Skip to main content Accessibility help
×
Home
Hostname: page-component-846f6c7c4f-s4lzp Total loading time: 0.605 Render date: 2022-07-06T14:53:50.509Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Distinct temporal brain dynamics in bipolar disorder and schizophrenia during emotion regulation

Published online by Cambridge University Press:  18 February 2019

Liwen Zhang
Affiliation:
Department of Pharmacology, National University of Singapore, Singapore
Hui Ai
Affiliation:
Shenzhen Key Laboratory of Affective and Social Neuroscience, Shenzhen University, Shenzhen, China
Esther M. Opmeer
Affiliation:
University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Center, Groningen, the Netherlands
Jan-Bernard C. Marsman
Affiliation:
University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Center, Groningen, the Netherlands
Lisette van der Meer
Affiliation:
Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, the Netherlands
Henricus G. Ruhé
Affiliation:
Department of Psychiatry, Radboud University Medical Center, Nijmegen, Netherlands
André Aleman
Affiliation:
University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Center, Groningen, the Netherlands Department of Psychology, University of Groningen, Groningen, the Netherlands
Marie-José van Tol*
Affiliation:
University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Center, Groningen, the Netherlands
*
Author for correspondence: Marie-José van Tol, E-mail: m.j.van.tol@umcg.nl
Rights & Permissions[Opens in a new window]

Abstract

Background

Disturbances in emotion regulation (ER) are characteristic of both patients with bipolar disorder (BD) and schizophrenia (SZ). We investigated the temporal dynamics of brain activation during cognitive ER in BD and SZ to understand the contribution of temporal characteristics of disturbed ER to their unique and shared symptomatology.

Method

Forty-six participants performed an ER-task (BD, n = 15; SZ, n = 16; controls, n = 15) during functional magnetic resonance imaging, in which they were instructed to use cognitive reappraisal techniques to regulate their emotional responses. Finite impulse response modeling was applied to estimate the temporal dynamics of brain responses during cognitive reappraisal (v. passive attending) of negative pictures. Group, time, and group × time effects were tested using multivariate modeling.

Results

We observed a group × time interaction during ER in the ventrolateral prefrontal cortex (VLPFC), supplementary motor area (SMA) and inferior occipital gyrus. Patients with SZ demonstrated initial hyper-activation of the VLPFC and SMA activation that was not sustained in later regulatory phases. Response profiles in the inferior occipital gyrus in SZ showed abnormal activation in the later phases of regulation. BD-patients showed general blunted responsivity in these regions.

Conclusions

These results suggest that ER-disturbances in SZ are characterized by an inefficient initialization and failure to sustain regulatory control, whereas in BD, a failure to recruit regulatory resources may represent initial deficits in formulating adequate representations of the regulatory needs. This may help to further understand how ER-disturbances give rise to symptomatology of BD and SZ.

Type
Original Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2019

Introduction

Inadequate emotion regulation (ER) poses a threat to personal well-being (Gross, Reference Gross2002) and is a central part of various psychiatric disorders, including bipolar disorder (BD) (Rowland et al., Reference Rowland, Hamilton, Vella, Lino, Mitchell and Green2013; Wolkenstein et al., Reference Wolkenstein, Zwick, Hautzinger and Joormann2014) and schizophrenia (SZ) (Livingstone et al., Reference Livingstone, Harper and Gillanders2009; van der Meer et al., Reference van der Meer, van't Wout and Aleman2009, Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014). Neural abnormalities during ER have been observed in both BD and SZ (Morris et al., Reference Morris, Sparks, Mitchell, Weickert and Green2012; Townsend et al., Reference Townsend, Torrisi, Lieberman, Sugar, Bookheimer and Altshuler2013; van der Meer et al., Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014; Kanske et al., Reference Kanske, Schonfelder, Forneck and Wessa2015), implying a shared pathology that might relate to the occurrence of psychotic symptoms (Modinos et al., Reference Modinos, Ormel and Aleman2010), which are also present in 20–50% of BD-patients (Keck et al., Reference Keck, McElroy, Havens, Altshuler, Nolen, Frye, Suppes, Denicoff, Kupka, Leverich, Rush and Post2003). On the other hand, BD and SZ are characterized by unique symptomatology, with strong fluctuations in affect during depression and mania in BD, and a predominantly flat affect in SZ (Phillips et al., Reference Phillips, Drevets, Rauch and Lane2003b). A direct comparison between BD and SZ indeed suggested a differential neural profile during cognitive ER, with BD-patients showing increased prefrontal involvement compared to SZ-patients (Morris et al., Reference Morris, Sparks, Mitchell, Weickert and Green2012). In this and other studies, lower lateral prefrontal recruitment was also observed in SZ-patients compared to controls (Morris et al., Reference Morris, Sparks, Mitchell, Weickert and Green2012; van der Meer et al., Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014), whereas BD has been associated with both lower (Townsend et al., Reference Townsend, Torrisi, Lieberman, Sugar, Bookheimer and Altshuler2013) and higher (Morris et al., Reference Morris, Sparks, Mitchell, Weickert and Green2012) lateral prefrontal activation during ER compared to controls. Of note, it has been demonstrated in healthy individuals that ER areas do not show continuous and stable but instead sequential, orchestrated involvement across time during reappraisal (Goldin et al., Reference Goldin, McRae, Ramel and Gross2008). However, in most ER-studies to date, the temporal variations of brain responses during regulatory attempts occurring over long periods of time were not taken into account. This raises the question whether abnormalities in SZ arise from a failure to engage frontal regulatory areas per se or from a failure to sustain initial frontal engagement during the full duration of ER. Similarly, it is unknown whether altered frontal recruitment is an early, late, or constant characteristic of BD. However, elucidating the temporal dynamics of the complex response underlying ER is essential to understand the contributions of emotion dysregulation to the unique symptomatology of both disorders.

