Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects

Lifespan investigation of brain volumetric changes associated with substance use disorders

Substance use disorder (SUD) stands as a critical public health concern, contributing to substantial morbidity, mortality and societal costs. The effects of SUD on structural brain changes have been well documented. However, the neural mechanisms underlying SUD and the spatial-temporal volumetric changes associated with SUD remained underexplored. In this investigation, neuroimaging, behavioral and genomic data across four large population cohorts jointly covering the full lifespan were harmonized, and whole-brain volumetric trajectories between substance use disorders (SUDs) and healthy controls (HCs) were compared, revealing the potential neurobiological mechanisms and the genomic basis underlying SUD. Results highlighted three distinct life stages critical for the development of SUD: 1) adolescence to early adulthood (before 25y), where SUD is suspected to be the consequence of prefrontal-subcortical imbalance during neurodevelopment; 2) early-to-mid adulthood (25y - 45y), where SUD was strongly associated with compulsivity-related brain volumetric changes; 3) mid-to-late adulthood (after 45y), where SUD-related brain structural changes could be explained by neurotoxicity. Results were externally validated both via longitudinal analysis of these population cohorts and in independent cross-sectional samples. In summary, our study demonstrated the lifespan whole-brain volumetric changes associated with SUD, revealed potential neurobehavioral mechanisms for the development of SUD, and suggested critical time window for effective prevention and treatment of SUD.

The effects of alcohol use severity and polygenic risk on gray matter volumes in young adults

Introduction

Genetic factors contribute to alcohol misuse. Chronic alcohol consumption is associated with decreases in gray matter volumes (GMVs) of the brain. However, it remains unclear whether or how genetic risks may alter GMVs independent of the effects of alcohol exposure.

Methods

Here, we employed the Human Connectome Project data of neurotypical adults (n = 995; ages 22-35; 534 women) and, with voxel-based morphometry analysis, computed the GMVs of 166 regions in the automated anatomical atlas 3. Alcohol use behaviors were assessed with the Semi-Structured Assessment for the Genetics of Alcoholism. Alcohol use severity was quantified by the first principal component (PC1) identified of principal component analysis of 15 drinking measures. Polygenic risk scores (PRS) for alcohol dependence were computed for all subjects using the Psychiatric Genomics Consortium study of alcohol dependence as the base sample. With age, sex, race, and total intracranial volume as covariates, we evaluated the relationships of regional GMVs with PC1 and PRS together in a linear regression.

Results

PC1 was negatively correlated with GMVs of right insula and Heschl's gyrus, and PRS was positively correlated with GMVs of left posterior orbitofrontal cortex, bilateral intralaminar nuclei of the thalamus and lingual gyri.

Discussion

These findings suggest distinct volumetric neural markers of drinking severity and genetic risks of alcohol misuse. Notably, in contrast to volumetric reduction, the genetic risks of dependent drinking may involve larger regional volumes in the reward, emotion, and saliency circuits.

Anabolic-androgenic steroid use disorder: case for recognition as a substance use disorder with specific diagnostic criteria

Approximately one in three people who use anabolic-androgenic steroids (AASs) develop dependence, characterised by both psychiatric and somatic symptoms. Despite this, AAS use disorder (AASUD) is not distinctly recognised in the latest versions of either the ICD or DSM, impeding both clinical care and research progress. It is clear that AASUD shares many features and correlates with substance use disorders (SUDs) that have specific diagnostic criteria in these classification systems, such as stimulants or opioids. We aim to outline the overlap between AASUD and more 'typical' SUDs as well as highlight the specific concerns related to AASUD that warrant recognition and distinct diagnostic criteria.

Striatal brain volume linked to severity of substance use in high-risk incarcerated youth

Substance use disorders among juveniles are a major public health concern and are often intertwined with other psychosocial risk factors including antisocial behavior. Identifying etiological risks and mechanisms promoting substance use disorders remains a high priority for informing more focused interventions in high-risk populations. The present study examined brain gray matter structure in relation to substance use severity among n = 152 high-risk, incarcerated boys (aged 14-20). Substance use severity was positively associated with gray matter volume across several frontal/striatal brain regions including amygdala, pallidum, putamen, insula, and orbitofrontal cortex. Effects were apparent when using voxel-based-morphometric analysis, as well as in whole-brain, data-driven, network-based approaches (source-based morphometry). These findings support the hypothesis that elevated gray matter volume in striatal reward circuits may be an endogenous marker for vulnerability to severe substance use behaviors among youth.

Measuring the associations between brain morphometry and polygenic risk scores for substance use disorders in drug-naive adolescents

Substance use has been associated with differences in adult brain morphology; however, it is unclear whether these differences precede or are a result of substance use substance use. We investigated the impact of polygenic risk scores (PRSs) for cannabis use disorder (CUD) and general substance use and substance use disorder liability (SU/SUD) on brain morphology in drug-naïve adolescents. Baseline data were used from 1,874 European-descent participants (ages 9-11) comprising 222, 328 and 387 pairs of MZ twins, DZ twins, and Non-Twin Siblings, respectively, in the Adolescent Brain Cognitive Development Study. We fitted multivariate twin models to estimate the putative effects of CUD, SU/SUD, and brain region-specific PRSs. These models assessed their influence on six subcortical and two cortical phenotypes. PRS for CUD and SU/SUD were created based on GWAS conducted by Johnson et al. (2020) and Hatoum et al. (2023), respectively. When decomposing variance in each brain region of interest (ROI), we used the corresponding ROI-specific PRS. Brain morphometry in drug-naive subjects was unrelated to CUD PRS. The variance explained in each ROI by its corresponding PRS ranged from 0.8-4.4%. The SU/SUD PRS showed marginally significant effects (0.2-0.4%) on cortical surface area and nucleus accumbens volume, but overall effect sizes were small. Our findings indicate that differences in brain morphometry among baseline drug-naive individuals are not associated with the genetic risk for CUD but show a weak association with general addiction and substance use risk (SU/SUD), particularly in nucleus accumbens volume and total cortical surface area.

