Shackman-Smith Mega-Analysis of the Maryland Threat Countdown Task

Description: Keywords: fear and anxiety; bed nucleus of the stria terminalis (BST/BNST); central nucleus of the amygdala (Ce/CeA); central extended amygdala (EAc) Nomenclature: CS - certain safety anticipation, CT - certain threat anticipation, US - uncertain safety anticipation, UT - uncertain threat anticipation OSF Resource Sharing - https://osf.io/fcvdj/ (DOI 10.17605/OSF.IO/FCVDJ) MEGA-ANALYSIS METHOD Overview of the Mega-Analysis The neuroimaging mega-analysis capitalized on data from two previously published fMRI studies focused on the neural circuits recruited by certain and uncertain threat-anticipation. The first study encompassed a sample of 220 psychiatrically healthy, first-year university students (Hur et al., 2022). The second study encompassed a mixed campus/community sample of 75 tobacco smokers (Kim et al., 2023). Both studies employed the same threat-anticipation paradigm (Maryland Threat Countdown task) and were collected using identical parameters on the same scanner. For the mega-analysis, all of the neuroimaging data were completely reprocessed using the identical pipeline, as detailed below. All participants provided informed written consent. Procedures were approved by the University of Maryland, College Park Institutional Review Board (protocols #659385 and #824438). Detailed descriptions of the study designs, enrollment criteria, participants, data collection procedures, and data exclusions are provided in the original reports (Hur et al., 2022; Kim et al., 2023). The mega-analysis was not pre-registered. Participants Across studies, an ethnoracially diverse sample of 295 participants provided usable neuroimaging data (45.4% female; 52.2% White Nonhispanic, 16.6% Asian, 19.0% African American, 4.1% Hispanic, 8.1% Multiracial/Other; M=21.6 years, SD=5.7). Power Analysis To enable readers to better gauge their confidence in our results, we performed a post hoc power analysis. G-power (version 3.1.9.2) indicated that the final sample of 295 usable fMRI datasets provides 80% power to detect “small” mean differences in region-of-interest activation (1-df contrasts, Cohen’s d=0.16, α=0.05, two-tailed) (Cohen, 1988; Faul et al., 2007). Threat-Anticipation Paradigm Paradigm Structure and Design Considerations. The Maryland Threat Countdown paradigm is a well-established, fMRI-optimized variant of temporally uncertain-threat assays that have been validated using fear-potentiated startle and acute anxiolytic administration (e.g., benzodiazepine) in mice, rats, and humans (Daldrup et al., 2015; Hefner et al., 2013; Lange et al., 2017; Miles et al., 2011; Moberg et al., 2017). The paradigm has been successfully used in several prior fMRI studies (Grogans et al., 2023; Hur et al., 2022; Hur et al., 2020; Kim et al., 2023). The paradigm 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). Participants 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. Stimulus presentation was controlled using Presentation software (version 19.0, Neurobehavioral Systems, Berkeley, CA). On Certain-Threat trials, participants 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 culminated with the presentation of a noxious electric shock, unpleasant photograph (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; M=18.75 s). Participants knew that something aversive was going to occur, but had no way of knowing precisely when. Consistent with recent recommendations (Shackman & Fox, 2016), the average duration of the anticipation epoch was identical across conditions, 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 (‘activation’) (Henson, 2007) and to allow sufficient time for sustained responses to become evident. Safety trials were similar, but terminated with the delivery of benign reinforcers (see below). Valence was continuously signaled during the anticipation epoch (‘countdown’) by the background color of the display. Temporal certainty was signaled by the nature of the integer stream. Certain trials always began with the presentation of the number 30. On Uncertain trials, integers were randomly drawn from a near-uniform distribution ranging from 1 to 45 to reinforce the impression that they could be much shorter or longer than Certain trials and to minimize incidental temporal learning (‘time-keeping’). To concretely demonstrate the variable duration of Uncertain trials, during scanning, the first three Uncertain trials featured short (8.75 s), medium (15.00 s), and long (28.75 s) anticipation epochs. 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 the persistence of visual reinforcers in iconic memory. Participants were periodically prompted (following the offset of the white-noise visual mask) to rate the intensity of fear/anxiety experienced a few seconds earlier, during the anticipation (‘countdown’) 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 (16.7% trials). Skin conductance was continuously acquired throughout. Procedures. Prior to scanning, participants practiced an abbreviated version of the paradigm (without electrical stimulation) until they indicated and 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. Aversive Stimulation. Participants received a 100 V stimulus and were asked whether it was “as unpleasant as you are willing to tolerate”—an instruction specifically chosen to maximize anxious distress and arousal. 