The most investigated strategy of cognitive ER is cognitive reappraisal, aiming at influencing the emotional intensity and connotation of a stimulus by reinterpreting its meaning (Gross, Reference Gross2001, Reference Gross2002; Gross and John, Reference Gross and John2003). Given that reappraisal is relevant for the occurrence and treatment of psychosis (Kuipers et al., Reference Kuipers, Garety, Fowler, Freeman, Dunn and Bebbington2006) that is prevalent in both SZ and BD, the current study focused on reappraisal. In healthy individuals, reappraisal relies on adequate interactions between the prefrontal cortex [PFC, including the ventrolateral PFC (VLPFC), dorsolateral PFC (DLPFC), and dorsomedial PFC (DMPFC)] and limbic affective areas (including the amygdala and ventral striatum) (Ochsner et al., Reference Ochsner, Silvers and Buhle2012; Buhle et al., Reference Buhle, Silvers, Wager, Lopez, Onyemekwu, Kober, Weber and Ochsner2014; Morawetz et al., Reference Morawetz, Bode, Baudewig and Heekeren2017Reference Morawetz, Bode, Baudewig and Heekerena, Reference Morawetz, Bode, Derntl and Heekeren2017Reference Morawetz, Bode, Derntl and Heekerenb). Interestingly, activation of the lateral and medial PFC was shown to be at maximum during an early phase (approximately 4.5 s after ER-onset), followed by attenuation of the amygdala response at a late stage (approximately 10.5 s after ER-onset) (Goldin et al., Reference Goldin, McRae, Ramel and Gross2008). This is consistent with the idea that the role of the VLPFC during regulation is to receive information about the affective value of the stimulus from the limbic areas, and forward this information to the DLPFC to call for prefrontal control over the limbic areas (Kohn et al., Reference Kohn, Eickhoff, Scheller, Laird, Fox and Habel2014). Moreover, this indicates that essential information for understanding the complex psychopathology related to ER-deficiencies may be missed when averaging the blood-oxygen-level dependent (BOLD) signal over long periods of time, as is often done in functional magnetic resonance imaging (fMRI) analysis. However, no study has investigated the temporal dynamics of ER in patients with BD and SZ yet.

We therefore aim to investigate the temporal dynamics underlying reappraisal in BD and SZ, using finite impulse response (FIR) modeling to estimate the timing and shape of the BOLD responses. Given the distinctive emotional symptoms seen in BD and SZ, we hypothesized differential temporal patterns within prefrontal and limbic areas between these two disorders. Moreover, healthy individuals were included to investigate shared abnormalities during ER in BD and SZ.

Materials and methods

Participants

Patients (23 BD-patients, 51 SZ-patients) were recruited through mental health care centers in the context of a study investigating insight (van der Meer et al., Reference van der Meer, de Vos, Stiekema, Pijnenborg, van Tol, Nolen, David and Aleman2013). Because illness insight is strongly associated with psychosis (David, Reference David1999) and emotional processing (van der Werf-Eldering et al., Reference van der Werf-Eldering, van der Meer, Burger, Holthausen, Nolen and Aleman2011), we only included patients with good insight and matched BD and SZ with insight scores for the current analysis, leading to a sample of 20 SZ-patients and 21 BD-patients. Also, 17 healthy controls (HC) were included. For patients, the following inclusion criteria were applied: (1) no change in medication at least 1 week before scanning; (2) no electroconvulsive therapy in the year prior to scanning; (3) no psychiatric disorders other than BD or SZ and no comorbidity with other psychiatric disorders (e.g. substance use disorder); (4) no somatic/neurological disorders with known influence on brain functioning. Because patients were recruited in the context of studying insight in psychosis, a history of psychotic symptoms was required for patients with BD. HC were required to have no current or past psychiatric disorders. Presence of MRI-incompatibilities and somatic or neurological disorder affecting the central nervous system was an exclusion criterion for all participants.

This study was approved by the Medical Ethical Committee of the University Medical Center Groningen and was performed according to the Helsinki Declaration (2008). All participants provided written informed consent.

Clinical assessments

To confirm the diagnosis of BD (including a history of psychotic symptoms) or SZ, and to exclude life-time psychiatric disorders in HC, the Mini International Neuropsychiatric Interview-Plus 5.0.0 (MINI-Plus; Sheehan et al., Reference Sheehan, Lecrubier, Sheehan, Amorim, Janavs, Weiller, Hergueta, Baker and Dunbar1998) was administered. For all participants, severity of current depressive symptoms was measured with the Quick Inventory of Depressive Symptomatology (QIDS; Rush et al., Reference Rush, Trivedi, Ibrahim, Carmody, Arnow, Klein, Markowitz, Ninan, Kornstein, Manber, Thase, Kocsis and Keller2003), intelligence was estimated using the Dutch Adult Reading Test (DART; Schmand et al., Reference Schmand, Bakker, Saan and Louman1991), and daily preferred ER-strategy was assessed with the Emotion Regulation Questionnaire (ERQ; Gross and John, Reference Gross and John2003). Only in patients, severity of current positive and negative symptoms was measured with the Positive and Negative Syndrome Scale (PANSS; Kay et al., Reference Kay, Fiszbein and Opler1987), severity of current mania with the Young Mania Rating Scale (YMRS; Young et al., Reference Young, Biggs, Ziegler and Meyer1978), and clinical and cognitive insight with the Schedule of Assessment of Insight-Expanded version (SAI-E; Kemp and David, Reference Kemp and David1997), and the Beck Cognitive Insight Scale (BCIS; Beck et al., Reference Beck, Baruch, Balter, Steer and Warman2004) respectively.

Emotion regulation task

The ER-task has been described in detail in a previous ER-study (van der Meer et al., Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014). Briefly, 88 negative and 22 neutral pictures were selected from the International Affective Picture System (Lang et al., Reference Lang, Bradley and Cuthbert2005; van der Meer et al., Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014). In each trial, a neutral or negative picture was shown with the instruction to view (2 s). Afterward, participants were asked to either reappraise or attend to this picture for 4 s, leading to three conditions: attend-neutral, attend-negative (viewing the picture without changing the experienced emotion) and reappraise-negative (reinterpreting to decrease the negative emotional intensity). Following the picture, a black screen was presented (lingering; 2 s), followed by affective rating on a four-point scale (1 – not negative at all; 4 – extremely negative) (3 s). Subsequently, a screen with the word ‘relax’ cued participants to relax (4 s). Finally, a black screen appeared to signal the next trial (0.5 s) (Fig. 1). The whole experiment was split into two runs, with a rest period in between during which the anatomical scan was acquired. See online Supplement for task details. Within runs, conditions were presented pseudorandomized, with equal distribution of conditions over blocks of 9 or 10 trials. In between blocks, a fixation-cross of 20 s was presented.

Fig. 1. Illustration of the task procedure during a reappraise trial and the time window used for FIR modeling. During attend-negative trials, participants were instructed to continue to attend to the picture during 4 s following the cue and preceding lingering. The black triangles indicate the start/end of each task phase. FIR, finite impulse response.

