P300 Predicts the Effect of a Brief Intervention on Alcohol Consumption in Habitual Drinkers

Abstract

Background

This study investigated whether P300, a component of event-related potentials (ERPs), and cognitive performance related to frontal lobe function can predict responsiveness to a brief cognitive behavioral intervention in individuals who consume alcohol on ≥3 days per week.

Methods

Participants were 35 habitual drinkers (drinking alcohol on ≥3 days per week). At baseline, they completed self-report questionnaires assessing alcohol consumption, neuropsychological tests reflecting frontal lobe function (eg, the Trail Making Test, Stroop Test, Digit Symbol Substitution Test, verbal fluency tests), and P300 assessment during a visual oddball task with alcohol-related images. All participants then received ∼30 min of structured counseling on drinking behavior, followed by a second brief intervention at 3 months. Alcohol intake was reported at both time points, and total daily alcohol consumption (in standard drinks) was calculated. Participants were categorized as successful reducers if their daily alcohol intake at three months was lower than at baseline (n = 27), and as unsuccessful if intake was unchanged or increased (n = 8). Baseline ERP and neuropsychological measures were compared between groups.

Results

Participants who reduced their drinking had significantly lower P300 amplitudes at P3 and Fz at baseline compared with those who did not. No significant between-group differences were found in neuropsychological performance.

Conclusions

Lower P300 amplitudes may serve as a neurophysiological marker for predicting responsiveness to brief psychosocial interventions for alcohol reduction in individuals with high-risk drinking, potentially providing greater sensitivity than traditional neuropsychological tests.

Keywords

alcohol use P300 event-related potentials cognitive function brief intervention EEG

Introduction

Alcohol Use Disorder (AUD) is one of the most prevalent mental health problems worldwide.^1,2 According to a report by the World Health Organization (WHO), in 2016, 8.6% of adult men and 1.7% of adult women were affected by AUD globally.³ Alcohol use was associated with the loss of 117.2 million disability-adjusted life years (DALYs) and caused approximately two million premature deaths.⁴ Despite the substantial health burden, fewer than one in five individuals with AUD receive treatment, making it one of the most undertreated mental health conditions.^5–7 Early intervention for individuals with alcohol-related problems is therefore essential to prevent progression to more severe health outcomes.

An effective approach to addressing this issue is the implementation of Brief Interventions (BIs), a preventive strategy aimed at identifying and reducing harmful alcohol consumption before serious health consequences arise.⁸ Screening for hazardous or harmful drinking, followed by the delivery of BIs, has been implemented in primary care settings, where both the efficacy and effectiveness of this approach have been demonstrated.⁹ The content of BIs varies, but typically involves the provision of structured advice, including individual risk assessment with personalized feedback and guidance, brief motivational interviewing that adopts a more patient-centered approach, or a combination of these methods.¹⁰ In relation to this, previous meta-analyses have revealed a reduction in alcohol consumption in men achieved by BIs, with benefits sustained up to one-year post-intervention⁸; the unclear effectiveness of BIs in women due to insufficient research data⁸; the minimal additional benefit of longer counseling sessions over brief interventions⁸; and the particularly positive role of nurses in delivering BIs.¹¹ In Japan, the 2018 revision of the national clinical guidelines continues to advocate abstinence from alcohol as the primary goal, but also acknowledges drinking reduction as a realistic alternative for patients with low motivation for abstinence or without significant physical or social complications.¹² In line with this perspective, early intervention strategies for high-risk drinkers—those whose alcohol consumption elevates the risk of lifestyle-related diseases—have been implemented across various settings. BIs have demonstrated significant efficacy in reducing alcohol consumption in Japan and other locations.^9,13 Despite these advances, the individual characteristics that predict responsiveness to BIs remain poorly understood.

