web viewall seventeen of the participants completed these three tasks during both sated and...

Supplemental Materials Avery et al.

Supplementary Information

Supplemental Participant InformationSubjects underwent clinical screening assessments including a Structured

Clinical Interview for DSM-IV Axis-I Disorders (SCID-I) conducted by trained Master’s or Doctorate level clinicians with experience in psychiatric diagnosis. Volunteers were excluded from participation if they were taking psychotropic medications or other drugs likely to affect brain function or blood flow within three weeks of participation (six weeks for fluoxetine), or had a history of substance abuse, a past history of traumatic brain injury, or current pregnancy. Volunteers were also excluded for having met criteria for any Axis I psychiatric disorder, except for major depressive disorder, at any point in their lifetime. Individuals who met criteria for a current major depressive episode at the time of participation were likewise excluded from study participation. Before scanning, subjects completed a series of behavioral assessment measures including the Hamilton Depression Rating Scale (HDRS) and the Hamilton Anxiety Rating Scale (HARS), in order to measure current levels of depression and anxiety, respectively. Participants also completed the Questionnaire of Smoking Urges (QSU) before scanning, to assess current nicotine craving.

Three subjects (two female) had experienced previous episodes (# of MDEs ≤ 2) of depression several years prior to study participation, but were not currently depressed, as assessed by clinical interview and Hamilton Depression Rating Scale (HDRS ≤ 5).

All seventeen of the participants completed these three tasks during both sated and abstinent scan days. However the IA task fMRI data for two of the participants was not included in the group analyses due to excessive head motion, which was evaluated following the procedures outlined below (see Motion Censoring). Resting-state fMRI data was included for all seventeen subjects.

Stimulus PresentationPrior to the start of the IA task scans; each subject performed a three-

minute practice version of the task. The practice version of the task included both the interoceptive and exteroceptive conditions, as well as rating periods for both conditions. Participants were monitored throughout this practice session to ensure that they were able to use the MR-compatible scroll-wheel to make responses and that they fully understood the requirements of the task. Immediately following each task scanning run, subjects reported current levels of cigarette craving (on a 1-to-9 scale; Craving Rating Scale - CRS) using an MR-compatible scroll wheel. The IA task conditions were presented in a pseudo-random order optimized for fMRI analysis by Optseq2 (http://surfer.nmr.mgh.harvard.edu/optseq/).

The visual stimuli for all tasks were projected onto a screen located inside the scanner bore and viewed through a mirror system mounted on the head-coil. Stimulus presentation and response collection were controlled using Eprime2 software (www.pstnet.com). During the resting scan, subjects fixated on a cross

1

http://www.pstnet.com/


that was presented at the center of the screen for the duration of the scan. They were instructed to clear their mind and to try not to think of anything in particular.

Imaging Parameters Functional and structural MR images were collected using a General

Electric Discovery MR750 (GE Healthcare, Milwaukee, WI) whole-body 3-Tesla MRI scanner, using a scalable 32-channel digital MRI receiver capable of performing massively-parallel fMRI. A receive-only 32-element head coil array (Nova Medical Inc, Wilmington, MA), optimized for parallel imaging, was used for MRI signal reception. During fMRI scans, physiological pulse-oximetry and respiration waveforms were simultaneously recorded. A single-shot gradient-recalled echo-planar imaging (EPI) sequence with Sensitivity Encoding (SENSE) depicting blood oxygenation level-dependent (BOLD) contrast was used for functional scans.

EPI imaging parameters: FOV/slice/gap = 240/2.9/0mm, slices/volume (axial) = 46, acquisition matrix = 96×96, repetition/echo time TR/TE = 2500/30ms, SENSE acceleration factor R = 2 in the phase encoding (anterior-posterior) direction, flip angle = 90º. EPI image matrix = 128x128, fMRI voxel volume = 1.875x1.875x2.9mm³. Scan duration IA task: 3 scans, 220 volumes/scan, 550s/scan. The following echo-planar imaging (EPI) imaging parameters were used for the resting scans: field of view (FOV)/slice/gap = 240/2.9/0 mm, axial slices per volume = 46, acquisition matrix = 96 × 96, repetition/echo time TR/TE = 2500/25 ms, SENSE acceleration factor R = 2 in the phase encoding (anterior-posterior) direction, flip angle = 90°, sampling bandwidth = 250 kHz, number of volumes = 180, scan time = 450 sec.

