Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users

Triple network resting-state functional connectivity patterns of alcohol heavy drinking

AIMS

Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain's salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse.

METHODS

Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30 days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures.

RESULTS

Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms-right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r = .25, p = .03).

CONCLUSIONS

These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

Global, interhemispheric and intrahemispheric functional connection patterns in male adults with alcohol use disorder

A growing body of evidence indicates the existence of abnormal local and long-range functional connection patterns in patients with alcohol use disorder (AUD). However, it has yet to be established whether AUD is associated with abnormal interhemispheric and intrahemispheric functional connection patterns. In the present study, we analysed resting-state functional magnetic resonance imaging data from 55 individuals with AUD and 32 healthy nonalcohol users. For each subject, whole-brain functional connectivity density (FCD) was decomposed into ipsilateral and contralateral parts. Correlation analysis was performed between abnormal FCD and a range of clinical measurements in the AUD group. Compared with healthy controls, the AUD group exhibited a reduced global FCD in the anterior and middle cingulate gyri, prefrontal cortex and thalamus, along with an enhanced global FCD in the temporal, parietal and occipital cortices. Abnormal interhemispheric and intrahemispheric FCD patterns were also detected in the AUD group. Furthermore, abnormal global, contralateral and ipsilateral FCD data were correlated with the mean amount of pure alcohol and the severity of alcohol addiction in the AUD group. Collectively, our findings indicate that global, interhemispheric and intrahemispheric FCD may represent a robust method to detect abnormal functional connection patterns in AUD; this may help us to identify the neural substrates and therapeutic targets of AUD.

Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence

AIM

Alcohol use disorder (AUD) is the most prevalent form of addiction, with a great burden on society and limited treatment options. A recent clinical trial reported significant clinical benefits of deep transcranial magnetic stimulations (Deep TMS) targeting midline frontocortical areas. However, the underlying biological substrate remained elusive. Here, we report the effect of Deep TMS on the microstructure of white matter.

METHODS

A total of 37 (14 females) AUD treatment-seeking patients were randomized to sham or active Deep TMS. Twenty (six females) age-matched healthy controls were included. White matter integrity was evaluated by fractional anisotropy (FA). Secondary measures included brain functional connectivity and self-reports of craving and drinking units in the 3 months of follow-up period.

RESULTS

White matter integrity was compromised in patients with AUD relative to healthy controls, as reflected by the widespread reduction in FA. This alteration progressed during early abstinence (3 weeks) in the absence of Deep TMS. However, stimulation of midline frontocortical areas arrested the progression of FA changes in association with decreased craving and relapse scores. Reconstruction of axonal tracts from white-matter regions showing preserved FA values identified cortical regions in the posterior cingulate and dorsomedial prefrontal cortices where functional connectivity was persistently modulated. These effects were absent in the sham-stimulated group.

CONCLUSIONS

By integrating brain structure and function to characterize the alcohol-dependent brain, this study provides mechanistic insights into the TMS effect, pointing to myelin plasticity as a possible mediator.

Resting state functional connectivity as a predictor of brief intervention response in adults with alcohol use disorder: A preliminary study

BACKGROUND

Brief interventions for alcohol use disorder (AUD) are generally efficacious, albeit with variability in response. Resting state functional connectivity (rsFC) may characterize neurobiological indicators that predict the response to brief interventions and is the focus of the current investigation.

MATERIALS AND METHODS

Forty-six individuals with AUD (65.2% female) completed a resting state functional magnetic resonance imaging (fMRI) scan immediately followed by a brief intervention aimed at reducing alcohol consumption. Positive clinical response was defined as a reduction in alcohol consumption by at least one World Health Organization (WHO) risk drinking level at 3-month follow-up. rsFC was analyzed using seed-to-voxel analysis with seed regions from four networks: salience network, reward network, frontoparietal network, and default mode network.

RESULTS

At baseline, responders had greater rsFC between the following seed regions in relation to voxel-based clusters than non-responders: (i) anterior cingulate cortex (ACC) in relation to left postcentral gyrus and right supramarginal gyrus (salience network); (ii) right posterior parietal cortex in relation to right ventral ACC (salience network); (iii) right interior frontal gyrus (IFG) pars opercularis in relation to right cerebellum and right occipital fusiform gyrus (frontoparietal); and (iv) right primary motor cortex in relation to left thalamus (default mode). Lower rsFC in responders vs. nonresponders was seen between the (i) right rostral prefrontal cortex in relation to left IFG pars triangularis (frontoparietal); (ii) right IFG pars triangularis in relation to right cerebellum (frontoparietal); (iii) right IFG pars triangularis in relation to right frontal eye fields and right angular gyrus (frontoparietal); and (iv) right nucleus accumbens in relation to right orbital frontal cortex and right insula (reward).

CONCLUSIONS

Resting state functional connectivity in the frontoparietal, salience, and reward networks predicts the response to a brief intervention in individuals with AUD and could reflect greater receptivity or motivation for behavior change.

Transcranial Magnetic Stimulation as a Tool to Promote Smoking Cessation and Decrease Drug and Alcohol Use

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive, drug-free, neural-circuit-based therapeutic tool that was recently cleared by the United States Food and Drug Associate for the treatment of smoking cessation. TMS has been investigated as a tool to reduce consumption and craving for many other substance use disorders (SUDs). This review starts with a discussion of neural networks involved in the addiction process. It then provides a framework for the therapeutic efficacy of TMS describing the role of executive control circuits, default mode, and salience circuits as putative targets for neuromodulation (via targeting the DLPFC, MPFC, cingulate, and insula bilaterally). A series of the largest studies of TMS in SUDs are listed and discussed in the context of this framework. Our review concludes with an assessment of the current state of knowledge regarding the use of rTMS as a therapeutic tool in reducing drug, alcohol, and nicotine use and identifies gaps in the literature that need to be addressed in future studies. Namely, while the presumed mechanism through which TMS exerts its effects is by modulating the functional connectivity circuits involved in executive control and salience of drug-related cues, it is also possible that TMS has direct effects on subcortical dopamine, a hypothesis that could be explored in greater detail with PET imaging.