Various studies have explored cognitive and neurophysiological markers that may reflect the underlying pathophysiological mechanisms of AUD. Among these, attentional bias has been considered a key cognitive feature in alcohol dependence.^14,15 The theoretical basis of attentional bias lies in the incentive sensitization theory proposed by Robinson and Berridge.¹⁶ This theory posits that repeated drug use induces neuroadaptations in motivation and reward-related systems, rendering them hypersensitive to drugs and drug-related stimuli. The incentive salience attributed to such stimuli causes attention to be automatically directed toward them—this is referred to as attentional bias.¹⁷

Indeed, numerous studies have demonstrated the presence of biased cognitive processing toward alcohol-related stimuli in individuals with alcohol dependence.^18–24 Historically, these biases were assessed behaviorally using modified neuropsychological tasks, such as the Stroop task and Dot Probe Detection task. More recently, however, neuroscientific techniques have gained attention for their ability to reveal brain abnormalities that may not be detectable through behavioral data alone. In particular, event-related potentials (ERPs) derived from electroencephalography (EEG) have proven useful as non-invasive tools to examine the neural mechanisms underlying the cognitive processing of substance-related cues.^25–27 ERP studies have shown that brain activity recorded during the processing of alcohol-related images is heightened in individuals with alcohol dependence compared to healthy controls. Notably, the P300 component (P3)—a large positive deflection (electrical brainwave) typically occurring 300–800 ms after stimulus presentation—has been found to be significantly increased in alcohol-dependent individuals in response to alcohol-related stimuli.^28,29 The P300 component is generally maximal at midline central and parietal electrode sites and is thought to reflect the allocation of attentional resources and the evaluation of stimuli.^30,31 Moreover, enhanced P300 amplitudes in response to alcohol-related stimuli have been associated with an increased risk of relapse, suggesting its potential utility as a prognostic biomarker.^17,32

Previous attempts have been made to examine whether P300 is associated with treatment response in individuals with problematic drinking enrolled in detoxification programs.^17,32 However, limited evidence exists regarding the predictive value of this ERP component for determining the effectiveness of brief psycho-social interventions aimed at reducing alcohol consumption. Moreover, previous studies focused on patients with a formal AUD diagnosis, whereas no research has investigated community-based habitual drinkers without a prior diagnosis of alcohol dependence by a medical institution. Examining such non-clinical drinkers may help identify neurophysiological markers before pathological alcohol use has fully developed.

Based on previous findings, we hypothesized that individuals exhibiting larger P300 amplitudes at baseline would be less likely to reduce their alcohol intake in response to a BI, and that this predictive relationship would be less evident when using conventional neurocognitive tests. Indeed, prior work has suggested that electrophysiological markers such as P300 may demonstrate greater sensitivity than neuropsychological measures in predicting clinical outcomes.^17,32–34

Accordingly, the present study aimed to investigate whether P300 amplitudes, as a neurophysiological index, together with cognitive domain test performance—including attention, processing speed, executive function, and decision-making—can predict responsiveness to a BI among individuals consuming alcohol at levels that increase the risk of lifestyle-related diseases.

Methods

Methods and Materials

Participants

The study recruitment period ran from March 2024 through to June 2024. The final follow-up was completed in October 2024 (Figure 1). Participants were recruited through a staffing agency (Randstad K.K.). Randstad contacted registered temporary workers aged 20–65 via email, providing information about the study and its participation requirements. The study inclusion criteria were as follows: (1) regular alcohol consumption on three or more days per week; (2) alcohol intake of more than four standard drinks per occasion for men and more than two standard drinks per occasion for women; (3) no prior diagnosis of alcohol dependence by a medical institution; (4) age between 20 and 65 years at the time of consent; and (5) the ability to understand the purpose and procedures of the study and to provide written informed consent; while exclusion criteria were: (1) a current or past diagnosis of severe organic brain lesions, head trauma involving a loss of consciousness lasting more than 10 min, or epilepsy; (2) a history of, or current treatment for, neurodegenerative disorders; (3) alcohol withdrawal syndrome accompanied by impaired consciousness; (4) a diagnosis or suspected diagnosis of dementia; (5) receipt of support or treatment for alcohol-related problems (eg, from public health centers, mental health and welfare centers, or hospitals) within the past year; (6) current pregnancy; (7) presence of suicidal ideation; or (8) any other condition deemed inappropriate for study participation by the principal investigator. Individuals who expressed interest completed a pre-screening questionnaire. The research team conducted a final eligibility confirmation, and enrolled those temporary workers deemed eligible to participate into the study.

