Resting-state functional connectivity and nicotine addiction: prospects for biomarker development

The Relationship of glutamate signaling to cannabis use and schizophrenia

PURPOSE OF REVIEW

This review examines the literature associating cannabis with schizophrenia, glutamate dysregulation in schizophrenia, and cannabis involvement in glutamate pathways. Cannabis use is widespread among adolescents world-wide and is sold legally in many countries for recreational use in a variety of forms. Most people use it without lasting effects, but a portion of individuals have negative reactions that manifest in acute psychotic symptoms, and in some, symptoms continue even after the use of cannabis has ceased. To date, there is a huge gap in our understanding of why this occurs.

RECENT FINDINGS

Recent studies have focused on abnormalities in the glutamate pathway in schizophrenia, the effect of cannabis on the glutamate system, and the role of glutamate in the brain Default Mode Network.

SUMMARY

Given these observations, we hypothesize that perturbance of glutamate neuronal connectivity by cannabis in the brains of individuals genetically at high risk for psychosis will initiate a schizophrenia-like psychosis. Future studies may tie together these diverse observations by combining magnetic resonance spectroscopy (MRS) and functional magnetic resonance imaging (fMRI) of the default resting state network in patients with new onset schizophrenia who do and do not use cannabis compared with nonpsychotic individuals who do and do not use cannabis.

Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24 539 Individuals

INTRODUCTION

Nicotine addiction, a multifaceted neuropsychiatric disorder, profoundly impacts brain functions through interactions with neural pathways. Despite its significance, the impact of tobacco smoking on the whole-brain functional connectome remains largely unexplored.

AIMS AND METHODS

We conducted a whole-brain analysis on 24 539 adults aged 40 and above from the United Kingdom Biobank cohort. Subjects were categorized into individuals who use nicotine and those who do not use nicotine based on current and chronic tobacco smoking information. Functional connectivity was assessed using resting-state functional magnetic resonance imaging. We employed a network analysis method to assess the systematic effects of tobacco smoking on brain connectome by identifying subnetworks that show nicotine-use-related differences.

RESULTS

Our analyses revealed two nicotine-use-related subnetworks with distinct network structure (permutation p < .001). In the first network, there is a significant decrease in resting-state functional connectivity (rsFC) between the basal ganglia regions (eg, nucleus accumbens) and 73% of the remaining brain regions, emphasizing the central hub role of basal ganglia in addictive smoking behaviors. Additionally, a data-driven subnetwork, mainly involving regions from frontal and occipital lobes, showed reduced rsFC among individuals who use nicotine.

CONCLUSIONS

The results suggest significant alterations in the communication and coordination among the basal ganglia and the broader network of brain regions. The observed changes in rsFC indicate a widespread disruption in the connectivity patterns associated with nicotine use.

IMPLICATIONS

This study identifies rsFC subnetworks related to chronic nicotine use through whole-brain connectome analysis. The findings confirm that widespread alterations in rsFC are centered around hub nodes within the basal ganglia, including bilateral nucleus accumbens, putamen, caudate, and globus pallidus. In addition, our analysis found a clique-forming subnetwork vulnerable to tobacco smoking consisting of regions from the visual, dorsal/ventral attention, and frontoparietal networks.

Central Executive Network drives delta-9-tetrahydrocannabinol (THC)-induced nonlinear changes in large-scale functional connectivity in adolescent nonhuman primates

Adolescent cannabinoid exposure has been implicated in enduring modifications to adult brain circuitry; however, well-controlled, systematic analyses investigating dose-dependent effects of chronic delta-9-tetrahydrocannabinol (THC) exposure on brain connectivity are lacking. It is hypothesized that large-scale intrinsic networks, such as default mode (DMN), central executive (CEN), and salience networks (SN), are critically involved in vulnerability to deficits in cognitive processing often associated with adolescent cannabis use. The present study aimed to elucidate the effects of chronic THC exposure on functional connectivity (FC) of these putative large-scale networks in nonhuman primates. Separate groups of adolescent squirrel monkeys (aged 2.0-yrs [female] and 2.5-yrs [male]) were administered intramuscular injections of vehicle or THC daily (0.32 or 3.2mg/kg) for 6-months during adolescence. Resting state functional connectivity from scans conducted in awake subjects was measured before dosing, at 6-months of chronic dosing, and 60-days following discontinuation of daily THC exposure. Utilizing two distinct analytical methodologies, we observed a non-linear, dosage-dependent alteration in DMN-CEN FC across scan intervals. Specifically, exposure to a low THC dosage increased FC during chronic exposure compared to both the pre-dosing and discontinuation periods. This pattern, however, was not observed in either the vehicle or high THC dosage groups. Dual-regression unveiled a similar non-linear effect within the CEN, but not DMN, suggesting the effect on DMN-CEN FC may be driven by modifications within the CEN. Taken together, these results suggest adolescent THC exposure differentially affects large-scale brain networks and contributes to a nuanced understanding of CEN's role in disrupting brain connectivity following chronic THC exposure.

Increasing the smoking cessation success rate by enhancing improvement of self-control through sleep-amplified memory consolidation: protocol of a randomized controlled, functional magnetic resonance study

BACKGROUND

Tobacco use disorder (TUD) remains a global health crisis characterized by high relapse rates despite extensive cessation efforts. This study aims to enhance treatment outcomes by addressing the cognitive and neural imbalances associated with habitual and goal-directed behaviours among individuals with TUD. We hypothesise that by integrating high-intensity interval training (HIIT), cognitive remediation treatment (CRT) via app-based chess training and a standard smoking cessation program (SCP) for cognitive control and sleep quality will be improved, thereby facilitating smoking cessation.

METHODS

The study will enrol 140 treatment-seeking smokers aged 18-65 years who meet the DSM-5 criteria for TUD. The participants will be randomly assigned to four groups: CRT + HIIT in the morning, CRT + HIIT in the evening, HIIT alone in the morning, and HIIT alone in the evening. Assessments will be conducted at baseline (T1), postintervention (T2), and at a three-month follow-up (T3) at the Central Institute of Mental Health in Mannheim, Germany. The primary outcomes include abstinence days or amount of alcohol consumed in cases of relapse, as well as craving reduction. Secondary outcomes include improvements in cognitive functions (working memory, response inhibition, and cognitive control), measured through neuropsychological tasks, functional magnetic resonance imaging (fMRI), polysomnography, and self-report questionnaires. The repeated-measures design allows for within-subject comparisons to evaluate intervention effectiveness.

DISCUSSION

This study aims to provide insights into the mechanisms through which combined CRT and evening HIIT, alongside improvements in sleep quality, can enhance smoking cessation outcomes. The hypothesised benefits on cognitive control and neural activity changes are expected to support better treatment adherence and reduced relapse rates among individuals with TUD. Addressing potential challenges such as high dropout rates through comprehensive participant support is crucial for the study's success. Findings from this research could inform future therapeutic strategies for TUD, potentially advancing addiction treatment approaches. The integration of novel interventions with established cessation programs underscores the study's significance in exploring holistic approaches to improving public health outcomes related to tobacco addiction.

TRIAL REGISTRATION

Registered at clinicaltrials.gov/ct2/show/NCT05726045 (Date 04.04.2024).

Hypnosis efficacy on nicotine addiction: An analysis of EEG microstates and brain oscillation entropy

Despite hypnosis showing efficacy in treating nicotine dependence, its neurobiological impacts on a smokers' brain function remain underexplored. Thirty-three smokers underwent electroencephalography (EEG) recording during pre- and post-hypnosis sessions, each 8 minutes long, alongside Tobacco Craving Questionnaire (TCQ) assessments. Four distinct EEG microstate classes (A, B, C, D) were identified. Daily cigarette consumption negatively correlated with the microstate A duration (r = -0.39, P = 0.03). Hypnosis increased the microstate A parameters while decreasing those of microstate B. Reduced microstate B parameters positively correlated with lower TCQ scores (r = 0.46, P = 0.02). Post-hypnosis, there was a decreased variability and sample entropy in low-frequency theta-band signals, indicating a shift towards more ordered theta oscillations. This shift was inversely related to the microstate D parameters and positively correlated with the microstate C occurrences. Dynamic changes in the brain microstates and theta oscillations elucidate the neurological mechanisms underlying hypnotherapy's effectiveness in treating smoking addiction. These findings provide new insights into the mechanisms by which hypnosis influences brain function and offer potential biomarkers for the treatment of smoking addiction, thus deepening our understanding of therapeutic approaches for substance use disorders.

