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      FMRI response to acute psychological stress differentiates patients with psychogenic non-epileptic seizures from healthy controls – A biochemical and neuroimaging biomarker study

      research-article
      a , * , a , a , a , a , a , a , 1 , a , a , b
      NeuroImage : Clinical
      Elsevier
      Psychogenic non-epileptic seizures (PNES), fMRI, Cortisol, Alpha-amylase, Psychological stress, Emotion, AA, salivary alpha-amylase, BAI, Beck Anxiety Inventory, BDI-II, Beck Depression Inventory-II, CMT, control math task, CORT, salivary cortisol, dAA, percent change in alpha-amylase, dCORT, percent change in cortisol, dHR, change in heart rate, DSSQ, Dundee Stress State Questionnaire, fMRI, functional magnetic resonance imaging, FND, functional neurological disorders, IQR, interquartile range, HCs, healthy controls, HPA-axis, hypothalamic pituitary adrenal axis, HR, heart rate, PANAS, Positive Affect and Negative Affect Schedule, PNES, psychogenic non-epileptic seizures, POMS, Profile of Mood States, PSS, Perceived Stress Scale, QOL, quality of life, ROIs, regions of interest, rs-FC, resting state functional connectivity, rs-fMRI, resting state functional magnetic resonance imaging, SF-36, Short Form-36, SMT, stress math task, SNS, sympathetic nervous system, STAI-t, trait- related State-Trait Anxiety Inventory, STAI-s, state-related State-Trait Anxiety Inventory, TMD, total mood disturbance (from POMS), UAB, University of Alabama at Birmingham, WCQ, Ways of Coping Questionnaire

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          Abstract

          We investigated psychological stress response in the brain regions involved in emotion-motor-executive control in psychogenic non-epileptic seizures (PNES). 12 PNES patients and 12 healthy controls (HCs) underwent stress task and resting state functional MRI (fMRI), mood and quality of life (QOL) assessments, and measurements of salivary cortisol, alpha-amylase, and heart rate. Group differences were assessed, and we correlated beta values from a priori selected brain regions showing stress task fMRI group differences with other stress response measures. We also used the regions showing stress task fMRI group differences as seeds for resting state functional connectivity (rs-FC) analysis. Mood and QOL were worse in PNES versus HCs. Physiological and assessment measures were similar except ‘Planful Problem Solving’ coping that was greater for HCs (p = .043). Perceived stress associated negatively with heart rate change ( r s  = −0.74, p = .0063). There was stress fMRI hyporeactivity in left/right amygdala and left hippocampus in PNES versus HCs (corrected p < .05). PNES exhibited a positive association between alpha-amylase change and right amygdala activation ( r s  = 0.71, p = .010). PNES versus HCs exhibited greater right amygdala rs-FC to left precentral and inferior/middle frontal gyri (corrected p < .05). Our findings of fMRI hyporeactivity to psychological stress, along with greater emotion-motor-executive control network rs-FC in PNES when compared to HCs suggest a dysregulation in stress response circuitry in PNES.

          Highlights

          • HPA-axis and autonomic system activation to acute stress are similar in PNES and HC.

          • PNES vs. HC show hyporeactivity to psychological stress in emotion-control regions.

          • PNES vs. HC show stronger emotion-motor/executive control functional connectivity.

          • Level of perceived stress modulates heart rate response to psychological stress.

          • Alpha-amylase and amygdala stress reactivity are positively associated in PNES.

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          Most cited references41

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          Dynamics of a stressful encounter: cognitive appraisal, coping, and encounter outcomes.

          Despite the importance that is attributed to coping as a factor in psychological and somatic health outcomes, little is known about actual coping processes, the variables that influence them, and their relation to the outcomes of the stressful encounters people experience in their day-to-day lives. This study uses an intraindividual analysis of the interrelations among primary appraisal (what was at stake in the encounter), secondary appraisal (coping options), eight forms of problem- and emotion-focused coping, and encounter outcomes in a sample of community-residing adults. Coping was strongly related to cognitive appraisal; the forms of coping that were used varied depending on what was at stake and the options for coping. Coping was also differentially related to satisfactory and unsatisfactory encounter outcomes. The findings clarify the functional relations among appraisal and coping variables and the outcomes of stressful encounters.
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            If it changes it must be a process: study of emotion and coping during three stages of a college examination.

