Introduction

Alcohol is the most commonly used addictive substance in the world and, because it is estimated that 107 million people worldwide suffer from alcohol use disorders (AUD) and that it also causes 2.8 million premature deaths every year, AUD has emerged as a major public health issue on a global scale (Wigger et al., 2022). AUD is a type of substance use disorder, mainly manifested by excessive and uncontrollable cravings for alcohol, and a common disorder: it can accelerate the course of other clinical or psychiatric disorders, thereby shortening the patient's life expectancy by >10 years (Schuckit, 2009).

Alcohol use affects the gray and white matter of the brain and also alters the electrophysiology of the brain, which then leads to addiction through the process of neuroplasticity (a trait of the nervous system that permits lifelong adaptation to change) (Khan et al., 2021). The neuronal alterations of alcohol exposure have been found extensively. Acute alcohol exposure selectively over-activates primary motor cortex neurons resulting in reduced motor performance (Zhang et al., 2022a), and repeated alcohol exposure over-activates dentate gyrus neurons of the hippocampus along with spatial memory damage (Zhang et al., 2022b). Many existing studies have revealed molecular targets for AUD at the genetic level, but because different alcoholics have different genetic substrates, are at different social levels, and experience different life circumstances, these factors pose significant limitations to research in areas such as risk genes in alcoholics (Ferraguti et al., 2015). Therefore, we want to investigate structural and functional alterations in the brain of AUD patients by a new approach—neuroimaging—that could overcome these limitations while also expanding our systematic knowledge of the physiology and pathology of the human nervous system.

Neuroimaging research has evolved from a focus on relatively isolated functional brain regions, where different brain regions corresponded to different brain functions, to the integration of functional regions, where there are many functional and efficacy connections across functional regions, even at a more subtle level (Oberlin et al., 2020). In this paper, we focus on the main noninvasive neuroimaging methods, structural magnetic resonance imaging (MRI), functional MRI (fMRI) and electroencephalography (EEG), to analyze and summarize the research on alcohol addiction. As more researchers concentrate on simultaneous transcranial magnetic stimulation (TMS) and neuroimaging devices, we introduced transcranial magnetic techniques, which are known as one of the four major technological tools for studying brain science in the 21st century, to investigate the impact of transcranial magnetic techniques on the examination and intervention of patients with AUD (Table 1).

Table 1: Summary of the four brain techniques.

Brain structural alterations in AUD

There is a consensus that the volume density of the prefrontal lobe, especially the medial frontal lobe, is reduced in patients with AUD. Structural MRI studies provide ample evidence of reduced gray matter (GM) volume associated with alcohol dependence, with the most pronounced damage in the frontal lobes (Yang et al., 2016). An analysis by anatomical likelihood estimation found that GM changes in patients with AUD were distributed in different parts of the cingulate and medial frontal gyri, the paracentral lobe, the left postcentral and precentral gyri, the left anterior and right posterior insulae, and the left superior frontal gyrus (Spindler et al., 2021). With these findings, we hypothesize that the reduction of GM in AUD may disrupt network communication and lead to neurocognitive impairment associated with chronic alcohol consumption. However, studies have also shown that the associated brain imaging deficits are also reversed and partially recovered, usually after a few weeks to months of abstinence from alcohol, which emphasizes the importance of abstinence from alcohol (Nutt et al., 2021). An analysis of the effects of gender and age on the GM of alcoholics' brains using voxel-based morphometry and surface-based morphometry found that women had more adverse effects from alcohol use on the left orbitofrontal cortex thickness than men did, and had a more pronounced negative correlation between age and right insula volume (Galandra et al., 2018, Thayer et al., 2016). The influence of gender, age, and other factors on the structure and function of the brain in alcohol-addicted patients could also be a major breakthrough in our understanding of this disease.

There is mounting evidence that AUD patients experience significant changes in their white matter in addition to the commonly described defects in the GM. In individuals with AUD, an investigation using anatomical likelihood estimation found four separate sets of aggregated macro- and microstructural white matter changes in the fornix, the anterior and posterior cingulum, the right posterior limb of the internal capsule, and the genu and body of the corpus callosum (Spindler et al., 2022). For different levels of alcohol consumption, studies have found that any level of alcohol consumption affects brain volume and white matter microstructure, especially the corpus callosum, resulting in altered cognitive function in patients (Nutt et al., 2021). We hope to learn more about how white matter changes as the disease progresses in future studies.

