scholarly journals Structure of population activity in primary motor cortex for single finger flexion and extension

2020 ◽  
Author(s):  
Spencer A. Arbuckle ◽  
Jeff Weiler ◽  
Eric A. Kirk ◽  
Charles L. Rice ◽  
Marc Schieber ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Activity Patterns ◽  
Muscular Activity ◽  
Finger Movements ◽  
Population Activity ◽  
Single Finger ◽  
Finger Flexion ◽  
Electrophysiological Measurements ◽  
Flexion And Extension

AbstractHow is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants, and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1.Significance statementThe primary motor cortex (M1) is important for producing individuated finger movements. Recent evidence shows that movements that commonly co-occur are associated with more similar activity patterns in M1. Flexion and extension of the same finger, which never co-occur, should therefore be associated with distinct representations. However, using carefully controlled experiments and multivariate analyses, we demonstrate that human fMRI activity patterns for flexion or extension of the same finger are highly similar. In contrast, spiking patterns measured in monkey M1 are clearly distinct. This suggests that populations controlling opposite movements of the same finger, while producing distinct outputs, may cluster together and share inputs and local processing. These results provide testable hypotheses about the organization of hand control in M1.

scholarly journals Structure of Population Activity in Primary Motor Cortex for Single Finger Flexion and Extension

2020 ◽  
Vol 40 (48) ◽  
pp. 9210-9223
Author(s):  
Spencer A. Arbuckle ◽  
Jeff Weiler ◽  
Eric A. Kirk ◽  
Charles L. Rice ◽  
Marc Schieber ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Activity ◽  
Single Finger ◽  
Finger Flexion ◽  
Structure Of Population ◽  
Flexion And Extension

scholarly journals Does human primary motor cortex represent sequences of finger movements?

2017 ◽  
Author(s):  
Atsushi Yokoi ◽  
Spencer A. Arbuckle ◽  
Jörn Diedrichsen
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Activity Patterns ◽  
Finger Movements ◽  
First Response ◽  
Single Finger ◽  
True Representation ◽  
Sequence Representation ◽  
Dexterous Hand ◽  
Fmri Analysis

AbstractHuman primary motor cortex (M1) is an essential structure for the production of dexterous hand movements. While distinct sub-populations of neurons are activated during single finger movements, it remains unknown whether M1 also represents sequences of multiple finger movements. Using novel multivariate fMRI analysis techniques, we show here that even after 5 days of intense practice there was little or no evidence for a true sequence representation in M1. Rather, the activity patterns for sequences in M1 could be explained by linear combination of patterns associated with the constituent individual finger movements, with the strongest weight on the finger making the first response of the sequence. These results suggest that M1 only represents single finger movements, but receives increased input at the start of a sequence. In contrast, the reliable differences between different sequences in premotor and parietal areas could not be explained by a strong weighting of the first finger, supporting the view that these regions exhibit a true representation of sequences.

Decoding M1 Neurons During Multiple Finger Movements

2007 ◽  
Vol 98 (1) ◽  
pp. 327-333 ◽  
Author(s):  
S. Ben Hamed ◽  
M. H. Schieber ◽  
A. Pouget
Keyword(s):  
Motor Cortex ◽  
Information Content ◽  
Primary Motor Cortex ◽  
Extended Techniques ◽  
Hand Movements ◽  
Finger Movements ◽  
Neural Prosthetics ◽  
Single Finger ◽  
Simultaneous Movement ◽  
Large Repertoire

We tested several techniques for decoding the activity of primary motor cortex (M1) neurons during movements of single fingers or pairs of fingers. We report that single finger movements can be decoded with >99% accuracy using as few as 30 neurons randomly selected from populations of task-related neurons recorded from the M1 hand representation. This number was reduced to 20 neurons or less when the neurons were not picked randomly but selected on the basis of their information content. We extended techniques for decoding single finger movements to the problem of decoding the simultaneous movement of two fingers. Movements of pairs of fingers were decoded with 90.9% accuracy from 100 neurons. The techniques we used to obtain these results can be applied, not only to movements of single fingers and pairs of fingers as reported here, but also to movements of arbitrary combinations of fingers. The remarkably small number of neurons needed to decode a relatively large repertoire of movements involving either one or two effectors is encouraging for the development of neural prosthetics that will control hand movements.

