Altered brain dynamic functional network connectivity in heavy smokers

doi:10.21203/rs.3.rs-4875266/v1

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Altered brain dynamic functional network connectivity in heavy smokers

https://doi.org/10.21203/rs.3.rs-4875266/v1

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Cigarette smoking is associated with altered static functional connectivity, however, studies on functional connectivity dynamics may provide new insightful perspectives for understanding the neural mechanisms of smoking addiction. The aim of this study was to investigate the characteristics of dynamic functional network connectivity (dFNC) in heavy smokers. DFNC analysis based on sliding window approach and k-means clustering was performed to the resting-state functional magnetic resonance imaging data of 34 heavy smokers and 36 healthy non-smokers. The between-group differences in temporal properties of dFNC states were assessed, followed by a correlation analysis of these differences with smoking-related factors in heavy smokers. Compared to non-smokers, heavy smokers showed a lower occurrence rate and mean dwell time in state 2, characterized by negative connectivity between the default-mode network and the other networks. Heavy smokers also had a trend toward higher occurrence rate and mean dwell time in state 1, characterized by global weak connectivity. Network-based statistics identified cognitive control and cerebellar domains played an important role in the impaired subnetworks. Correlation analyses demonstrated that in heavy smokers, both the occurrence rate and the mean dwell time were negatively associated with the duration of smoking in state 2, characterized by high connectivity within the sensory domains. Our findings suggest that dFNC abnormalities in heavy smokers may become new neuroimaging biomarkers and provide a deeper understanding of the pathophysiological mechanisms of smoking addiction.

Resting-state functional magnetic resonance imaging

dynamic functional network connectivity

heavy smokers

group independent component analysis

Cigarette smoking is a serious public health problem worldwide, with adverse effects on the cardiac, pulmonary, and vascular systems (Ng et al., 2014). Long-term smoking is associated with damages in neuroanatomical and functional organizations, as well as poor cognition, including memory, impulse control, attention, and executive function (Durazzo, Meyerhoff, & Nixon, 2010). Smoking affects not only functional activities in local brain regions, but also functional interactions between brain regions (Fox et al., 2016). A growing number of studies have shown that exploring brain connectivity can provide insights into the pathological mechanisms of smoking addiction (Fedota & Stein, 2015; Fox et al., 2016; Niu et al., 2023; Yip et al., 2022).

Resting-state functional connectivity (FC), statistical correlations between blood oxygen level dependent signals of two distinct brain regions based on resting-state functional magnetic resonance imaging (rs-fMRI), was widely used to explore pathological mechanisms in many psychiatric and neurological disorders (Barch, 2017; Fedota & Stein, 2015; Fox et al., 2016). For example, smokers, but not nonsmokers, show an inverse relationship between the strength of resting-state FC between the right anterior insula seed and the ventromedial prefrontal cortex and higher personality trait alexithymia, which is in turn associated with a deficiency in subjective awareness and regulation of emotions (Sutherland, Carroll, Salmeron, Ross, & Stein, 2013). In addition, the salience network appears to be posited to moderate the dynamics between internal default-mode network and external executive control network information processing which may be related to the alteration of attentional resources to interoceptive states in smokers (Sutherland, McHugh, Pariyadath, & Stein, 2012; Yip et al., 2022). Despite these advances, most studies assumed that FC is temporal stationary (i.e., static FC), without considering the fact that the brain is a dynamic system. Therefore, static FC may ignore spontaneous fluctuations in information interaction of the brain.

Recently, some studies have observed that brain regions interact not in a static manner, but in a dynamic process (Capouskova, Kringelbach, & Deco, 2022; Preti & Van De Ville, 2017; Van De Ville, 2019). Dynamic FC (dFC) aims to evaluate FC fluctuations and assess how functional organization evolves over time (Calhoun, Miller, Pearlson, & Adali, 2014). Compared to static FC, dFC can explore rs-fMRI time series on a much finer scale and capture brain connectivity in more detail, which is crucial for understanding the temporal variability in the intrinsic organization of the brain (Preti, Bolton, & Van De Ville, 2017). Therefore, dFC has attracted attention as an effective tool for revealing the dynamic spatiotemporal organization of the brain. Many studies have demonstrated that dFC is related to a wide range of cognitive and behavioral traits (Beaty, Benedek, Silvia, & Schacter, 2016; Capouskova et al., 2022; Marchitelli et al., 2022). Alterations in FC dynamics have also been observed in many brain disorders, such as schizophrenia, major depression disorder, autism, Alzheimer’s and Parkinson’s diseases (Fu et al., 2021; Gu et al., 2020; Kim et al., 2017; Marchitelli et al., 2022; Xie et al., 2022).

