SUPPLEMENTARY MATERIALS
Maladaptive emotion regulation traits predict altered corticolimbic recovery from positive social evaluation
Ryan J. Murray, Kalliopi Apazoglou, Zeynep Knight-Celen, Alexandre Dayer, Jean-Michel Aubry, Dimitri Van De Ville, Patrik Vuilleumier, & Camille Piguet
Corresponding Author:
Ryan J. Murray
Department of Psychiatry
Faculty of Medicine, University of Geneva Synapsy, University of Geneva
Campus Biotech Chemin des Mines 9 1202 Geneva, Switzerland Email: ryan.murray@unige.ch
1. Methods 1.1. Physiology
The oximeter was placed on the index finger of the participant’s left hand, if the person was right-handed, and vice versa otherwise. Data were recorded via Biopac Systems (Santa Barbara, CA). Heart rate analysis was performed offline with AcqKnowledge software (version 4.2; BIOPAC Systems Inc., Goleta, CA). Data were transformed using FIR High Pass filter fixed at 0.5Hz. Heart rate was calculated in a window of 40-180 BPM with 1-5% of the peak noise rejection. Subjects with corrupt recordings were excluded. Artifacts on the heart rate channel were interpolated by connecting endpoints only when the pulse signal was regular. Statistical analyses were conducted using IBM SPSS Statistics 25 for Windows (Corp, 2017).
1.2. fMRI data analysis
1.2.1. Acquisition parameters
Each run of the behavioral task comprised a mean number of 380 scans. Visual stimuli were displayed using an LCD projector (CP-SX1350, Hitachi, Japan) and shown on a screen at the rear of the scanner, which the participants could comfortably see through a mirror. Image quality was inspected for each participant to ensure the absence of signal drop out in ventral prefrontal regions. Anatomical images were also acquired for precise localization and normalization to standard templates, using a T1-weighted 3D sequence (TR/TI/TE: 1900/ 900/2.32 ms, flip angle = 9°, field of view = 230 mm, PAT factor = 2, voxel dimensions: 1 mm, isotropic 256 x 256 x 192 voxel).
1.2.2. Preprocessing
Individual functional images (EPI series) were first realigned using iterative rigid body transformations.
Participants with abrupt head movement of greater than 1 voxel (i.e. 3.2 mm) were ultimately removed from any subsequent analyses. EPI series for each participant were realigned to their mean functional
image, co-registered to their high-resolution T1-weighted structural image. Functional images were slice- time corrected, and then warped into alignment with the mean functional image and normalized to MNI EPI template (2D spline, voxel size: 3.2 mm). Normalized functional images were finally spatially smoothed using a Gaussian kernel with full-width at half maximum (FWHM) of 8 mm.
1.2.3. First-Level
Data were analyzed using robust weighted least squares (RWLS) method, which controls for noise and artifacts in fMRI data time series (Diedrichsen and Shadmehr, 2005). At the first-level (participant) analysis, movement corrections (realignment parameters) were imputed as covariates (i.e. nuisance regressors) into the general linear model. Contrast images were generated for the conditions of interest in each participant. Statistical analysis of functional images at the first-level was conducted using SPM12 (www.fil.ion.ucl.ac.uk/spm).
1.2.4. Second-Level
As stated in the main text, second-level analyses and multiple comparison corrections were implemented using threshold-free cluster enhancement (TFCE) nonparametric permutation testing via the FSL
randomize algorithm (Winkler et al., 2014). Contrary to cluster-based thresholding, which requires an initial and often arbitrary cluster-forming threshold, the TFCE method allows for a data-driven
thresholding approach, yielding outputs in which voxel-wise values represent "the amount of cluster-like local spatial support" (Smith and Nichols, 2009). The TFCE method has been shown to exhibit greater sensitivity, yielding increased signal-to-noise values (Smith and Nichols, 2009). We, therefore,
constructed the null distribution, generating 10'000 permutations of the input data, of the maximum (across voxels) TFCE score and then tested the resulting TFCE image against it. The TFCE image is then thresholded once the 95th percentile in the null distribution is located, thus allowing for an inference at the p<0.05 (corrected) level (Smith and Nichols, 2009). In cases where this level yielded clusters too large
to extract meaningful data, we decreased our p-corrected level even further (i.e. p<.025, p<.01, p<.005, p<.0025, p<.001, etc.).