Default Mode Network Modulation by Psychedelics: A Systematic Review

Psychedelics are a unique class of drug that commonly produce vivid hallucinations as well as profound psychological and mystical experiences. A grouping of interconnected brain regions characterized by increased temporal coherence at rest have been termed the Default Mode Network (DMN). The DMN has been the focus of numerous studies assessing its role in self-referencing, mind wandering, and autobiographical memories. Altered connectivity in the DMN has been associated with a range of neuropsychiatric conditions such as depression, anxiety, post-traumatic stress disorder, attention deficit hyperactive disorder, schizophrenia, and obsessive-compulsive disorder. To date, several studies have investigated how psychedelics modulate this network, but no comprehensive review, to our knowledge, has critically evaluated how major classical psychedelic agents-lysergic acid diethylamide, psilocybin, and ayahuasca-modulate the DMN. Here we present a systematic review of the knowledge base. Across psychedelics there is consistent acute disruption in resting state connectivity within the DMN and increased functional connectivity between canonical resting-state networks. Various models have been proposed to explain the cognitive mechanisms of psychedelics, and in one model DMN modulation is a central axiom. Although the DMN is consistently implicated in psychedelic studies, it is unclear how central the DMN is to the therapeutic potential of classical psychedelic agents. This article aims to provide the field with a comprehensive overview that can propel future research in such a way as to elucidate the neurocognitive mechanisms of psychedelics.

MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models

Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction.

Cortical thickness and intrinsic activity changes in middle-aged men with alcohol use disorder

BACKGROUND

Previous studies reported the alterations of brain structure or function in people with alcohol use disorder (AUD). However, a multi-modal approach combining structural and functional studies is essential to understanding the neural mechanisms of AUD. Hence, we examined regional differences in cortical thickness (CT) and amplitude of low-frequency fluctuation (ALFF) in patients with AUD.

METHODS

Thirty male patients with AUD and thirty age- and education-matched healthy male controls were recruited. High-resolution anatomical and resting-state functional MRI (rs-fMRI) data were collected, and the CT and ALFF were computed.

RESULTS

Behaviorally, males with AUD showed a cognitive decline in multiple domains. Structurally, they presented prominent reductions in CT in the bilateral temporal, insular, precentral, and dorsolateral prefrontal gyri (p < 0.05, voxel-wise family-wise error [FWE]). Functionally, a significant decrease in ALFF in the bilateral temporal, dorsolateral prefrontal, insular, putamen, cerebellum, right precuneus, mid-cingulate, and precentral gyri were observed (p < 0.05, FWE).

CONCLUSIONS

Our findings demonstrate the dual alterations of alcohol-related brain structure and function in male patients with AUD. These results may be useful in understanding the neural mechanisms in AUD.

Alcohol use disorder-associated structural and functional characteristics of the insula

Based on our current understanding of insular regions, effects of chronic alcohol use on the insula may affect the integration of sensory-motor, socio-emotional, and cognitive function. There is no comprehensive understanding about these differences in individuals with alcohol use disorder that accounts for both structural and functional differences related to chronic alcohol use. The purpose of this study was to investigate these variations in both the anterior and posterior insula in persons with alcohol use disorder. We investigated insula gray matter volume, morphometry, white matter structural connectivity, and resting state functional connectivity in 75 participants with alcohol use disorder (females = 27) and 75 age-matched healthy control participants (females = 39). Results indicated structural differences mostly in the anterior regions, while functional connectivity differences were observed in both the anterior and posterior insula in those with alcohol use disorder. Differing connectivity was observed with frontal, parietal, occipital, cingulate, cerebellar, and temporal brain regions. While these results align with prior studies showing differences primarily in anterior insular regions, they also contribute to the existing literature suggesting differences in anterior insular connectivity with brain regions shown to be engaged during higher cognitive and emotional tasks.

Differential association between the GLP1R gene variants and brain functional connectivity according to the severity of alcohol use

Growing evidence suggests that the glucagon-like peptide-1 (GLP-1) system is involved in mechanisms underlying alcohol seeking and consumption. Accordingly, the GLP-1 receptor (GLP-1R) has begun to be studied as a potential pharmacotherapeutic target for alcohol use disorder (AUD). The aim of this study was to investigate the association between genetic variation at the GLP-1R and brain functional connectivity, according to the severity of alcohol use. Participants were 181 individuals categorized as high-risk (n = 96) and low-risk (n = 85) alcohol use, according to their AUD identification test (AUDIT) score. Two uncommon single nucleotide polymorphisms (SNPs), rs6923761 and rs1042044, were selected a priori for this study because they encode amino-acid substitutions with putative functional consequences on GLP-1R activity. Genotype groups were based on the presence of the variant allele for each of the two GLP-1R SNPs of interest [rs6923761: AA + AG (n = 65), GG (n = 116); rs1042044: AA + AC (n = 114), CC (n = 67)]. Resting-state functional MRI data were acquired for 10 min and independent component (IC) analysis was conducted. Multivariate analyses of covariance (MANCOVA) examined the interaction between GLP-1R genotype group and AUDIT group on within- and between-network connectivity. For rs6923761, three ICs showed significant genotype × AUDIT interaction effects on within-network connectivity: two were mapped onto the anterior salience network and one was mapped onto the visuospatial network. For rs1042044, four ICs showed significant interaction effects on within-network connectivity: three were mapped onto the dorsal default mode network and one was mapped onto the basal ganglia network. For both SNPs, post-hoc analyses showed that in the group carrying the variant allele, high versus low AUDIT was associated with stronger within-network connectivity. No significant effects on between-network connectivity were found. In conclusion, genetic variation at the GLP-1R was differentially associated with brain functional connectivity in individuals with low versus high severity of alcohol use. Significant findings in the salience and default mode networks are particularly relevant, given their role in the neurobiology of AUD and addictive behaviors.