scholarly journals Coherence Between Motor Cortical Activity and Peripheral Discontinuities During Slow Finger Movements

2009 ◽  
Vol 102 (2) ◽  
pp. 1296-1309 ◽  
Author(s):  
Elizabeth R. Williams ◽  
Demetris S. Soteropoulos ◽  
Stuart N. Baker
Keyword(s):  
Primary Motor Cortex ◽  
Phase Relationship ◽  
Conduction Delay ◽  
Finger Movements ◽  
Antidromic Activation ◽  
Spike Triggered Averaging ◽  
Finger Flexion ◽  
Flexion Extension ◽  
Corticomotoneuronal Cells ◽  
Flexion And Extension

Slow finger movements in man are not smooth, but are characterized by 8- to 12-Hz discontinuities in finger acceleration thought to have a central source. We trained two macaque monkeys to track a moving target by performing index finger flexion/extension movements and recorded local field potentials (LFPs) and spike activity from the primary motor cortex (M1); some cells were identified as pyramidal tract neurons by antidromic activation or as corticomotoneuronal cells by spike-triggered averaging. There was significant coherence between finger acceleration in the approximately 10-Hz range and both LFPs and spikes. LFP–acceleration coherence was similar for flexion and extension movements (0.094 at 9.8 Hz and 0.11 at 6.8 Hz, respectively), but substantially smaller during steady holding (0.0067 at 9.35 Hz). The coherence phase showed a significant linear relationship with frequency over the 6- to 13-Hz range, as expected for a constant conduction delay, but the slope indicated that LFP lagged acceleration by 18 ± 14 or 36 ± 8 ms for flexion and extension movements, respectively. Directed coherence analysis supported the conclusion that the dominant interaction was in the acceleration to LFP (i.e., sensory) direction. The phase relationships between finger acceleration and both LFPs and spikes shifted by about π radians in flexion compared with extension trials. However, for a given trial type the phase relationship with acceleration was similar for cells that increased their firing during flexion or during extension trials. We conclude that movement discontinuities during slow finger movements arise from a reciprocally coupled network, which includes M1 and the periphery.

scholarly journals Reduced Interhemispheric Coherence in Cerebellar Kainic Acid-Induced Lateralized Dystonia

2020 ◽  
Vol 11 ◽  
Author(s):  
Elena Laura Georgescu Margarint ◽  
Ioana Antoaneta Georgescu ◽  
Carmen Denise Mihaela Zahiu ◽  
Stefan-Alexandru Tirlea ◽  
Alexandru Rǎzvan Şteopoaie ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Kainic Acid ◽  
Primary Motor Cortex ◽  
Low Frequency ◽  
Muscular Activity ◽  
Cerebellar Hemisphere ◽  
Interhemispheric Coherence ◽  
General Locomotor Activity ◽  
Complex Circuits ◽  
The Right

The execution of voluntary muscular activity is controlled by the primary motor cortex, together with the cerebellum and basal ganglia. The synchronization of neural activity in the intracortical network is crucial for the regulation of movements. In certain motor diseases, such as dystonia, this synchrony can be altered in any node of the cerebello-cortical network. Questions remain about how the cerebellum influences the motor cortex and interhemispheric communication. This research aims to study the interhemispheric cortical communication between the motor cortices during dystonia, a neurological movement syndrome consisting of sustained or repetitive involuntary muscle contractions. We pharmacologically induced lateralized dystonia to adult male albino mice by administering low doses of kainic acid on the left cerebellar hemisphere. Using electrocorticography and electromyography, we investigated the power spectral densities, cortico-muscular, and interhemispheric coherence between the right and left motor cortices, before and during dystonia, for five consecutive days. Mice displayed lateralized abnormal motor signs, a reduced general locomotor activity, and a high score of dystonia. The results showed a progressive interhemispheric coherence decrease in low-frequency bands (delta, theta, beta) during the first 3 days. The cortico-muscular coherence of the affected side had a significant increase in gamma bands on days 3 and 4. In conclusion, lateralized cerebellar dysfunction during dystonia was associated with a loss of connectivity in the motor cortices, suggesting a possible cortical compensation to the initial disturbances induced by cerebellar left hemisphere kainate activation by blocking the propagation of abnormal oscillations to the healthy hemisphere. However, the cerebellum is part of several overly complex circuits, therefore other mechanisms can still be involved in this phenomenon.