Recruiting participants through a nationally operating staffing agency offered several practical and methodological advantages. Such agencies maintain well-defined registries of workers with detailed demographic and employment information, enabling efficient pre-screening and quota-setting (eg, by sex and location) to align with population targets. This recruitment route is conceptually similar to other registry- or panel-based approaches used in psychiatric and behavioral research.^35,36 Because registered workers have an existing contractual relationship with the agency, the study invitation carried the credibility of a trusted intermediary, which may have enhanced participation.

This study was approved by the ethics committee at the National Center of Neurology and Psychiatry, Tokyo (approval number: A2023-112). Written informed consent was obtained from all participants after they were given a detailed explanation of the study's aims, procedures, and duration. They were informed that participation was voluntary and that they could withdraw at any time without penalty.

Procedure

At baseline, participants completed the following self-report questionnaires: the Alcohol Use Disorders Identification Test (AUDIT),³⁷ the Japanese version of the Alcohol Quality of Life Scale (AQoLS-J),³⁸ the Japanese version of the Alcohol Relapse Risk Scale (ARRS),³⁹ and the Japanese version of the K6 scale.⁴⁰ A substance use questionnaire covering alcohol and drug use history, usage characteristics, and family history of alcoholism was also administered. Participants were then asked to rate their current urge (craving) to drink alcohol using a 100-mm visual analog scale (VAS).⁴¹ Subsequently, participants were seated approximately 1 meter from a computer monitor in a dimly lit room. After receiving instructions from a member of the research team, they completed a visual oddball task involving the presentation of alcohol-related and neutral images (see below). A follow-up assessment was conducted three months later. Participants completed self-report questionnaires to evaluate the effectiveness of the BI in reducing consumption, and the same BI protocol as at baseline was administered again in order to continue alcohol reduction efforts in the study population (Figure 2).

Oddball Task

The task consisted of a visual oddball paradigm in which participants were presented with one regularly repeated standard stimulus and deviant target ones. Deviant stimuli consisted of pictures of different beverages. Three of these beverages were alcohol-related (A) and three others were not (NA). The alcohol-related beverages were beer, whiskey, and wine, and the non-alcoholic beverages were orange juice, Coca-Cola, and tea.⁴² The frequent stimulus was the same picture of drinking water. These pictures were repeated in four blocks. In each block, 144 stimuli were presented: the frequent stimulus appeared 114 times (79%), and the three deviant pictures for each condition (A and NA) each appeared five times, for a total of 15 deviant A and 15 deviant NA stimuli (21%). Each picture was presented for 800 ms. A black screen was displayed between pictures for a random duration of 600–1000 ms¹⁷ (see Figure 3). While having a time of 1200 ms to answer from the onset of the stimulus, participants were instructed to indicate as quickly as possible (but not at the cost of accuracy) the occurrence of any deviant stimulus with a right finger tap. The response times and percentage of correct answers were recorded.

Neurocognition

Attention, processing speed, and verbal fluency were assessed using the selected subsets of the Japanese version of the Brief Assessment of Cognition in Schizophrenia (BACS).⁴³ The Digit Symbol Substitution Test (DSST) presents a key pairing digits (1–9) with specific symbols at the top of the page. Below this key, a randomized sequence of digits is shown, and participants are required to draw the corresponding symbol in adjacent boxes as quickly and accurately as possible within a fixed time limit (typically 90 or 120 s). The total number of correct substitutions constitutes the test score. The verbal fluency domain consists of two types of verbal fluency test: the category fluency test and the letter fluency test. For the category fluency test, participants are asked to generate as many words as possible orally within 1 min, with “animal” as the category cue. The letter fluency test requires participants to generate as many words as possible beginning with designated letters (ie, “KA” and “TA”). We also administered the Trail Making Test (TMT), which is widely used to measure attention and executive function.⁴⁴ The procedures were adapted into Japanese (TMT) for this study (eg, 1–A–2–B).^45,46 In addition, the Stroop/reverse-Stroop Test, which is a widely used neuropsychological task that assesses selective attention and cognitive inhibition and serves as an indicator of frontal lobe function, particularly in the anterior cingulate cortex and dorsolateral prefrontal cortex,⁴⁷ was also used. For this study, the procedures were adapted into the Japanese version of the Stroop Test.⁴⁸

Interviews

Intervention Session

Participants received approximately 30 min of advice and counseling focused on drinking behavior.¹³ The intervention was administered by licensed clinical psychologists and certified public psychologists, and was based on components previously demonstrated to be effective in Japan.¹³ Participants were instructed to complete six structured tasks listed on a worksheet. The worksheet included elements of cognitive behavioral therapy (CBT), an explanation of the AUDIT with feedback on individual scores, a discussion of the pros and cons of drinking, goal setting regarding drinking behavior, and strategies for coping with high-risk drinking situations. In cases where participants had an AUDIT score of 15 or higher, indicating suspected alcohol dependence, they were provided with a leaflet containing information on relevant medical and support services.

