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

Zorlu, Nabi; Capraz, N.; Oztekin, E.; Bağcı, Başak; Biase, Maria A Di; Zalesky, Andrew; Gelal, Fazıl; Bora, Emre; Durmaz, Ercan; Beşiroğlu, Lütfullah; Sarıçiçek, Aybala

doi:10.1111/adb.12599

Cited by 18 publications

(20 citation statements)

References 51 publications

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“…The current results are the first to provide a brain-based factor indicative of future drinking in this model, suggesting that premorbid alcohol-naïve differences in Rich Club characteristics of functional brain networks also predict future drinking in this model. These results extend recent findings in human participants demonstrating that white matter brain networks of individuals with AUD displayed lower Rich Club characteristics compared to their non-abusing siblings, who displayed lower levels compared to control participants 44 . While Zorlu, Capraz, Oztekin, Bagci, Di Biase, Zalesky, Gelal, Bora, Durmaz, Besiroglu, Saricicek 44 suggest potential premorbid differences in white matter network structure may be a marker of risk or susceptibility to AUD, the results of the current study provide direct empirical support for this hypothesis, showing that Rich Club characteristics of premorbid functional brain networks are directly related to future drinking levels.…”

Section: Discussionsupporting

confidence: 89%

Functional Brain Networks and Alcohol Consumption: From the Naïve State to Chronic Heavy Drinking

Rowland

Stapleton-Kotloski

Alberto

et al. 2020

Preprint

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A fundamental question for alcohol use disorder is how naïve brain networks are reorganized in response to the consumption of alcohol. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during the transition from naïve, to early, to chronic consumption. Resting-state brain networks of six female monkeys were acquired using magnetoencephalography prior to alcohol exposure, after early exposure, and after free-access to alcohol using a well-established model of chronic heavy alcohol use. Functional brain network metrics were derived at each time point. Assortativity, average connection frequency, and number of gamma connections changed significantly over time. All metrics remained relatively stable from naïve to early drinking, and displayed significant changes following increased quantity of alcohol consumption. The assortativity coefficient was significantly less negative (p=.043), connection frequency increased (p=.03), and gamma connections increased (p=.034). Further, brain regions considered hubs (p=.037) and members of the Rich Club (p=.012) became less common across animals following the introduction of alcohol. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r=-.88, p<.001). Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters the topology of 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 AUD phenotype.

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Section: Discussionsupporting

confidence: 89%

Functional Brain Networks and Alcohol Consumption: From the Naïve State to Chronic Heavy Drinking

Rowland

Stapleton-Kotloski

Alberto

et al. 2020

Preprint

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“…It has been suggested that alterations in these hubs or interconnections could likely lead to severe impairments because of their roles in whole‐brain integrative processes (van den Heuvel and Sporns, ). For instance, abnormal rich club organization has been reported in structural connectivity analysis of patients with AUD (Zorlu et al., ). The differences seen here may represent a network phenotype associated with a predisposition to develop AUD.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…To our knowledge, only 1 other structural connectivity study used graph theory analyses in AUD and demonstrated that the AUD group had significantly weaker connectivity, primarily in the right hemisphere (Zorlu et al., ). The edges or tracts of significance included connections of the putamen and hippocampus with other brain regions (Zorlu et al., ). We add to these findings by reporting AUD had lower coclassification of nodes between ventral attention (supplementary motor and inferior insula/superior temporal areas) and default mode (superior middle temporal gyrus) networks and higher coclassification between nodes of visual (middle occipital/temporal areas) and somatomotor (supplementary motor area) networks.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chumin

Grecco

Džemidžić

et al. 2019

Alcoholism Clin & Exp Res

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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.

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“…In addition, the community index indicates which community or modularity a given node belongs to. Changes in the community index generally suggest the changes of locally connected clusters or modules implicated in specialized information processing (43). Both of these were used to describe the location and efficiency of nodes in the information transfer.…”

Section: Discussionmentioning

confidence: 99%

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

Cui

Yang

et al. 2021

Front. Psychiatry

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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.

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

Cited by 18 publications

References 51 publications

Functional Brain Networks and Alcohol Consumption: From the Naïve State to Chronic Heavy Drinking

Functional Brain Networks and Alcohol Consumption: From the Naïve State to Chronic Heavy Drinking

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

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

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