Although dFC has been used to explore spatiotemporal dynamic architectures in brain disorders, only one study reported dFC changes in modular communities and across community boundaries during the acute nicotine abstinence (Fedota et al., 2021). Hence, there is still lack of systematic examinations of FC dynamics in smokers at the whole-brain level. Actually, smoking-related brain abnormalities are not limited to local regions but spread throughout the whole brain, thus, it is important to conduct a whole-brain dFC analysis for smokers. Moreover, whole-brain dFC analysis can obtain a reproducible set of brain states exhibiting distinct patterns of brain connectivity, which are related to different cognitive states (Beaty et al., 2016) as well as psychiatric and neurological diseases (Fornito, Zalesky, & Breakspear, 2015). Therefore, exploring abnormalities of whole-brain dFC in smokers may bring new discoveries in brain pathology of smoking addiction.

In this study, using dynamic functional network connectivity (dFNC) analysis based on rs-fMRI, we investigated the dynamics of whole-brain functional networks as well as their relationship with smoking-related variables in heavy smokers. The group independent component analysis (GICA) was first conducted to identify independent components (ICs) representing intrinsic connectivity networks (ICNs). A sliding-window method and k-means clustering were used to identify dFNC states (i.e., brain states) and their connectivity patterns. For each dFNC state, network-based statistics (NBS)(Zalesky, Fornito, & Bullmore, 2010) was applied to explore group differences of network connectivity between heavy smokers and non-smokers, and correlation analysis was performed to investigate relationships between the temporal proprieties of dFNC states and smoking-related variables.

Subjects

Seventy-four right-handed subjects including 37 heavy smokers (HS) and 37 healthy non-smokers (NS) were enrolled in this study. All participants were screened for psychiatric and nonpsychiatric disorders by the mini-international neuropsychiatric interview (Sheehan et al., 1998). They have no history of medical, neurological disorders, intellectual disability or psychiatric diseases (other than nicotine dependence for heavy smokers). None of subjects reported daily alcohol consumption, or any history of difficulty ceasing alcohol intake. Heavy smokers met the criteria for tobacco use disorder in the Diagnostic and Statistical Manual of Mental Disorders, 5th ed. They smoked at least 20 cigarettes per day and had no period of smoking abstinence for more than 3 months during the past years. The severity of nicotine addiction was evaluated by Fagerström Test for Nicotine Dependence (FTND) (Heatherton, Kozlowski, Frecker, & Fagerström, 1991). Non-smokers used no more than 5 cigarettes in their lifetime. There was no statistically significant difference in body mass index between heavy smokers and non-smokers (p = 0.91). There is no special instruction on whether heavy smokers should smoke or not when coming into the study and heavy smokers was in a normal state not in satiety or abstinence. During the scanning, none of the heavy smokers was confirmed to feel the urge to smoke or experience any withdrawal symptoms by questionnaire. We referred such the state as spontaneous, rather than abstinence or satiety. Three heavy smokers and one non-smoker were excluded for excessive head motion (see Data preprocessing section). As a result, 34 heavy smokers (age range: 35–58 years) and 36 non-smokers (age range: 33–58 years) were analyzed in the following analysis. The demographic and clinical information of subjects is shown in Table 1. The data was used in our previous study based on static functional connectivity and network analysis (Lin, Wu, Zhu, & Lei, 2015), but the analysis method of this study was different; here we investigated the dFNC based on GICA and state analysis.

Table 1

Demographic information for subjects in each group and between-group comparisons.
	HS (n = 34)	NS (n = 36)	p value
	(Mean ± SD)	(Mean ± SD)	p value
Age (years)/age range	47.35 ± 7.47	46.86 ± 8.55	0.80
Gender (male/female)	27/7	29/7	0.91
Years of education (years)	8.32 ± 2.24	9.00 ± 3.06	0.30
FTND score	8.85 ± 0.70	--	--
Cigarettes smoked per day	37.06 ± 10.23	--	--
Onset of first smoking (years)	20.91 ± 5.45	--	--
Duration of smoking (years)	26.44 ± 9.04	--	--
HS: heavy smokers; NS: non-smokers; FTND: Fagerström test for Nicotine Dependence.
Two-sample two tailed t-test was used for age and years of education comparisons between HS and NS. Chi-square test was used for gender comparison between HS and NS.

