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. Author manuscript; available in PMC: 2025 Sep 16.
Published in final edited form as: Schizophr Res. 2023 Mar 27;255:93–101. doi: 10.1016/j.schres.2023.03.038

Neural Correlates of Daily-Life Affective Stress Reactivity in Early Psychosis: A Study Combining Functional MRI and Experience Sampling Methodology

Thomas Vaessen a,b, Ulrich Reininghaus c, Evelyne van Aubel a, Annelie Klippel a,d, Henrietta Steinhart a, Inez Myin-Germeys a,†, James Waltz e,†
PMCID: PMC12434802  NIHMSID: NIHMS2107539  PMID: 36989675

Abstract

Affective reactivity to daily stressors are increased in individuals in the early stages of psychosis. Studies in psychosis patients and healthy individuals at increased psychosis risk show altered neural reactivity to stress in limbic (i.e., hippocampus [HC] and amygdala), prelimbic (i.e., ventromedial prefrontal cortex [vmPFC] and ventral anterior cingulate cortex [vACC]), and salience areas (i.e., anterior insula [AI]). We investigated whether a similar pattern of neural reactivity is present in early psychosis individuals and if brain activity in these regions is associated with daily-life stress reactivity.

Twenty-nine early psychosis individuals (11 at-risk mental state and 18 first-episode psychosis) completed the Montreal Imaging Stress Task in conjunction with functional MRI. The study was part of a large-scale randomized controlled trial on the efficacy of an acceptance and commitment therapy-based ecological momentary intervention for early psychosis. All participants also provided experience sampling methodology (ESM) data on momentary affect and stressful activities in their everyday environment. Multilevel regression models were used to estimate if daily-life stress reactivity was moderated by activity in (pre)limbic and salience areas.

Task-induced stress was associated with increased activation of the right AI and decreased activation in the vmPFC, vACC, and HC. Task-induced changes in vmPFC and vACC activity were associated with affective stress reactivity, whereas changes in HC and amygdala activity were associated with higher overall stress ratings.

These preliminary results suggest region-specific roles in affective and psychotic daily-life stress reactivity in early psychosis. The observed pattern suggests that chronic stress plays a role in neural stress reactivity.

Keywords: stress reactivity, experience sampling method, functional magnetic resonance imaging, early psychosis, affect

1. Introduction

The role of stress, the biological and psychological reaction to threatening circumstances (Lazarus and Folkman, 1984), in the development of psychotic illness has long been established (Corcoran et al., 2003; Walker and Diforio, 1997). In vulnerable individuals, stress may elicit psychotic-like experiences through increased affective reactivity (Klippel et al., 2017; Klippel et al., 2021; Myin-Germeys and van Os, 2007), or the extend to which negative affect (NA) increases and positive affect (PA) decreases following stress. Experience sampling method (ESM) studies have consistently shown increased affective reactivity to everyday stressful situations in individuals with chronic psychosis, first episode psychosis (FEP), at-risk mental state for psychosis (ARMS), and familial risk for psychosis (Collip et al., 2011; Lataster et al., 2010; Myin-Germeys et al., 2001; Palmier-Claus et al., 2012; Reininghaus et al., 2016; van der Steen et al., 2017). Of these groups, FEP and ARMS comprise the early stages of psychosis (Fusar-Poli et al., 2013; Linscott and van Os, 2013; van Os and Linscott, 2012; van Os and Reininghaus, 2016; Yung et al., 2005), with the construct of the ARMS (also known as the “clinical high risk” [CHR] or “prodromal phase”) having evolved to capture the prepsychotic phase, describing people presenting with potentially prodromal symptoms (Fusar-Poli et al., 2013; van Os and Linscott, 2012; van Os and Reininghaus, 2016). These groups show the most pronounced changes in affect following stressful situations (Palmier-Claus et al., 2012; van der Steen et al., 2017), highlighting the important role of daily hassles and putting affective stress reactivity forward as putative mechanisms in the development of psychotic illness via an affective pathway.

The neural processes associated with this altered affective reactivity to daily stressors in psychosis are not yet clear, and investigating them could further our understanding of the development of psychosis in the early stages. A limited number of functional magnetic resonance imaging (fMRI) studies have investigated neural responses to experimentally-induced stress in populations across the psychosis continuum, all of them using the Montreal Imaging Stress Task (MIST) or tasks based on the MIST, a paradigm involving the performance of mental arithmetic under social pressure. These types of tasks aim to induce psychosocial stress, which, compared to more physiological stress tasks (e.g., pain induction), arguably more closely mirror real-life stressors and hence have greater ecological validity.