Follow-up Interview

A follow-up interview was conducted three months later using the same worksheet as at baseline.¹³ As with the intervention session, interviews were conducted by licensed clinical psychologists and certified public psychologists. Participants who obtained an AUDIT score of 15 or higher were again provided with a leaflet offering information about available medical and support services, based on the suspicion of alcohol dependence. Of the 47 participants initially enrolled, 12 discontinued their participation, and 35 completed the 3-month follow-up assessment (Figure 1).

Assessment Measures

(1)

Japanese version of the Alcohol Quality of Life Scale (AQoLS-J)³⁸

The AQoLS is a 34-item, 7-factor self-administered questionnaire designed to assess the impact of alcohol use disorders on health-related quality of life. Its reliability and validity in the Japanese population have been demonstrated by Higuchi and colleagues at the Kurihama Medical and Addiction Center.³⁸ The administration time is approximately 10 min. (2)

Japanese version of the Alcohol Use Disorders Identification Test (AUDIT)³⁷

Originally developed by the World Health Organization (WHO), the AUDIT is a widely used screening tool for identifying problematic alcohol use and facilitating early intervention that has been used across many countries.⁴⁹ In Japan, it is routinely used in clinical and public health settings. The AUDIT consists of 10 items, and the total score (maximum 40 points) reflects the severity of alcohol-related problems. The administration time is approximately 5 min.³⁷ (3)

Japanese version of the Alcohol Relapse Risk Scale (ARRS)³⁹

The ARRS is a 32-item, 5-factor self-administered questionnaire developed by the Tokyo Metropolitan Institute of Medical Science to multidimensionally assess and predict the risk of relapse (slip) in individuals with alcohol dependence. It also includes a 5-item screening scale to identify respondents with significantly impaired insight into their condition. The administration time is approximately 10 min. (4)

Japanese version of the K6 scale⁴⁰

The K6 is a psychological screening scale developed to assess mental health status, particularly to identify mood and anxiety disorders.⁴⁰ It consists of six items, with responses scored on a 5-point scale ranging from “none of the time” (0 points) to “all of the time” (4 points). Higher total scores indicate poorer mental health. A score of 5 or more suggests the presence of mild mood or anxiety disorders. The administration time is approximately 3 min.

EEG Recording and Analysis

Electroencephalographic (EEG) activity was recorded using 19 electrodes mounted on an MCS-Cap, positioned according to the international 10–20 system and intermediate locations.⁵⁰ EEG signals were referenced to linked earlobes (A1 + A2). Data acquisition was performed using the MED-1260 system (NIHON KOHDEN, Japan). The recording bandwidth was set at 0.5–100 Hz (or 0.5-70 Hz, depending on the session), and electrode impedance was kept below 10 kΩ. EEG signals were continuously recorded at a sampling rate of either 500 or 1000 Hz.

Nearly all of the participants’ responses were correct (98.2%, ie, a finger tap given for deviant stimuli). Only correct answers were considered for analysis of reaction times and EEG activity. The trials contaminated by eye movements or muscular artifacts were manually eliminated offline. Epochs were created with a 100 ms pre-stimulus baseline and a total duration of 900 ms relative to stimulus onset. Each stimulus was presented for 800 ms. The data were filtered with a 30 Hz lowpass filter. To compute the P3 averages to the target stimuli for each subject, two parameters were coded for each stimulus: (i) the type of stimulus (A; NA); and (ii) the type of response (key press for deviant stimulus, no key press for frequent stimulus). A general time window was first determined globally for the identification of the component of interest (the P300) based on the research literature (eg Polich,⁵¹ Campanella et al⁵²). The measurement window was then individually tailored: for each subject, the P300 was investigated by gathering individual maximum peak amplitude values and peak latency values for each stimulus type in a 300–450 ms time range. These data were obtained from the following electrodes: P3, P4, Pz, Cz, and Fz.

