Fast gamma oscillations in areas MT and MST occur during visual stimulation, but not during visually guided manual tracking

2002 ◽  
Vol 147 (3) ◽  
pp. 360-373 ◽  
Author(s):  
Wolfgang Kruse ◽  
Klaus-Peter Hoffmann
Keyword(s):  
Visual Stimulation ◽  
Gamma Oscillations ◽  
Manual Tracking ◽  
Visually Guided

scholarly journals Both ongoing alpha and visually induced gamma oscillations show reliable diversity in their across-site phase-relations

2015 ◽  
Vol 113 (5) ◽  
pp. 1556-1563 ◽  
Author(s):  
Freek van Ede ◽  
Stan van Pelt ◽  
Pascal Fries ◽  
Eric Maris
Keyword(s):  
Phase Relation ◽  
Visual Stimulation ◽  
Low Frequency ◽  
Gamma Oscillations ◽  
Phase Relations ◽  
Neural Oscillations ◽  
High Signal ◽  
Noninvasive Methods ◽  
Healthy Human ◽  
Systems Neuroscience

Neural oscillations have emerged as one of the major electrophysiological phenomena investigated in cognitive and systems neuroscience. These oscillations are typically studied with regard to their amplitude, phase, and/or phase coupling. Here we demonstrate the existence of another property that is intrinsic to neural oscillations but has hitherto remained largely unexplored in cognitive and systems neuroscience. This pertains to the notion that these oscillations show reliable diversity in their phase-relations between neighboring recording sites (phase-relation diversity). In contrast to most previous work, we demonstrate that this diversity is restricted neither to low-frequency oscillations nor to periods outside of sensory stimulation. On the basis of magnetoencephalographic (MEG) recordings in humans, we show that this diversity is prominent not only for ongoing alpha oscillations (8–12 Hz) but also for gamma oscillations (50–70 Hz) that are induced by sustained visual stimulation. We further show that this diversity provides a dimension within electrophysiological data that, provided a sufficiently high signal-to-noise ratio, does not covary with changes in amplitude. These observations place phase-relation diversity on the map as a prominent and general property of neural oscillations that, moreover, can be studied with noninvasive methods in healthy human volunteers. This opens important new avenues for investigating how neural oscillations contribute to the neural implementation of cognition and behavior.

Kinematic Analysis of Manual Tracking in Monkeys: Characterization of Movement Intermittencies During a Circular Tracking Task

2004 ◽  
Vol 91 (2) ◽  
pp. 901-911 ◽  
Author(s):  
A. V. Roitman ◽  
S. G. Massaquoi ◽  
K. Takahashi ◽  
T. J. Ebner
Keyword(s):  
Cross Correlation ◽  
Manual Tracking ◽  
Speed Profile ◽  
Visually Guided ◽  
Scaling Properties ◽  
Direction Error ◽  
Speed Error ◽  
Cross Correlation Analysis ◽  
Profile Control ◽  
Acceleration Force

Segmentation of the velocity profiles into the submovements has been observed in reaching and tracking limb movements and even in isometric tasks. Submovements have been implicated in both feed-forward and feedback control. In this study, submovements were analyzed during manual tracking in the nonhuman primate with the focus on the amplitude-duration scaling of submovements and the error signals involved in their control. The task consisted of the interception and visually guided pursuit of a target moving in a circle. The submovements were quantified based on their duration and amplitude in the speed profile. Control experiments using passive movements demonstrated that these intermittencies were not instrumentation artifacts. Submovements were prominent in both the interception and tracking phases and their amplitude scaled linearly with duration. The scaling factors increased with tracking speed at the same rate for both interception and pursuit. A cross-correlation analysis between a variety of error signals and the speed profile revealed that direction and speed errors were temporally coupled to the submovements. The cross-correlation profiles suggest that submovements are initiated when speed error reaches a certain limit and when direction error is minimized. The scaling results show that in monkeys submovements characterize both the interception and pursuit portions of the task and that these submovements have similar scaling properties consistent with 1) the concept of stereotypy and 2) adding constant acceleration/force at a specific tracking speed. The correlation results show involvement of speed and direction error signals in controlling the submovements.

