Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

paper Cited Public

Authors: Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A
Year: 2014
Journal: JAMA psychiatry
PMID: 24622915
DOI: 10.1001/jamapsychiatry.2013.4091
PMCID: PMC4097018

IMPORTANCE: Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. OBJECTIVES: To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. DESIGN, SETTING, AND PARTICIPANTS: A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. INTERVENTIONS: Twenty-four hours of abstinence vs smoking satiety. MAIN OUTCOMES AND MEASURES: Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). RESULTS: The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). CONCLUSIONS AND RELEVANCE: Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

Figure 1

Functional Magnetic Resonance Images Demonstrating Results of Independent Component Analysis (ICA)Four networks generated from group ICA of the resting state data were identified as the salience network (SN), default mode network (DMN), and left and right executive control network (ECN). Spatial maps were converted to z score images and then thresholded at z = 3.5 via a mixture model fit. Network maps are displayed in red-yellow overlaid onto the Talairach standard brain map based on neurological (left = left) convention. Slice coordinates are given in millimeters. The key SN regions included the dorsal anterior cingulate cortex (ACC)/paracingulate gyrus (Talairach coordinates: 3, 21, 33), bilateral anterior insula (left, –39, 9, 3; right, 51, 15, 0), dorsolateral prefrontal cortex (DLPFC) (left, –30, 45, 30; right, 30, 48, 30), supramarginal gyrus (left, –57, –45, 30; right, 57, –42, 33), and precuneus (12, –69, 36). Together, the left and right ECN covered the bilateral DLPFC (Talairach coordinates: left, –42, 45, 6; right, 45, 33, 25), parietal cortices (left, –39, –51, 42; right, 39, –57, 48), middle temporal gyrus (left, –51, –54, –9; right, 51, –57, –9), and dorsal medial frontal gyrus (left, –6, 27, 45; right, 6, 30, 45). The DMN included the posterior cingulate cortex (Talairach coordinates: 6, –57, 27), bilateral angular gyrus (left, –42, –69, 33; right, 48, –63, 30), ventromedial prefrontal cortex/rostral ACC (3, 54, 3), and temporal gyrus (left, –57, –9, –15; right, 60, –18, –15).

LLM interpretation

This figure presents functional MRI (fMRI) spatial maps generated from group Independent Component Analysis (ICA) of resting-state data. Four distinct networks—the salience network (SN), left executive control network (Left ECN), right executive control network (Right ECN), and default mode network (DMN)—are displayed as red-yellow overlays on a Talairach standard brain map across multiple axial slices (z = -10 to 46 mm) and one sagittal view. A color scale indicates z-score values ranging from a threshold of 3.5 to 10.0.

Figure 2

Resource Allocation Index (RAI) in the Abstinence vs Smoking StatesRendered brain figures present network components identified by group independent component analysis. Compared with the smoking state, the RAI for the left hemisphere (mL) decreased in abstinence (P = .002); the RAI for the right hemisphere (mR) showed a decreasing trend (P = .04). A thinner path connecting the salience network (SN) and executive control network (ECN) and the path connecting the SN and default mode network (DMN) in the abstinence (vs smoking) states illustrates decreased coupling between the networks. – Indicates negative correlation; +, positive correlation.aDifferences were significant.

LLM interpretation

This figure combines a bar chart with brain network diagrams to compare the Resource Allocation Index (RAI) between smoking and abstinence states. The bar chart shows a significant decrease in RAI for the left hemisphere ($m_L$, $P = .002$) and a decreasing trend for the right hemisphere ($m_R$, $P = .04$) during abstinence compared to smoking. Accompanying diagrams illustrate the salience network (SN) connecting to the executive control network (ECN, positive correlation) and default mode network (DMN, negative correlation), with thinner paths in the abstinence state indicating decreased coupling.

Figure 3

Between-Network Coupling in the Abstinence vs Smoking StatesSignificant reduction in correlation between the salience network (SN) and default mode network (DMN) (P = .02) in the abstinence vs smoking states. LECN indicates left executive control network; RECN, right executive control network. Whiskers indicate SD.aDifferences were significant.