Although the task also included a suppress (i.e. inhibit emotional expressions) and an increase (i.e. effortfully increase the intensity of negative emotions) condition, we focused on reappraisal in the current study (see online Supplement for the results in relation to suppress and increase).

Data acquisition

fMRI-data were collected using a 3.0 Tesla scanner (Philips Intera, Best, NL) at the University Medical Center Groningen, equipped with an 8-channel SENSE head-coil. Using a T2-weighted echo planar imaging sequence, 37 axial slices were acquired per volume in an ascending, interleaved order (slice-thickness = 3.5 mm, slice-gap = 0 mm; TR = 2.0 s; TE = 30 ms; flip-angle = 70°; in-plane resolution 64 × 62 pixels; voxel size of 3.5 × 3.5 mm). Additionally, a T1-weighted 3D anatomical image (170 slices; TR = 9 ms; TE = 3.54 ms; voxel size of 1 × 1 × 1 mm; flip-angle = 8°) was acquired.

Statistical analysis

Clinical variables and behavioral data

Demographics, psychometric assessments, and behavioral data were analyzed in SPSS (v.22.0), using one-way analysis of variances (ANOVAs), t tests, and χ2 tests where appropriate.

A repeated-measures ANOVA was performed to analyze the ratings during the task, with condition (3; attend-neutral, attend-negative, reappraise) as within-subjects factor and group (3; HC, SZ, BD) as between-subjects factor. Significance level was set to p < 0.05 (two-tailed) for all analyses.

fMRI data pre-processing

fMRI-data were pre-processed with statistical parametric mapping [SPM12b (v5970); Wellcome Trust Centre for Neuroimaging, London, UK] in Matlab 7.8.0 (R2009a; Mathworks, Natick, MA, USA). Both functional and anatomical images were manually reoriented to the anterior–posterior commissure plane before pre-processing. Pre-processing consisted of slice-time correction, realignment, co-registration of the functional images to the anatomical image, normalization to Montreal Neurological Institute (MNI) (2 × 2 × 2 mm) space and smoothing with a full-width at half-maximum Gaussian kernel of 8 mm.

Finite impulse response modeling

To quantify the shape/timing of the hemodynamic response (HR) during reappraisal, a general linear model with a FIR basis set was built to estimate condition-specific time-courses without a priori assumptions on their shape. Notably, this FIR-modeling does not take into consideration the known delay of the HR-function. Also, given the late-cueing paradigm, activation in response to negative pictures during the initial viewing phase was assumed to be comparable between conditions, especially since pictures under different conditions were matched on valence, arousal, and complexity. Therefore, to estimate ER-relevant signals and to avoid including activation related to the last part of the previous trial, a 10 s-time-window was set following the start of the regulation instruction, which was not fixed to the TR (Fig. 1). The 10 s-time-window was modeled with five triangular basis functions (‘tent function’), each of which spanned 2 s. This way, activation in response to both viewing and regulating were included within our time window of interest when assuming the known delay in HR function peak response of approximately 5 s. We however started modeling at the onset of the cue to either regulate or attend, as differences related to effortful regulation compared to passive attend were our primary interest.

In each participant's first-level model, regressors (with five tents) were included per condition (attend-neutral, attend-negative, reappraise, increase and suppress). FIR-estimates of the HR were calculated for each time bin (5) and condition (5) per participant. To investigate the temporal characteristics specific to reappraisal, we specified the contrast reappraise > attend-negative per time-bin at the first level and these contrasts were then entered in our main analysis (see below: 3dMVM modeling). To check whether effects were driven by reappraising or attending negative-pictures, we repeated our analyses for the contrast attend-negative > attend-neutral.

Whole-brain multivariate modeling (3dMVM in AFNI) was used to examine the temporal dynamics during reappraise > attend-negative, with group (3; HC, BD, SZ) as between-subjects factor, and time (5; 1, 2, 3, 4, 5) as within-subjects factor. 3dMVM allows quantification of the inter-correlation among the within-subject variables, which is likely high for the consecutive time-bins (Chen et al., Reference Chen, Adleman, Saad, Leibenluft and Cox2014, Reference Chen, Saad, Adleman, Leibenluft and Cox2015), and has proven to be effective at detecting subtle shape changes of HR (Chen et al., Reference Chen, Saad, Adleman, Leibenluft and Cox2015). Since our goal was to identify temporal differences between groups over the regulation period and to be able to detect commonalities in abnormal temporal patterns between SZ and BD as compared to HC, we tested for group (3) × time (5) interactions across all time bins, and for main effects of group and time. Because we were additionally interested in the differences in the temporal profile between patients with BD and SZ, we additionally tested for group (2) × time (5) interactions for BD v. SZ. Furthermore, to check their deviations from normal across the regulatory period, we explored BD and SZ v. HC. Notably, all above-mentioned contrasts were defined in one 3dMVM model, and group differences were not examined for each time bin separately. We repeated our analyses after adding QIDS score in the model including all three groups and PANSS score (total scores) in the model including BD- and SZ-patients as covariates of no interest, respectively. In addition, the possible influence of medication was explored by adding dummy variables coding for usage of medication types [i.e. antipsychotics (yes/no), anti-depressants (yes/no), mood-stabilizers (yes/no), benzodiazepines (yes/no), with no medication as the implicit option when all other dummies were coded ‘no’ (i.e. 0)] in the model including patients only.

For all analyses, Monte Carlo simulations were used to correct for multiple comparisons (i.e. 3d ClustSim in AFNI v18.1.04 updated on April 2018) (Cox et al., Reference Cox, Reynolds and Taylor2016). All results were thresholded at a voxel-wise threshold of p < 0.001 uncorrected, and should meet minimum cluster extent criteria (k > 74 for the main three group model and k > 70 for the models in which we controlled for medication and PANSS within patients) to hold a family-wise error (FWE) correction of p < 0.05. See online Supplemental material for details of cluster extent definition.

Results

Sample characteristics

Due to incomplete imaging data, excessive head-movements (i.e. >3 mm/3° in any direction) or missing Eprime log-files, 11 cases were excluded (BD: n = 5; SZ: n = 4; HC: n = 2). To match groups on age, another BD-patient was excluded. For the final analyses, 15 HC, 15 BD-patients, and 16 SZ-patients were included. One HC missed questionnaires/behavioral data and was excluded from behavioral analyses but included in the MRI analyses. Notably, a part of our sample (12 SZ-patients and 12 HC) was included in a previous study (van der Meer et al., Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014), without FIR-analysis being applied.

