Altered spontaneous brain activity in chronic smokers revealed by fractional ramplitude of low-frequency fluctuation analysis: a preliminary study

Temporal dynamic changes of intrinsic brain regional activity in depression with smoking

BACKGROUND

Depression is often accompanied by high levels of smoking behavior, and smoking can act as a risk factor for depression. However, there is limited neuroimaging evidence regarding the association between depression and smoking, especially the impact of this association on the brain stability remains unclear. Therefore, this study aimed to assess the interaction effect between smoking and depression from a neurodynamic perspective.

METHOD

We assessed the resting-state functional magnetic resonance imaging from 193 participants (55 depressed smokers; 51 depressed non-smokers; 25 healthy smokers; 62 healthy non-smokers) and calculated 3 regional activity dynamic indicators, including dynamic regional homogeneity (dReHo), dynamic amplitude of low-frequency fluctuations (dALFF), and dynamic fractional ALFF (dfALFF). Principal component analysis was conducted on these 3 dynamic indicators, and the first component was extracted for the subsequent 2 × 2 factor designs statistical analysis.

RESULT

We observed the interaction between smoking and depression increases the instability of regional activity in the precentral gyrus and precuneus. Compared with HCs, patients with depression showed increased instability of regional activity across widespread regions such as the precentral gyrus, thalamus, and medial frontal gyrus. No main effects of smoking were observed. In depressed smokers, the instability of regional activity in left precuneus is positively correlated with anxiety symptoms.

CONCLUSIONS

Our findings indicate that smoking potentially exacerbates brain abnormal instability in depression, implying a clinical need to require patients with depression to abstain from smoking.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Substance use disorders are characterised by increased voxel-wise intrinsic measures in sensorimotor cortices: An ALE meta-analysis

Substance use disorders (SUDs) are severe psychiatric illnesses. Seed region and independent component analyses are currently the dominant connectivity measures but carry the risk of false negatives due to selection. They can be complemented by a data-driven and whole-brain usage of voxel-wise intrinsic measures (VIMs). We meta-analytically integrated VIMs, namely regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), voxel-mirrored homotopy connectivity (VMHC) and degree centrality (DC) across different SUDs using the Activation Likelihood Estimation (ALE) algorithm, functionally decoded emerging clusters, and analysed their connectivity profiles. Our systematic search identified 51 studies including 1439 SUD participants. Although no overall convergent pattern of alterations across VIMs in SUDs was found, sensitivity analyses demonstrated two ALE-derived clusters of increased ReHo and ALFF in SUDs, which peaked in the left pre- and postcentral cortices. Subsequent analyses showed their involvement in action execution, somesthesis, finger tapping and vibrotactile monitoring/discrimination. Their numerous clinical correlates across included studies highlight the under-discussed role of sensorimotor cortices in SUD, urging a more attentive exploration of their clinical significance.

The resting-state brain activity signatures for addictive disorders

BACKGROUND

Addiction is a chronic and relapsing brain disorder. Despite numerous neuroimaging and neurophysiological studies on individuals with substance use disorder (SUD) or behavioral addiction (BEA), currently a clear neural activity signature for the addicted brain is lacking.

METHODS

We first performed systemic coordinate-based meta-analysis and partial least-squares regression to identify shared or distinct brain regions across multiple addictive disorders, with abnormal resting-state activity in SUD and BEA based on 46 studies (55 contrasts), including regional homogeneity (ReHo) and low-frequency fluctuation amplitude (ALFF) or fractional ALFF. We then combined Neurosynth, postmortem gene expression, and receptor/transporter distribution data to uncover the potential molecular mechanisms underlying these neural activity signatures.