Repeated exposure to high-dose nicotine induces prefrontal gray matter atrophy in adolescent male rats

Incidences of seizure after e-cigarette use in adolescents and young adults have been reported, raising the concern about the risk of nicotine overconsumption. Few previous studies have investigated the effects of nicotine at high doses on brain and behavior in adolescent animals. In this study, the effects of a 15-day repeated nicotine treatment at a daily dose of 2 mg/kg body weight were investigated in adolescent and adult male rats. Nicotine treatment abolished body weight gain in the adults, but did not affect the body weight significantly in the adolescents. Only the nicotine-treated adolescents showed significant changes in brain anatomy 1 day post-treatment, which manifested as a significant reduction of whole-brain gray matter (GM) volume, a further reduction of regional GM volume in the medial prefrontal cortex (mPFC) and altered GM volume covariations between the mPFC and a number of brain regions. The mPFC of nicotine-treated adolescent rats did not exhibit evident signs of neuronal degeneration and reactive astrocytosis, but showed a significantly decreased expression of presynaptic marker synaptophysin (SYN), along with a significantly increased oxidative stress and a significantly elevated expressions of microglial marker ionized calcium binding adaptor molecule 1 (IBA1). Together, these results suggested that repeated nicotine overdosing may shift regional redox, modulate microglia-mediated pruning, and give rise to structural/connectivity deficits in the mPFC of adolescent male rats.

Functional connectivity changes in males with nicotine addiction: A triple network model study

BACKGROUND

Nicotine addiction (NA) is recognized as a significant neurobehavioral disorder that affects both individuals and society. It is suggested that alterations in functional network connectivity (FNC) within specific brain networks underlie its neurobiological basis.

METHODS

The default mode network (DMN), executive control network (ECN), and salience network (SN) are identified using data from the Human Connectome Project. The study includes 47 individuals with NA and 35 normal controls (NC), all of whom undergo resting-state fMRI alongside smoking-related clinical assessments. A sliding window analysis is employed to assess connectivity metrics, including static functional network connectivity (FNC), standard deviation (SD), and coefficient of variation (CV), to compare information integration between the groups. Participants with NA are classified based on longitudinal changes in Fagerström Test for Nicotine Dependence (FTND) scores over six years into three categories: addiction tendency (AT), withdrawal tendency (WT), and stable tendency (ST). Correlation analyses are conducted to explore relationships between FNC abnormalities and clinical assessments.

RESULTS

Individuals with NA exhibit reduced static FNC (p_FDR = 0.029) between the dorsal DMN and the right ECN, accompanied by increased SD (p_FDR = 0.029) and CV (p_FDR = 0.029). A significant increase in SD (p_FDR = 0.049) is also observed in the dorsal DMN and left ECN. Correlations indicate that the SD of the dorsal DMN and right ECN relates to the pharmacological dimension of the Russell Smoking Reasons Questionnaire (RRSQ) scale (r = 0.416, p_FDR = 0.044), while CV correlates with changes in the FTND over six years (r = -0.391, p_FDR = 0.044) and the pharmacological dimension of the RRSQ scale (r = 0.402, p_FDR = 0.044). Post-hoc subgroup analyses reveal that these FNC intensity changes are present among WT patients (p_FDR < 0.05).

CONCLUSIONS

Alterations in brain network function within the DMN and ECN are suggested to precede behavioral changes in NA. These findings are interpreted as potential neurobiological markers of nicotine addiction.

Interoceptive Processing in Substance Use Disorders (SUDs): From the Neuroanatomy to Insights from Computational Models and Predictive Coding Frameworks

Substance use disorders (SUDs) represent complex public health challenges characterized by a blend of genetic, cognitive, environmental, and psychosocial factors. This chapter explores the critical role of interoceptive processing - the internal sensing of physiological states - in the neurobiology and treatment of SUDs. Interoceptive dysfunctions are highlighted as central to the craving, emotional regulation, and decision-making processes that underpin addictive behaviors. The importance of the insula in sustaining drug use, particularly nicotine, underscores a broader involvement of interoceptive pathways in SUDs. Altered interoceptive processing is evident across various SUDs, where individuals demonstrate both a heightened sensitivity to drug-related cues and a diminished ability to process aversive stimuli, suggesting substantial neurobiological underpinnings that complicate treatment outcomes. Moreover, we delve into the theoretical and computational approach to understanding interoceptive processing in SUDs. This perspective utilizes a predictive coding framework, positing that the brain continuously generates and updates predictions about internal states based on sensory inputs. In SUDs, disruptions in this predictive mechanism can lead to inaccuracies in interoceptive perception, contributing significantly to the compulsive nature of drug-seeking behaviors and the challenges associated with treatment. We explore how computational models, such as Bayesian inference, provide insights into the interplay between expected and received interoceptive signals, highlighting the role of hyper-precise prior beliefs in the persistence of craving and impulsivity. This theoretical approach not only deepens our understanding of the neural and cognitive bases of addiction but also suggests novel intervention strategies. By recalibrating interoceptive predictions through targeted therapies, such as neuromodulation and mindfulness training, we can potentially restore the interoceptive accuracy, thereby offering new avenues for effective treatment of SUDs.

The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis

BACKGROUND

Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.

METHODS

We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.

RESULTS

The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.

CONCLUSION

MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

Smoking-Related Increases in Alcohol Outcomes and Preliminary Evidence for the Protective Effect of a Functional Nicotine Receptor Gene (CHRNA5) Variant on Alcohol Consumption in Individuals Without Alcohol Use Disorder

BACKGROUND

Alcohol and nicotine interact with the nicotinic acetylcholine receptor system to alter reward-related responses, thereby contributing to the co-use and misuse of these drugs. A missense polymorphism rs16969968 (G>A) in the CHRNA5 gene has shown a strong association with nicotine-related phenotypes. However, less is known about the impact of this variant on alcohol-related phenotypes.

METHODS

We assessed the main and interactive effect of smoking and rs16969968 polymorphism on alcohol consumption using the Alcohol Use Disorders Identification Test (AUDIT), Timeline Follow Back (TLFB), and Lifetime Drinking History (LDH) in 980 healthy adults without alcohol use disorder. We further examined the effect of the rs16969968 polymorphism on acute alcohol consumption using a free-access i.v. alcohol self-administration (IV-ASA) human laboratory paradigm in a subset of 153 nonsmoking participants. Subjective alcohol responses, alcohol sensitivity, and expectancy measures were compared between genotype groups (GG; AA/AG).

RESULTS

We observed a significant association of smoking with AUDIT, TLFB, and LDH measures across genotype groups, with smokers showing higher scores compared with nonsmokers. Additionally, we found an association between genotype and TLFB-total drinks in the IV-ASA subset, with the GG group showing higher scores than AA/AG group. Relatedly, the alcohol negative expectancy score was significantly lower in the GG group than the AA/AG group.

CONCLUSIONS

Our findings underscore the association of smoking with alcohol measures. We found preliminary evidence for the protective effect of the functional CHRNA5 polymorphism on alcohol consumption and its association with increased negative alcohol expectancies, which highlights the substantial heterogeneity in alcohol responses.

