MRI economics: Balancing sample size and scan duration in brain wide association studies.

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity.

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

Selective D3 receptor antagonism modulates neural response during negative emotional processing in substance dependence.

Introduction: Negative affective states contribute to the chronic-relapsing nature of addiction. Mesolimbic dopamine D3 receptors are well placed to modulate emotion and are dysregulated in substance dependence. Selective antagonists might restore dopaminergic hypofunction, thus representing a potential treatment target. We investigated the effects of selective D3 antagonist, GSK598809, on the neural response to negative emotional processing in substance dependent individuals and healthy controls. Methodology: Functional MRI BOLD response was assessed during an evocative image task, 2 h following acute administration of GSK598809 (60 mg) or placebo in a multi-site, double-blind, pseudo-randomised, cross-over design. Abstinent drug dependent individuals (DD, n = 36) comprising alcohol-only (AO, n = 19) and cocaine-alcohol polydrug (PD, n = 17) groups, and matched controls (n = 32) were presented with aversive and neutral images in a block design (contrast of interest: aversive > neutral). Whole-brain mixed-effects and a priori ROI analyses tested for group and drug effects, with identical models exploring subgroup effects. Results: No group differences in task-related BOLD signal were identified between DD and controls. However, subgroup analysis revealed greater amygdala/insular BOLD signal in PD compared with AO groups. Following drug administration, GSK598809 increased BOLD response across HC and DD groups in thalamus, caudate, putamen, and pallidum, and reduced BOLD response in insular and opercular cortices relative to placebo. Multivariate analyses in a priori ROIs revealed differential effects of D3 antagonism according to subgroup in substantia nigra; GSK598809 increased BOLD response in AO and decreased response in PD groups. Conclusion: Acute GSK598809 modulates the BOLD response to aversive image processing, providing evidence that D3 antagonism may impact emotional regulation. Enhanced BOLD response within D3-rich mesolimbic regions is consistent with its pharmacology and with attenuation of substance-related hypodopaminergic function. However, the lack of group differences in task-related BOLD response and the non-specific effect of GSK598809 between groups makes it difficult to ascertain whether D3 antagonism is likely to be normalising or restorative in our abstinent populations. The suggestion of differential D3 modulation between AO and PD subgroups is intriguing, raising the possibility of divergent treatment responses. Further study is needed to determine whether D3 antagonism should be recommended as a treatment target in substance dependence.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study.

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Sensory-motor cortices shape functional connectivity dynamics in the human brain.

Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. We developed a spatially heterogeneous large-scale dynamical circuit model that allowed for variation in local synaptic properties across the human cortex. Here we show that parameterizing local circuit properties with both anatomical and functional gradients generates more realistic static and dynamic resting-state functional connectivity (FC). Furthermore, empirical and simulated FC dynamics demonstrates remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. Time-varying regional fMRI amplitude may track multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggests that sensory-motor regions are a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matches the principal gradient of gene expression that encompasses certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior.

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

Correction: Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

[This corrects the article DOI: 10.1371/journal.pbio.3000602.].

Disturbances across whole brain networks during reward anticipation in an abstinent addiction population.

The prevalent spatial distribution of abnormalities reported in cognitive fMRI studies in addiction suggests there are extensive disruptions across whole brain networks. Studies using resting state have reported disruptions in network connectivity in addiction, but these studies have not revealed characteristics of network functioning during critical psychological processes that are disrupted in addiction populations. Analytic methods that can capture key features of whole brain networks during psychological processes may be more sensitive in revealing additional and widespread neural disturbances in addiction, that are the provisions for relapse risk, and targets for medication development. The current study compared a substance addiction (ADD; n = 83) group in extended abstinence with a control (CON; n = 68) group on functional MRI (voxel-wise activation) and global network (connectivity) measures related to reward anticipation on a monetary incentive delay task. In the absence of group differences on MID performance, the ADD group showed reduced activation predominantly across temporal and visual regions, but not across the striatum. The ADD group also showed disruptions in global network connectivity (lower clustering coefficient and higher characteristic path length), and significantly less connectivity across a sub-network comprising frontal, temporal, limbic and striatal nodes. These results show that an addiction group in extended abstinence exhibit localised disruptions in brain activation, but more extensive disturbances in functional connectivity across whole brain networks. We propose that measures of global network functioning may be more sensitive in highlighting latent and more widespread neural disruptions during critical psychological processes in addiction and other psychiatric disorders.