Statistical Analysis

Of the 35 participants who completed the study, 27 were categorized as being in the successful reduction group and 8 as being in the unsuccessful reduction group. Participants reported their alcohol consumption at baseline and at the 3-month follow-up using a self-report questionnaire assessing the number of drinking days per month and the average amount consumed per day. Total daily alcohol intake (in standard drinks) was calculated for each time point. Participants were categorized into the successful reduction group if their total daily intake at 3 months was lower than at baseline, and into the unsuccessful group if their intake was unchanged or increased. Group differences in demographic and baseline clinical variables (including neurocognitive function and alcohol-related problems) were examined using independent-samples t-tests for continuous variables and χ² tests for categorical variables. ERP and behavioral data were analyzed using analyses of variance (ANOVAs). The primary aim was to investigate whether group status (successful reduction vs unsuccessful reduction) was associated with differences in behavioral and electrophysiological responses to both stimulus types—Alcohol(A) and Non-alcohol-related (NA)—at baseline.

To examine whether group status influenced reaction times to the two stimulus types, a 2 (Type: A vs NA) × 2 (Group: Successful vs Unsuccessful) ANOVA was conducted on reaction times for correct responses. To examine whether group status influenced P3 parameters in response to both stimulus types, a 2 × 2 × 5 ANOVA was performed separately for P300 latencies and amplitudes, with stimulus type (A vs NA) and electrode site (P3, P4, Pz, Cz, Fz) as within-subject factors, and group as the between-subject factor. Simple effects were explored, and sources of interaction were systematically examined using Student's independent or paired t-tests.

All p values were two-tailed, with p < .05 considered statistically significant. All statistical analyses were performed using IBM SPSS Statistics, Version 29.0 (SPSS Japan, Inc.).

Results

Demographic and Alcohol Consumption Characteristics

The demographic and alcohol consumption characteristics of the two groups are summarized in Table 1. Of the 35 participants who completed the study, 27 were classified into the successful reduction group (participants who successfully reduced their alcohol consumption compared with baseline), and 8 were classified into the unsuccessful reduction group (participants who did not reduce their alcohol consumption compared with baseline).

Table 1.

Demographic Characteristics of the Participants.

Characteristic	Successful Reduction Group (n = 27)		Unsuccessful Reduction Group (n = 8)		p-Value
Females (n, %)	12	44.4	2	25.0	.32
Age (mean, SD)	42.5	14.0	50.3	13.2	.16
Onset of drinking alcohol (age, mean, SD)	19.3	0.9	19.6	1.1	.32
Education level (in years, mean, SD)	14.6	1.7	15.0	2.4	.60
Non-communicable disease (n, %)	12	44.4	2	25.0	.32
Family history of alcoholism (n, %)	10	37.0	2	25.0	.69
Smoking (n, %)	8	29.6	3	37.5	.69
Drinking days per month (n, %)					.59
12–15	1	3.7	3	37.5
16–20	4	14.8	1	12.5
21–25	10	37.0	1	12.5
26–30	12	44.4	3	37.5
Binge drinking (mean, SD)	8.0	8.5	5.6	10.4	.52
AUDIT (mean, SD)	13.1	4.8	10.8	3.6	.21
AQoLS (mean, SD)	6.8	5.8	5.4	5.7	.55
ARRS (mean, SD)	1.7	0.2	1.7	0.3	.92
Craving before experiment (mean, SD)	18.2	25.1	23.9	23.5	.57
Craving after experiment (mean, SD)	19.1	20.4	20.0	26.1	.93
K6 (mean, SD)	2.6	2.9	3.4	3.6	.52
DSST (mean, SD)	64.2	11.0	63.0	12.3	.80
TMT(B/A) (mean, SD)	1.7	0.5	1.9	0.7	.34
Stroop (mean, SD)	4.5	13.1	9.9	10.3	.28
Reverse-Stroop (mean, SD)	9.9	9.2	11.3	6.8	.71
Category Fluency Test (mean, SD)	20.7	4.9	18.8	5.1	.34
Letter Fluency Test (mean, SD)	26.3	6.8	23.3	10.2	.44

Successful reduction group: Participants who successfully reduced their alcohol consumption compared with baseline. Unsuccessful reduction group: Participants who did not reduce their alcohol consumption compared with baseline. Significant p values are shown in bold type (p < .05, Bonferroni correction).