Neural mechanisms underlying empathy during alcohol abstinence: evidence from connectome-based predictive modeling

Empathy impairments have been linked to alcohol dependence even during abstinent periods. Nonetheless, the neural underpinnings of abstinence-induced empathy deficits remain unclear. In this study, we employed connectome-based predictive modeling (CPM) by using whole brain resting-state functional connectivity (rs-FC) to predict empathy capability of abstinent alcoholics (n = 47) versus healthy controls (n = 59). In addition, the generalizability of the predictive model (i.e., one group treated as a training dataset and another one treated as a test dataset) was performed to determine whether healthy controls and abstinent alcoholics share common neural fingerprints of empathy. Our results showed that abstinent alcoholics relative to healthy controls had decreased empathy capacity. Although no predictive models were observed in the abstinence group, we found that individual empathy scores in the healthy group can be reliably predicted by functional connectivity from the default mode network (DMN) to the sensorimotor network (SMN), occipital network, and cingulo-opercular network (CON). Moreover, the identified connectivity fingerprints of healthy controls could be generalized to predict empathy in the abstinence group. These findings indicate that neural circuits accounting for empathy may be disrupted by alcohol use and the impaired degree varies greatly among abstinent individuals. The large inter-individual variation may impede identification of the predictive model of empathy in alcohol abstainers.

Functional connectivity in the central executive network predicts changes in binge drinking behavior during emerging adulthood: an observational prospective study

BACKGROUND AND AIMS

Binge drinking contributes to the immense public health burden associated with alcohol use, especially among younger drinkers. Little is known about the underlying neurobiology of changes in this behavior over time. This preliminary study aimed to identify neurobiological markers of binge drinking behavior change during emerging adulthood.

DESIGN

Observational prospective investigation of neurobiological predictors of binge drinking behavior.

SETTING

Communities surrounding a large, public university in the northeastern United States.

PARTICIPANTS

A total of 42 emerging adults (48% female), approximately half meeting criteria for an alcohol use disorder.

MEASUREMENTS

Past month binge drinking, the dependent variable, was assessed at two time-points (T1, T2) via self-report. Ten indices of resting-state functional connectivity within the central executive network (CEN), a brain network involved in executive function, were collected at T1 and specified as independent variables in cross-sectional and prospective Poisson models. All models controlled for age, sex, and alcohol use disorder status.

FINDINGS

The cross-sectional model yielded five significant associations between CEN connectivity and binge drinking incidence. Connections anchored primarily in the anterior CEN exhibited negative associations with binge drinking incidence (P = 0.001, 0.004, 0.011), and connections stemming from the right posterior parietal cortex exhibited positive associations with binge drinking incidence (P = 0.041, 0.045). In prospective models, stronger frontoparietal connectivity between the right dorsolateral prefrontal cortex and left posterior parietal cortex predicted greater increases in binge drinking incidence over time (P = 0.003).

CONCLUSIONS

There is an association between central executive network connectivity and heavy drinking, as well as evidence that functional pathways within the central executive network may contribute to changes in problematic drinking behaviors.

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity (rsFNC) derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve (AUC) of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity.

Identifying alcohol misuse biotypes from neural connectivity markers and concurrent genetic associations

Alcohol use behaviors are highly heterogeneous, posing significant challenges to etiologic research of alcohol use disorder (AUD). Magnetic resonance imaging (MRI) provides intermediate endophenotypes in characterizing problem alcohol use and assessing the genetic architecture of addictive behavior. We used connectivity features derived from resting state functional MRI to subtype alcohol misuse (AM) behavior. With a machine learning pipeline of feature selection, dimension reduction, clustering, and classification we identified three AM biotypes-mild, comorbid, and moderate AM biotypes (MIA, COA, and MOA)-from a Human Connectome Project (HCP) discovery sample (194 drinkers). The three groups and controls (397 non-drinkers) demonstrated significant differences in alcohol use frequency during the heaviest 12-month drinking period (MOA > MIA; COA > non-drinkers) and were distinguished by connectivity features involving the frontal, parietal, subcortical and default mode networks. Further, COA relative to MIA, MOA and controls endorsed significantly higher scores in antisocial personality. A genetic association study identified that an alcohol use and antisocial behavior related variant rs16930842 from LINC01414 was significantly associated with COA. Using a replication HCP sample (28 drinkers and 46 non-drinkers), we found that subtyping helped in classifying AM from controls (area under the curve or AUC = 0.70, P < 0.005) in comparison to classifiers without subtyping (AUC = 0.60, not significant) and successfully reproduced the genetic association. Together, the results suggest functional connectivities as important features in classifying AM subgroups and the utility of reducing the heterogeneity in connectivity features among AM subgroups in advancing the research of etiological neural markers of AUD.

Repetitive Transcranial Magnetic Stimulation in Alcohol Dependence: A Randomized, Double-Blind, Sham-Controlled Proof-of-Concept Trial Targeting the Medial Prefrontal and Anterior Cingulate Cortices

BACKGROUND

Alcohol addiction is associated with a high disease burden, and treatment options are limited. In a proof-of-concept study, we used deep repetitive transcranial magnetic stimulation (dTMS) to target circuitry associated with the pathophysiology of alcohol addiction. We evaluated clinical outcomes and explored associated neural signatures using functional magnetic resonance imaging.

