Cervelet

Résumé

Le cervelet (du cerebellum, « petit cerveau ») est une structure de l'encéphale des vertébrés qui joue un rôle important dans le contrôle moteur et est impliqué, dans une moindre mesure, dans certaines fonctions cognitives, telles que l'attention, le langage et la régulation des réactions de peur et de plaisir. Le cervelet n'est généralement pas à l'origine du mouvement, il contribue à la coordination et la synchronisation des gestes, et à la précision des mouvements. Il reçoit des signaux en provenance des différents systèmes sensoriels, ainsi que d'autres parties du cerveau et de la moelle épinière. Il intègre ces signaux pour ajuster au mieux l'activité motrice. En plus de son rôle direct dans le contrôle moteur, le cervelet est nécessaire pour différents types d'apprentissages moteurs, le plus remarquable étant l'apprentissage de l'ajustement aux variations dans les relations sensorimotrices. Du fait de ces fonctions d'ajustement, des dommages au cervelet ne provoquent pas la pa

Source officielle

https://fr.wikipedia.org/wiki/Cervelet

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Reverse engineering the motor control system

Berat Denizdurduran

Mammalian motor control is implemented by a combination of different networks and system, working coherently to plan the movement of the body or a limb and to execute this movement a dynamical environment. While it is believed that complex, voluntary movements are planned in the motor areas of the cortex, the execution of the movement is controlled by a combination of cortical and cerebellar networks together with central pattern generators and reflex circuits in the spinal cord. In this thesis, we propose an abstract model that captures the basic properties of mammalian motor control, using the example of two different movements: arm movements as well as locomotion. Our model consists of three parts: first a high-level network, that learns movements, using a combination of actor-critic based reinforcement learning and Optimal Control theory. The second part corresponds to spinal reflex circuits that execute basic movements of the musculo-skeletal system. They are modeled by a simple neural network, that learns the dynamic properties of the muscoloskeletal system by the mechanism of spontaneous motor activity (muscle twithing) combined with a Hebbian learning rule. We demonstrate that this network can learn the antagonistic control of joint movements. The final part of the model is a cerebellar network, that translates a complex movement trajectory, such as reaching, and activates the spinal reflex circuits to execute the movement. The mapping between the cerebellar neurons and the spinal reflex circuits are trained with artificial neural networks. Using a musculoskeletal arm model, we demonstrate that the proposed neural motor control model can generate the movement to arbitrary goal position.

EPFL2019

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The development of novel strategies to augment motor training success is of great interest for healthy persons and neurological patients. A promising approach is the combination of training with transcranial electric stimulation. However, limited reproducibility and varying effect sizes make further protocol optimization necessary. We tested the effects of a novel cerebellar transcranial alternating current stimulation protocol (tACS) on motor skill learning. Furthermore, we studied underlying mechanisms by means of transcranial magnetic stimulation and analysis of fMRI-based resting-state connectivity. N=15 young, healthy participants were recruited. 50 Hz tACS was applied to the left cerebellum in a double-blind, sham-controlled, cross-over design concurrently to the acquisition of a novel motor skill. Potential underlying mechanisms were assessed by studying short intracortical inhibition at rest (SICIrest) and in the premovement phase (SICImove), intracortical facilitation at rest (ICFrest), and seed-based resting-state fMRI-based functional connectivity (FC) in a hypothesis-driven motor learning network. Active stimulation did not enhance skill acquisition or retention. Minor effects on striato-parietal FC were present. Linear mixed effects modelling identified SICImove modulation and baseline task performance as the most influential determining factors for predicting training success. Accounting for the identified factors may allow to stratify participants for future training-based interventions.

NATURE RESEARCH2020

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Metabolic and hemodynamic changes in the activated human brain and cerebellar organisation measured by MRS/MRl at 7 Tesla

Yohan Boillat

Although less often investigated than the positive blood-oxygen-level-dependent (BOLD) signal, a negative BOLD signal has also been observed under certain conditions, which could have a neuronal origin. To further investigate the negative BOLD signal from a metabolic point of view, a functional 1H magnetic resonance spectroscopy (fMRS) study was conducted using visual stimulations to trigger a negative BOLD response. The latter was linked to decreases of [Glu] and [Lac] with also a decrease in GABA concentration. These results suggest that the negative BOLD signal is linked to a neuronal deactivation and maybe to a local increase of inhibitory activity. Although both glycolytic and oxidative metabolisms are involved during neuronal activity, it is still unclear how they are respectively modulated by the latter. To investigate this, a functional magnetic resonance imaging (fMRI)-fMRS study was carried out on participants performing a finger tapping task at either 1Hz, 2Hz or 3Hz. In addition to the BOLD signal, the CBF signal was also obtained. As previously observed, the BOLD and CBF signals increased for larger finger tapping frequencies confirming the overall increase of metabolic demand. [Glu] and [Lac] were used as biomarker of oxidative and glycolytic metabolisms, respectively. [Glu] changes were the largest for 3Hz while [Lac] changes were more important at 2Hz. These results highlights the different involvement of the two metabolic pathways. The positive BOLD signal differs in its timings and shape across brain regions. Using a high temporal resolution sequence combined with short stimulations, the positive BOLD responses of six motor regions, including the cerebellum, were extracted. The main difference between motor regions was the positive peak and undershoot amplitudes that were the smallest in the cerebellum, which also showed delayed onsets. In addition to the BOLD response, the cerebellum has some very specific characteristics such as the representation of body parts in both the anterior and posterior lobes. In order to obtain more precise and complete maps of the cerebellar somatotopy, an fMRI motor task was performed testing for the eyes, the tongue, the little fingers, the thumbs or the toes. The anterior lobe showed a consistent and robust somatotopic gradient with also the presence of such a gradient in the posterior lobe, albeit less obvious. These results show that multiple representations of the body are present in the cerebellum and organized in an orderly manner. In addition, the cerebellum can be parcellated based on several features like cell packing, olive fibre input or molecular biomarker. To test whether such tissue characteristics can be observed at macroscale level, T1 and T2* mapping was performed on cerebellar surfaces generated at three different cortical depths. T1 surfaces showed an alternation of lower and higher T1 values when going from the median to the lateral part of the cerebellar hemispheres with shorter T1 values observed in the deeper grey matter layers. T2* maps showed a similar longitudinal pattern, but no change related to the cortical depths. These patterns reflect potential myelin and iron content variations over the cerebellar cortex. Whether these tissue variations are linked to the somatotopic organization is not clear.

EPFL2020

Chargement

Metabolic and hemodynamic changes in the activated human brain and cerebellar organisation measured by MRS/MRl at 7 Tesla

Yohan Boillat

EPFL2020