LLM interpretation

This bar chart compares between-network correlations in the smoking state (light blue) versus the abstinence state (dark blue) across three network couplings: SN-LECN, SN-RECN, and SN-DMN. The y-axis measures "Between-Network Correlation," with error bars representing standard deviation. Statistically significant reductions in correlation during the abstinence state are observed for SN-LECN ($P = .04$) and SN-DMN ($P = .02$), while the difference for SN-RECN is not significant ($P = .45$).

Figure 4

Resource Allocation Index (RAI) Correlation With Smoking Urge ScorePartial regression plots of the difference of brief Questionnaire on Smoking Urges score between the abstinence and smoking states (ΔQSUa − s) against the difference of the RAI in the right hemisphere between states (ΔmRa − s).

LLM interpretation

This is a scatter plot with a linear regression line showing the correlation between the difference in Resource Allocation Index ($\Delta m_{Ra-s}$) on the x-axis and the difference in Questionnaire on Smoking Urges score ($\Delta QSU_{a-s}$) on the y-axis. The data points exhibit a negative correlation, indicating that as the difference in RAI increases, the difference in smoking urge scores generally decreases. The x-axis ranges from approximately -0.70 to 0.50, and the y-axis ranges from -10 to 60.

Figure 5

Resource Allocation Index (RAI) Correlations With Working Memory (WM) Task ActivationPartial regression plots of the difference between the abstinence vs smoking states of the WM task-induced blood oxygenation level–dependent change ([dS/S0]a − s) averaged across memory load in the ventromedial prefrontal cortex (A) and posterior cingulate cortex regions (B) against the difference of the RAI in the left hemisphere (ΔmL,a − s). Markers indicate individual participants.

LLM interpretation

This figure consists of two partial regression scatter plots (A and B) showing the correlation between the difference in Resource Allocation Index ($\Delta m_{La-s}$) and the difference in BOLD signal change during a working memory task between abstinence and smoking states. Plot A represents the ventromedial prefrontal cortex and Plot B represents the posterior cingulate cortex. Both plots display a negative linear trend, with individual participants represented by open circles.

Figure 6

Schematic of Changes in the Association of the Resource Allocation Index (RAI) Between the Abstinence vs Smoking SessionsChanges in the working memory (WM)–induced signal percentage change between states (dS/S)a − s, the RAI in the right and left hemispheres between states (ΔmR,a − s and ΔmL,a − s, respectively), and the brief Questionnaire on Smoking Urges (QSU) score between states (ΔQSUa − s) are depicted. PCC indicates posterior cingulate cortex; VMPFC, ventromedial prefrontal cortex. Upward arrow indicates increase; downward arrows, decrease.

LLM interpretation

This figure is a schematic diagram illustrating changes in the Resource Allocation Index (RAI) between abstinence and smoking sessions. It shows a decrease in working memory-induced deactivation in the VMPFC and PCC, which is associated with a decrease in the left hemisphere RAI ($\Delta m_{La-s}$). This sequence, along with a decrease in the right hemisphere RAI ($\Delta m_{Ra-s}$), is linked to an increase in the urge score ($\Delta \text{QSU-brief}_{a-s}$).