1.2.5. Quality Check
As stated in the main text, we wished to control for feedback incongruence and participant suspicion.
Firstly, we conducted a factorial analysis examining neural responses specific to feedback as a function of expectancy violation; i.e., congruent or incongruent feedback with respect to performance. Positive performance was defined as a success rate of 60% or higher on the five calculation trials whereas negative performance was defined as 40% or lower on the five calculation trials. Congruencies were defined as feedback that corresponded with participant's performance (e.g. receiving positive feedback after positive performance), whereas incongruences were defined as feedback that did not correspond with participant's performance (e.g. receiving positive feedback after negative performance). We then compared expectancy affirmations (i.e. congruencies) with expectancy violations (i.e. incongruences). Secondly, we divided our sample into two groups based on debriefing at the end of the experiment, those who reported suspicion of the computer’s automatic responding and those who did. Given that the debrief questions were open, not all participants volunteered a clear response, thus limiting the sample size for this comparison.
1.2.6. Post-Hoc Moderation Analyses
As a post-hoc analysis, we wished to correlate heart rate (HR) with stress recovery BOLD response as a function of maladaptive cognitive emotion regulation traits (M-cER). To this aim, we ran individual regressions using a general linear model design, wherein we imputed BOLD signal of each contrast of interest (i.e. positive recovery, negative recovery, and positive<>negative recovery) as the intercept.
Additionally, we included average HR of each respective contrast and either non-adaptive cER (CERQ) or brooding rumination (RRS) as the slope of physiology and M-cER, respectively. Both physiology and
M-cER slopes were initially detrended separately. We then multiplied these detrended slopes to produce the slope of the interaction between HR and M-cER, which, after a final detrending, was ultimately modelled in our analyses. As in our main analyses, these post-hoc analyses were implemented using TFCE nonparametric permutation testing via the FSL randomize algorithm (Winkler et al., 2014).
2. Experimental Task
In our adapted MIST task (Dedovic et al., 2005), participants were first asked to perform mental
arithmetic calculations, in blocks of 5 trials. Each calculation trial lasted until the participant answered, or up to a maximum of 9 seconds. This random variation across participants thus removed the need for a jitter as each trial had a different length. At the beginning of every trial, a response cursor appeared at a default position of 5, and participants used a button box to move the cursor in either direction. Response choices always ranged from 0 to 10. At the beginning of a block, participants had to perform semi- complex calculations (Calculation Condition) within a limited time (9 secs maximum), represented by a white bar within which a black bar moved from left to right to indicate the passage of time (see figure 1).
Difficulty level was similar for all participants and chosen after piloting to result in approximatively 60%
correct answers. The calculation result was a number between 0 and 10 that participants had to indicate on a scale at the bottom of the screen, using a button box to move the cursor along the scale and validate their response. Five successive calculation screens were presented, after which a feedback screen was displayed for 8s (figure 1). At the end of the fifth trial in the respective block, participants were presented with an 8-second feedback screen, which could be positive, negative, or neutral (control condition).
Participants were given the cover story that their performance was compared to that of all other
participants studied so far. The feedback, therefore, included a ranking that placed the participant amongst a group of his/her peers, whose total number always equalled 35 participants. Although the real accuracy and reaction times were displayed on screen, written feedback was manipulated in order to have equal blocks of positive (e.g. “very good 2/35”) and negative (e.g. “try harder 34/35”) screens. The ratio is in
relation to the participant's ranking placement. The lower the numerator, the better the placement of the participant. Thus 2/35 indicates participant placed 2nd in their performance whereas a ratio of 34/35 suggests the participant placed second to last, with respect to the other fictitious participants. Positive feedback ranking ranged from 1/35 to 12/35 whereas negative feedback ranking ranged from 24/35 to 35/35. Following the feedback, a recovery period of 90s, eyes closed, was added to assess recovery subsequent to stress, with a sound indicating that the recovery period was over. During recovery, participants were instructed to let their mind wander freely and wait for the next calculation block.
