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174 results for “Motor cortex”
Propagating spatio-temporal activity patterns across macaque motor cortex carry kinematic information
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Data from: Speech motor cortex enables BCI cursor control and click
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Cell-type specific responses to associative learning in the primary motor cortex
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Data from: An output-null signature of inertial load in motor cortex
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Post-processed data for: Diverse operant control of different motor cortex populations during learning
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motor cortex circuit scheme
Drawing uploaded to scidraw.io on: 22 June 2020
Data from: Control of adaptive action selection by secondary motor cortex during flexible visual categorization
<p>Adaptive action selection during stimulus categorization is an important feature of flexible behavior. To examine neural mechanism underlying this process, we trained mice to categorize the spatial frequencies of visual stimuli according to a boundary that changed between blocks of trials in a session. Using a model with a dynamic decision criterion, we found that sensory history was important for adaptive action selection after the switch of boundary. Bilateral inactivation of the secondary motor cortex (M2) impaired adaptive action selection by reducing the behavioral influence of sensory history. Electrophysiological recordings showed that M2 neurons carried more information about upcoming choice and previous sensory stimuli when sensorimotor association was being remapped than when it was stable. Thus, M2 causally contributes to flexible action selection during stimulus categorization, with the representations of upcoming choice and sensory history regulated by the demand to remap stimulus-action association.</p>
Neural population dynamics in motor cortex are different for reach and grasp
<p>Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.</p>
Data from: MRI-based visualization of rTMS-induced cortical plasticity in the primary motor cortex
<p>Repetitive transcranial magnetic stimulation (rTMS) induces changes in cortical excitability for minutes to hours after the end of intervention. However, it has not been precisely determined to what extent cortical plasticity prevails spatially in the cortex. Recent studies have shown that rTMS induces changes in "interhemispheric" functional connectivity, the resting-state functional connectivity between the stimulated region and the symmetrically corresponding region in the contralateral hemisphere. In the present study, quadripulse stimulation (QPS) was applied to the index finger representation in the left primary motor cortex (M1), while the position of the stimulation coil was constantly monitored by an online navigator. After QPS application, resting-state functional magnetic resonance imaging was performed, and the interhemispheric functional connectivity was compared with that before QPS. A cluster of connectivity changes was observed in the stimulated region in the central sulcus. The cluster was spatially extended approximately 10 mm from the center [half width at half maximum (HWHM): approximately 3 mm] and was extended approximately 20 mm long in depth (HWHM: approximately 7 mm). A localizer scan of the index finger motion confirmed that the cluster of interhemispheric connectivity changes overlapped spatially with the activation related to the index finger motion. These results indicate that cortical plasticity in M1 induced by rTMS was relatively restricted in space and suggest that rTMS can reveal functional dissociation associated with adjacent small areas by inducing neural plasticity in restricted cortical regions.</p>
Data for paper "Information flow between motor cortex and striatum reverses during skill learning"
<p>Simultaneous spiking activity and local field potential (LFP) recordings from motor cortex (M1) and dorsolateral striatum (DLS) during learning of a reach-to grasp task.<br>Deposited data include spiking activity and LFP recordings, reaching trajectories, andi single-trial success data for the 8 animals included in the paper "Information flow between motor cortex and striatum reverses during skill learning".</p>
Nonhuman Primate Center-Out and Random-Walk Reaching with Multichannel Motor Cortex Electrophysiology
