The collaborative study on the genetics of alcoholism: Brain function

Abstract

Alcohol use disorder (AUD) and related health conditions result from a complex interaction of genetic, neural and environmental factors, with differential impacts across the lifespan. From its inception, the Collaborative Study on the Genetics of Alcoholism (COGA) has focused on the importance of brain function as it relates to the risk and consequences of alcohol use and AUD, through the examination of noninvasively recorded brain electrical activity and neuropsychological tests. COGA's sophisticated neurophysiological and neuropsychological measures, together with rich longitudinal, multi-modal family data, have allowed us to disentangle brain-related risk and resilience factors from the consequences of prolonged and heavy alcohol use in the context of genomic and social-environmental influences over the lifespan. COGA has led the field in identifying genetic variation associated with brain functioning, which has advanced the understanding of how genomic risk affects AUD and related disorders. To date, the COGA study has amassed brain function data on over 9871 participants, 7837 with data at more than one time point, and with notable diversity in terms of age (from 7 to 97), gender (52% female), and self-reported race and ethnicity (28% Black, 9% Hispanic). These data are available to the research community through several mechanisms, including directly through the NIAAA, through dbGAP, and in collaboration with COGA investigators. In this review, we provide an overview of COGA's data collection methods and specific brain function measures assessed, and showcase the utility, significance, and contributions these data have made to our understanding of AUD and related disorders, highlighting COGA research findings.

Keywords: EEG; ERO; ERP; alcohol use disorder; family; genomics; neurocognitive; neurodevelopment; neurophysiology; neuropsychological.

FIGURE 1

EEG Electrodes and Coherence Pairs used for Electrophysiological Recordings and Analyses in COGA. (A) Sixty‐one scalp electrodes are used to record EEG and ERPs in frontal (F), central (C), parietal (P), occipital (O), left‐temporal (LT) and right‐temporal (RT) regions. Even numbers signify the right side of the head, while odd numbers signify the left side, and Z signifies the center; the numbers indicate coordinates ascending from the center to the periphery of the scalp. (B) Bipolar electrode pairs used as the coherence measures of resting state EEG.

FIGURE 2

Brain wave characteristics in individuals with AUD (left panels, A1–A3) and their children, who are at high risk to develop AUD, due in part to genetic influences through family history (right panels, B1–B3) during a mental task called a “visual oddball task” in which they were asked to press a button only to a specific “target” image on a computer screen, while ignoring other more frequent images. Brain responses only to the target stimulus are illustrated in this figure. In A1 and B1 (top panels) you can see that both individuals with AUD, (red lines, left panel) and their high‐risk children from AUD families (red line, right panel) had smaller P3 or P300 waves (the large peak in top panel occurring between 300 and 700 ms after the target) compared to those unaffected (blue lines, left panel) and at low risk from community comparison families (blue lines, right panel). In A2 and A3, and B2 and B3 (middle and bottom panels) you can see the lower magnitude of brain waves oscillating at theta (4–7 Hz) and delta (1–3 Hz) frequencies during the P300 response in both individuals with AUD (left panel) and their high‐risk children (right panel) compared to those unaffected (left panel) and offspring from comparison community families (right panel); note that theta oscillations have a frontal focus while delta oscillations have a more posterior focus. These differences in brain wave characteristics between individuals affected compared to unaffected with AUD, and between offspring from AUD families compared to offspring from community comparison families indicate less activation and/or weaker synchronization of neural activity during this cognitive task in those with AUD and their high‐risk children.