Of interest, another recently discovered brain region associated with alcoholism is the cerebellum. Alcoholics are often associated with shrinkage of the cerebellum. The cerebellar Purkinje fibers, granule cells, and white matter fibers are the main targets of neurodegeneration in alcoholics, with the most pronounced atrophy in the anterior middle part of the vermis (de la Monte & Kril, 2014). This atrophy is progressive. In a recent study, cerebellar volume loss was found to increase with age in patients with additional neurological complications such as Wernicke's Korsakoff syndrome (Nutt et al., 2021). In functional connectivity (FC) analysis, Abdallah et al. abandoned previous static FC measurements and used the sliding window method and multilayer community assay to investigate dynamic brain-cerebellar FC in AUD patients and found that the AUD group showed significant FC variability between the cerebellum and both the frontoparietal executive control network and ventral attention network, with significantly less cerebellar flexibility and greater integration (Abdallah et al., 2021). However, the small sample size limits the reliability of the findings, and in the future, we hope to explore the characteristics of dynamic FC at the level of cerebellar sub-modules to gain a more comprehensive understanding of cerebellar alterations associated with AUD.

Brain functional alterations in AUD

Since its introduction in the early 1990s, fMRI has grown in popularity. fMRI has a much higher spatial resolution when looking at the activation of brain regions, even to the order of seconds in temporal resolution. Blood-oxygen-level-dependent- (BOLD-)fMRI is one of these methods, which uses the ratio of oxyhemoglobin to deoxyhemoglobin in the local blood to represent neuronal activity in the brain (Zakiniaeiz et al., 2017). The temporal sensitivity of the physiological blood flow response largely determines the extent to which active neurons can be detected in BOLD-fMRI (Weingarten & Strauman, 2015).

Task-state fMRI is an fMRI of the brain while performing a specific task, which necessitates the use of a complex task paradigm and can reflect different activation patterns of brain regions under different tasks. The tapping finger task state fMRI results revealed reduced FC between the prefrontal cortex and parts of the cerebellum in alcoholics, implying that alcoholism is associated with dysfunction of thalamocortical cerebellar neural pathways (Dupuy & Chanraud, 2016). Another finger-tapping experiment found that AUD patients did not commandeer the anterior cerebellar network as normal during maximal self-paced tapping, but instead recruited parietal function to perform the tapping task (Parks et al., 2010). According to the studies mentioned before, alcohol-dependent patients have impaired neural pathway function and require more compensatory increases in brain areas to complete the assigned task. Applying a monetary incentive delay task to participants, some scholars found that the ventral striatum and posterior cingulate cortex in AUD patients were associated with reduced reward responsiveness, while the anterior cingulate cortex and dorsal striatum were associated with reduced punishment responsiveness (Aloi et al., 2019). This suggests that the severity of AUD patients is negatively correlated with the activity of reward processing neural circuits. A recent substance-related visual cueing trial using alcohol versus nonalcoholic beverages showed that AUD patients showed more BOLD responses in the left posterior cingulate cortex when confronted with drinking behavior (Fukushima et al., 2020); this also provides evidence that patients with AUD have different patterns of brain activity in response to different visual stimuli, which may help clinicians develop treatments for patients with AUD.

