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. Author manuscript; available in PMC: 2023 Dec 1.
Published in final edited form as: Addict Behav. 2022 Aug 12;135:107458. doi: 10.1016/j.addbeh.2022.107458

The association of amygdala-insula functional connectivity and adolescent e-cigarette use via sleep problems and depressive symptoms

Benjelene D Sutherland 1, Patricio M Viera Perez 1, Katharine E Crooks 1, Jessica S Flannery 3, Lauren D Hill-Bowen 1, Michael C Riedel 2, Angela R Laird 2, Elisa M Trucco 1,4,5, Matthew T Sutherland 1
PMCID: PMC9730909  NIHMSID: NIHMS1849926  PMID: 35998541

Abstract

Background:

Adolescent electronic cigarette (e-cigarette) use remains high. Elucidating contributing factors may enhance prevention strategies. Neurobiologically, amygdala-insula resting-state functional connectivity (rsFC) has been linked with aspects of sleep, affect, and substance use (SU). As such, we hypothesized that amygdala’s rsFC with the insula would be associated with e-cigarette use via sleep problems and/or depression levels.

Methods:

An adolescent sample (N=146) completed a rs-fMRI scan at time 1 and self-reports at time 2 (~15 months later). Given consistent associations between mental health outcomes and the rsFC of the laterobasal amygdala (lbAMY) with the anterior insula, we utilized a seed region (lbAMY) to region of interest (ROI) analysis approach to characterize brain-behavior relationships. Two serial mediation models tested the interrelations between amygdala’s rsFC with distinct anterior insula subregions (i.e., ventral insula [vI], dorsal insula [dI]), sleep problems, depression levels, and days of e-cigarette use.

Results:

An indirect effect was observed when considering the lbAMY’s rsFC with the vI. Greater rsFC predicted more sleep problems, more sleep problems were linked with greater depressive symptoms, and greater depressive symptoms were associated with more e-cigarette use (indirect effect=0.08, CI [0.01,0.21]). Indicative of a neurobiological dissociation, a similar indirect effect linking these variables was not observed when considering the lbAMY’s rsFC with the dI (indirect effect=0.03, CI [−0.001,0.10]).

Conclusions:

These outcomes highlight functional interactions between the amygdala and insula as a neurobiological contributor to sleep problems, depressive symptoms, and ultimately SU thereby suggesting potential intervention points to reduce teen e-cigarette use.

Keywords: e-cigarettes, adolescence, amygdala, insula, sleep problems, depression

1. INTRODUCTION

Adolescent e-cigarette use remains alarmingly high and continues to be a public health concern (U.S. Food and Drug Administration, 2018). While use rates declined by ~9% from 2020-levels, 22% of adolescents reported past-year e-cigarette use in 2021 (Johnston et al., 2022). These rates are concerning given that vaping is linked with potential impacts to the cardiovascular (Moheimani et al., 2017) and respiratory systems (Hamberger & Halpern-Felsher, 2020; Shin et al., 2019) as well as an increased risk of combustible cigarette use. Specifically, ~30% of adolescent e-cigarette users may transition to combustible cigarettes, compared to only 8% of e-cigarette non-users (Leventhal et al., 2015). Additionally, nicotine, found in most e-cigarettes, is addictive (England et al., 2021) and can impact the brain (Shin et al., 2019; Sutherland et al., 2016). Characterizing factors linked with adolescent e-cigarette use is critical to mitigate negative health outcomes.

Multiple reasons exist for the popularity of e-cigarettes among teens. Some adolescents may start using given the false belief that e-cigarettes are a safer tobacco alternative and are more socially acceptable (McKeganey et al., 2017; Romijnders et al., 2018), or to cope with negative affect (Cristello et al., 2020; Kong et al., 2019). Risk factors associated with adolescent e-cigarette use include being male, having a friend/family member who uses, being Hispanic/Latino(a), and specific temperament profiles (Carey et al., 2019; Hartmann et al., 2021; Jane Ling et al., 2022). However, enhanced understanding of factors associated with use remains necessary. This may facilitate reductions in use by informing prevention and intervention programming. Other potential factors linked with nicotine use include sleep problems (Riehm et al., 2019), depressive symptoms (Leventhal et al., 2016), and emotion-related neurocircuitry (e.g., amygdala, insula; Sutherland et al., 2013).

Adequate sleep is critical for mental and physical well-being and is vital for development (Kwon et al., 2019; Storfer-Isser et al., 2013). Yet most teens (~70%) do not get the recommended amount of sleep (National Center for Chronic Disease Prevention and Health Promotion, 2021). Adequate sleep may reduce the risk of internalizing problems (i.e., depression), cognitive difficulties, and even SU (Brown et al., 2002). Indeed, prior studies have identified an association between sleep and SU across the lifespan. Sleep problems during childhood appear predictive of subsequent adolescent cigarette use (Warren et al., 2017; Wong et al., 2009), sleep-related complaints by teens has been linked with e-cigarette use (Riehm et al., 2019), and subjective sleep quality from adult users appears predictive of e-cigarette use disorder (Zvolensky et al., 2020).

Depression is another risk factor for SU and is predictive of e-cigarette use onset among adolescents (Green et al., 2018; Leventhal et al., 2016; Trucco et al., 2018). Indeed, adolescents reporting more depressive symptoms are at a greater risk of using nicotine (Lechner et al., 2017) potentially given the belief that nicotine use will enhance mood (Leventhal et al., 2016). Adolescents may use e-cigarettes as a means to cope with negative emotions, which is consistent with a self-medication hypothesis (Khantzian, 1985; Weinstein & Mermelstein, 2013).

Associations between sleep problems and depression among adolescents are also commonly observed. Sleep disturbance may be a precursor to the development of depression, whereas depression appear to be less predictive of the development of sleep disturbances (Lovato & Gradisar, 2014). Sleep deprivation is also predictive of depression one year later among adolescents even after controlling for baseline depression (Roberts & Duong, 2014). Sleep deprivation is also linked with enhanced negative emotional reactivity and subdued positive emotional reactivity (Vandekerckhove & Cluydts, 2010; Zohar et al., 2005). Thus, a reasonable perspective is that sleep problems may contribute to elevated depression.

The amygdala, a key node in emotion-related neurocircuitry, is implicated in aspects of SU (Koob & Volkow, 2010; Sutherland et al., 2013). The amygdala assesses sensory stimuli and emotional significance, and supports emotional memory formation (Höistad & Barbas, 2008). The amygdala has been divided into at least three functionally distinct subregions: laterobasal amygdala (lbAMY), associated with high-level sensory input; centromedial amygdala (cmAMY), linked with attentional and motor responses; and superficial amygdala (sfAMY), linked with olfactory-gustatory processes (Bzdok et al., 2013). Developmental neuroimaging studies have highlighted a role for the amygdala in adolescent SU. For example, adolescents at risk for SU disorders exhibit left amygdala hyper-reactivity to emotional faces, potentially indicative of impaired affect regulation abilities (Lindsay et al., 2014).

Sleep problems and depressive symptoms represent a plausible link in the association between amygdala functioning and adolescent SU. The amygdala’s functional interactions with other emotion-related regions such as the anterior insula, assessed via resting-state functional connectivity (rsFC), has been associated with inadequate sleep and insomnia, and internalizing symptoms (Huang et al., 2012; Klumpp et al., 2018; Paulus & Stein, 2010). Indeed, anatomical connections exist between the amygdala and insula (Augustine, 1996; Höistad & Barbas, 2008; Mufson et al., 1981). Notably, associations between the amygdala, sleep, and internalizing symptoms have been found (Klumpp et al., 2018). Importantly, prior work has demonstrated a significant interaction between the anterior insula and sleep disturbance on depression (Kim et al., 2022). As sleep problems may increase the development of future depression and depression seems to be less predictive of the development of sleep problems (Lovato & Gradisar, 2014; Roberts & Duong, 2014), we hypothesized that elevated amygdala-insula rsFC would be predictive of later sleep problems which, in turn, would relate to higher levels of depression and ultimately e-cigarette use.