METHODS

This was a double-blind, randomized, sham-controlled trial. A total of 51 recently abstinent treatment-seeking patients with alcohol use disorder (moderate to severe) were randomized to sham or active dTMS, using an H7 coil targeting midline frontocortical areas, including the medial prefrontal and anterior cingulate cortices. Treatment included 15 sessions over 3 weeks, followed by five sessions over 3 months of follow-up. Each session delivered 100 trains of 30 pulses at 10 Hz. The primary predefined outcome was reduction in percentage of heavy drinking days, obtained using timeline follow-back interviews. Secondary analyses included self-reports of craving, ethyl glucuronide in urine, and brain imaging measures.

RESULTS

Both craving after treatment and percentage of heavy drinking days during follow-up were significantly lower in the active versus sham control group (percentage of heavy drinking days = 2.9 ± 0.8% vs. 10.6 ± 1.9%, p = .037). Active dTMS was associated with decreased resting-state functional connectivity of the dorsal anterior cingulate cortex with the caudate nucleus and decreased connectivity of the medial prefrontal cortex to the subgenual anterior cingulate cortex.

CONCLUSIONS

We provide initial proof-of-concept for dTMS targeting midline frontocortical structures as a treatment for alcohol addiction. These data strongly support a rationale for a full-scale confirmatory multicenter trial. Therapeutic benefits of dTMS appear to be associated with persistent changes in brain network activity.

Translational Structural and Functional Signatures of Chronic Alcohol Effects in Mice

BACKGROUND

Alcohol acts as an addictive substance that may lead to alcohol use disorder. In humans, magnetic resonance imaging showed diverse structural and functional brain alterations associated with this complex pathology. Single magnetic resonance imaging modalities are used mostly but are insufficient to portray and understand the broad neuroadaptations to alcohol. Here, we combined structural and functional magnetic resonance imaging and connectome mapping in mice to establish brain-wide fingerprints of alcohol effects with translatable potential.

METHODS

Mice underwent a chronic intermittent alcohol drinking protocol for 6 weeks before being imaged under medetomidine anesthesia. We performed open-ended multivariate analysis of structural data and functional connectivity mapping on the same subjects.

RESULTS

Structural analysis showed alcohol effects for the prefrontal cortex/anterior insula, hippocampus, and somatosensory cortex. Integration with microglia histology revealed distinct alcohol signatures, suggestive of advanced (prefrontal cortex/anterior insula, somatosensory cortex) and early (hippocampus) inflammation. Functional analysis showed major alterations of insula, ventral tegmental area, and retrosplenial cortex connectivity, impacting communication patterns for salience (insula), reward (ventral tegmental area), and default mode (retrosplenial cortex) networks. The insula appeared as a most sensitive brain center across structural and functional analyses.

CONCLUSIONS

This study demonstrates alcohol effects in mice, which possibly underlie lower top-down control and impaired hedonic balance documented at the behavioral level, and aligns with neuroimaging findings in humans despite the potential limitation induced by medetomidine sedation. This study paves the way to identify further biomarkers and to probe neurobiological mechanisms of alcohol effects using genetic and pharmacological manipulations in mouse models of alcohol drinking and dependence.

Brain Network Allostasis after Chronic Alcohol Drinking Is Characterized by Functional Dedifferentiation and Narrowing

Alcohol use disorder (AUD) causes complex alterations in the brain that are poorly understood. The heterogeneity of drinking patterns and the high incidence of comorbid factors compromise mechanistic investigations in AUD patients. Here we used male Marchigian Sardinian alcohol-preferring (msP) rats, a well established animal model of chronic alcohol drinking, and a combination of longitudinal resting-state fMRI and manganese-enhanced MRI to provide objective measurements of brain connectivity and activity, respectively. We found that 1 month of chronic alcohol drinking changed the correlation between resting-state networks. The change was not homogeneous, resulting in the reorganization of pairwise interactions and a shift in the equilibrium of functional connections. We identified two fundamentally different forms of network reorganization. First is functional dedifferentiation, which is defined as a regional increase in neuronal activity and overall correlation, with a concomitant decrease in preferential connectivity between specific networks. Through this mechanism, occipital cortical areas lost their specific interaction with sensory-insular cortex, striatal, and sensorimotor networks. Second is functional narrowing, which is defined as an increase in neuronal activity and preferential connectivity between specific brain networks. Functional narrowing strengthened the interaction between striatal and prefrontocortical networks, involving the anterior insular, cingulate, orbitofrontal, prelimbic, and infralimbic cortices. Importantly, these two types of alterations persisted after alcohol discontinuation, suggesting that dedifferentiation and functional narrowing rendered persistent network states. Our results support the idea that chronic alcohol drinking, albeit at moderate intoxicating levels, induces an allostatic change in the brain functional connectivity that propagates into early abstinence.SIGNIFICANCE STATEMENT Excessive consumption of alcohol is positioned among the top five risk factors for disease and disability. Despite this priority, the transformations that the nervous system undergoes from an alcohol-naive state to a pathologic alcohol drinking are not well understood. In our study, we use an animal model with proven translational validity to study this transformation longitudinally. The results show that shortly after chronic alcohol consumption there is an increase in redundant activity shared by brain structures, and the specific communication shrinks to a set of pathways. This functional dedifferentiation and narrowing are not reversed immediately after alcohol withdrawal but persist during early abstinence. We causally link chronic alcohol drinking with an early and abstinence-persistent retuning of the functional equilibrium of the brain.

Multimodal neuroimaging of metabotropic glutamate 5 receptors and functional connectivity in alcohol use disorder

BACKGROUND

People recovering from alcohol use disorder (AUD) show altered resting brain connectivity. The metabotropic glutamate 5 (mGlu5) receptor is an important regulator of synaptic plasticity potentially linked with synchronized brain activity and a target of interest in treating AUD. The goal of this work was to assess potential relationships of brain connectivity at rest with mGlu5 receptor availability in people with AUD at two time points early in abstinence.