In healthy volunteers, stress induced by the MIST has resulted in decreased neural activity in the limbic system (Dedovic et al., 2009a; Pruessner et al., 2008; Soliman et al., 2011), a set of brain structures (including hippocampus [HC] and amygdala) associated with experience and expression of emotion. While the role of limbic regions in emotion processing has been long-since acknowledged (e.g., see LeDoux, 2000), prelimbic areas such as the ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), and anterior cingulate cortex (vACC), appear to play important roles as well. Mainly, they have been associated with emotion regulation – or attempts to influence emotions (McRae and Gross, 2020) – and have been implicated in disturbed emotion regulation across the psychopathology spectrum (Hiser and Koenigs, 2018; Rive et al., 2013; Wilcox et al., 2016). Furthermore, neural activity in the ACC is associated with limbic responses to emotional stimuli through connections with hippocampal and amygdaloid regions (Stevens et al., 2011), emphasizing the potential relevance of this prelimbic-limbic complex for affective stress reactivity.

Another region involved in stress reactivity – and strongly connected to the vACC as well – is the anterior insula (AI); in addition to its role in the limbic system, the AI is hub of the salience network (Hermans et al., 2014; Seeley, 2019), which has been assigned an active role in the development of psychosis and psychotic symptoms (Kapur, 2003; Palaniyappan and Liddle, 2012; Reininghaus et al., 2016). In this light, stress-related activity in the AI may be of particular relevance for the development of psychosis. Considering this evidence, the vmPFC/vACC complex, limbic regions, and AI may be associated with affective stress reactivity in psychosis.

Specifically, neuroimaging studies of stress reactivity in healthy volunteers have revealed that neural responses to stress (when compared with a no-stress control condition) were characterized by decreased activity in the ACC, hypothalamus, amygdala, OFC, and HC, with deactivation in the latter region being directly correlated with increased release of the stress hormone cortisol, highlighting its relevance in the stress response (Pruessner et al., 2008). Healthy volunteers who reported above-average levels of physical anhedonia showed greater stress-related deactivation in the ACC, amygdala, HC, striatum, fusiform gyrus, and superior temporal gyrus (Soliman et al., 2011) compared to healthy volunteers reporting lower levels of physical anhedonia, possibly reflecting increased sensitivity to stress at the neural level. While results are mixed, they generally suggest that (risk for) psychosis is associated with a reduced stress-related neural response in areas involved in all of the emotion-related processes listed above (emotion processing, emotion regulation, and salience attribution), which would be in line with the affective pathway to psychosis hypothesis (Myin-Germeys and van Os, 2007). One study (Castro et al., 2015) directly compared stress-induced brain activity in healthy volunteers to that in schizophrenia patients (all medicated with second-generation antipsychotic medications). These authors found that, whereas stress resulted in activation of the frontal and limbic regions in healthy volunteers, stress was not found to modulate activity in these areas in schizophrenia patients, with the largest between-group difference being observed in ACC. Two additional studies have investigated stress-related changes in brain activity in individuals at familial risk psychosis. One study reported activation in the ventral ACC (vACC), OFC, and HC in healthy volunteers but not in siblings of schizophrenia patients (Castro et al., 2017); a second study reported deactivations in, among other regions, the vmPFC and AI in healthy volunteers, but again not in siblings (van Leeuwen et al., 2018).

To date, no study has investigated neural stress responses in early psychosis, where individuals show the strongest evidence for altered affective stress reactivity (Palmier-Claus et al., 2012; Reininghaus et al., 2016; Vaessen et al., 2019; van der Steen et al., 2017). Moreover, no study has investigated whether a neural stress response seen in psychosis is associated with affective reactivity to stressors in daily life. Investigating these neural correlates of stress reactivity in early psychosis individuals may shed light on the mechanism underlying development of psychosis. We therefore sought to identify the neural correlates of affective daily life stress reactivity in a combined fMRI – ESM study in ARMS and FEP individuals. We hypothesized that: 1) exposure of early psychosis individuals to a psychosocial stress task would be associated with decreased activity in limbic areas (i.e., amygdala and HC), areas associated with emotion regulation (i.e., vmPFC, OFC, and vACC), and nodes of the salience network (i.e., AI); 2) that affective reactivity to experimentally induced stress in the lab would be associated with stress-related deactivation of these regions; and 3) that affective reactivity to daily-life stress would be associated with deactivation of these regions. Considering the relative scarceness of prior literature on neural correlates of stress reactivity in psychosis, we could not justify any specific hypotheses regarding associations between specific brain region and outcome measures. Therefore, hypotheses 2 and 3 are of a more explorative nature.