A T1-weighted magnetization-prepared rapid gradient-echo sequence with SENSE was used to provide an anatomical reference for the fMRI analysis. Anatomical Image: FOV = 240mm, slices/volume (axial) =176, slice thickness = 0.9mm, image matrix = 256x256, voxel volume = 0.938x0.938x0.9mm³, TR/TE = 5/2.02ms, acceleration factor R = 2, flip angle = 8º, inversion/delay time TI/TD= 725/1400ms, scan time = 372s.

Image PreprocessingAnatomical images were registered to the first volume of the first IA task

EPI scan (referred to as the base-EPI volume), using AFNI’s anatomical-to-epi alignment procedure. Those aligned anatomical scans were then transformed to the stereotaxic array of Talairach and Tournoux (Talairach and Tournoux, 1988) using AFNI’s automated algorithm, and the spatial transformation parameters were saved for use later in preprocessing. The first 4 volumes of each EPI time-course (10 seconds) were excluded from data analysis to allow the fMRI signal to reach longitudinal equilibrium, after which a slice timing correction was applied to all EPI volumes. Those EPI volumes were registered to the base-EPI volume using a six-parameter (3 translations and 3 rotations) motion correction algorithm, and those parameter estimates were subsequently saved for use in the subject-level regression analysis.

2


Volume-registration and spatial transformation matrices were combined to enable motion correction and transformation of the EPI data to Talairach space in one step, minimizing the amount of spatial interpolation applied to the functional data. During this step, all EPI volumes were resampled to a 1.75 x 1.75 x 1.75 mm3 grid, after which a 6-mm full-width at half-maximum (FWHM) Gaussian smoothing kernel was applied. Finally, the signal intensity for each EPI volume was normalized to reflect percent signal change from each voxel’s mean intensity across the time-course.

Resting Scan PreprocessingPreprocessing of the resting-state scans was performed using a modified

version of the ANATICOR method (Jo et al , 2010 ), implemented through the AFNI program afni_restproc.py. As with the task EPI data, the first 4 volumes of the resting state-scan were excluded to allow the fMRI signal to reach steady state. Following this, a de-spiking interpolation algorithm (AFNI’s 3dDespike) was used to remove transient signal spikes from the EPI data that might otherwise artificially inflate correlation estimates between voxel time-series. This was then followed with a slice time correction. Each resting state EPI volume was registered to the first volume of the time course, which was itself registered to the anatomical scan. Using the high-resolution anatomical scan, masks of the subject’s ventricles and white matter were constructed using FreeSurfer (http://surfer.nmr.mgh.harvard.edu/). After resampling the masks to the same resolution as the EPI scan, each mask was then eroded by a single voxel in each direction to prevent partial volume effects that might include signal from gray matter voxels in the mask. The average time course during the resting-state scan was extracted from the ventricle mask and subsequently used to account for any components of the MR signal due to cerebrospinal fluid in the ventricles. Next, local physiological noise present in white matter was estimated using the AFNI program 3dLocalstat, which calculated the average signal time-course for all white matter voxels within a 1.5 cm radius of each gray matter voxel. Respiration and cardiac data collected by pulse-oximetry, and respiration belt recording during scanning was used to calculate RETROICOR (Glover et al , 2000 ) and respiration volume per time (RVT) (Birn et al , 2008 ) parameters using the RetroTS.m plugin for MATLAB. Additional regressors of non-interest were constructed from the mean, linear, quadratic, and cubic signal trends, as well as the 6 normalized motion parameters (3 translations, 3 rotations) computed during the image registration preprocessing. In total, the estimates of physiological and non-physiological noise included the 6 motion parameters, the average ventricle signal, the average local white matter signal, and 13 respiration regressors from RETROICOR and RVT. The predicted time-course for these nuisance variables was constructed using AFNI’s 3dTfitter program and then subtracted from each resting-state voxel time-course, yielding a residual time-course for each voxel. The residual EPI time series was then smoothed with a 6 mm FWHM Gaussian kernel, resampled to a 1.75x1.75x1.75 mm3 grid, and spatially transformed to Talairach stereotaxic space (Talairach et al , 1988 ) for all subsequent analyses.