METHODS

Forty-eight image data sets were acquired with a multimodal neuroimaging battery that included resting-state functional magnetic resonance imaging (fMRI) and mGlu5 receptor positron emission tomography (PET) with the radiotracer [¹⁸ F]FPEB. Participants with AUD (n = 14) were scanned twice, at approximately 1 and 4 weeks after beginning supervised abstinence. [¹⁸ F]FPEB PET results were published previously. Primary comparisons of fMRI outcomes were performed between the AUD group and healthy controls (HCs; n = 23) and assessed changes over time within the AUD group. Relationships between resting-state connectivity measures and mGlu5 receptor availability were explored within groups.

RESULTS

Compared to HCs, global functional connectivity of the orbitofrontal cortex was higher in the AUD group at 4 weeks of abstinence (p = 0.003), while network-level functional connectivity within the default mode network (DMN) was lower (p < 0.04). Exploratory multimodal analyses showed that mGlu5 receptor availability was correlated with global connectivity across all brain regions (HCs, r = 0.41; AUD group at 1 week of abstinence, r = 0.50 and at 4 weeks, r = 0.46; all p < 0.0001). Furthermore, a component of cortical and striatal mGlu5 availability was correlated with connectivity between the DMN and salience networks in HCs (r = 0.60, p = 0.003) but not in the AUD group (p > 0.3).

CONCLUSIONS

These preliminary findings of altered global and network connectivity during the first month of abstinence from drinking may reflect the loss of efficient network function, while exploratory relationships with mGlu5 receptor availability suggest a potential glutamatergic relationship with network coherence.

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.

From a systems view to spotting a hidden island: A narrative review implicating insula function in alcoholism

Excessive use of alcohol promotes the development of alcohol addiction, but the understanding of how alcohol-induced brain alterations lead to addiction remains limited. To further this understanding, we adopted an unbiased discovery strategy based on the principles of systems medicine. We used functional magnetic resonance imaging data from patients and animal models of alcohol addiction-like behaviors, and developed mathematical models of the 'relapse-prone' network states to identify brain sites and functional networks that can be selectively targeted by therapeutic interventions. Our systems level, non-local, and largely unbiased analyses converged on a few well-defined brain regions, with the insula emerging as one of the most consistent finding across studies. In proof-of-concept experiments we were able to demonstrate that it is possible to guide network dynamics towards increased resilience in animals but an initial translation into a clinical trial targeting the insula failed. Here, in a narrative review, we summarize the key experiments, methodological developments and knowledge gained from this completed round of a discovery cycle moving from identification of 'relapse-prone' network states in humans and animals to target validation and intervention trial. Future concerted efforts are necessary to gain a deeper understanding of insula function a in a state-dependent, circuit-specific and cell population perspective, and to develop the means for insula-directed interventions, before therapeutic targeting of this structure may become possible.

The effects of nalmefene on the impulsive and reflective system in alcohol use disorder: A resting-state fMRI study

RATIONALE

Central aspects of alcohol use disorder (AUD) are the irresistible desire for alcohol and impaired control over its intake. According to the triadic neurocognitive model of addiction, this arises from aberrant functioning of different neural and cognitive systems: an impulsive system, a reflective system, and the abnormal dynamics between both systems based on an insular-dependent system.

OBJECTIVES

In this study, we examined the effects of a single dose of nalmefene on resting-state functional connectivity (rsFC) patterns within and between these addiction-related neural systems in AUD.

METHODS

Non-treatment seeking participants with AUD (N = 17; 19-66 years, 6 female) took part in a randomized, placebo-controlled, double-blind, crossover study and received either a single dose of 18 mg nalmefene or a placebo. Using seed-based correlation analyses on resting-state functional magnetic resonance imaging data, we examined the effects of nalmefene on key nodes related to the (1) impulsive system; (2) reflective system; (3) salience network; and (4) default mode network.

RESULTS

Under nalmefene, participants showed reduced rsFC between components of the impulsive system (Nucleus accumbens-putamen/pallidum/insula). Reduced rsFC was found between elements of the reflective system and impulsive system (orbitofrontal cortex-insula/putamen/pallidum), salience network (orbitofrontal cortex-insula/inferior frontal gyrus), and default mode network (lateral prefrontal cortex-precuneus/cuneus). Components of the salience network showed both increased (anterior cingulate cortex) and decreased (insular cortex) rsFC to elements of the reflective system.

CONCLUSION

A single dose of nalmefene impacts rsFC and alters the interaction between key nodes of addiction-related neural systems in non-treatment seeking participants with AUD. Nalmefene may normalize rsFC patterns by weakening the impulsive system while strengthening the reflective system.

TRIAL REGISTRATION

clinicaltrials.gov: NCT02372318.

Deep TMS H7 Coil: Features, Applications & Future

INTRODUCTION

Transcranial magnetic stimulation (TMS) uses magnetic pulses to induce electrical current in the underlying neuronal tissue. A variety of TMS coils exist on the market, differing primarily in configuration, orientation, and flexibility of the wire windings of the coil. Deep TMS^TM utilizes H-Coils, flexible coils with different configurations for stimulating different brain regions implicated in different neuropsychiatric disorders. The H7 Coil, designed to target primarily the medial prefrontal cortex and the anterior cingulate cortex, is FDA-cleared for obsessive-compulsive disorder (OCD). It was chosen as the focus of this review since it recently showed promise in various neuropsychiatric populations in addition to growing understanding of its mechanism of action (MOA).

AREAS COVERED

Here we assembled all peer-reviewed publications on the H7 Coil to showcase its efficacy in: (a) various OCD patient populations (e.g., different degrees of symptom severity, treatment resistance, comorbidities) (b) other neuropsychiatric populations (e.g., addiction, major depressive disorder and autism spectrum disorder).

EXPERT OPINION

While substantial evidence pertaining to the H7 Coil's efficacy as well as its MOA has accumulated, much work remains. In the final section of this review, we highlight areas of ongoing and future research that will further elucidate the coil's MOA as well as its full efficacy potential.