#	Section	Preview
0	Methods — Participants	Fifty-four smokers (≥10 cigarettes per day for ≥6 months) gave written informed consent as…
1	Methods — Design	Smokers participated in the following 2 imaging sessions: after 24 hours of biochemically confirmed…
2	Methods — Functional Magnetic Resonance Imaging Data	Data were collected on a commercially available scanner (3T Trio; Siemens). Participants were…
3	Methods — Functional Magnetic Resonance Imaging Data	Data preprocessing (using AFNI software29) included slice timing and motion correction, spatial…
4	Methods — Functional Magnetic Resonance Imaging Data	Excessive head motion can induce spurious correlation structures in resting connectivity…
5	Methods — Resting Functional Networks	We applied group independent component analysis (GICA) to the resting data using MELODIC (FMRIB…
6	Methods — Resting Functional Networks	Using the GICA component maps as spatial predictors for each participant's 4-dimensional data, a…
7	Methods — RAI Analyses	Linear mixed-effects modeling (SPSS, version 20.0; IBM Corporation) tested session effects,…
8	Methods — RAI Analyses	The relationship between changes in the RAI and changes in WM task performance (accounting for…
9	Methods — RAI Analyses	The correlation between the RAI and the WM task activation changes was investigated as in the…
10	Methods — RAI Analyses	All hypotheses were tested at aglobal type I error of α = 0.05. Based on the high correlation…
11	Results — Descriptive Data	The 37 smokers (including 19 women) had a mean age of 40.8 (13.2) years (range, 19-61 years) and…
12	Results — Resting Networks	Of the 20 GICA components, 4 were identified as SN, ECN, and DMN15,17 and were thresholded for…
13	Results — Smoking Abstinence Effects	When we considered the LECN and RECN independently, the 2 RAIs were defined corresponding to…
14	Results — Smoking Abstinence Effects	The RAI combined SN-ECN and SN-DMN coupling. To better understand which between-network connection…
15	Results — Subjective Measures	The change of the right hemisphere RAI (abstinence – smoking) was negatively correlated with…
16	Results — Working Memory	We found significant RAI-task activation correlations in all task-negative but not task-positive…
17	Results — Working Memory	We found a negative trend for an association between the right RAI and WM task performance (r =…
18	Results — Working Memory	Finally, to further instantiate that the RAI has unique merit, we investigated the relationship…
19	Discussion	Dysregulated interactions within and between 3 core brain networks (ie, the ECN, DMN, and SN) may…