2. Methods

2.1. Study design

The study was part of a large-scale randomized controlled trial (RCT) on the efficacy of an acceptance and commitment therapy (ACT)-based ecological momentary intervention for early psychosis individuals (INTERACT: NTR4252 http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4252). The scanning protocol described in the current manuscript was added in a later amendment and applied in a subsample. For a full description of the larger RCT, processes, and measures, see Reininghaus et al. (2019). All eligible participants were invited for two scan sessions involving fMRI; here we provide data from the pre-treatment assessments.

2.2. Participants

A total of 33 individuals in the early stages of psychosis were recruited from secondary mental health institutions across The Netherlands and Belgium for the larger study (see Supplementary Methods for study eligibility criteria). The study was approved by the medical-ethical committee of the academic hospital in Leuven, Belgium (reference: B322201629214; study number: s59127, Interact), and in accordance with the Helsinki Declaration (2013).

2.3. Clinical and Neuropsychological Assessments

The Brief Psychiatric Rating Scale (BPRS)(Ventura et al., 1993) was used to estimate rate symptom severity in participants. The BPRS total scale and subscales were calculated by summing all items of the respective scale. An estimation of premorbid IQ was obtained with the Dutch Adult Reading Test (Schmand et al., 1991).

2.4. Lab session

The MIST was used to induce mild levels of psychosocial stress (Dedovic et al., 2005; Pruessner et al., 2004). Briefly, the task consists of three conditions (i.e., rest, control, experimental) that are presented in a fixed order over the course of four identical runs. During the experimental blocks, participants solve mathematical equations under time pressure and receive scripted negative feedback on their performance (Supplementary Methods). Before and after the scan session, as well as after every run, participants filled out a short questionnaire assessing self-reported stress, NA and PA (table 1).

Table 1.

Operationalization of questionnaire measures and internal consistencies

Cronbach’s alpha
measure operationalization lab daily life
self-reported stress “I feel judged”, “I am under pressure”, “I do not live up to the expectations” between=0.865
within=0.713		 .
activity-related stress Following the question: “At the moment, what are you doing?”
“I can do this well” (reversed coded), “This is difficult”, and “I would rather do something else” 		. between=0.453
within=0.402
negative affect “I feel down, I feel insecure, I feel anxious, I feel guilty” between=0.811
within=0.646		 between=0.833
within=0.660
positive affect “I feel relaxed, I feel cheerful” between=0.851
within=0.449		 between=0.782
within=0.612
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All items were rated on 7-point Likert scales ranging from 1 (“Not at all”) to 7 (“Very”). For each construct, the mean scores over all items in the scale were computed per measurement point.

2.5. Daily Life

Within one month time prior to or following the scan, participants engaged in a 6-day ESM period, being notified at 10 semi-random moments per day between 7.30 AM and 10.30 PM to fill out a brief questionnaire assessing NA, PA, and activity-related stress as described in Table 1. Questionnaires were presented using the PsyMate™ app (www.psymate.eu).

2.6. Neuroimaging data acquisition and preprocessing

Whole-brain functional EPI images (for measurement of T2*-weighted BOLD effects) were acquired simultaneous with task performance, using a Philips 3-tesla scanner (Eindhoven, Netherlands). Functional images were acquired using the following parameters: 36 3.75-mm axial slices; 80 × 80 matrix; FOV=22 × 22 cm; TR=2 s (function voxels resampled to 1.5 mm3 for final analyses). In conjunction with performance of the MIST, 840 images were acquired across 4 runs (28 min 0 sec). In each session, we acquired a whole-brain T1-weighted structural (FISP) image for anatomical reference (1-mm3 isotropic voxels; shot interval=3000 ms; TR=9.6 ms; TE=4.6 ms; FA=8°). Our ROI’s (i.e., vmPFC, vACC, HC, amygdala, right AI, and left AI) were determined based on prior literature (Supplementary Methods).