Resting-state fMRI is the focus of current fMRI research, and of the total energy consumed by the brain, resting-state energy expenditure is much higher than task-related neuronal metabolic activity (O'Connor & Zeffiro, 2019). In terms of FC, evidence suggests that alcohol-dependent individuals in withdrawal show significantly enhanced BOLD signals in reward-related anterior striatal brain regions, particularly the prefrontal cortex, ventral striatum, orbitofrontal cortex, and anterior cingulate cortex, and, conversely, BOLD responses to anticipated nondrug rewards become blunted in the ventral striatum and dorsal striatum (Nutt et al., 2021). The default mode network is a major hotspot in the study of resting-state functional brain networks, with abnormal default mode network connectivity in nonwithdrawn AUD patients, impaired posterior cingulate cortex-cerebellar connectivity, and increased connectivity with the midbrain (Fritz et al., 2022), and abnormal FC between the prefrontal, parietal, and cerebellar lobes (Liu et al., 2018). Another finding was that at the limbic level, the FC strength of the cerebellar–thalamic–striatal–cortical circuit altered in patients with AUD, suggesting a disruption in the topology of the patient's motor executive network, which may underlie AUD-related movement disorders (Zhu et al., 2018). Liu et al. used the receiver operating characteristic curve and the Pearson correlation to show that amplitude of low frequency fluctuations (ALFF) differences in specific brain regions of AUD patients have high sensitivity and specificity, and that ALFF analysis can be used as a biological indicator to detect spontaneous brain activity in alcohol-dependent patients (Liu et al., 2018). In another study related to the ALFF, Hong et al. found that the frame frontal cortex of 56 sober alcoholics and 56 healthy controls varied in frequency-dependent oscillatory power, and that low scores on psychomotor and situational memory tests were significantly correlated with abnormal frame frontal high-frequency power in alcoholics, suggesting that overactivation of the frame frontal cortex contributes to increased relapse (Hong et al., 2018).

For the heterogeneity of AUD, some scholars have conducted phenotypic analysis by the underlying motivation of individuals to drink alcohol, and it was found that remission/habitual drinkers (i.e. negative reinforcement/normalization) showed greater dorsal striatum activation to visual alcohol cues than reward drinkers (i.e. positive reinforcement), while cue-induced ventral striatum activation did not differ significantly between groups (Burnette et al., 2021). Our understanding of regional brain activation has improved thanks to the widespread use of fMRI, which will also play a part in predicting the clinical outcome of AUD medication therapy (Table 2).

Table 2: Studies about MRI for AUD.

EEG applications in AUD

EEG is a noninvasive test of brain activity that captures electrical impulses from the brain (Table 3). A study about EEG, event-related potentials, and event-related oscillations discovered that early fast activity associated with sensory reception occurred in the visual cortex (i.e. occipital lobe), while slower activity associated with higher cognitive function involved the parietal and frontal lobes (Porjesz & Begleiter, 2003). Decreased visual and auditory P3 amplitude commonly occurs in alcohol-dependent patients, and to a lesser extent in women than in men (Cofresí et al., 2022). It has long been shown that in addition to P3, delta oscillations, theta oscillations, and resting beta power are also abnormal in alcoholics and even in the offspring of alcoholics (Rangaswamy et al., 2004). Alcoholics exhibited higher energy in the theta to high beta bands than controls, and the magnitude and anterior–posterior range of these effects varied between bands (Fein & Allen, 2005). Meanwhile, dimensional complexity can be used as a measure of EEG complexity, with significant increases in EEG dimensional complexity values in frontal (F3, F4), right posterior temporal (T6), and occipital (O1, O2) regions after viewing alcohol cues in alcoholics (Kim et al., 2003). These regions could be targeted brain areas for future studies of alcohol craving and addiction.

Table 3: Studies about EEG for AUD.

Recently, several researchers have proposed a machine learning approach based on resting-state EEG data that uses synchronization likelihood features as objective markers for screening AUD patients and healthy controls, and this machine learning approach suggests that EEG-based computer aided design tools could be developed that could help make AUD screening an automated and standard procedure (Mumtaz et al., 2018). However, due to the lack of a deep learning architecture for extracting spatiotemporal characteristics from EEG data, Neeraj et al. presented a combination of fast Fourier transform, a convolutional neural network, long short-term memory, and an attention mechanism. This design has a 98.83% accuracy in determining whether an individual is an alcoholic or not (Neeraj et al., 2021). Meanwhile, the wavelet scattering transform together with the intentional classifier can replace the convolutional neural network with extremely high accuracy and sensitivity, where the features based on the occipital and parietal regions of wavelet scattering transform are most beneficial to distinguish alcoholic patients from normal people (Buriro et al., 2021).

