the assumption that grip strength is a simple measure of physical performance that provides a marker of muscle status in general, and sarcopenia in particular. It is now evident that while differences in grip strength between individuals are influenced by musculoskeletal factors, "lifespan" changes in grip strength within individuals are exquisitely sensitive to integrity of neural systems that mediate the control of coordinated movement. The close and pervasive relationships between age-related declines in maximum grip strength and expressions of cognitive dysfunction can therefore be understood in terms of the convergent functional and structural mediation of cognitive and motor processes by the human brain. In the context of aging, maximum grip strength is a discriminating measure of neurological function and brain health.

fMRI connectivity of expressive language in young children and adolescents

Studies of language representation in development have shown a bilateral distributed pattern of activation that becomes increasingly left-lateralized and focal from young childhood to adulthood. However, the level by which canonical and extra-canonical regions, including subcortical and cerebellar regions, contribute to language during development has not been well-characterized. In this study, we employed fMRI connectivity analyses (fcMRI) to characterize the distributed network supporting expressive language in a group of young children (age 4-6) and adolescents (age 16-18). We conducted an fcMRI analysis using seed-to-voxel and seed-to-ROI (region of interest) strategies to investigate interactions of left pars triangularis with other brain areas. The analyses showed significant interhemispheric connectivity in young children, with a minimal connectivity of the left pars triangularis to subcortical and cerebellar regions. In contrast, adolescents showed significant connectivity between the left IFG seed and left perisylvian cortex, left caudate and putamen, and regions of the right cerebellum. Importantly, fcMRI analyses indicated significant differences between groups at 3 anatomical clusters, including left IFG, left supramarginal gyrus, and right cerebellar crura, suggesting a role in the functional development of language.

The antipsychotic landscape: dopamine and beyond

Until recently, the actions of antipsychotic and pro-psychotic drugs have largely been evaluated in the framework of neuronal doctrine - namely, that neurons communicate by releasing transmitters, and that psychiatric disorders are caused by neurotransmitter imbalances. Moreover, the majority of studies have focused on single transmitter systems - neglecting the fact that in the nervous system, different transmitter systems work in concert and impact on not only their immediate receptors but also downstream pathways that shape structural plasticity. In this review, we discuss the history of understanding the antipsychotic and pro-psychotic actions of drugs, recent developments and future perspectives.

Sensorimotor anatomy of gait, balance, and falls

The review demonstrates that control of posture and locomotion is provided by systems across the caudal-to-rostral extent of the neuraxis. A common feature of the neuroanatomic organization of the postural and locomotor control systems is the presence of key nodes for convergent input of multisensory feedback in conjunction with efferent copies of the motor command. These nodes include the vestibular and reticular nuclei and interneurons in the intermediate zone of the spinal cord (Rexed's laminae VI-VIII). This organization provides both spatial and temporal coordination of the various goals of the system and ensures that the large repertoire of voluntary movements is appropriately coupled to either anticipatory or reactive postural adjustments that ensure stability and provide the framework to support the intended action. Redundancies in the system allow adaptation and compensation when sensory modalities are impaired. These alterations in behavior are learned through reward- and error-based learning processes implemented through basal ganglia and cerebellar pathways respectively. However, neurodegenerative processes or lesions of these systems can greatly compromise the capacity to sufficiently adapt and sometimes leads to maladaptive changes that impair movement control. When these impairments occur, the risk of falls can be significantly increased and interventions are required to reduce morbidity.

Automatic Online Motor Control Is Intact in Parkinson's Disease With and Without Perceptual Awareness

In the double-step paradigm, healthy human participants automatically correct reaching movements when targets are displaced. Motor deficits are prominent in Parkinson's disease (PD) patients. In the lone investigation of online motor correction in PD using the double-step task, a recent study found that PD patients performed unconscious adjustments appropriately but seemed impaired for consciously-perceived modifications. Conscious perception of target movement was achieved by linking displacement to movement onset. PD-related bradykinesia disproportionately prolonged preparatory phases for movements to original target locations for patients, potentially accounting for deficits. Eliminating this confound in a double-step task, we evaluated the effect of conscious awareness of trajectory change on online motor corrections in PD. On and off dopaminergic therapy, PD patients (n = 14) and healthy controls (n = 14) reached to peripheral visual targets that remained stationary or unexpectedly moved during an initial saccade. Saccade latencies in PD are comparable to controls'. Hence, target displacements occurred at equal times across groups. Target jump size affected conscious awareness, confirmed in an independent target displacement judgment task. Small jumps were subliminal, but large target displacements were consciously perceived. Contrary to the previous result, PD patients performed online motor corrections normally and automatically, irrespective of conscious perception. Patients evidenced equivalent movement durations for jump and stay trials, and trajectories for patients and controls were identical, irrespective of conscious perception. Dopaminergic therapy had no effect on performance. In summary, online motor control is intact in PD, unaffected by conscious perceptual awareness. The basal ganglia are not implicated in online corrective responses.

