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"description": "== Quick Start ==\r\n6mm = 6-mm spatial smoothing kernel; 0mm = no spatial smoothing filter was applied; U = temporally uncertain/unpredictable anticipation ('countdown'); P = temporally certain/predictable anticipation ('countdown'); T = threat (aversive shock, photo, and auditory clip); S = safety (benign or neutral electrical stimulation, photo, and auditory clip); m = minus; FDR05 = FDR q<.05, whole-brain corrected; e.g. 6mm_UTmUS = the contrast showing greater action during the anticipation of temporally uncertain threat compared to the anticipation of temporally uncertain safety (6-mm smoothing)\r\n == DETAILED METHOD == \r\nParticipants and Enrollment Criteria\r\nA total of 78 daily tobacco smokers were enrolled. Eligibility was determined using a multi-stage procedure that included on-line, telephone, and face-to-face assessments. Eligibility criteria included: 18-40 years old; smoke at least 10 cigarettes/day for at least 6 months; baseline breath carbon monoxide (CO) level of ≥8 parts per million (ppm) at baseline; normal or corrected-to-normal color vision; English proficiency; and self-reported absence of a non-nicotine substance use disorder, lifetime psychotic or bipolar disorder, lifetime neurological or pervasive developmental disorder, very premature birth, current psychiatric treatment, MRI contraindications, or prior experience with aversive electrical stimulation. All participants provided informed written consent. Procedures were approved by the University of Maryland, College Park Institutional Review Board. \r\n\r\nThree individuals were excluded from all analyses due to study withdrawal (n=1) or inadequate task compliance (n=2; see below), yielding a racially diverse sample of 75 participants (M = 30.05 years, SD = 5.64; 33.3% female; 49.3% African American, 25.3% White Non-Hispanic, 12.0% Asian, 9.4% Multiracial/Other, 4.0% Hispanic or Latino/a). As detailed in Table 1, the Smoke-as-Usual and 24-Hour Abstinence groups were demographically and clinically matched. The two groups also did not differ in their general engagement with the MTC paradigm, as indexed by the proportion of ratings completed (Table 1). Analyses performed using Welch’s t-test yielded identical conclusions (not reported). All participants showed acceptable levels of head-motion artifact, as detailed below.\r\n\r\nPower Analysis \r\nSample size was determined a priori as part of the application for the grant that supported data collection (R21-DA040717). The target sample size (n≈72) was chosen to afford acceptable power and precision given available resources. At the time of study design, Gpower (version 3.1.9.2) indicated 88.3% power to detect a benchmark “large” group difference (d=.80) with 10% planned attrition (n=32/group) using αtwo-tailed=.05 (Erdfelder, Faul, & Buchner, 1996). In practice, funds were available to support the enrollment of 78 participants. With the exception of quality assurance checks performed using data from the first few participants, all analyses were performed following the acquisition of the entire dataset. The final sample (n=75; Table 1) was comparable to or larger than many prior studies focused on threat reactivity in acutely abstinent tobacco smokers (Bradford, Starr, Shackman, & Curtin, 2015; Grillon, Avenevoli, Daurignac, & Merikangas, 2007; Hogle et al., 2010). A post hoc power analysis indicated that the final sample was powered to detect medium-to-large effects (power >80% for d>.66). \r\n\r\nGeneral Procedures\r\nRecruitment. Daily tobacco smokers from the DC-Baltimore metropolitan region were recruited using combination of on-line (e.g., posts to social media platforms and groups) and off-line advertisements (e.g., fliers and business cards distributed at high-traffic local restaurants, coffee shops, and libraries). Preliminary eligibility was determined using a multi-stage screening process that included on-line surveys, and a telephone screening. \r\n\r\nBaseline Laboratory Session. Potentially eligible individuals were invited to a baseline laboratory session. Smoking status was verified using a Micro+ Smokerlyzer (coVita, Santa Barbara, CA). All participants demonstrated a CO level of at least 8 ppm, averaged across three serial tests. Participants also completed a battery of standardized measures of tobacco use and dependence. Participants were then randomly assigned to either the Smoke-as-Usual (SAU) or 24-Hour Abstinence group, stratified by age and sex. The Smoke-as-Usual group was instructed to continue their normal smoking habits prior to their neuroimaging session, whereas the 24-Hour Abstinence group was instructed to refrain from smoking or using any other nicotine products for 24 hours prior to the scheduled neuroimaging session. To encourage protocol compliance, text message reminders were sent 24 hours prior to the neuroimaging session.