FINDINGS

The overall comparison between addiction cohorts and healthy subjects indicated significantly increased ReHo and ALFF in the right striatum (putamen) and bilateral supplementary motor area, as well as decreased ReHo and ALFF in the bilateral anterior cingulate cortex and ventral medial prefrontal cortex, in the addiction group. On the other hand, neural activity in cingulate cortex, ventral medial prefrontal cortex, and orbitofrontal cortex differed between SUD and BEA subjects. Using molecular analyses, the altered resting activity recapitulated the spatial distribution of dopaminergic, GABAergic, and acetylcholine system in SUD, while this also includes the serotonergic system in BEA.

CONCLUSIONS

These results indicate both common and distinctive neural substrates underlying SUD and BEA, which validates and supports targeted neuromodulation against addiction.

FUNDING

This work was supported by the National Natural Science Foundation of China and Intramural Research Program of the National Institute on Drug Abuse, National Institutes of Health.

Increased spontaneous activity of the superior frontal gyrus with reduced functional connectivity to visual attention areas and cerebellum in male smokers

Background

Chronic smokers have abnormal spontaneous regional activity and disrupted functional connectivity as revealed by previous neuroimaging studies. Combining different dimensions of resting-state functional indicators may help us learn more about the neuropathological mechanisms of smoking.

Methods

The amplitude of low frequency fluctuations (ALFF) of 86 male smokers and 56 male non-smokers were first calculated. Brain regions that displayed significant differences in ALFF between two groups were selected as seeds for further functional connectivity analysis. Besides, we examined correlations between brain areas with abnormal activity and smoking measurements.

Results

Increased ALFF in left superior frontal gyrus (SFG), left medial superior frontal gyrus (mSFG) and middle frontal gyrus (MFG) as well as decreased ALFF in right calcarine sulcus were observed in smokers compared with non-smokers. In the seed-based functional connectivity analysis, smokers showed attenuated functional connectivity with left SFG in left precuneus, left fusiform gyrus, left lingual gyrus, left cerebellum 4 5 and cerebellum 6 as well as lower functional connectivity with left mSGF in left fusiform gyrus, left lingual gyrus, left parahippocampal gyrus (PHG), left calcarine sulcus, left cerebellum 4 5, cerebellum 6 and cerebellum 8 (GRF corrected, Pvoxel < 0.005, Pcluster<0.05). Furthermore, attenuated functional connectivity with left mSGF in left lingual gyrus and PHG displayed a negative correlation with FTND scores (r = −0.308, p = 0.004; r = −0.326, p = 0.002 Bonferroni corrected).

Conclusion

Our findings of increased ALFF in SFG with reduced functional connectivity to visual attention areas and cerebellum subregions may shed new light on the pathophysiology of smoking.

Reciprocal modulation between cigarette smoking and internet gaming disorder on participation coefficient within functional brain networks

Many reports indicated that cigarette smoking was associated with internet gaming disorder (IGD). However, the underlying mechanism of comorbidity between smoking and IGD and whether they had interaction effects on topological organization of brain functional network are still unknown. Therefore, we investigated the interaction between smoking and IGD in resting-state brain functional networks for 60 healthy controls, 46 smokers, 38 IGD individuals and 34 IGD comorbid with smoking participants. The modular structures of functional networks were explored and participation coefficient (Pc) was used to characterize the importance of each brain region in the communication between modules. Significant main effect of IGD was found in the left superior frontal gyrus, bilateral medial part of superior frontal gyrus and bilateral posterior cingulate gyrus with lower Pc in IGD group than in non-IGD group. Significant interaction effects between smoking and IGD were found in the left posterior orbital gyrus, right lateral orbital gyrus, left supramarginal gyrus, left middle temporal gyrus and left inferior temporal gyrus. The interaction in these brain regions was characterized by no significant difference or significantly decreased Pc in smokers or IGD individuals while significantly increased Pc in IGD comorbid with smoking group under the influence of IGD or smoking. Our findings provide valuable information underlying the neurophysiological mechanisms of smoking and IGD, and also offer a potential target for future clinical treatment of smoking and IGD comorbidity.

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration.

RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas

Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.

