Behavioral and electroencephalographic correlates of 40-Hz EEG biofeedback training in humans

Bird, Bruce L.; Newton, Frederick A.; Sheer, Daniel E.; Ford, Martin R.

doi:10.1007/bf00998560

Cited by 20 publications

(9 citation statements)

References 12 publications

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“…From the very early studies conducted in late 70s by Bird, Newton, Sheer, and Ford (Bird et al, 1978ab, Ford et al, 1980 ) on EEG gamma frequency neurofeedback, 40 Hz activity was considered as a psychophysiological biomarker of attention in humans, and further research on association of the 40 Hz-centered gamma activity with attention, especially in neurodevelopmental disorders such as ASD definitely warrants further explorations, either as stand-alone treatment or as an adjunct arm in a combined treatment similar to one used in this study.…”

Section: Discussionmentioning

confidence: 99%

Neuromodulation Integrating rTMS and Neurofeedback for the Treatment of Autism Spectrum Disorder: An Exploratory Study

Sokhadze

El-Baz

Tasman

et al. 2014

Appl Psychophysiol Biofeedback

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Autism spectrum disorder (ASD) is a pervasive developmental disorder characterized by deficits in social interaction, language, stereotyped behaviors, and restricted range of interests. In previous studies low frequency repetitive transcranial magnetic stimulation (rTMS) has been used, with positive behavioral and electrophysiological results, for the experimental treatment in ASD. In this study we combined prefrontal rTMS sessions with electroencephalographic (EEG) neurofeedback (NFB) to prolong and reinforce TMS-induced EEG changes. The pilot trial recruited 42 children with ASD (~14.5 yrs). Outcome measures included behavioral evaluations and reaction time test with event-related potential (ERP) recording. For the main goal of this exploratory study we used rTMS-neurofeedback combination (TMS-NFB, N=20) and waitlist (WTL, N=22) groups to examine effects of 18 sessions of integrated rTMS-NFB treatment or wait period) on behavioral responses, stimulus and response-locked ERPs, and other functional and clinical outcomes. The underlying hypothesis was that combined TMS-NFB will improve executive functions in autistic patients as compared to the waitlist group. Behavioral and ERP outcomes were collected in preand post-treatment tests in both groups. Results of the study supported our hypothesis by demonstration of positive effects of combined TMS-NFB neurotherapy in active treatment group as compared to control waitlist group, as the TMS-NFB group showed significant improvements in behavioral and functional outcomes as compared to the waitlist group.

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Section: Discussionmentioning

confidence: 99%

Neuromodulation Integrating rTMS and Neurofeedback for the Treatment of Autism Spectrum Disorder: An Exploratory Study

Sokhadze

El-Baz

Tasman

et al. 2014

Appl Psychophysiol Biofeedback

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“…At the same time, although oscillatory bursts with a frequency of ∼20 Hz were found in area 5, the oscillatory bursts in area 7 had a lower frequency. These relatively high frequency synchronous oscillations at the neuronal level are likely to correspond to the local cortical beta/gamma EEG rhythmic activities recorded in freely behaving animals, particularly during attentive visual states (23–25) and in humans (26,27). Both neurophysiologic and computational studies showed that oscillations in the gamma frequency range in hippocampal and neocortical networks may be caused by changes in the dynamics of inhibitory neuronal populations (28–33).…”

Section: Neuronal Network Synchrony and Oscillatory Behaviormentioning

confidence: 91%

Epilepsies as Dynamical Diseases of Brain Systems: Basic Models of the Transition Between Normal and Epileptic Activity