Smoking Progression and Nicotine-Enhanced Reward Sensitivity Predicted by Resting-State Functional Connectivity in Salience and Executive Control Networks

INTRODUCTION

The neural underpinnings underlying individual differences in nicotine-enhanced reward sensitivity (NERS) and smoking progression are poorly understood. Thus, we investigated whether brain resting-state functional connectivity (rsFC.) during smoking abstinence predicts NERS and smoking progression in young light smokers. We hypothesized that high rsFC between brain areas with high densities of nicotinic receptors (insula, anterior cingulate cortex [ACC], hippocampus, thalamus) and areas involved in reward-seeking (nucleus accumbens [NAcc], prefrontal cortex [PFC]) would predict NERS and smoking progression.

AIMS AND METHODS

Young light smokers (N = 64, age 18-24, M = 1.89 cigarettes/day) participated in the study. These individuals smoked between 5 and 35 cigarettes per week and lifetime use never exceeded 35 cigarettes per week. Their rsFC was assessed using functional magnetic resonance imaging after 14 hours of nicotine deprivation. Subjects also completed a probabilistic reward task after smoking a placebo on 1 day and a regular cigarette on another day.

RESULTS

The probabilistic-reward-task assessed greater NERS was associated with greater rsFC between the right anterior PFC and right NAcc, but with reduced rsFC between the ACC and left inferior prefrontal gyrus and the insula and ACC. Decreased rsFC within the salience network (ACC and insula) predicted increased smoking progression across 18 months and greater NERS.

CONCLUSIONS

These findings provide the first evidence that differences in rsFCs in young light smokers are associated with nicotine-enhanced reward sensitivity and smoking progression.

CLINICAL TRIAL REGISTRATION

NCT02129387 (preregistered hypothesis: www.clinicaltrials.gov).

IMPLICATIONS

Weaker rsFC within the salience network predicted greater NERS and smoking progression. These findings suggest that salience network rsFC and drug-enhanced reward sensitivity may be useful tools and potential endophenotypes for reward sensitivity and drug-dependence research.

Altered Physiological, Affective, and Functional Connectivity Responses to Acute Stress in Patients With Alcohol Use Disorder

Background

There is evidence that the processing of acute stress is altered in alcohol use disorder (AUD), but little is known about how this is manifested simultaneously across different stress parameters and which neural processes are involved. The current study examined physiological and affective responses to stress and functional connectivity in AUD.

Methods

Salivary cortisol samples, pulse rate, and affect ratings were collected on 2 days from 34 individuals with moderate or severe AUD during early abstinence and 34 control participants. On one of the days, stress was induced, and on the other day, a nonstressful control task was performed. Following the intervention, participants underwent functional magnetic resonance imaging to assess functional connectivity, with a focus on cortical and subcortical seed regions previously reported to be involved in AUD and/or stress.

Results

For pulse rate and cortisol, stress responses were blunted in AUD, whereas the affective response was stronger. Neuroimaging analyses revealed stress-related group differences in functional connectivity, involving the connectivity of striatal seeds with the posterior default mode network, cerebellum, and midcingulate cortex and of the posterior default mode network seed with the striatum and thalamus.

Conclusions

The results suggest a dissociation between subjectively experienced distress and the physiological stress response in AUD as well as stress-related alterations in functional connectivity. These findings highlight the complex interplay between chronic alcohol use and acute stress regulation, offering valuable considerations for the development of therapeutic strategies.

The Association Between Chronic Tobacco Smoking and Brain Alterations in Schizophrenia: A Systematic Review of Magnetic Resonance Imaging Studies

BACKGROUND AND HYPOTHESIS

The high co-occurrence of tobacco smoking in patients with schizophrenia spectrum disorders (SSD) poses a serious health concern, linked to increased mortality and worse clinical outcomes. The mechanisms underlying this co-occurrence are not fully understood.

STUDY DESIGN

Addressing the need for a comprehensive overview of the impact of tobacco use on SSD neurobiology, we conducted a systematic review of neuroimaging studies (including structural, functional, and neurochemical magnetic resonance imaging studies) that investigate the association between chronic tobacco smoking and brain alterations in patients with SSD.

STUDY RESULTS

Eight structural and fourteen functional studies were included. Structural studies show widespread independent and additive reductions in gray matter in relation to smoking and SSD. The majority of functional studies suggest that smoking might be associated with improvements in connectivity deficits linked to SSD. However, the limited number of and high amount of cross-sectional studies, and high between-studies sample overlap prevent a conclusive determination of the nature and extent of the impact of smoking on brain functioning in patients with SSD. Overall, functional results imply a distinct neurobiological mechanism for tobacco addiction in patients with SSD, possibly attributed to differences at the nicotinic acetylcholine receptor level.

CONCLUSIONS

Our findings highlight the need for more longitudinal and exposure-dependent studies to differentiate between inherent neurobiological differences and the (long-term) effects of smoking in SSD, and to unravel the complex interaction between smoking and schizophrenia at various disease stages. This could inform more effective strategies addressing smoking susceptibility in SSD, potentially improving clinical outcomes.

Data-driven connectivity profiles relate to smoking cessation outcomes

At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.

Neurofeedback for alcohol addiction: Changes in resting state network activity✰

Alcohol dependence continues to be a major global burden despite significant research progress and treatment development. The aim of this study was to investigate whether neurofeedback training can alter resting state fMRI activity in brain regions that play a crucial role in addiction disorders in patients with alcohol dependence. For this purpose, a total of 52 patients were recruited for the present study, randomized, and divided into an active and a sham group. Patients in the active group received three sessions of neurofeedback training. We compared the resting state data in the active group as part of the NF training on six measurement days. When comparing the results of the active group from neurofeedback day 3 with baseline 1, a significant reduction in activated voxels in the ventral attention network area was seen. This suggests that reduced activity over the course of therapy in subjects may lead to greater independence from external stimuli. Overall, a global decrease in activated voxels within all three analysed networks compared to baseline was observed in the study. The use of resting-state data as potential biomarkers, as activity changes within these networks, may be to help restore cognitive processes and alcohol abuse-related craving and emotions.

Factors influencing JUUL e-cigarette nicotine vapour-induced reward, withdrawal, pharmacokinetics and brain connectivity in rats: sex matters

Though vaping likely represents a safer alternative to smoking, it is not without risks, many of which are not well understood, especially for vulnerable populations. Here we evaluate the sex- and age-dependent effects of JUUL nicotine vapour in rats. Following passive nicotine vapour exposures (from 59 mg/ml JUUL nicotine pods), rats were evaluated for reward-like behaviour, locomotion, and precipitated withdrawal. Pharmacokinetics of nicotine and its metabolites in brain and plasma and the long-term impact of nicotine vapour exposure on functional magnetic resonance imaging-based brain connectivity were assessed. Adult female rats acquired conditioned place preference (CPP) at a high dose (600 s of exposure) of nicotine vapour while female adolescents, as well as male adults and adolescents did not. Adult and adolescent male rats displayed nicotine vapour-induced precipitated withdrawal and hyperlocomotion, while both adult and adolescent female rats did not. Adult females showed higher venous and arterial plasma and brain nicotine and nicotine metabolite concentrations compared to adult males and adolescent females. Adolescent females showed higher brain nicotine concentration compared to adolescent males. Both network-based statistics and between-component group connectivity analyses uncovered reduced connectivity in nicotine-exposed rats, with a significant group by sex interaction observed in both analyses. The short- and long-term effects of nicotine vapour are affected by sex and age, with distinct behavioural, pharmacokinetic, and altered network connectivity outcomes dependent on these variables.

Changes in distinct brain systems identified with fMRI during smoking cessation treatment with varenicline: a review

BACKGROUND

Varenicline is considered one of the most effective treatment options for smoking cessation. Nonetheless, it is only modestly effective. A deeper comprehension of the effects of varenicline by means of the in-depth review of relevant fMRI studies may assist in paving the development of more targeted and effective treatments.

METHODOLOGY

A search of PubMed and Google Scholar databases was conducted with the keywords "functional magnetic resonance imaging" or "fMRI", and "varenicline". All peer-reviewed articles regarding the assessment of smokers with fMRI while undergoing treatment with varenicline and meeting the predefined criteria were included.