Association Between Functional and Structural Brain Connectivity of the Default Mode Network in Non-treatment Seeking Individuals With Alcohol Use Disorder

AIMS

Alcohol use disorder (AUD) is associated with alterations within the default mode network (DMN) at rest. Also, impaired white matter structures have been observed in individuals with AUD. This study developed a workflow for examining the relation between functional and structural connectivity, exemplary for nodes of the DMN within a sample of non-treatment seeking individuals with AUD. Furthermore, AUD severity was correlated with both measures independently.

METHODS

The functional magnetic resonance imaging (fMRI) protocol included anatomical, resting state and diffusion weighted imaging measurements. Independent component analyses and deterministic fiber tracking as well as correlation analyses, including the severity of AUD, were performed. N = 18 out of 23 adult study participants took part in the fMRI examination, and N = 15 were included in the final analyses.

RESULTS

Established resting-state networks were reliably identified in our sample. Structural connections were found between several nodes of the DMN, whereas only fibers between the medial prefrontal cortex and the posterior cingulate cortex were reliably detected in all individuals. A negative correlation was observed between brain activation during rest and AUD severity in left parietal and temporal regions and the putamen. A more severe AUD predicted impairments in white matter integrity of the cingulum.

CONCLUSION

In AUD, information obtained from a combination of resting-state, diffusion weighted data and clinical information has great potential to provide a more profound understanding of the disorder since alterations may already become apparent at earlier stages of the disorder, e.g. in non-treatment seeking individuals.

SUMMARY

Alcohol use disorder leads to alterations in the default mode network of the resting brain that is associated with the severity of the disorder. Following our workflow, white matter impairments can be observed between some of the nodes of the default mode network using diffusion tensor imaging. Both, resting-state functional and structural connectivity relate to the severity of alcohol use disorder.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Impact of binge drinking during college on resting state functional connectivity

AIM

The current study examined the longitudinal effects of standard binge drinking (4+/5+ drinks for females/males in 2 hours) and extreme binge drinking (8+/10+ drinks for females/males in 2 hours) on resting-state functional connectivity.

METHOD

119 college students (61 males) were recruited in groups of distinct bingeing patterns at baseline: non-bingeing controls, standard and extreme bingers. Resting-state scans were first obtained when participants were freshmen/sophomores and again approximately two years later. Associations between longitudinal bingeing (reported during this two-year gap) and network connectivity were examined. Network connectivity was calculated by aggregating all edges affiliated with the same network (an edge is a functional connection between two brain regions). The relationship between longitudinal bingeing and connectivity edges was also studied using connectome-based predictive modeling (CPM).

RESULTS

Greater standard bingeing was negatively associated with change in connectivity between Default Mode Network and Ventral Attention Network (DMN-VAN; False Discovery Rate corrected), controlling for initial binge groups, longitudinal network changes, motions, scanner, SES, sex, and age. The correlations between change in DMN-VAN connectivity and change in cognitive performance (Stroop, Digit Span, Letter Fluency, and Trail Making) were also tested, but the results were not significant. Lastly, CPM failed to identify a generalizable predictive model of longitudinal bingeing from change in connectivity edges.

CONCLUSIONS

Binge drinking is associated with abnormality in networks implicated in attention and self-focused processes, which, in turn, have been implicated in rumination, craving, and relapse. More extensive alterations in functional connectivity might be observed with heavier or longer binge drinking pattern.

Rich Club Characteristics of Alcohol-Naïve Functional Brain Networks Predict Future Drinking Phenotypes in Rhesus Macaques

Purpose: A fundamental question for Alcohol use disorder (AUD) is how and when naïve brain networks are reorganized in response to alcohol consumption. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during transition from the naïve state to chronic consumption.

Procedures: Resting-state brain networks of six female rhesus macaque (Macaca mulatta) monkeys were acquired using magnetoencephalography (MEG) prior to alcohol exposure and after free-access to alcohol using a well-established model of chronic heavy alcohol consumption. Functional brain network metrics were derived at each time point.

Results: The average connection frequency (p < 0.024) and membership of the Rich Club (p < 0.022) changed significantly over time. Metrics describing network topology remained relatively stable from baseline to free-access drinking. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r = −0.88, p < 0.001).

Conclusions: Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters functional brain networks, shifting hubs and Rich Club membership away from previous regions in a non-systematic manner. Further work to refine these relationships may lead to the identification of a high-risk drinking phenotype.

Effect of alcohol on the central nervous system to develop neurological disorder: pathophysiological and lifestyle modulation can be potential therapeutic options for alcohol-induced neurotoxication

The central nervous system (CNS) is the major target for adverse effects of alcohol and extensively promotes the development of a significant number of neurological diseases such as stroke, brain tumor, multiple sclerosis (MS), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). Excessive alcohol consumption causes severe neuro-immunological changes in the internal organs including irreversible brain injury and it also reacts with the defense mechanism of the blood-brain barrier (BBB) which in turn leads to changes in the configuration of the tight junction of endothelial cells and white matter thickness of the brain. Neuronal injury associated with malnutrition and oxidative stress-related BBB dysfunction may cause neuronal degeneration and demyelination in patients with alcohol use disorder (AUD); however, the underlying mechanism still remains unknown. To address this question, studies need to be performed on the contributing mechanisms of alcohol on pathological relationships of neurodegeneration that cause permanent neuronal damage. Moreover, alcohol-induced molecular changes of white matter with conduction disturbance in neurotransmission are a likely cause of myelin defect or axonal loss which correlates with cognitive dysfunctions in AUD. To extend our current knowledge in developing a neuroprotective environment, we need to explore the pathophysiology of ethanol (EtOH) metabolism and its effect on the CNS. Recent epidemiological studies and experimental animal research have revealed the association between excessive alcohol consumption and neurodegeneration. This review supports an interdisciplinary treatment protocol to protect the nervous system and to improve the cognitive outcomes of patients who suffer from alcohol-related neurodegeneration as well as clarify the pathological involvement of alcohol in causing other major neurological disorders.