3

http://surfer.nmr.mgh.harvard.edu/


Motion CensoringIn addition to volume registration, motion-censoring algorithms were also

implemented to guard against potential artifactual confounds induced by uncontrolled subject motion (Power et al , 2012 ). The motion-censoring procedure employed herein is identical to that implemented in previously published neuroimaging studies (Avery et al , 2014 ; Lerman et al , 2014 ). Briefly, the Euclidean-normalized derivative of the subject’s motion parameters was calculated at each TR, and a list of time points was created in which that value was greater than 0.3 (roughly 0.3mm motion). The TRs within this list were then censored during the subsequent subject-level regression analysis. Additionally, any subject with an average Euclidean-normalized derivative of greater than 0.2 during either the nicotine-sated or nicotine-abstinent scan session was excluded from the group-level analysis. Two subjects were thus excluded from the analysis of functional imaging data due to excessive head motion, leaving 15 smokers in the analysis.

Heart Rate AnalysisThe pulse oximetry recordings obtained during the EPI scan sessions

were used to calculate average heart rate during the IA task. These recordings were analyzed using a custom peak-detection algorithm implemented in the suite of 1D analysis tools available within the AFNI software package. The result was a binary-valued output with a ‘1’ representing each individual heartbeat. The output of this procedure was visually confirmed alongside the raw pulse oximeter recording to ensure accurate labeling of each heartbeat. The total number of recorded heartbeats during a scan was then divided by the scan time in minutes to calculate the number of heartbeats per minute. This procedure was applied to the pulse oximeter recording from each subject’s IA task scans, and the average of the three IA scans was used to obtain overall heart rate during the task.

Subject-level Statistical Analyses – Imaging DataThe data collected during the IA task were analyzed at the single-subject

level using a multiple linear regression model, which included regressors for each interoception condition and the exteroception condition, as well as regressors for the response periods following those conditions. To adjust the model for the shape and delay of the BOLD function, the task regressors were constructed by convolution of a gamma-variate hemodynamic response function with a boxcar function having a 10-second (or 5 second) width beginning at the onset of each trial period. The regression model for the IA task also included regressors of non-interest to account for each run’s mean, linear, quadratic, and cubic signal trends, as well as the 6 normalized motion parameters (3 translations, 3 rotations) computed during the volume registration preprocessing.

Resting-state functional connectivity analysesThe average time course from the left dorsal mid-insula ROI (identified in

the IA task contrast; Figure 1) during the resting-state task was used to examine differences in subjects’ resting-state functional connectivity between sated and

4


abstinent scans. The AFNI program 3dmaskave was first used to extract the average time-series from the pre-processed resting-state data within the left mid-insula seed. Next, multiple regression analyses were used to produce voxel-wise maps of the correlation coefficients between the time-course of the insula seed region and all other voxels in the brain. The r-value maps made by this process were then Fisher-transformed into z-score maps for use in the group analysis.

At the group level, the AFNI program 3dttest++ was used to perform a paired t-test of the difference in left mid-insula functional connectivity maps, with the difference in pleasantness ratings used as a covariate. This enabled the identification of regions of the brain whose difference in connectivity to the left dorsal mid-insula (between sated and abstinent scan sessions) was predictive of the difference in inferred pleasantness of smoking-related stimuli. Resultant maps were cluster-size corrected for multiple comparisons at p < .05 as described below.

Anatomical ROI Definitions and Multiple Comparison Corrections The T-statistical maps derived from the analysis of IA task data and

resting-state functional connectivity data were separately corrected for multiple comparisons, using cluster-size FWE correction within small-volume a-priori-defined anatomical regions, as follows: An initial voxel-wise p-value of 0.005 was applied to the T-statistical maps, within a priori-defined anatomical regions of interest. The group-level statistical maps were then small-volume corrected for multiple comparisons at p<0.05 using Monte Carlo simulations of cluster-size implemented in AFNI’s 3dClustsim. In order to control for Type II statistical error, in which significant activations that lie outside our pre-defined anatomical regions might be overlooked, outside of our a priori-defined anatomical regions, a voxel-wise threshold of p<0.001 and cluster-size correction of p<0.05 was utilized.