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology.

BACKGROUND: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain. METHODS: A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components. RESULTS: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks. CONCLUSIONS: We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

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Introduction: Two cases of idiopathic granolomatous mastitis were diagnosed by histological examination in our Surgical Department in 2017. The idiopathic granulomatous mastitis is a rare, benign inflammatory laesion of the breast which can mimic malignancy in it's clinical appearance. We would like to draw attention to this differential diagnostic problem based on the cases of our Surgery Department.

Resting brain dynamics at different timescales capture distinct aspects of human behavior.

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior.

Global signal regression strengthens association between resting-state functional connectivity and behavior.

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain.

We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Naltrexone differentially modulates the neural correlates of motor impulse control in abstinent alcohol-dependent and polysubstance-dependent individuals.

Identifying key neural substrates in addiction disorders for targeted drug development remains a major challenge for clinical neuroscience. One emerging target is the opioid system, where substance-dependent populations demonstrate prefrontal opioid dysregulation that predicts impulsivity and relapse. This may suggest that disturbances to the prefrontal opioid system could confer a risk for relapse in addiction due to weakened 'top-down' control over impulsive behaviour. Naltrexone is currently licensed for alcohol dependence and is also used clinically for impulse control disorders. Using a go/no-go (GNG) task, we examined the effects of acute naltrexone on the neural correlates of successful motor impulse control in abstinent alcoholics (AUD), abstinent polysubstance-dependent (poly-SUD) individuals and controls during a randomised double blind placebo controlled fMRI study. In the absence of any differences on GNG task performance, the AUD group showed a significantly greater BOLD response compared to the control group in lateral and medial prefrontal regions during both placebo and naltrexone treatments; effects that were positively correlated with alcohol abstinence. There was also a dissociation in the positive modulating effects of naltrexone in the orbitofrontal cortex (OFC) and anterior insula cortex (AIC) of the AUD and poly-SUD groups respectively. Self-reported trait impulsivity in the poly-SUD group also predicted the effect of naltrexone in the AIC. These results suggest that acute naltrexone differentially amplifies neural responses within two distinct regions of a salience network during successful motor impulse control in abstinent AUD and poly-SUD groups, which are predicted by trait impulsivity in the poly-SUD group.

Effects of caffeine and phosphodiesterase inhibitors on activation of neonatal T lymphocytes.

Caffeine and selective PDE inhibitors are widely used in clinical management of preterm and term neonates. However, little is known about how these compounds interact with the neonatal adaptive immune system. We aimed to describe the effects of caffeine, milrinone and sildenafil on the activation and cytokine production of T cells from umbilical cord blood (UCB) compared to adult peripheral blood (APB). We isolated mononuclear cells from 10 APB and 6 UCB samples. We assessed intracellular cytokine production (IFN-γ, IL-2, IL-4, IL-6, IL-17) of stimulated CD4 cells and parameters of calcium influx and ROS production following treatment with caffeine, milrinone, sildenafil, dbcAMP or a specific A2A receptor antagonist, ZM241385 using flow cytometry. In ABP, only ZM241385 caused a 1.14-fold increase in calcium influx, while all compounds increased calcium influx in UCB. This effect was more pronounced in case of caffeine (1.41-fold) and dbcAMP (1.3-fold) compared to milrinone (1.22-fold), sildenafil (1.23-fold) or ZM241385 (1.23-fold). Intracellular levels of the studied cytokines were unaffected by the applied compounds in both APB and UCB samples. Caffeine increases calcium influx upon activation in neonatal T lymphocytes to a larger extent than milrinone or sildenafil. This effect appears to be mediated primarily via increased cAMP levels rather than A2A receptor inhibition. Overall, the application of caffeine, sildenafil or milrinone does not appear to have immunosuppressive effects on neonatal T cells.

