Introduction

Alcohol dependence (AD) is a chronic relapsing disease characterized by an impaired ability to control alcohol consumption despite adverse consequences. As the most prevalent drug of all substance use disorders, alcohol use disorder (AUD) is one of the leading global causes of disease burden and substantial health loss. A high proportion of disease burden is attributable to complex outcomes, including unintentional injuries, cancer, cardiovascular and cerebrovascular diseases, cirrhosis, and suicide. The risk of mortality is positively associated with the level of alcohol consumption, which is potentially harmful at any level.

It has been reported the prognosis of AD was unfavorable after patients achieve abstinence, which attributed to a high risk of relapse. Impulsivity plays a central role in the transfer from recreational alcohol use to alcohol dependence and relapse. Impulsivity increases the risk of relapse which may be related to impaired inhibitory control and enhanced motivation after cue exposure, leading more serious state of alcohol dependence. Impulsivity is a complex structure including impulsive traits and impulsive behavior, and there are some relationships between subtypes of impulsivity, with evidence of go/no-go performance and Barratt Impulsiveness Scale scores. Previous studies found multiple subtypes of impulsivity were associated with a common biological factor: low dopamine D2 receptor function in striatum. The striatum is a complex structure interconnected with the cerebral cortex, projecting in “loops” to executive, motor, and limbic regions of the brain. The impulsive system includes amygdala–striatum regions, reflective system which contains the prefrontal cortex, and insula cortex which plays a key role in modulating the dynamics between these two systems. It was reported that individuals with more severe alcohol dependence exhibit weaker frontal (e.g., the insula, medial prefrontal cortex, and anterior cingulate) functional connectivity with the striatum, and these networks are important for response inhibition. These findings suggest the fronto-striatal pathway underlying inhibition control is weakened in AD. Thus, a network of interacting brain regions and associated circuits has been shown to mediate impulsivity and inhibition control behaviors of AD, including striatum, dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), supplementary motor area (SMA), insular, and amygdala.

White matter (WM) structures efficiently propagate neural signals between spatially distinct cortical regions and then improve brain connectivity. WM damage is one of the characteristic injuries of AD. The clinical features and pathological behaviors in alcoholics are related to the integrity of WM, especially more evidence of the structural damage on prefrontal–striatal circuits was found in AD. The reduced FA value of the orbitofrontal cortex and nucleus accumbens (OFC-NAcc) network suggests structural network alterations in AD. The increased OFC-NAcc functional connection is associated with craving. Besides, frontal reduced WM integrity as predictors of the alcohol treatment outcome. Diffusion tensor imaging (DTI) is a quantitative non-invasive method to assess the integrity of WM mainly by using the value of fractional anisotropy (FA). The reduced FA can be attributed to degradation of both myelin sheaths and axonal membranes. Tract-based spatial statistics (TBSS) is generally used for voxel-wise analysis of whole-brain white matter. In spite that TBSS has advantages over other methods such as voxel-based morphometry, however, TBSS also has limited anatomical specificity and lack of information between different regions of interest (ROI). As a result, we will utilize the probability tractography to detect the white matter differences within specific tracts in our study.

Most prior studies have employed TBSS analysis to investigate the relationship between FA and impulsivity. In addition, the exact relationship between WM integrity and impulsivity is still inconsistent in AD. Our previous TBSS analysis found there is no relationship between FA of whole-brain skeleton and impulsive trait (BIS-11). Another tractography-based segmentation study found higher impulsivity level was associated with lower FA in corpus callosum extending to the orbitofrontal cortex in AD. So far, it is still unclear whether abnormal FA of systematic striatal circuits is associated with impulsivity and the severity of dependence in AD. The aim of our study was to investigate the integrity of striatal circuits by using seed-based classification with DTI probabilistic tractography and its relationship with impulsivity and severity of alcohol dependence in AD.

The aim of our study was to investigate the integrity of striatal circuits by using seed-based classification with DTI probabilistic tractography and its relationship with impulsive trait and severity of alcohol dependence in AD.

