scholarly journals Behavioral context determines network state and variability dynamics in monkey motor cortex

2017 ◽  
Author(s):  
Alexa Riehle ◽  
Thomas Brochier ◽  
Martin Paul Nawrot ◽  
Sonja Grün
Keyword(s):  
Motor Cortex ◽  
Computational Models ◽  
Phase Locking ◽  
Motor Task ◽  
Field Potential ◽  
Fano Factor ◽  
Movement Preparation ◽  
Contextual Dependence ◽  
Monkey Motor Cortex ◽  
Spiking Activity

AbstractVariability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependence. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the local coefficient of variation (CV2), reflect variability on long and short time scales, respectively. We co-analyzed FF and CV2 in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that FF significantly decreases from wait to movement, while CV2 increases. The more regular firing (low CV2) during wait is related to an increased power of local field potential beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF≈CV2. This expectation was met during movement, but not during wait where FF≫CV22. We conclude that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. We discuss our results in the light of recent computational models that generate non-stationary network conditions.

Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation

2000 ◽  
Vol 94 (5-6) ◽  
pp. 569-582 ◽  
Author(s):  
Alexa Riehle ◽  
Franck Grammont ◽  
Markus Diesmann ◽  
Sonja Grün
Keyword(s):  
Motor Cortex ◽  
Movement Preparation ◽  
Temporal Precision ◽  
Monkey Motor Cortex ◽  
Spiking Activity

scholarly journals Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex

2017 ◽  
Vol 117 (4) ◽  
pp. 1524-1543 ◽  
Author(s):  
Michael E. Rule ◽  
Carlos E. Vargas-Irwin ◽  
John P. Donoghue ◽  
Wilson Truccolo
Keyword(s):  
Steady State ◽  
Motor Cortex ◽  
Local Field ◽  
Single Neuron ◽  
Field Potential ◽  
Movement Preparation ◽  
Phase Coupling ◽  
Firing Rates ◽  
Sources Of Variation ◽  
Transient Events

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation. NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs.

scholarly journals On The Relationship Between LFP Oscillations And Spiking Activity In Monkey Motor Cortex

2015 ◽  
Vol 9 ◽  
Author(s):  
Kilavik Bj�rg Elisabeth ◽  
Brochier Thomas ◽  
Gr�n Sonja ◽  
Riehle Alexa
Keyword(s):  
Motor Cortex ◽  
The Relationship ◽  
Monkey Motor Cortex ◽  
Spiking Activity

Phase synchronization between LFP and spiking activity in motor cortex during movement preparation

2007 ◽  
Vol 70 (10-12) ◽  
pp. 2096-2101 ◽  
Author(s):  
Michael Denker ◽  
Sébastien Roux ◽  
Marc Timme ◽  
Alexa Riehle ◽  
Sonja Grün
Keyword(s):  
Motor Cortex ◽  
Phase Synchronization ◽  
Movement Preparation ◽  
Spiking Activity

scholarly journals On the complexity of resting state spiking activity in monkey motor cortex

2020 ◽  
Author(s):  
Paulina Anna Dąbrowska ◽  
Nicole Voges ◽  
Michael von Papen ◽  
Junji Ito ◽  
David Dahmen ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Population Level ◽  
Eyes Open ◽  
Simulated Network ◽  
Monkey Motor Cortex ◽  
And Task ◽  
Spiking Activity

AbstractResting state has been established as a classical paradigm of brain activity studies, mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results to task- or stimulus-driven experiments, which might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons’ spiking activity in monkey motor cortex and show the differences from spontaneous and task-induced movement conditions. The resting state shows a higher dimensionality, reduced firing rates and less balance between population level excitation and inhibition than behavior-related states. Additionally, our results stress the importance of distinguishing between rest with eyes open and closed.

scholarly journals Population interactions between parietal and primary motor cortices during reach

2014 ◽  
Vol 112 (11) ◽  
pp. 2959-2984 ◽  
Author(s):  
David L. Menzer ◽  
Naveen G. Rao ◽  
Adrian Bondy ◽  
Wilson Truccolo ◽  
John P. Donoghue
Keyword(s):  
Primary Motor Cortex ◽  
Linear Prediction ◽  
Phase Locking ◽  
Field Potential ◽  
Population Level ◽  
Movement Direction ◽  
Movement Preparation ◽  
Directional Bias ◽  
Population Interactions ◽  
And Performance