The interrelations between amygdala-centric rsFC, sleep problems, depressive symptoms, and e-cigarette use have not been examined among adolescents. Using a serial mediation model framework, we characterized the degree to which lbAMY-anterior insula rsFC related to e-cigarette use via sleep problems and depressive symptoms. Our focus on lbAMY-anterior insula rsFC was rooted in prior work that has linked the functioning of these regions with sleep and mental health outcomes (Baur et al., 2013; Huang et al., 2012; Kim et al., 2022). Moreover, as the insula integrates major functional systems, such as those related to emotions, sensorimotor, and olfaction-gustation and is composed of functionally distinct subregions (Kurth et al., 2010), we examined potential nuances across anterior insula subregions. Namely, we separately considered ventral (vI) and dorsal (dI) insula subregions as these areas have been differentiated (Kurth et al., 2010). Examining multiple mediators at the same time may help elucidate intervention and prevention avenues by targeting adolescents at higher risk for e-cigarette use.

2. METHODS

2.1. Participants

Data from a subsample of adolescents (n = 146; 48% female, 88% White, 84% Hispanic/Latino(a), Mage = 14.9 at initial visit) who had completed an MRI scan within a larger project (N = 264) were assessed. The subsample did not differ from the full study sample regarding demographics, sleep problems, depressive symptoms, or e-cigarette use (p’s range=0.16–0.96). Freshmen and sophomores enrolled in local schools, as well as caregivers, completed wave-1 (W1; adolescent’s age range=13–17) and wave-2 (W2; ~15 months later; adolescent’s age range=15–18) of the larger study examining factors impacting e-cigarette use (Cristello et al., 2020; Hartmann et al., 2021; Trucco et al., 2021). W1 data were collected from March 2018 through December 2019, and W2 data were collected from June 2019 to March 2021. Exclusionary criteria included: a learning disorder, intellectual/physical disability, neurological diseases, severe mental illness, and English non-fluency. MRI scanning exclusion criteria included: left-handedness, non-removable metal, claustrophobia, and for females, pregnancy.

2.2. Procedures

Recruitment was conducted at public schools. Families interested in participating completed an eligibility screen. Those meeting criteria were scheduled for W1 data collection. W1 involved two in-person visits. During visit W1a, participants completed set of questionnaires. Adolescent questionnaires took ~90min to complete while caregiver questionnaires took ~45min. Following providing consent/assent, adolescents and caregivers were escorted to separate rooms to ensure confidentiality. During visit W1b (scheduled within a month of W1a), adolescents completed additional questionnaires (~15min) and an MRI session (~90min). The MRI session consisted of two tasks (see Supplemental Information for task details) and a functional resting-state fMRI (rs-fMRI) scan completed between the two tasks. W2 assessments were conducted ~15 months after W1a via similar procedures. Due to COVID-19 restrictions, some appointments were remotely. Questionnaires were administered on an iPad during in-person visits and on personal electronic devices during remote visits using REDCap (Research Electronic Data Capture; Harris et al., 2019; Harris et al., 2009). Participants were compensated and the University’s Institutional Review Board approved study procedures.

2.3. Measures

Youth Self Report (YSR).

To quantify sleep problems and depressive symptoms, raw scores from the YSR of the Achenbach System of Empirical Behavioral Assessment (ASEBA; Achenbach & Rescorla, 2001) were used. Items were rated on a 3-point Likert scale (0=not true, 2=very true/often true). Sleep problems were derived by summing five items (“I have nightmares”, “I feel overtired without good reason”, “I sleep less than most kids”, “I sleep more than most kids during the day and/or night”, “I have trouble sleeping”; Cronbach’s α=0.47). While neither the YSR nor the parent-report version of the YSR (i.e., ASEBA CBCL) include a validated sleep scale, these sleep-related items have been associated with other validated sleep measures (Becker et al., 2015), and they have been used to assess sleep problems among adolescents and children (Poznanski et al., 2018; Thompson et al., 2020). While acknowledging that the internal consistency for this scale is low, the Cronbach’s α estimate is consistent with prior work employing a similar strategy quantifying sleep problems among adolescents and children (Becker et al., 2015; Fava et al., 2022; Narmandakh et al., 2020). The YSR’s withdrawn/depressed subscale, comprised of eight items (e.g., “I am unhappy, sad or depressed”; Cronbach’s α=0.77), quantified depressive symptoms. This subscale has shown consistency with other validated measures of depression (Ivarsson et al., 2002; Youngstrom et al., 2021). The YSR was administered to participants at both W1 and W2 allowing for sleep problems and depressive symptoms at W1 to serve as covariates.

Adolescent e-cigarette use.

Use was assessed at W2 using an item adapted from the Population Assessment of Tobacco and Health Survey (PATH) study, which assesses adolescent SU at the national level (Hyland et al., 2016). Utilizing a continuous variable to account for variation in e-cigarette use behavior, participants answered, “Since your last visit, on how many days did you use an Electronic Nicotine Delivery System (ENDS) product?” Participants were able to enter a value spanning 0 days to 455 days of use. Of note, national studies such as Monitoring the Future (Johnston et al., 2022) also adopt single items to assess SU. Prior lifetime e-cigarette use was also assessed at W1 via a dichotomous question (“Have you ever used an ENDS product, such as NJOY, Blu, Smoking Everywhere, a vape pen, or a vape mod, even one or two times?”), and served as a covariate to get a proxy of e-cigarette use initiation based on prior work recommending that baseline measures of outcomes be included in data analytic models to reduce biased estimates (Landau et al., 2018).

2.4. Neuroimaging Data Acquisition and Preprocessing

Whole-brain blood oxygenation level-dependent (BOLD) echo-planar imaging (EPI) data were acquired on a 3T Siemens MAGNETOM Prisma scanner equipped with a 32-channel head coil. Participants completed a 10min rs-fMRI run with eyes closed. During rest, 60 slices (2.4mm thick) were obtained in the transverse plane (750 volumes, repetition time=800ms, echo time=30ms, flip angle=52°, voxel size=2.4mm3; field of view=216mm, 90×90 matrix, multiband acceleration=6). Structural T1-weighted images (T1w) were acquired using a magnetization prepared rapid gradient-echo sequence (MPRAGE: TR=2,500ms; TE=2.9ms; flip angle=8°; voxel size=1mm3).

Neuroimaging data were preprocessed using fMRIPrep 20.2.1 (Esteban, Blair, et al., 2018; Esteban, Markiewicz, et al., 2018; RRID: SCR_016216), and analyzed with AFNI (Cox & Hyde, 1997). For detailed MRI data preprocessing, see Supplemental Information.

2.5. Functional data analysis

Separate first-level rsFC analyses were conducted for three seed regions in the left amygdala. lbAMY, cmAMY, and sfAMY were defined using a parcellation based on a meta-analytic connectivity modeling framework (Bzdok et al., 2013). These seeds were resampled to each participants’ rs-fMRI data space. Timeseries from voxels within each seed were averaged and extracted from each participant’s unsmoothed data. Data were subsequently smoothed to 4mm FWHM (3dBlurToFWHM). The three seeds’ average time-series were entered into a whole-brain multiple regression analysis (3dDeconvole & 3dREMLfit, AFNI). Connectivity maps representing each amygdala subregion’s rsFC with respect to (i.e., controlling for) the rsFC of the other two amygdala subregions were generated.