Materials and methods

Subjects

A case–control, cross-sectional study was conducted in the Peking University Institute of Mental Health. Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria were assessed with the mini-international neuropsychiatric interview. Fifty-one males meeting the DSM-IV criteria for AD were recruited, and the age range was 31 and 59 years old. All AD participants were patients in hospital of the Peking University Sixth hospital. AD patients had finished acute withdrawal with abstinence for at least 2 weeks. In the study sample, the average Michigan Alcoholism Screening Test (MAST) score of all 51 subjects was 13.51 ± 4.15 (1–40) (see Table 1). Inclusion criteria included male, aged 30–60, right-handed, meeting the DSM-IV diagnostic criteria for AD, and the score of the Clinic Institute Alcohol Withdrawal Syndrome Scale (CIWA-AR) was less than seven. Exclusion criteria included any other Axis I psychiatric disorder or other substance use disorder (except for nicotine), any systemic or neurological disease, claustrophobia, or any other contraindication for magnetic resonance examination (MRI). Meanwhile, a total number of age- and gender-matched 27 participants were involved as healthy control (HC). The control participants were recruited from the community and, based on structured interview, had never met DSM-IV criteria for AD or any other DSM-IV Axis I disorder. All participants completed informed consent.

Table 1. Demographics and clinical traits of AD and HC individuals.

Procedure

Our study consisted of two sessions. General and clinical data were collected in the first session including sociodemographic, alcohol use-related, and clinical characteristics. Trait impulsivity was measured by the BIS-11, which is one of the most widely used self-report measures of impulsivity trait. It includes 30-item self-administered questionnaire. MAST was used as a severity index for alcohol dependence in our study. MAST is a rapid, reliable, inexpensive measure of a severity index for alcoholism. CIWA-Ar was used for clinical quantitation of the severity of the alcohol withdrawal syndrome in the baseline measurement. A neuropsychological assessment of cue reactivity paradigm was made. Participants were exposed to visual, olfactory, and proprioceptive stimuli associated with the beverage in alcohol cue trial. Alcohol-related cue reactivity was assessed by subjective responses (visual analog scales of craving, C-VAS, and Alcohol Urge Questionnaire, AUQ) and physiological responses (heart rate, HR; systolic blood pressure, SBP; and diastolic blood pressure, DBP) before and immediately after alcohol exposure. All subjects were requested to evaluate the intensity of their craving of alcohol consumption on a 100-point Visual Analog Scale (VAS) ranging from zero (not at all) to 100 (extremely high). The AUQ was also used to evaluate the level of intensity of alcohol craving. FTND is a widely used test for assessing physical nicotine dependence. FTND was used to assess dependence of nicotine in this study. After 15 min of rest, a second session was finished by the MRI scan.

MRI data acquisition

The experiment was carried out on the GE Discovery MR750 3.0T at the Peking University Sixth Hospital. The three-dimensional (3D) T1-weighted images were acquired using an MPRAGE pulse sequence with a voxel size of 1 × 1 × 1 mm³ [repetition time (TR) = 6.7 ms; echo time (TE) = 2.9 ms; data matrix = 240 × 240 mm²; slices = 170; field of view (FOV) = 240 × 240 mm²]. DTI data were collected through a single-shot EPI sequence with a voxel size 2 × 2 × 2 mm³ (TR = 8,900 ms, TE = 92 ms, data matrix = 120 × 120, FOV = 240 × 240 mm², slice =72). To improve the signal-to-noise ratio, 64 repeats of the 32 non-collinear directions (b = 1,000 s/mm²) were applied with eight acquisitions without diffusion weighting (b = 0 s/mm²).

TBSS analysis

A voxel-wise analysis (TBSS) along whole-brain WM tracts was applied to detect the difference on the diffusivity parameters (such as FA and MD) among AD and HC. After preprocessing, we visually checked all FA maps of subjects for data quality insurance. The FA image was first normalized into to the standard MNI (Montreal Neurological Institute) space by using the fnirt command implemented in FSL. Then, an averaged FA map was created for skeleton generation step. Those voxels that lower on 0.2 in FA value were excluded based on prior research. Finally, FA image of each subject was projected onto the FA skeleton image. A permutation t-test was used to test FA differences between groups in FSL (FSL randomize procedure). The threshold-free cluster enhancement at P-value < 0.05 (5,000 permutations) was used to control the Type I error induced by multiple comparisons across voxels.