RESULTS

Several studies utilizing different methodologies and targeting different aspects of brain function were identified. During nicotine withdrawal, decreased mesocorticolimbic activity and increased amygdala activity, as well as elevated amygdala-insula and insula-default-mode-network functional connectivity are alleviated by varenicline under specific testing conditions. However, other nicotine withdrawal-induced changes, including the decreased reward responsivity of the ventral striatum, the bilateral dorsal striatum and the anterior cingulate cortex are not influenced by varenicline suggesting a task-dependent divergence in neurocircuitry activation. Under satiety, varenicline treatment is associated with diminished cue-induced activation of the ventral striatum and medial orbitofrontal cortex concomitant with reduced cravings; during the resting state, varenicline induces activation of the lateral orbitofrontal cortex and suppression of the right amygdala.

CONCLUSIONS

The current review provides important clues with regard to the neurobiological mechanism of action of varenicline and highlights promising research opportunities regarding the development of more selective and effective treatments and predictive biomarkers for treatment efficacy.

Resting-state functional connectivity and structural differences between smokers and healthy non-smokers

Previous studies have shown an association between cigarette use and altered resting-state functional connectivity (rsFC) in many large-scale networks, sometimes complemented by measures of cortical atrophy. In this study, we aimed to further explore the neural differences between smokers and healthy non-smokers through the integration of functional and structural analyses. Imaging data of fifty-two smokers and forty-five non-smokers were analyzed through an independent component analysis for group differences in rsFC. Smokers showed lower rsFC within the dorsal attention network (DAN) in the left superior and middle frontal gyrus and left superior division of the lateral occipital cortex compared to non-smokers; moreover, cigarette use was found to be associated with reduced grey matter volume in the left superior and middle frontal gyrus and right orbitofrontal cortex, partly overlapping with functional findings. Within smokers, daily cigarette consumption was positively associated with increased rsFC within the cerebellar network and the default mode network and decreased rsFC within the visual network and the salience network, while carbon monoxide level showed a positive association with increased rsFC within the sensorimotor network. Our results suggest that smoking negatively impacts rsFC within the DAN and that changes within this network might serve as a circuit-based biomarker for structural deficits.

Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: a narrative review

In recent years, the scientific community has begun tо explore the efficacy оf an integrated neurofeedback + biofeedback approach іn various conditions, both pathological and non-pathological. Although several studies have contributed valuable insights into its potential benefits, this review aims tо further investigate its effectiveness by synthesizing current findings and identifying areas for future research. Our goal іs tо provide a comprehensive overview that may highlight gaps іn the existing literature and propose directions for subsequent studies. The search for articles was conducted on the digital databases PubMed, Scopus, and Web of Science. Studies to have used the integrated neurofeedback + biofeedback approach published between 2014 and 2023 and reviews to have analyzed the efficacy of neurofeedback and biofeedback, separately, related to the same time interval and topics were selected. The search identified five studies compatible with the objectives of the review, related to several conditions: nicotine addiction, sports performance, Autism Spectrum Disorder (ASD), and Attention Deficit Hyperactivity Disorder (ADHD). The integrated neurofeedback + biofeedback approach has been shown to be effective in improving several aspects of these conditions, such as a reduction in the presence of psychiatric symptoms, anxiety, depression, and withdrawal symptoms and an increase in self-esteem in smokers; improvements in communication, imitation, social/cognitive awareness, and social behavior in ASD subjects; improvements in attention, alertness, and reaction time in sports champions; and improvements in attention and inhibitory control in ADHD subjects. Further research, characterized by greater methodological rigor, is therefore needed to determine the effectiveness of this method and the superiority, if any, of this type of training over the single administration of either. This review іs intended tо serve as a catalyst for future research, signaling promising directions for the advancement оf biofeedback and neurofeedback methodologies.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

An electroencephalographic signature predicts craving for methamphetamine

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

The changes of intrinsic connectivity contrast in young smokers

Previous studies demonstrated that reward circuit plays an important role in smoking. The differences of functional and structural connectivity were found among several brain regions such as thalamus and frontal lobe. However, few studies focused on functional connectivity (FC) in whole-brain voxel level of young smokers. In this study, intrinsic connectivity contrast (ICC) was used to perform voxel-based whole-brain analyses in 55 young smokers and 55 matched non-smokers to identify brain regions with significant group differences. ICC results showed that the connectivity of young smokers in medial frontal cortex (MedFC), supramarginal gyrus anterior division left (L_aSMG), central opercular cortex left (L_CO) and middle frontal gyrus left (L_MidFG) showed a significantly lower trend compared with the non-smokers. The seed-based FC analysis about MedFC indicated that young smokers showed reduced connectivity between the MedFC and left hippocampus, left amygdala compared to non-smokers. Correlation analysis showed that the ICC of MedFC in young smokers was significantly negatively correlated with Fagerstrom test for nicotine dependence (FTND) and Questionnaire on Smoking Urges (QSU). The FC between the MedFC and left hippocampus, left amygdala was significantly negatively correlated with Pack_years. The mediation analysis indicated that ICC of MedFC completely mediated FTND and QSU of young smokers. The results suggest that nicotine accumulation may affect the communication of the frontal lobe with the whole brain to some extent, leading to changes in smoking cravings. The above research also provides in-depth insights into the mechanism of adolescent smoking addiction and related intervention treatment.

Smoking, tobacco dependence, and neurometabolites in the dorsal anterior cingulate cortex

Cigarette smoking has a major impact on global health and morbidity, and positron emission tomographic research has provided evidence for reduced inflammation in the human brain associated with cigarette smoking. Given the consequences of inflammatory dysfunction for health, the question of whether cigarette smoking affects neuroinflammation warrants further investigation. The goal of this project therefore was to validate and extend evidence of hypoinflammation related to smoking, and to examine the potential contribution of inflammation to clinical features of smoking. Using magnetic resonance spectroscopy, we measured levels of neurometabolites that are putative neuroinflammatory markers. N-acetyl compounds (N-acetylaspartate + N-acetylaspartylglutamate), glutamate, creatine, choline-compounds (phosphocholine + glycerophosphocholine), and myo-inositol, have all been linked to neuroinflammation, but they have not been examined as such with respect to smoking. We tested whether people who smoke cigarettes have brain levels of these metabolites consistent with decreased neuroinflammation, and whether clinical features of smoking are associated with levels of these metabolites. The dorsal anterior cingulate cortex was chosen as the region-of-interest because of previous evidence linking it to smoking and related states. Fifty-four adults who smoked daily maintained overnight smoking abstinence before testing and were compared with 37 nonsmoking participants. Among the smoking participants, we tested for associations of metabolite levels with tobacco dependence, smoking history, craving, and withdrawal. Levels of N-acetyl compounds and glutamate were higher, whereas levels of creatine and choline compounds were lower in the smoking group as compared with the nonsmoking group. In the smoking group, glutamate and creatine levels correlated negatively with tobacco dependence, and creatine correlated negatively with lifetime smoking, but none of the metabolite levels correlated with craving or withdrawal. The findings indicate a link between smoking and a hypoinflammatory state in the brain, specifically in the dorsal anterior cingulate cortex. Smoking may thereby increase vulnerability to infection and brain injury.