Spatial GICA and ICN identification

Data acquisition and preprocessing could be seen in the Supplementary materials. Spatial GICA with model order of 100 was used to decompose the preprocessed data by GICA of fMRI Toolbox (GIFT v4.0c). First, principal components analysis (PCA) was applied for each subject to reduce the data into 150 principal components. Then, the reduced data were concatenated across all subjects and decomposed into 100 independent components (ICs). To guarantee the reliability and stability, ICA algorithm was repeated 100 times using the ICASSO toolbox. GICA back-reconstruction method was performed to obtain subject-specific spatial maps and TCs. Finally, the mean power spectrum of TC was computed.

Among the 100 ICs, 42 ICs were selected as meaningful ICNs according to the following criteria (Allen et al., 2014): 1) spatial maps overlapped with known brain regions; 2) peak coordinates located mainly in gray matter without overlap with ventricles, blood vessels, or susceptibility artifacts; and 3) TC dominated by low-frequency fluctuations, i.e., a high power ratio of less than 0.10 Hz to 0.15–0.25 Hz.

Static and dynamic FNC calculation

For subject-specific TCs, the following post-procedures were performed to remove remaining noise and artifacts: 1) detrending linear, quadratic, and cubic trends. 2) regressing 6 realignment parameters and their first derivatives. 3) de-spiking detected outliers. 3dDespike (http://afni.nimh.nih.gov/afni) was used in the detrending and de-spiking procedures. 4) low-pass filtering with cut-off frequency of 0.15 Hz. The static FNC (sFNC) was calculated using the pair-wise correlations between the whole TCs.

Based on the post-processed TCs, dFNC for each subject was estimated using the sliding window method. A tapered window created by convolving a rectangular window (size = 22 TRs) with a Gaussian kernel (σ = 3 TRs) was used. The window was slid in a step of 1 TR, resulting in 178 windows. The covariance matrices within the tapered window were calculated to measure the dFNC. For each subject, the covariance matrices of each window were concatenated to form a C×C×T array (C denotes the number of ICNs, T denotes the number of windows), representing the dFNC between ICNs as function of time.

Dynamic FNC state analysis

To evaluate the frequency and structure of dFNC state, k-means clustering was applied to the dFNC matrices from all participants. The optimal number of cluster (k) was determined 4 according to the elbow criterion of the cluster validity index, which is computed as the ratio between within-cluster distances to between-cluster distance. All dFNC matrices were classified into one of 4 states based on the similarity to cluster centroids, and a subject-specific state vector, representing which state each window is assigned to, was obtained. To assess temporal properties of dFNC states, we calculated three temporal properties: (1) occurrence rate, the percentage of time windows assigned to each state; (2) mean dwell time, average number of successive windows of the same state (if the state of the next window is the same as this window, but the one after that is different, the dwell time is 2); (3) number of transitions (if the state of the next window is different from this window, it is considered as a transition), total number of translations between different states.

A general linear model with age, sex, years of education, and mean framewise displacement as covariates was applied to explore differences on temporal properties of dFNC states between heavy smokers and non-smokers. False discovery rate correction at q = 0.05 was used for multiple comparisons. Additionally, the Spearman's correlation analysis was conducted to investigate associations between the altered temporal properties of dFNC states and the smoking-related variables (FTND score, Cigarettes smoked per day, Onset of first smoking, Duration of smoking) in heavy smokers.

Between-group differences on sFNC and dFNC

NBS was used to identify sub-networks with significant between-group differences on sFNC and dFNC in each state. Two-sample t test was employed for each connection between the ICNs, and those with uncorrected p < 0.001 were searched for connected sub-networks. A null distribution of component size with family wise error (FWE) corrected at p < 0.05 was computed for each connected sub-network using a 5,000 nonparametric permutation method. For each connected sub-network, the FWE-corrected p value was determined as the proportion of permutations yielding a relatively large or equal-sized interconnected network. Before NBS, multiple linear regression was performed to eliminate effects of age, gender, years of education and mean frame-wise displacement.