Sex differences in normative modeling of cortical thickness in cannabis use disorder

Cannabis use disorder (CUD) is associated with sexually dimorphic behavioral and neurobiological effects, but sex differences in a broader sampling of brain structures in CUD assessed relative to normative reference values have not been examined. Here, we assessed sex differences in brain regions measured via 3 T MRI in 72 adults (50 males, 22 females) with CUD. T1-weighted images, segmented via FreeSurfer, were used to derive Normative Morphometry Imaging Statistics z-scores (accounting for age, sex, intracranial volume, and image quality). Z-scores were then compared between sexes and associated with behavioral data. We found that average z-scores were within normative ranges for both sexes. There were no sex differences in total brain, cerebral white matter, and subcortical gray matter z-scores, but total cortical thickness z-scores were greater in females. Fourteen cortical regions surrounding the central and lateral sulci had greater z-scores in females than in males, but the medial orbitofrontal cortex z-score was greater in males. Of these regions, 3 were positively correlated with cannabis-related problems. Findings suggest sexual dimorphism in brain structure in CUD primarily in the frontal, medial parietal, and superior temporal lobes, with some association with cannabis-related problems even in the context of normative brain structure. Future research is needed to clarify causal mechanisms of morphometric differences in CUD.

Structural Cerebellar and Lateral Frontoparietal Networks are altered in CUD: An SBM Analysis

Repetitive drug use results in enduring structural and functional changes in the brain. Addiction research has consistently revealed significant modifications in key brain networks related to reward, habit, salience, executive function, memory and self-regulation. Techniques like Voxel-based Morphometry have highlighted large-scale structural differences in grey matter across distinct groups. Source-based Morphometry (SBM) takes this a step further by incorporating the Independent Component Analysis to detect shared patterns of grey matter variation, all without requiring prior selection of regions of interest. However, SBM has yet to be employed in the study of structural alteration patterns related to cocaine addiction. Therefore, we performed this analysis to explore alterations in structural covariance specific to cocaine addiction. Our study involved 40 individuals diagnosed with Cocaine Use Disorder (CUD) and 40 matched healthy controls. Participants with CUD completed clinical questionnaires assessing the severity of their dependence and other relevant clinical variables. Following the adjustment for age-related effects, we observed notable disparities between groups in two structural independent components, which we identified as the structural cerebellar network and the structural lateral frontoparietal network, which display opposing trends. Specifically, the individuals with CUD exhibited a heightened contribution to the cerebellar network but simultaneously demonstrated a reduced contribution to the lateral frontoparietal network compared to the healthy controls. These findings unveil distinctive covariance patterns of neuroregulation linked with cocaine addiction, which indicates an interruption in the typical structural development in an affected lateral frontoparietal network, while suggesting an extended pattern of neuroregulation within the cerebellar network in individuals with CUD.

Brain acid sphingomyelinase controls addiction-related behaviours in a sex-specific way

Addiction is a chronic and severe mental disorder with high gender- and sex-specificity. However, the pathogenesis of this disorder is not fully elucidated, and no targeted pharmacotherapy is available. A growing body of evidence points out the potential involvement of the ceramide system in the pathophysiology of addiction. A pathogenic pathway for several mental disorders based on the overexpression of an enzyme involved in ceramide formation, acid sphingomyelinase (ASM), was recently proposed. Here we show a crucial role of ASM specifically overexpressing in the forebrain for various types of addiction-related behaviours in a drug- and sex-specific way. In male mice, a forebrain ASM overexpression led to enhanced alcohol consumption in a free-choice paradigm. It also diminished the reinforcing properties of alcohol and cocaine, but not that of amphetamine, ketamine, or a natural reinforcer fat/carbohydrate-rich food in the conditioned place preference (CPP) test in males. In female mice, a forebrain ASM overexpression enhanced alcohol binge-like drinking, while moderate alcohol consumption was preserved. ASM overexpression in females contributed to CPP establishment for amphetamine, but not for other psychoactive substances. Altogether, this study shows a specific involvement of forebrain ASM in the development of conditioned reinforcing effects of different types of substances with addictive properties in a sex-specific way. Our data enlarge the current knowledge on the specific molecular mechanisms of dependence from various drugs of abuse and might serve as a basis for the development of drug- and sex-specific targeted therapy.

The effects of alcohol use severity and polygenic risk on gray matter volumes in young adults

Genetic factors contribute to alcohol misuse. Chronic alcohol consumption is associated with decreases in gray matter volumes (GMVs) of the brain. However, it remains unclear whether or how genetic risks may alter GMVs independent of the effects of alcohol exposure. Here, we employed the Human Connectome Project data of neurotypical adults (n = 995; age 22-35; 618 women) and, with voxel-based morphometry analysis, computed the GMVs of 166 regions in the automated anatomical atlas 3. Alcohol use behaviors were assessed with the Semi-Structured Assessment for the Genetics of Alcoholism. Alcohol use severity was quantified by the first principal component (PC1) identified of principal component analysis of 15 drinking measures. Polygenic risk scores (PRS) for alcohol dependence were computed for all subjects using the Psychiatric Genomics Consortium study of alcohol dependence as the base sample. With age, sex, race, and total intracranial volume as covariates, we evaluated the relationships of regional GMVs with PC1 and PRS together in a linear regression. PC1 was negatively correlated with GMVs of right insula and Heschl's gyrus, and PRS was positively correlated with GMVs of left posterior orbitofrontal cortex, bilateral intralaminar nuclei of the thalamus and lingual gyri. These findings suggest distinct volumetric neural markers of drinking severity and genetic risks of alcohol misuse. Notably, in contrast to volumetric reduction, the genetic risks of dependent drinking may involve larger regional volumes in the reward, emotion, and saliency circuits.

Brain structural differences in cocaine use disorder: Insights from multivariate and neurotransmitter analyses

Cocaine use disorder (CUD) is a chronic and relapsing neuropsychiatric disorder characterized by structural and functional brain lesions, posing a significant public health challenge. While the disruptive effects of cocaine on neurotransmitter systems (receptors/transporters) have been well established, the patterns of brain structural abnormalities in CUD and its interaction with other factors remain an ongoing topic of investigation. We employed source-based morphometry (SBM), a multivariate approach on 50 CUD participants and 50 matched healthy controls from the public SUDMEX CONN dataset. This method allowed us to identify co-varying patterns of brain tissue volume differences, and further explore the effect of average cocaine dosage through moderation analysis. Spatial correlation analysis was also performed to examine micro-macro structural consistency between tissue volume variations and chemoarchitectural distribution of dopamine and serotonin. Our SBM analysis findings were consistent with reward-related neuroadaptations in the striato-thalamo-cortical and limbic pathways and also exhibited co-localization with the distribution of dopamine and serotonin systems. The moderation analysis suggested that the average dosage positively strengthens cocaine consumption years' effect on brain structures. By integrating our findings of gray and white matter volume differences and corresponding neurotransmitter profiles, this comprehensive view not only strengthens our understanding of the brain's structural abnormalities in CUD, but also reveals potential mechanisms underlying its development and progression.

Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures

BACKGROUND

Craving is a core factor driving drug-seeking and -taking, representing a significant risk factor for relapse. This study aims to identify neuroanatomical biomarkers for quantifying and predicting craving.

METHODS

The study enrolled 94 ketamine-dependent users and 103 healthy controls (HC). Utilizing support vector regression (SVR) with 10-fold cross-validated framework, we developed a neuroanatomical craving model based on measures of regional cortical thickness (CT), surface area (SA), and subcortical volume (SV) derived from T1 images. The generalizability of neuroanatomical craving model was examined in an independent set. Spatial correlation analysis was employed to assess the relationship between the regional contribution to craving and density maps of receptors/transporters from previous molecular imaging studies.

RESULTS

The neuroanatomical craving model identified neuroanatomical biomarkers that predicted self-report craving (r = 0.635). The most importance of predictors of craving included the SA of the left medial orbitofrontal cortex and the left supramarginal gyrus, CT in the left caudal anterior cingulate, the left cuneus, the right lateral occipital cortex and the right lingual gyrus, as well as the left amygdala GMV. Importantly, these predictors were generalized to an independent sample. Moreover, nodal contribution to predicted craving scores were associated with DA2, 5-HTa, 5-HTb receptor and serotonin reuptake transporter densities.

CONCLUSION

The results offer a key perspective on craving prediction among ketamine-dependent users, and identify neuroanatomical areas associated with craving in the frontal and parietal regions. Additionally, the underlying neuroanatomical structures involved in the craving process may be linked to the dopaminergic and serotonergic systems.

Association of altered cortical gyrification and working memory in male early abstinent alcohol-dependent individuals

BACKGROUND

Alcohol dependence (AD) is an addictive disorder with multifaceted neurobiological features. Recent research on the pathophysiological mechanisms of AD has emphasized the important role of dysconnectivity. Cortical gyrification is known to be a reliable marker of neural connectivity. This study aimed to explore cortical gyrification using the local gyrification index (LGI) between alcohol-dependent patients and controls.

METHODS

Magnetic resonance images were collected from 60 early abstinent patients with AD (5-12 days after stopping alcohol consumption) and 59 controls and preprocessed using FreeSurfer, followed by surface-based morphometry (SBM) analysis to compare the LGI between the two groups. Cognitive performance was assessed using the Spatial Working Memory (SWM) test in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The relationship between LGI, cognitive performance, and clinical variables was also explored in the patient group.

RESULTS

Compared with controls, patients with AD exhibited significantly decreased LGI in several regions, including the postcentral gyrus, precentral gyrus, middle frontal, superior temporal, middle temporal, insula, superior parietal, and inferior parietal cortex. AD patients did worse than controls in several SWM measures. Furthermore, decreased LGI in the left postcentral was negatively correlated with working memory performance after multiple comparison corrections in the patient group.

CONCLUSION

Alcohol-dependent individuals exhibit abnormal patterns of cortical gyrification, which may be underlying neurobiological markers of AD. Our findings further indicate that working memory deficits may be related to abnormalities in cortical gyrification in alcohol addiction.

Sex differences in inter-temporal decision making and cortical thickness of the orbitofrontal and insula in young adult cannabis users: Evidence from 1111 subjects

To test for sex differences in the impact of cannabis use on decision-making and brain correlates, we employed cortical thickness (CT) analysis of brain regions involved in intertemporal decision-making namely bilateral orbitofrontal cortex(OFC) and insula in young adult nondependent cannabis-users(CU) and non-users(NU) and their scores on delay discounting task. Neuroimaging analyzes of previously collected data were performed on 608CU and 503NU. CT analysis was performed on MRI images. OFC and insula thickness, scores on the delay discounting test were compared between groups and correlated. Controlling alcohol-use and intra-cranial-volume, CU exhibited sex differences in CT. The bilateral insula was significantly thinner in male CU. OFC was thinner in females relative to controls. Female CU had thinner Right-medial OFC, Left-lateral-OFC. While male CU scored significantly lower on items within delay discounting task, female CU delay-discounting scores were within normal range. Our results demonstrate that cannabis-use differentially affects decision-making across sexes. Cortical morphology mirrors this dimorphism. CU subjects did not show a correlation between CT of OFC or insula and delay discounting, implying that thinner cortices and lower DD scores in males may be independent alterations which may be premorbid to cannabis use and may lead to cognitive deficits in later years.

Neuroanatomical Variability and Substance Use Initiation in Late Childhood and Early Adolescence

Importance

The extent to which neuroanatomical variability associated with early substance involvement, which is associated with subsequent risk for substance use disorder development, reflects preexisting risk and/or consequences of substance exposure remains poorly understood.

Objective

To examine neuroanatomical features associated with early substance use initiation and to what extent associations may reflect preexisting vulnerability.

Design, Setting, and Participants

Cohort study using data from baseline through 3-year follow-up assessments of the ongoing longitudinal Adolescent Brain Cognitive Development Study. Children aged 9 to 11 years at baseline were recruited from 22 sites across the US between June 1, 2016, and October 15, 2018. Data were analyzed from February to September 2024.

Exposures

Substance use initiation through 3-year follow-up (ie, age <15 years).

Main Outcomes and Measures

Self-reported alcohol, nicotine, cannabis, and other substance use initiation and baseline magnetic resonance imaging (MRI)-derived estimates of brain structure (ie, global and regional cortical volume, thickness, surface area, sulcal depth, and subcortical volume). Covariates included family (eg, familial relationships), pregnancy (eg, prenatal exposure to substances), child (eg, sex and pubertal status), and MRI (eg, scanner model) variables.

Results

Among 9804 children (mean [SD] baseline age, 9.9 [0.6] years; 5160 boys [52.6%]; 213 Asian [2.2%], 1474 Black [15.0%], 514 Hispanic/Latino [5.2%], 29 American Indian [0.3%], 10 Pacific Islander [0.1%], 7463 White [76.1%], and 75 other [0.7%]) with nonmissing baseline neuroimaging and covariate data, 3460 (35.3%) reported substance use initiation before age 15. Initiation of any substance or alcohol use was associated with thinner cortex in prefrontal regions (eg, rostral middle frontal gyrus, β = -0.03; 95% CI, -0.02 to -0.05; P = 6.99 × 10-6) but thicker cortex in all other lobes, larger globus pallidus and hippocampal volumes, as well as greater global indices of brain structure (eg, larger whole brain volume, β = 0.05; 95% CI, 0.03 to 0.06; P = 2.80 × 10-8) following Bonferroni or false discovery rate multiple testing correction. Cannabis use initiation was associated with lower right caudate volume (β = -0.03; 95% CI, -0.01 to -0.05; P = .002). Post hoc examinations restricting to postbaseline initiation suggested that the majority of associations, including thinner prefrontal cortex and greater whole brain volume, preceded initiation.