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

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

Prelimbic cortex and nucleus accumbens core resting state signaling dynamics as a biomarker for cocaine seeking behaviors

Substance use disorders (SUDs) are characterized by maladaptive signaling in the prefrontal cortex and associated regions, however precisely how these drug-induced abnormalities may be linked to drug seeking/taking behaviors is not well understood. Here, in vivo local field potential (LFP) electrophysiology was used in rats to examine the relationship between overall spontaneous (resting state) activity within the prelimbic cortex (PrL) and nucleus accumbens (NAc) core, and their functional connectivity, to cocaine taking and seeking behaviors. Adult, male Sprague-Dawley rats were trained to self-administer either intravenous cocaine (0.33 mg/inf) or water reinforcement during 6-hour daily sessions over 2 weeks; extinction sessions were completed immediately after self-administration training and following 30 days experimenter-imposed abstinence. Rest LFP recordings were completed during 3 recording periods (15 min each in a chamber different from the self-administration context) conducted (1) prior to self-administration training (rest LFP 1) (2) immediately after 2 weeks of self-administration training (rest LFP 2) and (3) following 1 month abstinence (rest LFP 3). Our findings show that resting state LFP power in the PrL recorded prior to training (Rest LFP 1) was positively correlated with total cocaine intake and escalation of cocaine seeking at the beta frequency range. Immediately after self-administration training (Rest LFP 2) power in the NAc core at gamma frequency was negatively correlated with incubation of cocaine craving. For rats trained to self-administer water, no significant correlations were observed. Together, these findings show that resting state LFP at specific timepoints in the addiction cycle can serve as unique predictors (biomarkers) of cocaine use disorders.

Resting-state electroencephalography theta predicts neurofeedback treatment 4-month follow-up response in nicotine addiction

Background

The high rate of long-term relapse is a major cause of smoking cessation failure. Recently, neurofeedback training has been widely used in the treatment of nicotine addiction; however, approximately 30% of subjects fail to benefit from this intervention. Our previous randomised clinical trial (RCT) examined cognition-guided neurofeedback and demonstrated a significant decrease in daily cigarette consumption at the 4-month follow-up. However, significant individual differences were observed in the 4-month follow-up effects of decreased cigarette consumption. Therefore, it is critical to identify who will benefit from pre-neurofeedback.

Aims

We examined whether the resting-state electroencephalography (EEG) characteristics from pre-neurofeedback predicted the 4-month follow-up effects and explored the possible mechanisms.

Methods

This was a double-blind RCT. A total of 60 participants with nicotine dependence were randomly assigned to either the real-feedback or yoked-feedback group. They underwent 6 min closed-eye resting EEG recordings both before and after two neurofeedback sessions. A follow-up assessment was conducted after 4 months.

Results

The frontal resting-state theta power spectral density (PSD) was significantly altered in the real-feedback group after two neurofeedback visits. Higher theta PSD in the real-feedback group before neurofeedback was the only predictor of decreased cigarette consumption at the 4-month follow-up. Further reliability analysis revealed a significant positive correlation between theta PSD pre-neurofeedback and post-neurofeedback. A leave-one-out cross-validated linear regression of the theta PSD pre-neurofeedback demonstrated a significant correlation between the predicted and observed reductions in cigarette consumption at the 4-month follow-up. Finally, source analysis revealed that the brain mechanisms of the theta PSD predictor were located in the orbital frontal cortex.

Conclusions

Our study demonstrated changes in the resting-state theta PSD following neurofeedback training. Moreover, the resting-state theta PSD may serve as a prognostic marker of neurofeedback effects. A higher resting-state theta PSD predicts a better long-term response to neurofeedback treatment, which may facilitate the selection of individualised interventions.

Trial registration number

ChiCTR-IPR-17011710.

Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers

Introduction

Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers.

Methods

A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing.

Results

As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks.

Discussion

These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.

Right inferior frontal gyrus theta-burst stimulation reduces smoking behaviors and strengthens fronto-striatal-limbic resting-state functional connectivity: a randomized crossover trial

Introduction

Functional and anatomical irregularities in the right inferior frontal gyrus (rIFG), a ventrolateral prefrontal region that mediates top-down inhibitory control over prepotent behavioral responding, are implicated in the ongoing maintenance of nicotine dependence (ND). However, there is little research on the effects of neuromodulation of the rIFG on smoking behavior, inhibitory control, and resting-state functional connectivity (rsFC) among individuals with ND.

Methods

In this double-blind, crossover, theta-burst stimulation (TBS) study, adults with ND (N = 31; female: n = 15) completed a baseline session and were then randomized to two counterbalanced sessions of functionally neuronavigated TBS to the rIFG: continuous TBS (cTBS) on 1 day and intermittent TBS (iTBS) on another. Differences in cigarette cravings, smoking, and fronto-striatal-limbic rsFC were assessed.

Results

Relative to baseline, cTBS significantly reduced appetitive and withdrawal cravings immediately after treatment. The effects of cTBS on withdrawal craving persisted for 24 h, as well as produced a reduction in smoking. Furthermore, cTBS significantly strengthened rsFC between the rIFG pars opercularis and subcallosal cingulate (fronto-striatal circuit), and between the rIFG pars opercularis and the right posterior parahippocampal gyrus (fronto-limbic circuit). At post-24 h, cTBS-induced increase in fronto-striatal rsFC was significantly associated with less appetitive craving, while the increase in fronto-limbic rsFC was significantly associated with less withdrawal craving and smoking.

Discussion

These findings warrant further investigation into the potential value of rIFG cTBS to attenuate smoking behavior among individuals with ND.

Hyperconnectivity of Two Separate Long-Range Cholinergic Systems Contributes to the Reorganization of the Brain Functional Connectivity during Nicotine Withdrawal in Male Mice

Chronic nicotine results in dependence with withdrawal symptoms on discontinuation of use, through desensitization of nicotinic acetylcholine receptors and altered cholinergic neurotransmission. Nicotine withdrawal is associated with increased whole-brain functional connectivity and decreased network modularity; however, the role of cholinergic neurons in those changes is unknown. To identify the contribution of nicotinic receptors and cholinergic regions to changes in the functional network, we analyzed the contribution of the main cholinergic regions to brain-wide activation of the immediate early-gene Fos during withdrawal in male mice and correlated these changes with the expression of nicotinic receptor mRNA throughout the brain. We show that the main functional connectivity modules included the main long-range cholinergic regions, which were highly synchronized with the rest of the brain. However, despite this hyperconnectivity, they were organized into two anticorrelated networks that were separated into basal forebrain-projecting and brainstem-thalamic-projecting cholinergic regions, validating a long-standing hypothesis of the organization of the brain cholinergic systems. Moreover, baseline (without nicotine) expression of Chrna2, Chrna3, Chrna10, and Chrnd mRNA of each brain region correlated with withdrawal-induced changes in Fos expression. Finally, by mining the Allen Brain mRNA expression database, we were able to identify 1755 gene candidates and three pathways (Sox2-Oct4-Nanog, JAK-STAT, and MeCP2-GABA) that may contribute to nicotine withdrawal-induced Fos expression. These results identify the dual contribution of the basal forebrain and brainstem-thalamic cholinergic systems to whole-brain functional connectivity during withdrawal; and identify nicotinic receptors and novel cellular pathways that may be critical for the transition to nicotine dependence.

Towards neuromarkers for tailored smoking cessation treatments

Vulnerability to compulsive drug use stems from dysregulated activity within the neural networks that underlie reward and executive functions. Empirical evidence suggests that a) attributing high motivational salience to drug-related stimuli leads to compulsive drug seeking and b) cognitive control deficits lead to compulsive drug taking. Noninvasive neuroimaging techniques enable brain activity monitoring during affective and cognitive processing and are paving the way to precision medicine for substance use disorders. Identifying robust neuromarkers of affective and cognitive dysregulation would allow clinicians to personalize treatments by targeting individual psychophysiological vulnerabilities. However, methodological choices have biased the field toward experimental paradigms that cannot optimally assess individual differences in the motivational salience of drug-related cues and in the ability to control drug-related decisions, choices which have hindered the identification of clinically relevant neuromarkers. Here, we show that once these shortcomings are amended, replicable neuromarkers of the tendency to attribute motivational salience to drug-related cues and the ability to control drug-related decisions emerge. While we use tobacco use disorder as a model, we also show that the methodological issues highlighted here are relevant to other disorders characterized by maladaptive appetitive behaviors.