2.7. Statistical analyses

In order to determine which brain areas had their activity modulated by the MIST (hypothesis 1), we first performed a 1-sample whole-brain t-test on the contrast of the stress block and control block beta coefficients (the rest blocks were not used for any of the analyses), using the AFNI 3dttest++ function (Cox, 1996). For the whole-brain analysis, we used a voxel-wise threshold of p=0.001 and a cluster-size threshold of 448 voxels, determined by Monte Carlo simulations (Cox, 2019; Cox et al., 2017). We then performed a paired-samples t-test on mean betas during control and stress conditions extracted from each ROI listed above to see if they significantly differed. To see if ARMS and FEP differed in their brain response, we then performed an additional regression analysis for each ROI with the binary variable status (0=ARMS; 1=FEP) as predictor to see if MIST-induced brain activity in these regions differed between groups.

Next, in order to test if task-induced changes in ROI activity were associated with changes in outcomes (hypothesis 2), we ran six (one for each ROI) multilevel regression models for each of the three outcomes (self-reported stress, NA, and PA) with the dichotomous factor phase (0=baseline; 1=stress), ROI difference score (stress–control), and the ROI*phase interaction term as predictors. In each model, individual observations (level 1) were nested within individuals (level 2).

Finally, for the analysis of the ESM data, we used the ROI difference scores to predict three daily-life outcomes: activity-related stress, NA and PA (hypothesis 3). First, we tested whether task-induced changes in activity of our ROIs were associated with overall (i.e., mean levels over the assessment period) daily-life measures. We therefore ran separate multilevel regression models with each of the six ROIs as predictor for each of the three outcomes, where individual observations (level 1) were nested within days (level 2), which were nested within individuals (level 3). Then, to test if task-induced changes in activity of our ROIs were associated with daily-life stress reactivity – operationalized as the association between activity-related stress and NA (Myin-Germeys et al., 2001) – we reran each model, adding the interaction term ROI*activity-related stress.

In all models, we controlled for sex, age, and premorbid IQ as potential confounders. Intercepts were allowed to vary randomly within participants and days, and an unstructured variance-covariance matrix was assumed for all models. Given the explorative nature of the study, we did not correct for multiple testing. All analyses were carried out in Stata version 13.1 (StataCorp, 2013). We corrected for multiple comparisons per hypothesis per outcome measure, by multiplying the obtained p-values by six for each of the six ROIs.

3. Results

3.1. Sample and demographics

Of the 33 individuals who completed MRI scanning, one participant was excluded from analyses due to excessive head motion during the scan session, and one participant did not finish the entire task. Two more participants were excluded because they did not provide any ESM data. Therefore, the final sample consisted of 29 participants (16 male, 13 female), with a mean age of 24.5 years (Table 2; Supplementary Results).

Table 2:

Sample characteristics.

total (n=29) ARMS (n=11) FEP (n=18) p-value
age 24.47 (5.94) 21.98 (3.39) 25.99 (6.70) p=0.078
sex p=0.111
 male 16 4 12
 female 13 7 6
race p=0.193
caucasian 28 10 18
black 1 1 0
education p=0.108
 pre-vocational education 5 1 4
 vocational training 7 2 5
 secondary education 8 6 2
 higher education 8 2 6
living situation p=0.177
 alone 3 0 3
 w partner/own family 4 3 1
 w parents/family 10 3 7
 student housing/ w roommates 4 3 1
 psychiatric setting 7 2 5
 single parent w children 1 0 1
work situation p=0.031
 school/education 8 6 2
 employed (fulltime or parttime) 4 2 2
 volunteered /sheltered work 4 0 4
 non-structured activities 11 2 9
 other 1 1 0
estimated premorbid IQ * 100.07 (12.61) 104.64 (12.04) 97.28 (12.46) p=0.130
medications
 Antipsychotic 17 (59%) 0 (0%) 17 (94%) p<0.001
 Antidepressant/Mood Stabilizers 12 (43%) 7 (64%) 5 (29%) p=0.304
 Anxiolytics 9 (31%) 0 (0%) 9 (50%) P<0.001
 Stimulants 3 (11%) 2 (18%) 1 (6%) p=0.304
clinical symptoms scores from the BPRS
 total 48.38 (9.03) 52.44 (7.62) 46.24 (9.18) p=0.096
 positive 11.00 (4.16) 10.55 (3.59) 11.27 (4.55) p=0.654
 depression 13.03 (4.03) 15.09 (2.88) 11.78 (4.18) p=0.029
 negative 12.73 (3.75) 14.22 (2.77) 11.94 (4.02) p=0.143
 mania 7.12 (1.14) 6.78 (1.09) 7.29 (1.16) p=0.282
 disorganisation 3.72 (1.51) 4.09 (1.92) 3.50 (1.20) p=0.315
GAF general 41.41 (9.66) 47.09 (6.17) 37.94 (9.88) p=0.011
ESM variables
 activity-related stress 3.39 (0.69) 3.78 (0.33) 3.15 (0.75) p=0.014
 negative affect 2.68 (1.16) 3.34 (1.06) 2.20 (0.86) p=0.002
 positive affect 3.88 (1.09) 3.53 (1.01) 4.09 (1.10) p=0.179
 n filled out beeps 35.24 (11.44) 36.55 (10.96) 34.44 (11.96) p=0.640
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*

from the Dutch Adult Reading Test (Schmand et al., 1991)