Demographic information

No significant difference was found in age (p = 0.80), sex (p = 0.91), and years of education (p = 0.30) between heavy smokers and nonsmokers (Table 1). For heavy smokers, the average daily cigarettes were 37.06 ± 10.23, and the average FTND score was 8.85 ± 0.70 (range 8–10), indicating heavy nicotine dependence. The heavy smokers had smoked for 26.44 ± 9.04 years, and their first smoking age was 20.91 ± 5.45 years old.

ICNs identification

The spatial maps of the 42 identified ICNs were displayed in Fig. 1A. The 42 ICNs were categorized into 7 functional domains: subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VS), cognitive control (CC), default-mode (DM), and cerebellar (CB) networks. The peak coordinates of the ICNs are listed in Table S1, and the spatial maps of the ICNs are provided in Figure S1. Overall, the derived ICNs were similar to those observed in previous high-model ICA decompositions (Allen et al., 2014). Figure 1B shows the sFNC averaged across all subjects. The pattern of sFNC was consistent with prior literature, showing modular organizations within sensory systems (i.e., AUD, SM, and VS networks) and DM regions, as well as anti-correlations between these domains.

Dynamic FNC states

Four distinct dFNC states were identified. Figure 2A showed connectivity matrices, reflecting 4 brain states arranged in order of emergence. Figure 2B represented the functional profile of each state, retaining the 5% strongest connectivity. The histogram of connectivity strength for each state was displayed in Fig. 2C. Different brain states represent distinct connectivity patterns, indicating evident differences among these dFNC states.

State 1 accounted for over 50% of all windows, characterized by overall weak connections within and between each pair of ICNs, while a few positive connections within SM, VS and DM domains. The histogram of this state showed that the connectivity strength was almost between − 0.2 to 0.2, indicating weak connectivity. The characteristics of state 2 (29% of all windows) were a partly strong positive correlation within the SM, VS and CC domains, as well as almost positive correlation within the DM domain, while a negative correlation between the DM and other domains such as SC, AUD, SM, and CC networks. This pattern was validated by the functional profile of connectivity strength, showing many blue lines between the DM and SM, as well CC domains (Fig. 2B). State 3 (10% of all windows) and state 4 (9% of all windows) were distinguished by the predominance of strong positive connections within and between the sensory domains (i.e., AUD, SM, and VS), while negative correlations between the sensory domains and other domains. However, compared to state 4, state 3 exhibited much stronger positive connections within and between the sensory domains, as well as negative connections between the sensory domains and DM network.

The clustering method was also repeated for k = 3 and k = 5 to evaluate whether the number of clusters would change the results. We observed similar patterns of dFNC state with different k values. The results are shown in Figure S2.

Alterations of dFNC strengths and temporal properties of dFNC states

NBS found impaired connected sub-networks in dFNC in states 1–3 (Figs. 3A&B). See the supplementary material for more details.

Compared with non-smokers, heavy smokers had a significantly lower occurrence rate in state 2 (HS: 0.227 ± 0.206, NS: 0.356 ± 0.242, p = 0.015), while a trend toward higher occurrence rate in state 1 (HS: 0.579 ± 0.299, NS: 0.458 ± 0.244, p = 0.052). There was no significant difference in occurrence rate in other states (Fig. 4A). Similarly, heavy smokers showed a significantly lower mean dwell time in state 2 (HS: 13.499 ± 10.414, NS: 25.192 ± 25.405, p = 0.013), while a trend toward longer mean dwell time in state 1 (HS: 43.927 ± 46.407, NS: 28.440 ± 24.319, p = 0.082), and no significant differences in mean dwell time in other states (Fig. 4B). Further, there was no significant difference in number of transitions between the two groups (HS: 5.941 ± 3.455, NS: 6.722 ± 2.794, p = 0.269) (Fig. 4C).

Correlation analysis manifested the occurrence rate and mean dwell time of state 2 were negatively correlated with the duration of smoking in heavy smokers (r = -0.467, p = 0.005; p = -0.393, p = 0.022 (Figs. 5A&B). Moreover, negative associations were found between the occurrence rate and mean dwell time of state 3 and FTND in heavy smokers (r = -0.350, p = 0.046; r = -0.356, p = 0.042) (Figs. 5C&D). Similar results of temporal properties and their correlation results with k = 3 and k = 5 are shown in Figures S3&S4 and Figures S5&S6. Repeatability and reproducibility analysis could be seen in the supplementary marterial.