Whole-brain one-sample t-tests (two-tailed, 3dMEMA) were used to create overall rsFC maps for each amygdala seed (e.g., Figure 1a). Pairwise dependent-samples t-tests (two-tailed, 3dTtest++) were conducted to compare amygdala subregion rsFC (e.g., lbAMY vs. cmAMY rsFC maps; Figure 1b). Following preliminary group-level rsFC analyses for each amygdala subregion, the functional connections between the lbAMY seed and both the vI and dI subregions of interest were quantified given our a priori focus. The vI and dI ROIs were defined using Chang and colleagues (2013) parcellation based on neuroimaging metanalytic work assessing a variety of tasks in the literature (Chang et al., 2013). The average z-values (i.e., rsFC values) from the vI and dI ROIs (Figure 2a) were extracted from the left lbAMY rsFC map using AFNI’s 3dROIstats for graphical assessment and follow-up analyses.

Figure 1. Differential amygdala subregion resting-state functional connectivity (rsFC).

Figure 1.

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(a) Whole brain rsFC of laterobasal (1; pFWE-corrected < 0.05, pvoxel-wise=1.0–9), centromedial (2; pFWE-corrected < 0.05, pvoxel-wise=1.0–4) and superficial (3; pFWE-corrected < 0.05, pvoxel-wise=1.−11) amygdala seed regions. (b) Pairwise comparisons between amygdala subregions highlighted significant rsFC differences between (1) laterobasal versus centromedial, (2) centromedial versus superficial, and (3) superficial versus laterobasal amygdala subregions (pFWE-corrected <0.05, pvoxel-wise=1.0–5).

Figure 2. Serial mediation model linking amygdala-insula rsFC, sleep problems, depressive symptoms, and e-cigarette use.

Figure 2.

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(a) We utilized a seed region to region of interest (ROI) analysis strategy to a priori focus assessment on the left laterobasal amygdala’s [blue] rsFC with the anterior insula (both dorsal [dI, pink] and ventral [vI, yellow] areas). (b) A significant indirect effect from the serial mediation model indicated that higher amygdala-insula rsFC was linked with more e-cigarette use via sleep problems and depressive symptoms when considering the ventral (top), but not the dorsal anterior insula subregions, *p < 0.05, **p < 0.01, ***p < 0.001; W1=Wave 1; W2=Wave 2.

2.6. Data Analytic Plan

Serial mediation analyses with two mediators were conducted using the PROCESS macro version 3.3 (Hayes, 2019) for SASv9.4 (SAS Institute, 2002–2012). Two separate serial mediation models considered whether a participant’s self-reported sleep problems at W2 (M1) and depressive symptoms (M2) mediated the relationship between lbAMY-anterior insula rsFC subregions (vI, dI) at W1 (X) and e-cigarette use at W2 (Y). Apart from estimating indirect effects with bootstrapped confidence intervals (CIs) for serial mediation, the PROCESS macro also provides indirect effects with CIs for the simple mediation pathways (Hayes, 2018). Study variables were normally distributed (skewness range=0.29–0.79, kurtosis range=−1.72–1.47) apart from e-cigarette use at W2 (skewness=5.08, kurtosis=27.36). Given the non-normality of this variable, a logarithm transformation was applied (Kline, 2016). Descriptive statistics and zero-order correlations were calculated for all variables. The following covariates were included: W1 sleep problems, W1 depressive symptoms, W1 lifetime e-cigarette use, biological sex, age, ethnicity, and mean framewise displacement (FD) values to account for in-scanner head motion (Burgess et al., 2016; Power et al., 2012); see Supplemental Information, Figure S1 for covariation paths).

3. RESULTS

Table 1 displays means, standard deviations, and correlations for study variables. We observed that e-cigarette use (since W1) was positively correlated with lifetime e-cigarette use, as well as self-reported sleep problems and depressive symptoms assessed at W2. Depressive symptoms at W2 positively correlated with sleep problems at both W1 and W2, as well as depressive symptoms at W1. Notably, sleep problems at W2 positively correlated with left lbAMY-vI and lbAMY-dI rsFC, as well as sleep problems assessed at W1.

Table 1.

Means, Standard Deviations, and Correlations for Study Variables.

M SD Correlations
Study Variables 1 2 3 4 5 6 7 8 9 10 11 12
1. Age (W1a) 14.90 0.68 –
2. Sexa (W1a) 0.49 0.50 0.02 –
3. Ethnicityb 0.84 0.36 0.08 −0.12 * –
4. Lifetime E-Cig. Usec (W1a) 0.33 0.47 0.07 0.03 0.10 –
5. Sleep Problems (W1a) 0.56 0.38 −0.01 −0.08 0.08 0.13 * –
6. Depressive Symptoms (W1a) 4.26 2.86 −0.13 * −0.08 0.03 0.14 * 0.52 *** –
7. FD Mean (W1b) 0.16 0.07 −0.25 *** 0.09 −0.22 ** −0.12 0.09 0.05 –
8. lbAMY-vI rsFC (W1b) 0.28 0.38 0.10 0.01 −0.02 −0.02 0.01 0.10 −0.06 –
9. lbAMY-dI rsFC (W1b) 0.34 0.52 0.13 0.04 0.13 0.05 0.09 0.16 * 0.08 0.59 *** –
10. E- Cig. Use Since W1a (W2) 10.93 43.43 0.07 −0.01 0.04 0.29 ** 0.10 0.05 0.07 0.13 0.09 –
11. Sleep Problems (W2) 0.50 0.37 −0.01 −0.11 0.01 −0.01 0.60 *** 0.45 *** 0.06 0.23 ** 0.17 * 0.17 * –
12. Depressive Symptoms(W2) 4.11 2.99 −0.04 −0.10 0.01 0.08 0.38 *** 0.59 *** 0.07 0.12 0.12 0.18 * 0.59 *** –
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Note.

*

p < 0.05

**

p < 0.01

***

p < 0.001

W1a=Wave 1, Visit 1; W1b=Wave 1, Visit 2; W2=Wave 2

a

Female=0 (51%, n = 134), Male=1 (49%, n = 130)

b

non-Hispanic/Latino(a)=0 (16%, n = 41), Hispanic/Latino(a)=1 (84%, n = 223)

c

Non-User=0 (67%, n = 176), User=1 (33%, n = 88)

sample information based on larger on-going project’s W1a sample size (N = 264).

3.1. Amygdala subregion rsFC

Assessment of whole-brain rsFC maps from the three amygdala subregions revealed patterns largely consistent with prior reports (Figure 1a). The lbAMY demonstrated prominent rsFC with medial prefrontal, temporal, and parietal regions; the cmAMY with the striatum, thalamus, insula, dorsal ACC, and cerebellum; and the sfAMY with the posterior insula and hippocampus (Roy et al., 2009). Assessment of pairwise comparisons between amygdala subregion rsFC maps further highlighted dissociations in connectivity patterns (Figure 1b). For example, the lbAMY (blue) demonstrated significantly greater rsFC with temporal regions relative to the other subregions; the cmAMY (red) demonstrated greater connectivity with limbic and cerebellar regions; and the sfAMY (green) showed greater rsFC with the hippocampus

3.2. Serial Mediation Models for amygdala-insula rsFC impact on e-cigarette use

A significant indirect effect was observed when considering the influence of lbAMY-vI rsFC on e-cigarette use via sleep problems and depressive symptoms (Figure 2b[top], Table 2). First, lbAMY-vI rsFC at W1 was a significant predictor of sleep problems at W2 (effect=0.22, p=0.001), such that greater rsFC indicated more sleep problems. Second, sleep problems (but not rsFC) were significantly related to depressive symptoms (effect=3.32, p<0.001), such that more sleep problems indicated greater depressive symptoms. Lastly, depressive symptoms (but not rsFC nor sleep problems) were significantly related to e-cigarette use (effect=0.11, p=0.002), such that more depressive symptoms indicated more days of e-cigarette use. Taken together, these outcomes evidenced a significant indirect effect of lbAMY-vI rsFC (X) on e-cigarette use (Y) through sleep problems (M1) and depressive symptoms (M2; indirect effect=0.08, CI [0.01,0.21]).