Cerebellar TMS Evokes a Long Latency Motor Response in the Hand during a Visually Guided Manual Tracking Task

2010 ◽  
Vol 9 (3) ◽  
pp. 454-460 ◽  
Author(s):  
Koichi Hiraoka ◽  
Kenichi Horino ◽  
Atsuko Yagura ◽  
Akiyoshi Matsugi
Keyword(s):  
Motor Response ◽  
Tracking Task ◽  
Manual Tracking ◽  
Visually Guided ◽  
Long Latency

scholarly journals Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

2016 ◽  
Vol 115 (4) ◽  
pp. 1821-1835 ◽  
Author(s):  
Cristin G. Welle ◽  
Diego Contreras
Keyword(s):  
Visual Cortex ◽  
Visual Stimulation ◽  
Oscillation Amplitude ◽  
Gamma Oscillations ◽  
Gamma Oscillation ◽  
Inhibitory Neurons ◽  
Sensory Representation ◽  
Cortical Circuitry ◽  
Mouse Visual Cortex

Gamma oscillations are a robust component of sensory responses but are also part of the background spontaneous activity of the brain. To determine whether the properties of gamma oscillations in cortex are specific to their mechanism of generation, we compared in mouse visual cortex in vivo the laminar geometry and single-neuron rhythmicity of oscillations produced during sensory representation with those occurring spontaneously in the absence of stimulation. In mouse visual cortex under anesthesia (isoflurane and xylazine), visual stimulation triggered oscillations mainly between 20 and 50 Hz, which, because of their similar functional significance to gamma oscillations in higher mammals, we define here as gamma range. Sensory representation in visual cortex specifically increased gamma oscillation amplitude in the supragranular (L2/3) and granular (L4) layers and strongly entrained putative excitatory and inhibitory neurons in infragranular layers, while spontaneous gamma oscillations were distributed evenly through the cortical depth and primarily entrained putative inhibitory neurons in the infragranular (L5/6) cortical layers. The difference in laminar distribution of gamma oscillations during the two different conditions may result from differences in the source of excitatory input to the cortex. In addition, modulation of superficial gamma oscillation amplitude did not result in a corresponding change in deep-layer oscillations, suggesting that superficial and deep layers of cortex may utilize independent but related networks for gamma generation. These results demonstrate that stimulus-driven gamma oscillations engage cortical circuitry in a manner distinct from spontaneous oscillations and suggest multiple networks for the generation of gamma oscillations in cortex.

scholarly journals Balance between inhibitory cell types is necessary for flexible frequency switching in adult mouse visual cortex

2020 ◽  
Author(s):  
Justin W. M. Domhof ◽  
Paul H. E. Tiesinga
Keyword(s):  
Visual Cortex ◽  
Visual Stimulation ◽  
Time Window ◽  
Pyramidal Cells ◽  
Gamma Oscillations ◽  
Neuron Network ◽  
Beta Oscillations ◽  
Frequency Bands ◽  
Adult Mice ◽  
Gamma Rhythms

Neuronal networks in rodent primary visual cortex (V1) can generate oscillations in different frequency bands depending on the network state and the level of visual stimulation. High-frequency gamma rhythms, for example, dominate the network’s spontaneous activity in adult mice but are attenuated upon visual stimulation, during which the network switches to the beta band instead. The spontaneous LFP of juvenile mouse V1, however, mainly contains beta oscillations and presenting a stimulus does not elicit drastic changes in collective network oscillations. We study, in a spiking neuron network model, the mechanism in adult mice that allows for flexible switches between multiple frequency bands and contrast this to the network structure in juvenile mice that do not posses this flexibility. The model is comprised of excitatory pyramidal cells (PCs) and two types of inhibitory interneurons: the parvalbumin expressing (PV) interneuron, which produces gamma oscillations, and the somatostatin expressing (SOM) cell, which generates beta rhythms. Our model simulations suggest that both of these oscillations are generated by a pyramidal-interneuron gamma (PING) mechanism. Furthermore, prominent gamma and beta oscillations in, respectively, the spontaneous and visually evoked activity of the simulated network only occurred within the same network configuration when there was a balance between both types of interneurons so that SOM neurons are able to shape the dynamics of the pyramidal-PV cell subnetwork without dominating dynamics. Taken together, our results demonstrate that the effective strengths of PV and SOM cells must be balanced for experimentally observed V1 dynamics in adult mice. Moreover, since spontaneous gamma rhythms emerge during the well-known critical period, our findings support the notion that PV cells become integrated in the circuit of this cortical area during this time window and additionally indicate that this integration comprises an overall increase in their synaptic strength.

scholarly journals No evidence for entrainment: endogenous gamma oscillations and rhythmic flicker responses coexist in visual cortex