scholarly journals Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

2021 ◽  
Author(s):  
Shreya Saxena ◽  
Abigail A. Russo ◽  
John P. Cunningham ◽  
Mark M. Churchland
Keyword(s):  
Motor Cortex ◽  
Muscle Activity ◽  
Single Neuron ◽  
Activity Patterns ◽  
Network Activity ◽  
Movement Speed ◽  
Neural Population ◽  
Recurrent Networks ◽  
Neural Signals ◽  
Population Activity

AbstractLearned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling, and yielded quantitative and qualitative predictions. To evaluate predictions, we recorded motor cortex population activity during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

scholarly journals Primary motor cortex activity traces distinct trajectories of population dynamics during spontaneous facial motor sequences in mice

2021 ◽  
Author(s):  
Juan Carlos Boffi ◽  
Tristan Wiessalla ◽  
Robert Prevedel
Keyword(s):  
Population Dynamics ◽  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Neuronal Population ◽  
Future Research ◽  
Sequencing Analysis ◽  
Population Activity ◽  
Neuronal Populations ◽  
Custom Made ◽  
Facial Area

AbstractWe explore the link between on-going neuronal activity at primary motor cortex (M1) and face movement in awake mice. By combining custom-made behavioral sequencing analysis and fast volumetric Ca2+-imaging, we simultaneously tracked M1 population activity during many different facial motor sequences. We show that a facial area of M1 displays distinct trajectories of neuronal population dynamics across different spontaneous facial motor sequences, suggesting an underlying population dynamics code.Significance statementHow our brain controls a seemingly limitless diversity of body movements remains largely unknown. Recent research brings new light into this subject by showing that neuronal populations at the primary motor cortex display different dynamics during forelimb reaching movements versus grasping, which suggests that different motor sequences could be associated with distinct motor cortex population dynamics. To explore this possibility, we designed an experimental paradigm for simultaneously tracking the activity of neuronal populations in motor cortex across many different motor sequences. Our results support the concept that distinct population dynamics encode different motor sequences, bringing new insight into the role of motor cortex in sculpting behavior while opening new avenues for future research.

Neuronal Activity Patterns in Primate Primary Motor Cortex Related to Trained or Semiautomatic Jaw and Tongue Movements

2002 ◽  
Vol 87 (5) ◽  
pp. 2531-2541 ◽  
Author(s):  
Dongyuan Yao ◽  
Kensuke Yamamura ◽  
Noriyuki Narita ◽  
Ruth E. Martin ◽  
Gregory M. Murray ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Activity Patterns ◽  
Rhythmic Activity ◽  
Firing Frequency ◽  
Orofacial Movements ◽  
Jaw Opening ◽  
The Face ◽  
Tongue Movements ◽  
Opening Phase

The present study was undertaken to determine the firing patterns and the mechanoreceptive field (RF) properties of neurons within the face primary motor cortex (face-MI) in relation to chewing and other orofacial movements in the awake monkey. Of a total of 107 face-MI neurons recorded, 73 of 74 tested had activity related to chewing and 47 of 66 neurons tested showed activity related to a trained tongue task. Of the 73 chewing-related neurons, 52 (71.2%) showed clear rhythmic activity during rhythmic chewing. A total of 32 (43.8%) also showed significant alterations in activity in relation to the swallowing of a solid food (apple) bolus. Many of the chewing-related neurons (81.8% of 55 tested) had an orofacial RF, which for most was on the tongue dorsum. Tongue protrusion was evoked by intracortical microstimulation (ICMS) at most (63.6%) of the recording sites where neurons fired during the rhythmic jaw-opening phase, whereas tongue retraction was evoked by ICMS at most (66.7%) sites at which the neurons firing during the rhythmic jaw-closing phase were recorded. Of the 47 task-related neurons, 21 of 22 (95.5%) examined also showed chewing-related activity and 29 (61.7%) demonstrated significant alteration in activity in relation to the swallowing of a juice reward. There were no significant differences in the peak firing frequency among neuronal activities related to chewing, swallowing, or the task. These findings provide further evidence that face-MI may play an important role not only in trained orofacial movements but also in chewing as well as swallowing, including the control of tongue and jaw movements that occur during the masticatory sequence.