Table 2.

Regression Coefficients, Standard Errors, and Summary Information of the Laterobasal Amygdala-Ventral Insula (lbAMY-vI) Serial Mediation Model Predicting E-Cigarette Use.

Sleep Problems W2 (M1) Depression W2 (M2) E-Cigarette Use W2 (Y)
Coefficient (95% CI) SE t Value Coefficient (95% CI) SE t Value Coefficient (95% CI) SE t Value
Intercept 0.11 (−1.05, 1.27) 0.59 0.19 1.65 (−7.23, 10.52) 4.49 0.37 −2.84 (−7.59, 1.91) 2.40 −1.18
Age −0.002 (−0.08, 0.07) 0.04 −0.05 −0.08 (−0.65, 0.49) 0.29 −0.29 0.17 (−0.14, 0.47) 0.15 1.09
Sexa −0.01 (−0.11, 0.08) 0.05 −0.30 −0.08 (−0.83, 0.67) 0.38 −0.22 0.11 (−0.29, 0.51) 0.20 0.53
Ethnicityb −0.08 (−0.22, 0.06) 0.07 −1.12 0.19 (−0.89, 1.27) 0.55 0.35 −0.26 (−0.84, 0.31) 0.29 −0.90
Lifetime E-Cig. Use (W1a) −0.01 (−0.11, 0.10) 0.05 −0.15 0.20 (−0.61, 1.01) 0.41 0.50 0.80 *** (0.37, 1.24) 0.22 3.66
Sleep Problems (W1a) 0.46 *** (0.30, 0.62) 0.08 5.61 −0.62 (−1.99, 0.76) 0.70 −0.88 0.25 (−0.49, 0.98) 0.37 0.66
Depressive Symptoms(W1a) 0.03 * (0.01, 0.05) 0.01 2.46 0.48 *** (0.32, 0.64) 0.08 5.98 −0.04 (−0.14, 0.05) 0.05 −0.89
FD Mean (W1b) 0.37 (−0.35, 1.08) 0.36 1.01 0.75 (−4.74, 6.25) 2.78 0.27 1.91 (−1.03, 4.86) 1.49 1.29
lbAMY-vI rsFC (W1b) 0.22 *** (0.10, 0.35) 0.06 3.48 0.02 (−0.99, 1.03) 0.51 0.04 0.24 (−0.30, 0.78) 0.27 0.88
Sleep Problems (W2) - - - - 3.32 *** (2.03, 4.61) 0.65 5.09 −0.10 (−0.85, 0.65) 0.38 −0.27
Depressive Symptoms(W2) - - - - - - - - 0.11 * (0.02, 0.20) 0.05 2.44
R2 = 0.39 R2 = 0.47 R2 = 0.18
F (8, 137) = 10.86, p < 0.001 F (9, 136) = 13.53, p < 0.001 F (10, 135) = 3.00, p = 0.002
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Note.

*

p < 0.05

**

p < 0.01

***

p < 0.001.

lbAMY-vI=resting-state functional connectivity between the laterobasal amygdala and ventral insula; W1a=Wave 1-Visit 1; W1b=Wave 1-Visit 2; W2=Wave 2

a

Female=0, Male=1

b

non-Hispanic/Latino(a)=0, Hispanic/Latino(a)=1

FD Mean=mean framewise displacement.

In contrast, we did not observe a significant indirect effect when considering lbAMY-dI rsFC (Figure 2b[bottom], Supplemental Information Table S1). Specifically, lbAMY-dI rsFC at W1 was not a significant predictor of sleep problems at W2 (effect=0.09, p=0.06). As such, this model did not detect a significant indirect effect of lbAMY-dI on e-cigarette use via sleep problems and depression (indirect effect=0.03, CI [−0.001,0.10]).

3.3. Sensitivity Analyses

Given the YSR-derived sleep problems score possessed low internal consistency (Cronbach’s α=0.47), we conducted ancillary analyses utilizing an alternative measure (i.e., Adolescent Sleep Hygiene Scale; (Storfer-Isser et al., 2013) which was collected only at W2. Specifically, the ASHS’s cognitive/emotional subscale was used as prior work has demonstrated that less pre-sleep cognitive/emotional arousal among adolescents is linked with earlier bedtime routines, decreased sleep latency, and longer sleep (Bartel et al., 2016). We arrived at similar outcomes and interpretations as those described above for the YSR-derived variable (Supplemental Information Figure S2, Tables S2 and S3). Given the YSR was administered at both W1 and W2, we elected to present those outcomes in the main text since W1-values were used as a covariate.

4. DISCUSSION

Adolescent e-cigarette use remains problematic (Johnston et al., 2022) and elucidating associated factors may help reduce use rates. Individual differences in emotion-related neurocircuitry (e.g., amygdala, insula) may confer risk for adolescent SU (Chaplin et al., 2019; Hardee et al., 2017; Lindsay et al., 2014). This study bridged multiple lines of research and examined a pathway through which amygdala-insula rsFC may contribute to e-cigarette use by considering the potential mediating roles of sleep problems and depressive symptoms. The main objective of the current study was to examine a continuous measure of early stages of e-cigarette use by assessing the role of amygdala-insula rsFC, sleep problems, and depression on e-cigarette use controlling for lifetime e-cigarette use. Namely, controlling for the baseline measure of a continuous clinical outcome variable can reduce biased estimates (Landau et al., 2018). Hence, the inclusion of lifetime e-cigarette use at wave 1 as a proxy of initiation.

We first observed that amygdala subregion rsFC patterns were largely consistent with prior reports (Roy et al., 2009). Additionally, we observed that greater lbAMY-vI rsFC predicted more sleep problems, more sleep problems were associated with greater depressive symptoms, and that greater depressive symptoms were associated with more e-cigarette use. Our outcomes indicated that left lbAMY-vI rsFC was not directly associated with e-cigarette use, rather other factors mediated this association. Prior work has suggested that the link between amygdala-insula functioning and SU may operate via negative affect or internalizing pathways (Chaplin et al., 2019; Hardee et al., 2017). Indeed, lbAMY-anterior insula rsFC is linked to internalizing symptoms (Baur et al., 2013; Paulus & Stein, 2010). Yet, our findings showed that lbAMY-anterior insula rsFC was not directly associated to subsequent depression; rather, sleep problems linked this relationship. Importantly, an interaction between the anterior insula and sleep disturbance on depressive symptoms has been previously found (Kim et al., 2022). Moreover, it has been suggested that sleep modulates amygdala rsFC in depression (Klumpp et al., 2018). Plausibly, the link between amygdala-insula and depression is explained by other factors like sleep problems. Our findings expand on previous work by demonstrating that lbAMY-vI predicted sleep problems and that in turn was associated with depressive symptoms.

