Understanding alcohol use disorders with neuroelectrophysiology

Abstract

Neurocognitive deficits associated with impairments in various brain regions and neural circuitries, particularly involving frontal lobes, have been associated with chronic alcoholism, as well as with a predisposition to develop alcohol use and related disorders (AUDs). AUD is a multifactorial disorder caused by complex interactions between behavioral, genetic, and environmental liabilities. Neuroelectrophysiological techniques are instrumental in understanding brain and behavior relationships and have also proved very useful in evaluating the genetic diathesis of alcoholism. This chapter describes findings from neuroelectrophysiological measures (electroencephalogram, event-related potentials, and event-related oscillations) related to acute and chronic effects of alcohol on the brain and those that reflect underlying deficits related to a predisposition to develop AUDs and related disorders. The utility of these measures as effective endophenotypes to identify and understand genes associated with brain electrophysiology, cognitive networks, and AUDs has also been discussed.

Testing the effects of adolescent alcohol use on adult conflict-related theta dynamics

“…This may suggest the presence of some compensatory mechanism (e.g., sensorimotor beta; attention-related alpha) that prevents behavioral slips even when prefrontal response conflict processes are lacking, that other cognitive control-related processes important to optimal task performance are relatively intact in the presence of decreased conflict-related theta, or that EEG correlates of response conflict/cognitive control may be more closely associated with adolescent drinking than performance-based measures. In addition, despite the finding that theta dynamics were significantly related to drinking only during response conflict trials, given the lack of association between AAU and behavioral performance, it is possible that individual differences in other more global cognitive states that may affect EEG characteristics, such as wakefulness or general vigilance (Rangaswamy and Porjesz, 2014; Clayton et al, 2015), may have contributed to the observed effects. Further work is needed to tease apart the relationship between adolescent drinking and the various subdomains (e.g., sustained attention, conflict processing) of cognitive control.…”

Alcohol and the sleeping brain

“…While there have been no studies evaluating genotype influences on sleep EEG, the data from wake EEG studies are clearly relevant, especially the known association between the GABA gene GABRA2 and beta oscillations (Porjesz et al 2002) and between the cholinergic gene CHRM2 and delta oscillations (Porjesz and Rangaswamy 2007; Rangaswamy and Porjesz 2013). It should be noted however that EEG during wakefulness that is measured in association with cognitive or behavioral tasks, reflects the activity in task specific pathways and networks, whereas sleep EEG is measuring a baseline state.…”

A KCNJ6 gene polymorphism modulates theta oscillations during reward processing

“…ERO measures have been used as effective tools to understand brain mechanisms underlying alcoholism and its predisposition (for reviews, see Porjesz et al, 2005; Pandey et al, 2012; Rangaswamy and Porjesz, 2014; Kamarajan and Porjesz, 2015). Further, as reported in the combined analyses of ERP and ERO data, ERO measures yielded additional information than the traditional ERP measures to discriminate alcoholics from controls (e.g., Jones et al, 2006b) as well as high-risk from low-risk individuals (e.g., Rangaswamy et al, 2007).…”

“…In the Collaborative Study on the Genetics of Alcoholism (COGA), we have successfully used EROs as endophenotypes in the search for genes involved in alcoholism and related disorders (for reviews, see Porjesz et al, 2005; Pandey et al, 2012; Rangaswamy and Porjesz, 2014). Genetic studies of the theta ERO phenotype in a visual oddball task has been associated with several genes, including CHRM2 (Jones et al, 2004; Jones et al, 2006a), GRM8 (Chen et al, 2009), and HTR7 (Zlojutro et al, 2011).…”