EEG coherency

Nunez, Paul L.; Srinivasan, Ramesh; Westdorp, Andrew F.; Wijesinghe, Ranjith S.; Tucker, Don M.; Silberstein, Richard B.; Cadusch, Peter J.

doi:10.1016/s0013-4694(97)00066-7

Cited by 1,124 publications

(368 citation statements)

References 49 publications

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“…6). Such patterns of synchrony can be found in various brain rhythms (16) [theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-70 Hz)], suggesting that DNS should be studied in a wide frequency range.…”

Section: Integration Of First-person Data With Electroencephalogram (mentioning

confidence: 99%

“…Further work is needed to improve this strategy. For example, the lack of spatial resolution of the EEG makes it difficult to interpret the synchrony patterns directly in terms of precise neural networks (26). Also, neural interactions may take multiple forms that are not detected by linear measures such as synchrony (27).…”

Section: First-person Clusters or Behavioral Clusters?mentioning

confidence: 99%

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Guiding the study of brain dynamics by using first-person data: Synchrony patterns correlate with ongoing conscious states during a simple visual task

Lutz

Lachaux

Martinerie

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

401

244

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Even during well-calibrated cognitive tasks, successive brain responses to repeated identical stimulations are highly variable. The source of this variability is believed to reside mainly in fluctuations of the subject's cognitive ''context'' defined by his͞her attentive state, spontaneous thought process, strategy to carry out the task, and so on . . . As these factors are hard to manipulate precisely, they are usually not controlled, and the variability is discarded by averaging techniques. We combined first-person data and the analysis of neural processes to reduce such noise. We presented the subjects with a three-dimensional illusion and recorded their electrical brain activity and their own report about their cognitive context. Trials were clustered according to these first-person data, and separate dynamical analyses were conducted for each cluster. We found that (i) characteristic patterns of endogenous synchrony appeared in frontal electrodes before stimulation. These patterns depended on the degree of preparation and the immediacy of perception as verbally reported. (ii) These patterns were stable for several recordings. (iii) Preparatory states modulate both the behavioral performance and the evoked and induced synchronous patterns that follow. (iv) These results indicated that first-person data can be used to detect and interpret neural processes. FrameworkW hen a subject is stimulated during an experiment, his͞her brain is not idle or in a state of suspension but is engaged in cognitive activity. The brain response is derived from the active interaction between this cognitive background and the stimulation that disturbs it: the neural response is ''shaped'' by the ongoing activity (refs. 1-5; see ref. 6 for review). As this ongoing state has not been carefully monitored, most of the brain response is not understood: successive exposure to the same stimulus elicits highly variable responses, and this variability is treated as unintelligible noise (6). Although it is common to control, at least indirectly, for some of the factors that condition this ongoing state, such as attention, vigilance, or motivation (for reviews, see refs. 7 and 8), the ongoing activity has not yet been analyzed systematically. One strategy would be to precisely describe the ongoing cognitive activity by obtaining refined verbal reports from human subjects. These should reveal subtle changes in the subject's experience (conditioned, for instance, by his͞her cognitive strategy, attention level, and inner speech). This type of qualitative first-person data is usually omitted from brain-imaging studies. We show that if methodological precautions are taken when gathering first-person data, they can indeed be used to shed light on cognition via a joint analysis with quantitative measures of neural activity. Collection of First-Person Data: Phenomenological Clusters (PhCs).It is not easy to collect reports about inner experience, because verbal reports can be biased or untrue (9). The definition of a precise and rigorous method to c...

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Section: Integration Of First-person Data With Electroencephalogram (mentioning

confidence: 99%

Section: First-person Clusters or Behavioral Clusters?mentioning

confidence: 99%

Guiding the study of brain dynamics by using first-person data: Synchrony patterns correlate with ongoing conscious states during a simple visual task

Lutz

Lachaux

Martinerie

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

401

244

View full text Add to dashboard Cite

show abstract

“…signals was calculated to achieve a reference-free evaluation (Perrin et al, 1989;Nunez et al, 1997). The exact mathematical procedure is explained in detail in the work by Perrin et al (1989).…”

Section: Eeg Data Processingmentioning

confidence: 99%

The Role of the BDNF Val66Met Polymorphism for the Synchronization of Error-Specific Neural Networks

Beste¹,

Kolev²,

Yordanova³

et al. 2010

J. Neurosci.

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Behavioral adaptation depends on the recognition of response errors and processing of this error-information. Error processing is a specific cognitive function crucial for behavioral adaptation. Neurophysiologically, these processes are reflected by an event-related potential (ERP), the error negativity (Ne/ERN). Even though synchronization processes are important in information processing, its role and neurobiological foundation in behavioral adaptation are not understood. The brain-derived neurotrophic factor (BDNF) strongly modulates the establishment of neural connectivity that determines neural network dynamics and synchronization properties. Therefore altered synchronization processes may constitute a mechanism via which BDNF affects processes of error-induced behavioral adaptation. We investigate how variants of the BDNF gene regulate EEG-synchronization processes underlying error processing. Subjects (n ϭ 65) were genotyped for the functional BDNF Val66Met polymorphism (rs6265). We show that Val/Val genotype is associated with stronger error-specific phase-locking, compared with Met allele carriers. Posterror behavioral adaptation seems to be strongly dependent on these phase-locking processes and efficacy of EEG-phase-locking-behavioral coupling was genotype dependent. After correct responses, neurophysiological processes were not modulated by the polymorphism, underlining that BDNF becomes especially necessary in situations requiring behavioral adaptation. The results suggest that alterations in neural synchronization processes modulated by the genetic variants of BDNF Val66Met may be the mechanism by which cognitive functions are affected.

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“…The activity of even a small cortical area is recorded by several sensors, leading to severe spreading in sensor-based measures. The spreading is particularly problematic when describing interdependencies between signals (16)(17)(18).…”

mentioning

confidence: 99%

Dynamic imaging of coherent sources: Studying neural interactions in the human brain

Groß

Kujala

Hämäläinen

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

1,642

1,349

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Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept (''binding'') is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto-or electroencephalography (MEG͞EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.oscillations ͉ functional connectivity ͉ coherence ͉ magnetoencephalography ͉ synchronization T he hypothesis that relevant information in the brain is coded by accurate timing of neuronal discharges has received strong support from recent reports of synchronization of neuronal firing within and across areas of the cat visual cortex (1). The synchronization of neural activity, which was modulated by gammaband oscillations, was shown to depend on stimulus properties like continuity, vicinity, and common motion, and on receptive field constellations (for review, see ref.2). This and similar findings seem to support the concept that synchronized rhythmic neural firing has a role in solving the binding problem, i.e., the integration of distributed information into a unified representation (1-4).To investigate cortico-cortical synchrony noninvasively in the human brain, new analysis tools must be developed. In functional magnetic resonance imaging (fMRI) studies, structural equation models have been used to estimate connectivities between brain areas (5, 6). Although this is a very promising approach, it lacks the temporal resolution required to measure oscillatory activity and to observe the expected transient formation of neuronal assemblies (7).Magnetoencephalography (MEG) and electroencephalography (...

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EEG coherency

Cited by 1,124 publications

References 49 publications

Guiding the study of brain dynamics by using first-person data: Synchrony patterns correlate with ongoing conscious states during a simple visual task

Guiding the study of brain dynamics by using first-person data: Synchrony patterns correlate with ongoing conscious states during a simple visual task

The Role of the BDNF Val66Met Polymorphism for the Synchronization of Error-Specific Neural Networks

Dynamic imaging of coherent sources: Studying neural interactions in the human brain

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