Rich club and reward network connectivity as endophenotypes for alcohol dependence: a diffusion tensor imaging study

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.

Disrupted white matter structural networks in individuals with alcohol dependence

Previous diffusion tensor imaging (DTI) studies have demonstrated widespread white matter microstructure damage in individuals with alcoholism. However, very little is known about the alterations in the topological architecture of white matter structural networks in alcohol dependence (AD). This study included 67 AD patients and 69 controls. The graph theoretical analysis method was applied to examine the topological organization of the white matter structural networks, and network-based statistics (NBS) were employed to detect structural connectivity alterations. Compared to controls, AD patients exhibited abnormal global network properties characterized by increased small-worldness, normalized clustering coefficient, clustering coefficient, and shortest path length; and decreased global efficiency and local efficiency. Further analyses revealed decreased nodal efficiency and degree centrality in AD patients mainly located in the default mode network (DMN), including the precuneus, anterior cingulate and paracingulate gyrus, median cingulate and paracingulate gyrus, posterior cingulate gyrus, and medial part of the superior frontal gyrus. Furthermore, based on NBS approaches, patients displayed weaker subnetwork connectivity mainly located in the region of the DMN. Additionally, altered network metrics were correlated with intelligence quotient (IQ) scores and global assessment function (GAF) scores. Our results may reveal the disruption of whole-brain white matter structural networks in AD individuals, which may contribute to our comprehension of the underlying pathophysiological mechanisms of alcohol addiction at the level of white matter structural networks.

Win and Loss Responses in the Monetary Incentive Delay Task Mediate the Link between Depression and Problem Drinking

Depression and alcohol misuse, frequently comorbid, are associated with altered reward processing. However, no study has examined whether and how the neural markers of reward processing are shared between depression and alcohol misuse. We studied 43 otherwise-healthy drinking adults in a monetary incentive delay task (MIDT) during fMRI. All participants were evaluated with the Alcohol Use Disorders Identification Test (AUDIT) and Beck's Depression Inventory (BDI-II) to assess the severity of drinking and depression. We performed whole brain regressions against each AUDIT and BDI-II score to investigate the neural correlates and evaluated the findings at a corrected threshold. We performed mediation analyses to examine the inter-relationships between win/loss responses, alcohol misuse, and depression. AUDIT and BDI-II scores were positively correlated across subjects. Alcohol misuse and depression shared win-related activations in frontoparietal regions and parahippocampal gyri (PHG), and right superior temporal gyri (STG), as well as loss-related activations in the right PHG and STG, and midline cerebellum. These regional activities (β's) completely mediated the correlations between BDI-II and AUDIT scores. The findings suggest shared neural correlates interlinking depression and problem drinking both during win and loss processing and provide evidence for co-morbid etiological processes of depressive and alcohol use disorders.

Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia

Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.

Strengthened and posterior-shifted structural rich-club organization in people who use cocaine

BACKGROUND

People with cocaine use disorder (CUD) often have abnormal cognitive function and brain structure. Cognition is supported by brain networks that typically have characteristics like rich-club organization, which is a group of regions that are highly connected across the brain and to each other, and small worldness, which is a balance between local and long-distance connections. However, it is unknown whether there are abnormalities in structural brain network connectivity of CUD.

METHODS

Using diffusion-weighted imaging, we measured structural connectivity in 37 people with CUD and 38 age-matched controls. We identified differences in rich-club organization and whether such differences related to small worldness and behavior. We also tested whether rich-club reorganization was associated with caudate and putamen structural connectivity due to the relevance of the dopamine system to cocaine use.

RESULTS

People with CUD had a higher normalized rich-club coefficient than controls, more edges connecting rich-club nodes to each other and to non-rich-club nodes, and fewer edges connecting non-rich-club nodes. Rich-club nodes were shifted posterior and lateral. Rich-club reorganization was related to lower clustered connectivity around individual nodes found in CUD, to increased impulsivity, and to a decrease in caudate connectivity.

CONCLUSIONS

These findings are consistent with previous work showing increased rich-club connectivity in conditions associated with a hypofunctional dopamine system. The posterior shift in rich-club nodes in CUD suggests that the structural connectivity of posterior regions may be more impacted than previously recognized in models based on brain function and morphology.