2020 ◽  
Author(s):  
Katharina Duecker ◽  
Tjerk P. Gutteling ◽  
Christoph S. Herrmann ◽  
Ole Jensen
Keyword(s):  
Visual Cortex ◽  
Human Brain ◽  
Visual Stimulation ◽  
Gamma Oscillations ◽  
Photic Stimulation ◽  
Neuronal Populations ◽  
Frequency Entrainment ◽  
Flicker Response ◽  
Neuronal Processes

AbstractMotivated by the plethora of studies associating gamma oscillations (∼30-100 Hz) with various neuronal processes, including inter-regional communication and neuroprotection, we asked if endogenous gamma oscillations in the human brain can be entrained by rhythmic photic stimulation. The photic drive produced a robust Magnetoencephalography (MEG) response in visual cortex up to frequencies of about 80 Hz. Strong, endogenous gamma oscillations were induced using moving grating stimuli as repeatedly shown in previous research. When superimposing the flicker and the gratings, there was no evidence for phase or frequency entrainment of the endogenous gamma oscillations by the photic drive. Rather – as supported by source modelling – our results show that the flicker response and the endogenous gamma oscillations coexist and are generated by different neuronal populations in visual cortex. Our findings challenge the notion that neuronal entrainment by visual stimulation generalises to cortical gamma oscillations.

scholarly journals The visual cortex produces a signal in the gamma band in response to broadband visual flicker

2021 ◽  
Author(s):  
Alexander Zhigalov ◽  
Katharina Duecker ◽  
Ole Jensen
Keyword(s):  
Visual Cortex ◽  
Visual Stimulation ◽  
Gamma Oscillations ◽  
Early Response ◽  
Dominant Frequency ◽  
Gamma Band ◽  
Input Current ◽  
Alpha Band ◽  
Constant Input ◽  
Gamma Range

AbstractThe aim of this study is to uncover the network dynamics of the human visual cortex by driving it with a broadband random visual flicker. We here applied a broadband flicker (1–720 Hz) while measuring the MEG and then estimated the temporal response function (TRF) between the visual input and the MEG response. This TRF revealed an early response in the 40–60 Hz gamma range as well as in the 8–12 Hz alpha band. While the gamma band response is novel, the latter has been termed the alpha band perceptual echo. The gamma echo preceded the alpha perceptual echo. The dominant frequency of the gamma echo was subject-specific thereby reflecting the individual dynamical properties of the early visual cortex. To understand the neuronal mechanisms generating the gamma echo, we implemented a pyramidal-interneuron gamma (PING) model that produces gamma oscillations in the presence of constant input currents. Applying a broadband input current mimicking the visual stimulation allowed us to estimate TRF between the input current and the population response (akin to the local field potentials). The TRF revealed a gamma echo that was similar to the one we observed in the MEG data. Our results suggest that the visual gamma echo can be explained by the dynamics of the PING model even in the absence of sustained gamma oscillations.

Accounting for direction and speed of eye motion in planning visually guided manual tracking

2013 ◽  
Vol 110 (8) ◽  
pp. 1945-1957 ◽  
Author(s):  
Guillaume Leclercq ◽  
Gunnar Blohm ◽  
Philippe Lefèvre
Keyword(s):  
Smooth Pursuit ◽  
Visual Motion ◽  
Motor Planning ◽  
Brain Network ◽  
Movement Direction ◽  
Manual Tracking ◽  
Visually Guided ◽  
Eye Motion ◽  
Eye Velocity ◽  
The Brain

Accurate motor planning in a dynamic environment is a critical skill for humans because we are often required to react quickly and adequately to the visual motion of objects. Moreover, we are often in motion ourselves, and this complicates motor planning. Indeed, the retinal and spatial motions of an object are different because of the retinal motion component induced by self-motion. Many studies have investigated motion perception during smooth pursuit and concluded that eye velocity is partially taken into account by the brain. Here we investigate whether the eye velocity during ongoing smooth pursuit is taken into account for the planning of visually guided manual tracking. We had 10 human participants manually track a target while in steady-state smooth pursuit toward another target such that the difference between the retinal and spatial target motion directions could be large, depending on both the direction and the speed of the eye. We used a measure of initial arm movement direction to quantify whether motor planning occurred in retinal coordinates (not accounting for eye motion) or was spatially correct (incorporating eye velocity). Results showed that the eye velocity was nearly fully taken into account by the neuronal areas involved in the visuomotor velocity transformation (between 75% and 102%). In particular, these neuronal pathways accounted for the nonlinear effects due to the relative velocity between the target and the eye. In conclusion, the brain network transforming visual motion into a motor plan for manual tracking adequately uses extraretinal signals about eye velocity.

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