Striatum structural connectivity analysis

Masks of target regions, including four cortical regions [dlPFC (3,7/4, 8), vlPFC (7,15/10, 16), SMA (19/20), and insula (29/30)] and one subcortical region [amygdala (41/42)], and mask of seed region [striatum (71,73/72,74)] were extracted from the standard Anatomical Automatic Labeling (AAL) template and then were applied to individual brain space through a two-step normalization of ANTs tool (Advanced Normalization Tools) to build white matter connectivity within striatum.

Probabilistic tractography

Preprocessing steps of diffusion images in FDT (FMRIB's Diffusion Toolbox) included image quality check, 8 b0 images averaging, eddy current correction for distortions coming from EPI artifacts and motion correction, a rotation of b-vector, and tensor fitting, to eventually obtain FA maps.

Probabilistic tractography method based on seed was used to tract fibers between striatum and target regions. Calculation of fiber orientation distribution for each voxel through BEDPOSTX (GPU version) was then used in fiber tracking from seeds to targets (one seed and one target at a time) by the probtrackx algorithm (GPU version) with the following parameters: streamlines = 5,000; step length = 0.5 mm; curvature threshold = 0.29. Tractography was separately conducted for each hemisphere. Voxels in fiber that had streamlines below the threshold of 5% of the maximum streamlines from seed to target were excluded by fslstat function. Eventually, we kept those voxels beyond threshold as a fiber mask in individual diffusion space to compute the mean FA as FA measurement of the fiber.

Statistical analysis

The statistical analyses were conducted using SPSS, version 24 (IBM, Armonk, NY). Independent t-tests, Kruskal–Wallis test, and chi-square test were used to compare demographic and clinical characteristics between AD and HC. Then, we further employed correlations analysis to test the relationship between BIS-11 and clinical and behavioral data (such as alcohol use features and cue reactivity) in AD. We included age and education as covariates in DTI data. TBSS and seed-based probabilistic fiber tracking method were used to compare the FA of whole-brain skeleton and striatal circuits in AD and HC. After that, we employed partial correlations analysis to test whether the FA of striatal circuits and abnormal FA values of TBSS were associated with BIS-11 in AD and HC. To investigate the relationship between white matter integrity of striatal circuits and severity of alcohol dependence (MAST) we performed a single-level mediation analysis with impulsivity as a mediator. We tested whether the association between FA of right striatum-vlPFC (X) and severity of alcohol dependence (Y), measured by the MAST, was mediated by self-reported impulsivity (M), measured by the BIS-11. This tests whether FA reduction is associated with impulsivity, which in turn leads to severer drinking behavior. A false discovery rate (FDR) method was used to control the rate of type I error caused by multi-comparisons in all statistics.

Results

Demographics, clinical characteristics, and behavior data

There are no significant differences in age, education, and marital status between the two groups. The onset of alcohol use in AD patients is earlier (Z = −2.366, p < 0.05), and the score of MAST is much higher than HC [t (76) = −6.436, p < 0.001]. The nicotine use in AD is much more serious than HC [t (76) = −5.897, p < 0.001] (Table 1).

The correlation analyses were made between impulsivity and alcohol-related cue reactivity (subjective responses, e.g., C-VAS and AUQ, physiological responses e.g., HR and blood pressure) and alcohol use features (mean ethanol intake per day and age at first use) in AD. Significant positive correlations were found between the BIS-11 and cue-induced craving changes (C-VAS: r = 0.332, p = 0.021; AUQ: r = 0.302, p = 0.031). The mean alcohol intake per day is also significantly positively correlated with impulsivity (r = 0.353, p = 0.011) (Supplementary Table 1). However, the correlations between BIS-11 and cue-induced craving variations were not significant after FDR correction. There is no significant relationship in HC.