Regional Homogeneity Abnormalities and Its Correlation With Impulsivity in Male Abstinent Methamphetamine Dependent Individuals

Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation.

Altered patterns of fractional amplitude of low-frequency fluctuation and regional homogeneity in abstinent methamphetamine-dependent users

Methamphetamine (MA) could induce functional and structural brain alterations in dependent subjects. However, few studies have investigated resting-state activity in methamphetamine-dependent subjects (MADs). We aimed to investigate alterations of brain activity during resting-state in MADs using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). We analyzed fALFF and ReHo between MADs (n = 70) and healthy controls (HCs) (n = 84) and performed regression analysis using MA use variables. Compared to HCs, abstinent MADs showed increased fALFF and ReHo values in the bilateral striatum, decreased fALFF in the left inferior frontal gyrus, and decreased ReHo in the bilateral anterior cingulate cortex, sensorimotor cortex, and left precuneus. We also observed the fALFF values of bilateral striatum were positively correlated with the age of first MA use, and negatively correlated with the duration of MA use. The fALFF value of right striatum was also positively correlated with the duration of abstinence. The alterations of spontaneous cerebral activity in abstinent MADs may help us probe into the neurological pathophysiology underlying MA-related dysfunction and recovery. Since MADs with higher fALFF in the right striatum had shorter MA use and longer abstinence, the increased fALFF in the right striatum might implicate early recovery during abstinence.

Increased thalamic volume and decreased thalamo-precuneus functional connectivity are associated with smoking relapse

The thalamus, with the highest density of nicotinic acetylcholine receptor (nAChR) in the brain, plays a central role in thalamo-cortical circuits that are implicated in nicotine addiction. However, little is known about whether the thalamo-cortical circuits are potentially predictive of smoking relapse. In the current study, a total of 125 participants (84 treatment-seeking male smokers and 41 age-matched male nonsmokers) were recruited. Structural and functional magnetic resonance images (MRI) were acquired from all participants. After a 12-week smoking cessation treatment with varenicline, the smokers were then divided into relapsers (n = 54) and nonrelapsers (n = 30). Then, we compared thalamic volume and seed-based thalamo-cortical resting state functional connectivity (rsFC) prior to the cessation treatment among relapsers, nonrelapsers and nonsmokers to investigate the associations between thalamic structure/function and smoking relapse. Increased thalamic volume was detected in smokers relative to nonsmokers, and in relapsers relative to nonrelapsers, especially on the left side. Moreover, decreased left thalamo-precuneus rsFC was detected in relapsers relative to nonrelapsers. Additionally, a logistic regression analysis showed that the thalamic volume and thalamo-precuneus rsFC predicted smoking relapse with an accuracy of 75.7%. These novel findings indicate that increased thalamic volume and decreased thalamo-precuneus rsFC are associated with smoking relapse, and these thalamic measures may be used to predict treatment efficacy of nicotine addiction and serve as a potential biomarker for personalized medicine.

Dynamic Neuroimaging Biomarkers of Smoking in Young Smokers

OBJECTIVE

To examine potential changes in the dynamic characteristics of regional neural activity in young smokers and to detect whether the changes were associated with smoking behavior.

METHODS

The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) in 40 young smokers and 42 nonsmokers were compared. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and smoking behavior [e.g., the Fagerström Test for Nicotine dependence (FTND) scores and pack-years].

RESULTS

Significantly differences in dReHo variability were observed in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), medial frontal gyrus (MFG), insula, cuneus, postcentral gyrus, inferior semi-lunar lobule, orbitofrontal gyrus, and inferior temporal gyrus (ITG). Young smokers also showed significantly increased dALFF variability in the anterior cingulate cortex (ACC) and ITG. Furthermore, a significant positive correlation was found between dALFF variability in the ACC and the pack-years; whereas a significant negative correlation between dReHo variability in the IFG and the FTND scores was found in young smokers.

