Alterations of resting state functional network connectivity in the brain of nicotine and alcohol users

Deep Parametric Mixtures for Modeling the Functional Connectome

Functional connectivity between brain regions is often estimated by correlating brain activity measured by resting-state fMRI in those regions. The impact of factors (e.g, disorder or substance use) are then modeled by their effects on these correlation matrices in individuals. A crucial step in better understanding their effects on brain function could lie in estimating connectomes, which encode the correlation matrices across subjects. Connectomes are mostly estimated by creating a single average for a specific cohort, which works well for binary factors (such as sex) but is unsuited for continuous ones, such as alcohol consumption. Alternative approaches based on regression methods usually model each pair of regions separately, which generally produces incoherent connectomes as correlations across multiple regions contradict each other. In this work, we address these issues by introducing a deep learning model that predicts connectomes based on factor values. The predictions are defined on a simplex spanned across correlation matrices, whose convex combination guarantees that the deep learning model generates well-formed connectomes. We present an efficient method for creating these simplexes and improve the accuracy of the entire analysis by defining loss functions based on robust norms. We show that our deep learning approach is able to produce accurate models on challenging synthetic data. Furthermore, we apply the approach to the resting-state fMRI scans of 281 subjects to study the effect of sex, alcohol, and HIV on brain function.

Leveraging Neural Networks in Preclinical Alcohol Research

Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural "hubs" involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.

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

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

Determining the number of states in dynamic functional connectivity using cluster validity indexes

BACKGROUND

Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.

NEW METHOD

Currently employed indexes do not provide a crisp answer on what is the best number of clusters. In addition, there is a lack of CVI testing in the context of dFC data. This work tests a comprehensive set of twenty four cluster validity indexes applied to addiction data and suggest the best ones for clustering dynamic functional connectivity.

RESULTS

Out of the twenty four considered CVIs, Davies-Bouldin and Ray-Turi were the most suitable methods to find the number of clusters in both simulation and real data. The solution for these two CVIs is to find a local minimum critical point, which can be automated using computational algorithms.

COMPARISON WITH EXISTING METHODS

Elbow-Criterion, Silhouette and GAP-Statistic methods have been widely used in dFC studies. These methods are included among the tested CVIs where the performances of all twenty four CVIs are compared.

CONCLUSIONS

Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis.

Altered habenula resting state functional connectivity in deprived veteran tobacco smokers: A pilot study

This study aimed to examine habenular resting state functional connectivity (RSFC) abnormalities in tobacco-smoking veterans. The authors explored RSFC in sated smokers (n = 3D 18), overnight deprived smokers (n = 3D 13), and nonsmoker controls (n = 3D 26). Seed-to-voxel analysis was used to explore RSFC in the habenula. Compared to sated smokers, deprived smokers demonstrated higher RSFC between the right habenula and two clusters of voxels: one in the right fusiform gyrus, and one in the left lingual gyrus. To study nicotine withdrawal, the authors used the Shiffman-Jarvik Withdrawal Questionnaire (SJWQ) score as a regressor and found higher RSFC between the right habenula and the left frontal pole in deprived compared to sated smokers. Right habenula RSFC distinguished between sated and deprived smokers and differentiated between sated and deprived smokers when using SJWQ as a regressor, suggesting a habenular role in tobacco withdrawal.

Healthy Subjects With Extreme Patterns of Performance Differ in Functional Network Topology and Benefits From Nicotine

Do subjects with atypical patterns in attentional and executive behaviour show different brain network topology and react differently towards nicotine administration? The efficacy of pro-cognitive drugs like nicotine considerably varies between subjects and previous theoretical and empirical evidence suggest stronger behavioural nicotine effects in subjects with low performance. One problem is, however, how to best define low performance, especially if several cognitive functions are assessed for subject characterisation. We here present a method that used a multivariate, robust outlier detection algorithm to identify subjects with suspicious patterns of performance in attentional and executive functioning. In contrast to univariate approaches, this method is sensitive towards extreme positions within the multidimensional space that do not have to be extreme values in the individual behavioural distributions. The method was applied to a dataset of healthy, non-smoking subjects (n = 34) who were behaviorally characterised by an attention and executive function test on which N = 12 volunteers were classified as outliers. All subjects then underwent a resting-state functional magnetic resonance imaging (fMRI) scan to characterise brain network topology and an experimental behavioural paradigm under placebo and nicotine (7 mg patch) that gauged aspects of attention and executive function. Our results indicate that subjects with an atypical multivariate pattern in attention and executive functioning showed significant differences in nodal brain network integration in visual association and pre-motor brain regions during resting state. These differences in brain network topology significantly predicted larger individual nicotine effects on attentional processing. In summary, the current approach successfully identified a subgroup of healthy volunteers with low behavioural performance who differ in brain network topology and attentional benefit from nicotine.