Neural interactions between parietal area 2/5 and primary motor cortex (M1) were examined to determine the timing and behavioral correlates of cortico-cortical interactions. Neural activity in areas 2/5 and M1 was simultaneously recorded with 96-channel microelectrode arrays in three rhesus monkeys performing a center-out reach task. We introduce a new method to reveal parietal-motor interactions at a population level using partial spike-field coherence (PSFC) between ensembles of neurons in one area and a local field potential (LFP) in another. PSFC reflects the extent of phase locking between spike times and LFP, after removing the coherence between LFPs in the two areas. Spectral analysis of M1 LFP revealed three bands: low, medium, and high, differing in power between movement preparation and performance. We focus on PSFC in the 1–10 Hz band, in which coherence was strongest. PSFC was also present in the 10–40 Hz band during movement preparation in many channels but generally nonsignificant in the 60–200 Hz band. Ensemble PSFC revealed stronger interactions than single cell-LFP pairings. PSFC of area 2/5 ensembles with M1 LFP typically rose around movement onset and peaked ∼500 ms afterward. PSFC was typically stronger for subsets of area 2/5 neurons and M1 LFPs with similar directional bias than for those with opposite bias, indicating that area 2/5 contributes movement direction information. Together with linear prediction of M1 LFP by area 2/5 spiking, the ensemble-LFP pairing approach reveals interactions missed by single neuron-LFP pairing, demonstrating that cortico-cortical communication can be more readily observed at the ensemble level.

scholarly journals Sensory feedback-dependent coding of arm position in local field potentials of the posterior parietal cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paul VanGilder ◽  
Ying Shi ◽  
Gregory Apker ◽  
Christopher A. Buneo
Keyword(s):  
Local Field ◽  
Sensory Feedback ◽  
Posterior Parietal Cortex ◽  
Spectral Power ◽  
Field Potential ◽  
Beta Activity ◽  
Beta Band ◽  
Arm Position ◽  
Multisensory Interactions ◽  
Spiking Activity

AbstractAlthough multisensory integration is crucial for sensorimotor function, it is unclear how visual and proprioceptive sensory cues are combined in the brain during motor behaviors. Here we characterized the effects of multisensory interactions on local field potential (LFP) activity obtained from the superior parietal lobule (SPL) as non-human primates performed a reaching task with either unimodal (proprioceptive) or bimodal (visual-proprioceptive) sensory feedback. Based on previous analyses of spiking activity, we hypothesized that evoked LFP responses would be tuned to arm location but would be suppressed on bimodal trials, relative to unimodal trials. We also expected to see a substantial number of recording sites with enhanced beta band spectral power for only one set of feedback conditions (e.g. unimodal or bimodal), as was previously observed for spiking activity. We found that evoked activity and beta band power were tuned to arm location at many individual sites, though this tuning often differed between unimodal and bimodal trials. Across the population, both evoked and beta activity were consistent with feedback-dependent tuning to arm location, while beta band activity also showed evidence of response suppression on bimodal trials. The results suggest that multisensory interactions can alter the tuning and gain of arm position-related LFP activity in the SPL.

scholarly journals Superimposed gratings induce diverse response patterns of gamma oscillations in primary visual cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bin Wang ◽  
Chuanliang Han ◽  
Tian Wang ◽  
Weifeng Dai ◽  
Yang Li ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Neural Mechanisms ◽  
Model Parameters ◽  
Response Patterns ◽  
Region Difference ◽  
High Gamma ◽  
Spiking Activity

AbstractStimulus-dependence of gamma oscillations (GAMMA, 30–90 Hz) has not been fully understood, but it is important for revealing neural mechanisms and functions of GAMMA. Here, we recorded spiking activity (MUA) and the local field potential (LFP), driven by a variety of plaids (generated by two superimposed gratings orthogonal to each other and with different contrast combinations), in the primary visual cortex of anesthetized cats. We found two distinct narrow-band GAMMAs in the LFPs and a variety of response patterns to plaids. Similar to MUA, most response patterns showed that the second grating suppressed GAMMAs driven by the first one. However, there is only a weak site-by-site correlation between cross-orientation interactions in GAMMAs and those in MUAs. We developed a normalization model that could unify the response patterns of both GAMMAs and MUAs. Interestingly, compared with MUAs, the GAMMAs demonstrated a wider range of model parameters and more diverse response patterns to plaids. Further analysis revealed that normalization parameters for high GAMMA, but not those for low GAMMA, were significantly correlated with the discrepancy of spatial frequency between stimulus and sites’ preferences. Consistent with these findings, normalization parameters and diversity of high GAMMA exhibited a clear transition trend and region difference between area 17 to 18. Our results show that GAMMAs are also regulated in the form of normalization, but that the neural mechanisms for these normalizations might differ from those of spiking activity. Normalizations in different brain signals could be due to interactions of excitation and inhibitions at multiple stages in the visual system.

Sign in / Sign up

Export Citation Format

Share Document