The a priori-defined anatomical brain regions included: the bilateral insula, amygdala, orbitofrontal cortex, and basal ganglia. These regions were chosen based on their well-established roles in the neurocircuitry involved in interoceptive awareness of the body (Craig, 2002), reward processing (Berridge and Kringelbach, 2015), the generation of pleasantness inferences (Simmons et al , 2014 ), and drug addiction (Koob and Volkow, 2010). Anatomical masks of these regions were selected from a pre-rendered stereotaxic brain atlas available within AFNI (DD Desai Maximum Probability Map Atlas), based on probability maps generated for 35 cortical areas (Desikan et al , 2006 ) and the parcellation of cortical and subcortical structures (of the AFNI Talairach N27 atlas brain) generated by the FreeSurfer program. This atlas is freely available for reference in the AFNI software distribution (http://afni.nimh.nih.gov). The cortical and sub-cortical anatomical masks derived from this atlas included: the bilateral amygdala, the (lateral and medial) orbitofrontal cortex, the basal ganglia (including: caudate, putamen, accumbens area, and pallidum masks). The left and right insula anatomical masks were generated by the FreeSurfer program, which was applied to the AFNI Talairach N27 atlas brain. All of the anatomical masks were subsequently resampled to the spatial resolution of the EPI data for use in multiple-comparison correction.

5


Importantly, recent evidence has suggested that Gaussian spatial auto-correlation functions (used by most fMRI analysis software packages) may underestimate the spatial auto-correlations present within fMRI data, leading to underestimations of the minimum cluster-size required to achieve family-wise error (FWE) correction (Eklund et al , 2015 ). In order to accurately estimate the cluster-sizes required to achieve FWE correction, revised versions of AFNI’s 3dFWHMx and 3dClustsim (compiled on December 9th, 2015) were used to generate smoothness and cluster-size estimates using a spherical non-Gaussian spatial autocorrelation function.

SUPPLEMENTAL RESULTS

We did not observe any significant relationships between nicotine dependence, as measured by the Fagerstrom Test for Nicotine Dependence (FTND) and change in interoceptive task activity in the left dorsal mid-insula (ΔIA: p = 0.91; See Table SII), change in exhaled carbon monoxide (ΔCO: p = 0.11), or change in heart rate during scanning (p = 0.90). Likewise, we observed no relationships between FTND and change in self-reported cigarette craving (CRS: p = 0.25; QSU: p = 0.72), change in IA task intensity ratings (p = 0.52), or change in Smoking Pleasantness task ratings (ΔSP: p = 0.15; Table SII).

During both nicotine-sated (NS) and nicotine-abstinent (NA) scans, exhaled carbon monoxide (CO) was positively correlated with heart rate during scanning (NS: r(15) = 0.62, p < 0.01; NA: r(15) = 0.59, p < 0.01) and negatively correlated with CRS-reported cigarette craving during scanning (NS: r(15) = -0.64, p < 0.01; NA: r(15) = -0.53, p < 0.03). We observed significant relationships between difference in CO between scan sessions (ΔCO) and ΔIA (r(13) = 0.56, p < 0.03), difference in CRS-reported cigarette craving (r(15) = -0.47, p = 0.05; Table SI), and difference in Smoking Pleasantness task ratings (r(15) = -0.54, p < 0.02; Table SI). However, we did not observe any relationship between self-reported length of cigarette abstinence prior to nicotine-abstinent scans and differences in exhaled CO, heart rate, cigarette craving, SP task ratings, or IA task activity (p>0.49; Table SII).

Mediation Analysis The mediation analysis we performed was based upon the method

devised by Baron and Kenny (1986), and is only performed if certain basic conditions are met: There must be: 1) a significant relationship between the independent variable (IV) and the dependent variable (DV) (PATH C); 2) a significant relationship between the independent variable and the mediator (PATH A); and 3) a significant relationship between the mediator and the dependent variable (PATH B)(Baron and Kenny, 1986). The mediation of the IV-DV relationship is then tested through a multiple linear regression model to determine whether 1) M is significantly related to the DV, when controlling for the IV (PATH B’); 2) The IV is not significantly related to the DV, when controlling for M (PATH C’).

6


For the mediation analysis performed in the present study, all three initial conditions were met, as we observed significant relationships between the independent variable (ΔCO), the dependent variable (ΔSP), and the mediator (ΔIA) (See Table S1 & S4). For ease of interpretation, values entered into the various regression analyses were normalized to z-scores, so the beta coefficients listed in Table S4 are normalized beta coefficients. When controlling for ΔIA, ΔCO was not significantly related to ΔSP (PATH C’). But ΔIA was significantly related to ΔSP, when controlling for ΔCO (PATH B’). This met the criteria for partial mediation of the ΔCO ΔSP relationship (by convention, full mediation would only be indicated if the beta coefficient for PATH C’ were exactly zero.)