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Altered intrinsic connectivity distribution in internet gaming disorder and its associations with psychotherapy treatment outcomes.	Liu L et al.	—	2021	→
A multivariate pattern analysis of resting-state functional MRI data in Naïve and chronic betel quid chewers.	Linli Z et al.	—	2021	→
Assessing effect of long-term abstinence on coupling of three core brain networks in male heroin addicts: A resting-state functional magnetic resonance imaging study.	Chen J et al.	—	2021	→
Better living through understanding the insula: Why subregions can make all the difference.	Centanni SW et al.	—	2021	→
Exploration of the influence of body mass index on intra-network resting-state connectivity in chronic cigarette smokers.	Ely AV et al.	—	2021	→
Functional connectivity of dorsolateral prefrontal cortex predicts cocaine relapse: implications for neuromodulation treatment.	Zhai T et al.	—	2021	→
Functional connectivity of posterior cingulate gyrus in heroin dependents treated by methadone maintenance and protracted abstinence measures: an event-related fMRI study.	Wei X et al.	—	2021	→
Functional connectivity of the anterior insula during withdrawal from cigarette smoking.	Ghahremani DG et al.	—	2021	→
Nicotine acutely alters temporal properties of resting brain states.	Wang KS et al.	—	2021	→
Resting state functional connectivity in adolescent synthetic cannabinoid users with and without attention-deficit/hyperactivity disorder.	Yüncü Z et al.	—	2021	→
Task-induced brain functional connectivity as a representation of schema for mediating unsupervised and supervised learning dynamics in language acquisition.	Akama H et al.	—	2021	→
The changes of brain functional networks in young adult smokers based on independent component analysis.	Wang X et al.	—	2021	→
Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.	Fedota JR et al.	—	2021	→
Transdiagnostic connectome-based prediction of craving	Garrison KA et al.	—	2021	—
Value-based cognition and drug dependency.	Loganathan K	—	2021	→
Anterior Cingulate Cortex in Addiction: New Insights for Neuromodulation.	Zhao Y et al.	—	2020	→
Converging Structural and Functional Evidence for a Rat Salience Network.	Tsai PJ et al.	—	2020	→
Craving and Cue Reactivity in Nicotine-Dependent Tobacco Smokers Is Associated With Different Insula Networks.	Janes AC et al.	—	2020	→
Damaged Insula Network Contributes to Depression in Parkinson's Disease.	Huang P et al.	—	2020	→
Dosing parameters for the effects of high-frequency transcranial magnetic stimulation on smoking cessation: study protocol for a randomized factorial sham-controlled clinical trial.	Carl E et al.	—	2020	→
Effects of nicotine on pupil size and performance during multiple-object tracking in non-nicotine users.	Wardhani IK et al.	—	2020	→
Evaluating the resource allocation index as a potential fMRI-based biomarker for substance use disorder.	Moradi M et al.	—	2020	→
Functional connectivity in a triple-network saliency model is associated with real-life self-control.	Krönke KM et al.	—	2020	→
Increased Resting State Triple Network Functional Connectivity in Undergraduate Problematic Cannabis Users: A Preliminary EEG Coherence Study.	Imperatori C et al.	—	2020	→
Independent and Combined Effects of Nicotine or Chronic Tobacco Smoking and HIV on the Brain: A Review of Preclinical and Clinical Studies.	Chang L et al.	—	2020	→
Networks Are Associated With Depression in Patients With Parkinson's Disease: A Resting-State Imaging Study.	Liao H et al.	—	2020	→
Neural cue reactivity during acute abstinence predicts short-term smoking relapse.	Allenby C et al.	—	2020	→
Neuroplastic changes in resting-state functional connectivity after rTMS intervention for methamphetamine craving.	Su H et al.	—	2020	→
Resting-State Connectivity in Former, Current, and Never Smokers.	Claus ED et al.	—	2020	→
Short-term nicotine deprivation alters dorsal anterior cingulate glutamate concentration and concomitant cingulate-cortical functional connectivity.	Abulseoud OA et al.	—	2020	→
Temporal Dynamics of Large-Scale Networks Predict Neural Cue Reactivity and Cue-Induced Craving.	Wang KS et al.	—	2020	→
The effects of selective serotonin reuptake inhibitors on brain functional networks during goal-directed planning in obsessive-compulsive disorder.	Kim M et al.	—	2020	→
