Beta power in the EEG of alcoholics

Rangaswamy, Madhavi; Porjesz, Bernice; Chorlian, David B.; Wang, Kongming; Jones, Kevin; Bauer, Lance O.; Rohrbaugh, John; O’Connor, Sean; Kuperman, Samuel; Reich, Theodore; Begleiter, Henri

doi:10.1016/s0006-3223(02)01362-8

Cited by 293 publications

(255 citation statements)

References 60 publications

(73 reference statements)

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“…85 Alcohol-dependent groups have been shown to have increased power in the beta frequency, particularly in the parietal and frontal regions of the brain. 86 Taken together, these findings suggest that specific GABA A receptor gene variants (e.g., GABRA2) are associated with central nervous system (CNS) hyper-excitability, as reflected in brain oscillation patterns and that these patterns of CNS activity may underlie the behavioral disinhibition and impulsivity that characterizes alcohol dependence and other externalizing spectrum disorders. Understanding the role of GABA A in neuronal activity provided a framework for COGA researchers to effectively hypothesize that GABRA2 would be correlated with personality characteristics related to impulse control (e.g., antisocial personality characteristics), which in turn would be correlated with a measure of interpersonal functioning as reflected in marital status.…”

Section: Genotype-environment Associations: Challenges In Identifyingmentioning

confidence: 94%

Gene–environment correlations: a review of the evidence and implications for prevention of mental illness

2007

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Family studies have demonstrated genetic influences on environmental exposure: the phenomenon of gene-environment correlation (rGE). A few molecular genetic studies have confirmed the results, but the identification of rGE in studies that measure genes and environments faces several challenges. Using examples from studies in psychology and psychiatry, we integrate the behavioral and molecular genetic literatures on rGE, describe challenges in identifying rGE and discuss the implications of molecular genetic findings of rGE for future research on gene-environment interplay and for attempts to prevent disease by reducing environmental risk exposure. Genes affect environments indirectly, via behavior and personality characteristics. Associations between individual genetic variants and behaviors are typically small in magnitude, and downstream effects on environmental risk are further attenuated by behavioral mediation. Genotype-environment associations are most likely to be detected when the environment is behaviorally modifiable and highly specified and a plausible mechanism links gene and behavior. rGEs play an important causal role in psychiatric illness. Although research efforts should concentrate on elucidating the genetic underpinnings of behavior rather than the environment itself, the identification of rGE may suggest targets for environmental intervention even in highly heritable disease. Prevention efforts must address the possibility of confounding between rGE and gene-environment interaction (G Â E).

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Section: Genotype-environment Associations: Challenges In Identifyingmentioning

confidence: 94%

Gene–environment correlations: a review of the evidence and implications for prevention of mental illness

2007

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“…However, most reports of alcoholic patients agree in describing alterations mainly within the beta (Bauer, 1997(Bauer, , 2001bCosta & Bauer, 1997;Rangaswamy et al, 2002;Rangaswamy et al, 2004) and=or alpha bands (Finn & Justus, 1999).…”

Section: Qeeg In Sudmentioning

confidence: 97%

“…Alcohol-dependent individuals have different synchronization of brain activity than light drinkers as reflected by differences in resting EEG coherence (Kaplan, Glueck, Hesselbrock, & Reed, 1985;Michael, Mirza, Mukundan, & Channabasavanna, 1993;Winterer, Enoch, et al, 2003) and power (e.g., Bauer, 2001aBauer, , 2001bEnoch, White, Harris, Rohrbaugh, & Goldman, 2002;Rangaswamy et al, 2002;Saletu-Zyhlarz et al, 2004). Most differences in EEG coherence and power are found in the alpha and beta bands.…”

Section: Qeeg In Sudmentioning

confidence: 99%

“…Most differences in EEG coherence and power are found in the alpha and beta bands. Non-alcohol-dependent relatives of alcohol-dependent individuals also have EEG differences in alpha and beta coherence (Michael et al, 1993) and power Finn & Justus, 1999;Rangaswamy et al, 2002;Rangaswamy et al, 2004) as compared to those without alcohol-dependent relatives. This indicates that differences in functional brain activity as measured with qEEG in alcoholdependent patients relate not only to the impact of long-term alcohol intake but possibly also to genetic factors related to alcohol dependence.…”

Section: Qeeg In Sudmentioning

confidence: 99%

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EEG Biofeedback as a Treatment for Substance Use Disorders: Review, Rating of Efficacy and Recommendations for Further Research

Sokhadze¹,

Cannon²,

Trudeau³

2008

Journal of Neurotherapy

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“…Başar et al (2003) showed that beta oscillation is an integral part of the process of face recognition, especially the recognition of one's own grandmother in a photograph. Begleiter and colleagues Rangaswamy et al, 2002Rangaswamy et al, , 2004 found the biochemical, and genetic basis, specifically the GABA A receptor genes, for beta activity in the EEG at rest. The authors further showed that the power density of beta oscillation was elevated in alcoholics suggesting that this may be the electrophysiological index of imbalance in the excitationinhibition homeostasis in the cortex.…”

Section: Comparison Of Our Results With Tfca With Those Of Previous Smentioning

confidence: 99%

Time–frequency component analyser and its application to brain oscillatory activity

Özdemir

Karakaş

Çakmak

et al. 2005

Journal of Neuroscience Methods

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Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.

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Beta power in the EEG of alcoholics

Cited by 293 publications

References 60 publications

Gene–environment correlations: a review of the evidence and implications for prevention of mental illness

Gene–environment correlations: a review of the evidence and implications for prevention of mental illness

EEG Biofeedback as a Treatment for Substance Use Disorders: Review, Rating of Efficacy and Recommendations for Further Research

Time–frequency component analyser and its application to brain oscillatory activity

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