Effects of moderate alcohol levels on default mode network connectivity in heavy drinkers

BACKGROUND

It is well established that even moderate levels of alcohol affect cognitive functions such as memory, self-related information processing, and response inhibition. Nevertheless, the neural mechanisms underlying these alcohol-induced changes are still unclear, especially on the network level. The default mode network (DMN) plays an important role in memory and self-initiated mental activities; hence, studying functional interactions of the DMN may provide new insights into the neural mechanisms underlying alcohol-related changes.

METHODS

We investigated resting-state functional connectivity (rsFC) of the DMN in a cohort of 37 heavy drinkers at a breath alcohol concentration of 0.8 g/kg. Alcohol and saline were infused in a single-blind crossover design.

RESULTS

Intranetwork connectivity analyses revealed that participants showed significantly decreased rsFC of the right hippocampus and right middle temporal gyrus during acute alcohol exposure. Moreover, follow-up analyses revealed that these rsFC decreases were more pronounced in participants who reported stronger craving for alcohol. Exploratory internetwork connectivity analyses of the DMN with other resting-state networks showed no significant alcohol-induced changes, but suffered from low statistical power.

CONCLUSIONS

Our results indicate that acute alcohol exposure affects rsFC within the DMN. Functionally, this finding may be associated with impairments in memory encoding and self-referential processes commonly observed during alcohol intoxication. Future resting-state functional magnetic resonance imaging studies might therefore also investigate memory function and test whether DMN-related connectivity changes are associated with alcohol-induced impairments or craving.

Neural network involving medial orbitofrontal cortex and dorsal periaqueductal gray regulation in human alcohol abuse

Prompted by recent evidence of neural circuitry in rodent models, functional magnetic resonance imaging and functional connectivity analyses were conducted for a large adolescent population at two ages, together with alcohol abuse measures, to characterize a neural network that may underlie the onset of alcoholism. A network centered on the medial orbitofrontal cortex (mOFC), as well as including the dorsal periaqueductal gray (dPAG), central nucleus of the amygdala, and nucleus accumbens, was identified, consistent with the rodent models, with evidence of both inhibitory and excitatory coregulation by the mOFC over the dPAG. Furthermore, significant relationships were detected between raised baseline excitatory coregulation in this network and impulsivity measures, supporting a role for negative urgency in alcohol dependence.

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.

Reduced Inhibitory Inputs On Basolateral Amygdala Principal Neurons Following Long-Term Alcohol Consumption

Recent studies have shown that manipulating basolateral amygdala (BLA) activity can affect alcohol consumption, particularly following chronic and/or long-term intake. Although the mechanisms underlying these effects remain unclear, the BLA is highly sensitive to emotional stimuli including stress and anxiety. Negative emotional states facilitate alcohol craving and relapse in patients with alcohol use disorders. Consequently, the aim of this study was to determine the effect of long-term (10 weeks) alcohol drinking on synaptic activity in BLA principal neurons. We utilized an intermittent drinking paradigm in rats, which facilitated escalating, binge-like alcohol intake over the 10 week drinking period. We then recorded spontaneous excitatory and inhibitory postsynaptic currents of BLA principal neurons from long-term alcohol drinking rats and aged-matched water drinking controls. Excitatory postsynaptic current properties from long-term alcohol drinking rats were unchanged compared to those from age-matched water drinking controls. Conversely, we observed significant reductions of inhibitory postsynaptic current amplitude and frequency in long-term ethanol drinking rats compared to age-matched water drinking controls. These results highlight substantive decreases in basal inhibitory synaptic activity of BLA principal neurons following long-term alcohol consumption. A loss of inhibitory control in the BLA could explain the high incidence of compulsive drinking and stress- or anxiety-induced relapse in patients with alcohol use disorders.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

Sex Differences in the Neurobiology of Alcohol Use Disorder

Sex differences may play a critical role in modulating how chronic or heavy alcohol use impacts the brain to cause the development of alcohol use disorder (AUD). AUD is a multifaceted and complex disorder driven by changes in key neurobiological structures that regulate executive function, memory, and stress. A three-stage framework of addiction (binge/intoxication; withdrawal/negative affect; preoccupation/anticipation) has been useful for conceptualizing the complexities of AUD and other addictions. Initially, alcohol drinking causes short-term effects that involve signaling mediated by several neurotransmitter systems such as dopamine, corticotropin releasing factor, and glutamate. With continued intoxication, alcohol leads to dysfunctional behaviors that are thought to be due in part to alterations of these and other neurotransmitter systems, along with alterations in neural pathways connecting prefrontal and limbic structures. Using the three-stage framework, this review highlights examples of research examining sex differences in drinking and differential modulation of neural systems contributing to the development of AUD. New insights addressing the role of sex differences in AUD are advancing the field forward by uncovering the complex interactions that mediate vulnerability.

Machine-learning approaches to substance-abuse research: emerging trends and their implications

PURPOSE OF REVIEW

To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice.

RECENT FINDINGS

Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting current abuse, assessing future risk and predicting treatment success. These models cover a wide range of machine-learning techniques and data types including physiological measures, longitudinal surveys, treatment outcomes, national surveys, medical records and social media.

SUMMARY

The application of machine-learning models to substance use disorder data shows significant promise, with some use cases and data types showing high predictive accuracy, particularly for models of physiological and behavioral measures for predicting current substance use, portending potential clinical diagnostic applications; however, these results are uneven, with some models performing poorly or at chance, a limitation likely reflecting insufficient data and/or weak validation methods. The field will likely benefit from larger and more multimodal datasets, greater standardization of data recording and rigorous testing protocols as well as greater use of modern deep neural network models applied to multimodal unstructured datasets.