Noteworthy, there was a lack of support for simple mediation via sleep problems or depressive symptoms in the association between amygdala-insula rsFC and e-cigarette use. However, as expected, there was support for serial mediation whereby sleep problems were linked to depressive symptoms, which in turn were associated with more days of e-cigarette use, but only for the left lbAMY-vI model. A non-significant association was observed when considering the influence of lbAMY-dI on sleep problems, perhaps suggesting the relative importance of the anterior vI. The anterior insula has been linked with inadequate sleep and insomnia (Huang et al., 2012; Klumpp et al., 2018), and elevated anterior insula activity is linked with higher depression among individuals reporting insomnia and sleep disturbances (Chen et al., 2014; Kim et al., 2022). However, most work has focused on the anterior insula as a whole and not the potential nuances across anterior-ventral and anterior-dorsal insula subregions. Importantly, the anterior-vI has been associated with social-emotional tasks and affective processes whereas the anterior-dI has been linked to cognitive task performance (Kurth et al., 2010).

Our findings suggest that altered left lbAMY-vI rsFC may contribute to sleep problems, such sleep problems may increase depressive symptoms, and higher levels of depressive symptoms contribute to more days of e-cigarette use. These observations are consistent with prior work linking amygdala-insula functioning with sleep problems (Chen et al., 2014; Huang et al., 2012; Klumpp et al., 2018), work linking sleep problems with depression among adolescents (Lovato & Gradisar, 2014), and work linking hyperactivity of the left anterior insula to negatively valenced emotional stimuli and adolescent SU (Chaplin et al., 2019). We suggest that adolescents with elevated left lbAMY-vI rsFC, sleep problems, and/or high depressive symptoms may use e-cigarettes as a means to cope with their symptomatology.

4.1. Limitations

While clear associations were observed between left lbAMY-anterior insula rsFC (W1) with sleep problems, depressive symptoms, and e-cigarette use, bidirectional relationships may exist among W2 variables. Namely, bidirectional associations have been observed among sleep, depression, and e-cigarette use (Lechner et al., 2017; Lovato & Gradisar, 2014; Merianos et al., 2021). Including at least one additional timepoint to account for temporal precedence is critical for future work to further elucidate causality (e.g., replicate prior work on sleep problems as a precursor of future depression; (Lovato & Gradisar, 2014). Second, this sample consisted of primarily Hispanic/Latino(a) adolescents, who appear to be at increased risk for clinical syndromes (Anderson & Mayes, 2010), and for e-cigarette initiation at an early age compared to other ethnicities (Lanza et al., 2017). As such, our sample may have reported higher depression and e-cigarette use and the degree to which results may generalize to students from other locations remains unclear. Third, this study used self-report measures and individuals may demonstrate biased perceptions of their sleep, emotional states, and SU. Factors related to cognitive (e.g., understanding of the question) or situational factors (e.g., stigma) may have impacted adolescent’s self-reports of e-cigarette use (Brener et al., 2003). Fourth, specific stages of SU should be examined as our findings could be interpreted as initiation, maintenance, and/or escalation of use. A dichotomous variable to control for baseline e-cigarette use may not have been sensitive enough for our analyses to get a proxy of initiation. Limiting analyses to a e-cigarette use naïve youth sample could help to disentangle nuances between onset and maintenance of use. Finally, future work should consider examining sleep problems via additional sleep measures given the low internal consistency of the YSR sleep scale.

4.2. Clinical implications

Identifying neurobiological differences linked with adolescent e-cigarette use could help increase the armamentarium of behavioral and pharmacologic therapies to decrease sleep problems and depressive symptoms that increase risk for e-cigarette use. Moreover, these outcomes suggest that promoting healthy sleep, especially among adolescents with symptoms of depression, may have utility in reducing e-cigarette use. Fostering sleep hygiene, such as bedtime routines and limiting the use of technology prior to bedtime may provide benefits to adolescents with sleep problems. (Hall & Nethery, 2019). This could reduce the risk of e-cigarette use onset and/or continuation.

4.3. Conclusion

This study provides enhanced insight into potential neurobiological, sleep-related, and mental health factors associated with adolescent e-cigarette use. We observed that adolescents with higher rsFC between the amygdala and anterior insula reported greater sleep problems, and, in turn higher depression levels, and ultimately more days of e-cigarette use.