The Role of Social Reward and Corticostriatal Connectivity in Substance Use.	Sazhin D et al.	—	2020	→
Transcranial Direct Current Stimulation Applied to the Dorsolateral and Ventromedial Prefrontal Cortices in Smokers Modifies Cognitive Circuits Implicated in the Nicotine Withdrawal Syndrome.	Aronson Fischell S et al.	—	2020	→
12 h Abstinence-Induced ERP Changes in Young Smokers: Electrophysiological Evidence From a Go/NoGo Study.	Liu C et al.	—	2019	→
Alterations in resting-state functional connectivity in substance use disorders and treatment implications.	Wilcox CE et al.	—	2019	→
A Novel Hierarchical LATER Process Model: Evaluating Latent Sources of Variation in Reaction Times of Adult Daily Smokers.	Roberts NJ et al.	—	2019	→
Brain default-mode network dysfunction in addiction.	Zhang R et al.	—	2019	→
Classic psychedelics: An integrative review of epidemiology, therapeutics, mystical experience, and brain network function.	Johnson MW et al.	—	2019	→
Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data.	Sakoglu U et al.	—	2019	→
Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI.	Wetherill RR et al.	—	2019	→
Current understanding of the neurobiology of opioid use disorder: An overview.	Moningka H et al.	—	2019	→
Diverse functional connectivity patterns of resting-state brain networks associated with good and poor hand outcomes following stroke.	Hong W et al.	—	2019	→
Dynamic Functional Connectivity Within the Fronto-Limbic Network Induced by Intermittent Theta-Burst Stimulation: A Pilot Study.	Tang Y et al.	—	2019	→
Effects of Naltrexone on Large-Scale Network Interactions in Methamphetamine Use Disorder.	Kohno M et al.	—	2019	→
Estimation of brain functional connectivity from hypercapnia BOLD MRI data: Validation in a lifespan cohort of 170 subjects.	Hou X et al.	—	2019	→
Exploring common changes after acute mental stress and acute tryptophan depletion: Resting-state fMRI studies.	Zhang X et al.	—	2019	→
Lack of effect of transcranial direct current stimulation (tDCS) on short-term smoking cessation: Results of a randomized, sham-controlled clinical trial.	Falcone M et al.	—	2019	→
Neural basis of smoking-induced relief of craving and negative affect: Contribution of nicotine.	Faulkner P et al.	—	2019	→
Neuroinflammation in addiction: A review of neuroimaging studies and potential immunotherapies.	Kohno M et al.	—	2019	→
Regional excitation-inhibition balance predicts default-mode network deactivation via functional connectivity.	Gu H et al.	—	2019	→
The Insula: A Brain Stimulation Target for the Treatment of Addiction.	Ibrahim C et al.	—	2019	→
The insula in nicotine use disorder: Functional neuroimaging and implications for neuromodulation.	Regner MF et al.	—	2019	→
The Role of Cognitive Control in the Self-Regulation and Reinforcement of Smoking Behavior.	Evans DE et al.	—	2019	→
Triple Network Resting State Connectivity Predicts Distress Tolerance and Is Associated with Cocaine Use.	Reese ED et al.	—	2019	→
Aberrant Time-Varying Cross-Network Interactions in Children With Attention-Deficit/Hyperactivity Disorder and the Relation to Attention Deficits.	Cai W et al.	—	2018	→
Altered interhemispheric resting-state functional connectivity in young male smokers.	Yu D et al.	—	2018	→
A mechanistic model of connector hubs, modularity and cognition.	Bertolero MA et al.	—	2018	→
Changes in resting state functional brain connectivity and withdrawal symptoms are associated with acute electronic cigarette use.	Hobkirk AL et al.	—	2018	→
Cognitive Effects of Nicotine: Recent Progress.	Valentine G et al.	—	2018	→
Combined rTMS/fMRI Studies: An Overlooked Resource in Animal Models.	Seewoo BJ et al.	—	2018	→
Default mode network deactivation to smoking cue relative to food cue predicts treatment outcome in nicotine use disorder.	Wilcox CE et al.	—	2018	→
Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men.	Li Q et al.	—	2018	→
Distinct intrinsic functional brain network abnormalities in methamphetamine-dependent patients with and without a history of psychosis.	Ipser JC et al.	—	2018	→
Functional Neurocircuits and Neuroimaging Biomarkers of Tobacco Use Disorder.	Sutherland MT et al.	—	2018	→
Neurobiological Considerations for Tobacco Use Disorder.	Chawla M et al.	—	2018	→