Conclusions and Relevance

In this cohort study of children, preexisting neuroanatomical variability was associated with substance use initiation. In addition to putative neurotoxic effects of substance exposure, brain structure variability may reflect predispositional risk for initiating substance use earlier in life with potential cascading implications for development of later problems.

Let's focus on the insula in addiction: A refined anatomical exploration of insula in severe alcohol and cocaine use disorders

BACKGROUND

Theoretical and empirical contributions have identified insula as key in addiction. However, anatomical modifications of the insula in addictive states, and their variations across substance use disorders (SUDs), remain to be specifically explored. We therefore explored the specificities and commonalities of insula gray matter (GM) alterations in severe alcohol use disorder (sAUD) and severe cocaine use disorder (sCUD).

METHODS

We explored insula GM volume through a refined parcellation in 12 subregions (six bilateral): anterior inferior cortex (AIC), anterior short gyrus, middle short gyrus, posterior short gyrus, anterior long gyrus (ALG), and posterior long gyrus (PLG). Using a linear mixed model analysis, we explored the insula volume profiles of 50 patients with sAUD, 61 patients with sCUD, and 36 healthy controls (HCs).

RESULTS

In both sAUD and sCUD, we showed overall insular lower volume with a right-sided lateralization effect, and a major volume deficit in bilateral ALG. Moreover, differences emerged across groups, with higher left AIC and PLG volume deficits in sCUD compared to sAUD and HC.

CONCLUSIONS

We offered the first joint exploration of GM insular volumes in two SUD through refined parcellation, thus unveiling the similarities and dissimilarities in volume deficit profiles. Our results bring evidence complementing prior ones suggesting the core role of the right and posterior insula in craving and interoception, two crucial processes in addiction. Left AIC and PLG group differences also show that, while insula is a region of interest in SUD, sCUD and sAUD generate distinct insular profiles, which might parallel clinical differences across SUD.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study

BACKGROUND AND AIMS

The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls.

DESIGN

This was a cross-sectional study.

SETTING

This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites.

PARTICIPANTS

This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls.

MEASUREMENTS

This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls.

FINDINGS

Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models.

CONCLUSIONS

People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence

While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.

Compensation patterns and altered functional connectivity in alcohol use disorder with and without Korsakoff's syndrome

Alcohol use disorder is a chronic disease characterized by an inappropriate pattern of drinking, resulting in negative consequences for the individual's physical, mental and social health. Korsakoff's syndrome is a complication of alcohol use disorder and is characterized by severe memory and executive deficits. The fronto-cerebellar and Papez circuits are structurally affected in patients with alcohol use disorder with and without Korsakoff's syndrome. The first objective of the present study was to measure the effect of chronic and excessive alcohol consumption on resting-state functional connectivity of these two functional brain networks. The second objective was to identify, for the first time, resting-state functional connectivity abnormalities specific to amnesic patients with Korsakoff's syndrome. In the present study, a neuropsychological assessment and a resting-state functional magnetic resonance imaging examination were conducted in 31 healthy controls (43.6 ± 6.1 years) and 46 patients (46.6 ± 9.1 years) with alcohol use disorder including 14 patients with Korsakoff's syndrome (55.5 ± 5.3 years) to examine the effect of chronic and heavy alcohol consumption on functional connectivity of the fronto-cerebellar and the Papez circuits at rest and the specificity of functional connectivity changes in Korsakoff's syndrome compared to alcohol use disorder without Korsakoff's syndrome. The resting-state functional connectivity analyses focused on the nodes of the fronto-cerebellar and Papez circuits and combined region of interest and graph theory approaches, and whether these alterations are associated with the neuropsychological profile. In patients pooled together compared to controls, lower global efficiency was observed in the fronto-cerebellar circuit. In addition, certain regions of the fronto-cerebellar and Papez circuits were functionally hyperconnected at rest, which positively correlated with executive functions. Patients with Korsakoff's syndrome showed lower resting-state functional connectivity, lower local and global efficiency within the Papez circuit compared to those without Korsakoff's syndrome. Resting-state functional connectivity positively correlated with several cognitive scores in patients with Korsakoff's syndrome. The fronto-cerebellar and Papez circuits, two normally well-segregated networks, are functionally altered by alcohol use disorder. The Papez circuit attempts to compensate for deficits in the fronto-cerebellar circuit, albeit insufficiently as evidenced by patients' overall lower cognitive performance. Korsakoff's syndrome is characterized by altered functional connectivity in the Papez circuit known to be centrally involved in memory.

Neuroimaging Biomarkers in Addiction

As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.

[Cannabis use and cannabis use disorders]

Cannabis use and cannabis use disorders have taken on a new social significance as a result of partial legalization. In 2021 a total of 4.5 million adults (8.8%) in Germany used the drug. The number of users as well as problematic use have risen in the last decade. Cannabis products with a high delta-9-tetrahydrocannabinol (THC) content and their regular use lead to changes in cannabinoid receptor distribution in the brain and to modifications in the structure and functionality of relevant neuronal networks. The consequences of cannabinoid use are particularly in the psychological functioning and can include intoxication, harmful use, dependence with withdrawal symptoms and cannabis-induced mental disorders. Changes in the diagnostics between ICD-10 and ICD-11 are presented. Interdisciplinary S3 guidelines on cannabis-related disorders are currently being developed and will be finalized shortly.

Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.

Alcohol and brain structure across the lifespan: A systematic review of large-scale neuroimaging studies

Alcohol exposure affects brain structure, but the extent to which its effects differ across development remains unclear. Several countries are considering changes to recommended guidelines for alcohol consumption, so high-quality evidence is needed. Many studies have been conducted among small samples, but recent efforts have been made to acquire large samples to characterize alcohol's effects on the brain on a population level. Several large-scale consortia have acquired such samples, but this evidence has not been synthesized across the lifespan. We conducted a systematic review of large-scale neuroimaging studies examining effects of alcohol exposure on brain structure at multiple developmental stages. We included studies with an alcohol-exposed sample of at least N = 100 from the following consortia: ABCD, ENIGMA, NCANDA, IMAGEN, Framingham Offspring Study, HCP and UK BioBank. Twenty-seven studies were included, examining prenatal (N = 1), adolescent (N = 9), low-to-moderate-level adult (N = 11) and heavy adult (N = 7) exposure. Prenatal exposure was associated with greater brain volume at ages 9-10, but contemporaneous alcohol consumption during adolescence and adulthood was associated with smaller volume/thickness. Both low-to-moderate consumption and heavy consumption were characterized by smaller volume and thickness in frontal, temporal and parietal regions, and reductions in insula, cingulate and subcortical structures. Adolescent consumption had similar effects, with less consistent evidence for smaller cingulate, insula and subcortical volume. In sum, prenatal exposure was associated with larger volume, while adolescent and adult alcohol exposure was associated with smaller volume and thickness, suggesting that regional patterns of effects of alcohol are similar in adolescence and adulthood.