ARMS=at-risk mental state for psychosis; FEP=first episode psychosis; BPRS=brief psychiatric rating scale; GAF=global assessment of functioning; ESM=experience sampling methodology.

3.2. Associations between measures of task-evoked neural-activity and task-induced stress

3.2.1. Whole-brain analysis of fMRI data

We first checked whether the task had been successful in inducing changes in self-reported stress and affect (see supplementary results). Whole-brain analyses on the [stress - control] contrast revealed stress-related activation in six main clusters (Figure S3, Table 3). The largest cluster encompassed the superior occipital gyrus/precuneus and parts of the posterior parietal cortex. A smaller cluster was located around the bilateral thalamus. A third cluster overlapped mostly with the left posterior superior temporal gyrus. Two clusters overlapped with the right superior frontal gyrus and the right middle frontal gyrus. Finally, there was a small cluster in the right AI. We did not find any clusters that significantly deactivated during stress compared to control conditions. Unfortunately, signal quality in the OFC was of such low quality that we could not use this region in any of our analyses.

Table 3.

Coordinates of significant whole-brain contrasts [Stress - Control].

Brain Region L/R/B x , y , z Vol (ml)
Superior Occipital Gyrus/Posterior Parietal Cortex Bilateral 0 , −66 , 55 187373
Thalamus Bilateral 2 , −20 , 12 2025
Superior Temporal Gyrus Left −59 , −59 , 10 1961
Superior Frontal Gyrus Right 36 , 62 , 19 1256
Middle Frontal Gyrus Right 45 , 20 , 24 432
Anterior Insula Right 42 , 23 , −8 388
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3.2.2. ROI analyses of MRI responses as a function of clinical status

We then tested whether there was a significant change in activity between control and stress conditions in our predefined ROIs (Table 4). Results indicated that there was a significant increase in brain activity during stress compared to control in the right AI, but significant decreases in activity in the vmPFC, vACC, and HC. Clinical status did not predict task-related change in ROI activity (all p-values>.1), suggesting that ARMS and FEP individuals are comparable in their neural response to the MIST in these regions. After correction of multiple comparisons, none of the results were statistically significant anymore.

Table 4.

Coordinates of a priori ROIs.

ROI L/R Source X Y Z Mean control (SD) Mean stress (SD) t p
vmPFC B NEUROSYNTH.ORG 0 52 −12 −0.42 (0.99) −0.94 (1.65) 2.20 0.04
vACC B NEUROSYNTH.ORG 0 38 −4 −0.19 (0.57) −0.50 (0.91) 2.35 0.03
r_AI R Millman et al. (2019) 32 18 2 0.14 (0.20) 0.21 (0.27) −2.65 0.01
l_AI L Millman et al. (2019) −33 19 3 0.17 (0.22) 0.19 (0.32) −0.61 0.55
HC L Pruessner et al. (2008) −25 −10 −16 −0.12 (0.26) −0.19 (0.33) 2.22 0.03
AMY B Talairach daemon ±24 −5 −14 −0.12 (0.21) −0.16 (0.29) 1.24 0.23
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3.2.3. Associations between task-evoked brain responses and outcome variables in the MRI session

Task-induced increases in self-reported stress were negatively associated with task-induced changes in vmPFC (B=−.38; p=.005; 95%CI=−.65, −.11) and vACC (B=−.62; p=.016; 95%CI=-1.12, −.12), suggesting that individuals experiencing greater increases of stress during the task deactivated these regions more so than individuals reporting smaller increases of self-reported stress. After inspection of the plots, these results appear to be mainly driven by higher experienced stress before the task in individuals showing less deactivation of these brain areas during stress (Figure 2A/B). Task-induced increases in NA were positively associated with task-induced changes in the right AI (B=2.88; p=.03; 95%CI=.29, 5.48), such that individuals reporting a stronger NA reaction to the task showed greater task-induced activation of the right AI (Figure 2C). Task-induced decreases in PA were negatively associated with task-induced decreases in the vACC, although not significantly so (B=−.45; p=.071; 95%CI=−.94, .04; Figure 2D). After correcting for multiple comparisons, only the association between vmPFC activity and self-reported stress remained statistically significant.