Visuo-manual tracking: does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?

KEY POINTS

A human controlling an external system is described most easily and conventionally as linearly and continuously translating sensory input to motor output, with the inevitable output remnant, non-linearly related to the input, attributed to sensorimotor noise. Recent experiments show sustained manual tracking involves repeated refractoriness (insensitivity to sensory information for a certain duration), with the temporary 200-500 ms periods of irresponsiveness to sensory input making the control process intrinsically non-linear. This evidence calls for re-examination of the extent to which random sensorimotor noise is required to explain the non-linear remnant. This investigation of manual tracking shows how the full motor output (linear component and remnant) can be explained mechanistically by aperiodic sampling triggered by prediction error thresholds. Whereas broadband physiological noise is general to all processes, aperiodic sampling is associated with sensorimotor decision making within specific frontal, striatal and parietal networks; we conclude that manual tracking utilises such slow serial decision making pathways up to several times per second.

ABSTRACT

The human operator is described adequately by linear translation of sensory input to motor output. Motor output also always includes a non-linear remnant resulting from random sensorimotor noise from multiple sources, and non-linear input transformations, for example thresholds or refractory periods. Recent evidence showed that manual tracking incurs substantial, serial, refractoriness (insensitivity to sensory information of 350 and 550 ms for 1st and 2nd order systems respectively). Our two questions are: (i) What are the comparative merits of explaining the non-linear remnant using noise or non-linear transformations? (ii) Can non-linear transformations represent serial motor decision making within the sensorimotor feedback loop intrinsic to tracking? Twelve participants (instructed to act in three prescribed ways) manually controlled two systems (1st and 2nd order) subject to a periodic multi-sine disturbance. Joystick power was analysed using three models, continuous-linear-control (CC), continuous-linear-control with calculated noise spectrum (CCN), and intermittent control with aperiodic sampling triggered by prediction error thresholds (IC). Unlike the linear mechanism, the intermittent control mechanism explained the majority of total power (linear and remnant) (77-87% vs. 8-48%, IC vs. CC). Between conditions, IC used thresholds and distributions of open loop intervals consistent with, respectively, instructions and previous measured, model independent values; whereas CCN required changes in noise spectrum deviating from broadband, signal dependent noise. We conclude that manual tracking uses open loop predictive control with aperiodic sampling. Because aperiodic sampling is inherent to serial decision making within previously identified, specific frontal, striatal and parietal networks we suggest that these structures are intimately involved in visuo-manual tracking.

Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex

Despite increasing evidence suggesting the cerebellum works in concert with the cortex and basal ganglia, the nature of the reciprocal interactions between these three brain regions remains unclear. This consensus paper gathers diverse recent views on a variety of important roles played by the cerebellum within the cerebello-basal ganglia-thalamo-cortical system across a range of motor and cognitive functions. The paper includes theoretical and empirical contributions, which cover the following topics: recent evidence supporting the dynamical interplay between cerebellum, basal ganglia, and cortical areas in humans and other animals; theoretical neuroscience perspectives and empirical evidence on the reciprocal influences between cerebellum, basal ganglia, and cortex in learning and control processes; and data suggesting possible roles of the cerebellum in basal ganglia movement disorders. Although starting from different backgrounds and dealing with different topics, all the contributors agree that viewing the cerebellum, basal ganglia, and cortex as an integrated system enables us to understand the function of these areas in radically different ways. In addition, there is unanimous consensus between the authors that future experimental and computational work is needed to understand the function of cerebellar-basal ganglia circuitry in both motor and non-motor functions. The paper reports the most advanced perspectives on the role of the cerebellum within the cerebello-basal ganglia-thalamo-cortical system and illustrates other elements of consensus as well as disagreements and open questions in the field.