Neuronal biomarkers as potential therapeutic targets for drug addiction related to sex differences in the brain: Opportunities for personalized treatment approaches

Biological sex disparities manifest at various stages of drug addiction, including craving, substance abuse, abstinence, and relapse. These discrepancies are underpinned by notable distinctions in neurobiological substrates, encompassing brain structures, functions, and neurotransmitter systems implicated in drug addiction. Neuronal biomarkers, such as neurotransmitters, signaling proteins, and genes may be associated with the diagnosis, prognosis, and treatment outcomes in both biological sexes afflicted by drug abuse. Sex differences in the neural reward system, mainly through dopaminergic transmission during drug abuse, can be attributed to modifications in neurotransmitter systems and signaling pathways. This results in distinct patterns of neural activation and responsiveness to addictive substances in males and females. Sex hormones, the estrus/menstrual cycle, and cerebral neurochemistry contribute to the progression of psychological and physiological dependence in both male and female individuals grappling with addiction. Moreover, the alteration of sex hormone balance and neurotransmitter release plays a pivotal role in substance use disorders, subsequently modulating cognitive functions pertinent to reward, including memory formation, decision-making, and locomotor activity. Comparative investigations reveal distinctions in brain region volume, gene expression, neuronal firing, and circuitry in substance use disorders affecting individuals of both biological sexes. This review examines prevalent substance use disorders to elucidate the impact of sex hormones as therapeutic biomarkers on the mesocorticolimbic neurotransmitter systems via diverse mechanisms within the addicted brain. We underscore the imperative necessity of considering these variations to gain a deeper comprehension of addiction mechanisms and potentially discern sex-specific neuronal biomarkers for tailored therapeutic interventions.

Transcranial magnetic stimulation for methamphetamine use disorder: A scoping review within the neurocircuitry model of addiction

The use of methamphetamine in the United States is increasing, contributing now to the "fourth wave" in the national opioid epidemic crisis. People who suffer from methamphetamine use disorder (MUD) have a higher risk of death. No pharmacological interventions are approved by the FDA and psychosocial interventions are only moderately effective. Transcranial Magnetic Stimulation (TMS) is a relatively novel FDA-cleared intervention for the treatment of Major Depressive Disorder (MDD) and other neuropsychiatric conditions. Several lines of research suggest that TMS could be useful for the treatment of addictive disorders, including MUD. We will review those published clinical trials that show potential effects on craving reduction of TMS when applied over the dorsolateral prefrontal cortex (DLPFC) also highlighting some limitations that affect their generalizability and applicability. We propose the use of the Koob and Volkow's neurocircuitry model of addiction as a frame to explain the brain effects of TMS in patients with MUD. We will finally discuss new venues that could lead to a more individualized and effective treatment of this complex disorder including the use of neuroimaging, the exploration of different areas of the brain such as the frontopolar cortex or the salience network and the use of biomarkers.

Mapping potential pathways from polygenic liability through brain structure to psychological problems across the transition to adolescence

BACKGROUND

We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition.

METHODS

Three annual assessments of child conduct problems, attention-deficit/hyperactivity problems, and internalizing problems were conducted across across 9-13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®).

RESULTS

The extPGS predicted conduct problems in each wave (R2 = 2.0%-2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%-2.1%), but also variance that conduct problems shared with other measured problems (R2 = .8%-1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 = .4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%-2.1%) and the variance common to all problems in each wave (R2 = 1.6%-3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV.

CONCLUSIONS

Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.

Brain alterations in Cocaine Use Disorder: Does the route of use matter and does it relate to the treatment outcome?

AIMS

Cocaine Use Disorder (CUD) is an important health issue, associated with structural brain abnormalities. However, the impact of the route of administration and their predictive value for relapse remain unknown.

METHODS

We conducted an anatomical MRI study in 55 CUD patients (26 CUD-Crack and 29 CUD-Hydro) entering inpatient detoxification, and 38 matched healthy controls. In patients, a 3-months outpatient follow-up was carried out to specify the treatment outcome status (relapser when cocaine was consumed once or more during the past month). A Voxel-Based Morphometry approach was used.

RESULTS

Compared with controls, CUD patients had widespread gray matter alterations, mostly in frontal and temporal cortices, but also in the cerebellum and several sub-cortical structures. We then compared CUD-Crack with CUD-Hydro patients and found that crack-cocaine use was associated with lower volume in the right inferior and middle temporal gyri, and the right fusiform gyrus. Cerebellar vermis was smaller during detoxification in subsequent relapsers compared to three-months abstainers.

CONCLUSIONS

Patients with CUD display widespread cortical and subcortical brain shrinkage. Patients with preferential crack-cocaine use and subsequent relapsers showed specific gray matter volume deficits, suggesting that different patterns of cocaine use and different clinical outcome are associated with different brain macrostructure.

Brain alterations in individuals with exercise dependence: A multimodal neuroimaging investigation

Background

Exercise dependence (ED) is characterised by behavioural and psychological symptoms that resemble those of substance use disorders. However, it remains inconclusive whether ED is accompanied by similar brain alterations as seen in substance use disorders. Therefore, we investigated brain alterations in individuals with ED and inactive control participants.

Methods

In this cross-sectional neuroimaging investigation, 29 individuals with ED as assessed with the Exercise Dependence Scale (EDS) and 28 inactive control participants (max one hour exercising per week) underwent structural and functional resting-state magnetic resonance imaging (MRI). Group differences were explored using voxel-based morphometry and functional connectivity analyses. Analyses were restricted to the striatum, amygdala, and inferior frontal gyrus (IFG). Exploratory analyses tested whether relationships between brain structure and function were differently related to EDS subscales among groups.

Results

No structural differences were found between the two groups. However, right IFG and bilateral putamen volumes were differently related to the EDS subscales "time" and "tolerance", respectively, between the two groups. Resting-state functional connectivity was increased from right IFG to right superior parietal lobule in individuals with ED compared to inactive control participants. Furthermore, functional connectivity of the angular gyrus to the left IFG and bilateral caudate showed divergent relationships to the EDS subscale "tolerance" among groups.