Functional and structural connectivity of the executive control network in college binge drinkers

Binge Drinking (BD) is a pattern of excessive alcohol consumption highly prevalent among college students, and has been associated with structural and functional alterations of brain networks. Recent advances in the resting-state connectivity analysis have boosted the research of the network-level connectivity disturbances associated with many psychiatric and neurological disorders, including addiction. Accordingly, atypical functional connectivity patterns in resting-state networks such as the Executive Control Network (ECN) have been found in substance users and alcohol-dependent individuals. In this study, we assessed for the first time the ECN functional and structural connectivity in a group of 34 college students, 20 (10 women) binge drinkers (BDs) in comparison with a group of 14 (8 women) alcohol abstinent controls (AACs). Overall, our findings documented increased resting-state functional connectivity (rsFC) in the BDs left middle frontal cortex of the left ECN in comparison to the AACs, while no structural connectivity differences were observed between groups. Pearson correlations revealed a positive association between the left middle frontal gyrus rsFC and the frequency of BD episodes per month, in the BD group. These findings suggest that maintaining a pattern of acute and intermittent alcohol consumption during important stages of brain development, as the transition from adolescence to adulthood, is associated with impaired ECN rsFC despite no group differences being yet noticed in the ECN structural connectivity.

Executive Impairment in Alcohol Use Disorder Reflects Structural Changes in Large-Scale Brain Networks: A Joint Independent Component Analysis on Gray-Matter and White-Matter Features

Alcohol Use Disorder (AUD) entails chronic effects on brain structure. Neurodegeneration due to alcohol toxicity is a neural signature of executive impairment typically observed in AUD, previously related to both gray-matter volume/density and white-matter abnormalities. Recent studies highlighted the role of meso-cortico-limbic structures supporting the salience and executive networks, in which the extent of neurostructural damage is significantly related to patients’ executive performance. Here we aim to integrate multimodal information on gray-matter and white-matter features with a multivariate data-driven approach (joint Independent Component Analysis, jICA), and to assess the relationship between the extent of damage in the resulting neurostructural superordinate components and executive profile in AUD. Twenty-two AUD patients and 18 matched healthy controls (HC) underwent a Magnetic Resonance Imaging (MRI) protocol, alongside clinical and neuropsychological examinations. We ran jICA on five neurostructural features, including gray-matter density and different diffusion tensor imaging metrics. We extracted 12 Independent Components (ICs) and compared the resulting mixing coefficients in patients vs. HC. Finally, we correlated significant ICs with executive and clinical variables. One out of 12 ICs (IC11) discriminated patients from healthy controls and correlated positively both with executive performance in all subjects, and with lifetime duration of alcohol abuse in patients. In line with previous related evidence, this component involved widespread gray-matter and white-matter patterns including key nodes and fiber tracts of salience, default-mode and central executive networks. These findings highlighted the role of multivariate data integration as a valuable approach revealing superordinate hallmarks of neural changes related to cognition in neurological and psychiatric populations.

Brain-behavior relations and effects of aging and common comorbidities in alcohol use disorder: A review

OBJECTIVE

Alcohol use disorder (AUD) is a complex, dynamic condition that waxes and wanes with unhealthy drinking episodes and varies in drinking patterns and effects on brain structure and function with age. Its excessive use renders chronically heavy drinkers vulnerable to direct alcohol toxicity and a variety of comorbidities attributable to nonalcohol drug misuse, viral infections, and accelerated or premature aging. AUD affects widespread brain systems, commonly, frontolimbic, frontostriatal, and frontocerebellar networks.