Nicotine dependence and insula subregions: functional connectivity and cue-induced activation

Nicotine dependence is a major predictor of relapse in people with Tobacco Use Disorder (TUD). Accordingly, therapies that reduce nicotine dependence may promote sustained abstinence from smoking. The insular cortex has been identified as a promising target in brain-based therapies for TUD, and has three major sub-regions (ventral anterior, dorsal anterior, and posterior) that serve distinct functional networks. How these subregions and associated networks contribute to nicotine dependence is not well understood, and therefore was the focus of this study. Sixty individuals (28 women; 18-45 years old), who smoked cigarettes daily, rated their level of nicotine dependence (on the Fagerström Test for Nicotine Dependence) and, after abstaining from smoking overnight (~12 h), underwent functional magnetic resonance imaging (fMRI) in a resting state. A subset of these participants (N = 48) also completing a cue-induced craving task during fMRI. Correlations between nicotine dependence and resting-state functional connectivity (RSFC) and cue-induced activation of the major insular sub-regions were evaluated. Nicotine dependence was negatively correlated with connectivity of the left and right dorsal, and left ventral anterior insula with regions within the superior parietal lobule (SPL), including the left precuneus. No relationship between posterior insula connectivity and nicotine dependence was found. Cue-induced activation in the left dorsal anterior insula was positively associated with nicotine dependence and negatively associated with RSFC of the same region with SPL, suggesting that craving-related responsivity in this subregion was greater among participants who were more dependent. These results may inform therapeutic approaches, such as brain stimulation, which may elicit differential clinical outcomes (e.g., dependence, craving) depending on the insular subnetwork that is targeted.

Lifestyle risks associated with brain functional connectivity and structure

Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.

Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

Hyperconnectivity of two separate long-range cholinergic systems contributes to the reorganization of the brain functional connectivity during nicotine withdrawal in male mice

Chronic nicotine results in dependence with withdrawal symptoms upon discontinuation of use, through desensitization of nicotinic acetylcholine receptors and altered cholinergic neurotransmission. Nicotine withdrawal is associated with increased whole-brain functional connectivity and decreased network modularity, however, the role of cholinergic neurons in those changes is unknown. To identify the contribution of nicotinic receptors and cholinergic regions to changes in the functional network, we analyzed the contribution of the main cholinergic regions to brain-wide activation of the immediate early-gene FOS during withdrawal in male mice and correlated these changes with the expression of nicotinic receptor mRNA throughout the brain. We show that the main functional connectivity modules included the main long-range cholinergic regions, which were highly synchronized with the rest of the brain. However, despite this hyperconnectivity they were organized into two anticorrelated networks that were separated into basal forebrain projecting and brainstem-thalamic projecting cholinergic regions, validating a long-standing hypothesis of the organization of the brain cholinergic systems. Moreover, baseline (without nicotine) expression of Chrna2 , Chrna3 , Chrna10 , and Chrnd mRNA of each brain region correlated with withdrawal-induced changes in FOS expression. Finally, by mining the Allen Brain mRNA expression database, we were able to identify 1755 gene candidates and three pathways (Sox2-Oct4-Nanog, JAK-STAT, and MeCP2-GABA) that may contribute to nicotine withdrawal-induced FOS expression. These results identify the dual contribution of the basal forebrain and brainstem-thalamic cholinergic systems to whole-brain functional connectivity during withdrawal; and identify nicotinic receptors and novel cellular pathways that may be critical for the transition to nicotine dependence.

Significance Statement

Discontinuation of nicotine use in dependent users is associated with increased whole-brain activation and functional connectivity and leads to withdrawal symptoms. Here we investigated the contribution of the nicotinic cholinergic receptors and main cholinergic projecting brain areas in the whole-brain changes associated with withdrawal. This not only allowed us to visualize and confirm the previously described duality of the cholinergic brain system using this novel methodology, but also identify nicotinic receptors together with 1751 other genes that contribute, and could thus be targets for treatments against, nicotine withdrawal and dependence.

Altered white matter functional network in nicotine addiction

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity?

Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design. Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs). Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions. Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02991781.

Using computer-based habit versus chess-based cognitive remediation training as add-on therapy to modify the imbalance between habitual behavior and cognitive control in tobacco use disorder: protocol of a randomized controlled, fMRI study

BACKGROUND

Although the vast majority of smokers are aware of the enormous preventable health hazards caused by smoking, only a small percentage of smokers manage to remain abstinent in the long term. One possible explanation for this discrepancy lies in the inflexibility of addictive behavior and associated disadvantageous decision-making. According to a dual-process theory of decision-making, two distinct decision systems can be identified. One slow deliberate system based on desirable expectations of outcome value described as goal-directed behavior and a fast reflexive system based on habitual instrumental behavior and driven by reinforcement experienced in the past. In the course of addiction development, an imbalance occurs between habitual behavior and goal-directed. The present study aims to investigate the modifiability of the balance between habitual and goal-directed behavior at the neurobiological and behavioral level in smokers using two different novel add-on therapies. We hypothesize that both interventions change the balance between goal-directed and habitual behavior, but by different mechanisms. Whereas a cognitive remediation treatment should directly improve cognitive control, in contrast an implicit priming task should affect the early processing and the emotional valence of smoking and smoking cues.

METHODS

We will conduct a randomized controlled study in treatment-seeking individuals with tobacco use disorder applying either chess-based cognitive remediation training (N = 30) or implicit computer-based habit-modifying training (N = 30) as add on therapy compared to the standard smoking cessation group therapy (N = 30) only. We will address neurobiological and neuropsychological correlates associated with craving, reward devaluation, cue reactivity and attentional bias. In addition, various effects of treatment and prediction of treatment outcome will be examined using behavioral and neural measures.

DISCUSSION

The present study will apply different examination methods such as functional magnetic resonance imaging, neuropsychological tests, and self-report before and after the interventions. This allows the identification of intervention-specific mechanisms and therefore potential neurobiology-based specific treatment targets for individuals with Tobacco Use Disorder.

TRIAL REGISTRATION

Registered at clinicaltrials.gov/ct2/show/NCT03764969 (05 December 2018).

Active versus sham transcranial direct current stimulation (tDCS) as an adjunct to varenicline treatment for smoking cessation: Study protocol for a double-blind single dummy randomized controlled trial

BACKGROUND

Smoking is a chronic and relapsing disease, with up to 60% of quitters relapsing within the first year. Transcranial Direct Current Stimulation (tDCS), targets cortical circuits and acutely reduces craving and withdrawal symptoms among cigarette smokers. However, the efficacy of tDCS as an adjunct to standard smoking cessation treatments has not been studied. This study aims to investigate the effectiveness of tDCS in combination with varenicline for smoking cessation. We hypothesize that active tDCS combined with varenicline will improve cessation outcomes compared to sham tDCS combined with varenicline.

METHODS

This is a double-blind, sham-controlled randomized clinical trial where fifty healthy smokers will be recruited in Toronto, Canada. Participants will be randomized 1:1 to either active tDCS (20 minutes at 2 mA) or sham tDCS (30 seconds at 2 mA, 19 minutes at 0 mA) for 10 daily sessions (2 weeks) plus 5 follow up sessions, occurring every two weeks for 10 weeks. All participants will be given standard varenicline treatment concurrently for the 12-week treatment period. The primary outcome is 30 day continuous abstinence at end of treatment, confirmed with urinary cotinine. Measurements made at each study visit include expired carbon monoxide, self-reported craving and withdrawal. Three magnetic resonance imaging (MRI) scans will be conducted: two at baseline and one at end of treatment, to assess any functional or structural changes following treatment.

DISCUSSION

For every two smokers who quit, one life is saved from a tobacco-related mortality. Therefore, it is important to develop new and more effective treatment approaches that can improve and maintain long-term abstinence, in order to decrease the prevalence of tobacco-related deaths and disease. Furthermore, the addition of longitudinal neuroimaging can shed light on neural circuitry changes that might occur as a result of brain stimulation, furthering our understanding of tDCS in addiction treatment.

TRIAL REGISTRATION

This trial has been registered with Clinicaltrials.gov: NCT03841292 since February 15th 2019 (https://clinicaltrials.gov/ct2/show/NCT03841292)-retrospectively registered.