Discussion

The findings suggest that ED may be accompanied by alterations in cognition-related brain structures, but also functional changes that may drive compulsive habitual behaviour. Further prospective studies are needed to disentangle beneficial and detrimental brain effects of ED.

A Cognitive Pathway to Persistent, Maladaptive Choice

BACKGROUND

Correctly recognising that alcohol or other substances are causing problems is a necessary condition for those problems to spur beneficial behaviour change. Yet such recognition is neither immediate nor straightforward. Recognition that one's alcohol or drug use is causing negative consequences often occurs gradually. Contemporary addiction neuroscience has yet to make progress in understanding and addressing these recognition barriers, despite evidence that a lack of problem recognition is a primary impediment to seeking treatment.

SUMMARY

Based on our recent empirical work, this article shows how recognition barriers can emerge from dual constraints on how we learn about the negative consequences of our actions. One constraint is imposed by the characteristics of negative consequences themselves. A second constraint is imposed by the characteristics of human cognition and information processing. In some people, the joint action of these constraints causes a lack of correct awareness of the consequences of their behaviour and reduced willingness to update that knowledge and behaviour when confronted with counterevidence.

KEY MESSAGES

This "cognitive pathway" can drive persistent, maladaptive choice.

White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers

Excessive use of the internet, which is a typical scenario of self-control failure, could lead to potential consequences such as anxiety, depression, and diminished academic performance. However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self-control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self-control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (p < 0.001), but not with self-control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC-right VS pathway was negatively (p = 0.011) and positively (p = 0.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self-control (p = 0.036). These associations were not observed for the dACC-left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self-control on the relationship between FA of the dACC-right VS pathway and internet addiction severity. Our findings suggest that the dACC-right VS pathway is a critical neural substrate for both internet addiction and self-control. Deficits in this pathway may lead to impaired self-regulation over internet usage, exacerbating the severity of internet addiction.

Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response

Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

Influence of sex and liver cirrhosis on the expression of miR-146a-5p and its target genes, IRAK1 and TRAF6

Long-term alcohol misuse triggers cellular adaptions in susceptible regions of the human brain, resulting in neurodegeneration, neuroinflammation and altered gene expression. Previous studies have identified ∼35 miRNAs, including miR-146a-5p, which are up-regulated in the frontal cortex of males with alcohol use disorder (AUD), but the influence of liver cirrhosis and sex is unknown. The expression of miR-146a-5p, IRAK1, and TRAF6 was measured in the prefrontal cortex of controls and individuals with AUD with and without cirrhosis of the liver. Further, individuals were genotyped for two SNPs, rs2910164 and rs57095329. The expression of miR-146a-5p was significantly different between sexes. In males the expression of miR-146a-5p was increased in individuals with AUD with and without liver cirrhosis compared with controls. In females miR-146a-5p expression was significantly lower in individuals with AUD compared with both controls and those with AUD and cirrhosis, suggesting that both the severity of alcohol misuse and the sex of the individual influences the expression of miR-146a-5p. The expression of TRAF6 was significantly lower in individuals with uncomplicated AUD compared with those with AUD and cirrhosis. The expression of IRAK1 did not differ between groups or sexes. There was no influence of genotype on expression. Increased expression of miR-146a-5p did not correlate with decreased IRAK1 or TRAF6 expression suggesting a loss of regulatory control of the TLR4 pathway. Understanding sex-specific differences in the regulation of gene expression in AUD is key to determine which inflammatory pathways could be targeted for therapeutic intervention.

Neuroanatomical variability associated with early substance use initiation: Results from the ABCD Study

The extent to which neuroanatomical variability associated with substance involvement reflects pre-existing risk and/or consequences of substance exposure remains poorly understood. In the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study, we identify associations between global and regional differences in brain structure and early substance use initiation (i.e., occurring <15 years of age; nsanalytic=6,556-9,804), with evidence that associations precede initiation. Neurodevelopmental variability in brain structure may confer risk for substance involvement.

Effects of current alcohol use on brain volume among older adults in the Gothenburg H70 Birth Cohort study 2014-16

Brain gray- and white matter changes is well described in alcohol-dependent elderly subjects; however, the effect of lower levels of alcohol consumption on the brain is poorly understood. We investigated the impact of different amounts of weekly alcohol consumption on brain structure in a population-based sample of 70-year-olds living in Gothenburg, Sweden. Cross-sectional data from 676 participants from The Gothenburg H70 Birth Cohort Study 2014-16 were included. Current alcohol consumers were divided into seven groups based on self-reported weekly amounts of alcohol consumption in grams (g) (0-50 g/week, used as reference group, 51-100 g/week, 101-150 g/week, 151-200 g/week, 201-250 g/week, 251-300 g/week, and > 300 g/week). Subcortical volumes and cortical thickness were assessed on T1-weighted structural magnetic resonance images using FreeSurfer 5.3, and white matter integrity assessed on diffusion tensor images, using tract-based statistics in FSL. General linear models were carried out to estimate associations between alcohol consumption and gray- and white matter changes in the brain. Self-reported consumption above 250 g/week was associated with thinning in the bilateral superior frontal gyrus, the right precentral gyrus, and the right lateral occipital cortex, in addition to reduced fractional anisotropy (FA) and increased mean diffusivity (MD) diffusively spread in many tracts all over the brain. No changes were found in subcortical gray matter structures. These results suggest that there is a non-linear relationship between alcohol consumption and structural brain changes, in which loss of cortical thickness only occur in non-demented 70-year-olds who consume more than 250 g/week.

Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders

BACKGROUND

Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD.

METHODS

Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses.

RESULTS

Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets.

CONCLUSIONS

Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.

A genetic and transcriptomic assessment of the KTN1 gene in Parkinson's disease risk

Parkinson's disease (PD) is a progressive neurological disorder caused by both genetic and environmental factors. An association has been described between KTN1 genetic variants and changes in its expression in the putamen and substantia nigra brain regions and an increased risk for PD. Here, we examine the link between PD susceptibility and KTN1 using individual-level genotyping data and summary statistics from the most recent genome-wide association studies (GWAS) for PD risk and age at onset from the International Parkinson's Disease Genomics Consortium (IPDGC), as well as whole-genome sequencing data from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative. To investigate the potential effect of changes in KTN1 expression on PD compared to unaffected individuals, we further assess publicly available expression quantitative trait loci (eQTL) results from GTEx v8 and BRAINEAC and transcriptomics data from AMP-PD. Overall, we found no genetic associations between KTN1 and PD in our cohorts but found potential evidence of differences in mRNA expression, which needs to be further explored.