CONCLUSION

The pattern of resting state regional neural activity variability was different between young smokers and nonsmokers. Dynamic regional indexes might be a novel neuroimaging biomarker of smoking behavior in young smokers.

Interaction Between Smoking and Internet Gaming Disorder on Spontaneous Brain Activity

Converging lines of evidence indicates that smoking and internet gaming disorder (IGD) affect spontaneous brain activity, respectively. However, little is known about whether these two factors work together on the human brain. In this study, we investigated the interaction between smoking and IGD on local spontaneous brain activity using amplitude of low-frequency fluctuation (ALFF) based on resting-state fMRI (rs-fMRI). Forty-six cigarette smokers, 38 IGD individuals, 34 participants with both IGD and cigarette smoking (IGD-Smoking), and 60 healthy individuals involved in the study. Voxel-wise analysis of covariance of ALFF revealed that there were significant interactions between IGD by smoking in the right medial pre-frontal cortex (MPFC)/ventral striatum, bilateral cerebellar, and visual-related regions as well as the left temporal gyrus. In the right MPFC/ventral striatum and left temporal gyrus, ALFF in smoking group was significantly higher than healthy group while there were no significant ALFF differences between IGD-Smoking group and IGD group. While in the bilateral cerebellar and visual-related regions, ALFF in the smoking group was significantly lower than healthy group while ALFF in IGD-Smoking group did not show significant difference with IGD group. In addition, in the smoking group, ALFF of the right MPFC/ventral striatum was associated positively with anxiety and depression scores while the ALFF value in the smoking group had a trend toward negative correlation with SDS scores in the bilateral cerebellar and visual-related regions. The ALFF value in the smoking group was associated positively with anxiety score in the left temporal gyrus. These findings indicate that smoking and IGD interacted with each other in the human brain. Our results, in terms of spontaneous brain activity, may imply the fact that IGD people are more tended to get smoking. Moreover, it is possible to predict that smokers may be more easily to get internet addiction than healthy people.

Increased interregional functional connectivity of anterior insula is associated with improved smoking cessation outcome

Damage to the insular cortex has been shown to disrupt smoking behavior. However, whether smoking cessation outcomes are associated with abnormal functions of insula and its subregions remains unclear. In this study, we investigated the relationship between insular functions (interregional functional connectivity and regional activity) and treatment outcomes of cigarette smoking. Thirty treatment-seeking smokers were recruited into the treatment study and underwent magnetic resonance imaging (MRI) scans immediately before and after the treatment. Sixteen participants remained abstinent from smoking (quitters), while 14 relapsed to smoking (relapers). Changes in resting-state functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) across groups and visits were assessed using repeated measures ANCOVA. Significant interaction effects were detected: 1) between the left anterior insula and left precuneus; and 2) between the right anterior insula and left precuneus and medial frontal gyrus. Post-hoc region-of-interest analyses in brain areas showing interaction effects indicated significantly increased functional connectivity after treatment compared with before treatment in quitters but opposite longitudinal changes in relapsers. However, no significant effects in fALFF were observed. These novel findings suggest that increased interregional functional connectivity of the anterior insula is associated with improved smoking cessation outcome: individuals with increased functional connectivity of the anterior insula during the treatment would more likely quit smoking successfully. These insular circuits may serve as therapeutic targets for more efficacious treatment of nicotine addiction.

Temporal dynamic changes of intrinsic brain activity in schizophrenia with cigarette smoking