We additionally performed a series of regression analyses, in order to identify whether change in heart rate between scans (ΔHR) mediates any of the relationships observed in our initial analysis (Table S4). We were able to confirm that ΔHR does not mediate any of the observed in this analysis (ΔCO ΔIA, ΔIA ΔSP, or ΔCO ΔSP; See Table SVI below).

SUPPLEMENTAL DISCUSSION

LimitationsNicotine Dependence . Even though the smokers examined within this

study ranged from low to high levels of nicotine dependence, we observed no relationship between nicotine dependence, as measured by the FTND score or by number of cigarettes smoked per day, and any of the changes in behavioral or physiological parameters measured within this study (see Table SII). This is likely due to our use of a within-subjects repeated-measures design, which allowed for greater control over subject-level variability as well as greater sensitivity for state effects, rather than trait effects. Another possibility is that these measurements are biased by smokers’ own inaccurate representations of the degree of their nicotine dependence or the number of cigarettes they typically smoke per day.

This does not entirely rule out the possibility of an effect of varying dependence levels upon the neural mechanisms of positive alliesthesia, however. While individuals with low vs. high drug dependence may exhibit a similar degree of positive alliesthesia after drug withdrawal, the underlying brain mechanisms that drive that positive alliesthesia may indeed be quite different. Specifically, negative reinforcement may play a much more prominent role in the behavior of more dependent smokers (Koob et al , 2010 ). Future studies with large sample sizes could build on the present work by contrasting individuals with low vs. high levels of nicotine dependence.

Duration of Abstinence. This study employed a relatively short duration of nicotine abstinence (4 hours or greater), compared to some prior studies of nicotine abstinence and withdrawal that have employed 12 to 24-hour abstinence periods. However, research into the time course of nicotine withdrawal symptoms has identified that abstinence periods of as brief as four hours are sufficient to induce the physiological and psychological symptoms of withdrawal (Morrell et al , 2008). Additionally, we verified that smokers were indeed abstinent, as they exhibited significantly lower heart rates and levels of exhaled carbon monoxide,

7


on abstinent vs. sated scan sessions. They also reported significantly greater craving for cigarettes, both prior to and during scanning, on nicotine-abstinent scan days. Finally, we observed no relationship between the reported duration of abstinence prior to abstinent scans, and any of the changes in behavioral or physiological parameters measured within this study (see Table SII). However, we did observe significant relationships between the differences in exhaled CO between scans and differences in IA task activity, cigarette craving, and pleasantness ratings for smoking pictures (Table SI & SIV). Thus, we did observe a relationship between the degree of cigarette abstinence and positive alliesthesia for cigarette pictures, but we did so using an objective and potentially more reliable metric than smokers’ self-reported duration of abstinence.

Interoceptive Signaling. In daily cigarette smokers, nicotine abstinence significantly alters the activity of viscerosensory regions of the insular cortex while attending to interoceptive sensations. However, it remains unclear, whether this altered interoceptive insula activity during nicotine abstinence results from reduced adrenergic stimulation of the heart (as evidenced by the significant reduction in heart rate during nicotine abstinence), reduced blood nicotine levels, or reduced airway stimulation by cigarette smoke. This question may be addressed by future studies that directly track blood nicotine levels, as well as by experimental devices such as electronic or de-nicotinized cigarettes, which may be used to isolate the specific bio-chemical and viscerosensory components of interoceptive insula activity responsive to cigarette smoking.

8


Table SI. Relationships among measures of behavioral, physiological, and hemodynamic differences between scan sessions

Behavioral and Physiological Measures ΔCO ΔHR ΔCRS ΔIIR

r15

ΔCO – exhaled carbon monoxide (ppm)

ΔHR (bpm) – Heart Rate 0.38

ΔCRS – Cigarette craving during scanning -0.47* -0.28

ΔIIR – Interoceptive Intensity Ratings -0.17 -0.08 0.04

ΔSP – Smoking Pleasantness ratings -0.54* -0.13 0.50* 0.58*

ΔIA a - dorsal mid-insula IA task activity 0.56* 0.26 -0.60* -0.38*p < 0.05. ar13; subscript denotes degree of freedom

