5,797 research outputs found
Learning alters theta-nested gamma oscillations in inferotemporal cortex
How coupled brain rhythms influence cortical information processing to support learning is unresolved. Local field potential and neuronal activity recordings from 64- electrode arrays in sheep inferotemporal cortex showed that visual discrimination learning increased the amplitude of theta oscillations during stimulus presentation. Coupling between theta and gamma oscillations, the theta/gamma ratio and the regularity of theta phase were also increased, but not neuronal firing rates. A neural network model with fast and slow inhibitory interneurons was developed which generated theta nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity similar learning-evoked changes could be produced. The model revealed that altered theta nested gamma could potentiate downstream neuron responses by temporal desynchronization of excitatory neuron output independent of changes in overall firing frequency. This learning-associated desynchronization was also exhibited by inferotemporal cortex neurons. Changes in theta nested gamma may therefore facilitate learning-associated potentiation by temporal modulation of neuronal firing
Failure of Delayed Feedback Deep Brain Stimulation for Intermittent Pathological Synchronization in Parkinson's Disease
Suppression of excessively synchronous beta-band oscillatory activity in the
brain is believed to suppress hypokinetic motor symptoms of Parkinson's
disease. Recently, a lot of interest has been devoted to desynchronizing
delayed feedback deep brain stimulation (DBS). This type of synchrony control
was shown to destabilize the synchronized state in networks of simple model
oscillators as well as in networks of coupled model neurons. However, the
dynamics of the neural activity in Parkinson's disease exhibits complex
intermittent synchronous patterns, far from the idealized synchronous dynamics
used to study the delayed feedback stimulation. This study explores the action
of delayed feedback stimulation on partially synchronized oscillatory dynamics,
similar to what one observes experimentally in parkinsonian patients. We employ
a model of the basal ganglia networks which reproduces experimentally observed
fine temporal structure of the synchronous dynamics. When the parameters of our
model are such that the synchrony is unphysiologically strong, the feedback
exerts a desynchronizing action. However, when the network is tuned to
reproduce the highly variable temporal patterns observed experimentally, the
same kind of delayed feedback may actually increase the synchrony. As network
parameters are changed from the range which produces complete synchrony to
those favoring less synchronous dynamics, desynchronizing delayed feedback may
gradually turn into synchronizing stimulation. This suggests that delayed
feedback DBS in Parkinson's disease may boost rather than suppress
synchronization and is unlikely to be clinically successful. The study also
indicates that delayed feedback stimulation may not necessarily exhibit a
desynchronization effect when acting on a physiologically realistic partially
synchronous dynamics, and provides an example of how to estimate the
stimulation effect.Comment: 19 pages, 8 figure
Desynchronizing effect of high-frequency stimulation in a generic cortical network model
Transcranial Electrical Stimulation (TCES) and Deep Brain Stimulation (DBS)
are two different applications of electrical current to the brain used in
different areas of medicine. Both have a similar frequency dependence of their
efficiency, with the most pronounced effects around 100Hz. We apply
superthreshold electrical stimulation, specifically depolarizing DC current,
interrupted at different frequencies, to a simple model of a population of
cortical neurons which uses phenomenological descriptions of neurons by
Izhikevich and synaptic connections on a similar level of sophistication. With
this model, we are able to reproduce the optimal desynchronization around
100Hz, as well as to predict the full frequency dependence of the efficiency of
desynchronization, and thereby to give a possible explanation for the action
mechanism of TCES.Comment: 9 pages, figs included. Accepted for publication in Cognitive
Neurodynamic
Proficient brain for optimal performance: the MAP model perspective
Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances. Methods. Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged. Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources. Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback technique
Neural markers of performance states in an Olympic athlete: An EEG case study in air-pistol shooting
This study focused on identifying the neural markers underlying optimal and suboptimal performance experiences of an elite air-pistol shooter, based on the tenets of the multi-action plan (MAP) model. According to the MAP model’s assumptions, skilled athletes’ cortical patterns are expected to differ among optimal/automatic (Type 1), optimal/controlled (Type 2), suboptimal/controlled (Type 3), and suboptimal/automatic (Type 4) performance experiences. We collected performance (target pistol shots), cognitive-affective (perceived control, accuracy, and hedonic tone), and cortical activity data (32-channel EEG) of an elite shooter. Idiosyncratic descriptive analyses revealed differences in perceived accuracy in regard to optimal and suboptimal performance states. Event-Related Desynchronization/Synchronization analysis supported the notion that optimal-automatic performance experiences (Type 1) were characterized by a global synchronization of cortical arousal associated with the shooting task, whereas suboptimal controlled states (Type 3) were underpinned by high cortical activity levels in the attentional brain network. Results are addressed in the light of the neural efficiency hypothesis and reinvestment theory. Perceptual training recommendations aimed at restoring optimal performance levels are discussed
Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex.
