Our long-term objective is to understand the neural control of individuated movements, those in which one body part moves relatively independently of the motion or posture of other body parts. Individuated movements are the first lost and last recovered when neurologic lesions affect the motor cortex or corticospinal tract, and play an increasingly important role in the motor repertoire of primates, especially humans. Modern evidence contradicts the notion that movements of different fingers are controlled from distinct, somatotopically arrayed regions of the primary motor cortex (M1), is if labeled lines extended from separate cortical regions to each finger. We hypothesize instead that each finger movement is controlled by a network of neurons distributed throughout the M1 hand region, and that this network reorganizes during motor skill learning. To test these hypotheses experimentally, we will identify M1 neurons that provide direct connections to spinal motoneuron averaging of electromyographic (EMG) activity. Each CM neuron and its target muscles will be recorded simultaneously during performance of 12 individuated finger and wrist movements. By comparing the activity of CM neurons and their target muscles across these 12 movements, we will (Aim 1) examine where CM neurons lie along a spectrum of functional possibilities-from labeled-lines to diversified elements of a distributed network;
and (Aim 2) the extent to which the activity of CM neurons combined through the physiologically identified connections to spinal motoneuron pools, can account for the patterns of EMG activity actually recorded. We also will use the identified CM neuron-target muscle connections to map the spatial distribution of output effects from M1 to muscles at the single neuron level to determine (Aim 3) whether outputs to selected muscles that act on radial versus ulnar digits are spatially segregated or entirely overlapping. Finally, we will map M1 repeatedly during motor skill learning to determine (Aim 4) whether any spatial segregation diminishes as the M1 territories that provide output to trained muscles progressively enlarge and increasingly overlap.
| Choi, Hwayoung; You, Kyung-Jin; Thakor, Nitish V et al. (2018) Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements. IEEE Trans Neural Syst Rehabil Eng 26:2240-2248 |
| Kim, Yong-Hee; Thakor, Nitish V; Schieber, Marc H et al. (2015) Neuron selection based on deflection coefficient maximization for the neural decoding of dexterous finger movements. IEEE Trans Neural Syst Rehabil Eng 23:374-84 |
| Perel, Sagi; Schwartz, Andrew B; Ventura, Valérie (2014) Automatic scan test for detection of functional connectivity between cortex and muscles. J Neurophysiol 112:490-9 |
| Perel, Sagi; Schwartz, Andrew B; Ventura, Valérie (2014) Single-snippet analysis for detection of postspike effects. Neural Comput 26:40-56 |
| Aggarwal, Vikram; Tenore, Francesco; Acharya, Soumyadipta et al. (2009) Cortical decoding of individual finger and wrist kinematics for an upper-limb neuroprosthesis. Conf Proc IEEE Eng Med Biol Soc 2009:4535-8 |
| Schieber, Marc H; Lang, C E; Reilly, K T et al. (2009) Selective activation of human finger muscles after stroke or amputation. Adv Exp Med Biol 629:559-75 |
| Acharya, Soumyadipta; Tenore, Francesco; Aggarwal, Vikram et al. (2008) Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area. IEEE Trans Neural Syst Rehabil Eng 16:15-23 |
| Reilly, Karen T; Schieber, Marc H; McNulty, Penelope A (2008) Selectivity of voluntary finger flexion during ischemic nerve block of the hand. Exp Brain Res 188:385-97 |
| Aggarwal, Vikram; Singhal, Girish; He, Jiping et al. (2008) Towards closed-loop decoding of dexterous hand movements using a virtual integration environment. Conf Proc IEEE Eng Med Biol Soc 2008:1703-6 |
| Aggarwal, Vikram; Acharya, Soumyadipta; Tenore, Francesco et al. (2008) Asynchronous decoding of dexterous finger movements using M1 neurons. IEEE Trans Neural Syst Rehabil Eng 16:3-14 |
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