Finally, the task also comprised control blocks (Control Condition) during which participants had to indicate the number shown on the screen instead of an arithmetic calculation, with 9s maximum per trial.
Then, after five consecutive arithmetic control trials, a neutral feedback was displayed (“ok”) for 8s followed by 90secs of recovery. During the control condition, performance feedback pertaining to ranking, percentage and response time was absent.
The task was implemented using E-Prime 2.0 software (Psychology Software Tools Inc., USA), and reaction times were recorded using a response button box (HH - 1 x 4 - CR, Current Designs Inc., USA).
Two calculation sessions of 12-minutes each were performed (figure 1) for each condition (positive, negative, neutral) for each of two separate blocks, thus representing 4 events for each condition. Previous imaging work using this type of design as well as piloting indicate that this number of repetition is sufficient to reliably measure the impact of emotional events (e.g. stress) on short recovery periods (cf.
Eryilmaz et al., 2011, 2014). Each condition session (positive, negative, neutral) thus included 5 blocks of calculations (9s maximum for each), 5 blocks of feedback (8s each) and 5 blocks of recovery (90s each).
3. Results 3.1. fMRI
3.1.1. Expectancy violation
In our quality check analysis, we investigated any possible effects during the recovery period from incongruent feedback, relative to one's performance, as well as from any overall suspicion. We thus
conducted a 2x2 repeated-measures flexible factorial design testing within-subject effects of Condition (congruent, incongruent feedback relative to performance) and between-subject effects of Group (suspicious, unsuspicious) across the three recovery period conditions. Suspicious participants were labelled as those who spontaneously admitted a suspicion that the feedback did not match their
performance, whereas unsuspicious participants showed no indication of suspicion toward the feedback.
All participants (N=47) had at least one congruent and at least one incongruent trial during the
experiment, of whom 43 volunteered meaningful information regarding their attitude toward the cover story (19 suspicious, 24 unsuspicious). Results yielded no main effect of condition, of group or an effect of their interaction. The results of the quality check thus affirm the validity of the feedback, whose periodic incongruence likely has a negligible effect on neural stress recovery.
As an additional post-hoc analysis, however, we wished to ensure the effects of non-adaptive cER and brooding rumination, shown in the main text, could not be explained by either incongruent feedback or participant suspicion of the cover story during the recovery period subsequent to positive feedback. We thus conducted a 2x2 flexible factorial analysis testing the effects of Condition (congruent, incongruent feedback) and Group (suspicious, unsuspicious participants) and tested any effects of their interaction within the positive recovery only. Whereas we again witnessed no significant effect of Condition, we did observe a significant effect of Group, whereby suspicious (versus unsuspicious) participants exhibited significantly increased neural activity in the right precuneus (posterior part), bilateral orbitofrontal cortex, left insula left striatum, bilateral thalamus, and cerebellum when recovery subsequent to positive feedback (see figure S1). We observed no significant Condition x Group interaction effects. Despite their
significance, however, these effects did not overlap with those of non-adaptive cER and brooding rumination shown in the main text. We can thus be confident that the effects of non-adaptive cER and brooding rumination on corticolimbic activity in the positive (vs. negative) recovery condition are not due to suspicion.
Figure S1. Effect of suspicion during recovery subsequent to positive feedback only. Figure illustrates significant neural activations during recovery subsequent to positive feedback in individuals who suspect the task feedback is not genuine with regards to their performance (suspicious) as compared to participants who do not suspect such a case (unsuspicious) (corrected-p<.05). Abbreviations. Precun: Precuneus.