<p><strong>General Description</strong></p> <p>A rhesus macaque was implanted with a 96-channel microelectrode array (Blackrock Neurotech, Inc.) in the arm area of primary motor cortex (M1). The monkey performed each reaching task with the arm contralateral to the array. We collected broadband data from each electrode at 30 kHz using a 128-channel acquisition system (Cerebus, Blackrock Neurotech, Inc.). To extract spikes, we high pass filtered the broadband data (1st order causal filter, 300 Hz cutoff) and thresholded it using a threshold manually set for each channel (average threshold = 5.2 standard deviations above mean waveform potential). To extract LFPs, we first bandpass filtered the broadband data from 0.3-500 Hz (1st order causal filter), then resampled the signal at 2 kHz, and finally notch filtered it at harmonics of 60 Hz for powerline noise removal. </p> <p>The first task we analyzed was an eight-target center-out reaching task. On each trial, the monkey began by holding at the center of a 10 cm-radius circle of targets for 0.5-0.6 s. Then, one of eight 2 cm square targets spaced at 45° intervals around the circle was illuminated. The monkey had to reach the outer target within 1.5 s and hold for a random time between 0.2-0.4s to obtain a liquid reward. The second task was a random target reaching task. On each trial, the monkey had to acquire a series of 6 randomly positioned targets appearing one-at-a-time, holding each for 0.1 s, to obtain the reward. The targets spanned the majority of the 20-by-20 pixel workspace. </p> <p><strong>Possible Uses</strong></p> <p>These data may be used for testing new BCI decoders or otherwise investigating the motor cortical electrophysiological signals (spikes, local field potentials) during reaching.</p> <p><strong>Variable Names</strong></p> <p>Inside each .mat file is a structure, traditionally called ‘bdf’, or in some cases, ‘out_struct’. We delineate important fields below: </p> <ul> <li> <p><em>bdf.units</em> contains spike timestamps </p> </li> <ul> <li> <p><em>bdf.units.id </em>is a two-element vector [channel, unit]</p> </li> <li> <p><em>bdf.units.ts </em>is an array of spike timestamps for each unit </p> </li> <li> <p>A note on resorting: The<em> bdf.units</em> structure has elements for every sorted unit, plus one element per channel for the unsorted waveforms. In the above example, that amounts to 198 elements. Generally, all these elements will have a unit ID array and some timestamps. However, the waveform data for all recorded spike waveforms in a channel will be found in only one element for that channel (the 255 unit). To illustrate, here is the bdf.units information about channel 16 in the file used to make the above structure map.</p> </li> </ul> </ul> <div> <table> <tbody> <tr> <td> <p>id </p> </td> <td> <p>ts </p> </td> <td> <p>tsAll </p> </td> <td> <p>wv </p> </td> </tr> <tr> <td> <p>[16,0] </p> </td> <td> <p>38x1 double </p> </td> <td> <p>[] </p> </td> <td> <p>[] </p> </td> </tr> <tr> <td> <p>[16,1] </p> </td> <td> <p>3438x1 double </p> </td> <td> <p>[] </p> </td> <td> <p>[] </p> </td> </tr> <tr> <td> <p>[16,255] </p> </td> <td> <p>4199x1 double </p> </td> <td> <p>7675x1 double </p> </td> <td> <p>48x7675 double </p> </td> </tr> </tbody> </table> </div> <ul> <li> <p>There are two sorted units 0 and 1, and a 255 element for unsorted waveforms. Notes: </p> </li> <ul> <li> <p><em>tsAll</em> and <em>wv</em> are only filled out for unit 255 of channel 16. </p> </li> <li> <p>The <em>tsAll</em> field, as its name implies, contains all timestamps recorded on that channel, including those for units 0 and 1, and the unsorted waveforms. A sanity check is that 38+3438+4199 = 7675.</p> </li> <li> <p>The <em>wv</em> field contains the waveforms corresponding to tsAll. Here, there are 48 points per waveform.</p> </li> <li> <p>In general, <em>tsAll</em> will accompany the highest numbered unit.</p> </li> </ul> </ul> <ul> <li> <p><em>bdf.analog.data</em> contains the local field potential (LFP) data</p> </li> <li> <p><em>bdf.analog.ts</em> contains the timestamps for the LFP data as well as the kinematic data </p> </li> <li> <p><em>bdf.pos</em> contains manipulandum position </p> </li> <li> <p><em>bdf.vel </em>contains manipulandum velocity </p> </li> <li> <p><em>bdf.acc</em> contains manipulandum acceleration</p> </li> <li> <p><em>bdf.words</em> contains information about how the run occurred, such as times of trial onset, whether the trial was a success or failure, etc </p> </li> <ul> <li> <p>For <span>center-out</span> data </p> </li> <ul> <li> <p>The <em>bdf.words</em> field can be processed by running the function: tt = co_trial_table(bdf); </p> </li> <li> <p>The numeric array tt will then have Nx10 