METHOD AND RESULTS

Multimodal assessment using selective neuropsychological testing and whole-brain neuroimaging provides evidence for AUD-related specific brain structure-function relations established with double dissociations. Longitudinal study using noninvasive imaging provides evidence for brain structural and functional improvement with sustained sobriety and further decline with relapse. Functional imaging suggests the possibility that some alcoholics in recovery can compensate for impairment by invoking brain systems typically not used for a target task but that can enable normal-level performance.

CONCLUSIONS

Evidence for AUD-aging interactions, indicative of accelerated aging, together with increasing alcohol consumption in middle-age and older adults, put aging drinkers at special risk for developing cognitive decline and possibly dementia. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Association Between Age and Familial Risk for Alcoholism on Functional Connectivity in Adolescence

OBJECTIVE

Youth with a family history of alcohol use disorder (family history positive [FHP]) are at increased risk for developing maladaptive substance use relative to family history negative (FHN) peers. Building on earlier studies demonstrating morphological differences and distinct patterns of neural activation in FHP, the purpose of the present study was to investigate differential intrinsic functional connectivity among brain networks indexing premorbid risk of developing alcohol use disorder (AUD).

METHOD

The current study examined intrinsic functional connectivity using resting state functional magnetic resonance imaging in 191 adolescents 13 to 18 years of age with and without family history of AUD via independent component analysis, a method enabling data-driven investigation of internetwork and intranetwork connectivity among brain regions at rest.

RESULTS

Analyses revealed significantly lower intranetwork connectivity in FHP compared to FHN participants between the dorsal premotor cortex and other sensorimotor network regions. Reduced intranetwork connectivity in this region was further correlated with the number of biological family members with AUD and mood disorders. Robust differences were also evident in internetwork connectivity as a function of age. However, there was no evidence for family history by age interactions.

CONCLUSION

Intra- but not internetwork connectivity appears to differentiate FHP and FHN adolescents, whereas age differences within adolescence are marked by differences in internetwork connectivity.

Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI

Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n = 108; 63 male) and matched nonsmoking controls (n = 108; 63 male) and multi-dimensional scaling to visualize the heterogeneity of NUD in individual smokers based on rsFC measures. Machine-based learning models identified five resting-state networks that played a role in distinguishing smokers from controls: the posterior and anterior default mode networks, the sensorimotor network, the salience network and the right executive control network. The classification method constructed classifiers with an average correct classification rate of 88.1 percent and an average area under the curve of 0.93. Compared with controls, individuals with NUD had weaker functional connectivity measures within these networks (P < 0.05, false discovery rate corrected). Further, multi-dimensional scaling visualization demonstrated that controls were similar to each other whereas individuals with NUD had less similarity to controls and to other individuals with NUD. Our findings build upon previous literature demonstrating that machine learning-based approaches to classifying rsFC data offer a valuable technique to understanding network-level differences in nicotine-related neurobiology and extend previous findings by improving classification accuracy and demonstrating the heterogeneity in resting-state networks of individuals with NUD.

Association between the oral microbiome and brain resting state connectivity in smokers

Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut-microbiome-brain axis. However, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli, such as smoking, within the oral microenvironment. Additionally, given the complex structural and functional networks in brain, our knowledge about the relationship between microbiome and brain function through specific brain circuits is still very limited. In this pilot study, we leveraged next generation sequencing for microbiome and functional neuroimaging technique to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex-matched nonsmokers were recruited for 16S sequencing of their oral microbial community. Among them, 56 subjects were scanned by resting-state functional magnetic resonance imaging to derive brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the oral microbial composition, functional network connectivity, and the associations between microbial shifts and functional network connectivity alternations. Compared to nonsmokers, we found a significant decrease of beta diversity (P = 6 × 10^-3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) with significant alterations in microbial abundance. Pathway analysis on the predicted KEGG pathways shows that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (P = 0.032), mainly including connectivity between brain default network and other task-positive networks. This brain functional component was also significantly associated with smoking related microbiota, suggesting a correlated cross-individual pattern between smoking-induced oral microbiome dysbiosis and brain functional connectivity alternation, possibly involving immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity.