Nicotine Abstinence Influences the Calculation of Salience in Discrete Insular Circuits.	Fedota JR et al.	—	2018	→
Phase IIb Trial of an α7 Nicotinic Receptor Partial Agonist With and Without Nicotine Patch for Withdrawal-Associated Cognitive Deficits and Tobacco Abstinence.	Schuster RM et al.	—	2018	→
Salience network coupling is linked to both tobacco smoking and symptoms of attention deficit hyperactivity disorder (ADHD).	Janes AC et al.	—	2018	→
The relationship between interleukin-6 and functional connectivity in methamphetamine users.	Kohno M et al.	—	2018	→
The thalamo-cortical resting state functional connectivity and abstinence-induced craving in young smokers.	Wang C et al.	—	2018	→
Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder.	Moeller SJ et al.	—	2018	→
Transcranial DC stimulation modifies functional connectivity of large-scale brain networks in abstinent methamphetamine users.	Shahbabaie A et al.	—	2018	→
12h abstinence-induced right anterior insula network pattern changes in young smokers.	Bi Y et al.	—	2017	→
Altered coupling of default-mode, executive-control and salience networks in Internet gaming disorder.	Zhang JT et al.	—	2017	→
Altered spontaneous brain activity in chronic smokers revealed by fractional ramplitude of low-frequency fluctuation analysis: a preliminary study.	Wang C et al.	—	2017	→
A protocadherin gene cluster regulatory variant is associated with nicotine withdrawal and the urge to smoke.	Jensen KP et al.	—	2017	→
Cingulate cortex functional connectivity predicts future relapse in alcohol dependent individuals.	Zakiniaeiz Y et al.	—	2017	→
Closed-loop brain training: the science of neurofeedback.	Sitaram R et al.	—	2017	→
Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction.	Ding X et al.	—	2017	→
CYP2A6 Genetic Variation Alters Striatal-Cingulate Circuits, Network Hubs, and Executive Processing in Smokers.	Li S et al.	—	2017	→
Dorsal-CA1 Hippocampal Neuronal Ensembles Encode Nicotine-Reward Contextual Associations.	Xia L et al.	—	2017	→
Executive Control and Striatal Resting-State Network Interact with Risk Factors to Influence Treatment Outcomes in Alcohol-Use Disorder.	Kohno M et al.	—	2017	→
Executive control network connectivity strength protects against relapse to cocaine use.	McHugh MJ et al.	—	2017	→
Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder.	Wilcox CE et al.	—	2017	→
Model-free functional connectivity and impulsivity correlates of alcohol dependence: a resting-state study.	Zhu X et al.	—	2017	→
Multiparametric imaging of brain hemodynamics and function using gas-inhalation MRI.	Liu P et al.	—	2017	→
Neural correlates of 12-h abstinence-induced craving in young adult smokers: a resting-state study.	Li Y et al.	—	2017	→
Resting-State Functional Connectivity of the Basal Nucleus of Meynert in Cigarette Smokers: Dependence Level and Gender Differences.	Zhang S et al.	—	2017	→
The first day is always the hardest: Functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt.	Zelle SL et al.	—	2017	→
The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue-induced craving of smokers.	Yuan K et al.	—	2017	→
White matter integrity of central executive network correlates with enhanced brain reactivity to smoking cues.	Bi Y et al.	—	2017	→
Abnormal gray matter volume and resting-state functional connectivity in former heroin-dependent individuals abstinent for multiple years.	Wang L et al.	—	2016	→
Atomoxetine could improve intra-individual variability in drug-naïve adults with attention-deficit/hyperactivity disorder comparably with methylphenidate: A head-to-head randomized clinical trial.	Ni HC et al.	—	2016	→
Can brain games help smokers quit?: Results of a randomized clinical trial.	Loughead J et al.	—	2016	→
Constituents and functional implications of the rat default mode network.	Hsu LM et al.	—	2016	→
Cue Reactivity in Nicotine and Alcohol Addiction: A Cross-Cultural View.	Lv W et al.	—	2016	→
Dorsal anterior cingulate glutamate is associated with engagement of the default mode network during exposure to smoking cues.	Janes AC et al.	—	2016	→
Functional Connectivity During Exposure to Favorite-Food, Stress, and Neutral-Relaxing Imagery Differs Between Smokers and Nonsmokers.	Garrison KA et al.	—	2016	→
