Anticipatory reward dysfunction in alcohol dependence: an electroencephalography monetary Incentive delay task study

Mica Komarnyckyj, Chris Retzler, Robert Whelan, Elsa Fouragnan, Anna Murphy

Research output: Contribution to journalMeeting Abstractpeer-review

Abstract

Background: A wealth of functional magnetic resonance imaging monetary incentive delay task (MIDT) research has shown alcohol dependency is associated with a hypoactive striatal response during gain anticipation (gain > neutral) and loss anticipation (loss > neutral) 1, which supports the ‘reward deficiency syndrome’ theory of drug and alcohol dependence 2. Despite the popularity of the fMRI-MIDT in prior addiction research, the temporal resolution of the fMRI blood-oxygen-level-dependent (BOLD) signal (in the range of seconds) is not optimal for studying reward-related processing that occurs in the sub-second range. Furthermore, the prohibitive costs of fMRI (£2.5 million investment, £500/hour rental) have limiting effects on study design and present a barrier for translation into clinical practice. Electroencephalography (EEG) holds clinical advantages over fMRI (high temporal resolution, low cost, portable) however its use to study reward processing in alcohol dependence is limited. We aimed to carry out the first EEG MIDT (eMIDT) study in alcohol dependence.
Methods: 21 abstinent alcohol dependent individual and 26 controls performed an MIDT while brain activity was recorded under 64-channel EEG. During the task participants were shown reward incentive cues, followed by a target stimulus and feedback informing them if they had successfully won, or avoided losing money. Trial averaged event related potential (ERP) and single trial machine learning discriminant analyses was applied to the EEG data 3. Clinical variables related to severity of dependence were collected and relationships with ERP data explored. Machine learning discriminator performance was quantified by calculating the area under a receiver operating characteristic (ROC) curve (termed Az value) using a leave-one-out trial (LOO) cross validation approach. Group averaged Az values were statistically compared using SPM1d, a method which corrects for multiple comparisons using random field theory to account for covariance between timepoints 4.
Results: Alcohol dependents individuals, compared with healthy controls, had blunted cue-P3 amplitudes for gain and loss anticipation (interaction: p = 0.019); and elevated contingent negative variation amplitudes for all conditions (gain, loss, neutral)(main effect: p < 0.001) which was associated with an increase of alcohol units consumed per year of active drinking (p = 0.002). The machine learning analyses demonstrated alcohol dependent individuals had reduced ability to discriminate between loss and neutral cues between 328 – 350 ms (p = 0.040), 354 – 367 ms (p = 0.047) and 525 – 572 ms (p = 0.022).
Conclusions: Here, we demonstrate a low-cost eMIDT approach for detecting dysfunctional anticipatory reward processing in alcohol dependence. EEG was found to be sensitive to a hypoactive cue-P3 response during gain and loss anticipation in alcohol dependence, a similar ventral striatum response has previously been demonstrated with fMRI [1]. Furthermore, during preparation for motor response, we found increased alcohol consumption was associated with a general hyperactive CNV signal in alcohol dependency. The eMIDT approach may be useful for proof-of-concept evaluation of novel treatments for alcohol dependency and to guide clinical decision-making.
Original languageEnglish
Article number101095
Number of pages1
JournalNeuroscience Applied
Volume2
Issue numberS1
DOIs
Publication statusPublished - 16 Mar 2023
EventECNP Workshop for Early Career Scientists in Europe 2023 - Nice, France
Duration: 16 Mar 202319 Mar 2023

Cite this