Table SII. Relationships between measures of nicotine dependence and usage and measures of behavioral and physiological differences between scan sessions

Behavioral and Physiological Measures FTND TAbs CPD

r15 p r15 p r15 p

ΔCO – exhaled carbon monoxide (ppm) 0.40 0.11 0.02 0.95 -0.08 0.75

ΔHR (bpm) – Heart Rate -0.03 0.90 0.18 0.58 -0.15 0.58

ΔSP – Smoking Pleasantness ratings -0.36 0.15 0.11 0.66 0.00 0.99

ΔIA* - dorsal mid-insula IA task activity -0.03 0.91 0.06 0.83 -0.35 0.17

ΔQSU – Questionnaire of Smoking Urges -0.10 0.72 0.17 0.50 -0.01 0.97

ΔCRS – Cigarette craving during scanning -0.29

0.250.13

0.61-0.02

0.94

ΔIIR – Interoceptive Intensity Ratings 0.17 0.52 -0.28 0.28 0.36 0.15*r13; subscript denotes degrees of freedomAbbreviations: FTND - Fagerstrom Test for Nicotine Dependence; Tabs – self-reported duration of nicotine abstinence prior to abstinent scans; CPD – number of cigarettes smoked per day.

Table SIII. Relationship between change in smoking pleasantness ratings (ΔSP) and change in hemodynamic activity during interoception within brain regions exhibiting abstinence-induced decreases in interoceptive activity.Side / Location r13 r2 pL Postcentral Gyrus -0.18 0.03 0.52L Supplementary Motor Area 0.14 0.02 0.61L Dorsal Mid-Insula -0.70 0.48 0.004*R Dorsal Mid-Insula -0.30 0.09 0.28L Lateral Orbitofrontal Cortex 0.20 0.04 0.48R Amygdala -0.05 0.00 0.85R Lateral Orbitofrontal Cortex -0.03 0.00 0.92L Amygdala -0.09 0.01 0.74

*Statistically significant after Bonferroni correction for multiple comparisons.

9


Table SIV. Mediation Analysis of the relationship between change in exhaled carbon monoxide (ΔCO), change in smoking pleasantness ratings (ΔSP), and change in interoceptive activity in the left dorsal mid-insula (ΔIA).

β* SE t13 p r2

Path A: ΔCO ΔIA 0.56 0.23 -2.52 0.03 0.33

Path B: ΔIA ΔSP -0.70 0.20 -3.50 0.004 0.48

Path C: ΔCO ΔSP -0.57 0.23 2.40 0.03 0.31

Path B’: ΔIA ΔSP, controlling for ΔCO -0.55 0.24 -2.31 0.04 0.54

Path C’: ΔCO ΔSP, controlling for ΔIA -0.27 0.24 -1.14 0.28*Normalized beta-coefficients

Table SV. Brain regions exhibiting a significant relationship between change in smoking pleasantness ratings and change in resting-state functional connectivity to the left dorsal mid-insula.Side / Location Peak Coordinatesa Peak t16 Volume

X Y Z (mm3)L Ventral Striatum / Accumbens area -17 +6 -8 5.3 364

R Ventral Pallidum +17 +1 -1 4.9 134a All coordinates reported according to Talairach stereotaxic atlas.

Table SVI. Multiple regression analyses examining the effect of change in heart rate (ΔHR) upon relationships between pleasantness ratings (ΔSP), carbon monoxide (ΔCO), and interoceptive mid-insula activity (ΔIA).

β* SE t13 p

Path B’: ΔCO ΔIA, controlling for ΔHR -0.52 0.25 2.13 0.05

Path B’: ΔIA ΔSP, controlling for ΔHR -0.71 0.21 -3.35 0.006

Path B’: ΔCO ΔSP, controlling for ΔHR -0.58 0.24 -2.39 0.03

*Normalized beta coefficient

10


Figure S1: Experimental Design17 cigarette smokers (9 Female; age [mean(SD)]: 33(9); age range: 22-51) were scanned twice, once while nicotine-sated, and once while nicotine-abstinent. Smokers were abstinent for an average of 7(4) hours. Prior to both scan sessions, subjects’ levels of exhaled carbon monoxide (CO) were measured to verify smoking status. Note: the task pictures below are representative images of each task, and are not all exemplars of task stimuli (see Figure S1 for specific task stimuli.)