Anomalies in global network connectivity associated with early recovery from alcohol dependence: A network transcranial magnetic stimulation and electroencephalography study

Although previous research in alcohol dependent populations identified alterations within local structures of the addiction ‘reward’ circuitry, there is limited research into global features of this network, especially in early recovery. Transcranial magnetic stimulation (TMS) is capable of non‐invasively perturbing the brain network while electroencephalography (EEG) measures the network response. The current study is the first to apply a TMS inhibitory paradigm while utilising network science (graph theory) to quantify network anomalies associated with alcohol dependence. Eleven individuals with alcohol‐dependence (ALD) in early recovery and 16 healthy controls (HC) were administered 75 single pulses and 75 paired‐pulses (inhibitory paradigm) to both the left and right prefrontal cortex (PFC). For each participant, Pearson cross‐correlation was applied to the EEG data and correlation matrices constructed. Global network measures (mean degree, clustering coefficient, local efficiency and global efficiency) were extracted for comparison between groups. Following administration of the inhibitory paired‐pulse TMS to the left PFC, the ALD group exhibited altered mean degree, clustering coefficient, local efficiency and global efficiency compared to HC. Decreases in local efficiency increased the prediction of being in the ALD group, while all network metrics (following paired‐pulse left TMS) were able to adequately discriminate between the groups. In the ALD group, reduced mean degree and global clustering was associated with increased severity of past alcohol use. Our study provides preliminary evidence of altered network topology in patients with alcohol dependence in early recovery. Network anomalies were predictive of high alcohol use and correlated with clinical features of alcohol dependence. Further research using this novel brain mapping technique may identify useful network biomarkers of alcohol dependence and recovery.

Topological alteration is associated with non-dependent alcohol use in bipolar disorder

INTRODUCTION

Structural alterations in cortical thickness and the microstructural organisation of white matter are independently associated with non-dependent alcohol consumption and bipolar disorder(BD). Identifying their interactive and network level effects on brain topology may identify the impact of alcohol on reward and emotion circuitry, and its contribution to relapse in BD.

METHODS

Thirty-four BD-I (DSM-IV-TR) and 38 healthy controls underwent T1 and diffusion-weighted MRI scanning, and the AUDIT-C to assess alcohol use. Connectomes comprised of 34 cortical and nine subcortical nodes bilaterally (Freesurfer v5.3) connected by fractional anisotropy-weighted edges derived from non-tensor based deterministic constrained spherical deconvolution tractography (ExploreDTI v4.8.6) underwent permutation-based topological analysis (NBS v1.2) and were examined for effects of alcohol use and diagnosis-by-alcohol use accounting for age, sex and diagnosis.

RESULTS

Alcohol was significantly related to a subnetwork, encompassing connections between fronto-limbic, basal ganglia and temporal nodes (Frange=5-8.4, p=0.031) and was not detected to have an effect on global brain integration or segregation. A portion of this network (18%), involving cortico-limbic and basal ganglia connections, was differentially impacted by alcohol in the BD relative to the control group (Frange=5-8.8, p=0.033), despite the groups' consuming similar amounts of alcohol (BD: mean±SD 4.95±3.0; HC 3.62±3.0, T=1.88, p=0.06).

DISCUSSION

Non-dependent alcohol use impacts brain architectural organization and connectivity within salience, reward, and affective circuitry. The relationship between alcohol use and topology of the network in BD suggests an interactive effect between specific biological vulnerability and alcohol use, which may explain susceptibility to increased risk of relapse in the disorder.

Alcohol use in emerging adults associated with lower rich-club connectivity and greater connectome network disorganization

BACKGROUND

Emerging adulthood is a critical neurodevelopmental stage, with alcohol use during this period consistently associated with brain abnormalities and damage in anatomical structure and white matter integrity. However, it is less clear how alcohol use is associated with the brain's structural organization (i.e., white matter connections between anatomical regions). Recent connectome research has focused on rich-club regions, a collection of highly-interconnected hubs that are critical in brain communication and global network organization and disproportionately vulnerable to insults.

METHODS

For the first time, we examined alcohol use associations with structural rich-club and connectome organization in emerging adults (N = 66).

RESULTS

Greater lifetime drinks and current monthly drinks were significantly associated with lower rich-club organization (rs =-0.38, ps < 0.003) and lower rich-club connectivity (rs <-0.34, ps < 0.007). Additionally, rich-club connectivity was significantly more negatively correlated with alcohol use than connectivity among non-rich-club regions (ps < 0.035). Examining overall structural organization, greater lifetime drinks and current monthly drinks were significantly associated with lower network density (i.e., lower network resilience; rs <-0.36, ps = 0.004). Additionally, greater lifetime drinks and current monthly drinks were significantly associated with higher network segregation (i.e., network's tendency to divide into subnetworks; rs >0.33, ps<0.008). Alcohol use was not significantly associated with network integration (i.e., network's efficiency in combining information across the brain; ps > 0.064).