All groups were comparable on age, sex, level of education, intelligence and ERQ-scores, but differed on QIDS-score (HC < BD < SZ; ps < 0.01). Moreover, BD and SZ were comparable on YMRS and insight scores (i.e. SAI-E/BCIS; ps > 0.55), but SZ showed higher PANSS-scores than BD (p = 0.048). See Table 1 and online Supplementary Table S2 for details.

Table 1. Demographic and clinical characteristics in all groups

BCIS, Beck cognitive insight scale; DART, Dutch Reading Test for Adults; ERQ, emotion regulation questionnaire; M, mean; PANSS, Positive and Negative Syndrome Scale; QIDS, Quick Inventory of Depressive Symptomatology; SAI_E, Schedule of Assessment of Insight-Expanded version; s.d., standard error; YMRS, Young Mania Rating Scale.

a Comparison between two patients groups (BD and SZ).

b The ERQ consists of ten items: six items measure the cognitive reappraisal strategy and four items the suppression strategy. Because of the unequal number of items between the two subscales, the total score for each subscale was divided by the number of items per subscale.

c We defined a depressive state with a QIDS score of >10 (http://www.ids-qids.org/) and a manic state with a cut-off score of 8 on the YMRS (Mercer and Becerra, Reference Mercer and Becerra2013).

* p < 0.05.

Behavioral results

Across all participants, there was a main effect of condition on emotion ratings (F (2,84) = 132.38, p < 0.01; see online Supplementary Fig. S1 for ratings in all conditions). Post-hoc t tests indicated most negative ratings after attend-negative (M = 2.63, s.e.m. = 0.08) and lowest after attend-neutral (M = 1.20, s.e.m. = 0.03), with the ratings following reappraisal in between (M = 2.23, s.e.m. = 0.09) (attend-negative < reappraise-negative < attend-neutral; ps < 0.001). No significant main effect of group (F (2,42) = 2.70, p = 0.08) and interaction of group × condition were found (F (4,84) = 0.337, p = 0.85).

FIR modeling results during reappraise > attend-negative

Comparisons between SZ, BD, and HC

Reappraisal-sensitive temporal profiles are illustrated in Table 2 and Fig. 2. A main effect of time was shown in the VLPFC, DLPFC, DMPFC, temporal pole, middle temporal gyrus (MTG), STG, and supplementary motor area (SMA). No main effect of group was observed, but a group × time interaction on reappraisal > attend-negative was found in the left VLPFC, right inferior occipital gyrus, and right SMA. Plotting of the time-courses showed that SZ had normal to hyper-engagement of the VLPFC and inferior occipital gyrus in the early phases (time bin 1–3), with a drop in activation during the later phases (time bin 4–5), where HC showed more sustained higher activation in the later time bins when comparing reappraise-negative to attend-negative (Fig. 2a, c). For the SMA, SZ showed hyper-activation in the early phases (time bin 2–3) relative to HC, followed by a gradual decrease toward normal at time bin 4 and hypoactivation at time bin 5 (Fig. 2b) when contrasting reappraise-negative to attend-negative. BD failed to show heightened responses in the VLPFC and SMA in the early phases relative to HC, but showed some normalization of responses in the later time bins (time bin 4–5; Fig. 2a, b). In the inferior occipital gyrus, BD was characterized by blunted response relative to HC (Fig. 2c). Similar results were obtained after controlling for QIDS (online Supplementary Table S3). Observed findings in relation to reappraisal could not be attributed to the group difference over time during the attend-negative (online Supplementary Fig. S3). See online Supplementary Fig. S6 for plots including the viewing phase preceding the reappraise or attend cue.

Fig. 2. Interaction effects between group and time in HC, patients with BD, and SZ at the contrast reappraise > attend-negative. Effects were observed (a) in the VLPFC, (b) SMA, and (c) inferior occipital gyrus. Time bins start at the presentation of regulation cue (i.e. time bin 1, 0–2 s post instruction). The presented time course is averaged over significant voxels. The line connecting the mean responses in each time bin visualizes the temporal response for each group (BD, dashed dark gray; HC, dashed light gray; SZ, straight dark gray). BD, bipolar disorder; HC, healthy controls; SZ, schizophrenia; SMA, supplementary motor area; VLPFC, ventrolateral prefrontal cortex.

Table 2. Group comparisons during reappraise > attend negative (cluster extent threshold k > 74)

Significance at P FWE < 0.05 with a height threshold of p < 0.001.

Comparisons between BD and SZ

Planned comparisons between BD and SZ revealed no main effect of group or group × time effects. Controlling for PANSS scores and medication types did not change this observation (online Supplementary Tables 4 and 6).

Comparisons between SZ and BD to HC

To objectify deviations from normal, we compared SZ-patients and BD-patients to HC separately. The comparison between SZ and HC showed a main effect of group in the left VLPFC extending toward the DLPFC, and right inferior occipital gyrus, resulting from lower activation in SZ than HC (Table 2). Results of group × time interaction effects showed that SZ demonstrated an abnormal response over time in the right VLPFC, with early abnormal recruitment in time bin 2 (online Supplementary Fig. 2). In the model comparing BD to HC, a main effect of group was observed in the left DLPFC extending to the VLPFC and right MTG, with lower activation in BD compared to HC. No group × time effect was observed.

No effects of group × time were observed for the conditions suppress and increase. See online Supplement for post-hoc explorations.

Discussion

In this study, we investigated for the first time the temporal dynamics of cognitive ER in patients with BD, SZ, and HC. We demonstrated that BD and SZ were characterized by distinct temporal profiles of engaging brain regions during reappraisal compared to attending negative pictures, with unique deviations from normal in the VLPFC, SMA, and inferior occipital gyrus. Of those regions, both the VLPFC and SMA have been consistently indicated as part of an ER-network (Ochsner et al., Reference Ochsner, Silvers and Buhle2012; Kohn et al., Reference Kohn, Eickhoff, Scheller, Laird, Fox and Habel2014). In short, SZ-patients showed initial normal- to hyper-recruitment of the VLPFC, but failed to sustain this activation in late phases of regulation, together with early hyperactivation and non-sustained activation of the SMA in the late phase of activation. BD-patients showed blunted responses in these areas, especially during early regulation phases. This suggests that inefficient initialization and a failure to sustain regulatory control may underlie ER-abnormalities in SZ, whereas a failure to engage regulatory areas may underlie ER-abnormalities in BD. These disorder-specific temporal fingerprints may help understand how abnormalities in reappraisal contribute to the unique symptomatology in different psychiatric disorders.