Striatal GPR88 Modulates Foraging Efficiency

The striatum is anatomically and behaviorally implicated in behaviors that promote efficient foraging. To investigate this function, we studied instrumental choice behavior in mice lacking GPR88, a striatum-enriched orphan G-protein-coupled receptor that modulates striatal medium spiny neuron excitability. Our results reveal that hungry mice lacking GPR88 (KO mice) were slow to acquire food-reinforced lever press but could lever press similar to controls on a progressive ratio schedule. Both WT and KO mice discriminated between reward and no-reward levers; however, KO mice failed to discriminate based on relative quantity-reward (1 vs 3 food pellets) or effort (3 vs 9 lever presses). We also demonstrate preference for the high-reward (3 pellet) lever was selectively reestablished when GPR88 expression was restored to the striatum. We propose that GPR88 expression within the striatum is integral to efficient action-selection during foraging.SIGNIFICANCE STATEMENT Evolutionary pressure driving energy homeostasis favored detection and comparison of caloric value. In wild and laboratory settings, neural systems involved in energy homeostasis bias foraging to maximize energy efficiency. This is observed when foraging behaviors are guided by superior nutritional density or minimized caloric expenditure. The striatum is anatomically and functionally well placed to perform the sensory and motor integration necessary for efficient action selection during foraging. However, few studies have examined this behavioral phenomenon or elucidated underlying molecular mechanisms. Both humans and mice with nonfunctional GPR88 have been shown to present striatal dysfunctions and impaired learning. We demonstrate that GPR88 expression is necessary to efficiently integrate effort and energy density information guiding instrumental choice.

Mechanisms of automaticity and anticipatory control in fluid intelligence

The constructs of fluid (Gf) and crystalized (Gc) intelligence represent an early attempt to describe the mechanisms of problem solving in the vertebrate brain. Modern neuroscience demonstrates that problem solving involves interplay between the mechanisms of automaticity and anticipatory control, enabling nature's elegant solution to the challenges animals face in their environment. Studies of neural functioning are making clear the primary role of cortical-subcortical interactions in the manifestation of intelligent behavior in humans and other vertebrates. A tridimensional model of intelligent problem solving is explored, wherein the basal ganglia system (BGS) and cerebrocerebellar system (CCS) interact within large scale brain networks. The BGS and CCS work together to enable automaticity to occur. The BGS enables the organism to learn what to do through a powerful instrumental learning system. The BGS also regulates when behavior is released through an inhibitory system which is incredibly sensitive to context. The CCS enables the organism to learn how to perform adaptive behaviors. Internal cerebellar models enable gradual improvements in the quality of behavioral output. The BGS and CCS interact within large scale brain networks, including the dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN) and frontoparietal network (FPN). The interactions of these systems enable vertebrate organisms to develop a vast array of complex adaptive behaviors. The benefits and importance of developing clinical tests to measure the integrity of these systems is considered.

Target-distractor synchrony affects performance in a novel motor task for studying action selection

The study of action selection in humans can present challenges of task design since our actions are usually defined by many degrees of freedom and therefore occupy a large action-space. While saccadic eye-movement offers a more constrained paradigm for investigating action selection, the study of reach-and-grasp in upper limbs has often been defined by more complex scenarios, not easily interpretable in terms of such selection. Here we present a novel motor behaviour task which addresses this by limiting the action space to a single degree of freedom in which subjects have to track (using a stylus) a vertical coloured target line displayed on a tablet computer, whilst ignoring a similarly oriented distractor line in a different colour. We ran this task with 55 subjects and showed that, in agreement with previous studies, the presence of the distractor generally increases the movement latency and directional error rate. Further, we used two distractor conditions according to whether the distractor’s location changes asynchronously or synchronously with the location of the target. We found that the asynchronous distractor yielded poorer performance than its synchronous counterpart, with significantly higher movement latencies and higher error rates. We interpret these results in an action selection framework with two actions (move left or right) and competing ‘action requests’ offered by the target and distractor. As such, the results provide insights into action selection performance in humans and supply data for directly constraining future computational models therein.

Mapping Critical Language Sites in Children Performing Verb Generation: Whole-Brain Connectivity and Graph Theoretical Analysis in MEG

A classic left frontal-temporal brain network is known to support language processes. However, the level of participation of constituent regions, and the contribution of extra-canonical areas, is not fully understood; this is particularly true in children, and in individuals who have experienced early neurological insult. In the present work, we propose whole-brain connectivity and graph-theoretical analysis of magnetoencephalography (MEG) source estimates to provide robust maps of the pediatric expressive language network. We examined neuromagnetic data from a group of typically-developing young children (n = 15, ages 4–6 years) and adolescents (n = 14, 16–18 years) completing an auditory verb generation task in MEG. All source analyses were carried out using a linearly-constrained minimum-variance (LCMV) beamformer. Conventional differential analyses revealed significant (p < 0.05, corrected) low-beta (13–23 Hz) event related desynchrony (ERD) focused in the left inferior frontal region (Broca’s area) in both groups, consistent with previous studies. Connectivity analyses were carried out in broadband (3–30 Hz) on time-course estimates obtained at the voxel level. Patterns of connectivity were characterized by phase locking value (PLV), and network hubs identified through eigenvector centrality (EVC). Hub analysis revealed the importance of left perisylvian sites, i.e., Broca’s and Wernicke’s areas, across groups. The hemispheric distribution of frontal and temporal lobe EVC values was asymmetrical in most subjects; left dominant EVC was observed in 20% of young children, and 71% of adolescents. Interestingly, the adolescent group demonstrated increased critical sites in the right cerebellum, left inferior frontal gyrus (IFG) and left putamen. Here, we show that whole brain connectivity and network analysis can be used to map critical language sites in typical development; these methods may be useful for defining the margins of eloquent tissue in neurosurgical candidates.