The alteration of EEG differences is multifaceted, and it is recommended to improve the accuracy of observing EEG differences from a combination of multiple perspectives and techniques. It has been suggested to use machine learning and artificial intelligence to analyze EEG signals from at least five perspectives, including individual electrodes, cortical subregions, left and right hemispheres, anterior and posterior axes, and the entire cortex, to diagnose and explore the prognosis of patients with substance use disorder (Minnerly et al., 2021). The combination of artificial intelligence and EEG promises to be a powerful tool for the rapid and low-cost diagnosis of mental health in AUD patients. Although the magnetoencephalographic is used as a superior form of EEG, some researchers recommend synchronized magnetoencephalographic-EEG experiments to better meet the traceability requirements of experimental data by taking into account the detection of surface and deep sources on the one hand, and improving the spatial resolution of EEG data on the other (Hauk et al., 2022). However, due to the inherent limitations of magnetoencephalographic or EEG, its traceability results cannot be very accurate.

TMS applications and future directions

TMS is accomplished by passing a rapidly alternating current via a coil close to the scalp, which forms a magnetic field in a targeted area of the brain below and generates a current in the brain via neuronal depolarization, thus influencing metabolism and neural electrical activity in the brain. The duration of the ipsilateral silent phase and contralateral silent phase are potential indicators of central nervous system hyperexcitability. In one study, it was found that participants at high-risk of AUD had a significantly shorter contralateral silent phase (indicating diminished intracortical inhibition) and ipsilateral silent phase (indicating diminished interhemispheric, transcallosal inhibition) compared to participants at low risk for AUD (Muralidharan et al., 2008).

TMS stimulation of the dorsolateral prefrontal cortex (DLPFC) has been shown to be effective as a target of action (Table 4). A single-blind, sham-controlled randomized controlled trial of patients with AUD showed a significant reduction in alcohol craving (Mishra et al., 2010), a significant increase in days of abstinence, and a significant reduction in alcohol consumption after high-frequency rTMS (HF-rTMS) in the right DLPFC compared to sham surgery (Antonelli et al., 2021), whereas no significant differences were found in the reduction of craving and alcohol intake for the HF-rTMS intervention on left-sided DLPFC (Del Felice et al., 2016, Hoppner et al., 2011). However, at the same time, some scholars also randomly divided 20 AUD patients into two groups, one group had the left side of DLPFC stimulated and the other group had the right side stimulated, and found a significant reduction in craving after the TMS in both (Ceccanti et al., 2015, Mishra et al., 2015). This difference in results may be due to bias in single-blind trials, limitations of small sample sizes, or participants receiving treatment that interfered with the assessment of craving. For the mechanistic study of rTMS for alcoholism, a randomized, double-blind, placebo-controlled study randomized 18 alcoholics into two groups: nine in the true stimulation group, and nine in the sham stimulation group. The true stimulation group indicated that rTMS significantly decreased cortisol levels and prolactin levels, thus suggesting an increase in dopamine, while the sham stimulation group showed no significant effect (Ceccanti et al., 2015). Observations on the modulation of the dopamine system will be useful for the study of AUD. A recent small randomized trial showed the most significant efficacy of HF-rTMS in right-sided DLPFC at a 3-month follow-up (Belgers et al., 2022). It gives us a direction for future research to explore how to change the intervention protocol, such as stimulation frequency, duration of each stimulation, and stimulation interval, to induce a longer treatment effect.

Table 4: Studies about rTMS of the DLPFC for AUD.

Simultaneous TMS-EEG and TMS-fMRI experiments are technically feasible and provide insights into brain function beyond what is possible when each method is used alone (Table 5). TMS-EEG can assess various properties of the cortex such as excitability and connectivity (Tremblay et al., 2019). Through the combination of TMS-EEG, it was more clearly discovered that postabstinence AUD patients have changed cortical function related to GABAergic neurotransmission (Kaarre et al., 2018), including decreased frontal cortex excitability and increased motor cortex excitability (Naim-Feil et al., 2016). In addition, in another TMS-EEG trial of ethanol consumption in 10 healthy participants, ethanol was found to possibly alter the FC between the prefrontal and motor cortices (Kahkonen et al., 2001). TMS-fMRI is a viable tool that can explore the potential mechanisms of TMS-mediated neuronal modulation (Mizutani-Tiebel et al., 2022). Hanlon et al. found by comparing the effects of TMS on BOLD signals before and after continuous theta-burst stimulation of the frontal pole in alcoholics that continuous theta-burst stimulation of the frontal pole significantly reduced activity in the orbitofrontal region and indirectly reduced activity in several functionally relevant nodes in the salience network, such as the anterior insula and anterior cingulate gyrus, which becomes a powerful new adjunct to addiction treatment (Hanlon et al., 2017). The combination of EEG and fMRI covers both temporal and spatial resolution, and recent technical improvements have demonstrated the feasibility of simultaneous TMS-EEG-fMRI (Janssens & Sack, 2021, Peters et al., 2020, Peters et al., 2013). A study covering four healthy right-handed volunteers also showed us that TMS-EEG-fMRI can directly monitor the relationship between oscillatory states and signal propagation in the entire cortical–subcortical network (Peters et al., 2020), opening a new avenue for studying dynamic cognitive loops and their dysfunction (Fig. 1).