Alterations in resting-state functional connectivity in substance use disorders and treatment implications

Substance use disorders (SUD) are diseases of the brain, characterized by aberrant functioning in the neural circuitry of the brain. Resting state functional connectivity (rsFC) can illuminate these functional changes by measuring the temporal coherence of low-frequency fluctuations of the blood oxygenation level-dependent magnetic resonance imaging signal in contiguous or non-contiguous regions of the brain. Because this data is easy to obtain and analyze, and therefore fairly inexpensive, it holds promise for defining biological treatment targets in SUD, which could help maximize the efficacy of existing clinical interventions and develop new ones. In an effort to identify the most likely "treatment targets" obtainable with rsFC we summarize existing research in SUD focused on 1) the relationships between rsFC and functionality within important psychological domains which are believed to underlie relapse vulnerability 2) changes in rsFC from satiety to deprived or abstinent states 3) baseline rsFC correlates of treatment outcome and 4) changes in rsFC induced by treatment interventions which improve clinical outcomes and reduce relapse risk. Converging evidence indicates that likely "treatment target" candidates, emerging consistently in all four sections, are reduced connectivity within executive control network (ECN) and between ECN and salience network (SN). Other potential treatment targets also show promise, but the literature is sparse and more research is needed. Future research directions include data-driven prediction analyses and rsFC analyses with longitudinal datasets that incorporate time since last use into analysis to account for drug withdrawal. Once the most reliable biological markers are identified, they can be used for treatment matching, during preliminary testing of new pharmacological compounds to establish clinical potential ("target engagement") prior to carrying out costly clinical trials, and for generating hypotheses for medication repurposing.

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

BACKGROUND

In the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences.

METHODS

Adults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24).

RESULTS

The resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model.

CONCLUSIONS

These findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.

Real-Time fMRI Neurofeedback in Patients With Tobacco Use Disorder During Smoking Cessation: Functional Differences and Implications of the First Training Session in Regard to Future Abstinence or Relapse

One of the most prominent symptoms in addiction disorders is the strong desire to consume a particular substance or to show a certain behavior (craving). The strong association between craving and the probability of relapse emphasizes the importance of craving in the therapeutic process. Former studies have demonstrated that neuromodulation using real-time fMRI (rtfMRI) neurofeedback (NF) can be used as a treatment modality in patients with tobacco use disorder. The aim of the present project was to determine whether it is possible to predict the outcome of NF training plus group psychotherapy at the beginning of the treatment. For that purpose, neuronal responses during the first rtfMRI NF session of patients who remained abstinent for at least 3 months were compared to those of patients with relapse. All patients were included in a certified smoke-free course and took part in three NF sessions. During the rtfMRI NF sessions tobacco-associated and neutral pictures were presented. Subjects were instructed to reduce their neuronal responses during the presentation of smoking cues in an individualized region of interest for craving [anterior cingulate cortex (ACC), insula or dorsolateral prefrontal cortex]. Patients were stratified to different groups [abstinence (N = 10) vs. relapse (N = 12)] according to their individual smoking status 3 months after the rtfMRI NF training. A direct comparison of BOLD responses during the first NF-session of patients who had remained abstinent over 3 months after the NF training and patients who had relapsed after 3 months showed that patients of the relapse group demonstrated enhanced BOLD responses, especially in the ACC, the supplementary motor area as well as dorsolateral prefrontal areas, compared to abstinent patients. These results suggest that there is a probability of estimating a successful withdrawal in patients with tobacco use disorder by analyzing the first rtfMRI NF session: a pronounced reduction of frontal responses during NF training in patients might be the functional correlate of better therapeutic success. The results of the first NF sessions could be useful as predictor whether a patient will be able to achieve success after the behavioral group therapy and NF training in quitting smoking or not.

Aberrations of anterior insular cortex functional connectivity in nontreatment-seeking alcoholics

An emergent literature suggests that resting state functional magnetic resonance imaging (rsfMRI) functional connectivity (FC) patterns are aberrant in alcohol use disorder (AUD) populations. The salience network (SAL) is an established set of brain regions prominent in salience attribution and valuation, and includes the anterior insular cortex (AIC). The SAL is thought to play a role in AUD through directing increased attention to interoceptive cues of intoxication. There is very little information on the salience network (SAL) in AUD, and, in particular, there are no data on SAL FC in currently drinking, nontreatment seeking individuals with AUD (NTS). rsfMRI data from 16 NTS and 21 social drinkers (SD) were compared using FC correlation maps from ten seed regions of interest in the bilateral AIC. As anticipated, SD subjects demonstrated greater insular FC with frontal and parietal regions. We also found that, compared to SD, NTS had higher insular FC with hippocampal and medial orbitofrontal regions. The apparent overactivity in brain networks involved in salience, learning, and behavioral control in NTS suggests possible mechanisms in the development and maintenance of AUD.