Many studies have recently explored the fluctuations of spontaneous brain activity and come to a preliminary conclusion that dynamic brain connectivity may be an effective diagnostic biomarker for neuropsychiatric disorders. To our knowledge, this is the first study to investigate dFNC alterations between heavy smokers and non-smokers using rs-fMRI. Four distinct dFNC states were revealed, and NBS identified that the cognitive control and cerebellar domains play an important role in these impaired sub-networks. Compared to non-smokers, heavy smokers showed decreased occurrence rate and mean dwell time in state 2, while a trend toward higher occurrence rate and mean dwell time in state 1. In addition, both the occurrence rate and the mean dwell time were negatively associated with the duration of smoking in state 2 and FTND in state 3 in heavy smokers. Our results suggest that dFNC analysis may provide new discoveries in pathophysiological mechanisms of smoking addiction.

Mounting evidence suggests that the dynamic information embedded in the brain can be well-captured by the sliding window approach even using a small number of time points (Allen et al., 2014). The recurring dFNC state, a short period during which FC topography remains quasi-stable in a conceptual analogy to EEG microstate, is a widely accepted representation of brain state (Allen, Damaraju, Eichele, Wu, & Calhoun, 2018). In this study, we identified 4 distinct brain states that recur in both heavy smokers and non-smokers using the sliding window method and k-means clustering. Resting-state is an unconstrained condition which might contain different mental processes associating with different connectivity patterns and flexibility in functional coordination between distinct brain systems (Kringelbach & Deco, 2020). State 1 was the most frequently reoccurring state with weak and diffuse connectivity between all ICNs, which resembles the static FNC patterns, signifying the average of a large number of additional states that are not sufficiently distinct or frequent to be separated and might be correlated with amount of self-focused thought (Viviano, Raz, Yuan, & Damoiseaux, 2017). The high occurrence of this state may suggest that the human brain prefers to be in a state with less information transfer but a more energy reservation pattern. State 2, characterized by synchrony within DMN and anticorrelation between DMN and CC, was likely to be associated with thinking about others and more thoughts about the past (Baggio et al., 2015). States 3 and 4 were characterized by highly synchronous connectivity within the sensory domains (i.e., AUD, SM, and VS) and antagonism with other functional domains. But their synchronous patterns were different. That is, a large DM module is present in state 3 while state 4 was characterized with strongly asynchronous activation between subcortical regions and sensorimotor cortex. These findings might indicate that as time continues, subjects are more likely to move from wakefulness to sleep from state 3 to state 4 (Hutchison & Morton, 2015). These dynamic connectivity states are nonrandom patterns and each potentially evoked or exploited to some degree by cognition. Maturation appears to lead to a brain with greater functional variability which can facilitate the formation of functional networks and more accurate performance and this variability may reflect a broader repertoire of metastable brain states and more fluid transitions among them that enable optimum responses (Baggio et al., 2015; Hutchison & Morton, 2015).

Heavy smokers spent more time in state 1 with weak connectivity dFNC patterns while non-smokers spent more time in a globally modularized state 2 with both positive and negative connectivity within and between networks which more resembles the pattern of connectivity as previously found in adult subjects’ studies (Allen et al., 2014). These results might imply that the organized state 2 in heavy smokers was transferred to more discrete and unorganized state 1. This phenomenon has also been observed in other psychiatric disorders, such as autism and schizophrenia (Damaraju et al., 2014; Rashid et al., 2018). The weaker intra/inter-FNC in heavy smokers may be interpreted as a reduced segregation/integration, that is, the flexibility of neural networks. Our previous results also verified disrupted small-world properties and thus less efficiency in heavy smokers (Lin et al., 2015). Therefore, these findings may indicate that heavy smokers are less frequently tapping into the resources that they have and are even less likely to efficiently use such connections and may have less capacity. Additionally, smoking may lead to the neuroplastic changes during the development of nicotine dependence (Shen et al., 2016), which was further confirmed by our additional results that the occurrence rate and mean dwell time in state 2 in heavy smokers were negatively correlated with duration of smoking. Recent literature has suggested an association between self-focused thought and the brain state with weak and diffused connectivity (Marusak et al., 2017). Heavy smokers showed less negative connectivity between DM and other networks in state 1 in our study may suggest that heavy smokers also have exaggerated self-focus, which may ultimately lead smokers to have difficulties interacting with non-smoking related cue stimuli.