In SZ, consistent with previous findings (Morris et al., Reference Morris, Sparks, Mitchell, Weickert and Green2012; van der Meer et al., Reference van der Meer, Swart, van der Velde, Pijnenborg, Wiersma, Bruggeman and Aleman2014), we observed lower activation in the VLPFC during reappraisal compared to HC. Notably, our results add to previous findings that although hypo-activation was lower in general in the pars triangular part of the VLPFC bordering the DLPFC, activation in a more ventral part of the triangular part of the VLPFC was characterized by a failure to sustain activation following a subtle initial over-recruitment. The VLPFC is a key component of the neural circuitry involved in appraisals that initiates controlled ER (Dixon et al., Reference Dixon, Thiruchselvam, Todd and Christoff2017), and has been linked to response selection and inhibition during reappraisal (Ochsner and Gross, Reference Ochsner and Gross2005; Ochsner et al., Reference Ochsner, Silvers and Buhle2012; Morawetz et al., Reference Morawetz, Bode, Baudewig, Kirilina and Heekeren2016), inhibition of self-perspective during theory of mind tasks (van der Meer et al., Reference van der Meer, Groenewold, Nolen, Pijnenborg and Aleman2011), top-down control over emotional information (Phillips et al., Reference Phillips, Drevets, Rauch and Lane2003a, Reference Phillips, Ladouceur and Drevets2008), detection of salience to signal the need for control (Kohn et al., Reference Kohn, Eickhoff, Scheller, Laird, Fox and Habel2014), and self-reflection (van der Meer et al., Reference van der Meer, Costafreda, Aleman and David2010, Reference van der Meer, de Vos, Stiekema, Pijnenborg, van Tol, Nolen, David and Aleman2013; Murray et al., Reference Murray, Schaer and Debbané2012). This suggests that the VLPFC is an important area for forming a meta-cognitive representation of emotional information and for detecting a need for control during reappraisal. Based on the suggested roles of the VLPFC in ER, we propose that in SZ, the salience of the emotional information is initially processed but in a possibly inefficient way as indicated by the early over-recruitment of this area compared with HC. This suggestion is in line with the emotion paradox in SZ that they have shown reduced emotional expression (flat affect), but with normal or even enhanced emotional experience compared to healthy individuals (Aleman and Kahn, Reference Aleman and Kahn2005; Kring and Moran, Reference Kring and Moran2008; Cohen and Minor, Reference Cohen and Minor2010). Also, this suggests that the emotional information is processed for further elaboration, but that SZ-patients fail to keep an adequate emotional reappraisal online, indicated by the non-sustained VLPFC activation, that may hinder controlled ER (Dixon et al., Reference Dixon, Thiruchselvam, Todd and Christoff2017). This resulting suboptimal reappraisal may be associated with a higher negative affect (compared to HC) based on the depression severity scores, and might be investigated further in relation to inappropriate affect featured in SZ.

In BD on the other hand, we observed a stable blunted response of a cluster encompassing the VLPFC extending to the DLPFC, and in the MTG. The DLPFC is important for maintaining reappraisal goals in working memory during ER (Wager et al., Reference Wager, Jonides and Reading2004; Ochsner et al., Reference Ochsner, Silvers and Buhle2012) and for explicit reasoning (Ochsner and Gross, Reference Ochsner and Gross2005; Kohn et al., Reference Kohn, Eickhoff, Scheller, Laird, Fox and Habel2014). The MTG has been proposed to be important for ER success (Morawetz et al., Reference Morawetz, Bode, Derntl and Heekeren2017b) and to play an important intermediate role together with the DLPFC and amygdala during ER related to cognitive control over emotions, similar to the VLPFC (Ochsner et al., Reference Ochsner, Silvers and Buhle2012). In line with this previous reasoning, the blunted VLPFC/DLPFC and temporal response pattern may indicate that abnormal ER in BD results from a general failure to form an adequate reappraisal of the emotion situation and associated need for regulatory cognitive control.

The suggested dissociation between BD and SZ (as compared to HC) based on adequate recognition of saliency and associated need for control resources in relation to VLPFC functioning is further supported by the observed differential responses in the SMA and the inferior occipital gyrus extending to the middle occipital gyrus. In comparison with HC, we observed hyper-recruitment of the SMA in SZ in the early phases of regulation, whereas BD showed blunted SMA activation during the entire regulation window. The SMA has been suggested to be an essential intermediate unit between the prefrontal areas and limbic affective areas during ER (Kohn et al., Reference Kohn, Eickhoff, Scheller, Laird, Fox and Habel2014). Specifically, the SMA has been involved in stimulus reconceptualization (Kohn et al., Reference Kohn, Eickhoff, Scheller, Laird, Fox and Habel2014) and cognitive demands (Urry et al., Reference Urry, van Reekum, Johnstone and Davidson2009) during ER. Therefore, the over-recruitment of the SMA in the early phase of regulation together with low recruitment in the late phase may indicate a counterproductive call upon cognitive control. As the hyperrecruitment of the SMA was most pronounced at the second time bin, and the BOLD response usually peaks five seconds post-event, this may suggest that this hyperrecruitment might be related to the initial viewing phase and thus may represent abnormalities related to stimulus attribution. This is supported by online Supplemental Fig. S6. In BD, blunted VLPFC response during reappraisal was accompanied by blunted responses of the SMA compared to HC, suggesting that these regions are recruited to a less extent, which may reflect a diminished call for adequate cognitive regulatory control. Though less well known, the inferior occipital gyrus has been implicated as an important region for ER in mood disorders (Pico-Perez et al., Reference Pico-Perez, Radua, Steward, Menchon and Soriano-Mas2017) and emotion recognition (Wiggins et al., Reference Wiggins, Adleman, Kim, Oakes, Hsu, Reynolds, Chen, Pine, Brotman and Leibenluft2016). Near-normal early recruitment of the inferior occipital gyrus in SZ may therefore represent initial correct recognition of negative emotional information which seemed not sustained in later phases (time bin 4–5). In BD, the blunted pattern of response in the inferior occipital gyrus, VLPFC and SMA, implies a general failure to identify regulatory needs. We therefore propose that ER-deficits in SZ are primarily driven by an inefficient initialization and failure to sustain regulatory control, while ER-deficits in BD are characterized by a primary failure to index regulatory needs. Though testing for group × time × instruction was beyond the scope of this paper, we explored post-hoc whether similar group differences were observed during the increase and suppress conditions. Plotting of the time courses in the regions resulting from the main analyses indicated similar direction of response differences between groups during increasing negative emotions, but not during suppression (see online Supplement). However, no group or group × time effect during either increase (>attend-negative) or suppress (>attend-negative) reached statistical significance. Though clearly in need of further study, these differential temporal patterns of activation in BD and SZ in the VLPFC, SMA and inferior occipital gyrus may be specific to effortful downregulation of negative emotional material, and not for intensifying or suppressing emotional experience.