Abbreviations: SD: Standard Deviation; AUDIT: the Alcohol Use Disorders Identification Test; ARRS: the Alcohol Relapse Risk Scale; AQoLS: the Alcohol Quality of Life Scale; DSST: the Digit Symbol Substitution Test; TMT: Trail Making Test.

The unsuccessful reduction group included 2 females (25.0%) with a mean age of 50.3 years (SD = 13.2), while the successful reduction group included 12 females (44.4%) with a mean age of 42.5 years (SD = 14.0). The mean age at onset of alcohol use was comparable between the groups (Reduced: M = 19.3, SD = 0.9; Not reduced: M = 19.6, SD = 1.1). Educational attainment and the prevalence of a family history of alcoholism were also similar between groups. Regarding drinking patterns, a comparable proportion of participants reported drinking on 26–30 days per month in the successful (44.4%) and unsuccessful (37.5%) reduction groups.

Clinical Measures

No significant group differences were observed for any of the clinical parameters examined; scores on assessments of alcohol-related problems (AUDIT, AQoLS, ARRS), depressive symptoms (K6), and neuropsychological performance (DSST, TMT, Stroop, Reverse-Stroop, Categorical Fluency Test, Letter Fluency Test) did not differ significantly between the groups (Table 1).

P300 Reaction Times

An independent samples t-test revealed no significant differences in reaction times between the two groups for either alcohol-related or non-alcohol-related stimuli. Specifically, for alcohol-related stimuli, the group difference was not statistically significant, t(33) = −0.89, p = .382, d = .071. Similarly, no significant difference was observed for non-alcohol-related stimuli, t(33) = −0.341, p = .736, d = .077 (see Table 2).

Table 2.

Baseline Reaction Times, P3 Amplitudes (µV), and P3 Latencies (ms) for Deviant Stimuli as a Function of Group (Successful vs Unsuccessful Reduction) and Type (Alcohol-Related vs non-Alcohol-Related).

Measure	Successful Reduction Group (n = 27)		Unsuccessful Reduction Group (n = 8)		Statistic
Reaction Times - Alcohol-related pictures (ms, SD)	0.45	0.07	0.42	0.06	t(33) = −.89, p = .382, d = .071
Reaction Times - Non-alcohol related pictures (ms, SD)	0.46	0.08	0.45	0.06	t(33) = −.34, p = .736, d = .077
P3 Amplitudes - Alcohol-related pictures (μV, SE)
P3	5.87	1.14	12.48	2.09	F(1, 33) = 7.69, p = .009, $η_{p}^{2} = .189$
P4	7.13	0.93	9.02	1.71	F(1, 33) = .95, p = .338, $η_{p}^{2} = .028$
Pz	6.21	0.96	7.94	1.76	F(1, 33) = .75, p = .393, $η_{p}^{2} = .022$
Cz	2.31	1.36	7.64	2.51	F(1, 33) = 3.49, p = .071, $η_{p}^{2} = .096$
Fz	1.05	1.91	9.67	3.50	F(1, 33) = 4.69, p = .038, $η_{p}^{2} = .124$
P3 Amplitudes - Non-alcohol related pictures (μV, SE)
P3	3.93	0.97	3.06	1.79	F(1, 33) = .19, p = .669, $η_{p}^{2} = .006$
P4	4.32	0.98	4.31	1.81	F(1, 33) = 6.0 × 10⁻⁵, p = .993, $η_{p}^{2} < .001$
Pz	3.79	0.95	2.54	1.75	F(1, 33) = .39, p = .535, $η_{p}^{2} = .012$
Cz	−0.58	1.17	1.14	2.14	F(1, 33) = .50, p = .487, $η_{p}^{2} = .015$
Fz	−1.90	1.57	1.03	2.88	F(1, 33) = .80, p = .378, $η_{p}^{2} = .024$
P3 Latencies - Alcohol-related pictures (ms, SE)
P3	351.9	8.0	351.4	14.7	F(1, 33) = .001, p = .977, $η_{p}^{2} < .001$
P4	356.4	8.7	356.6	15.9	F(1, 33) = 1.4 × 10⁻⁴, p = .990, $η_{p}^{2} < .001$
Pz	343.2	7.4	356.6	13.6	F(1, 33) = .75, p = .394, $η_{p}^{2} = .022$
Cz	333.7	6.3	352.4	11.6	F(1, 33) = 1.99, p = .167, $η_{p}^{2} = .057$
Fz	343.9	7.0	336.1	12.8	F(1, 33) = .29, p = .597, $η_{p}^{2} = .009$
P3 Latencies - Non-alcohol related pictures(ms, SE)
P3	345.2	7.7	345.9	14.2	F(1, 33) = .002, p = .966, $η_{p}^{2} < .001$
P4	345.0	7.0	338.6	12.9	F(1, 33) = .19, p = .664, $η_{p}^{2} = .006$
Pz	340.6	6.8	346.5	12.6	F(1, 33) = .17, p = .682, $η_{p}^{2} = .005$
Cz	341.2	7.8	357.9	14.4	F(1, 33) = 1.04, p = .316, $η_{p}^{2} = .030$
Fz	349.8	8.0	357.4	14.7	F(1, 33) = .21, p = .654, $η_{p}^{2} = .006$