Dysfunctions of the basal ganglia-cerebellar-thalamo-cortical system produce motor tics in Tourette syndrome

Motor tics are a cardinal feature of Tourette syndrome and are traditionally associated with an excess of striatal dopamine in the basal ganglia. Recent evidence increasingly supports a more articulated view where cerebellum and cortex, working closely in concert with basal ganglia, are also involved in tic production. Building on such evidence, this article proposes a computational model of the basal ganglia-cerebellar-thalamo-cortical system to study how motor tics are generated in Tourette syndrome. In particular, the model: (i) reproduces the main results of recent experiments about the involvement of the basal ganglia-cerebellar-thalamo-cortical system in tic generation; (ii) suggests an explanation of the system-level mechanisms underlying motor tic production: in this respect, the model predicts that the interplay between dopaminergic signal and cortical activity contributes to triggering the tic event and that the recently discovered basal ganglia-cerebellar anatomical pathway may support the involvement of the cerebellum in tic production; (iii) furnishes predictions on the amount of tics generated when striatal dopamine increases and when the cortex is externally stimulated. These predictions could be important in identifying new brain target areas for future therapies. Finally, the model represents the first computational attempt to study the role of the recently discovered basal ganglia-cerebellar anatomical links. Studying this non-cortex-mediated basal ganglia-cerebellar interaction could radically change our perspective about how these areas interact with each other and with the cortex. Overall, the model also shows the utility of casting Tourette syndrome within a system-level perspective rather than viewing it as related to the dysfunction of a single brain area.

Tourette syndrome is a neuropsychiatric disorder characterized by vocal and motor tics. Tics represent a cardinal symptom traditionally associated with a dysfunction of the basal ganglia leading to an excess of the dopamine neurotransmitter. This view gives a restricted clinical picture and limits therapeutic approaches because it ignores the influence of altered interactions between the basal ganglia and other brain areas. In this respect, recent evidence supports a more articulated framework where cerebellum and cortex are also involved in tic production. Building on these data, we propose a computational model of the basal ganglia-cerebellar-thalamo-cortical network to investigate the specific mechanisms underlying motor tic production. The model reproduces the results of recent experiments and suggests an explanation of the system-level processes underlying tic production. Moreover, it furnishes predictions related to the amount of tics generated when there are dysfunctions in the basal ganglia-cerebellar-thalamo-cortical circuits. These predictions could be important in identifying new brain target areas for future therapies based on a system-level view of Tourette syndrome.

The thalamus as a relay station and gatekeeper: relevance to brain disorders

Here, we provide a review of behavioural, cognitive, and neural studies of the thalamus, including its role in attention, consciousness, sleep, and motor processes. We further discuss neuropsychological and brain disorders associated with thalamus function, including Parkinson’s disease, Alzheimer’s disease, Korsakoff’s syndrome, and sleep disorders. Importantly, we highlight how thalamus-related processes and disorders can be explained by the role of the thalamus as a relay station.

Parkinson's disease as a system-level disorder

Traditionally, the basal ganglia have been considered the main brain region implicated in Parkinson’s disease. This single area perspective gives a restricted clinical picture and limits therapeutic approaches because it ignores the influence of altered interactions between the basal ganglia and other cerebral components on Parkinsonian symptoms. In particular, the basal ganglia work closely in concert with cortex and cerebellum to support motor and cognitive functions. This article proposes a theoretical framework for understanding Parkinson’s disease as caused by the dysfunction of the entire basal ganglia–cortex–cerebellum system rather than by the basal ganglia in isolation. In particular, building on recent evidence, we propose that the three key symptoms of tremor, freezing, and impairments in action sequencing may be explained by considering partially overlapping neural circuits including basal ganglia, cortical and cerebellar areas. Studying the involvement of this system in Parkinson’s disease is a crucial step for devising innovative therapeutic approaches targeting it rather than only the basal ganglia. Possible future therapies based on this different view of the disease are discussed.