Integrity difference between AD and HC in the whole-brain WM skeleton and correlation with impulsivity

Patients with AD had reduced FA of widespread microstructural compared with HC at p < 0.05, corrected for FDR (Figure 1), mainly located in the forceps minor, forceps major, left superior longitudinal fasciculus, and right inferior frontal-occipital fasciculus (Supplementary Table 2).

Figure 1. Differences between alcohol dependence (AD) cohorts and healthy controls (HC) in the white matter skeleton.

Tract-based statistical analysis shows cross-sectional differences in the white matter skeleton between controls and the AD cohort undergoing diffusion tensor imaging for fractional anisotropy. AD had widespread microstructural abnormalities, namely reduced FA compared with controls at p < 0.05.

Considering the impact factors of white matter integrity, age, education, and nicotine use were controlled in partial correlation analysis. All tracts with a significant difference between the two groups in the above TBSS analysis were included in the partial correlations with impulsivity (BIS-11). Our results found a positive correlation between FA of forceps minor (cluster with local maxima coordinates: x = 71.9, y = 178, z = 78.2, R = 0.548, p < 0.05 and cluster with local maxima coordinates: x = 76.4, y = 175, z = 58.6, R = 0.606, p < 0.05) with BIS-11 in HC (Figure 2; Supplementary Table 3). However, there is no correlation between FA of WM skeleton and BIS-11 in AD (Supplementary Table 4).

Figure 2. Correlation between FA of WM skeleton and BIS-11 in HC.

(A) A positive correlation between FA of forceps minor (cluster with local maxima coordinates: x = 71.9, y = 178, z = 78.2) with the impulsive trait (BIS-11); and (B) A positive correlation between FA of forceps minor (cluster with local maxima coordinates: y = 175, z = 58.6) with the impulsive trait (BIS-11).

WM integrity difference in the striatal circuits and the correlation with impulsivity

Compared with HC, AD showed much weaker white matter integrity in the left (t = 0.218, p = 0.011, FDR p = 0.037) and right (t = −3.251, p = 0.002, FDR p = 0.010) striatum–SMA, left striatum–amygdala (t = −3.492, p = 0.001, FDR p = 0.010), and right striatum–insular (t = −2.181, p = 0.032, FDR p = 0.080) (Figure 3).

Figure 3. White matter integrity of striatal circuit structural connection comparisons between AD and HC.

(A) The comparisons of FA of left and right striatum–supplementary motor area between two groups; (B) The comparisons of FA of left and right striatum–ventrolateral prefrontal cortex between two groups; (C) The comparisons of FA of left and right striatum–dorsolateral prefrontal cortex between two groups; (D) The comparisons of FA of left and right striatum–amygdala between two groups; and (E) The comparisons of FA of left and right striatum–insular between two groups. The * and ** symbols indicate the group differences at the p < 0.05 and p < 0.01 respectively.

Figure 4. Correlation between FA of striatal circuits and impulsivity and mediation role.

(A) Visualization of white matter connectivity target from the striatum to the ventrolateral prefrontal cortex; (B) Scatter plot with a significantly negative correlation between FA of right striatum–dorsolateral prefrontal cortex tract and BIS-11 score; and (C) Mediation analysis of FA of right striatum-vlPFC, impulsivity (BIS-11), and severity of alcohol dependence (MAST score).

Mediation analysis

We also performed a mediation analysis to test whether the association between reduced white matter integrity of the right striatum-vlPFC and severity of alcohol drinking (MAST score) could be mediated by impulsivity (BIS-11). We found the relationship between reduced FA of right striatum-vlPFC and MAST score was mediated by impulsivity [β = −41.3184 Boot 95% CI (−75.1251~−19.3450)] p = 0.012. However, the reduced FA could not directly predict the severity of alcohol use [β = −30.0038 Boot 95% CI (−86.2956~26.2880)] p = 0.856 (Figure 4). Considering the power to test the mediation analysis of right striatum-vlPFC, impulsivity, and AD severity, we calculated the post-hoc power through power Mediation package in R. The result of post-hoc power analysis indicated that the moderate sample size of the AD (n = 51) can achieve 0.75.