\r\n\r\nNeuroimaging Session. Upon arrival at the neuroimaging session, protocol compliance was assessed. For the 24-Hour Abstinence group, participants were allowed to proceed with scanning upon self-reporting nicotine abstinence and receiving a CO reading of <50% of their baseline level. Participants in the Smoke-as-Usual group demonstrated CO levels of at least 8 ppm. By design, measured CO levels were significantly reduced in the 24-Hour Abstinence group, both in comparison to the Smoke-as-Usual group (p<.001; Table 1) and to their own baseline levels (t(37)=11.27, p<.001). Prior to scanning, participants were offered a brief break, and those in the Smoke-as-Usual group were given the option to smoke. Prior to scanning, participants completed a battery of standardized questionnaires assessing smoking urges and withdrawal symptoms (see below). During scanning, foam inserts were used to immobilize the participant’s head within the head-coil and mitigate potential motion artifact. Participants were continuously monitored using an MRI-compatible eye-tracker (Eyelink 1000; SR Research, Ottawa, Ontario, Canada) and the AFNI real-time motion plugin (Cox, 1996). Measures of respiration and breathing were continuously acquired during scanning using a respiration belt and photo-plethysmograph affixed to the first digit of the non-dominant hand. Following the last scan, participants were removed from the scanner, debriefed, compensated, and discharged. \r\n\r\nThreat-Anticipation Paradigm \r\nParadigm Structure and Design Considerations. The Maryland Threat Countdown (MTC) is a well-established, fMRI-optimized version of temporally uncertain-threat assays previously validated using fear-potentiated startle and acute anxiolytic administration (e.g., benzodiazepine) in mice (Daldrup et al., 2015; Lange et al., 2017), rats (Miles, Davis, & Walker, 2011), and humans (Hefner, Moberg, Hachiya, & Curtin, 2013). The MTC has been successfully used in a number of human fMRI studies (Hur et al., 2022; Hur et al., 2020). \r\n\r\nThe MTC paradigm (Figure 1) takes the form of a 2 (Valence: Threat/Safety) × 2 (Temporal Certainty: Uncertain/Certain) randomized, event-related, repeated-measures design (3 scans; 6 trials/condition/scan). Subjects were completely informed about the task design and contingencies prior to scanning. Simulations were used to optimize the detection and deconvolution of task-related hemodynamic signals (variance inflation factors <1.54). Stimulus presentation and ratings acquisition were controlled using Presentation software (version 19.0, Neurobehavioral Systems, Berkeley, CA). \r\n\r\nValence was continuously signaled during the anticipation epoch by the background color of the display. Trial certainty was signaled by the nature of the integer stream. On Certain Threat trials, subjects saw a descending stream of integers (‘count-down;’ e.g. 30, 29, 28...3, 2, 1) for 18.75 s. To ensure robust distress, this anticipation epoch always culminated with the delivery of a noxious electric shock, unpleasant photographic image (e.g., mutilated body), and thematically related audio clip (e.g., scream, gunshot). Uncertain Threat trials were similar, but the integer stream was randomized and presented for an uncertain and variable duration (8.75-30.00 s). Here, participants knew that something aversive was going to occur, but they had no way of knowing precisely when. Safety trials were similar, but terminated with the delivery of benign reinforcers (i.e., just-perceptible electrical stimulation and neutral audiovisual stimuli). Mean duration of the anticipation epochs was identical across trial types, ensuring an equal number of measurements (TRs/condition). The specific mean duration was chosen to enhance detection of task-related differences in the blood oxygen level-dependent (BOLD) signal (Henson, 2007). To mitigate potential confusion and eliminate mnemonic demands, a lower-case ‘c’ or ‘u’ was presented at the lower edge of the display throughout the anticipatory epoch. White-noise visual masks (3.2 s) were presented between trials to minimize persistence of the visual reinforcers in iconic memory. Subjects were periodically prompted to rate the intensity of fear/anxiety experienced a few seconds earlier, during the anticipation period of the prior trial, using a 1 (minimal) to 4 (maximal) scale and an MRI-compatible response pad (MRA, Washington, PA). Each condition was rated once per scan. Skin conductance was continuously acquired throughout.