Combining lifestyle risks to disentangle brain structure and functional connectivity differences in older adults

Lifestyle contributes to inter-individual variability in brain aging, but previous studies focused on the effects of single lifestyle variables. Here, we studied the combined and individual contributions of four lifestyle variables - alcohol consumption, smoking, physical activity, and social integration - to brain structure and functional connectivity in a population-based cohort of 549 older adults. A combined lifestyle risk score was associated with decreased gyrification in left premotor and right prefrontal cortex, and higher functional connectivity to sensorimotor and prefrontal cortex. While structural differences were driven by alcohol consumption, physical activity, and social integration, higher functional connectivity was driven by smoking. Results suggest that combining differentially contributing lifestyle variables may be more than the sum of its parts. Associations generally were neither altered by adjustment for genetic risk, nor by depressive symptomatology or education, underlining the relevance of daily habits for brain health.

Lifestyle factors such as smoking and exercise contribute to the health of the brain during aging, but previous studies have focused on the effects of single lifestyle variables. Here, the authors examine the combined and individual effects of four lifestyle variables on brain structure and function.

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

PURPOSE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

Disrupted Brain Network Efficiency and Decreased Functional Connectivity in Multi-sensory Modality Regions in Male Patients With Alcohol Use Disorder

Background: Recent studies have reported altered efficiency in selective brain regions and functional networks in patients with alcohol use disorder (AUD). Inefficient processing can reflect or arise from the disorganization of information being conveyed from place to place. However, it remains unknown whether the efficiency and functional connectivity are altered in large-scale topological organization of patients with AUD.

Methods: Resting-state functional magnetic resonance imaging (rsfMRI) data were experimentally collected from 21 right-handed males with AUD and 21 right-handed, age-, gender- and education-matched healthy controls (HCs). Graph theory was used to investigate inter-group differences in the topological parameters (global and nodal) of networks and inter-regional functional connectivity. Correlations between group differences in network properties and clinical variables were also investigated in the AUD group.

Results: The brain networks of the AUD group showed decreased global efficiency when compared with the HC group. Besides, increased nodal efficiency was found in the left orbitofrontal cortex (OFC), while reduced nodal efficiency was observed in the right OFC, right fusiform gyrus (FFG), right superior temporal gyrus, right inferior occipital gyrus (IOG), and left insula. Moreover, hypo-connectivity was detected between the right dorsolateral prefrontal cortex (DLPFC) and right superior occipital gyrus (SOG) in the AUD group when compared with the HC group. The nodal efficiency of the left OFC was associated with cognitive performance in the AUD group.

Conclusions: AUD patients exhibited alterations in brain network efficiency and functional connectivity, particularly in regions linked to multi-sensory modalities. These disrupted topological properties may help to obtain a more comprehensive understanding of large-scale brain network activity. Furthermore, these data provide a potential neural mechanism of impaired cognition in individuals with AUD.

Increased Randomness of Functional Network Connectivity in Nicotine and Alcohol Consumers

Alcohol and nicotine are substances that alter the functional connectivity of the brain. These changes have been observed after pinpointing particular brain areas as well as studying the overall brain wiring structure. One property of this wiring structure is the level of randomness. Evidence strongly agrees that brain connectivity is not random, but that chemical substances can affect the connectivity structure. This work aims at studying changes in resting state functional connectivity randomness in relation to the consumption of nicotine and alcohol. Results suggest that randomness in whole brain connectivity is not affected by used substance. However, connectivity among particular brain areas does show changed randomness linked to substance use. Abnormal randomness was found between salience and default mode functional domains. This dysfunction is in line with some postulates of the network model of addiction. The study provides new information on the effects of substance use on the brain.