Supplementary Material

Supplementary material

5. REFERENCES

  1. Achenbach TM, & Rescorla LA (2001). Youth Self-Report for ages 11–18. Burlington, VT: ASEBA. [Google Scholar]
  2. Anderson ER, & Mayes LC (2010). Race/ethnicity and internalizing disorders in youth: A review. Clinical Psychology Review, 30, 338–348. 10.1016/j.cpr.2009.12.008 [DOI] [PubMed] [Google Scholar]
  3. Augustine J (1996). Circuitry and functional aspects of the insular lobe in primates including humans. Brain Research Reviews, 22(3), 229–244. 10.1016/s0165-0173(96)00011-2 [DOI] [PubMed] [Google Scholar]
  4. Avants B, Epstein C, Grossman M, & Gee J (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41. 10.1016/j.media.2007.06.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bartel K, Williamson P, Van Maanen A, Cassoff J, Meijer AM, Oort F, Knäuper B, Gruber R, & Gradisar M (2016). Protective and risk factors associated with adolescent sleep: findings from Australia, Canada, and The Netherlands. Sleep Medicine, 26, 97–103. 10.1016/j.sleep.2016.07.007 [DOI] [PubMed] [Google Scholar]
  6. Baur V, Hänggi J, Langer N, & Jäncke L (2013). Resting-state functional and structural connectivity within an insula–amygdala route specifically index state and trait anxiety. Biological Psychiatry, 73(1), 85–92. 10.1016/j.biopsych.2012.06.003 [DOI] [PubMed] [Google Scholar]
  7. Becker SP, Ramsey RR, & Byars KC (2015). Convergent validity of the Child Behavior Checklist sleep items with validated sleep measures and sleep disorder diagnoses in children and adolescents referred to a sleep disorders center. Sleep Medicine, 16(1), 79–86. 10.1016/j.sleep.2014.09.008 [DOI] [PubMed] [Google Scholar]
  8. Brener ND, Billy JOG, & Grady WR (2003). Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: evidence from the scientific literature. J Adolesc Health, 33(6), 436–457. 10.1016/s1054-139x(03)00052-1 [DOI] [PubMed] [Google Scholar]
  9. Brown FC, Buboltz WC, & Soper B (2002). Relationship of Sleep Hygiene Awareness, Sleep Hygiene Practices, and Sleep Quality in University Students. Behavioral Medicine, 28(1), 33–38. 10.1080/08964280209596396 [DOI] [PubMed] [Google Scholar]
  10. Burgess GC, Kandala S, Nolan D, Laumann TO, Power JD, Adeyemo B, Harms MP, Petersen SE, & Barch DM (2016). Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project. Brain Connectivity, 6(9), 669–680. 10.1089/brain.2016.0435 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bzdok D, Laird AR, Zilles K, Fox PT, & Eickhoff SB (2013). An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Human Brain Mapping, 34(12), 3247–3266. 10.1002/hbm.22138 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Carey FR, Rogers SM, Cohn EA, Harrell MB, Wilkinson AV, & Perry CL (2019). Understanding susceptibility to e-cigarettes: A comprehensive model of risk factors that influence the transition from non-susceptible to susceptible among e-cigarette naïve adolescents. Addictive Behaviors, 91, 68–74. 10.1016/j.addbeh.2018.09.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chang LJ, Yarkoni T, Khaw MW, & Sanfey AG (2013). Decoding the Role of the Insula in Human Cognition: Functional Parcellation and Large-Scale Reverse Inference. Cerebral Cortex, 23(3), 739–749. 10.1093/cercor/bhs065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chaplin TM, Poon JA, Thompson JC, Hansen A, Dziura SL, Turpyn CC, Niehaus CE, Sinha R, Chassin L, & Ansell EB (2019). Sex-differentiated associations among negative parenting, emotion-related brain function, and adolescent substance use and psychopathology symptoms. Social Development, 28(3), 637–656. 10.1111/sode.12364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chen MC, Chang C, Glover GH, & Gotlib IH (2014). Increased insula coactivation with salience networks in insomnia. Biological Psychology, 97, 1–8. 10.1016/j.biopsycho.2013.12.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cox RW, & Hyde JS (1997). Software tools for analysis and visualization of fMRI data. NMR in Biomedicine, 10, 171–178. [DOI] [PubMed] [Google Scholar]
  17. Cristello JV, Sutherland MT, & Trucco EM (2020). A preliminary validation of the adolescent e-cigarette consequences questionnaire. Drug and Alcohol Dependence, 213, 108118. 10.1016/j.drugalcdep.2020.108118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dale AM, Fischl B, & Sereno MI (1999). Cortical Surface-Based Analysis. NeuroImage, 9(2), 179–194. 10.1006/nimg.1998.0395 [DOI] [PubMed] [Google Scholar]
  19. England KJ, Edwards AL, Paulson AC, Libby EP, Harrell PT, & Mondejar KA (2021). Rethink Vape: Development and evaluation of a risk communication campaign to prevent youth E-cigarette use. Addictive Behaviors, 113, 106664. 10.1016/j.addbeh.2020.106664 [DOI] [PubMed] [Google Scholar]
  20. Esteban O, Blair R, Markiewicz CJ, Berleant SL, Moodie C, Ma F, Isik AI, & et al. (2018). FMRIPrep. In Software. Zenodo. [Google Scholar]
  21. Esteban O, Markiewicz C, Blair RW, Moodie C, Isik AI, Aliaga AE, Kent J, & et al. (2018). fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI. In Nature Methods. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fava NM, Meldrum RC, Villar MG, Zucker RA, & Trucco EM (2022). Adverse childhood experiences, sleep problems, low self-control, and adolescent delinquency: A longitudinal serial mediation analysis. Development and Psychopathology, 1–10. 10.1017/s0954579422000530 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fedota JR, Sutherland MT, Salmeron BJ, Ross TJ, Hong LE, & Stein EA (2015). Reward anticipation is differentially modulated by varenicline and nicotine in smokers. Neuropsychopharmacology, 40, 2038–2046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Green VR, Conway KP, Silveira ML, Kasza KA, Cohn A, Cummings KM, Stanton CA, Callahan-Lyon P, Slavit W, Sargent J, Hilmi N, Niaura R, Reissig CJ, Lambert E, Zandberg I, Brunette MF, Tanski SE, Borek N, Hyland A, & Compton WM (2018). Mental health problems and onset of tobacco use among 12–24 year-olds in the PATH study. J Am Acad Child Adolesc Psychiatry, 57(12), 944–954. 10.1016/j.jaac.2018.06.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Greve DN, & Fischl B (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72. 10.1016/j.neuroimage.2009.06.060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hagler DJ, Hatton S, Cornejo MD, Makowski C, Fair DA, Dick AS, Sutherland MT, Casey BJ, Barch DM, Harms MP, Watts R, Bjork JM, Garavan HP, Hilmer L, Pung CJ, Sicat CS, Kuperman J, Bartsch H, Xue F, Heitzeg MM, Laird AR, Trinh TT, Gonzalez R, Tapert SF, Riedel MC, Squeglia LM, Hyde LW, Rosenberg MD, Earl EA, Howlett KD, Baker FC, Soules M, Diaz J, De Leon OR, Thompson WK, Neale MC, Herting M, Sowell ER, Alvarez RP, Hawes SW, Sanchez M, Bodurka J, Breslin FJ, Morris AS, Paulus MP, Simmons WK, Polimeni JR, Van Der Kouwe A, Nencka AS, Gray KM, Pierpaoli C, Matochik JA, Noronha A, Aklin WM, Conway K, Glantz M, Hoffman E, Little R, Lopez M, Pariyadath V, Weiss SR, Wolff-Hughes DL, Delcarmen-Wiggins R, Feldstein Ewing SW, Miranda-Dominguez O, Nagel BJ, Perrone AJ, Sturgeon DT, Goldstone A, Pfefferbaum A, Pohl KM, Prouty D, Uban K, Bookheimer SY, Dapretto M, Galvan A, Bagot K, Giedd J, Infante MA, Jacobus J, Patrick K, Shilling PD, Desikan R, Li Y, Sugrue L, Banich MT, Friedman N, Hewitt JK, Hopfer C, Sakai J, Tanabe J, Cottler LB, Nixon SJ, Chang L, Cloak C, Ernst T, Reeves G, Kennedy DN, Heeringa S, Peltier S, Schulenberg J, Sripada C, Zucker RA, Iacono WG, Luciana M, Calabro FJ, Clark DB, Lewis DA, Luna B, Schirda C, Brima T, Foxe JJ, Freedman EG, Mruzek DW, Mason MJ, Huber R, McGlade E, Prescot A, Renshaw PF, Yurgelun-Todd DA, Allgaier NA, Dumas JA, Ivanova M, Potter A, Florsheim P, Larson C, Lisdahl K, Charness ME, Fuemmeler B, Hettema JM, Maes HH, Steinberg J, Anokhin AP, Glaser P, Heath AC, Madden PA, Baskin-Sommers A, Constable RT, Grant SJ, Dowling GJ, Brown SA, Jernigan TL, & Dale AM (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage, 202, 116091. 10.1016/j.neuroimage.2019.116091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hall WA, & Nethery E (2019). What does sleep hygiene have to offer children’s sleep problems? Paediatric Respiratory Reviews, 31, 64–74. 10.1016/j.prrv.2018.10.005 [DOI] [PubMed] [Google Scholar]