Misconfigured striatal connectivity profiles in smokers

Dysregulation of frontal cortical inputs to the striatum is foundational in the neural basis of substance use disorder (SUD). Neuroanatomical and electrophysiological data increasingly show that striatal nodes receive appreciable input from numerous cortical areas, and that the combinational properties of these multivariate "connectivity profiles" play a predominant role in shaping striatal activity and function. Yet, how abnormal configuration of striatal connectivity profiles might contribute to SUD is unknown. Here, we implemented a novel "connectivity profile analysis" (CPA) approach using resting-state functional connectivity data to facilitate detection of different types of connectivity profile "misconfiguration" that may reflect distinct forms of aberrant circuit plasticity in SUD. We examined 46 nicotine-dependent smokers and 33 non-smokers and showed that both dorsal striatum (DS) and ventral striatum (VS) connectivity profiles with frontal cortex were misconfigured in smokers-but in doubly distinct fashions. DS misconfigurations were stable across sated and acute abstinent states (indicative of a "trait" circuit adaptation) whereas VS misconfigurations emerged only during acute abstinence (indicative of a "state" circuit adaptation). Moreover, DS misconfigurations involved abnormal connection strength rank order arrangement, whereas VS misconfigurations involved abnormal aggregate strength. We found that caudal ventral putamen in smokers uniquely displayed multiple types of connectivity profile misconfiguration, whose interactive magnitude was linked to dependence severity, and that VS misconfiguration magnitude correlated positively with withdrawal severity during acute abstinence. Findings underscore the potential for approaches that more aptly model the neurobiological composition of corticostriatal circuits to yield deeper insights into the neural basis of SUD.

Convergent gray matter alterations across drugs of abuse and network-level implications: A meta-analysis of structural MRI studies

BACKGROUND

Neuroimaging studies often consider brain alterations linked with substance abuse within the context of individual drugs (e.g., nicotine), while neurobiological theories of addiction emphasize common brain network-level alterations across drug classes. Using emergent meta-analytic techniques, we identified common structural brain alterations across drugs and characterized the functionally-connected networks with which such structurally altered regions interact.

METHODS

We identified 82 articles characterizing gray matter (GM) volume differences for substance users vs. controls. Using the anatomical likelihood estimation algorithm, we identified convergent GM reductions across drug classes. Next, we performed resting-state and meta-analytic functional connectivity analyses using each structurally altered region as a seed and computed whole-brain functional connectivity profiles as the union of both maps. We characterized an "extended network" by identifying brain areas demonstrating the highest degree of functional coupling with structurally impacted regions. Finally, hierarchical clustering was performed leveraging extended network nodes' functional connectivity profiles to delineate subnetworks.

RESULTS

Across drug classes, we identified medial frontal/ventromedial prefrontal, and multiple regions in anterior cingulate (ACC) and insula as regions displaying convergent GM reductions among users. Overlap of these regions' functional connectivity profiles identified ACC, inferior frontal, PCC, insula, superior temporal, and putamen as regions of an impacted extended network. Hierarchical clustering revealed 3 subnetworks closely corresponding to default mode (PCC, angular), salience (dACC, caudate), and executive control networks (dlPFC and parietal).

CONCLUSIONS

These outcomes suggest that substance-related structural brain alterations likely have implications for the functioning of canonical large-scale networks and the perpetuation of substance use and neurocognitive alterations.

Connectome-based predictive modelling of smoking severity in smokers

The functional connectivity within and between networks could provide a framework to characterize the neurobiological mechanism of nicotine addiction. This study examined the brain regions that were functionally connected in response to smoking cues and established the brain-behaviour relationships in smokers. Sixty-seven male smokers were enrolled and scanned while performing the cue-reactivity and Stroop task. A whole-brain analysis approach, connectome-based predictive modelling (CPM), was conducted on the data from the cue-reactivity task to identify the networks that could predict the smoking severity with the Shen atlas as templates. Then, the brain-behaviour relationships were verified in a different brain state (Stroop task). CPM identified the smoking severity-related network, as indicated by a significant correlation between predicted and actual smoking severity scores (r = 0.31, p = 0.02). Identified networks mainly involved the canonical networks implicated in the reward process (motor/sensory network and salience network) and executive control (frontoparietal network). Network strength in the Stroop task marginally significantly predicted smoking severity scores (r = 0.23, p = 0.06), partially replicating the brain-behaviour relationship. The CPM results identified the whole-brain neural network related to smoking severity, which was cross-validated by the AAL and Shen atlas. These findings contribute to more profound insights into neural substrates underlying the smoking severity.

Effects of Smoking Status and State on Intrinsic Connectivity

BACKGROUND

Smoking behavior during the first 24 hours of a quit attempt is a significant predictor of longer-term abstinence, yet little is known about the neurobiology of early tobacco abstinence. Specifically, the effects of acute tobacco deprivation and reinstatement on brain function-particularly at the level of large-scale network dynamics and assessed across the entire brain-remain incompletely understood. To address this gap, this study used a mixed within- and between-subjects design to assess the effects of smoking status (yes/no smoker) and state (deprived vs. satiated) on whole-brain patterns of intrinsic connectivity.

METHODS

Participants included 42 tobacco smokers who underwent resting-state functional magnetic resonance imaging following overnight abstinence (deprived state) and following smoking reinstatement (satiated state, randomized order across participants). Sixty healthy control nonsmokers underwent a single resting-state scan using the same acquisition parameters. Functional connectivity data were analyzed using both a canonical network-of-interest approach and a whole-brain, data-driven approach, i.e., intrinsic connectivity distribution.

RESULTS

Network-of-interest-based analyses indicated decreased functional connectivity within frontoparietal and salience networks among smokers relative to nonsmokers as well as effects of smoking state on default mode connectivity. In addition, intrinsic connectivity distribution analyses identified novel between-group differences in subcortical-cerebellar and corticocerebellar networks that were largely smoking state dependent.

CONCLUSIONS

These data demonstrate the importance of considering smoking state and the utility of using both theory- and data-driven analysis approaches. These data provide much-needed insight into the functional neurobiology of early abstinence, which may be used in the development of novel treatments.

Shared network-level functional alterations across substance use disorders: A multi-level kernel density meta-analysis of resting-state functional connectivity studies

An increasing number of neuroimaging studies indicate functional alterations in cortico-striatal loops in individuals with substance use disorders (SUD). Dysregulations in these circuits may contribute to drug-seeking and drug-consuming behaviour by impeding inhibitory control, habit formation, and reward processing. Despite evidence of network-level changes in SUD, a shared pattern of functional alterations within and between spatially distributed brain networks has not been systematically investigated. The present meta-analytic investigation aims at identifying common alterations in resting-state functional connectivity patterns across different SUD, including stimulant, heroin, alcohol, cannabis, and nicotine use. To this aim, seed-based whole-brain connectivity maps for different functional networks were extracted and subjected to multi-level kernel density analysis to identify dysfunctional networks in individuals with SUD compared with healthy controls. In addition, an exploratory analysis examined substance-specific effects as well as the influence of drug use status on the main findings. Our findings indicate a hypoconnectivity pattern for the limbic, salience, and frontoparietal networks in individuals with SUD as compared with healthy controls. The default mode network additionally exhibited a complex pattern of hypo- and hyperconnectivity across the studies. The observed disrupted connectivity between networks in SUD may associate with deficient inhibitory control mechanisms that are thought to contribute to excessive craving and automatic drug-related behaviour as well as failure in substance use cessation. The identified network-based alterations in SUD represent potential treatment targets for neuromodulation, for example, network-based real-time functional magnetic resonance imaging (fMRI) neurofeedback. Such interventions can evaluate the behavioural relevance of the identified neural circuits.