Table 5: Studies on the integration of TMS, EEG, and fMRI in patients with AUD.

Conclusion and Future Perspectives

With the development of neuroimaging techniques, an increasing number of studies have revealed alterations in brain structure and function in alcohol dependence, providing neuroimaging evidence for early diagnosis, treatment, assessment of efficacy, and alcohol withdrawal in patients with AUD. Noninvasive brain stimulation techniques have also been used to explore the effects of DLPFC activity on cognitive processes and can focus on targeting the cortical cortex to improve cognitive function and treat related disorders (Liu et al., 2021). Among them, structural MRI allows clear visualization of morphological changes in the brain, meanwhile, fMRI and EEG are the most commonly used techniques, and both can be combined with TMS (the most commonly used noninvasive physical therapy) for efficacy evaluation and are widely used.

Current research has found that patients with alcohol dependence often have reduced GM, and the damage is most pronounced in the frontal lobes (Yang et al., 2016). Many studies have targeted the prefrontal cortex for addiction treatment, and various studies have confirmed that stimulation of this area is beneficial in reducing cravings and improving cognitive function changes due to substance dependence (Antonelli et al., 2021, Belgers et al., 2022, Mishra et al., 2010). After several weeks to months of abstinence, the corresponding brain imaging deficits were reversed (Nutt et al., 2021), affirming the importance of abstinence and the feasibility of treatment.

In future research, we would like to consider the following questions: (i) how to conduct studies with larger sample sizes to increase the likelihood of detecting small effects and improve the generalizability of findings; (ii) how machine learning techniques can improve the prediction of AUD across modalities; (iii) what additional evidence comparison of left- and right-sided DLPFC stimulation using a randomized double-blind controlled study would obtain for brain regions; (iv) how existing sham stimuli and real stimuli differ in some ways, and why the nature of sham stimuli remains a limitation of our study; and (v) how to conduct more joint TMS and neuroimaging studies to further investigate the mechanism of AUD.

Summary

Alcohol use disorder (AUD) presents a significant global public health concern, impacting millions and resulting in substantial mortality. The disorder is characterized by excessive and uncontrollable alcohol cravings, often exacerbating other health conditions. Neuroimaging techniques offer a valuable approach to investigating the structural and functional brain alterations associated with AUD, overcoming limitations inherent in genetic and socio-environmental analyses.

Neuroimaging Modalities in AUD Research

Neuroimaging research has transitioned from a focus on isolated brain regions to an understanding of integrated functional networks. This study examines structural magnetic resonance imaging (MRI), functional MRI (fMRI), and electroencephalography (EEG) to analyze AUD. The integration of transcranial magnetic stimulation (TMS) with neuroimaging is also explored as a promising avenue for investigation and intervention.

Brain Structural Alterations in AUD

Studies consistently demonstrate reduced grey matter (GM) volume in AUD patients, particularly within the prefrontal lobe. Structural MRI reveals GM alterations across various brain regions, including the cingulate and medial frontal gyri. These GM reductions potentially disrupt network communication, contributing to neurocognitive impairment. Notably, some brain imaging deficits demonstrate partial recovery following abstinence, highlighting the importance of treatment adherence. Further research is needed to explore the influence of gender, age, and other factors on these structural changes.