CONCLUSIONS

Results provide novel evidence that alcohol use is associated with decreased rich-club connectivity and structural network disorganization. Given that both are critical in global brain communication, these results highlight the importance of examining alcohol use and brain relationships in emerging adulthood.

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.

Treatment Response Prediction and Individualized Identification of Short-Term Abstinence Methamphetamine Dependence Using Brain Graph Metrics

Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress. Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics. Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23-74.17%), 66.05% (95% CI = 63.06-69.04%), and 80.35% (95% CI = 77.77-82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28-73.12%), 86.75% (95% CI = 84.48-88.92%), and 55.65% (95% CI = 52.61-58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance. Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

Delta resting-state functional connectivity in the cognitive control network as a prognostic factor for maintaining abstinence: An eLORETA preliminary study

BACKGROUND

Cortical regions that support cognitive control are increasingly well recognized, but the functional mechanisms that promote such control over emotional and behavioral hyperreactivity to alcohol in recently abstinent alcohol-dependent patients are still insufficiently understood. This study aimed to identify neurophysiological biomarkers of maintaining abstinence in alcohol-dependent individuals after alcohol treatment by investigating the resting-state EEG-based functional connectivity in the cognitive control network (CCN).

METHODS

Lagged phase synchronization between CCN areas by means of eLORETA as well as the Barratt Impulsiveness Scale (BIS-11) and Beck Depression Inventory (BDI) were assessed in abstinent alcohol-dependent patients recruited from treatment centers. A preliminary prospective study design was used to classify participants into those who did and did not maintain abstinence during a follow-up period (median 12 months) after discharge from residential treatment.

RESULTS

Alcohol-dependent individuals, who maintained abstinence (N = 18), showed significantly increased lagged phase synchronization between the left dorsolateral prefrontal cortex (DLPFC) and the left posterior parietal cortex (IPL) as well as between the right anterior insula cortex/frontal operculum (IA/FO) and the right inferior frontal junction (IFJ) in the delta band compared to those who later relapsed (N = 16). Regression analysis showed that the increased left frontoparietal delta connectivity in the early period of abstinence significantly predicted maintaining abstinence over the ensuing 12 months. Furthermore, right frontoinsular delta connectivity correlated negatively with impulsivity and depression measures.

CONCLUSIONS

These results suggest that the increased delta resting-state functional connectivity in the CCN may be a promising neurophysiological predictor of maintaining abstinence in individuals with alcohol dependence.

Alterations in White Matter Microstructure and Connectivity in Young Adults with Alcohol Use Disorder

BACKGROUND

Magnetic resonance imaging (MRI) studies have shown differences in volume and structure in the brains of individuals with alcohol use disorder (AUD). Most research has focused on neuropathological effects of alcohol that appear after years of chronic alcohol misuse. However, few studies have investigated white matter (WM) microstructure and diffusion MRI-based (DWI) connectivity during early stages of AUD. Therefore, the goal of this work was to investigate WM integrity and structural connectivity in emerging adulthood AUD subjects using both conventional DWI metrics and a novel connectomics approach.

METHODS

Twenty-two AUD and 18 controls (CON) underwent anatomic and diffusion MRI. Outcome measures were scalar diffusion metrics and structural network connectomes. Tract-Based Spatial Statistics was used to investigate group differences in diffusion measures. Structural connectomes were used as input into a community structure procedure to obtain a coclassification index matrix (an indicator of community association strength) for each subject. Differences in coclassification and structural connectivity (indexed by streamline density) were assessed via the Network Based Statistics Toolbox.

RESULTS

AUD had higher fractional anisotropy (FA) values throughout the major WM tracts, but also had lower FA values in WM tracts in the cerebellum and right insula (p_TFCE  < 0.05). Mean diffusivity was generally lower in the AUD group (p_TFCE  < 0.05). AUD had lower coclassification of nodes between ventral attention and default mode networks and higher coclassification between nodes of visual, default mode, and somatomotor networks. Additionally, AUD had higher fiber density between an adjacent pair of nodes within the default mode network.

CONCLUSIONS

Our results indicate that emerging adulthood AUD subjects may have differential patterns of FA and distinct differences in structural connectomes compared with CON. These data suggest that such alterations in microstructure and structural connectivity may uniquely characterize early stages of AUD and/or a predisposition for development of AUD.