Correlations Between the DMN and the Smoking Cessation Outcome of a Real-Time fMRI Neurofeedback Supported Exploratory Therapy Approach: Descriptive Statistics on Tobacco-Dependent Patients

The aim of this study was to explore the potential of default mode network (DMN) functional connectivity for predicting the success of smoking cessation in patients with tobacco dependence in the context of a real-time function al MRI (RT-fMRI) neurofeedback (NF) supported therapy.Fifty-four tobacco-dependent patients underwent three RT-fMRI-NF sessions including resting-state functional connectivity (RSFC) runs over a period of 4 weeks during professionally assisted smoking cessation. Patients were randomized into two groups that performed either active NF of an addiction-related brain region or sham NF. After preprocessing, the RSFC baseline data were statistically evaluated using seed-based ROI (SBA) approaches taking into account the smoking status of patients after 3 months (abstinence/relapse).The results of the real study group showed a widespread functional connectivity in the relapse subgroup (n = 10) exceeding the DMN template and mainly low correlations and anticorrelations in the within-seed analysis. In contrast, the connectivity pattern of the abstinence subgroup (n = 8) primarily contained the core DMN in the seed-to-whole-brain analysis and a left lateralized correlation pattern in the within-seed analysis. Calculated Multi-Subject Dictionary Learning (MSDL) matrices showed anticorrelations between DMN regions and salience regions in the abstinence group. Concerning the sham group, results of the relapse subgroup (n = 4) and the abstinence subgroup (n = 6) showed similar trends only in the within-seed analysis.In the setting of a RT-fMRI-NF-assisted therapy, a widespread intrinsic DMN connectivity and a low negative coupling between the DMN and the salience network (SN) in patients with tobacco dependency during early withdrawal may be useful as an early indicator of later therapy nonresponse.

Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies

Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.

The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN

Cocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of 74 CUD patients (9 female) and matched 64 healthy controls (6 female) that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1-weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. The repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform. Researchers can pursue brain variability between these groups or use a single group for a larger population sample.

Measurement(s)	functional brain measurement • Diffusion Weighted Imaging • Abnormality of brain morphology • Alteration Of Cognitive Function • Clinical Study
Technology Type(s)	Functional Magnetic Resonance Imaging • Diffusion Weighted Imaging • Turbo Field Echo MRI • neuropsychological test • Clinical Evaluation
Factor Type(s)	Cocaine Dependence
Sample Characteristic - Organism	Homo
Sample Characteristic - Location	Mexico City

Electronic Cigarette Vaping Did Not Enhance the Neural Process of Working Memory for Regular Cigarette Smokers

Background

Electronic cigarettes (e-cigs) as substitute devices for regular tobacco cigarettes (r-cigs) have been increasing in recent times. We investigated neuronal substrates of vaping e-cigs and smoking r-cigs from r-cig smokers.

Methods

Twenty-two r-cig smokers made two visits following overnight smoking cessation. Functional magnetic resonance imaging (fMRI) data were acquired while participants watched smoking images. Participants were then allowed to smoke either an e-cig or r-cig until satiated and fMRI data were acquired. Their craving levels and performance on the Montreal Imaging Stress Task and a 3-back alphabet/digit recognition task were obtained and analyzed using two-way repeated-measures analysis of variance. Regions-of-interest (ROIs) were identified by comparing the abstained and satiated conditions. Neuronal activation within ROIs was regressed on the craving and behavioral data separately.

Results

Craving was more substantially reduced by smoking r-cigs than by vaping e-cigs. The response time (RT) for the 3-back task was significantly shorter following smoking r-cigs than following vaping e-cigs (interaction: F (1, 17) = 5.3, p = 0.035). Neuronal activations of the right vermis (r = 0.43, p = 0.037, CI = [-0.05, 0.74]), right caudate (r = 0.51, p = 0.015, CI = [0.05, 0.79]), and right superior frontal gyrus (r = −0.70, p = 0.001, CI = [−0.88, −0.34]) were significantly correlated with the RT for the 3-back task only for smoking r-cigs.

Conclusion

Our findings suggest that insufficient satiety from vaping e-cigs for r-cigs smokers may be insignificant effect on working memory function.

Brain network dysfunctions in addiction: a meta-analysis of resting-state functional connectivity

Resting-state functional connectivity (rsFC) provides novel insights into variabilities in neural networks associated with the use of addictive drugs or with addictive behavioral repertoire. However, given the broad mix of inconsistent findings across studies, identifying specific consistent patterns of network abnormalities is warranted. Here we aimed at integrating rsFC abnormalities and systematically searching for large-scale functional brain networks in substance use disorder (SUD) and behavioral addictions (BA), through a coordinate-based meta-analysis of seed-based rsFC studies. A total of fifty-two studies are eligible in the meta-analysis, including 1911 SUD and BA patients and 1580 healthy controls. In addition, we performed multilevel kernel density analysis (MKDA) for the brain regions reliably involved in hyperconnectivity and hypoconnectivity in SUD and BA. Data from fifty-two studies showed that SUD was associated with putamen, caudate and middle frontal gyrus hyperconnectivity relative to healthy controls. Eight BA studies showed hyperconnectivity clusters within the putamen and medio-temporal lobe relative to healthy controls. Altered connectivity in salience or emotion-processing areas may be related to dysregulated affective and cognitive control-related networks, such as deficits in regulating elevated sensitivity to drug-related stimuli. These findings confirm that SUD and BA might be characterized by dysfunctions in specific brain networks, particularly those implicated in the core cognitive and affective functions. These findings might provide insight into the development of neural mechanistic biomarkers for SUD and BA.

How structural and functional MRI can inform dual-site tACS parameters: A case study in a clinical population and its pragmatic implications

BACKGROUND

Abnormalities in frontoparietal network (FPN) were observed in many neuropsychiatric diseases including substance use disorders. A growing number of studies are using dual-site-tACS with frontoparietal synchronization to engage this network. However, a computational pathway to inform and optimize parameter space for frontoparietal synchronization is still lacking. In this case study, in a group of participants with methamphetamine use disorders, we proposed a computational pathway to extract optimal electrode montage while accounting for stimulation intensity using structural and functional MRI.

METHODS

Sixty methamphetamine users completed an fMRI drug cue-reactivity task. Four main steps were taken to define electrode montage and adjust stimulation intensity using 4x1 high-definition (HD) electrodes for a dual-site-tACS; (1) Frontal seed was defined based on the maximum electric fields (EF) predicted by simulation of HD montage over DLPFC (F3/F4 in EEG 10-20), (2) frontal seed-to-whole brain context-dependent correlation was calculated to determine connected regions to frontal seeds, (3) center of connected cluster in parietal cortex was selected as a location for placing the second set of HD electrodes to shape the informed montage, (4) individualized head models were used to determine optimal stimulation intensity considering underlying brain structure. The informed montage was compared to montages with large electrodes and classic frontoparietal HD montages (F3-P3/F4-P4) in terms of tACS-induced EF and ROI-to-ROI task-based/resting-state connectivity.

RESULTS

Compared to the large electrodes, HD frontoparietal montages allow for a finer control of the spatial peak fields in the main nodes of the FPN at the cost of lower maximum EF (large-pad/HD: max EF[V/m] = 0.37/0.11, number of cortical sub-regions that EF exceeds 50% of the max = 77/13). For defining stimulation targets based on EF patterns, using group-level head models compared to a single standard head model results in comparable but significantly different seed locations (6.43mm Euclidean distance between the locations of the frontal maximum EF in standard-space). As expected, significant task-based/resting-state connections were only found between frontal-parietal locations in the informed montage. Cue-induced craving score was correlated with frontoparietal connectivity only in the informed montage (r = -0.24). Stimulation intensity in the informed montage, and not in the classic HD montage, needs 40% reduction in the parietal site to reduce the disparity in EF between sites.

CONCLUSION

This study provides some empirical insights to montage and dose selection in dual-site-tACS using individual brain structures and functions and proposes a computational pathway to use head models and functional MRI to define (1) optimum electrode montage for targeting FPN in a context of interest (drug-cue-reactivity) and (2) proper transcranial stimulation intensity.

Altered resting-state electroencephalography microstate characteristics in young male smokers

The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.