Induced Rhythms in the Brain (Brain Dynamics)

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Berger was not the first to discover that changes in electric potential can be recorded from the surface of the head, but it was he who first systematized the method, and it was he who first proposed that explanatory correlations might be found between the electroencephalogram, brain processes, and behavioral states.

Moreover, use of the method in experimental animals led to a further understanding of such state changes as attention-inattention, arousal, and sleep and wakefulness. These maps still form the initial guides to studies of the cortical mechanisms in sensation and perception.

Neural oscillation

The use of the event-related potential paradigm has proved useful in studies of the brain mechanisms of some cognitive functions of the brain. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Medicine Neurology. Brain Dynamics Free Preview. Buy eBook. Buy Softcover. Event-related potentials ERPs provide a critical link between the hemodynamic response, as measured by functional magnetic resonance imaging, and the dynamics of the underlying neuronal activity.

Single-trial ERP recordings capture the oscillatory activity that are hypothesized to underlie both communication between brain regions and amplified processing of behaviorally relevant stimuli. However, precise interpretations of ERPs are precluded by uncertainty about their neural mechanisms. One influential theory holds that averaged sensory ERPs are generated by partial phase resetting of ongoing electroencephalographic oscillations, while another states that ERPs result from stimulus-evoked neural responses.

We formulated critical predictions of each theory and tested these using direct, intracortical analyses of neural activity in monkeys. Our findings support a predominant role for stimulus-evoked activity in sensory ERP generation, and they outline both logic and methodology necessary for differentiating evoked and phase resetting contributions to cognitive and motor ERPs in future studies.

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For nearly 30 years, two opposing theories about the neural origins of sensory event-related potentials ERPs have been debated. Resolving this debate has become crucial with the advent of functional magnetic resonance imaging fMRI because ERPs provide a critical link between the hemodynamic response indexed by fMRI and the temporal dynamics of underlying neuronal activity Murray et al. Equally important, a growing body of literature shows interactions between event-related neural responses indexed by the ERP and ongoing brain oscillations Brandt et al. Earlier attempts at defining the neural mechanisms of the ERP were limited by two factors: i scalp ERPs are indirect measures, recorded at a distance from their neural sources, and ii the critical predictions of each model were not clearly delineated.

Here we address both limitations. Secondly, we report the direct evaluation of each requirement by analysis of single-trial, intracortical activity in awake, behaving monkeys. Data for these analyses were collected from two male macaques performing a visual oddball discrimination paradigm. The monkey pressed a switch to initiate stimulus presentation and held it until he detected an oddball.

Appropriate release of the switch earned a drop of juice. The deviant stimulus differed slightly in intensity or color. Data analyses concerned only the neural responses to the standard, non-target stimulus. Both of these monkeys also served in selective attention studies not covered by the present report. Further details are available in Mehta et al. An experimental session began with positioning a linear-array multielectrode in either V1 or posterior IT such that the contact array was perpendicular to the layering of the area Fig.

A detailed description of these methods can be found in Schroeder et al.

Brain Dynamics on Multiple Scales - Paradigms, their Relations, and Integrated Approaches

In one session, two multielectrodes arrays were inserted intracranially to record simultaneously from sites in V1 and IT. In all cases, electrode contacts had an impedance between 0. Data from each session were prepared for analyses by first epoching the EEG from — to ms, with zero denoting the onset of stimulus presentation. Secondly, the average CSD profile was computed, and the laminar positioning of each CSD channel was defined as either supragranular, granular, or infragranular according to the typical averaged CSD profile of that cortical area Schroeder et al.

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Thirdly, the full-wave rectified, average CSD signal for each supragranular and granular channel was integrated, and the two channels one in each layer with the largest integral areas were chosen for analyses. Fourth, the post-stimulus average CSD defined as the average CSD waveform from 0 to ms in these two channels was multiplied by a Hamming window, and the discrete Fourier transform DFT of the resultant was calculated.

The dominant frequency of the ERP see Table 2 in each layer was defined as the frequency bin with the greatest power in this spectrum. Fifth, single-trial CSD signals in both chosen channels were split into pre- and post-stimulus signals, multiplied by a Hamming window, and converted to the frequency domain by a point DFT yielding pre- and post-stimulus, single-trial frequency spectra.

With a sampling frequency of Hz and a sample DFT, the frequency bins were 3. Single-trial power and phase distributions at the dominant frequency i. The Wilcoxon Signed-Rank test was used to compare pre- and post-stimulus power distributions. Also, sample circular variance was calculated for the phase data because these values represent a circular population Fisher, Single-trial total power distributions for the pre- and post-stimulus periods were calculated as the integral of the single-trial, power spectra from 0 to Hz.

For each experimental session, these distributions were normalized so that the median post-stimulus total power equalled 1. The distributions for the supragranular tissue are displayed as box plots see Fig. The position of the median line with respect to the upper and lower lines indicates the skew of the distribution.