Cytokine production pattern of T lymphocytes in neonatal arterial ischemic stroke during the first month of life-a case study.

BACKGROUND: The perinatal period carries the highest risk for stroke in childhood; however, the pathophysiology is poorly understood and preventive, prognostic, and therapeutic strategies are not available. A new pathophysiological model describes the development of neonatal arterial ischemic stroke (NAIS) as the combined result of prenatal inflammation and hypoxic-ischemic insult. Neuroinflammation and a systemic inflammatory response are also important features of NAIS. Identifying key players of the inflammatory system is in the limelight of current research. CASE PRESENTATION: We present four NAIS cases, in whom detailed analysis of intracellular and plasma cytokine levels are available from the first month of life. All neonates were admitted with the initial diagnosis of hypoxic ischemic encephalopathy (HIE); however, early MRI examination revealed NAIS. Blood samples were collected between 3 and 6 h of life, at 24 h, 72 h, 1 week, and 1 month of life. Peripheral blood mononuclear cells were assessed with flow cytometry and plasma cytokine levels were measured. Pooled data from the cohort of four NAIS patients were compared to infants with HIE. At 6 and 72 h of age, the prevalence of IL10+ CD8+ lymphocytes remained lower in NAIS. At 6 h, CD8+ lymphocytes in NAIS produced more IL-17. At 72 h, CD8+ cells produced more IL-6 in severe HIE than in NAIS, but IL-6 production remained elevated in CD8 cells at 1 month in NAIS, while it decreased in HIE. At 1 week, the prevalence of TGF-ß + lymphocytes prone to enter the CNS was elevated in NAIS. On the other hand, by 1 month of age, the prevalence of TGF-ß + CD4+ lymphocytes decreased in NAIS compared to HIE. At 72 h, we found elevated plasma levels of IL-5, MCP-1, and IL-17 in NAIS. By 1 month, plasma levels of IL-4, IL-12, and IL-17 decreased in NAIS but remained elevated in HIE. CONCLUSIONS: Differences in the cytokine network are present between NAIS and HIE. CD8 lymphocytes appear to shift towards the pro-inflammatory direction in NAIS. The inflammatory response appears to be more pronounced at 72 h in NAIS but decreases faster, reaching lower plasma levels of inflammatory markers at 1 month.

Naltrexone ameliorates functional network abnormalities in alcohol-dependent individuals.

Naltrexone, an opioid receptor antagonist, is commonly used as a relapse prevention medication in alcohol and opiate addiction, but its efficacy and the mechanisms underpinning its clinical usefulness are not well characterized. In the current study, we examined the effects of 50-mg naltrexone compared with placebo on neural network changes associated with substance dependence in 21 alcohol and 36 poly-drug-dependent individuals compared with 36 healthy volunteers. Graph theoretic and network-based statistical analysis of resting-state functional magnetic resonance imaging (MRI) data revealed that alcohol-dependent subjects had reduced functional connectivity of a dispersed network compared with both poly-drug-dependent and healthy subjects. Higher local efficiency was observed in both patient groups, indicating clustered and segregated network topology and information processing. Naltrexone normalized heightened local efficiency of the neural network in alcohol-dependent individuals, to the same levels as healthy volunteers. Naltrexone failed to have an effect on the local efficiency in abstinent poly-substance-dependent individuals. Across groups, local efficiency was associated with substance, but no alcohol exposure implicating local efficiency as a potential premorbid risk factor in alcohol use disorders that can be ameliorated by naltrexone. These findings suggest one possible mechanism for the clinical effects of naltrexone, namely, the amelioration of disrupted network topology.