Successful reduction group: Participants who successfully reduced their alcohol consumption compared with baseline. Unsuccessful reduction group: Participants who did not reduce their alcohol consumption compared with baseline. Significant P values are shown in bold type (P < .05, Bonferroni correction).

Abbreviations: SD: Standard Deviation; SE: Standard Error.

P300 Amplitudes

A mixed-design ANOVA was conducted to examine group differences in P300 amplitudes in response to alcohol-related stimuli across five electrode sites (P3, P4, Pz, Cz, Fz). A significant main effect of group was observed at P3 (F(1, 33) = 7.693, p = .009, $η_{p}^{2} = .189$ ) and Fz (F(1, 33) = 4.69, p = .038, $η_{p}^{2} = .124$ ). In contrast, no statistically significant group differences in P300 amplitudes were found in response to non-alcohol-related stimuli at any electrode site (Table 2).

P300 Latencies

No significant differences in P300 latencies were observed between the successful and unsuccessful reduction groups for either alcohol-related or non-alcohol-related stimuli. Across all electrode sites, group effects failed to reach statistical significance, and no reliable interaction effects emerged. Latency values were comparable across conditions, suggesting no group-related modulation of processing speed (Table 2).

Discussion

In this study, we examined the potential utility of electrophysiological and neurocognitive indicators in predicting responsiveness to a BI among individuals engaged in habitual alcohol consumption. Specifically, we focused on P300 amplitudes and latencies elicited by alcohol-related and neutral stimuli during a visual oddball task. Importantly, unlike previous research that primarily investigated clinical populations undergoing detoxification or formal treatment for alcohol dependence, the present study targeted community-based habitual drinkers with no prior diagnosis of alcohol dependence. Examining neurophysiological predictors in such non-clinical drinkers allows potential markers to be explored before pathological alcohol use develops, which may provide insight into early intervention opportunities. This conceptual shift represents a meaningful extension of prior work on P300 and alcohol use.

Our findings revealed that individuals who subsequently reduced their alcohol intake following the intervention exhibited significantly lower P300 amplitudes in response to alcohol-related cues, particularly at midline electrode sites (P3 and Fz), compared to those who did not show such behavioral change. In contrast, no significant group differences were observed in P300 latency, reaction time, or neuropsychological task performance (eg, DSST, TMT-A, Stroop task). These findings build upon previous studies, which have demonstrated that individuals with alcohol dependence exhibit attentional bias toward alcohol-related cues,^34,53 and that reduced P300 amplitudes are associated with attenuated attentional bias and more favorable treatment outcomes.¹⁷ However, such studies have predominantly examined treatment-seeking populations. In contrast, the present study is novel in its focus on habitual drinkers with no history of treatment, and is the first to suggest that baseline P300 amplitude in response to alcohol-related cues may serve as a neurophysiological marker for predicting responsiveness to a BI. This represents a meaningful contribution to the literature.