            This natural experiment provides substantial evidence for the following major themes, which are based on a cognitively oriented, process-centered theory of stress and coping: First, a stressful encounter should be viewed as a dynamic, unfolding process, not as a static, unitary event. Emotion and coping (including the use of social support) were assessed at three stages of a midterm examination: the anticipation stage before the exam, the waiting stage after the exam and before grades were announced, and after grades were posted. For the group as a whole there were significant changes in emotions and coping (including the use of social support) across the three stages. Second, people experience seemingly contradictory emotions and states of mind during every stage of an encounter. In this study, for example, subjects experienced both threat emotions and challege emotions. The complexity of emotions and their cognitive appraisals reflects ambiguity regarding the multifaceted nature of the exam and its meanings, especially during the anticipation stage. Third, coping is a complex process. On the average, subjects used combinations of most of the available forms of problem-focused coping and emotion-focused coping at every stage of the exam. Different forms of coping were salient during the anticipation and waiting stages. Problem-focused coping and emphasizing the positive were more prominent during the former, and distancing more prominent during the latter. Finally, despite normatively shared emotional reactions at each stage, substantial individual differences remained. Using selected appraisal and coping variables, and taking grade point averages (GPA) into account, approximately 48% of the variances in threat and challenge emotions at the anticipation stage was explained. Controlling for variance due to the grade received, appraisal, and coping variables accounted for 28% of the variance in positive and negative emotions at the outcome stage. Including grade, 57% of the variance in positive emotions at outcome and 61% of the negative emotions at outcome were explained.
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              FMRI Clustering in AFNI: False-Positive Rates Redux.

              Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determining statistically significant clusters had greatly inflated FPRs ("up to 70%," mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially "countless" previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed "particularly high" FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depends on "task" paradigm and voxelwise p value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's bug effect was greatly overstated-their own results show that AFNI results were not "particularly" worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p ≤ 0.01.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                06 August 2019
                2019
                06 August 2019
                : 24
                : 101967
                Affiliations
                [a ]Department of Neurology, The UAB Epilepsy Center, University of Alabama at Birmingham, Birmingham, AL, USA
                [b ]Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
                Author notes
                [* ]Corresponding author at: Department of Neurology, University of Alabama at Birmingham (UAB) Epilepsy Center, 312 Civitan International Research Center, 1719 6th Avenue South, Birmingham, AL 35294, USA. jallendorfer@ 123456uabmc.edu
                [1]

                Current address: Department of Neurology, Ochsner Medical Center, New Orleans, LA, USA.

                Article
                S2213-1582(19)30317-1 101967
                10.1016/j.nicl.2019.101967
                6718876
                31446314
                54c8c393-44f9-42f0-aede-f69a6b556c08
                © 2019 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 2 April 2019
                : 24 July 2019
                : 1 August 2019
                Categories
                Regular Article

                psychogenic non-epileptic seizures (pnes),fmri,cortisol,alpha-amylase,psychological stress,emotion,aa, salivary alpha-amylase,bai, beck anxiety inventory,bdi-ii, beck depression inventory-ii,cmt, control math task,cort, salivary cortisol,daa, percent change in alpha-amylase,dcort, percent change in cortisol,dhr, change in heart rate,dssq, dundee stress state questionnaire,fmri, functional magnetic resonance imaging,fnd, functional neurological disorders,iqr, interquartile range,hcs, healthy controls,hpa-axis, hypothalamic pituitary adrenal axis,hr, heart rate,panas, positive affect and negative affect schedule,pnes, psychogenic non-epileptic seizures,poms, profile of mood states,pss, perceived stress scale,qol, quality of life,rois, regions of interest,rs-fc, resting state functional connectivity,rs-fmri, resting state functional magnetic resonance imaging,sf-36, short form-36,smt, stress math task,sns, sympathetic nervous system,stai-t, trait- related state-trait anxiety inventory,stai-s, state-related state-trait anxiety inventory,tmd, total mood disturbance (from poms),uab, university of alabama at birmingham,wcq, ways of coping questionnaire

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