Impact of Coffee, Wine, and Chocolate Consumption on Cognitive Outcome and MRI Parameters in Old Age

Coffee, wine and chocolate are three frequently consumed substances with a significant impact on cognition. In order to define the structural and cerebral blood flow correlates of self-reported consumption of coffee, wine and chocolate in old age, we assessed cognition and brain MRI measures in 145 community-based elderly individuals with preserved cognition (69 to 86 years). Based on two neuropsychological assessments during a 3-year follow-up, individuals were classified into stable-stable (52 sCON), intermediate (61 iCON) and deteriorating-deteriorating (32 dCON). MR imaging included voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and arterial spin labelling (ASL). Concerning behavior, moderate consumption of caffeine was related to better cognitive outcome. In contrast, increased consumption of wine was related to an unfavorable cognitive evolution. Concerning MRI, we observed a negative correlation of wine and VBM in bilateral deep white matter (WM) regions across all individuals, indicating less WM lesions. Only in sCON individuals, we observed a similar yet weaker association with caffeine. Moreover, again only in sCON individuals, we observed a significant positive correlation between ASL and wine in overlapping left parietal WM indicating better baseline brain perfusion. In conclusion, the present observations demonstrate an inverse association of wine and coffee consumption with cognitive performances. Moreover, low consumption of wine but also moderate to heavy coffee drinking was associated with better WM preservation and cerebral blood-flow notably in cognitively stable elders.

Problem Drinking, Alcohol Expectancy, and Thalamic Resting-State Functional Connectivity in Nondependent Adult Drinkers

Alcohol misuse is associated with thalamic dysfunction. The thalamus comprises subnuclei that relay and integrate information between cortical and subcortical structures. However, it is unclear how the subnuclei contribute to thalamic dysfunctions in problem drinking. We investigated resting-state functional connectivity (rsFC) of thalamic subregions in 107 nondependent drinkers (57 women), using masks delineated by white matter tractography. Thalamus was parceled into motor, somatosensory, visual, premotor, frontal association, parietal association, and temporal association subregions. Whole-brain linear regression, each against Alcohol Use Disorders Identification Test (AUDIT) and positive alcohol expectancy (AE) score with age as a covariate, was performed for each seed, for men and women combined, and separately. Overall, problem drinking was associated with increased thalamic connectivities, whereas AE was associated with a mixed pattern of increased and decreased connectivities. Motor, premotor, somatosensory, and frontal association thalamic connectivity with bilateral caudate head was positively correlated with AUDIT score in men and women combined. Connectivity of the right caudate head with frontal association and premotor thalamus was also positively correlated with AE score in men and women combined. In contrast, motor and premotor thalamic connectivity with a number of cortical and subcortical structures showed sex differences in the correlation each with AUDIT and AE score. In mediation analyses, AE score completely mediated the correlation between thalamic caudate connectivity and AUDIT score, whereas the model where AE contributed to problem drinking and, in turn, altered thalamic caudate connectivity was not supported. To conclude, thalamic subregional rsFCs showed both shared and distinct changes and sex differences in association with problem drinking and AE. Increased thalamic caudate connectivity may contribute to problem drinking via enhanced AE. The findings suggest the importance of examining thalamic subdivisions and sex in investigating the functional roles of thalamus in problem drinking.

The Impact of Combinations of Alcohol, Nicotine, and Cannabis on Dynamic Brain Connectivity

Alcohol, nicotine, and cannabis are among the most commonly used drugs. A prolonged and combined use of these substances can alter normal brain wiring in different ways depending on the consumed cocktail mixture. Brain connectivity alterations and their change with time can be assessed using functional magnetic resonance imaging (fMRI) because of its spatial and temporal content. Here, we estimated dynamic functional network connectivity (dFNC) as derived from fMRI data to investigate the effects of single or combined use of alcohol, nicotine, and cannabis. Data from 534 samples were grouped according to their substance use combination as controls (CTR), smokers (SMK), drinkers (DRN), smoking-and-drinking subjects (SAD), marijuana users (MAR), smoking-and-marijuana users (SAM), marijuana-and-drinking users (MAD), and users of all three substances (ALL). The DRN group tends to exhibit decreased connectivity mainly in areas of sensorial and motor control, a result supported by the dFNC outcome and the alcohol use disorder identification test. This trend dominated the SAD group and in a weaker manner MAD and ALL. Nicotine consumers were characterized by an increment of connectivity between dorsal striatum and sensorimotor areas. Where possible, common and separate effects were identified and characterized by the analysis of dFNC data. Results also suggest that a combination of cannabis and nicotine have more contrasting effects on the brain than a single use of any of these substances. On the other hand, marijuana and alcohol might follow an additive effect trend. We concluded that all of the substances have an impact on brain connectivity, but the effect differs depending on the dFNC state analyzed.

