Proprioceptive Movement Illusions Due to Prolonged Stimulation: Reversals and Aftereffects

Background. Adaptation to constant stimulation has often been used to investigate the mechanisms of perceptual coding, but the adaptive processes within the proprioceptive channels that encode body movement have not been well described. We investigated them using vibration as a stimulus because vibration of muscle tendons results in a powerful illusion of movement. Methodology/Principal Findings. We applied sustained 90 Hz vibratory stimulation to biceps brachii, an elbow flexor and induced the expected illusion of elbow extension (in 12 participants). There was clear evidence of adaptation to the movement signal both during the 6-min long vibration and on its cessation. During vibration, the strong initial illusion of extension waxed and waned, with diminishing duration of periods of illusory movement and occasional reversals in the direction of the illusion. After vibration there was an aftereffect in which the stationary elbow seemed to move into flexion. Muscle activity shows no consistent relationship with the variations in perceived movement. Conclusion. We interpret the observed effects as adaptive changes in the central mechanisms that code movement in direction-selective opponent channels

A neural network model for the intersensory coordination involved in goal-directed movements

International audienceA neural network model for a sensorimotor system, which was developed to simulate oriented movements in man, is presented. It is composed of a formal neural network comprising two layers: a sensory layer receiving and processing sensory inputs, and a motor layer driving a simulated arm. The sensory layer is an extension of the topological network previously proposed by Kohonen (1984). Two kinds of sensory modality, proprioceptive and exteroceptive, are used to define the arm position. Each sensory cell receives proprioceptive inputs provided by each arm-joint together with the exteroceptive inputs. This sensory layer is therefore a kind of associative layer which integrates two separate sensory signals relating to movement coding. It is connected to the motor layer by means of adaptive synapses which provide a physical link between a motor activity and its sensory consequences. After a learning period, the spatial map which emerges in the sensory layer clearly depends on the sensory inputs and an associative map of both the arm and the extra-personal space is built up if proprioceptive and exteroceptive signals are processed together. The senso-rimotor transformations occuring in the junctions linking the sensory and motor layers are organized in such a manner that the simulated arm becomes able to reach towards and track a target in extra-personal space. Proprioception serves to determine the final arm posture adopted and to correct the ongoing movement in cases where changes in the target location occur. With a view to developing a sensorimotor control system with more realistic salient features, a robotic model was coupled with the formal neural network. This robotic implementation of our model shows the capacity of formal neural networks to control the displacement of mechanical devices

Writing in two different scripts promotes fine motor control

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Early Differentiation of Pseudoprogression for Recurrent Glioblastoma in STERIMGLI Trial Using Multimodal MRI Radiomics Signature

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Comparison of variable selection methods for high-dimensional survival data with competing events

In the era of personalized medicine, it's primordial to identify gene signatures for each event type in the context of competing risks in order to improve risk stratification and treatment strategy. Until recently, little attention was paid to the performance of high-dimensional selection in deriving molecular signatures in this context. In this paper, we investigate the performance of two selection methods developed in the framework of high-dimensional data and competing risks: Random survival forest and a boosting approach for fitting proportional subdistribution hazards models

Detection of simultaneous movement at two human arm joints

To detect joint movement, the brain relies on sensory signals from muscle spindles that sense the lengthening and shortening of the muscles. For single-joint muscles, the unique relationship between joint angle and muscle length makes this relatively straightforward. However, many muscles cross more than one joint, making their spindle signals potentially ambiguous, particularly when these joints move in opposite directions. We show here that simultaneous movement at adjacent joints sharing biarticular muscles affects the threshold for detecting movements at either joint whereas it has no effect for non-adjacent joints. The angular displacements required for 70% correct detection were determined in 12 subjects for movements imposed on the shoulder, elbow and wrist at angular velocities of 0.25–2 deg s(−1). When moved in isolation, detection thresholds at each joint were similar to those reported previously. When movements were imposed on the shoulder and wrist simultaneously, there were no changes in the thresholds for detecting movement at either joint. In contrast, when movements in opposite directions at velocities greater than 0.5 deg s(−1) were imposed on the elbow and wrist simultaneously, thresholds increased. At 2 deg s(−1), the displacement threshold was approximately doubled. Thresholds decreased when these adjacent joints moved in the same direction. When these joints moved in opposite directions, subjects more frequently perceived incorrect movements in the opposite direction to the actual. We conclude that the brain uses potentially ambiguous signals from biarticular muscles for kinaesthesia and that this limits acuity for detecting joint movement when adjacent joints are moved simultaneously