NBS revealed that the CC domain involved in almost all impaired interconnected sub-networks in heavy smokers. The brain is constantly receiving information from multiple sensory channels, and it must ensure that only the information related to the target gets focused attention (Markram & Markram, 2010). This ability to efficiently process information and generate appropriate responses is known as cognitive control processes which is supported by the cognitive control network of the brain. Cognitive control deficits might lead to compulsive smoking seeking and use by attributing high motivational salience to drug-related stimuli from the onset of nicotine abuse to addiction and chronic relapse (Fan, 2014; Versace, Robinson, & Cinciripini, 2023). It is worth noting that CB domain played an important hub role in two of the hypo-connectivity sub-networks of state1 and 2. Specifically, the reduced FC were found between the CB and the CC, DM and AUD domains, which were consistent with previous studies (Niu et al., 2023; Shen et al., 2018). Each subregion of the cerebellum has been shown to have strong FC with each brain network such as the visual, auditory, sensorimotor, default-mode, fronto-parietal, task-positive, salience and limbic networks, and may act as a multimodal modulator to maintain the homeostatic balance of in the corticostriatal-limbic circuit involving in memory, reward/saliency, motivation/drive and cognitive control (Moulton, Elman, Becerra, Goldstein, & Borsook, 2014; Sang et al., 2012). The cerebellum has multiple nicotinic acetylcholine receptor subtypes, and repeated nicotine exposure can lead to alterations in synaptic plasticity which are crucial to the transition from a pattern of recreational drug taking to the compulsive behavioural phenotype (Miquel, Toledo, García, Coria-Avila, & Manzo, 2009). Our findings of cerebellum-based hypo-connectivity from a dynamic perspective, may provide evidence for the cerebellum regulating cognition, emotion, attention processing and decision-making associated with smokers and further emphasize the importance of incorporating the cerebellum into a part of cortical-limbic loops for drug addiction research.

Several issues in our study need to be mentioned. First, using blood oxygen level dependent fMRI, alterations of resting-state functional connectivity in heavy smokers might be a result of vascular and neuro-adaptations involved in chronic cigarette exposure (Duehlmeyer & Hester, 2019), thus further study should be performed to validate our findings. Second, alcohol consumption in the subjects was not assessed quantitatively using tools such as alcohol use disorders identification test. Although no subjects self-reported daily alcohol consumption, the possibility of false/deceptive reporting cannot be ruled out, as well as the effects of casual drinking. Third, we aimed to explore the chronic smoking effects on the brain functional networks in heavy smokers, and thus we only acquired information of heavy smokers at a spontaneous state rather than a craving state. Future work with information from craving state and determination of carbon monoxide level is needed to fully clarify the state of smokers.

In conclusion, we found altered FC dynamics of heavy smokers with different functional connectivity patterns and temporal properties of dFNC states which were negatively associated with smoking-related factors. In addition, NBS identified heavy smokers with impaired subnetworks and cognitive control and cerebellar domains played an important role among these impaired subnetworks. These findings of abnormal dFNC dynamics in heavy smokers may deepen the understanding of the pathological mechanisms of nicotine addiction.

Supplementary Information Supplementary data to this article can be found online.

Author contributions XQ: Visualization, Writing - original draft; and Writing - review & editing. GW: Conceptualization and Data curation. LZ: Data curation. FL: Conceptualization, Funding acquisition, Project administration, Super vision, Formal analysis, Writing - original draft; and Writing - review & editing.

Funding This work was supported by the Frontier Scientific Significant Breakthrough Project of CAS (grant number QYZDB-SSW-SLH046).

Data Availability The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethical approval The study was approved by the Medical Ethics Review Board of Zhongnan Hospital, Wuhan University and written informed consent was obtained after a complete description of the study.

Conflict of Interest The authors declare no competing interests.

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Altered brain dynamic functional network connectivity in heavy smokers

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Abstract

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Introduction

Methods

Subjects

Spatial GICA and ICN identification

Static and dynamic FNC calculation

Dynamic FNC state analysis

Between-group differences on sFNC and dFNC

Results

Demographic information

ICNs identification

Dynamic FNC states

Alterations of dFNC strengths and temporal properties of dFNC states

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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