Discussion

In our study, we found widespread abnormal white matter integrity of whole-brain skeleton and striatal circuits in AD compared with HC. We also found a negative relationship between FA of right striatum-vlPFC and BIS-11 in AD. Furtherly, the relationship between reduced FA of right striatum-vlPFC and severity of dependence was mediated by impulsive trait. Our study may provide the role of WM disruption of striatal circuits underlying the mechanism of impulsivity in AD.

In our study, we found mean alcohol intake per day was significantly positively correlated with impulsivity in AD. Our findings are consistent with some prior studies. Higher impulsivity with the BIS-11 shows higher levels of cue reactivity than less impulsive drinkers. Impulsivity is generally described as a construct consisting of a predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences. Thus, impulsivity has an influence on almost all stages of alcohol use and exacerbate disease progression. Therefore, it is necessary to investigate the mechanisms of impulsivity to improve the prognosis of AD.

We found widespread WM skeleton microstructural differences in patients with AD compared with HC, mainly in the corpus callosum, frontal area, and association fibers, which were in agreement with two previous literatures. Interestingly, a positive correlation was found between FA of forceps minor and BIS-11 in HC; however, there is no correlation found in AD. Impulsive traits are not necessarily pathological and likely reflect the desire/motivation to obtain high salience outcomes. Thus, this may suggest higher WM integrity in the frontal portion of the corpus callosum may promote adaptive social behavior in HC. The forceps minor is a large fiber bundle that connects the bilateral prefrontal cortices, which play an important role in motor control. A recent study found WM reduced along the forceps major and forceps minor and the FA was negatively correlated with the impulsivity score in attention deficit hyperactivity disorder. Similar finding not found in AD in our study may attribute to the severe disruption effect of alcohol on callosum forceps.

We also found weaker WM integrity of striatal circuits in AD compared with HC, including left and right striatum–SMA, and left striatum–amygdala. Alcohol produces dysfunctions in functional connectivity between the striatum and other cortical areas. Our DTI findings of decreasing microstructural connectivity of striatal circuits in AD performed a useful complement to previous work. As TBSS analysis fails to cover specific tracks between striatum and related ROIs, therefore, we selected ROIs associated with impulsive trait, including frontal cortex and the limbic basal ganglia. Specifically, the prefrontal cortex (dlPFC and vlPFC) has been linked to “top-down” cognitive control of inhibiting impulse that promotes risky behaviors and SMA has been related to the inhibition of motor impulse. Striatum acts in concert with portions of the PFC to modulate impulsive behavior. Insular cortex specifically outgoing projects to the striatum is also necessary for executive “top-down” control. The amygdala receives input from midbrain dopamine neurons and innervates the striatum, which regulates risky reward seeking. The disrupted functional and structural connectivity of cortico-limbic-striatal systems may be the mechanism of impulsive reward seeking in AD.

In our study, a negative correlation was found between WM integrity of right striatum-vlPFC connection and BIS-11 in AD. The vlPFC has been proposed as a key area for inhibitory control of inappropriate impulse. A recent study found AD displayed less modulation of activation in PFC when deciding to take risk decisions. Thus, diminished FA of striatum-vlPFC may presumably reflect ineffective PFC control over the striatum, which may account for disruption in inhibition control and impulsive behavior in AD. Our findings of abnormal microstructure of vlPFC to striatum were consistent with some functional MRI results. A previous study also found the reduced tract strength of striatum-vlPFC was associated with abstinence-induced increases in craving and the relapse in nicotine-dependent. Furthermore, we also found increased impulsivity mediated the correlation between lower FA of right striatum-vlPFC and the severity of alcohol dependence. This may suggest that WM disruption of certain cortico-striatal circuits could potentially reflect a vulnerability factor with increased impulsivity, which may promote severe alcohol consumption. It has been suggested that higher impulsivity may stem from the neurobiological effects of alcohol intake or, conversely, may be a premorbid deficiency of inhibitory control. However, more evidence is needed to address this causality relationship.

