Correlations between activity of motor cortex cells and arm muscles during operantly conditioned response patterns

1975 ◽  
Vol 23 (3) ◽  
Author(s):  
E.E. Fetz ◽  
D.V. Finocchio
2009 ◽  
Vol 102 (2) ◽  
pp. 1026-1039 ◽  
Author(s):  
W. S. Smith ◽  
E. E. Fetz

We investigated the synaptic interactions between neighboring motor cortex cells in monkeys generating isometric ramp-and-hold torques about the wrist. For pairs of cortical cells the response patterns were determined in response-aligned averages and their synaptic interactions were identified by cross-correlation histograms. Cross-correlograms were compiled for 215 cell pairs and 84 (39%) showed significant features. The most frequently found feature (65/84 = 77%) was a central peak, straddling the origin and representing a source of common synaptic input to both cells. One third of these also had superimposed lagged peaks, indicative of a serial excitatory connection. Pure lagged peaks and lagged troughs, indicative of serial excitatory or inhibitory linkages, respectively, both occurred in 5% of the correlograms with features. A central trough appeared in 13% of the correlograms. The magnitude of the synaptic linkage was measured as the normalized area of the correlogram feature. Plotting the strength of synaptic interaction against response similarity during alternating wrist torques revealed a positive relationship for the correlated cell pairs. A linear fit yielded a positive slope: the pairs with excitatory interactions tended to covary more often than countervary. This linear fit had a positive offset, reflecting a tendency for both covarying and countervarying cells to have excitatory common input. Plotting the cortical location of the cell pairs showed that the strongest interactions occurred between cells separated by <400 microns. The correlational linkages between cells of different cortical layers showed a large proportion of common input to cells in layer V.


2007 ◽  
Vol 97 (4) ◽  
pp. 2887-2899 ◽  
Author(s):  
Troy M. Herter ◽  
Isaac Kurtzer ◽  
D. William Cabel ◽  
Kirk A. Haunts ◽  
Stephen H. Scott

The present study examined neural activity in the shoulder/elbow region of primary motor cortex (M1) during a whole-limb postural task. By selectively imposing torques at the shoulder, elbow, or both joints we addressed how neurons represent changes in torque at a single joint, multiple joints, and their interrelation. We observed that similar proportions of neurons reflected changes in torque at the shoulder, elbow, and both joints and these neurons were highly intermingled across the cortical surface. Most torque-related neurons were reciprocally excited and inhibited (relative to their unloaded baseline activity) by opposing flexor and extensor torques at a single joint. Although coexcitation/coinhibition was occasionally observed at a single joint, it was rarely observed at both joints. A second analysis assessed the relationship between single-joint and multijoint activity. In contrast to our previous observations, we found that neither linear nor vector summation of single-joint activities could capture the breadth of neural responses to multijoint torques. Finally, we studied the neurons' directional tuning across all the torque conditions, i.e., in joint-torque space. Our population of M1 neurons exhibited a strong bimodal distribution of preferred-torque directions (PTDs) that was biased toward shoulder-extensor/elbow-flexor (whole-limb flexor) and shoulder-flexor/elbow-extensor (whole-limb extensor) torques. Notably, we recently observed a similar bimodal distribution of PTDs in a sample of proximal arm muscles. This observation illustrates the intimate relationship between M1 and the motor periphery.


2021 ◽  
Author(s):  
Basil C Preisig ◽  
Lars Riecke ◽  
Alexis Hervais-Adelman

What processes lead to categorical perception of speech sounds? Investigation of this question is hampered by the fact that categorical speech perception is normally confounded by acoustic differences in the stimulus. By using ambiguous sounds, however, it is possible to dissociate acoustic from perceptual stimulus representations. We used a binaural integration task, where the inputs to the two ears were complementary so that phonemic identity emerged from their integration into a single percept. Twenty-seven normally hearing individuals took part in an fMRI study in which they were presented with an ambiguous syllable (intermediate between /da/ and /ga/) in one ear and with a meaning-differentiating acoustic feature (third formant) in the other ear. Multi-voxel pattern searchlight analysis was used to identify brain areas that consistently differentiated between response patterns associated with different syllable reports. By comparing responses to different stimuli with identical syllable reports and identical stimuli with different syllable reports, we disambiguated whether these regions primarily differentiated the acoustics of the stimuli or the syllable report. We found that BOLD activity patterns in the left anterior insula (AI), the left supplementary motor cortex, the left ventral motor cortex and the right motor and somatosensory cortex (M1/S1) represent listeners' syllable report irrespective of stimulus acoustics. The same areas have been previously implicated in decision-making (AI), response selection (SMA), and response initiation and feedback (M1/S1). Our results indicate that the emergence of categorical speech sounds implicates decision-making mechanisms and auditory-motor transformations acting on sensory inputs.