Mounting evidence from multimodal neuroimaging studies has supported a neurobiological basis for schizophrenia-nicotine dependence comorbidity. However, this evidence comes exclusively from studies measuring static intrinsic activity/connectivity of the brain, while the dynamic effects of this comorbidity remain poorly understood. The current study therefore sought to examine whether temporal dynamic intrinsic brain activity interacted with diagnosis (schizophrenics vs. healthy controls) and smoking status (smokers vs. non-smokers). We used a mixed sample design and included the following four groups: i) schizophrenic smokers (n = 22), ii) schizophrenic non-smokers (n = 27), iii) healthy control smokers (n = 22), and iv) healthy control non-smokers (n = 21). All subjects underwent functional magnetic resonance imaging during the resting state. The temporal variability in intrinsic brain activity among the four groups was compared using a novel dynamic amplitude of low-frequency fluctuation (dALFF) method. A significant main effect of diagnosis was found in the left superior parietal gyrus (SPG; F_{(1, 88)} = 142.1, P < 0.0001). Moreover, the dALFF strength in the SPG was positively correlated with disease duration in patients with schizophrenia (Rho₍₄₆₎ = 0.43, P = 0.002). In addition, a significant interaction between diagnosis and smoking status was observed in the left dorsolateral prefrontal cortex (DLPFC; F_{(1, 88)} = 7.39, P = 0.008), which was consistent with the self-medication hypothesis. Together, this study has demonstrated for the first time that nicotine restores dynamic intrinsic brain activity in the left DLPFC in patients with schizophrenia. This interaction may be a clinical neuromarker for increased comorbid smoking in schizophrenia.

Temporal dynamics of spontaneous default-mode network activity mediate the association between reappraisal and depression

Cognitive reappraisal is associated with major depressive disorder (MDD), while spontaneous activity patterns of the default mode network (DMN) is implicated in reappraisal and MDD. However, neural mechanisms subserving the close association of spontaneous reappraisal and depression are unclear. Spontaneous reappraisal, depression and resting-state functional magnetic resonance imaging (rsfMRI) were measured from 105 healthy subjects. We assessed the temporal complexity (Hurst exponent), Regional Homogeneity (ReHo) and fractional Amplitude of Low Frequency Fluctuation (fALFF) profiles of DMN, a network involved in both reappraisal and depression. Mediation effects of these standard measures on the relationship between reappraisal and depression, and the contributions of each DMN subregion, were assessed. Results indicated that Hurst exponent (H) of DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with reappraisal scores. An individual with a higher reappraisal score has a lower Hurst value of DMN. Mediation analyses suggest that H of DMN partially mediates the association between reappraisal and the degree of depression, and this mediation effect arises from the contribution of medial prefrontal cortex. Neither ReHo nor fALFF showed a similar correlation or mediation effect. These findings suggest that temporal dynamics of DMN play an important role in emotion regulation and its association with depression. H of DMN may serve as a neural marker mediating the association between reappraisal and depression.

Altered function but not structure of the amygdala in nicotine-dependent individuals

Tobacco use disorder is frequently comorbid with emotional disorders, each exerting reciprocal influence on the other. As an important hub for emotional processing, amygdala may also play a critical role in tobacco addiction. Therefore, we aimed to investigate the volume and spontaneous activity of the amygdala in nicotine-dependent individuals and their relationships with cigarette use. A total of 84 smokers (aged 22-54 years) and 41 nonsmokers (aged 26-56 years) were enrolled in the present study. 3D-T1 weighted images and resting-state fMRI images were acquired from all participants. We used ROI-wise volume, fractional amplitude of low frequency fluctuation (fALFF) and resting-state functional connectivity (FC) to assess structural and functional changes of the amygdala in the smokers. There was no significant difference between smokers and nonsmokers on amygdala volume (p > 0.05). When compared to nonsmokers, increased fALFF in the right amygdala was observed in smokers (p = 0.024). In addition, increased FC between the left amygdala and the right precuneus and decreased FC between the right amygdala and the right orbitofrontal cortex (OFC) was found in smokers. In smokers, these amygdala measures did not correlate with any measures of cigarette use. The results revealed that the amygdala function but not volume was affected in nicotine addiction. When considering the fALFF and FC results, we propose that the OFC top-down control may regulate the amygdala activity in nicotine addicts. The pattern of amygdala-based FC in smokers revealed in our study may provide new information about the brain circuitry of tobacco dependence.