The strengths of this study were listed as follows. First, we explored the mechanisms of WM microstructures underlying impulsive trait in AD. Second, we addressed the WM integrity of striatal circuits by utilizing probabilistic tractography in AD to find the exact relationship with impulsive trait. However, there are also some limitations in our study. First, in spite that we examined the connectivity of the striatum circuits in AD, however, the segmentation of the striatum (e.g., ventral and dorsal striatum) may provide more information. Second, future studies with larger sample sizes could explore several other striatal tracts that might also be important for impulsive traits. Finally, like most of clinical studies, our study was a cross-sectional study. We cannot directly address the causal relationship between impulsive trait and long-term alcohol dependence.

Conclusion

In summary, we found abnormal white matter microstructure of striatal circuits in male AD patients. We also found a negative relationship between FA of right striatum-vlPFC and BIS-11 in AD. Meanwhile, the impulsive trait played a mediating effect between FA of striatum-vlPFC and severity of dependence. Thus, our findings could provide system-level insights into the abnormal integrity of striatal circuits in alcoholics and their potential roles as neuroimaging biomarkers for impulsive traits.

Summary

Alcohol dependence (AD) is a chronic relapsing disease significantly impacting global health. High relapse rates after abstinence highlight the need to understand underlying mechanisms, particularly impulsivity's role in transitioning from recreational use to dependence and relapse. Neuroimaging studies, specifically diffusion tensor imaging (DTI), offer a means to investigate the structural integrity of brain networks implicated in impulsivity and inhibitory control. This study utilized DTI probabilistic tractography to examine the relationship between white matter (WM) integrity of striatal circuits, impulsivity, and AD severity.

Subjects and Methods

A cross-sectional case-control study compared 51 male AD patients (meeting DSM-IV criteria) with 27 age- and gender-matched healthy controls (HC). Data included sociodemographic information, alcohol use history (assessed using the Michigan Alcoholism Screening Test, MAST), impulsivity (Barratt Impulsiveness Scale, BIS-11), and alcohol cue reactivity. DTI was employed to assess WM integrity using both voxel-wise analysis (TBSS) and seed-based probabilistic tractography focusing on striatal circuits. Statistical analyses included independent t-tests, correlation analyses, and mediation analysis to examine the relationships between WM integrity, impulsivity, and AD severity.

Results

Significant differences in alcohol use history and nicotine dependence were observed between AD patients and HC. Positive correlations were found between BIS-11 scores and alcohol cue reactivity measures in the AD group. TBSS analysis revealed widespread reduced fractional anisotropy (FA) in AD patients compared to HC, primarily in the corpus callosum and association fibers. Seed-based tractography showed significantly reduced FA in several striatal circuits in AD patients compared to HC, particularly those connecting the striatum with the supplementary motor area, amygdala, and insula. Importantly, a negative correlation was found between FA in the right striatum-ventrolateral prefrontal cortex (vlPFC) tract and BIS-11 scores in AD patients. Mediation analysis revealed that impulsivity mediated the relationship between reduced FA in the right striatum-vlPFC tract and AD severity.

Discussion

The findings suggest that widespread WM abnormalities, particularly within striatal circuits, contribute to impulsivity in AD. Reduced FA in the right striatum-vlPFC pathway may reflect impaired inhibitory control, potentially increasing vulnerability to relapse. These results highlight the complex interplay between WM integrity, impulsivity, and the severity of alcohol dependence. Limitations include the cross-sectional design and the need for larger sample sizes to further explore specific striatal subregions.

Conclusion

This study provides evidence for abnormal WM microstructure in striatal circuits in male AD patients, linking it to impulsivity and AD severity. The negative correlation between FA of the right striatum-vlPFC tract and BIS-11 scores, and the mediating role of impulsivity, highlight the potential of these neuroimaging biomarkers in understanding and possibly predicting the course of AD.