BACKGROUND: How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. RESULTS: Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. CONCLUSIONS: Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
Noise-induced synchronization and anti-resonance in excitable systems; Implications for information processing in Parkinson's Disease and Deep Brain Stimulation
We study the statistical physics of a surprising phenomenon arising in large
networks of excitable elements in response to noise: while at low noise,
solutions remain in the vicinity of the resting state and large-noise solutions
show asynchronous activity, the network displays orderly, perfectly
synchronized periodic responses at intermediate level of noise. We show that
this phenomenon is fundamentally stochastic and collective in nature. Indeed,
for noise and coupling within specific ranges, an asymmetry in the transition
rates between a resting and an excited regime progressively builds up, leading
to an increase in the fraction of excited neurons eventually triggering a chain
reaction associated with a macroscopic synchronized excursion and a collective
return to rest where this process starts afresh, thus yielding the observed
periodic synchronized oscillations. We further uncover a novel anti-resonance
phenomenon: noise-induced synchronized oscillations disappear when the system
is driven by periodic stimulation with frequency within a specific range. In
that anti-resonance regime, the system is optimal for measures of information
capacity. This observation provides a new hypothesis accounting for the
efficiency of Deep Brain Stimulation therapies in Parkinson's disease, a
neurodegenerative disease characterized by an increased synchronization of
brain motor circuits. We further discuss the universality of these phenomena in
the class of stochastic networks of excitable elements with confining coupling,
and illustrate this universality by analyzing various classical models of
neuronal networks. Altogether, these results uncover some universal mechanisms
supporting a regularizing impact of noise in excitable systems, reveal a novel
anti-resonance phenomenon in these systems, and propose a new hypothesis for
the efficiency of high-frequency stimulation in Parkinson's disease
Potential mechanisms for imperfect synchronization in parkinsonian basal ganglia
Neural activity in the brain of parkinsonian patients is characterized by the
intermittently synchronized oscillatory dynamics. This imperfect
synchronization, observed in the beta frequency band, is believed to be related
to the hypokinetic motor symptoms of the disorder. Our study explores potential
mechanisms behind this intermittent synchrony. We study the response of a
bursting pallidal neuron to different patterns of synaptic input from
subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe)
neuron is sensitive to the phase of the input from the STN cell and can exhibit
intermittent phase-locking with the input in the beta band. The temporal
properties of this intermittent phase-locking show similarities to the
intermittent synchronization observed in experiments. We also study the
synchronization of GPe cells to synaptic input from the STN cell with
dependence on the dopamine-modulated parameters. Dopamine also affects the
cellular properties of neurons. We show how the changes in firing patterns of
STN neuron due to the lack of dopamine may lead to transition from a lower to a
higher coherent state, roughly matching the synchrony levels observed in basal
ganglia in normal and parkinsonian states. The intermittent nature of the
neural beta band synchrony in Parkinson's disease is achieved in the model due
to the interplay of the timing of STN input to pallidum and pallidal neuronal
dynamics, resulting in sensitivity of pallidal output to the phase of the
arriving STN input. Thus the mechanism considered here (the change in firing
pattern of subthalamic neurons through the dopamine-induced change of membrane
properties) may be one of the potential mechanisms responsible for the
generation of the intermittent synchronization observed in Parkinson's disease.Comment: 27 pages, 9 figure
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