Cerebellar alterations in cannabis users: A systematic review.

Cannabis is the most used illicit substance in the world. As many countries are moving towards decriminalization, it is crucial to determine whether and how cannabis use affects human brain and behavior. The role of the cerebellum in cognition, emotion, learning, and addiction is increasingly recognized. Because of its high density in CB1 receptors, it is expected to be highly affected by cannabis use. The aim of this systematic review is to investigate how cannabis use affects cerebellar structure and function, as well as cerebellar-dependent behavioral tasks. Three databases were searched for peer-reviewed literature published until March 2018. We included studies that focused on cannabis effects on cerebellar structure, function, or cerebellar-dependent behavioral tasks. A total of 348 unique records were screened, and 40 studies were included in the qualitative synthesis. The most consistent findings include (1) increases in cerebellar gray matter volume after chronic cannabis use, (2) alteration of cerebellar resting state activity after acute or chronic use, and (3) deficits in memory, decision making, and associative learning. Age of onset and higher exposure to cannabis use were frequently associated with increased cannabis-induced alterations. Chronic cannabis use is associated with alterations in cerebellar structure and function, as well as with deficits in behavioral paradigms that involve the cerebellum (eg, eyeblink conditioning, memory, and decision making). Future studies should consider tobacco as confounding factor and use standardized methods for assessing cannabis use. Paradigms exploring the functional activity of the cerebellum may prove useful as monitoring tools of cannabis-induced impairment.

The perceptual shaping of anticipatory actions.

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behaviour relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts.

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.

Active Inference and Learning in the Cerebellum.

This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.

The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines.

In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines.

Chronic cannabis use affects cerebellum dependent visuomotor adaptation.

BACKGROUND: Cannabis is one of the most commonly used substances in the world. However, its effects on human cognition are not yet fully understood. Although the cerebellum has the highest density of cannabinoid receptor type 1 (CB1R) in the human brain, literature on how cannabis use affects cerebellar-dependent learning is sparse. This study examined the effect of chronic cannabis use on sensorimotor adaptation, a cerebellar-mediated task, which has been suggested to depend on endocannabinoid signaling. METHODS: Chronic cannabis users (n = 27) with no psychiatric comorbidities and healthy, cannabis-naïve controls (n = 25) were evaluated using a visuomotor rotation task. Cannabis users were re-tested after 1 month of abstinence (n = 13) to assess whether initial differences in performance would persist after cessation of use. RESULTS: Cannabis users showed lower adaptation rates compared to controls at the first time point. However, this difference in performance did not persist when participants were retested after one month of abstinence (n = 13). Healthy controls showed attenuated implicit learning in the late phase of the adaptation during re-exposure, which was not present in cannabis users. This explains the lack of between group differences in the second time point and suggests a potential alteration of synaptic plasticity required for cerebellar learning in cannabis users. CONCLUSIONS: Overall, our results suggest that chronic cannabis users show alterations in sensorimotor adaptation, likely due to a saturation of the endocannabinoid system after chronic cannabis use.

Motor Adaptation Impairment in Chronic Cannabis Users Assessed by a Visuomotor Rotation Task.

Background-The cerebellum has been recently suggested as an important player in the addiction brain circuit. Cannabis is one of the most used drugs worldwide, and its long-term effects on the central nervous system are not fully understood. No valid clinical evaluations of cannabis impact on the brain are available today. The cerebellum is expected to be one of the brain structures that are highly affected by prolonged exposure to cannabis, due to its high density in endocannabinoid receptors. We aim to use a motor adaptation paradigm to indirectly assess cerebellar function in chronic cannabis users (CCUs). Methods-We used a visuomotor rotation (VMR) task that probes a putatively-cerebellar implicit motor adaptation process together with the learning and execution of an explicit aiming rule. We conducted a case-control study, recruiting 18 CCUs and 18 age-matched healthy controls. Our main measure was the angular aiming error. Results-Our results show that CCUs have impaired implicit motor adaptation, as they showed a smaller rate of adaptation compared with healthy controls (drift rate: 19.3 +/- 6.8° vs. 27.4 +/- 11.6°; t(26) = -2.1, p = 0.048, Cohen's d = -0.8, 95% CI = (-1.7, -0.15)). Conclusions-We suggest that a visuomotor rotation task might be the first step towards developing a useful tool for the detection of alterations in implicit learning among cannabis users.

A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test.

Emulating the input-output functions performed by a brain structure opens the possibility for developing neuroprosthetic systems that replace damaged neuronal circuits. Here, we demonstrate the feasibility of this approach by replacing the cerebellar circuit responsible for the acquisition and extinction of motor memories. Specifically, we show that a rat can undergo acquisition, retention, and extinction of the eye-blink reflex even though the biological circuit responsible for this task has been chemically inactivated via anesthesia. This is achieved by first developing a computational model of the cerebellar microcircuit involved in the acquisition of conditioned reflexes and training it with synthetic data generated based on physiological recordings. Secondly, the cerebellar model is interfaced with the brain of an anesthetized rat, connecting the model's inputs and outputs to afferent and efferent cerebellar structures. As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response. However, non-stationarities in the recorded biological signals limit the performance of the cerebellar model. Thus, we introduce an updated cerebellar model and validate it with physiological recordings showing that learning becomes stable and reliable. The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region. These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control.

In the acquisition of adaptive motor reflexes to aversive stimuli, the cerebellar output fulfills a double purpose: it controls a motor response and it relays a sensory prediction. However, the question of how these two apparently incompatible goals might be achieved by the same cerebellar area remains open. Here we propose a solution where the inhibition of the Inferior Olive (IO) by the cerebellar Deep Nuclei (DN) translates the motor command signal into a sensory prediction allowing a single cerebellar area to simultaneously tackle both aspects of the problem: execution and prediction. We demonstrate that having a graded error signal, the gain of the Nucleo-Olivary Inhibition (NOI) balances the generation of the response between the cerebellar and the reflexive controllers or, in other words, between the adaptive and the reactive layers of behavior. Moreover, we show that the resulting system is fully autonomous and can either acquire or erase adaptive responses according to their utility.

A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.