Summary

Alcohol dependence (AD) is a chronic relapsing disease impacting the ability to control alcohol consumption despite negative consequences. A significant portion of the global disease burden is linked to AUD, resulting in injuries, cancers, cardiovascular issues, cirrhosis, and suicide. Mortality risk correlates directly with alcohol consumption levels. Relapse is a significant challenge in AD recovery, with impulsivity playing a crucial role in the transition from recreational use to dependence and subsequent relapse. This study investigates the relationship between white matter (WM) integrity in striatal circuits, impulsivity, and AD severity.

Subjects

This cross-sectional study, conducted at the Peking University Institute of Mental Health, comprised 51 male AD patients (ages 31-59) and 27 age- and gender-matched healthy controls (HC). AD participants met DSM-IV criteria, were abstinent for at least two weeks, and underwent a series of assessments, including the Michigan Alcoholism Screening Test (MAST), the Clinical Institute Withdrawal Assessment for Alcohol Scale (CIWA-AR), and the Barratt Impulsiveness Scale (BIS-11). HC participants had no history of AUD or other Axis I disorders.

Procedure

The study involved two sessions. Session one collected sociodemographic, alcohol use, and clinical data, along with impulsivity (BIS-11) and AD severity (MAST) assessments. Cue reactivity was assessed using visual analog scales and physiological measures. Session two consisted of magnetic resonance imaging (MRI) scans.

MRI Data Acquisition

MRI data was acquired using a GE Discovery MR750 3.0T scanner. Three-dimensional T1-weighted images and diffusion tensor imaging (DTI) data were collected.

TBSS Analysis

Voxel-wise analysis (TBSS) was used to identify differences in fractional anisotropy (FA) and mean diffusivity (MD) between AD and HC groups. The analysis focused on whole-brain white matter tracts.

Striatum Structural Connectivity Analysis

Masks defining specific brain regions (dlPFC, vlPFC, SMA, insula, amygdala, and striatum) were created using the Anatomical Automatic Labeling (AAL) template and applied to individual brain spaces.

Probabilistic Tractography

Probabilistic tractography was employed to trace white matter fibers between the striatum and target regions using the probtrackx algorithm. FA values were calculated for each tract.

Statistical Analysis

Statistical analyses used independent t-tests, Kruskal–Wallis tests, chi-square tests, and correlation analyses to compare demographic and clinical characteristics, and assess the relationship between impulsivity and alcohol-related factors. Partial correlation analyses examined the relationship between FA values and impulsivity, controlling for age and education. Mediation analysis assessed the role of impulsivity in the relationship between white matter integrity and AD severity.

Results

No significant differences were found in age, education, or marital status between groups. AD patients exhibited earlier alcohol use onset, significantly higher MAST scores, and more severe nicotine use compared to the HC group. Positive correlations were observed between BIS-11 scores and cue-induced craving changes in the AD group. AD patients showed reduced FA in widespread white matter regions compared to HC. A positive correlation was observed between FA of the forceps minor and BIS-11 in HC, but not in AD. AD patients exhibited weaker white matter integrity in several striatal circuits compared to HC. A negative correlation was found between FA of the right striatum-vlPFC connection and BIS-11 in AD. Mediation analysis indicated that impulsivity mediated the relationship between reduced FA of the right striatum-vlPFC and AD severity.

Discussion

The study findings suggest abnormal white matter microstructure in striatal circuits is associated with impulsivity and the severity of AD. The observed correlations highlight the complex interplay between white matter integrity, impulsivity, and alcohol dependence. Limitations of the study include the cross-sectional design and sample size, necessitating future longitudinal studies with larger samples.

Conclusion

This study provides evidence of abnormal white matter microstructure in striatal circuits in male AD patients, linking it to impulsivity and the severity of dependence. These findings may contribute to the development of neuroimaging biomarkers for impulsive traits in AD.