  28. Hamberger ES, & Halpern-Felsher B (2020). Vaping in adolescents: epidemiology and respiratory harm. Curr Opin Pediatr, 32(3), 378–383. 10.1097/MOP.0000000000000896 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hardee JE, Cope LM, Munier EC, Welsh RC, Zucker RA, & Heitzeg MM (2017). Sex differences in the development of emotion circuitry in adolescents at risk for substance abuse: a longitudinal fMRI study. Social Cognitive and Affective Neuroscience, 12(6), 965–975. 10.1093/scan/nsx021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, McLeod L, Delacqua G, Delasqua F, Kirby J, Duda SN, & consortium R (2019). The REDCap consortium: Building an international community of software partners. J Biomed Inform. 10.1016/j.jbi.2019.103208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Harris PA, Taylor R, Thielke R, Payne L, Gonzalez N, & Conde JG (2009). Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform, 42(2), 377–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hartmann SA, Hayes T, Sutherland MT, & Trucco EM (2021). Risk factors for early use of e-cigarettes and alcohol: Dimensions and profiles of temperament. Development and Psychopathology, 1–13. 10.1017/s0954579421001565 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hayes AF (2018). Introduction to mediation, moderation, and conditional process analysis. A regression-based approach (Second ed.). Copyright © 2018 The Guilford Press. [Google Scholar]
  34. Hayes AF (2019). PROCESS version 3.3 for SAS. In Copyright 2019 by Andrew F. Hayes. [Google Scholar]
  35. Höistad M, & Barbas H (2008). Sequence of information processing for emotions through pathways linking temporal and insular cortices with the amygdala. NeuroImage, 40(3), 1016–1033. 10.1016/j.neuroimage.2007.12.043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hu Y, Chen X, Gu H, & Yang Y (2013). Resting-State Glutamate and GABA Concentrations Predict Task-Induced Deactivation in the Default Mode Network. Journal of Neuroscience, 33(47), 18566–18573. 10.1523/jneurosci.1973-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Huang Z, Liang P, Jia X, Zhan S, Li N, Ding Y, Lu J, Wang Y, & Li K (2012). Abnormal amygdala connectivity in patients with primary insomnia: Evidence from resting state fMRI. European Journal of Radiology, 81(6), 1288–1295. 10.1016/j.ejrad.2011.03.029 [DOI] [PubMed] [Google Scholar]
  38. Hyland A, Ambrose BK, Conway KP, Borek N, Lambert E, Carusi C, Taylor K, Crosse S, Fong GT, Cummings KM, Abrams D, Pierce JP, Sargent J, Messer K, Bansal-Travers M, Niaura R, Vallone D, Hammond D, Hilmi N, Kwan J, Piesse A, Kalton G, Lohr S, Pharris-Ciurej N, Castleman V, Green VR, Tessman G, Kaufman A, Lawrence C, van Bemmel DM, Heather L Kimmel HL, Blount B, Yang L, O’Brien B, Tworek C, Alberding D, Hull LC, Cheng Y, Maklan D, Backinger CL, & Compton WM (2016). Design and methods of the Population Assessment of Tobacco and Health (PATH) Study. Tobacco Control, 26, 371–378. 10.1136/tobaccocontrol-2016-052934 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Ivarsson T, Gillberg C, Arvidsson T, & Broberg AG (2002). The Youth Self-Report (YSR) and the Depression Self-Rating Scale (DSRS) as measures of depression and suicidality among adolescents. European Child & Adolescent Psychiatry, 11(1), 31–37. 10.1007/s007870200005 [DOI] [PubMed] [Google Scholar]
  40. Jane Ling MY, Ahmad N, Mohd Yusoff MF, & Lim KH (2022). Current e-cigarette use among in-school adolescents in West Malaysia: Examining the interactions between sociodemographic characteristics and lifestyle risk behaviours. PLOS ONE, 17(1), e0263355. 10.1371/journal.pone.0263355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Jenkinson M, Bannister P, Brady M, & Smith S (2002). Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825–841. 10.1006/nimg.2002.1132 [DOI] [PubMed] [Google Scholar]
  42. Johnston LD, Miech RA, O’Malley PM, Bachman JG, Schulenberg JE, & Patrick ME (2022). Monitoring the Future national survey results on drug use 1975–2021: Overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, University of Michigan. [Google Scholar]
  43. Khantzian EJ (1985). The self-medication hypothesis of addictive disorders: focus on heroin and cocaine dependence. The American journal of psychiatry, 142(11), 1259–1264. 10.1176/ajp.142.11.1259 [DOI] [PubMed] [Google Scholar]
  44. Kim S-Y, Lee KH, Lee H, Jeon JE, Lee MH, Lee J, Oh S-M, Lee YJ, & Kim SJ (2022). Negative life stress, sleep disturbance, and depressive symptoms: The moderating role of anterior insula activity in response to sleep-related stimuli. Journal of Affective Disorders, 299, 553–558. 10.1016/j.jad.2021.12.072 [DOI] [PubMed] [Google Scholar]
  45. Klein A, Ghosh SS, Bao FS, Giard J, Häme Y, Stavsky E, Lee N, Rossa B, Reuter M, Chaibub Neto E, & Keshavan A (2017). Mindboggling morphometry of human brains. PLOS Computational Biology, 13(2), e1005350. 10.1371/journal.pcbi.1005350 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Kline RB (2016). Principles and practice of structural equation modeling. The Guilford Press. [Google Scholar]
  47. Klumpp H, Hosseini B, & Phan KL (2018, 2018-May-29). Self-Reported Sleep Quality Modulates Amygdala Resting-State Functional Connectivity in Anxiety and Depression [Original Research]. Frontiers in Psychiatry, 9. 10.3389/fpsyt.2018.00220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kong G, Bold KW, Morean ME, Bhatti H, Camenga DR, Jackson A, & Krishnan-Sarin S (2019). Appeal of JUUL among adolescents. Drug and Alcohol Dependence, 205, 107691. 10.1016/j.drugalcdep.2019.107691 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Koob GF, & Volkow ND (2010). Neurocircuitry of Addiction. Neuropsychopharmacology, 35(1), 217–238. 10.1038/npp.2009.110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Kurth F, Zilles K, Fox PT, Laird AR, & Eickhoff SB (2010). A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Structure and Function, 214(5–6), 519–534. 10.1007/s00429-010-0255-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Kwon M, Park E, & Dickerson SS (2019). Adolescent substance use and its association to sleep disturbances: A systematic review. Sleep Health, 5(4), 382–394. 10.1016/j.sleh.2019.06.001 [DOI] [PubMed] [Google Scholar]
  52. Lanczos C (1964). Evaluation of noisy data. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis, 1(1), 76–85. 10.1137/0701007 [DOI] [Google Scholar]
  53. Landau S, Emsley R, & Dunn G (2018). Beyond total treatment effects in randomised controlled trials: Baseline measurement of intermediate outcomes needed to reduce confounding in mediation investigations. Clinical Trials, 15(3), 247–256. 10.1177/1740774518760300 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Lanza ST, Russell MA, & Braymiller JL (2017). Emergence of electronic cigarette use in US adolescents and the link to traditional cigarette use. Addictive behaviors, 67, 38–43. 10.1016/j.addbeh.2016.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Lechner WV, Janssen T, Kahler CW, Audrain-McGovern J, & Leventhal AM (2017). Bi-directional associations of electronic and combustible cigarette use onset patterns with depressive symptoms in adolescents. Preventive Medicine, 96, 73–78. 10.1016/j.ypmed.2016.12.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Leventhal AM, Strong DR, Kirkpatrick MG, Unger JB, Sussman S, Riggs NR, Stone MD, Khoddam R, Samet JM, & Audrain-McGovern J (2015). Association of Electronic Cigarette Use With Initiation of Combustible Tobacco Product Smoking in Early Adolescence. JAMA, 314(7), 700–707. 10.1001/jama.2015.8950 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Leventhal AM, Strong DR, Sussman S, Kirkpatrick MG, Unger JB, Barrington-Trimis JL, & Audrain-McGovern J (2016). Psychiatric comorbidity in adolescent electronic and conventional cigarette use. 73, 71–78. 10.1016/j.jpsychires.2015.11.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Lindsay DL, Pajtek S, Tarter RE, Long EC, & Clark DB (2014). Amygdala Activation and Emotional Processing in Adolescents at Risk for Substance Use Disorders. Journal of Child & Adolescent Substance Abuse, 23(3), 200–204. 10.1080/1067828x.2014.889515 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Lovato N, & Gradisar M (2014). A meta-analysis and model of the relationship between sleep and depression in adolescents: Recommendations for future research and clinical practice. Sleep Medicine Reviews, 18(6), 521–529. 10.1016/j.smrv.2014.03.006 [DOI] [PubMed] [Google Scholar]
  60. McKeganey N, Barnard M, & Russell C (2017). Vapers and vaping: E-cigarettes users views of vaping and smoking. Drugs: Education, Prevention and Policy, 25(1), 13–20. 10.1080/09687637.2017.1296933 [DOI] [Google Scholar]