White matter abnormalities are also prevalent in AUD. Studies utilizing anatomical likelihood estimation have identified micro- and macrostructural alterations in various white matter tracts. Alcohol consumption, at any level, impacts brain volume and white matter microstructure, notably the corpus callosum, resulting in impaired cognitive function. Cerebellar atrophy is another significant finding, particularly affecting Purkinje fibers and granule cells, often progressing with age and associated neurological complications. Dynamic functional connectivity analyses reveal altered interactions between the cerebellum and other brain networks in AUD patients.

Brain Functional Alterations in AUD

fMRI, with its high spatial resolution, allows detailed investigation of brain activation patterns. Task-state fMRI studies in AUD reveal altered functional connectivity between the prefrontal cortex and cerebellum, suggesting dysfunction in thalamocortical cerebellar neural pathways. Studies using monetary incentive delay tasks and alcohol-related visual cues reveal altered reward and punishment responsiveness in AUD patients.

Resting-state fMRI research identifies alterations in default mode network connectivity, particularly involving the posterior cingulate cortex and cerebellum. Furthermore, alterations in the cerebellar–thalamic–striatal–cortical circuit suggest disruption of motor executive networks. Amplitude of low-frequency fluctuations (ALFF) analysis has shown promise as a biomarker for detecting spontaneous brain activity alterations in AUD. Phenotypic analysis reveals differing brain activation patterns based on the underlying motivations for alcohol consumption.

EEG Applications in AUD

EEG, a non-invasive measure of brain electrical activity, reveals abnormalities in various oscillatory patterns in AUD patients, including P3 amplitude, delta, theta, and beta oscillations. These alterations are observed not only in affected individuals but also in their offspring. Dimensional complexity analyses reveal increased complexity in specific brain regions following alcohol cue exposure.

Machine learning approaches utilizing EEG data show promise for automated AUD screening. Advanced techniques combining fast Fourier transform, convolutional neural networks, and long short-term memory networks achieve high accuracy in distinguishing alcoholics from controls. Future applications may benefit from incorporating multiple analytical perspectives to enhance diagnostic accuracy.

TMS Applications and Future Directions

TMS, by inducing magnetic fields to modulate neuronal activity, offers a therapeutic intervention. Studies indicate that high-frequency rTMS (HF-rTMS) to the dorsolateral prefrontal cortex (DLPFC) may reduce alcohol cravings and increase abstinence periods, although inconsistencies regarding left versus right DLPFC stimulation exist. Mechanistic studies suggest that rTMS modulates dopamine systems.

Simultaneous TMS-EEG and TMS-fMRI experiments offer further insights into brain function. TMS-EEG studies reveal alterations in cortical excitability and connectivity in AUD, while TMS-fMRI studies demonstrate effects on activity in relevant brain networks. The integration of TMS with EEG and fMRI offers a powerful approach to investigate AUD mechanisms and therapeutic targets.

Conclusion and Future Perspectives

Neuroimaging and TMS studies have significantly advanced our understanding of AUD. Future research should focus on larger sample sizes, enhanced machine learning techniques for cross-modal prediction, comprehensive comparisons of left and right DLPFC stimulation, improved sham stimulation methodologies, and further integration of TMS with neuroimaging to clarify underlying mechanisms and improve therapeutic strategies.

Alcohol Use Disorder: A Neuroimaging Perspective

Alcohol use disorder (AUD) presents a significant global health concern, affecting millions and contributing to substantial premature mortality. The disorder's impact stems from its effects on brain structure and function, leading to addiction through neuroplasticity. Neuroimaging techniques offer a powerful approach to investigate these neurological alterations, overcoming limitations inherent in solely genetic or socio-environmental analyses of AUD.

Neuroimaging Modalities in AUD Research

Neuroimaging research has transitioned from examining isolated brain regions to a more integrated understanding of functional connectivity across various regions. This study focuses on key non-invasive methods: structural magnetic resonance imaging (sMRI), functional MRI (fMRI), and electroencephalography (EEG), along with transcranial magnetic stimulation (TMS) which, in combination with neuroimaging, allows for interventional studies. These techniques provide a multi-faceted approach to studying brain alterations in AUD.