Summary

Alcohol dependence (AD) is a serious, chronic illness impacting the ability to control alcohol use despite negative consequences. It's a leading cause of health problems worldwide. Impulsivity plays a significant role in developing and relapsing AD. This study investigated the connection between impulsivity, the severity of AD, and the structural integrity of brain's white matter, particularly the pathways connecting the striatum (a brain region involved in reward and motivation) to other areas.

Subjects

Researchers studied 51 men diagnosed with AD and 27 healthy men (control group). All AD participants were hospitalized, had completed alcohol withdrawal, and met specific criteria. The groups were similar in age and other factors, except for alcohol use and nicotine dependence, which were significantly higher in the AD group.

Procedure

The study involved two sessions. The first collected information on demographics, alcohol use, and impulsivity levels (using the Barratt Impulsiveness Scale – BIS-11). Alcohol dependence severity was measured using the Michigan Alcoholism Screening Test (MAST). The second session included an MRI scan to assess brain white matter.

MRI data acquisition

A 3.0T MRI machine was used to obtain detailed brain images. Specific imaging techniques (DTI) were used to assess the integrity of the brain's white matter.

TBSS analysis

TBSS, a type of statistical analysis, was used to compare white matter differences between the AD and control groups across the whole brain.

Striatum structural connectivity analysis

Researchers focused on the connections between the striatum and other brain regions involved in impulse control, using a technique called probabilistic tractography.

Probabilistic tractography

This technique allowed for a detailed examination of the white matter pathways connecting the striatum to specific brain regions.

Statistical analysis

Statistical tests were used to compare the groups and to determine the relationships between impulsivity, white matter integrity, and the severity of AD. Mediation analysis explored if impulsivity explained the link between white matter integrity and alcohol dependence severity.

Results

The AD group showed reduced white matter integrity in several brain areas compared to the control group. In the AD group, impulsivity was negatively correlated with the integrity of the connection between the striatum and the ventrolateral prefrontal cortex (vlPFC), a brain area involved in impulse control. Impulsivity mediated the relationship between this white matter integrity and the severity of alcohol dependence.

Discussion

These findings suggest that disrupted white matter pathways connecting the striatum and other brain regions contribute to impulsivity in AD. The study highlighted the importance of white matter integrity in the brain's impulse control network.

Conclusion

The study provides evidence of abnormal white matter structure in the brains of men with AD, particularly in connections involving the striatum. This abnormality is linked to impulsivity and the severity of the condition, suggesting these brain structures could be potential targets for treatment and biomarkers.

Summary

This study looked at the brains of people with alcohol problems and compared them to the brains of people without alcohol problems. Scientists used special brain scans to see how well different parts of the brain were connected. They also looked at how impulsive the people were.

Subjects

The study included 51 men with alcohol problems and 27 men without alcohol problems. The men with alcohol problems were all in the hospital, but had stopped drinking for at least two weeks. The scientists made sure the two groups were similar in age.

Procedure

The study had two parts. First, scientists gathered information about the men, including how impulsive they were and how much alcohol they usually drank. They also gave the men tests to see how much they craved alcohol when shown alcohol-related things. Second, the men had brain scans.

MRI data acquisition

Special brain scans (MRIs) were used to create detailed pictures of the brain’s connections.

TBSS analysis

Scientists used a special method to look at the brain scans and see if the connections between brain areas were different in the two groups of men.

Striatum structural connectivity analysis

Scientists focused on connections between a part of the brain called the striatum and other important areas.

Probabilistic tractography

Another method was used to look at the brain scans and measure the strength of connections between different brain regions.

Statistical analysis

Scientists used math to compare the two groups and see if there were differences in how well their brains were connected and how impulsive they were.

Results

Men with alcohol problems had weaker connections in many parts of their brains compared to men without alcohol problems. There was a link between how impulsive a person was and how well some brain connections worked.

Discussion

The study showed that problems with brain connections are linked to impulsiveness in people with alcohol problems. This information may help in better understanding and treating alcohol problems.

Conclusion

This study found problems in how different parts of the brains of men with alcohol problems were connected. These problems were linked to how impulsive they were and how severe their alcohol problem was. This information may be useful in the future for understanding and treating alcohol problems.