3.1.2. Recovery
Region/Subregion k t-score Hem x y z
A. Main Effect of Social Feedback (Positive + Negative > Neutral)
Intracalcarine Gyrus 1868 5.73 L/R 0 -85 2
Fusiform 4.17 L -27 -82 -25
Intracalcarine Gyrus 5.4 L -6 -76 8
5.24 R 12 -76 8
Occipital Pole 4.57 L -27 -97 -7
Occipital Pole 4.51 L -18 -100 -7
B. Positive > Neutral
Supracalcarine Cortex 484 4.87 L/R 0 -85 5
Intracalcarine Cortex 4.63 L -6 -76 8
4.58 R 12 -76 8
C. Negative > Neutral
Intracalcarine Cortex 3320 5.29 R 3 -88 -1
Intracalcarine Cortex 5.16 L -9 -85 5
4.88 L -6 -73 11
Occipital Pole 4.75 L -18 -100 -7
4.71 R 12 -97 -1
Fusiform Gyrus 4.57 L -27 -85 -22
Hippocampus 691 4.56 L -27 -22 -10
Amygdala 4.01 L -24 -13 -10
Lateral orbitofrontal cortex 4.53 L -27 11 -19
4.51 L -33 17 -22
Temporal Pole 3.97 L -42 17 -22
vLPFC 3.89 L -45 44 -16
Frontal Pole/dLPFC 314 4.06 R 36 62 8
dLPFC 4.01 R 27 65 14
3.95 R 30 65 8
dMPFC 3.49 R 9 62 -4
3.48 R 6 65 14
Lateral orbitofrontal cortex 3.47 R 48 29 -4
SFG/dMPFC 111 3.9 L -3 53 41
SFG 3.27 L -3 23 62
2.86 L -6 38 29
Frontal Pole/vMPFC 110 3.98 L -6 62 -7
dLPFC 2.85 L -36 56 17
dMPFC 3.75 L -21 65 14
2.95 L -3 65 14
2.85 L -3 62 23
vMPFC 3.2 L -6 65 5
Mid Cingulate Gyrus 109 3.1 L/R 0 -1 38
Mid Cingulate Gyrus 2.86 R 3 17 38
2.67 L -9 8 35
2.59 L -3 17 41
Angular Gyrus 41 3.75 L -57 -55 41
Lateral Occipital cortex 3.6 L -57 -61 35
Supramarginal Gyrus 3.49 L -57 -46 47
Superior Frontal Gyrus 14 3.11 R 6 44 53
3.02 R 6 38 56
2.84 R 3 26 59
Angular Gyrus 12 3.44 L -39 -55 23
Table S1. Main effect of condition during Recovery. Whole-brain cluster peak activations to 90s* recovery period subsequent to social
feedback (p<.05). (A) Recovery period subsequent to both positive and negative, relative to neutral, feedback. (B) Recovery period subsequent to positive, relative to neutral, feedback. (C) Recovery period subsequent to negative, relative to neutral, feedback. Clusters with k<5 voxels are not shown. The results were thresholded using TFCE nonparametric permutation testing (Winkler et al., 2014). Abbreviations. dMPFC:
dorsomedial prefrontal cortex; dLPFC: dorsolateral prefrontal cortex; ; Hem: hemisphere; SFG: superior frontal gyrus; vMPFC: ventromedial prefrontal cortex; vLPFC: ventrolateral prefrontal cortex. *First 5 seconds of the 90s recovery period were excluded from analysis to reduce the likelihood of any visuoperceptual biases induced from the preceding instructions screen.
4. References
Corp, I. (2017). IBM SPSS Statistics for Windows. (ed. I. Corp.): Armonk, NY.
Dedovic, K., Renwick, R., Mahani, N. K., Engert, V., Lupien, S. J. & Pruessner, J. C. (2005). The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. J Psychiatry Neurosci 30, 319-25.
Diedrichsen, J. & Shadmehr, R. (2005). Detecting and adjusting for artifacts in fMRI time series data.
Neuroimage 27, 624-34. 10.1016/j.neuroimage.2005.04.039.
Eryilmaz, H., Van De Ville, D., Schwartz, S. & Vuilleumier, P. (2011). Impact of transient emotions on functional connectivity during subsequent resting state: a wavelet correlation approach. Neuroimage 54, 2481-91. 10.1016/j.neuroimage.2010.10.021.
Eryilmaz, H., Van De Ville, D., Schwartz, S. & Vuilleumier, P. (2014). Lasting impact of regret and gratification on resting brain activity and its relation to depressive traits. J Neurosci 34, 7825-35.
10.1523/JNEUROSCI.0065-14.2014.
Smith, S. M. & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83-98.
10.1016/j.neuroimage.2008.03.061.
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage 92, 381-97. 10.1016/j.neuroimage.2014.01.060.
