Polygenic risk for stress-related disease correlates with fMRI stress task pulse rate recovery
Poster No:
629
Submission Type:
Abstract Submission
Authors:
Mira Erhart1, Dorothee Poehlchen2, Darina Czamara1, Julia Fietz1, Natan Yusupov1, Anne Kühnel3, Tanja Brückl1, BeCOME study team1, Michael Czisch1, Elisabeth Binder1, Philipp Sämann1, Victor Spoormaker1
Institutions:
1Max-Planck-Institute of Psychiatry, Munich, Bavaria, 2Max-Plack-Institute of Psychiatry, Munich, Bavaria, 3University of Bonn, Bonn, Nordrhein-Westfalen
First Author:
Co-Author(s):
Introduction:
Abnormal responses to acute stress are one of the key elements in the research of stress-related disorders (van Oort et al., 2020; White et al., 2014; Zorn et al., 2017). However, these disorders are multicausal and influenced by long-term predispositions such as stress exposure and genetic variants, too (Dalvie et al., 2021). In previous work, we showed that the incorporation of individual pulse rate (PR) traces in the fMRI analysis accounted for the highly individual and dynamic stress response and revealed – among others – limbic structures and the insula to be involved in the stress response (Figure 1A). We aimed to reproduce and extend our previous findings to a larger sample including 104 additional patients to examine the robustness of the correlation between individual PR patterns and limbic activity. Moreover, we aimed to evaluate the correlations between individual PR from this stress-task, polygenic risk scores (PRS) and stress exposure.
Methods:
266 participants from a transdiagnostic study including healthy controls and subjects with disorders such as depression or anxiety (Brückl et al., 2020) (female=128, mean age=34.8years, standard deviation=11.6) underwent the imaging stress test (IST) during fMRI including simultaneous pulse plethysmography. In three different phases (PreStress, Stress, PostStress) participants were asked to solve mental arithmetic tasks. Each phase consists of 5 mini-blocks [60s active calculus, 40s rest], with additional psychosocial stress applied during the 2nd phase.
Postprocessing was conducted exactly as in our previous work (Erhart et al., submitted manuscript). In brief, the fMRI volumes were slice-time corrected, realigned to the mean, and normalized using the DARTEL technique. Denoising followed a 2-step-residualization, first against motion and differential motion, after which time courses from white matter and cerebrospinal fluid masks were extracted and forwarded to a CompCor correction. Finally, images were smoothed (Gaussian, FWHM 6 mm isometric).
The first level model was set up in the GLM framework of SPM with a single regressor capturing the 15 active blocks with mean individual PR per block as a parametric modulator.
Random effect second-level analyses comprised a one-sample t-test of the estimates of the parametric modulator for the complete sample. Maps were thresholded at pvoxel.FWE-corrected <0.05, with a cluster extent >25.
For correlations with lifetime stress and PRS proportional PR downswings were calculated by normalizing the mean change in PR from the Stress to the PostStress phase by the mean change from the PreStress to the Stress phase (upswing). PRS for major depressive disorder (MDD) (Howard, 2019) and post-traumatic stress disorder (PTBS) (Stein, 2021) were calculated using PRSice 2.3.5 software and thresholded at p<5e-05. Higher values indicate greater polygenic risk. Lifetime stress was assessed with the Munich Life Event List (Maier-Diewald, 1983). Higher values represent greater lifetime stress.
Postprocessing was conducted exactly as in our previous work (Erhart et al., submitted manuscript). In brief, the fMRI volumes were slice-time corrected, realigned to the mean, and normalized using the DARTEL technique. Denoising followed a 2-step-residualization, first against motion and differential motion, after which time courses from white matter and cerebrospinal fluid masks were extracted and forwarded to a CompCor correction. Finally, images were smoothed (Gaussian, FWHM 6 mm isometric).
The first level model was set up in the GLM framework of SPM with a single regressor capturing the 15 active blocks with mean individual PR per block as a parametric modulator.
Random effect second-level analyses comprised a one-sample t-test of the estimates of the parametric modulator for the complete sample. Maps were thresholded at pvoxel.FWE-corrected <0.05, with a cluster extent >25.
For correlations with lifetime stress and PRS proportional PR downswings were calculated by normalizing the mean change in PR from the Stress to the PostStress phase by the mean change from the PreStress to the Stress phase (upswing). PRS for major depressive disorder (MDD) (Howard, 2019) and post-traumatic stress disorder (PTBS) (Stein, 2021) were calculated using PRSice 2.3.5 software and thresholded at p<5e-05. Higher values indicate greater polygenic risk. Lifetime stress was assessed with the Munich Life Event List (Maier-Diewald, 1983). Higher values represent greater lifetime stress.
Results:
We reproduced our main findings in the extended sample (Figure 1). Participants with a higher polygenic risk for MDD showed lower downswings after the stress phase (r=-.2, p=.003, Figure 2). Furthermore, negative correlations were observed between PR downswings and the polygenic risk for PTSD and two of its symptom subscales (sum: r=-.15, p=.001, hyperarousal: r=-.08, p=.003, avoidance: r=-.16, p=.001).
Higher lifetime work distress was associated with decreased downswings after the stress phase in the IST (r=-.12, p=0.03).
Higher lifetime work distress was associated with decreased downswings after the stress phase in the IST (r=-.12, p=0.03).
Conclusions:
We could reproduce our previous finding of individual pulse rate markers in response to an intense psychosocial stress test being correlated with fMRI BOLD levels in the insula and amygdala/hippocampus, among other regions. We report in addition, that individual pulse rate recovery in turn is correlated with polygenic risk scores for depression and PTSD connecting an acute stress marker with more long-term risk factors for psychopathology.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 2
Keywords:
Affective Disorders
FUNCTIONAL MRI
Limbic Systems
Modeling
Psychiatric Disorders
Other - Stress
1|2Indicates the priority used for review
Provide references using author date format
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