Notches in the box around the median indicate the confidence interval about that median.


Vertical lines above and below the box show the extent of the data, minus outliers. Strict interpretations of the phase resetting and evoked models pose differing predictions for three specific properties of single-trial field potentials, as outlined in Table 1. Phase resetting makes three main predictions. Under property 1, it predicts that activity at the dominant frequency of the ERP must be present in the pre-stimulus period. Under property 2, phase resetting predicts that the transition between the pre- to the post-stimulus periods must involve phase concentration i.

These two predictions are actually critical requirements for the operation of phase resetting. On the other hand, the evoked model is tolerant of a wide range of pre-stimulus activity, and it predicts phase concentration because an evoked, single-trial response is relatively phase-locked to stimulus onset. Regarding property 3, a strict version of the phase resetting model predicts requires that the pre- to post-stimulus transition must occur without increase in power at the dominant frequency of the ERP. The evoked model, in contrast, requires a post-stimulus increase in power at that frequency.

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It is important to emphasize that the phase resetting model does not deny that evoked activity occurs. Rather it assumes that the evoked responses occur outside the dominant frequency of the ERP and serve as a trigger for resetting the phase of the local EEG oscillations. What the strict phase resetting model does hold is that in averaging over numerous trials, phase-reset EEG rhythms form the substrate for the ERP rather than the local evoked responses. Thus increase in EEG power at the dominant frequency of the ERP can rule out an exclusive account in terms of phase resetting.

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Our analysis considered cortical areas at both low and high levels of visual processing, namely primary visual cortex area V1 and inferotemporal IT cortex. Data were collected from two male macaque monkeys while they performed a task requiring discrimination between target and non-target visual stimuli. Local field potentials were sampled, and a second-derivative approximation of the current source density CSD was computed Freeman and Nicholson, ; Schroeder et al.

Quantitative analyses presented below utilized the CSD measure instead of the field potential because the CSD approximation eliminates activity generated outside the cortical area of interest, which may contaminate local field potential recordings. Moreover, the CSD profile directly addresses the electrogenesis of ERPs, because it is an index of the transmembrane current flow patterns responsible for generating the local field potential.

The local multiunit action potential MUA profile was sampled from the same electrode contacts to provide an independent link between the ERP and neuronal activity. In each animal, two V1 and two IT recordings were analyzed for a total of four sessions in each area.

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Figure 1 illustrates field potentials recorded from V1 of one subject during presentation of non-target stimuli. A concurrent EEG recording from an electrode over the occipital brain surface near the recording site is shown top trace along with recordings from all fourteen contacts of the multielectrode array. A prominent single-trial, stimulus-evoked response is observed as a negative field potential above layer 4, which inverts to a positive potential below layer 4 arrowhead and arrow in Fig. However, the pre-stimulus activity is relatively small and displays no apparent oscillations at the frequency of the evoked response.

Since previous studies show that the generator for this potential lies in the layer 4C Schroeder et al. Relatively few pre-stimulus oscillations are observed across trials, and the post-stimulus period shows a discrete field potential negativity, which, accompanied by a burst of MUA and a current sink in the CSD, indicates net local excitation. When viewed at this scale, the obvious event-related responses appear to support the evoked model, and the lack of comparable pre-stimulus oscillations discount the phase resetting model. Although these data exhibit few pre-stimulus EEG oscillations and crisp event-related responses, these aspects vary across experimental sessions.

Previous studies indicate that these two laminar divisions contain major generators of the average surface ERPs Schroeder et al. Figure 3 depicts simultaneous field potential recordings from multielectrodes in V1 and IT during a different experimental session VE7. Although the single-trial, evoked response is still apparent in V1 arrow , both recordings display more obvious pre-stimulus oscillations than those in recorded in session VA2 see Figures 1 and 2. Comparison of concurrent V1 and IT recordings underscores a further point that generalizes across all experimental sessions: the ratio of pre- to post-stimulus oscillatory activity is larger in higher-order area IT than in primary visual area V1 see Quantitative Analyses.

Quantitative analyses were performed to evaluate properties 1—3 of Table 1. To evaluate property 1, we computed the median ratio of pre- to post-stimulus power at the dominant frequency of the ERP across all trials, experimental sessions, and subjects. For the supragranular layer recordings, this ratio increased from 0. The corresponding comparison for the granular layer showed an increase from 0.

These results indicate two important properties. First, both cortical areas have some pre-stimulus activity at the dominant frequency, which is required for the phase resetting model and consistent with the evoked model under property 1. Second, the pre-stimulus power in IT is significantly greater than that in V1, suggesting that IT may be more prone to phase resetting, and hence the mechanisms of ERP generation may differ across levels of the hierarchy. Property 2 specifies model-relevant changes in the phase of oscillatory activity, and so we compared pre- and post-stimulus phase distributions at the dominant frequency of the ERP in our case, the dominant frequency of the averaged, post-stimulus CSD in each electrode contact considered for each session.