Some limitations need to be mentioned. First, most patients were taking medication and potential confounding effects could not be excluded. However, after controlling for medication, comparable results between patient groups were observed (online Supplementary Tables 2 and 6). Second, our sample size was modest, which increases the risk of inflated effect size estimates (Ioannidis, Reference Ioannidis2005). However, we believe that our observations are important to report as they result from highly selective and difficult to recruit patient populations. Comparability between patient groups was improved by selecting BD-patients with a history of psychosis and SZ-patients with comparable levels of illness insight as the BD-patients. Third, BD- and SZ-patients differed on current depression severity and positive/negative symptoms, but did so mainly with mild forms of symptomatology. The temporal dynamics of reappraisal during more severe mood states remain unclear, since differential neural correlates between depressed and euthymic states have been shown in BD (Rive et al., Reference Rive, Mocking, Koeter, van Wingen, de Wit, van den Heuvel, Veltman, Ruhe and Schene2015). Unfortunately, our small sample size of mainly euthymic/non-symptomatic patients prevented us from exploring these factors. Also, we could not control for the heterogeneity within the BD group, related to characteristics of the most recent episode and bipolar type I v. bipolar type II. Replication in a bigger (medication-free) sample with better matching of clinical variables and during different polarity states is recommended before strong conclusions can be drawn. Forth, although results of group × time interaction during reappraise > attend-negative indicate that the observed cortical temporal responses were specific to reappraisal compared to attend-negative, we did not find an association between PFC temporal-responses and negative affect ratings in an exploratory correlation analysis (see online Supplement). A narrow rating range (four-point scale) might have confounded this result. Furthermore, we did not have affect ratings of the participants in response to the same stimuli before regulation. Therefore, our calculation method of ER success (i.e. mean rating after attend-negative > mean rating after reappraisal) may not reflect regulation success sufficiently, which also prevented us from exploring dynamics of regulation success. Fifth, there was no effect of reappraisal on amygdala activation between the attend and regulate condition, which might be related to the late-cueing design (Ochsner et al., Reference Ochsner, Silvers and Buhle2012) and that the time window following the regulation instruction was too short to allow the amygdala to reach its maximum response (Goldin et al., Reference Goldin, McRae, Ramel and Gross2008). Finally, reappraisal has shown differential modulating effects for positive and negative stimuli (Rive et al., Reference Rive, Mocking, Koeter, van Wingen, de Wit, van den Heuvel, Veltman, Ruhe and Schene2015), while we only focused on negative stimuli. Including positive conditions in future work may further clarify the emotional disturbances in BD (e.g. problems with down-regulating positive affect during mania) and SZ (e.g. difficulty in up-regulating positive affect).

In conclusion, our findings of differential temporal responses between BD and SZ during reappraisal may indicate that SZ-patients predominantly show an inefficient initialization of regulatory attempts followed by a failure of sustaining activation in areas important for signaling reappraisal needs, whereas BD may have a more general deficiency in initiating reappraisal due to suboptimal recognition of the saliency of the stimuli and detecting the need for regulation. These differences between BD and SZ might be of potential interest as disorder-specific biomarkers and may enable a better understanding of these two disorders by linking the observed differential temporal dynamics to distinct emotional symptomatology of BD (e.g. switching between emotional poles) and SZ (e.g. inappropriate affect) in the future.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291719000217.

Acknowledgements

This study was supported by a grant from the European Science Foundation EURYI (NWO grant number 044.035.001) awarded to Prof. Dr A. Aleman. Dr H.G. Ruhé was supported by a VENI grant (NWO/ZonMW grant number 016.126.059), and Dr M.J. van Tol was supported by a VENI grant (NWO grant number 016.156.077). Dr L. Zhang and Dr H. Ai were supported by China Scholarship Council during their Ph.D. study at the University of Groningen. We thank Dr H.G. Knegtering, Dr R. Bruggeman, Dr L. Bais, Dr B. Haarman and Prof. Dr W.A. Nolen for their help with patient inclusion. We thank A. Sibeijn-Kuiper and J. Streurman for their help with scanning of the participants, and we appreciate the help on the study design from Dr R. J. Renken.

Conflict of interest

None.

Footnotes

*

These authors contributed equally to this study.