Importantly, no significant differences were observed in standard neuropsychological assessments between the group that reduced their alcohol consumption and the group that did not. This suggests that traditional cognitive performance measures may lack the sensitivity to detect subtle individual differences relevant to intervention responsiveness. One plausible explanation for this is that most participants in this study had no prior history of treatment for alcohol problems and retained relatively preserved social functioning, such as sustained employment. Although significant cognitive impairments have been documented in individuals with AUD,⁵⁴ such impairments were not evident in our sample. This may at least partially account for the absence of significant group differences in neuropsychological task performance.

Furthermore, prior studies employing alcohol-related Stroop tasks have shown differences in attentional bias between individuals with alcohol use problems and healthy controls.^22,34,55,56 However, the present study could not implement an alcohol-specific Stroop task due to the lack of a standardized Japanese version, and thus employed a conventional Stroop paradigm instead. Future research should consider incorporating more sensitive task paradigms (eg, alcohol-related Stroop tasks) to more accurately identify cognitive markers associated with the effectiveness of BIs.

The current findings must be interpreted in light of several limitations. First, the sample size—particularly within the unsuccessful group—was relatively small, which may have limited statistical power and prevented us from examining potential gender differences in the observed associations. Second, the follow-up period was restricted to three months, leaving longer-term drinking outcomes unassessed. Third, alcohol consumption was measured using self-reports, which may be subject to recall or social desirability biases. Fourth, although ERP measurement provides high temporal resolution, it does not capture the broader neural dynamics involved in motivational processes or decision-making. Finally, the absence of a non-drinking or low-risk drinking control group limits interpretation of absolute P300 amplitudes and latencies relative to normative values. While the present study focused on predictive relationships within habitual drinkers, incorporating non-drinking controls in future research would allow a clearer comparison of ERP characteristics and normative values across different drinking profiles.

Despite these limitations, this study contributes novel evidence to support the predictive validity of the P300 component in the context of alcohol interventions. Future studies should aim to replicate these findings in larger samples, explore the neural mechanisms underlying P300 modulation, and investigate the potential of combining ERP indices with behavioral and self-report data to optimize personalized intervention strategies. Expanding beyond the P3 component, future research should also consider analyzing earlier ERP components, such as N2, which may reflect conflict monitoring and early attentional processes relevant to alcohol-related cue evaluation.

Conclusions

This study suggests that lower P300 amplitudes to alcohol-related cues may predict responsiveness to BIs in non-treatment-seeking habitual drinkers. Individuals who reduced their drinking showed reduced P300 amplitudes at baseline, indicating attenuated salience or attention to alcohol cues. By contrast, standard neuropsychological tests did not distinguish between groups, highlighting the added value of ERP measures. Thus, P300 amplitude may serve as a sensitive marker to identify individuals more likely to benefit from low-intensity interventions. Future studies should confirm these findings in larger samples and over longer follow-up periods.

Footnotes

Acknowledgments

We would like to thank Ms. Yukari Honda, Ms. Son Heung, Dr Kazuki Iijima, and Toshiharu Kurita for their valuable contributions to and support of this study.

ORCID iDs

Risa Yamada

Tomiki Sumiyoshi

Ethical Approval and Informed Consent Statements

Ethical approval for the survey was provided by the ethics committee at the National Center of Neurology and Psychiatry, Tokyo (approval number: A2023-112). Patients were given an information sheet about the study and asked to provide written informed consent before the data collection began.

Author Contributions

Risa Yamada contributed to the conception and design; contributed to the acquisition, analysis, and interpretation; drafted the manuscript; critically revised the manuscript; gave final approval; agrees to be accountable for all aspects of the work ensuring integrity and accuracy. Andrew Stickley contributed to the conception; contributed to the interpretation; drafted the manuscript; critically revised the manuscript; gave final approval; agrees to be accountable for all aspects of the work ensuring integrity and accuracy. Tomiki Sumiyoshi contributed to the conception and design; contributed to the acquisition and interpretation; drafted the manuscript; critically revised the manuscript; gave final approval; agrees to be accountable for all aspects of the work ensuring integrity and accuracy.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by JSPS KAKENHI (grant number JP23K18998 and JP24K10696) to R.Y., as well as Intramural Research Grants for Neurological and Psychiatric Disorders (Grant number: 6-1, 5-3) from the National Center of Neurology and Psychiatry to T.S.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data are available in the article's supplementary material.

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