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Solomonow-Avnon, D.

Publications and source records attributed to Solomonow-Avnon, D..

2 recordsLinked to original sources

Cortically-evoked movement in humans reflects history of prior executions, not plan for upcoming movement

Human motor behavior involves planning and execution, but we often perform some actions more frequently. Experimentally manipulating the probability distribution of a movement through intensive repetition toward a certain direction causes physiological bias toward that direction, which can be cortically-evoked by transcranial magnetic stimulation (TMS). However, because movement execution and plan histories were indistinguishable to date, to what extent TMS-evoked biases are due to more frequently executed movement, or recent planning of movement, is unclear. Here, we use novel experimentation to separately manipulate recent history of movement plans and execution, and probe the effects of this on physiological biases using TMS, and on default plan for goal-directed actions using a behavioral timed-response task. At baseline, physiological biases shared similar low-level kinematic properties (direction) to default plan for upcoming movement. However, when recent movement execution history was manipulated via thumb movement repetitions toward a specific direction, we found a significant effect on physiological biases, but not plan-based goal-directed movement. To further determine if physiological biases reflect ongoing motor planning, we biased movement plan history by increasing the likelihood of a specific target location, and found a significant effect on the default plan for goal-directed movements. However, TMS-evoked movement during the preparation period did not become biased toward the most frequent plan. This suggests that physiological biases provide a readout of the default state of M1 population activity in the movement-related space, but not ongoing neural activation in the planning-related space, potentially ruling out relevance of cortically-evoked physiological biases to voluntary movements. HighlightsO_LIStimulating the human motor cortex selectively evoked thumb movements toward a specific direction (physiological bias) C_LIO_LIAt baseline, these physiological biases shared similar low-level kinematics with default plan for voluntary goal-directed movements C_LIO_LIModulating the probability distribution of prior movements had a significant effect on physiological biases C_LIO_LIHowever, biasing history of plans for upcoming movement toward a specific direction had no effect on evoked movement direction C_LIO_LIDuring ongoing planning of voluntary movement, evoked movements maintained the distinct and robust baseline bias, regardless of change in probability distribution of history of upcoming plans C_LI

neuroscience↗

Computational neural network provides naturalistic solution for recovery of finger dexterity after stroke

Finger dexterity is a fundamental movement skill of humans and the ability to individuate fingers imparts high motor flexibility. Disruption of dexterity due to brain injury reduces quality of life. Thus, understanding the neurological mechanisms responsible for recovery is critical to effective neurorehabilitation. Two neuronal pathways have been proposed to play crucial roles in finger individuation: the corticospinal tract, originating from primary motor cortex and premotor areas, and the subcortical reticulospinal tract, originating from the reticular formation in the brainstem. Finger individuation in patients with lesions to these pathways may recover. However, it remains an open question how the cortical-reticular network reorganizes and contributes to this recovery following a stroke. We hypothesized that interactive connections between cortical and subcortical neurons reflect dynamics appropriate for generating outgoing commands for finger movement. To test this hypothesis, we developed an Artificial Neural Network (ANN) representing a premotor planning input layer, a cortical layer including excitatory and inhibitory neurons and, a reticular layer that control motoneurons eliciting unilateral flexion of two fingers. The ANN was trained to reproduce "normal" activity of finger individuation and strength. Analysis of the trained ANN revealed that the natural dynamical solution was a near-linear relationship between the force of the instructed and uninstructed finger, resembling individuation patterns in humans. A simulated stroke lesion was then applied to the ANN and the resulting finger dexterity was assessed at multiple stages post stroke. Analysis revealed: (1) increased unintended force produced by uninstructed fingers (i.e., enslaving) and (2) weakening of the force in the instructed finger immediately after stroke, (3) improved finger control during recovery that typically occurs early after stroke, and (4) association of this behavior with increased neural plasticity of the residual neurons, as reflected by strengthening of connectivity weights between premotor and focal cortical excitatory and inhibitory neurons, but reduction in connectivity in shared cortical neurons. Interestingly, the network solution predicted that the reticulospinal pathway also contributed to the improved behavior. Lastly, the ANN also predicts the effect of cortical lesion size on finger individuation. Our model provides a framework by which to understand a number of experimental findings. The model solution suggests that a key mechanism of finger individuation is establishment of an interactive relationship between cortical and subcortical regions, appropriate to produce desired finger movement.

neuroscience↗