Figure 2.

Figure 2.

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Associations between task-related changes in neural activity the ROIs and ESM affective stress reactivity. Projected regression lines of unstandardized regression coefficients from different ROIs on ESM affective stress reactivity for the mean ROI beta value and for one and two standard deviations (SD) above and below the mean.

3.3. Associations between task-evoked brain responses and EMA measures

3.3.1. Associations between task-evoked brain responses and overall stress and affect in daily life

Individuals who deactivated their HC (B=1.82; p=.005; 95%CI=.54, 3.1) and amygdala (B=2.12; p=.001; 95%CI=.89, 3.35) more during stress reported lower overall activity-related stress in daily life. Task-induced changes in the right AI were positively associated with overall activity-related stress at trend-level. Similarly, there were significant positive associations between task-induced changes in right (B=3.53; p=.006; 95%CI=1.02, 6.03) and left AI activation (B=3.03; p=.012; 95%CI=.65, 5.42) and overall ratings of NA in daily life. We found no associations between task-induced changes in brain activity and overall ratings of PA in daily life. After correcting for multiple comparisons, only the associations between HC and amygdala activity and overall ratings of activity-related stress, and between right AI and overall ratings of NA remained statistically significant.

3.3.2. Associations between task-evoked brain responses and daily-life stress reactivity

Task-induced changes in vmPFC activity significantly moderated relationships between activity-related stress in daily-life and both NA (B=−.06; p=.001; 95%CI=−.10, −.03) and PA (B=.06; p=.006; 95%CI=.02, .11), such that a stronger deactivation during stress was associated with stronger increases in NA and decreases in PA in response to daily-life stressors. Similar results were observed for changes in vACC activity (NA: B=−.09; p=.012; 95%CI=−.15, −.02; PA: B=.09; p=.028; 95%CI=.01, .18). Again, based on the plots these effects appear to be mainly driven by lower ratings of NA and higher ratings of PA at moments of low activity-related stress in individuals who deactivated the vmPFC and vACC regions during the task (Figure 3A-D). Finally, there was an effect of task-induced changes in the right AI on PA reactivity to daily stressors (B=.70; p=.011; 95%CI=.15, 1.23; Figure 3E), where stronger stress-related activation of this area predicted blunted reactivity associated with lower ratings of PA when activity-related stress was low. After correcting for multiple comparisons, only the associations between vmPFC activity and NA and PA reactivity to daily-life stress remained statistically significant.

3.3.2. Post-Hoc Analyses Controlling for Psychotic Symptom Severity

To see if these results could be (partially) explained by the degree of psychotic symptom severity and status, we reran all multilevel models with the additional inclusion of BPRS positive symptom score and status (ARMS vs FEP) as additional covariates, but results remained the same.

4. Discussion

This study, investigating relationships between acute neural stress response in early psychosis and self-reported experiences of stress and affect in daily life, found that stress-related changes in limbic regions (HC and amygdala, specifically) were associated with higher overall stress ratings in daily life. Changes in vmPFC and vACC activity were negatively associated with reported stressfulness of the task and with affective reactivity in daily life. Stress-induced activation of the AI was associated with higher ratings of NA in daily life. Results suggested that higher ratings of negative affect in daily life are associated with reduced modulation of the vmPFC, vACC, HC, and amygdala, and increased modulation of the AI, by psychosocial stress.

4.1. Effects of stress on the brain

Consistent with Pruessner et al. (2008) and Dedovic et al. (2009b), we found evidence for deactivation of (pre)limbic regions (vmPFC, vACC, and HC), relative to baseline, when experiencing acute psychosocial stress during performance of a cognitive task. By contrast, both ROI and whole-brain analyses revealed activation of rAI, in response to acute psychosocial stress in early psychosis individuals. This pattern is more or less in line with results of Soliman et al. (2011), but not with Castro et al. (2015) who found no stress-induced changes in neural activity in any of these regions in schizophrenia patients. Multiple studies have observed deactivations of limbic regions during stress (Pruessner et al., 2008; Soliman et al., 2011) in healthy volunteers (but see Castro et al., 2015). Of note, the sample of Soliman et al. (2011) did not include schizophrenia patients; rather, it was comprised of healthy college students that scored above average on a questionnaire assessing perceptual aberrations and physical anhedonia. Our sample likely falls between these two groups on the continuum in terms of psychosis severity. Along that same line, we did not observe significant differences in neural response to stress between ARMS and FEP individuals. However, considering the relatively small sample size this may be due to a lack of power. Future studies directly comparing these and other groups along the psychosis continuum are necessary to determine the specificity of this altered neural stress reactivity for psychosis.