11


Figure S2: Behavioral and Neuroimaging tasksInteroceptive Attention (IA) task (top): During the interoception condition, the word “HEART”, “STOMACH”, or “BLADDER” was presented on the screen and subjects were instructed to focus their attention on interoceptive sensations from that organ. During the exteroception condition, the word “TARGET” was presented on screen and the color of the word alternated from black to a lighter shade of gray every second. The subjects were instructed to focus their attention on the intensity of these color changes. Smoking Pleasantness (SP) task (bottom): Subjects were presented with 40 tobacco-related images selected from the Geneva Smoking Pictures database (Khazaal et al , 2012 ). During the presentation of the tobacco-related images, the subject rated on a 7-point scale “How pleasant would it be to smoke this cigarette right now?”

12


Figure S3: Nicotine withdrawal alters smokers’ interoceptive awareness of cardiac sensationsSmokers’ heart rates during the IA task, measured using pulse-oximetry recording, were significantly greater during nicotine-sated than nicotine abstinent scans. However, their self-reported heartbeat intensity during the IA scan sessions was actually higher while nicotine-abstinent than while nicotine-sated, reflecting smokers’ altered perception of internal signals from their bodies. These heart rate findings agree with the literature on nicotine satiety effects on heart rate (see (Niedermaier et al , 1993 )), and demonstrate that our nicotine craving manipulation is a potent modulator of interoceptive tone. Importantly, the heart rate changes are accompanied by an inverse behavioral effect, suggesting dissociation between autonomic tone and interoception as a function of nicotine withdrawal. This inversion of behavioral responses is consistent with the reduced mid-insula BOLD signal during craving scans, and is consistent with other findings in the literature demonstrating inverse behavioral and mid-insula activity during states of heightened somatic signaling (Avery et al , 2014 ; Kerr et al , 2016).

13


REFERENCES:Avery JA, Drevets WC, Moseman SE, Bodurka J, Barcalow JC, Simmons WK (2014). Major depressive disorder is associated with abnormal interoceptive activity and functional connectivity in the insula. Biological psychiatry 76(3): 258-266.

Baron RM, Kenny DA (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6): 1173-1182.

Berridge KC, Kringelbach ML (2015). Pleasure systems in the brain. Neuron 86(3): 646-664.

Birn RM, Murphy K, Bandettini PA (2008). The effect of respiration variations on independent component analysis results of resting state functional connectivity. Human brain mapping 29(7): 740-750.

Craig AD (2002). How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 33: 655–666.

Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3): 968-980.

Eklund A, Nichols T, Knutsson H (2015). Can parametric statistical methods be trusted for fMRI based group studies? ArXiv 1511.01863.

Glover GH, Li TQ, Ress D (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44(1): 162-167.

Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW (2010). Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage 52(2): 571-582.

Kerr KL, Moseman SE, Avery JA, Bodurka J, Zucker NL, Simmons WK (2016). Altered Insula Activity during Visceral Interoception in Weight-Restored Patients with Anorexia Nervosa. Neuropsychopharmacology 41(2): 521-528.

Khazaal Y, Zullino D, Billieux J (2012). The Geneva Smoking Pictures: development and preliminary validation. Eur Addict Res 18(3): 103-109.

Koob GF, Volkow ND (2010). Neurocircuitry of addiction. Neuropsychopharmacology 35(1): 217-238.

14


Lerman C, Gu H, Loughead J, Ruparel K, Yang Y, Stein EA (2014). Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function. JAMA Psychiatry 71(5): 523-530.

Morrell HE, Cohen LM, al'Absi M (2008). Physiological and psychological symptoms and predictors in early nicotine withdrawal. Pharmacol Biochem Behav 89(3): 272-278.

Niedermaier ON, Smith ML, Beightol LA, Zukowska-Grojec Z, Goldstein DS, Eckberg DL (1993). Influence of cigarette smoking on human autonomic function. Circulation 88(2): 562-571.

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.

Simmons WK, Rapuano KM, Ingeholm JE, Avery J, Kallman S, Hall KD, et al (2014). The ventral pallidum and orbitofrontal cortex support food pleasantness inferences. Brain Struct Funct 219(2): 473-483.

Talairach J, Tournoux P (1988). Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging Georg Thieme: Stuttgart ; New York, 122 p.pp.

15

web viewall seventeen of the participants completed these three tasks during both sated and...

Documents