et al. 2003

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Summary:Purpose: The occurrence of abnormal dynamics in a physiological system can become manifest as a sudden qualitative change in the behavior of characteristic physiologic variables. We assume that this is what happens in the brain with regard to epilepsy. We consider that neuronal networks involved in epilepsy possess multistable dynamics (i.e., they may display several dynamic states). To illustrate this concept, we may assume, for simplicity, that at least two states are possible: an interictal one characterized by a normal, apparently random, steady-state of ongoing activity, and another one that is characterized by the paroxysmal occurrence of a synchronous oscillations (seizure).Methods: By using the terminology of the mathematics of nonlinear systems, we can say that such a bistable system has two attractors, to which the trajectories describing the system's output converge, depending on initial conditions and on the system's parameters. In phase-space, the basins of attraction corresponding to the two states are separated by what is called a "separatrix." We propose, schematically, that the transition between the normal ongoing and the seizure activity can take place according to three basic models:Model I: In certain epileptic brains (e.g., in absence seizures of idiopathic primary generalized epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is very small in contrast to that of a normal brain (possibly due to genetic and/or developmental factors). In the former, discrete random fluctuations of some variables can be sufficient for the occurrence of a transition to the paroxysmal state. In this case, such seizures are not predictable.Model II and model III: In other kinds of epileptic brains (e.g., limbic cortex epilepsies), the distance between "normal steadystate" and "paroxysmal" attractors is, in general, rather large, such that random fluctuations, of themselves, are commonly not capable of triggering a seizure. However, in these brains, neuronal networks have abnormal features characterized by unstable parameters that are very vulnerable to the influence of endogenous (model II) and/or exogenous (model III) factors. In these cases, these critical parameters may gradually change with time, in such a way that the attractor can deform either gradually or suddenly, with the consequence that the distance between the basin of attraction of the normal state and the separatrix tends to zero. This can lead, eventually, to a transition to a seizure.Results: The changes of the system's dynamics preceding a seizure in these models either may be detectable in the EEG and thus the route to the seizure may be predictable, or may be unobservable by using only measurements of the dynamical state. It is thinkable, however, that in some cases, changes in the excitability state of the underlying networks may be uncovered by using appropriate stimuli configurations before changes in the dynamics of the ongoing EEG activity are evident. A typical example of model III that we discuss...

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“…Thus, the EEG of an awake, alert and highly attentive person might contain oscillations at beta frequencies (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), while the same person's EEG during deep relaxation might display prominent 10-12 Hz alpha activity. EEG frequencies above 25-30 Hz are often said to be in the gamma band, within which frequencies near 40 Hz have been of particular interest in studies of attention (Freeman, 1975;Bird, Newton, Sheer, and Ford, 1978;Galambos, Makeig, and Talmachoff, 1981;Gray and Singer, 1989). On the other hand, high amplitude activity at lower frequencies regularly accompanies sleepiness, sleep and unconsciousness ( Makeig and Inlow, 1993;Ogilvie, Simons, Kuderian, et al, 1991; see also Colrain, Di Parsia, and Gora, this issue).…”

Section: Introductionmentioning

confidence: 99%

Awareness during drowsiness: Dynamics and electrophysiological correlates.

Makeig

Jung²,

Sejnowski³

2000

Canadian Journal of Experimental Psychology / Revue canadienne

109

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Summary: During drowsy periods, performance on tasks requiring continuous attention becomes intermittent. Previously, we have reported that during drowsy periods of intermittent performance 7 of 10 participants performing an auditory detection task exhibited episodes of non-responding lasting about 18 s . Further, the time patterns of these episodes were repeated precisely in subsequent sessions. The 18 s cycles were accompanied by counterbalanced power changes within two frequency bands in the vertex EEG (near 4 Hz and circa 40 Hz). In the present experiment, performance patterns and concurrent EEG spectra were examined in four participants performing a continuous visuomotor compensatory tracking task in 15-20 minute bouts during a 42-hour sleep deprivation study. During periods of good performance, participants made compensatory trackball movements about twice per second attempting to keep a target disk near a central ring. Autocorrelations of time series representing the distance of the target disk from the ring center showed that during periods of poor performance, marked near 18 s cycles in performance again appeared, with phases of poor or absent performance accompanied by an increase in EEG power that was largest at 3-4 Hz. These studies show that in drowsy humans, opening and closing of the gates of behavioral awareness is marked not by the appearance of (12-14 Hz) sleep spindles, but by prominent EEG amplitude changes in the low theta band. Further, both EEG and behavioral changes during drowsiness often exhibit stereotyped 18 s cycles.

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Behavioral and electroencephalographic correlates of 40-Hz EEG biofeedback training in humans

Cited by 20 publications

References 12 publications

Neuromodulation Integrating rTMS and Neurofeedback for the Treatment of Autism Spectrum Disorder: An Exploratory Study

Neuromodulation Integrating rTMS and Neurofeedback for the Treatment of Autism Spectrum Disorder: An Exploratory Study

Epilepsies as Dynamical Diseases of Brain Systems: Basic Models of the Transition Between Normal and Epileptic Activity

Awareness during drowsiness: Dynamics and electrophysiological correlates.

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