2009 ◽  
Vol 102 (2) ◽  
pp. 1040-1048 ◽  
Author(s):  
W. S. Smith ◽  
E. E. Fetz

To elucidate the cortical circuitry controlling primate forelimb muscles we investigated the synaptic interactions between neighboring motor cortex cells that had postspike output effects in target muscles. In monkeys generating isometric ramp-and-hold wrist torques, pairs of cortical cells were recorded simultaneously with independent electrodes and corticomotoneuronal (“CM”) cells were identified by their postspike effects on target forelimb muscles in spike-triggered averages (SpTAs) of electromyographs (EMGs). The response patterns of the cells were determined in response-aligned averages and their synaptic interactions were identified by cross-correlograms of action potentials. The possibility that synchronized firing between cortical cells could mediate spike-correlated effects in the SpTA of EMG was examined in several ways. Sixty-two pairs consisted of one CM cell and a non-CM cell; 15 of these had correlogram peaks of the same magnitude as that of other pairs, but the synchrony peaks did not mediate any postspike effect from the non-CM cell. Twelve pairs of simultaneously recorded CM cells were cross-correlated. Half had features (usually synchrony peaks) in their cross-correlograms and the cells of these pairs also shared some target muscles in common. The other half had flat correlograms and, in most of these pairs, the CM cells affected different muscles. The latter group included pairs of CM cells that facilitated synergistic muscles. These results indicate that common synaptic input specifically affects CM cells that have overlapping muscle fields. Reconstruction of the cortical locations of CM cells affecting 12 different muscles showed a wide and overlapping distribution of cortical colonies of forelimb muscles.


1981 ◽  
Vol 31 (3) ◽  
pp. 377-393 ◽  
Author(s):  
Wendon W. Henton ◽  
Benjamin R. Fisher

2019 ◽  
Author(s):  
K. Cora Ames ◽  
Mark M. Churchland

AbstractPrimary motor cortex (M1) has lateralized outputs, yet M1 neurons can be active during movements of either arm. What is the nature and role of activity in the two hemispheres? When one arm moves, are the contralateral and ipsilateral cortices performing similar or different computations? When both hemispheres are active, how does the brain avoid moving the “wrong” arm? We recorded muscle and neural activity bilaterally while two male monkeys (Macaca mulatta) performed a cycling task with one or the other arm. Neurons in both hemispheres were active during movements of either arm. Yet response patterns were arm-dependent, raising two possibilities. First, the nature of neural signals may differ (e.g., be high versus low-level) depending on whether the ipsilateral or contralateral arm is used. Second, the same population-level signals may be present regardless of the arm being used, but be reflected differently at the individual-neuron level. The data supported this second hypothesis. Muscle activity could be predicted by neural activity in either hemisphere. More broadly, we failed to find signals unique to the hemisphere contralateral to the moving arm. Yet if the same signals are shared across hemispheres, how do they avoid impacting the wrong arm? We found that activity related to the two arms occupied distinct, orthogonal subspaces of population activity. As a consequence, a linear decode of contralateral muscle activity naturally ignored signals related to the ipsilateral arm. Thus, information regarding the two arms is shared across hemispheres and neurons, but partitioned at the population level.


2007 ◽  
Vol 97 (6) ◽  
pp. 4235-4257 ◽  
Author(s):  
Mark M. Churchland ◽  
Krishna V. Shenoy

The relationship between neural activity in motor cortex and movement is highly debated. Although many studies have examined the spatial tuning (e.g., for direction) of cortical responses, less attention has been paid to the temporal properties of individual neuron responses. We developed a novel task, employing two instructed speeds, that allows meaningful averaging of neural responses across reaches with nearly identical velocity profiles. Doing so preserves fine temporal structure and reveals considerable complexity and heterogeneity of response patterns in primary motor and premotor cortex. Tuning for direction was prominent, but the preferred direction was frequently inconstant with respect to time, instructed-speed, and/or reach distance. Response patterns were often temporally complex and multiphasic, and varied with direction and instructed speed in idiosyncratic ways. A wide variety of patterns was observed, and it was not uncommon for a neuron to exhibit a pattern shared by no other neuron in our dataset. Response patterns of individual neurons rarely, if ever, matched those of individual muscles. Indeed, the set of recorded responses spanned a much higher dimensional space than would be expected for a model in which neural responses relate to a moderate number of factors—dynamic, kinematic, or otherwise. Complex responses may provide a basis-set representing many parameters. Alternately, it may be necessary to discard the notion that responses exist to “represent” movement parameters. It has been argued that complex and heterogeneous responses are expected of a recurrent network that produces temporally patterned outputs, and the present results would seem to support this view.


2018 ◽  
Vol 18 (10) ◽  
pp. 439
Author(s):  
Sara Fabbri ◽  
Michael Compton ◽  
Edward O'Neil ◽  
Lars Strother ◽  
Jacqueline Snow

2006 ◽  
Vol 96 (6) ◽  
pp. 3220-3230 ◽  
Author(s):  
Isaac Kurtzer ◽  
Troy M. Herter ◽  
Stephen H. Scott

The present study examined the activity of primate shoulder and elbow muscles using a novel reaching task. We enforced similar patterns of center-out movement while the animals countered viscous loads at their shoulder, elbow, both joints, or neither joint. Accordingly, we could examine reach-related activity during the unloaded condition and torque-related activity by comparing activity across load conditions. During unloaded reaching the upper arm muscles exhibited a bimodal distribution of preferred hand direction. Maximal reach-related activity occurred with hand movements mostly toward or away from the body. Arm muscles also exhibited a bimodal distribution of their preferred torque direction. Maximal torque-related activity typically occurred with shoulder-extension/elbow-flexion torque or shoulder-flexion/elbow-extension torque. Similar biases in reach-related and torque-related activity could be reproduced by optimizing a global measure of muscle activity. These biases were also observed in the neural activity of primary motor cortex (M1). The parallels between M1 and muscular activity demonstrate another link between motor cortical processing and the motor periphery and may reflect an optimization process performed by the sensorimotor system.


2019 ◽  
Vol 19 (8) ◽  
pp. 43
Author(s):  
Michael Compton ◽  
Sara Fabbri ◽  
Edward O’Neil ◽  
Lars Strother ◽  
Jacqueline Snow

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