\r\n\r\nProcedures. Prior to fMRI scanning, participants practiced an abbreviated version of the MTC paradigm without electrical stimulation until staff confirmed understanding. Benign and aversive electrical stimulation levels were individually titrated. Benign Stimulation. Participants were asked whether they could “reliably detect” a 20 V stimulus and whether it was “at all unpleasant.” If the participant could not detect the stimulus, the voltage was increased by 4 V and the process repeated. If the participant indicated that the stimulus was unpleasant, the voltage was reduced by 4V and the process was repeated. The final level chosen served as the benign electrical stimulation during the imaging assessment (M = 24.43 V, SD = 5.48 V). Aversive Stimulation. Participants received a 100 V stimulus and were asked whether it was “as unpleasant as you are willing to tolerate.” If the participant indicated that they were willing to tolerate more intense stimulation, the voltage was increased by 10 V and the process repeated. If the participant indicated that the stimulus was too intense, the voltage was reduced by 5 V and the process repeated. The final level chosen served as the aversive electrical stimulation during the imaging assessment (M = 121.33, SD = 45.14). Following each scan, staff verbally re-assessed whether the level of stimulation was sufficiently aversive and re-calibrated as necessary. Stimulation levels were similar to prior work in university samples (Hur et al., 2020). The groups did not significantly differ in the chosen intensity of benign or aversive electrical stimulation (Table 1). \r\n\r\nElectrical Stimuli. Electrical stimuli (100 ms; 2 ms pulses every 10 ms) were generated using an MRI-compatible constant-voltage stimulator system (STMEPM-MRI; Biopac Systems, Inc., Goleta, CA). Stimuli were delivered using MRI-compatible, disposable carbon electrodes (Biopac) attached to the fourth and fifth digits of the non-dominant hand. \r\n\r\nVisual Stimuli. Visual stimuli (1.8 s) were digitally back-projected (Powerlite Pro G5550, Epson America, Inc., Long Beach, CA) onto a semi-opaque screen mounted at the head-end of the scanner bore and viewed using a mirror mounted on the head-coil. A total of 72 aversive and benign photographs were selected from the International Affective Picture System (for details, see Hur et al., 2020). \r\n\r\nAuditory Stimuli. Auditory stimuli (0.80 s) were delivered using an amplifier (PA-1 Whirlwind) with in-line noise-reducing filters and ear buds (S14; Sensimetrics, Gloucester, MA) fitted with noise-reducing ear plugs (Hearing Components, Inc., St. Paul, MN). A total of 72 aversive and benign auditory stimuli were adapted from open-access online sources. \r\n\r\nSkin Conductance Data Acquisition\r\nSkin conductance was continuously acquired during each scan using a Biopac system (MP-150; Biopac Systems, Inc., Goleta, CA). Skin conductance (250 Hz; 0.05 Hz high-pass) was measured using MRI-compatible disposable electrodes (EL507) attached to the second and third digits of the non-dominant hand. \r\n\r\nMRI Data Acquisition \r\nMRI data were acquired using a Siemens Magnetom TIM Trio 3 Tesla scanner (32-channel head-coil). Sagittal T1-weighted anatomical images were acquired using a magnetization prepared rapid acquisition gradient echo sequence (TR=2,400 ms; TE=2.01 ms; inversion time=1,060 ms; flip angle=8°; sagittal slice thickness=0.8 mm; in-plane=0.8 × 0.8 mm; matrix=300 × 320; field-of-view=240 × 256). A T2-weighted image was collected co-planar to the T1-weighted image (TR=3,200 ms; TE=564 ms; flip angle=120°). To enhance resolution, a multi-band sequence was used to collect oblique-axial echo planar imaging (EPI) volumes (multiband acceleration=6; TR=1,250 ms; TE=39.4 ms; flip angle=36.4°; slice thickness=2.2 mm, number of slices=60; in-plane resolution=2.1875 × 2.1875 mm; matrix=96 × 96). Images were collected in the oblique axial plane (approximately −20° relative to the AC-PC plane) to minimize potential susceptibility artifacts. A total of three 478-volume EPI scans were acquired. The first seven volumes were automatically discarded by the scanner. To enable fieldmap correction, two oblique-axial spin echo (SE) images were collected in each of two opposing phase-encoding directions (rostral-to-caudal and caudal-to-rostral) at the same location and resolution as the functional volumes (i.e., co-planar; TR=7,220 ms; TE=73 ms). \r\n\r\nMRI Data Pipeline \r\nMethods were optimized to minimize spatial-normalization error and other potential sources of noise. Data were visually inspected before and after processing for quality assurance. \r\n\r\nAnatomical Data Processing. Methods are similar to those described in other recent reports by our group (Hur et al., 2022; Hur et al., 2020). T1-weighted images were inhomogeneity corrected using N4 (Tustison et al., 2010) and denoised using ANTS (Avants et al., 2011). The brain