Insula Demonstrates a Non-Linear Response to Varying Demand for Cognitive Control and Weaker Resting Connectivity With the Executive Control Network in Smokers.	Fedota JR et al.	—	2016	→
Intra-individual changes in Stroop-related activations linked to cigarette abstinence in adolescent tobacco smokers: Preliminary findings.	Yip SW et al.	—	2016	→
Intra-regional and inter-regional abnormalities and cognitive control deficits in young adult smokers.	Feng D et al.	—	2016	→
Lower gray matter density and functional connectivity in the anterior insula in smokers compared with never smokers.	Stoeckel LE et al.	—	2016	→
Midbrain functional connectivity and ventral striatal dopamine D2-type receptors: link to impulsivity in methamphetamine users.	Kohno M et al.	—	2016	→
Neuroscience of drug craving for addiction medicine: From circuits to therapies.	Ekhtiari H et al.	—	2016	→
Resting state functional connectivity analysis for addiction medicine: From individual loci to complex networks.	Pariyadath V et al.	—	2016	→
Smoking Abstinence-Induced Changes in Resting State Functional Connectivity with Ventral Striatum Predict Lapse During a Quit Attempt.	Sweitzer MM et al.	—	2016	→
Stress-Induced Reinstatement of Nicotine Preference Requires Dynorphin/Kappa Opioid Activity in the Basolateral Amygdala.	Nygard SK et al.	—	2016	→
The 5-HT2A serotonin receptor in executive function: Implications for neuropsychiatric and neurodegenerative diseases.	Aznar S et al.	—	2016	→
Transdisciplinary translational behavioral (TDTB) research: opportunities, barriers, and innovations.	Czajkowski SM et al.	—	2016	→
Translational Research on Nicotine Dependence.	Falcone M et al.	—	2016	→
Aberrant Cross-Brain Network Interaction in Children With Attention-Deficit/Hyperactivity Disorder and Its Relation to Attention Deficits: A Multisite and Cross-Site Replication Study.	Cai W et al.	—	2015	→
A CHRNA5 Smoking Risk Variant Decreases the Aversive Effects of Nicotine in Humans.	Jensen KP et al.	—	2015	→
Alterations in interhemispheric functional and anatomical connectivity are associated with tobacco smoking in humans.	Viswanath H et al.	—	2015	→
Altered spontaneous brain activity in heavy smokers revealed by regional homogeneity.	Wu G et al.	—	2015	→
Biomarkers in substance use disorders.	Volkow ND et al.	—	2015	→
Cannabis, cigarettes, and their co-occurring use: Disentangling differences in default mode network functional connectivity.	Wetherill RR et al.	—	2015	→
Cigarette smoking leads to persistent and dose-dependent alterations of brain activity and connectivity in anterior insula and anterior cingulate.	Zanchi D et al.	—	2015	→
Combined association of physical activity and diet with C-reactive protein among smokers.	Loprinzi PD et al.	—	2015	→
Cortical activity differs during nicotine deprivation versus satiation in heavy smokers.	Evans DE et al.	—	2015	→
Effects of chronic and acute stimulants on brain functional connectivity hubs.	Konova AB et al.	—	2015	→
Finding the self by losing the self: Neural correlates of ego-dissolution under psilocybin.	Lebedev AV et al.	—	2015	→
Increased Functional Connectivity in an Insula-Based Network is Associated with Improved Smoking Cessation Outcomes.	Addicott MA et al.	—	2015	→
Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity.	Lei Y et al.	—	2015	→
Neurobiological impact of nicotinic acetylcholine receptor agonists: an activation likelihood estimation meta-analysis of pharmacologic neuroimaging studies.	Sutherland MT et al.	—	2015	→
Prediction of smoking cessation with treatment: the emerging contribution of brain imaging research.	Brody AL et al.	—	2015	→
Reduced executive and default network functional connectivity in cigarette smokers.	Weiland BJ et al.	—	2015	→
Resting-state functional connectivity and nicotine addiction: prospects for biomarker development.	Fedota JR et al.	—	2015	→
Under pressure: adolescent substance users show exaggerated neural processing of aversive interoceptive stimuli.	Berk L et al.	—	2015	→
Working memory-related neural activity predicts future smoking relapse.	Loughead J et al.	—	2015	→
Machine learning classification of resting state functional connectivity predicts smoking status.	Pariyadath V et al.	—	2014	→
Neuroimaging and biomarkers in addiction treatment.	Garrison KA et al.	—	2014	→
Large-scale brain networks in the awake, truly resting marmoset monkey.	Belcher AM et al.	—	2013	→