4.2. Limbic regions and self-reported stress

We found positive associations between daily-life overall stress ratings and stress responses in the right AI, HC, and amygdala, supporting the hypothesis that these regions are implicated in the neural stress response, also in early psychosis. Specifically, a relative lack of modulation by stress in these areas predicted overall high stress ratings in daily life. It appears that stress-related deactivation of limbic regions is a normative response to stress, and individuals who report chronic higher ratings of activity-related stress in their daily lives show reduced deactivations of limbic regions during performance of the MIST. This pattern corresponds to findings on cortisol levels in psychosis patients, which are increased overall (Chaumette et al., 2016; Pruessner et al., 2017), whereas reactivity to stress appears blunted (Vaessen et al., 2018; Zorn et al., 2017). It is also in line with observations of blunted responses to stress in hippocampus and amygdala in schizophrenia patients (Castro et al., 2015). However, it is important to note that in our analyses, we have used difference scores to estimate brain reactivity to the stress task. Consequentially, we cannot exclude the possibility that those individuals showing less of a decreased response in our regions of interest during stress already deactivated these regions during the control task. In other words, individuals showing little activity modulation in these areas during stress, relative to the control condition, may have deactivated these regions chronically. The findings of Pruessner et al. (Pruessner et al., 2008) relating decreased HC activity during stress with HPA-axis activation underline this possibility.

4.3. Neural correlates of affective reactivity to stress

Our results help to characterize behavioral and neural responses to acute stressors in young adults with, and at risk for, psychotic illness. These behavioral and neural responses to acute stressors occur against a background of accumulated/chronic stress. The relative reduction in modulation of limbic activity in participants showing greater stress-reactivity in daily life may reflect a reduced ability to regulate activity in these regions in a top-down manner. At least three separate systematic reviews suggest that psychosis patients have more difficulties with emotion regulation (Lawlor et al., 2020; Liu et al., 2020; Ludwig et al., 2019). Importantly, psychosis patients apply emotion regulation strategies more frequently in their daily lives than healthy controls do; they are, however, less successful in doing so (Ludwig et al., 2020a; Visser et al., 2018). Altered connectivity between vmPFC and limbic regions could play a role here (Fan et al., 2013). In this light, individuals showing less activity reduction between the control and stress conditions may have had lower vmPFC/vACC activity levels to begin with. Direct comparison with healthy volunteers would elucidate whether early psychosis individuals indeed show decreased overall activity in these areas.

It is important to note that exaggerated and blunted affective responses to acute stressors are not unique to people with, or at risk for, psychosis. Rather, exaggerated and blunted affective responses to acute stressors occur in a wide range of individuals with and without mental illness. For example, the pattern of increased negative affect, in the absence of stress and blunted affective reactivity to stressors, is similar to that seen in depression (Peeters et al., 2003). Moreover, it has been suggested that increased affective reactivity to stress, in psychosis, is mainly associated with self-reported mood symptoms in this population (Booij et al., 2018). In young individuals with depressive complaints, emotion regulation has been associated with increased activation of the vmPFC (Stephanou et al., 2017), and as in psychosis, there appears to be altered neural connectivity between the vmPFC and limbic regions (Johnstone et al., 2007).

However, even if the neural stress response and emotion regulation issues are not specific to psychosis, they can still be very relevant for the development of psychotic disorder, through the affective pathway to psychosis (Klippel et al., 2017; Klippel et al., 2021; Kramer et al., 2014; Ludwig et al., 2020b; Myin-Germeys and van Os, 2007). Maladaptive emotion regulation is associated with the experience of psychotic symptoms (Liu et al., 2020; Ludwig et al., 2019), and a prior ESM study in psychosis patients showed that maladaptive emotion regulation strategies strengthen the prospective effect of negative affect on paranoia (Ludwig et al., 2020b). Future studies directly comparing individuals with different mental illnesses can shed more light on the nature of the neural basis of these affective disturbances in psychosis.