was then extracted using BEaST (Eskildsen et al., 2012) and brain-extracted and normalized reference brains from IXI (BIAC, 2022). Brain-extracted T1 images were normalized to a version of the brain-extracted 1-mm T1-weighted MNI152 (version 6) template (Grabner et al., 2006) modified to remove extracerebral tissue. Normalization was performed using the diffeomorphic approach implemented in SyN (version 2.3.4) (Avants et al., 2011). T2-weighted images were rigidly co-registered with the corresponding T1 prior to normalization. The brain extraction mask from the T1 was then applied. Tissue priors were unwarped to native space using the inverse of the diffeomorphic transformation (Lorio et al., 2016). Brain-extracted T1 and T2 images were segmented—using native-space priors generated in FAST (version 6.0.4) (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012)—to enable T1-EPI co-registration (see below). \r\n\r\nFieldmap Data Processing. SE images and topup were used to create fieldmaps. Fieldmaps were converted to radians, median-filtered, and smoothed (2-mm). The average of the distortion-corrected SE images was inhomogeneity corrected using N4 and masked to remove extracerebral voxels using 3dSkullStrip (version 19.1.00). \r\n\r\nFunctional Data Processing. EPI files were de-spiked using 3dDespike, slice-time corrected to the TR center using 3dTshift, and motion corrected to the first volume and inhomogeneity corrected using ANTS (12-parameter affine). Transformations were saved in ITK-compatible format for subsequent use (McCormick, Liu, Jomier, Marion, & Ibanez, 2014). The first volume was extracted for EPI-T1 coregistration. The reference EPI volume was simultaneously co-registered with the corresponding T1-weighted image in native space and corrected for geometric distortions using boundary-based registration (Jenkinson et al., 2012). This step incorporated the previously created fieldmap, undistorted SE, T1, white matter (WM) image, and masks. The spatial transformations necessary to transform each EPI volume from native space to the reference EPI, from the reference EPI to the T1, and from the T1 to the template were concatenated and applied to the processed EPI data in a single step to minimize incidental spatial blurring. Normalized EPI data were resampled (2 mm3) using fifth-order b-splines. Hypothesis testing focused on anatomically defined regions of interest (ROIs), as detailed below. To maximize anatomical resolution, no additional spatial filters were applied, consistent with recent recommendations (Tillman et al., 2018). By convention, exploratory whole-brain voxelwise analyses employed data that were spatially smoothed (6-mm) using 3DblurInMask. \r\n\r\nfMRI Data Exclusions and Modeling\r\nData Exclusions. Participants who responded to <50% of rating prompts—indicating poor task compliance—were excluded from all analyses (n=2). The remaining participants completed >83% of the ratings. Volume-to-volume displacement (>0.5 mm) was used to assess residual motion artifact. Scans with excessively frequent artifacts (>3 SD) were discarded. The remaining participants provided at least 2 scans of usable data. \r\n\r\nCanonical First-Level Modeling. Single-participant (‘first-level’) GLMs were used to separate hemodynamic signals associated with the anticipatory periods of the MTC paradigm from those evoked by other aspects of the task. GLMs were implemented in SPM12 (version 7771) using the default autoregressive model and the temporal band-pass filter set to the hemodynamic response function (HRF) and 128 s (Wellcome Centre for Human Neuroimaging, 2022). Anticipatory signals were modeled using variable-duration rectangular regressors time-locked to the countdown periods of the Uncertain Threat, Certain Threat, and Uncertain Safety trials; and convolved with a canonical HRF and its temporal derivative. To maximize design efficiency, Certain Safety anticipation—which is psychologically similar to a conventional inter-trial interval—served as the implicit baseline. Periods corresponding to the presentation of the reinforcers (separately for each trial type), visual masks, and rating prompts were simultaneously modeled using the same approach. Consistent with prior work using the MTC paradigm (Hur et al., 2022; Hur et al., 2020), nuisance variates included estimates of volume-to-volume displacement, motion (6 parameters × 3 lags), cerebrospinal fluid (CSF) signal, instantaneous pulse and respiration rates, and ICA-derived nuisance signals (e.g. brain edge, CSF edge, global motion, white matter) (Pruim et al., 2015). Volumes with excessive volume-to-volume displacement (>0.5 mm) and those during and immediately following reinforcer delivery were censored.\r\n",
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