columns, where each row is a trial and each column gives the following information (this is replicated in the documentation of the function file): </p> </li> <ul> <li> <p>1: Start time </p> </li> <li> <p>2: Bump direction -- -1 for none </p> </li> <li> <p>3: Bump phase -- H (hold), D (delay), or M (movement) </p> </li> <li> <p>4: Bump time </p> </li> <li> <p>5: Target -- -1 for none (e.g., a neutral bump) </p> </li> <li> <p>6: OT on time </p> </li> <li> <p>7: Go cue </p> </li> <li> <p>8: Movement start time </p> </li> <li> <p>9: Trial End time </p> </li> <li> <p>10: Trial result -- R (reward), A (aborted), I (incomplete), or N (no-result) </p> </li> </ul> </ul> <li> <p>For <span>random-walk</span> data </p> </li> <ul> <li> <p>In RW hand-control data, 1 trial consisted of N distinct movements, where N was usually 6 or 8. In other words, N targets were presented successively, and all required to be hit, prior to delivery of a reward. For each target presentation, there is a time-stamp in the field bdf.words. There is also a time-stamp for the event of the cursor first entering the target. This information is presented in two columns, where the first column is the timestamp and the second column is an event code, which indicates:</p> </li> <ul> <li> <p>18: trial-start event flag </p> </li> <li> <p>49: target presentation </p> </li> <li> <p>160: cursor enters target </p> </li> <li> <p>32: success </p> </li> <li> <p>33/35: failure</p> </li> </ul> </ul> </ul> </ul>
Differential influence of the dorsal premotor and primary somatosensory cortex on corticospinal excitability during kinesthetic and visual motor imagery: a low-frequency repetitive transcranial magnetic stimulation study
<p>Consistent evidence suggests that motor imagery involves activation of several sensorimotor areas also involved during action execution, including the dorsal premotor (dPMC) and primary somatosensory cortex (S1). However, it is still unclear whether their involvement is specific for either kinesthetic or visual imagery or whether they contribute to motor activation for both modalities. Although sensorial experience during motor imagery is often multimodal, identifying the modality exerting greater facilitation of the motor system may allow to optimize the functional outcomes of rehabilitation interventions. In a sample of healthy adults, we combined 1-HZ repetitive transcranial magnetic stimulation (TMS) to suppress neural activity of the dPMC, S1, and primary motor cortex (M1) with single-pulse TMS over M1 for measuring cortico-spinal excitability (CSE) during kinesthetic and visual motor imagery of finger movements as compared to static imagery conditions. We found that rTMS over both dPMC and S1, but not over M1, modulated the muscle-specific facilitation of CSE during kinesthetic, but not during visual motor imagery. Furthermore, dPMC-rTMS suppressed the facilitation of CSE, whereas S1-rTMS boosted it. The results highlight the differential pattern of cortico-cortical connectivity within the sensorimotor system during the mental simulation of the kinesthetic and visual consequences of actions.</p>
Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023</p> <p>Please download the data_v2.zip file, which contains the most updated and complete version of the data.</p> <p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Cyclization of Motor Cortex Stimulation
ClinicalTrials.gov study NCT02465918. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Cortex Motor Function Reorganization in Stroke Patients
ClinicalTrials.gov study NCT04794673. IPD Sharing: UNDECIDED. Countries: 1. Publications: 31.
Safety and Efficacy of Motor Cortex Stimulation in the Treatment of Advanced Parkinson Disease
ClinicalTrials.gov study NCT00159172. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Motor Cortex Reward Signaling in Parkinson Disease
ClinicalTrials.gov study NCT00558766. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effect of Reward on Learning in Motor Cortex
ClinicalTrials.gov study NCT00885131. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Neuromuscular Electrical Stimulation on Median Nerve Facilitates Low Motor Cortex Excitability in Human With Spinocerebellar Ataxia
ClinicalTrials.gov study NCT02103075. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Transcranial Direct Current Stimulation (tDCS) on the Excitability of the Diaphragmatic Primary Motor Cortex
ClinicalTrials.gov study NCT01548586. IPD Sharing: Not stated. Countries: 1. Publications: 10.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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