4.4. Involvement of the Anterior Insula

We found evidence that stress-induced activation of the AI was associated with NA reactivity to the task and with increased overall NA in daily life. The AI may play a double role (Uddin et al., 2017): in addition to being part of the limbic system, it has been identified as a hub of the salience network (Menon and Uddin, 2010; Seeley, 2019) and an important region in the pathophysiology of psychosis (Palaniyappan and Liddle, 2012). This incorporates another possibility, where altered affective reactivity to stress is associated with aberrant instances of salience attribution (Heinz et al., 2019; Kapur, 2003). Whereas difficulties with emotion regulation and stress reactivity are not unique to psychosis, aberrant salience attribution and its related cognitive biases are thought to underlie the development of psychotic experiences. In this sense, the AI may be a crucial hub, linking emotional experience to salience attribution. Possibly, difficulties with emotion regulation and a chronically hyperactive stress system impair modulation of the salience network during stress, resulting in the aberrant salience attribution that is believed to give rise to psychotic symptoms. Future studies should investigate the role of the AI in the connection of stress-related affect and aberrant salience attribution.

4.5. Clinical implications

The current findings suggest that chronic stress in early psychosis individuals may be associated with altered brain activity in key regions for emotion regulation, emotional experience, and the attribution of salience. Early interventions aimed at stress coping and emotion regulation may therefore not only lead to reductions in stress levels in these individuals, but also potentially have beneficial effects on their positive and negative symptoms by attenuating aberrant salience attribution (Hare et al., 2018; Kapur, 2003; Katthagen et al., 2016). Third-wave cognitive behavioral therapies, such as acceptance and commitment therapy, are aimed at improving psychological flexibility in the face of adversity and thus help an individual coping with stress. Acceptance and commitment therapy has been shown effective in reducing both positive and especially negative symptoms of psychosis (Tonarelli et al., 2016), and may therefore be an interesting approach for individuals in the early stages of psychosis.

4.6. Limitations

The current study has several limitations that should be taken into account when interpreting the results. First, the sample size is relatively small, increasing the risk of type II errors. Therefore, results must be interpreted with caution until they are replicated in future studies. Second, the study did not include a control group of healthy volunteers, limiting the generalizability of results and impeding interpretations. However, since we used a well-validated stress task applied in similar designs, we can still compare our results to those from previous studies. Third, the poor quality of signal in the OFC region prevents us from drawing any conclusions on stress reactivity in this relevant area. Finally, the fact that we did not assess emotion regulation directly prevents us from making stronger claims about its role during stress in early psychosis.

4.7. Conclusions

Taken together, these results, although preliminary, suggest that a lack of an adequate response to stress in vmPFC, vACC, HC, and amygdala, possibly driven by chronic stress, and an activation of the AI are associated with disturbances in affective reactivity to lab and daily life stressors. Stress-related difficulties with emotion regulation in prelimbic areas may stimulate the salience network, driving psychotic-like experiences and may thus be involved in an affective pathway to psychosis. However, replication of these results and direct comparison with a healthy control group and individuals with a more chronic course psychosis is a crucial next step for the interpretation of these findings.

Supplementary Material

Supplementary Material

Figure 1.

Figure 1.

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Associations between task-related changes in neural activity the ROIs and self-reports at baseline and during the task: Day of scanning. Projected regression lines of unstandardized regression coefficients from different ROIs on self-report measures from baseline to task for the mean ROI beta value and for one and two standard deviations (SD) above and below the mean.

Acknowledgements

We thank all the participating health services in Amsterdam (Academic Medical Centre, Arkin Basis GGZ), The Hague (Parnassia, PsyQ), Maastricht/Eindhoven (Mondriaan, Virenze, GGZE), Flemish-Brabant (UPC KU Leuven, VDIP Antwerp, Sint-Annendael, PCM Mortsel), and East/West Flanders (OLV Brugge, Karus Melle, VDIP Sint Niklaas). We thank all research coordinators (Silke Apers, Nele Volbragt, Wendy Beuken), research assistants (Dieuwke Siegmann, Davinia Verhoeven, Anna de Zwart, Iris de Wit, Lore Depraetere, Tessa Biesemans, Lotte Hendriks), and data managers (Martien Wampers, Jolien Bynens) past and present who were involved in the INTERACT trial. We also like to thank all individuals who participated in the study and were essential for its successful completion.

Funding

TV was funded by a postdoctoral fellowship grant from FWO (1243620N). UH was supported by a NWO VENI grant (451–13–022) and a DFG Heisenberg professorship (389624707). JAW was supported by an R01 grant (MH115031) from the National Institutes of Health of the United States. IMG was supported by an FWO Odysseus grant (GOF8416N).

Footnotes

Disclosures

None.

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