References

Aleman, A and Kahn, RS (2005) Strange feelings: do amygdala abnormalities dysregulate the emotional brain in schizophrenia? Progress in Neurobiology 77, 283298.Google Scholar
Beck, AT, Baruch, E, Balter, JM, Steer, RA and Warman, DM (2004) A new instrument for measuring insight: the Beck Cognitive Insight Scale. Schizophrenia Research 68, 319329.CrossRefGoogle ScholarPubMed
Buhle, JT, Silvers, JA, Wager, TD, Lopez, R, Onyemekwu, C, Kober, H, Weber, J and Ochsner, KN (2014) Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex (New York, N.Y.: 1991) 24, 29812990.CrossRefGoogle ScholarPubMed
Chen, G, Adleman, NE, Saad, ZS, Leibenluft, E and Cox, RW (2014) Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model. NeuroImage 99, 571588.CrossRefGoogle ScholarPubMed
Chen, G, Saad, ZS, Adleman, NE, Leibenluft, E and Cox, RW (2015) Detecting the subtle shape differences in hemodynamic responses at the group level. Frontiers in Neuroscience 9, 375.CrossRefGoogle ScholarPubMed
Cohen, AS and Minor, KS (2010) Emotional experience in patients with schizophrenia revisited: meta-analysis of laboratory studies. Schizophrenia Bulletin 36, 143150.CrossRefGoogle ScholarPubMed
Cox, RW, Reynolds, RC and Taylor, PA (2016) AFNI and clustering: false positive rates redux. BioRxiv.Google Scholar
David, AS (1999) ‘To see ourselves as others see us’. Aubrey Lewis's insight. The British Journal of Psychiatry 175, 210216.CrossRefGoogle Scholar
Dixon, ML, Thiruchselvam, R, Todd, R and Christoff, K (2017) Emotion and the prefrontal cortex: an integrative review. Psychological Bulletin 143, 10331081.CrossRefGoogle Scholar
Goldin, PR, McRae, K, Ramel, W and Gross, JJ (2008) The neural bases of emotion regulation: reappraisal and suppression of negative emotion. Biological Psychiatry 63, 577586.CrossRefGoogle ScholarPubMed
Gross, JJ (2001) Emotion regulation in adulthood: timing is everything. Current Directions in Psychological Science 10, 212219.CrossRefGoogle Scholar
Gross, JJ (2002) Emotion regulation: affective, cognitive, and social consequences. Psychophysiology 39, 281291.CrossRefGoogle ScholarPubMed
Gross, JJ and John, OP (2003) Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology 85, 348362.CrossRefGoogle ScholarPubMed
Kanske, P, Schonfelder, S, Forneck, J and Wessa, M (2015) Impaired regulation of emotion: neural correlates of reappraisal and distraction in bipolar disorder and unaffected relatives. Translational Psychiatry 5, e497.CrossRefGoogle ScholarPubMed
Kay, SR, Fiszbein, A and Opler, LA (1987) The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13, 261276.CrossRefGoogle Scholar
Keck, PE Jr, McElroy, SL, Havens, JR, Altshuler, LL, Nolen, WA, Frye, MA, Suppes, T, Denicoff, KD, Kupka, R, Leverich, GS, Rush, AJ and Post, RM (2003) Psychosis in bipolar disorder: phenomenology and impact on morbidity and course of illness. Comprehensive Psychiatry 44, 263269.CrossRefGoogle ScholarPubMed
Kemp, R and David, AS (1997) Treatment Compliance and the Therapeutic Alliance. Amsterdam: Harwood Academic Publishers.Google Scholar
Kohn, N, Eickhoff, SB, Scheller, M, Laird, AR, Fox, PT and Habel, U (2014) Neural network of cognitive emotion regulation – an ALE meta-analysis and MACM analysis. NeuroImage 87, 345355.CrossRefGoogle ScholarPubMed
Kring, AM and Moran, EK (2008) Emotional response deficits in schizophrenia: insights from affective science. Schizophrenia Bulletin 34, 819834.CrossRefGoogle ScholarPubMed
Kuipers, E, Garety, P, Fowler, D, Freeman, D, Dunn, G and Bebbington, P (2006) Cognitive, emotional, and social processes in psychosis: refining cognitive behavioral therapy for persistent positive symptoms. Schizophrenia Bulletin 32(suppl. 1), S24S31.CrossRefGoogle ScholarPubMed
Ioannidis, JP (2005) Why most published research findings are false. Plos Medicine 2, 06960701.CrossRefGoogle ScholarPubMed
Lang, PJ, Bradley, MM and Cuthbert, BN (2005) International affective picture system (IAPS): Technical manual and affective ratings Technical Report A-6, University of Florida, Gainesville, FL.Google Scholar
Livingstone, K, Harper, S and Gillanders, D (2009) An exploration of emotion regulation in psychosis. Clinical Psychology & Psychotherapy 16, 418430.CrossRefGoogle ScholarPubMed
Mercer, L and Becerra, R (2013) A unique emotional processing profile of euthymic bipolar disorder? A critical review. Journal of Affective Disorders 146, 295309.CrossRefGoogle ScholarPubMed
Modinos, G, Ormel, J and Aleman, A (2010) Altered activation and functional connectivity of neural systems supporting cognitive control of emotion in psychosis proneness. Schizophrenia Research 118, 8897.CrossRefGoogle ScholarPubMed
Morawetz, C, Bode, S, Baudewig, J, Kirilina, E and Heekeren, HR (2016) Changes in effective connectivity between dorsal and ventral prefrontal regions moderate emotion regulation. Cerebral Cortex 26, 19231937.CrossRefGoogle ScholarPubMed
Morawetz, C, Bode, S, Baudewig, J and Heekeren, HR (2017 a) Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation. Social Cognitive and Affective Neuroscience 12, 569585.CrossRefGoogle ScholarPubMed
Morawetz, C, Bode, S, Derntl, B and Heekeren, HR (2017 b) The effect of strategies, goals and stimulus material on the neural mechanisms of emotion regulation: a meta-analysis of fMRI studies. Neuroscience & Biobehavioral Reviews 72, 111128.CrossRefGoogle ScholarPubMed
Morris, RW, Sparks, A, Mitchell, PB, Weickert, CS and Green, MJ (2012) Lack of cortico-limbic coupling in bipolar disorder and schizophrenia during emotion regulation. Translational Psychiatry 2, e90.CrossRefGoogle ScholarPubMed
Murray, RJ, Schaer, M and Debbané, M (2012) Degrees of separation: a quantitative neuroimaging meta-analysis investigating self-specificity and shared neural activation between self- and other-reflection. Neuroscience and Biobehavioral Reviews 36, 10431059.CrossRefGoogle ScholarPubMed
Ochsner, KN and Gross, JJ (2005) The cognitive control of emotion. Trends in Cognitive Sciences 9, 242249.CrossRefGoogle ScholarPubMed
Ochsner, KN, Silvers, JA and Buhle, JT (2012) Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences 1251, E124.CrossRefGoogle ScholarPubMed
Phillips, ML, Drevets, WC, Rauch, SL and Lane, R (2003 a) Neurobiology of emotion perception I: the neural basis of normal emotion perception. Biological Psychiatry 54, 504514.CrossRefGoogle ScholarPubMed
Phillips, ML, Drevets, WC, Rauch, SL and Lane, R (2003 b) Neurobiology of emotion perception II: implications for major psychiatric disorders. Biological Psychiatry 54, 515528.CrossRefGoogle ScholarPubMed
Phillips, ML, Ladouceur, CD and Drevets, WC (2008) A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Molecular Psychiatry 13, 829, 833–57.CrossRefGoogle ScholarPubMed
Pico-Perez, M, Radua, J, Steward, T, Menchon, JM and Soriano-Mas, C (2017) Emotion regulation in mood and anxiety disorders: a meta-analysis of fMRI cognitive reappraisal studies. Progress in Neuro-Psychopharmacology & Biological Psychiatry 79, 96104.CrossRefGoogle ScholarPubMed
Rive, MM, Mocking, RJ, Koeter, MW, van Wingen, G, de Wit, SJ, van den Heuvel, OA, Veltman, DJ, Ruhe, HG and Schene, AH (2015) State-dependent differences in emotion regulation between unmedicated bipolar disorder and major depressive disorder. JAMA Psychiatry 72, 687696.CrossRefGoogle ScholarPubMed
Rowland, JE, Hamilton, MK, Vella, N, Lino, BJ, Mitchell, PB and Green, MJ (2013) Adaptive associations between social cognition and emotion regulation are absent in schizophrenia and bipolar disorder. Frontiers in Psychology 3, 607.CrossRefGoogle ScholarPubMed
Rush, AJ, Trivedi, MH, Ibrahim, HM, Carmody, TJ, Arnow, B, Klein, DN, Markowitz, JC, Ninan, PT, Kornstein, S, Manber, R, Thase, ME, Kocsis, JH and Keller, MB (2003) The 16-item quick inventory of depressive symptomatology (QIDS) clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry 54, 573583.CrossRefGoogle ScholarPubMed
Schmand, B, Bakker, D, Saan, R and Louman, J (1991) The Dutch Reading Test for Adults: a measure of premorbid intelligence level. Tijdschrift voor Gerontologie en Geriatrie 22, 1519.Google ScholarPubMed
Sheehan, DV, Lecrubier, Y, Sheehan, KH, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R and Dunbar, GC (1998) The mini-international neuropsychiatric interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 59(suppl. 20), 2233.Google ScholarPubMed
Townsend, JD, Torrisi, SJ, Lieberman, MD, Sugar, CA, Bookheimer, SY and Altshuler, LL (2013) Frontal-amygdala connectivity alterations during emotion downregulation in bipolar I disorder. Biological Psychiatry 73, 127135.CrossRefGoogle ScholarPubMed
Urry, HL, van Reekum, CM, Johnstone, T and Davidson, RJ (2009) Individual differences in some (but not all) medial prefrontal regions reflect cognitive demand while regulating unpleasant emotion. NeuroImage 47, 852863.CrossRefGoogle ScholarPubMed
van der Meer, L, van't Wout, M and Aleman, A (2009) Emotion regulation strategies in patients with schizophrenia. Psychiatry Research 170, 108113.CrossRefGoogle ScholarPubMed
van der Meer, L, Costafreda, S, Aleman, A and David, AS (2010) Self-reflection and the brain: a theoretical review and meta-analysis of neuroimaging studies with implications for schizophrenia. Neuroscience and Biobehavioral Reviews 34, 935946.CrossRefGoogle Scholar
van der Meer, L, Groenewold, NA, Nolen, WA, Pijnenborg, M and Aleman, A (2011) Inhibit yourself and understand the other: neural basis of distinct processes underlying Theory of Mind. NeuroImage 56, 23642374.CrossRefGoogle ScholarPubMed
van der Meer, L, de Vos, AE, Stiekema, AP, Pijnenborg, GH, van Tol, MJ, Nolen, WA, David, AS and Aleman, A (2013) Insight in schizophrenia: involvement of self-reflection networks? Schizophrenia Bulletin 39, 12881295.CrossRefGoogle ScholarPubMed
van der Meer, L, Swart, M, van der Velde, J, Pijnenborg, G, Wiersma, D, Bruggeman, R and Aleman, A (2014) Neural correlates of emotion regulation in patients with schizophrenia and non-affected siblings. PLoS ONE 9, e99667.CrossRefGoogle ScholarPubMed
van der Werf-Eldering, MJ, van der Meer, L, Burger, H, Holthausen, EA, Nolen, WA and Aleman, A (2011) Insight in bipolar disorder: associations with cognitive and emotional processing and illness characteristics. Bipolar Disorders 13, 343354.CrossRefGoogle ScholarPubMed
Wager, TD, Jonides, J and Reading, S (2004) Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage 22, 16791693.CrossRefGoogle ScholarPubMed
Wiggins, JL, Adleman, NE, Kim, P, Oakes, AH, Hsu, D, Reynolds, RC, Chen, G, Pine, DS, Brotman, MA and Leibenluft, E (2016) Developmental differences in the neural mechanisms of facial emotion labeling. Social Cognitive and Affective Neuroscience 11, 172181.CrossRefGoogle ScholarPubMed
Wolkenstein, L, Zwick, JC, Hautzinger, M and Joormann, J (2014) Cognitive emotion regulation in euthymic bipolar disorder. Journal of Affective Disorders 160, 9297.CrossRefGoogle ScholarPubMed
Young, RC, Biggs, JT, Ziegler, VE and Meyer, DA (1978) A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry 133, 429435.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Illustration of the task procedure during a reappraise trial and the time window used for FIR modeling. During attend-negative trials, participants were instructed to continue to attend to the picture during 4 s following the cue and preceding lingering. The black triangles indicate the start/end of each task phase. FIR, finite impulse response.