Structural Brain Alterations in AUD

Studies using sMRI consistently reveal reduced gray matter (GM) volume in AUD patients, particularly within the prefrontal cortex. These reductions are hypothesized to disrupt inter-regional communication, leading to cognitive impairments. Importantly, some of these deficits demonstrate reversibility following abstinence, underscoring the potential for recovery. Gender and age also appear to influence the impact of alcohol on brain structure, with women showing potentially greater vulnerability in certain regions. White matter alterations, including changes within the corpus callosum and other major tracts, also occur in AUD and are associated with cognitive dysfunction. Cerebellar atrophy, particularly in the vermis, is another consistent finding, potentially contributing to motor and cognitive impairments. Functional connectivity analyses further reveal dynamic changes in cerebellar interactions with other brain networks in AUD patients.

Functional Brain Alterations in AUD

fMRI, particularly resting-state fMRI, has become crucial in understanding functional alterations in AUD. Studies show disrupted connectivity within the default mode network and alterations in reward-related circuitry. Task-based fMRI studies reveal impaired neural pathway function and compensatory recruitment of brain regions in AUD patients performing cognitive tasks. The heterogeneity of AUD, including differences in underlying motivations for alcohol use, is reflected in distinct patterns of brain activation. EEG studies have identified abnormalities in various brain wave patterns, including event-related potentials (ERPs) and oscillations, offering further insights into the neurophysiological underpinnings of AUD. Furthermore, machine learning approaches using EEG data show promising potential for automated AUD screening and diagnosis.

TMS Applications and Future Directions

TMS, often targeted at the dorsolateral prefrontal cortex (DLPFC), has shown potential as an intervention for AUD. Studies demonstrate the effectiveness of repetitive TMS (rTMS) in reducing alcohol cravings and improving abstinence rates. However, inconsistencies in findings regarding the optimal DLPFC hemisphere for stimulation underscore the need for larger, double-blind controlled trials. Combined TMS-EEG and TMS-fMRI studies offer significant advantages, providing a more comprehensive understanding of the effects of TMS on brain activity and connectivity. These combined techniques reveal alterations in cortical excitability and connectivity in AUD and show potential for monitoring the effects of interventions.

Conclusion and Future Directions

Neuroimaging, along with TMS, provides valuable insights into the neurological basis of AUD, facilitating improved diagnostic tools and therapeutic strategies. Future research should prioritize larger sample sizes, refine machine learning applications for prediction and diagnosis, and conduct further investigation into the optimal parameters for TMS interventions, focusing on mechanistic studies and resolving inconsistencies in existing research. Moreover, a deeper understanding of the interaction between different neuroimaging measures and the development of combined TMS and multi-modal neuroimaging approaches will greatly advance our understanding of and treatment for AUD.

Alcohol Use Disorder and Neuroimaging

Alcohol use disorder (AUD) is a significant global health problem, affecting millions and contributing to premature deaths. AUD is characterized by intense, uncontrollable cravings for alcohol and can worsen other health issues. The disorder significantly impacts brain structure and function, leading to addiction through neuroplasticity—the brain's ability to adapt. Studies have shown alcohol's effects on specific brain regions, altering neuronal activity and impacting motor skills and memory. However, the genetic, social, and environmental factors influencing AUD make research challenging. Neuroimaging offers a new approach to overcome these obstacles and improve understanding of the disorder.

Neuroimaging Techniques in AUD Research

Neuroimaging has evolved from studying isolated brain regions to understanding the complex interactions between them. This research utilizes structural MRI (sMRI), fMRI, and EEG to analyze alcohol addiction. Transcranial magnetic stimulation (TMS), a cutting-edge brain science tool, is also included in the analysis to explore its impact on AUD treatment. These techniques provide insights into both structural and functional changes in the brains of individuals with AUD.

Structural Brain Alterations in AUD

Studies consistently show reduced gray matter (GM) volume in the prefrontal lobe, particularly the medial frontal lobe, of AUD patients. sMRI reveals GM volume reduction in various brain areas, potentially disrupting communication networks and causing cognitive impairment. Importantly, some brain imaging deficits reverse with abstinence, highlighting the importance of treatment. Gender and age also impact the brain's response to alcohol, with women exhibiting more significant effects in certain areas. White matter also shows considerable changes in AUD, affecting brain volume and microstructure, particularly the corpus callosum, impacting cognitive function. Cerebellar shrinkage is another key finding, impacting various cerebellar structures and contributing to progressive atrophy, especially in those with additional neurological complications. Functional connectivity (FC) analysis reveals dynamic changes in brain-cerebellar interactions in AUD, though further research with larger samples is needed.

Functional Brain Alterations in AUD

fMRI, with its high spatial resolution, is widely used to study brain activation during tasks. Task-state fMRI reveals impaired neural pathways in AUD patients, requiring increased brain activity to complete tasks. Studies using reward-based tasks demonstrate reduced responsiveness to rewards and punishments in AUD patients, highlighting the role of reward processing circuits in the disorder. Resting-state fMRI, focusing on brain activity during rest, reveals abnormal connectivity in various networks, including the default mode network and cerebellar–thalamic–striatal–cortical circuit, suggesting disruptions in brain networks vital for executive functions. Amplitude of low frequency fluctuations (ALFF) analysis helps identify specific brain regions with altered activity in AUD patients, showing high accuracy in detecting the disorder. Studies using fMRI also examine the distinct brain activation patterns in those with AUD, based on different drinking motivations (positive vs. negative reinforcement).

EEG Applications in AUD

EEG measures brain electrical activity, offering insights into various brainwave patterns. Studies using EEG, event-related potentials, and event-related oscillations reveal abnormalities in brainwave activity in AUD patients, impacting sensory processing and cognitive functions. Machine learning approaches using resting-state EEG data show great promise in automatically screening for AUD, offering high accuracy in distinguishing alcoholics from healthy controls. Advanced techniques, such as wavelet scattering transform and artificial intelligence, are enhancing the accuracy and sensitivity of EEG-based AUD diagnosis. Future research could improve diagnostic accuracy by integrating multiple EEG analysis perspectives.

TMS Applications and Future Directions

TMS uses magnetic fields to stimulate specific brain areas, influencing neuronal activity. Studies show that TMS stimulation of the dorsolateral prefrontal cortex (DLPFC) can reduce alcohol cravings and improve abstinence. While some studies show right-sided DLPFC stimulation as more effective, others show benefit from left-sided stimulation. This discrepancy may be due to methodological limitations. Combining TMS with EEG and fMRI provides a more comprehensive understanding of the effects of stimulation on brain activity and connectivity. This combined approach helps uncover the impact of AUD on cortical functions and neural pathways, potentially leading to more effective treatments. Future studies should focus on larger sample sizes, improved machine learning techniques, and more rigorous experimental designs to enhance the reliability and generalizability of findings.

Summary

Alcohol is a very common problem around the world. Many people struggle with it, and it can cause early deaths. Alcohol changes the brain, making it hard to stop drinking. Scientists are using special tools to look inside the brains of people with alcohol problems to better understand what's happening.

Brain Changes from Alcohol

Scientists use special brain scans to see how alcohol affects the brain. These scans show that alcohol can shrink parts of the brain, especially the front part, which is important for making decisions. It also affects the brain's "wiring" which connects different parts of the brain. These changes can make it hard to think clearly and remember things. The good news is that some of the damage can get better if a person stops drinking. Women and older people may be affected more by alcohol than men and younger people.

Brain Activity and Alcohol

Brain scans also show how alcohol changes brain activity. When people with alcohol problems do simple tasks, like tapping their fingers, different parts of their brains work harder than normal. This means their brains have to work extra to do things that are usually easy. They also respond differently to rewards and punishments. Brain scans show differences in brain activity when people with alcohol problems see pictures of alcoholic drinks.

Using EEG to Study Alcohol Problems

EEG is like a brain wave monitor. It shows the electrical activity in the brain. People with alcohol problems often have different brain waves than people who don't have alcohol problems. Scientists are using computers to help analyze these brain waves and better identify who has alcohol problems.

Using TMS to Help People Stop Drinking

TMS is a way to use magnets to gently stimulate parts of the brain. Studies show that stimulating a specific part of the brain can help people with alcohol problems reduce their cravings and drink less. Scientists are learning more about how this works and how to make it even better. They are also using TMS with other brain scanning methods to get a better understanding of how the brain changes in people with alcohol problems.

The Future of Research

Scientists are still learning a lot about how alcohol affects the brain and how to help people who struggle with alcohol. They want to do more studies with more people to get a clearer picture, and they want to use even better computer technology to analyze brain scans. They also want to improve the methods they use to help people quit drinking.