Partial Reconstruction of Muscle Activity From a Pruned Network of Diverse Motor Cortex Neurons

2007 ◽  
Vol 97 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Marc H. Schieber ◽  
Gil Rivlis
Keyword(s):  
Motor Cortex ◽  
Motor Neurons ◽  
Primary Motor Cortex ◽  
Activity Patterns ◽  
Neuron Activity ◽  
Emg Activity ◽  
Weighted Sum ◽  
Partial Reconstruction ◽  
Average Similarity ◽  
Upper Motor Neurons

Primary motor cortex (M1) neurons traditionally have been viewed as “upper motor neurons” that directly drive spinal motoneuron pools, particularly during finger movements. We used spike-triggered averages (SpikeTAs) of electromyographic (EMG) activity to select M1 neurons whose spikes signaled the arrival of input in motoneuron pools, and examined the degree of similarity between the activity patterns of these M1 neurons and their target muscles during 12 individuated finger and wrist movements. Neuron–EMG similarity generally was low. Similarity was unrelated to the strength of the SpikeTA effect, to whether the effect was pure versus synchrony, or to the number of muscles influenced by the neuron. Nevertheless, the sum of M1 neuron activity patterns, each weighted by the sign and strength of its SpikeTA effect, could be more similar to the EMG than the average similarity of individual neurons. Significant correlations between the weighted sum of M1 neuron activity patterns and EMG were obtained in six of 17 muscles, but showed R2 values ranging from only 0.26 to 0.42. These observations suggest that additional factors—including inputs from sources other than M1 and nonlinear summation of inputs to motoneuron pools—also contributed substantially to EMG activity patterns. Furthermore, although each of these M1 neurons produced SpikeTA effects with a significant peak or trough 6–16 ms after the triggering spike, shifting the weighted sum of neuron activity to lead the EMG by 40–60 ms increased their similarity, suggesting that the influence of M1 neurons that produce SpikeTA effects includes substantial synaptic integration that in part may reach the motoneuron pools over less-direct pathways.

Perturbation-evoked responses in primary motor cortex are modulated by behavioral context

2014 ◽  
Vol 112 (11) ◽  
pp. 2985-3000 ◽  
Author(s):  
Mohsen Omrani ◽  
J. Andrew Pruszynski ◽  
Chantelle D. Murnaghan ◽  
Stephen H. Scott
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Initial Perturbation ◽  
Motor Task ◽  
Initial Response ◽  
Mechanical Perturbation ◽  
Population Activity ◽  
Motor Action ◽  
Central Target ◽  
Postural Task

Corrective responses to external perturbations are sensitive to the behavioral task being performed. It is believed that primary motor cortex (M1) forms part of a transcortical pathway that contributes to this sensitivity. Previous work has identified two distinct phases in the perturbation response of M1 neurons, an initial response starting ∼20 ms after perturbation onset that does not depend on the intended motor action and a task-dependent response that begins ∼40 ms after perturbation onset. However, this invariant initial response may reflect ongoing postural control or a task-independent response to the perturbation. The present study tested these two possibilities by examining if being engaged in an ongoing postural task before perturbation onset modulated the initial perturbation response in M1. Specifically, mechanical perturbations were applied to the shoulder and/or elbow while the monkey maintained its hand at a central target or when it was watching a movie and not required to respond to the perturbation. As expected, corrective movements, muscle stretch responses, and M1 population activity in the late perturbation epoch were all significantly diminished in the movie task. Strikingly, initial perturbation responses (<40 ms postperturbation) remained the same across tasks, suggesting that the initial phase of M1 activity constitutes a task-independent response that is sensitive to the properties of the mechanical perturbation but not the goal of the ongoing motor task.

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