Addictions NeuroImaging Assessment (ANIA): Towards an integrative framework for alcohol use disorder

Alcohol misuse and addiction are major international public health issues. Addiction can be characterized as a disorder of aberrant neurocircuitry interacting with environmental, genetic and social factors. Neuroimaging in alcohol misuse can thus provide a critical window into underlying neural mechanisms, highlighting possible treatment targets and acting as clinical biomarkers for predicting risk and treatment outcomes. This neuroimaging review on alcohol misuse in humans follows the Addictions Neuroclinical Assessment (ANA) that proposes incorporating three functional neuroscience domains integral to the neurocircuitry of addiction: incentive salience and habits, negative emotional states, and executive function within the context of the addiction cycle. Here we review and integrate multiple imaging modalities focusing on underlying cognitive processes such as reward anticipation, negative emotionality, cue reactivity, impulsivity, compulsivity and executive function. We highlight limitations in the literature and propose a model forward in the use of neuroimaging as a tool to understanding underlying mechanisms and potential clinical applicability for phenotyping of heterogeneity and predicting risk and treatment outcomes.

The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk

Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.

Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures

: Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.

Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior-posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality – grey-matter density – by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

Does an Over-Connected Visual Cortex Undermine Efforts to Stay Sober After Treatment for Alcohol Use Disorder?

A fine-tuned interplay of highly synchronized activity within and between the brain's communities is a crucial feature of the brain's functional organization. We wanted to investigate in individuals with alcohol use disorder (AUD) the degree to which the interplay of the brain's community-architecture and the extended brain reward system (eBRS) is affected by drinking status (relapse or abstinence). We used Graph Theory Analysis of resting-state fMRI data from treatment seekers at 1 month of abstinence to model the brain's intrinsic community configuration and their follow-up data as abstainers or relapsers 3 months later to quantify the degree of global across-community interaction between the eBRS and the intrinsic communities at both timepoints. After 1 month of abstinence, the ventromedial PFC in particular showed a significantly higher global across-community interaction in the 22 future relapsers when compared to 30 light/non-drinking controls. These differences were no longer present 3 months later when the relapsers had resumed drinking. We found no significant differences between abstainers and controls at either timepoint. Post hoc tests revealed that one eBRS region, the ventromedial PFC, showed a significant global across-community interaction with a community comprising the visual cortex in relapsers at baseline. In contrast, abstainers showed a significant negative association of the ventromedial PFC with the visual cortex. The increased across-community interaction of the ventromedial PFC and the visual cortex in relapsers at timepoint 1 may be an early indicator for treatment failure in a subgroup of AUD patients.

Addiction resistance to alcohol: What about heavy drinkers who avoid alcohol problems?

BACKGROUND

Some individuals are resistant to alcohol use disorders despite high levels of intake. Addiction Resistance (AR) measures the disparity between alcohol consumption and alcohol use disorder (AUD) symptoms, such that some persons exhibit few (AUD) symptoms despite higher intake. The validity of the concept and the factors contributing to AR are not well understood. The aim of this study was to predict AR based on variables related to risk for addiction that are measured in the Family Health Patterns Project.

METHOD

Participants were healthy young adults (n = 1122) with and without a family history of alcohol and other substance use disorders who were given measures of mood stability and risk-taking tendencies, and were interviewed to determine alcohol intake, AUD symptoms, and other substance use disorders (SUD). AR was calculated using maximal lifetime alcohol intake and number of AUD symptoms.

RESULTS

A principal components analysis was run with varimax rotation, which yielded three components: Component 1 indexed behavioral and mood regulation, Component 2 encompassed family and environmental factors, and Component 3 included cognitive factors. A multiple regression analysis revealed that Component 1 and Component 2 were predictive of AR whereas Component 3 was not.

DISCUSSION

Individuals who reported greater emotional stability, norm adherence, risk avoidance, and fewer family members with substance use disorders were more resistant to AUD despite higher alcohol intake. These findings suggest that AUD risk and resistance may represent different points of the same continuum.

Frequency-Specific Abnormalities Of Functional Homotopy In Alcohol Dependence: A Resting-State Functional Magnetic Resonance Imaging Study

PURPOSE

Alcohol dependence (AD) is a relapsing mental disorder, typically occurring with concurrent tobacco misuse. Studies have reported disruption of the structural connectivity between hemispheres in the brain of individuals with AD. However, alterations in interhemispheric interactions and the specificity of frequency bands in individuals with AD remain unknown. Voxel-mirrored homotopic connectivity (VMHC) allows examination of functional interactions between mirrored interhemispheric voxels. Here, we use VMHC to investigate homotopic connectivity in AD and alcohol and nicotine co-dependence (AND) subjects.

PATIENTS AND METHODS

VMHC and seed-based functional connectivity (FC) in 24 AD, 30 AND, and 35 sex-, age-, and education-matched healthy control (HC) subjects were calculated for different frequency bands (slow-5, slow-4, and typical bands).

RESULTS

Individuals with AD demonstrated significantly reduced VMHC in bilateral cerebellum posterior lobe (CPL) and increased VMHC in bilateral middle frontal gyrus (MFG) compared to that in HCs in the typical and slow-4 bands; higher VMHC in the MFG was positively correlated with the dependence-severity score. In all bands of the VMHC analysis, no significant differences were found between the AND and other groups. Subsequent seed-based FC analysis demonstrated all regions with abnormal VMHC exhibited altered FC with its counterpart in the contralateral hemisphere in the typical and slow-4 frequency bands. The FC value between bilateral CPL within AD subjects negatively correlated with alcohol intake.

CONCLUSION

Our findings provide further evidence of the role of disruptions within the brain circuitry supporting cognitive control in the development of AD. Alterations in neural activities in the CPL and MFG might be a biomarker of dependence severity in AD patients as assessed using clinical questionnaire and features. Because of the frequency specificity in VMHC, we must consider frequency effects in future AD functional magnetic resonance imaging studies.