Brain structural covariance network features are robust markers of early heavy alcohol use

BACKGROUND AND AIMS

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

DESIGN AND SETTING

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

CASES

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

MEASUREMENTS

Graph theory metrics of segregation and integration were used to summarize SCN.

FINDINGS

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

CONCLUSION

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

The potential role of gray matter volume differences in the association between smoking and depression: A narrative review

Tobacco use and major depression are both leading contributors to the global burden of disease and are also highly comorbid. Previous research indicates bi-directional causality between tobacco use and depression, but the mechanisms that underlie this causality are unclear, especially for the causality from tobacco use to depression. Here we narratively review the available evidence for a potential causal role of gray matter volume in the association. We summarize the findings of large existing neuroimaging meta-analyses, studies in UK Biobank, and the Enhancing NeuroImaging Genetics through MetaAnalysis (ENIGMA) consortium and assess the overlap in implicated brain areas. In addition, we review two types of methods that allow us more insight into the causal nature of associations between brain volume and depression/smoking: longitudinal studies and Mendelian Randomization studies. While the available evidence suggests overlap in the alterations in brain volumes implicated in tobacco use and depression, there is a lack of research examining the underlying pathophysiology. We conclude with recommendations on (genetically-informed) causal inference methods useful for studying these associations.

Recalibrating single-study effect sizes using hierarchical Bayesian models

Introduction

There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.

Methods

We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.

Results

The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.

Discussion

Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Impact of Adolescent Cannabis Use on Neurocognitive and Brain Development

Research examining associations between frequent cannabis use in adolescence and brain-behavior outcomes has increased substantially over the past 2 decades. This review attempts to synthesize the state of evidence in this area of research while acknowledging challenges in interpretation. Although there is converging evidence that ongoing, frequent cannabis use in adolescence is associated with small reductions in cognitive functioning, there is still significant debate regarding the persistence of reductions after a period of abstinence. Similarly, there is controversy regarding the replicability of structural and functional neuroimaging findings related to frequent cannabis use in adolescence. Larger studies with informative designs are needed.

Association of smoking with brain gray and white matter volume: a Mendelian randomization study

BACKGROUND

Observational studies have found a significant association between smoking and smaller gray matter volume, but this finding was limited by the reverse causality bias and possible confounding factors. Therefore, we conducted a Mendelian randomization (MR) study to explore the causal association of smoking with brain gray and white matter volume from a genetic perspective, and to investigate the possible mediators influencing the association.

METHODS

Smoking initiation (ever being a regular smoker) was used as the primary exposure from the GWAS & Sequencing Consortium of Alcohol and Nicotine use in up to 1,232,091 individuals of European descent. Their associations with brain volume were acquired from a recent genome-wide association study of brain imaging phenotypes conducted among 34,298 individuals of the UK Biobank. The random-effects inverse-variance weighted method was applied as the main analysis. Multivariable MR analysis was performed to assess the potential interference of confounding factors on causal effect.

RESULTS

Genetic liability to smoking initiation was significantly associated with lower gray matter volume (beta, -0.100; 95% CI, -0.156 to -0.043; P=5.23×10-4) but not with white matter volume. Multivariable MR results suggested that the association with lower gray matter volume might be mediated by alcohol drinking. Regarding localized gray matter volume, genetic liability to smoking initiation was associated with lower gray matter volume in left superior temporal gyrus, anterior division and right superior temporal gyrus, posterior division.

CONCLUSIONS

This MR study supports the association between smoking and lower gray matter volume, and highlights the importance of never smoking.

Translating the hierarchical taxonomy of psychopathology (HiTOP) from potential to practice: Ten research questions

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a novel diagnostic system grounded in empirical research into the architecture of mental illness. Its basic units are continuous dimensions-as opposed to categories-that are organized into a hierarchy according to patterns of symptom co-occurrence observed in quantitative studies. Previous HiTOP discussions have focused on existing evidence regarding the model's structure and ability to account for neurobiological, social, cultural, and clinical variation. The present article looks ahead to the next decade of applied research and clinical practice using the HiTOP rubric. We highlight 10 topics where HiTOP has the potential to make significant breakthroughs. Research areas include genetic influences, environmental contributions, neural mechanisms, real-time dynamics, and lifespan development of psychopathology. We also discuss development of novel assessments, forecasting methods, and treatments. Finally, we consider implications for clinicians and educators. For each of these domains, we propose directions for future research and venture hypotheses as to what HiTOP will reveal about psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Unraveling Neural Complexity: Exploring Brain Entropy to Yield Mechanistic Insight in Neuromodulation Therapies for Tobacco Use Disorder

Neuromodulation therapies, such as repetitive transcranial magnetic stimulation (rTMS), have shown promise as treatments for tobacco use disorder (TUD). However, the underlying mechanisms of these therapies remain unclear, which may hamper optimization and personalization efforts. In this study, we investigated alteration of brain entropy as a potential mechanism underlying the neural effects of noninvasive brain stimulation by rTMS in people with TUD. We employed sample entropy (SampEn) to quantify the complexity and predictability of brain activity measured using resting-state fMRI data. Our study design included a randomized single-blind study with 42 participants who underwent 2 data collection sessions. During each session, participants received high-frequency (10Hz) stimulation to the dorsolateral prefrontal cortex (dlPFC) or a control region (visual cortex), and resting-state fMRI scans were acquired before and after rTMS. Our findings revealed that individuals who smoke exhibited higher baseline SampEn throughout the brain as compared to previously-published SampEn measurements in control participants. Furthermore, high-frequency rTMS to the dlPFC but not the control region reduced SampEn in the insula and dlPFC, regions implicated in TUD, and also reduced self-reported cigarette craving. These results suggest that brain entropy may serve as a potential biomarker for effects of rTMS, and provide insight into the neural mechanisms underlying rTMS effects on smoking cessation. Our study contributes to the growing understanding of brain-based interventions for TUD by highlighting the relevance of brain entropy in characterizing neural activity patterns associated with smoking. The observed reductions in entropy following dlPFC-targeted rTMS suggest a potential mechanism for the therapeutic effects of this intervention. These findings support the use of neuroimaging techniques to investigate the use of neuromodulation therapies for TUD.

Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies

Alcohol use is a growing global health concern and economic burden. Alcohol involvement (i.e., initiation, use, problematic use, alcohol use disorder) has been reliably associated with broad spectrum grey matter differences in cross-sectional studies. These findings have been largely interpreted as reflecting alcohol-induced atrophy. However, emerging data suggest that brain structure differences also represent pre-existing vulnerability factors for alcohol involvement. Here, we review evidence from human studies with designs (i.e., family-based, genomic, longitudinal) that allow them to assess the plausibility that these correlates reflect predispositional risk factors and/or causal consequences of alcohol involvement. These studies provide convergent evidence that grey matter correlates of alcohol involvement largely reflect predisposing risk factors, with some evidence for potential alcohol-induced atrophy. These conclusions highlight the importance of study designs that can provide causal clues to cross-sectional observations. An integrative model may best account for these data, in which predisposition to alcohol use affects brain development, effects which may then be compounded by the neurotoxic consequences of heavy alcohol use.

Brain volumes in alcohol use disorder: Do females and males differ? A whole-brain magnetic resonance imaging mega-analysis

Emerging evidence suggests distinct neurobiological correlates of alcohol use disorder (AUD) between sexes, which however remain largely unexplored. This work from ENIGMA Addiction Working Group aimed to characterize the sex differences in gray matter (GM) and white matter (WM) correlates of AUD using a whole-brain, voxel-based, multi-tissue mega-analytic approach, thereby extending our recent surface-based region of interest findings on a nearly matching sample using a complementary methodological approach. T1-weighted magnetic resonance imaging (MRI) data from 653 people with AUD and 326 controls was analyzed using voxel-based morphometry. The effects of group, sex, group-by-sex, and substance use severity in AUD on brain volumes were assessed using General Linear Models. Individuals with AUD relative to controls had lower GM volume in striatal, thalamic, cerebellar, and widespread cortical clusters. Group-by-sex effects were found in cerebellar GM and WM volumes, which were more affected by AUD in females than males. Smaller group-by-sex effects were also found in frontotemporal WM tracts, which were more affected in AUD females, and in temporo-occipital and midcingulate GM volumes, which were more affected in AUD males. AUD females but not males showed a negative association between monthly drinks and precentral GM volume. Our results suggest that AUD is associated with both shared and distinct widespread effects on GM and WM volumes in females and males. This evidence advances our previous region of interest knowledge, supporting the usefulness of adopting an exploratory perspective and the need to include sex as a relevant moderator variable in AUD.

Methylphenidate as a treatment option for substance use disorder: a transdiagnostic perspective

A transition in viewing mental disorders from conditions defined as a set of unique characteristics to one of the quantitative variations on a collection of dimensions allows overlap between disorders. The overlap can be utilized to extend to treatment approaches. Here, we consider the overlap between attention-deficit/hyperactivity disorder and substance use disorder to probe the suitability to use methylphenidate as a treatment for substance use disorder. Both disorders are characterized by maladaptive goal-directed behavior, impaired cognitive control, hyperactive phasic dopaminergic neurotransmission in the striatum, prefrontal hypoactivation, and reduced frontal cortex gray matter volume/density. In addition, methylphenidate has been shown to improve cognitive control and normalize associated brain activation in substance use disorder patients and clinical trials have found methylphenidate to improve clinical outcomes. Despite the theoretical basis and promising, but preliminary, outcomes, many questions remain unanswered. Most prominent is whether all patients who are addicted to different substances may equally profit from methylphenidate treatment.

Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: A pooled analysis in the European Lifebrain consortium

BACKGROUND

Lifestyle-related risk factors, such as obesity, physical inactivity, short sleep, smoking and alcohol use, have been associated with low hippocampal and total grey matter volumes (GMV). However, these risk factors have mostly been assessed as separate factors, leaving it unknown if variance explained by these factors is overlapping or additive. We investigated associations of five lifestyle-related factors separately and cumulatively with hippocampal and total GMV, pooled across eight European cohorts.

METHODS

We included 3838 participants aged 18-90 years from eight cohorts of the European Lifebrain consortium. Using individual person data, we performed cross-sectional meta-analyses on associations of presence of lifestyle-related risk factors separately (overweight/obesity, physical inactivity, short sleep, smoking, high alcohol use) as well as a cumulative unhealthy lifestyle score (counting the number of present lifestyle-related risk factors) with FreeSurfer-derived hippocampal volume and total GMV. Lifestyle-related risk factors were defined according to public health guidelines.

RESULTS

High alcohol use was associated with lower hippocampal volume (r = -0.10, p = 0.021), and overweight/obesity with lower total GMV (r = -0.09, p = 0.001). Other lifestyle-related risk factors were not significantly associated with hippocampal volume or GMV. The cumulative unhealthy lifestyle score was negatively associated with total GMV (r = -0.08, p = 0.001), but not hippocampal volume (r = -0.01, p = 0.625).

CONCLUSIONS

This large pooled study confirmed the negative association of some lifestyle-related risk factors with hippocampal volume and GMV, although with small effect sizes. Lifestyle factors should not be seen in isolation as there is evidence that having multiple unhealthy lifestyle factors is associated with a linear reduction in overall brain volume.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Binge drinking is associated with higher cortisol and lower hippocampal and prefrontal gray matter volume: Prospective association with future alcohol intake

Background

Cortisol is a significant driver of the biological stress response that is potently activated by acute alcohol intake and increased with binge drinking. Binge drinking is associated with negative social and health consequences and risk of developing alcohol use disorder (AUD). Both cortisol levels and AUD are also associated with changes in hippocampal and prefrontal regions. However, no previous research has assessed structural gray matter volume (GMV) and cortisol concurrently to examine BD effects on hippocampal and prefrontal GMV and cortisol, and their prospective relationship to future alcohol intake.

Methods

Individuals who reported binge drinking (BD: N = 55) and demographically matched non-binge moderate drinkers (MD: N = 58) were enrolled and scanned using high-resolution structural MRI. Whole brain voxel-based morphometry was used to quantify regional GMV. In a second phase, 65% of the sample volunteered to participate in prospective daily assessment of alcohol intake for 30 days post-scanning.

Results

Relative to MD, BD showed significantly higher cortisol and smaller GMV in regions including hippocampus, dorsal lateral prefrontal cortex (dlPFC), prefrontal and supplementary motor, primary sensory and posterior parietal cortex (FWE, p < 0.05). GMV in bilateral dlPFC and motor cortices were negatively associated with cortisol levels, and smaller GMV in multiple PFC regions was associated with more subsequent drinking days in BD.

Conclusion

These findings indicate neuroendocrine and structural dysregulation associated with BD relative to MD. Notably, BD-associated lower GMV regions were those involved in stress, memory and cognitive control, with lower GMV in cognitive control and motor regions also predicting higher levels of future alcohol intake in BD.