Aberrant prefrontal-parietal-cerebellar circuits in alcohol dependence

OBJECTIVE

To identify whether the amplitude of low-frequency fluctuations (ALFF) analysis has the potential to serve as a biological marker to detect alcohol-induced spontaneous brain activities and distinguish the patients with alcohol dependence from the healthy subjects.

METHODS

We utilized the ALFF analysis to report on the alcohol-induced spontaneous brain activities in 29 patients with alcohol dependence (9 female, 20 male) relative to 29 status-matched healthy subjects (11 female, 18 male). Receiver operating characteristic curve was used to test the ability of the ALFF analysis in discriminating the patients with alcohol dependence from the healthy subjects. Pearson correlation was used to evaluate the relationships between the signal value of those ALFF differences in brain areas and behavioral characteristics.

RESULTS

Alcohol-induced brain differences located in the right inferior parietal lobule and right supplementary motor area with significant higher ALFF values, and in the left precuneus and bilateral cerebellum posterior lobe with lower ALFF values. The movement-related areas were significantly correlated with each other (P<0.05). Receiver operating characteristic curve revealed good area under the curve values (mean, 0.86±0.079; 0.774-0.951) of the ALFF differences in those specific brain areas, as well as high degree of sensitivities (mean, 80.84%±14.01% or 80%±14.56%; 62.5%-100%) and specificities (mean, 83.32%±9.31%; 70.8%-95.8% or 84.16%±8%; 75%-95.8%).

CONCLUSION

The ALFF analysis may serve as a biological indicator to detect the spontaneous brain activities in patients with alcohol dependence. The prefrontal-parietal-cerebellar circuit appears to be disturbed by long-term alcoholism in patients with alcohol dependence.

Difference in the functional connectivity of the dorsolateral prefrontal cortex between smokers with nicotine dependence and individuals with internet gaming disorder

Background

It has been reported that internet gaming disorder (IGD) and smokers with nicotine dependence (SND) share clinical characteristics, such as over-engagement despite negative consequences and cravings. This study is to investigate the alterations in the resting-state functional connectivity (rsFC) of the dorsolateral prefrontal cortex (DLPFC) observed in SND and IGD. In this study, 27 IGD, 29 SND, and 33 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. DLPFC connectivity was determined in all participates by investigating the synchronized low-frequency fMRI signal fluctuations using a temporal seed-based correlation method.

Results

Compared with the HC group, the IGD and SND groups showed decreased rsFC with DLPFC in the right insula and left inferior frontal gyrus with DLPFC. Compared with SND group, the IGD subjects exhibited increased rsFC in the left inferior temporal gyrus and right inferior orbital frontal gyrus and decreased rsFC in the right middle occipital gyrus, supramarginal gyrus, and cuneus with DLPFC.

Conclusion

Our results confirmed that SND and IGD share similar neural mechanisms related to craving and impulsive inhibitions. The significant difference in rsFC with DLPFC between the IGD and SND subjects may be attributed to the visual and auditory stimulation generated by long-term internet gaming.

The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers

Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and strength, brain networks in farmworkers had more clustered and modular structures when compared to non-farmworkers. Our findings suggest increased functional specificity and decreased functional integration in farmworkers when compared to non-farmworkers. Cholinesterase activity was associated with population differences in community structure and the strength of brain network functional connections. Urinary cotinine, a marker of nicotine exposure, was associated with the differences in network community structure. Brain network differences between farmworkers and non-farmworkers, as well as pesticide and nicotine exposure effects on brain functional connections in this study, may illuminate underlying mechanisms that cause neurological implications in later life.