What aspects of movement are represented in the primary motor cortex (M1): relatively low-level parameters like muscle force, or more abstract parameters like handpath? To examine this issue, the activity of neurons in M1 was recorded in a monkey trained to perform a task that dissociates three major variables of wrist movement: muscle activity, direction of movement at the wrist joint, and direction of movement in space. A substantial group of neurons in M1 (28 out of 88) displayed changes in activity that were muscle-like. Unexpectedly, an even larger group of neurons in M1 (44 out of 88) displayed changes in activity that were related to the direction of wrist movement in space independent of the pattern of muscle activity that generated the movement. Thus, both "muscles" and "movements" appear to be strongly represented in M1.There has been long-standing controversy over whether "muscles" or "movements" are represented in the primary motor cortex (M1) (1). From a contemporary perspective, this question can be recast: What aspects of movement are encoded in the activity of M1 neurons: relatively low-level movement parameters like muscle force, or more abstract movement parameters like handpath? Since the pioneering work of Evarts (2), this question has been examined by recording the activity of single neurons in awake trained primates [for example, (3-10)]. Early experiments examined M1 activity in relation to simple finger and wrist movements (3). The discharge of many M1 neurons in these studies covaried with movement parameters such as static and dynamic force. These results led to the view that M1 is concerned with the generation of movement in terms of an "intrinsic" parameter space related to one or more of a number of variables including aspects of joint kinematics, joint torques, and the detailed pattern of muscle activity at a single joint.A different perspective has come from studies of M1 activity during reaching movements (4). In some experiments, the activity of M1 neurons, as a population, covaried with the trajectory of hand movement and signaled its instantaneous movement direction and velocity. These and similar results led to the view that M1 is concerned with the generation of movement in terms of an "extrinsic" parameter space related to the motion of the hand, the location of the target in space, or both (4, 9, 10). However, the results of other experiments of M1 activity during reaching movements made under altered load conditions or with different arm postures produced evidence for coding in an intrinsic parameter space (5).To address this controversy, we developed a paradigm that dissociates three different coordinate frames related to wrist movements: extrinsic (related to the direction of movement in space), muscle (related to the activity of individual or groups of muscles), and joint (related to the angle of the wrist joint) (11-13). Our paradigm takes advantage of two features of the wrist joint. First, the wrist rotates along two axes: flexion-extension and radial-ulnar deviation...

The ventral premotor area (PMv) is a major source of input to the primary motor cortex (M1). To examine the potential hierarchical processing between these motor areas, we recorded the activity of PMv neurons in a monkey trained to perform wrist movements in different directions with the wrist in three different postures. The task dissociated three major variables of wrist movement: muscle activity, direction of joint movement and direction of movement in space. Many PMv neurons were directionally tuned. Nearly all of these neurons (61/65, 94%) were 'extrinsic-like'; they seemed to encode the direction of movement in space independent of forearm posture. These results are strikingly different from results from M1 of the same animal, and suggest that intracortical processing between PMv and M1 may contribute to a sensorimotor transformation between extrinsic and intrinsic coordinate frames.

In the last few years, a lot of publications suggested that disabling cerebellar ataxias may develop through immune-mediated mechanisms. In this consensus paper, we discuss the clinical features of the main described immune-mediated cerebellar ataxias and address their presumed pathogenesis. Immune-mediated cerebellar ataxias include cerebellar ataxia associated with anti-GAD antibodies, the cerebellar type of Hashimoto’s encephalopathy, primary autoimmune cerebellar ataxia, gluten ataxia, Miller Fisher syndrome, ataxia associated with systemic lupus erythematosus, and paraneoplastic cerebellar degeneration. Humoral mechanisms, cell-mediated immunity, inflammation, and vascular injuries contribute to the cerebellar deficits in immune-mediated cerebellar ataxias.

This review surveys physiological, behavioral, and morphological evidence converging to the view of the cerebro-cerebellum as loci of internal forward models. The cerebro-cerebellum, the phylogenetically newest expansion in the cerebellum, receives convergent inputs from cortical, subcortical, and spinal sources, and is thought to perform the predictive computation for both motor control, motor learning, and cognitive functions. This predictive computation is known as an internal forward model. First, we elucidate the theoretical foundations of an internal forward model and its role in motor control and motor learning within the framework of the optimal feedback control model. Then, we discuss a neural mechanism that generates various patterns of outputs from the cerebro-cerebellum. Three lines of supporting evidence for the internal-forwardmodel hypothesis are presented in detail. First, we provide physiological evidence that the cerebellar outputs (activities of dentate nucleus cells) are predictive for the cerebellar inputs [activities of mossy fibers (MFs)]. Second, we provide behavioral evidence that a component of movement kinematics is predictive for target motion in control subjects but lags behind a target motion in patients with cerebellar ataxia. Third, we provide morphological evidence that the cerebellar cortex and the dentate nucleus receive separate MF projections, a prerequisite for optimal estimation. Finally, we speculate that the predictive computation in the cerebro-cerebellum could be deployed to not only motor control but also to non-motor, cognitive functions. This review concludes that the predictive computation of the internal forward model is the unifying algorithmic principle for understanding diverse functions played by the cerebro-cerebellum.

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