  61. Merianos AL, Jandarov RA, Choi K, Fiser KA, & Mahabee-Gittens EM (2021). Combustible and electronic cigarette use and insufficient sleep among U.S. high school students. Preventive Medicine, 147, 106505. 10.1016/j.ypmed.2021.106505 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Moheimani RS, Bhetraratana M, Yin F, Peters KM, Gornbein J, Araujo JA, & Middlekauff HR (2017). Increased Cardiac Sympathetic Activity and Oxidative Stress in Habitual Electronic Cigarette Users. JAMA Cardiology, 2(3), 278. 10.1001/jamacardio.2016.5303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Mufson EJ, Mesulam MM, & Pandya DN (1981). Insular interconnections with the amygdala in the rhesus monkey. Neuroscience, 6(7), 1231–1248. 10.1016/0306-4522(81)90184-6 [DOI] [PubMed] [Google Scholar]
  64. Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, & Mostofsky SH (2014). Reduction of motion-related artifacts in resting state fMRI using aCompCor. NeuroImage, 96, 22–35. 10.1016/j.neuroimage.2014.03.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Narmandakh A, Roest AM, Jonge PD, & Oldehinkel AJ (2020). The bidirectional association between sleep problems and anxiety symptoms in adolescents: a TRAILS report. Sleep Medicine, 67, 39–46. 10.1016/j.sleep.2019.10.018 [DOI] [PubMed] [Google Scholar]
  66. National Center for Chronic Disease Prevention and Health Promotion C (2021). Do Your Children Get Enough Sleep? Retrieved 12/10 from https://www.cdc.gov/chronicdisease/resources/infographic/children-sleep.htm
  67. Owen AM, McMillan KM, Laird AR, & Bullmore E (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59. 10.1002/hbm.20131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Paulus MP, & Stein MB (2010). Interoception in anxiety and depression. Brain Structure and Function, 214(5–6), 451–463. 10.1007/s00429-010-0258-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, & Petersen SE (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154. 10.1016/j.neuroimage.2011.10.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Poznanski B, Cornacchio D, Coxe S, Pincus DB, McMakin DL, & Comer JS (2018). The link between anxiety severity and irritability among anxious youth: Evaluating the mediating role of sleep problems. Child Psychiatry & Human Development, 49(3), 352–359. 10.1007/s10578-017-0769-1 [DOI] [PubMed] [Google Scholar]
  71. Riehm KE, Rojo-Wissar DM, Feder KA, Mojtabai R, Spira AP, Thrul J, & Crum RM (2019). E-cigarette use and sleep-related complaints among youth. Journal of Adolescence, 76, 48–54. 10.1016/j.adolescence.2019.08.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Roberts RE, & Duong HT (2014). The Prospective Association between Sleep Deprivation and Depression among Adolescents. Sleep, 37(2), 239–244. 10.5665/sleep.3388 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Romijnders KAGJ, Osch L, Vries H, & Talhout R (2018). Perceptions and reasons regarding e-cigarette use among users and non-users: A narrative literature review. International Journal of Environmental Research and Public Health, 15, 1190. 10.3390/ijerph15061190 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Roy AK, Shehzad Z, Margulies DS, Kelly AMC, Uddin LQ, Gotimer K, Biswal BB, Castellanos FX, & Milham MP (2009). Functional connectivity of the human amygdala using resting state fMRI. NeuroImage, 45(2), 614–626. 10.1016/j.neuroimage.2008.11.030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. SAS Institute s. (2002–2012). (Version 9.4) Copyright © [2014] SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. [Google Scholar]
  76. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, & Wolf DH (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256. 10.1016/j.neuroimage.2012.08.052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Shin SH, Conley D, Ksinan Jiskrova G, & Wills TA (2019). Adverse Childhood Experiences and E-Cigarette Use During Young Adulthood. The American Journal on Addictions, 28(4), 303–310. 10.1111/ajad.12890 [DOI] [PubMed] [Google Scholar]
  78. Storfer-Isser A, Lebourgeois MK, Harsh J, Tompsett CJ, & Redline S (2013). Psychometric properties of the Adolescent Sleep Hygiene Scale. Journal of Sleep Research, 22(6), 707–716. 10.1111/jsr.12059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Sutherland MT, Carroll AJ, Salmeron BJ, Ross TJ, Hong LE, & Stein EA (2013). Down-Regulation of Amygdala and Insula Functional Circuits by Varenicline and Nicotine in Abstinent Cigarette Smokers. Biological Psychiatry, 74(7), 538–546. 10.1016/j.biopsych.2013.01.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Sutherland MT, Riedel MC, Flannery JS, Yanes JA, Fox PT, Stein EA, & Laird AR (2016). Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations. Behavioral and Brain Functions, 12(1). 10.1186/s12993-016-0100-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Thompson EC, Fox KA, Lapomardo A, Hunt JI, & Wolff JC (2020). Youth Self Report Thought Problems and Sleep Difficulties Are Linked to Suicidal Ideation Among Psychiatrically Hospitalized Adolescents. Journal of Child and Adolescent Psychopharmacology, 30(8), 522–525. 10.1089/cap.2019.0160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Trucco EM, Cristello JV, & Sutherland MT (2021). Do Parents Still Matter? The Impact of Parents and Peers on Adolescent Electronic Cigarette Use. Journal of Adolescent Health, 68(4), 780–786. 10.1016/j.jadohealth.2020.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Trucco EM, Villafuerte S, Hussong A, Burmeister M, & Zucker RA (2018). Biological Underpinnings of an Internalizing Pathway to Alcohol, Cigarette, and Marijuana Use. Abnormal Psychology, 127(1), 79–91. 10.1037/abn0000310 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Tustison NJ, Avants BB, Cook PA, Yuanjie Z, Egan A, Yushkevich PA, & Gee JC (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. 10.1109/tmi.2010.2046908 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. U.S. Food and Drug Administration. (2018). Statement from FDA Commissioner Scott Gottlieb, M.D., on proposed new steps to protect youth by preventing access to flavored tobacco products and banning menthol in cigarettes. Retrieved 11/15 from https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-proposed-new-steps-protect-youth-preventing-access
  86. Vandekerckhove M, & Cluydts R (2010). The emotional brain and sleep: An intimate relationship. Sleep Medicine Reviews, 14(4), 219–226. 10.1016/j.smrv.2010.01.002 [DOI] [PubMed] [Google Scholar]
  87. Warren CM, Riggs NR, & Pentz MA (2017). Longitudinal relationships of sleep and inhibitory control deficits to early adolescent cigarette and alcohol use. Journal of Adolescence, 57, 31–41. 10.1016/j.adolescence.2017.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Weinstein SM, & Mermelstein RJ (2013). Influences of mood variability, negative moods, and depression on adolescent cigarette smoking. Psychology of Addictive Behaviors, 27(4), 1068–1078. 10.1037/a0031488 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Wong MM, Brower KJ, & Zucker RA (2009). Childhood sleep problems, early onset of substance use and behavioral problems in adolescence. Sleep Medicine, 10(7), 787–796. 10.1016/j.sleep.2008.06.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Youngstrom EA, Perez Algorta G, Youngstrom JK, Frazier TW, & Findling RL (2021). Evaluating and Validating GBI Mania and Depression Short Forms for Self-Report of Mood Symptoms. Journal of Clinical Child & Adolescent Psychology, 50(5), 579–595. 10.1080/15374416.2020.1756301 [DOI] [PubMed] [Google Scholar]
  91. Zhang Y, Brady M, & Smith S (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57. 10.1109/42.906424 [DOI] [PubMed] [Google Scholar]
  92. Zohar D, Tzischinsky O, Epstein R, & Lavie P (2005). The Effects of Sleep Loss on Medical Residents’ Emotional Reactions to Work Events: a Cognitive-Energy Model. Sleep, 28(1), 47–54. 10.1093/sleep/28.1.47 [DOI] [PubMed] [Google Scholar]
  93. Zvolensky MJ, D’Souza J, Garey L, Alfano CA, Mayorga NA, Peraza N, & Gallagher MW (2020). Subjective sleep quality and electronic cigarette dependence, perceived risks of use, and perceptions about quitting electronic cigarettes. Addictive Behaviors, 102, 106–199. 10.1016/j.addbeh.2019.106199 [DOI] [PubMed] [Google Scholar]

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