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. Following each scan, staff re-assessed whether stimulation was sufficiently intense and increased the level as necessary. Electrical 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) and delivered using MRI-compatible, disposable carbon electrodes (Biopac) attached to the fourth and fifth digits of the non-dominant hand. Visual Stimuli. Seventy-two aversive and benign photographs (1.8 s) were selected from the International Affective Picture System (for details, see Hur et al., 2020). Visual stimuli were 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. Auditory Stimuli. Seventy-two aversive and benign auditory stimuli (0.8 s) were adapted from open-access online sources and 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). MRI Data Acquisition Data were acquired using a Siemens Magnetom TIM Trio 3 Tesla scanner (32-channel head-coil). Foam inserts were used to immobilize the participant’s head within the head-coil. Participants were continuously monitored using an eye-tracker (Eyelink 1000; SR Research, Ottawa, Ontario, Canada) and the AFNI real-time motion plugin (Cox, 1996). Eye-tracking data were not recorded. 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=8°; 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=36.4°; slice thickness=2.2 mm, number of slices=60; in-plane resolution=2.1875×2.1875 mm; matrix=96×96). Data were collected in the oblique-axial plane (approximately −20° relative to the AC-PC plane) to minimize susceptibility artifacts. Three 478-volume EPI scans were acquired. The scanner automatically discarded the first 7 volumes. To enable fieldmap correction, two oblique-axial spin echo (SE) images were collected in 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). Respiration and pulse 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. MRI Pipeline Methods were optimized to minimize spatial normalization error and other potential sources of noise and are similar to other work by our group (Grogans et al., 2023; Hur et al., 2022; Hur et al., 2020; Kim et al., 2023). Data were visually inspected before and after processing for quality assurance. Anatomical Data Processing. T1- and T2-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 et al., 2012) for subsequent use in T1-EPI co-registration (see below). Fieldmap 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). The resulting mask was minimally eroded to further exclude extracerebral voxels. Functional 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 processing (McCormick et al., 2014). The first volume was extracted for EPI-T1 co-registration. 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. Voxelwise analyses employed data that were spatially smoothed (4-mm) using 3DblurInMask. To minimize signal mixing, smoothing was confined to the gray-matter compartment, defined using a variant of the Harvard-Oxford cortical and subcortical atlases that was expanded to include the bed nucleus of the stria terminalis (BST) and periaqueductal gray (PAG) (Desikan et al., 2006; Edlow et al., 2012; Frazier et al., 2005; Makris et al., 2006; Theiss et al., 2017). Focal analyses of the central extended amygdala (EAc) leveraged spatially unsmoothed data and anatomically defined regions of interest (ROIs; see below), consistent with prior work by our group (Grogans et al., 2023; Kim et al., 2023; Tillman et al., 2018). fMRI Data Modeling First-Level Modeling. For each participant, first-level modeling was performed using general linear models (GLMs) implemented in 3dREMLfit (ARMA1,1; 4th-order Legendre high-pass filter). Regressors were convolved with the SPM12 canonical HRF. For the threat-anticipation paradigm, hemodynamic activity was modeled using variable-duration rectangular (‘boxcar’) regressors that spanned the entirety of the anticipation (‘countdown’) epochs of the Uncertain-Threat, Certain-Threat, and Uncertain-Safety trials. To maximize design efficiency, Certain-Safety anticipation—which is psychologically similar to a conventional inter-trial interval—served as the reference condition and contributed to the baseline estimate. Epochs corresponding to the presentation of the four types of reinforcers, white-noise visual masks, and rating prompts were simultaneously modeled using the same approach. EPI volumes acquired before the first trial and following the final trial were unmodeled and also contributed to the baseline estimate. Consistent with prior work (Grogans et al., 2023; Hur et al., 2022; Hur et al., 2020; Kim et al., 2023), nuisance variates included volume-to-volume displacement and first derivative, 6 motion parameters and first derivatives, cerebrospinal fluid (CSF) signal, instantaneous pulse and respiration rates, and nuisance signals (e.g., brain edge, CSF edge, global motion, white matter, extracerebral soft tissue) (Anderson et al., 2011; Pruim et al., 2015). Volumes with excessive volume-to-volume displacement (>0.75 mm) and those during and immediately following reinforcer delivery were censored. Analytic Strategy Overview. Except where noted otherwise, neuroimaging analyses were performed using SPM12 (version 7771) (Wellcome Centre for Human Neuroimaging, 2022). Confirmatory Testing. We confirmed that the paradigm recruited the canonical threat-anticipation network, including the dorsal amygdala (Ce) and BST (Grogans et al., 2023; Hur et al., 2022; Hur et al., 2020; Kim et al., 2023; Shackman & Fox, 2021). Spatially smoothed (4-mm) data and whole-brain voxelwise (‘second-level’) repeated-measures GLMs (‘random effects’) were used to compare each threat-anticipation condition to its corresponding control condition (e.g., Uncertain-Threat vs. Uncertain-Safety anticipation), while controlling for nuisance variation in mean-centered study, age, and biological sex. Significance was assessed using p<0.05 (whole-brain FWE corrected). A minimum conjunction test (logical ‘AND’) was used to identify voxels sensitive to both Certain and Uncertain Threat anticipation (Nichols et al., 2005). 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