Figure 1

Table 1. Demographic and clinical characteristics in all groups

Figure 2

Fig. 2. Interaction effects between group and time in HC, patients with BD, and SZ at the contrast reappraise > attend-negative. Effects were observed (a) in the VLPFC, (b) SMA, and (c) inferior occipital gyrus. Time bins start at the presentation of regulation cue (i.e. time bin 1, 0–2 s post instruction). The presented time course is averaged over significant voxels. The line connecting the mean responses in each time bin visualizes the temporal response for each group (BD, dashed dark gray; HC, dashed light gray; SZ, straight dark gray). BD, bipolar disorder; HC, healthy controls; SZ, schizophrenia; SMA, supplementary motor area; VLPFC, ventrolateral prefrontal cortex.

Figure 3

Table 2. Group comparisons during reappraise > attend negative (cluster extent threshold k > 74)

Supplementary material: File

Zhang et al. supplementary material

Zhang et al. supplementary material 1

Download Zhang et al. supplementary material(File)
File 2 MB
You have Access Open access
7
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Distinct temporal brain dynamics in bipolar disorder and schizophrenia during emotion regulation
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Distinct temporal brain dynamics in bipolar disorder and schizophrenia during emotion regulation
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Distinct temporal brain dynamics in bipolar disorder and schizophrenia during emotion regulation
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *