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. 2025 May 22;35(5):bhaf118. doi: 10.1093/cercor/bhaf118

Brain-wide connectivity changes due to social–emotional regulation during a naturalistic fMRI task

Christopher J Hyatt 1,✉, Bruce E Wexler 2, Gretchen J Diefenbach 3,4, Gabriel S Dichter 5, Carla A Mazefsky 6, Lavinia C Uscatescu 7,8, Julie Wolf 9, Robert A Sahl 10,11, Brian Pittman 12, Godfrey D Pearlson 13,14, Michal Assaf 15,16
PMCID: PMC12096004  PMID: 40401354

Abstract

Social–emotional (SE) regulation is necessary for successful social interactions. Such emotion regulation (ER), however, has been examined in only a few studies using naturalistic SE tasks during functional neuroimaging. We examined ER in typically developed young adults (n = 62) watching and listening to a video of a person telling an emotional (positive, negative) or neutral story during functional MRI. We calculated brain-wide voxel-to-voxel functional connectivity (FC) using data-driven functional connectivity multivariate pattern analysis. Participants made two visits, the first involving only passive video viewing, while the second visit included application of an ER reappraisal strategy during video viewing. Contrasts of video emotion type across visits (main effect of Emotion) demonstrated regional FC differences depending on emotion type while contrasts between passive and ER visits (main effect of Regulation) showed significant FC differences involving left temporoparietal junction, left supramarginal gyrus, posterior cingulate cortex (PCC), and precuneus. We found no significant Emotion by Regulation interaction. Our results suggest prefrontal involvement in implicit ER (negative stimuli) and the role of medial posterior regions associated with the default mode network (PCC and precuneus) during explicit ER and provide insight into the neural substrates underlying introspective SE cognition central to ER strategies such as reappraisal and mindfulness.

Keywords: fMRI, functional connectivity, emotion regulation, naturalistic neuroscience, neurotypical

Introduction

Social–emotional (SE) perception and cognition are essential for effective social interactions and succeeding in social groups (Gallese et al. 2004). SE perception involves the identification of emotional states, in oneself and others, during social interactions (Phillips et al. 2003; Adolphs 2010; Spunt and Adolphs 2019), whereas SE cognition refers to processes such as mentalizing, which is defined as the ability to attribute mental states to others (Frith and Frith 2005; Muller-Pinzler et al. 2017; Spunt and Adolphs 2019). An additional critical component of interpersonal interactions, however, is the application of emotion regulation (ER) to modify the intensity, duration, or valence of emotions in order to meet social goals (Adolphs 2010; Gross 2013). Difficulty with ER has been well-documented across different psychopathologies, such as anxiety and mood disorders (Gross 2013) and autism (Mazefsky et al. 2013).

The neural correlates of SE perception, cognition, and regulation have been the focus of many functional MRI studies in the past few decades, in both neurotypical (Ochsner et al. 2012) and clinical (eg autism spectrum disorder [ASD]) populations (Hyatt et al. 2022; Richey et al. 2022). To examine these neural correlates, most studies used in-scanner tasks based on the viewing of still photos of emotional faces (Fusar-Poli et al. 2009; Breakspear et al. 2015). Such stimuli, however, lack the naturalistic social aspect of emotion processing and regulation that might be provided by, eg watching and listening to a video of a person telling an emotional story. The viewing of emotional stories, in contrast with the viewing of still photos of emotional faces, allows the integration of additional social cues such as dynamic facial expressions and tone of voice.

Both task-based activation and functional connectivity (FC) have been studied during facial emotional identification and ER (Fusar-Poli et al. 2009; Morawetz et al. 2011, 2016b; Underwood et al. 2021). A recent review of functional and effective connectivity studies examining emotion has highlighted the role of specific anatomical and functional structures involved in emotional salience, identification, and regulation (Underwood et al. 2021). This review demonstrated that most emotion studies focused on a priori seed regions, most often including the amygdala (Banks et al. 2007; Sladky et al. 2015), but other regions were included as well, such as insula (Singer et al. 2009; Denny et al. 2014), inferior frontal gyrus (Jabbi and Keysers 2008), fusiform gyrus (Edmiston et al. 2024), and anterior cingulate cortex (ACC) (Etkin et al. 2011; Edmiston et al. 2024). Connectivity within and between brain networks in these studies was usually measured using either psychophysiological interactions (PPIs seed-based FC) or dynamic causal modeling (effective connectivity) (Underwood et al. 2021). While these methods provided invaluable data depicting the networks underserving SE perception and regulation, they are based on a priori hypotheses and thus potentially miss other brain regions critically involved in these processes.

The current study is designed to bridge the gaps in imaging studies described above by using ecologically valid SE stimuli and data-driven broad analysis methods to delineate the neural architecture of SE processing and ER, in neurotypical adults. We have used a naturalistic fMRI task that combines both the visual and auditory sensory aspects of social and emotional personal interaction. It involves participants watching videos of actors talking to the camera, telling a personal story of either positive (ie happy) or negative (ie sad) affect, as well as neutral stories (eg how to cook lasagna). In this study, we expand on prior studies from our group that used the same emotion stimuli in a passive-viewing video task (Prohovnik et al. 2004; Hyatt et al. 2022) by including an fMRI session with explicit ER during video viewing.

Thus, participants underwent two separate fMRI sessions: the first session included a passive viewing of SE videos, while the second session involved training in ER, specifically reappraisal methods, before viewing an additional set of SE videos during neuroimaging that included instructions to apply the learned ER methods (ie explicit ER).

We examined FC using an entirely data-driven approach called functional connectivity multivariate pattern analysis (fc-MVPA), a method which measures voxel-to-voxel FC through dimensionality reduction, sharing some similarities to principal and independent component analysis (PCA and ICA, respectively) methods (Nieto-Castanon 2022). The fc-MVPA method, because it operates on the voxel level, can potentially uncover FC differences within and between groups not obtainable by seed-based connectivity (SBC) or ICA methods which rely on larger scale networks and/or regions of interest (ROIs).

Because the fc-MVPA method is a data-driven approach, we present the fc-MVPA findings without a formal hypothesis. However, we expected to confirm existing hypotheses on the neural networks involved in SE perception and regulation, and also to uncover new and potentially important network interactions involved in these processes. More specifically, we delineated the networks involved in positive versus negative emotion perception and passive viewing versus active ER. Given the recent evidence for potential therapeutic benefit of ER during positive emotional experiences and the need to further understand its neural substrates (Hoffman et al. 2024), we further explored the potential interaction between emotion perception and regulation to assess both the overlap and the differentiation underlying the ER of positive versus negative affect.

Materials and methods

Participants

Participant demographics and behavioral test scores (n = 62) are shown in Table 1. Individuals were recruited at the Olin Neuropsychiatry Research Center (ONRC), Institute of Living, Hartford, CT. Participants included in the study were typically developed (TD) adults, (n = 70 before participant exclusion for excess motion), ages 18 to 40 yrs. Exclusion criteria were intellectual disability (estimated full-scale IQ < 80), current substance use/abuse (assessed by clinical interview and urine screen prior to MRI), pregnancy (confirmed with urine pregnancy test prior to MRI), MRI contraindications (eg in-body metal), major medical condition (eg cancer), current DSM-5 Axis I diagnosis other than simple phobia, and/or history of psychotic disorders, as confirmed with Structured Clinical Interview for DSM-5, and a detailed health questionnaire.

Table 1.

Participant (n = 62) demographics, emotion measures, and video ratings according to Regulation level (Passive vs. Active).

  Mean (SD)  
Age 24.3 (5.9)
IQ (Estimated) 110.6 (11.7)
Gender (M/F) 26/36
Passive (Visit 1)
mean (SD)		 Active (Visit 2)
mean (SD)		 Paired Wilcoxon test
P-value
DASS-21 depression 3.19 (4.60) 2.68 (4.54) P = 0.2838
DASS-21 anxiety 2.90 (3.39) 1.84 (2.79) P = 0.0071
DASS-21 stress 5.39 (5.30) 4.65 (5.04) P = 0.0884
Valence (Neutral videos) passive only −0.11 (0.93)
P = 0.2854a 		NA NA
Valence (Positive videos) 1.92 (1.14)
P = 6.78e-11a 		2.45 (0.71)
P = 5.15e-12a 		P = 5.14e-4
Valence (Negative videos) −2.23 (0.95)
P = 7.30e-12a 		−1.17 (1.08)
P = 3.94e-9a 		P = 1.40e-6
Arousal (Neutral videos), passive only 2.77 (1.40)
P = 7.06e-12a 		NA NA
Arousal (Positive videos) 3.26 (1.81)
P = 7.39e-12a 		4.31 (1.88)
P = 7.37e-12a 		P = 6.58e-4
Arousal (Negative videos) 3.09 (1.54)
P = 7.34e-12a 		3.10 (1.31)
P = 7.05e-12a 		P = 0.796
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SD, standard deviation, DASS-21, Depression Anxiety and Stress Scale 21.

a1-Sample Wilcoxon test (vs. mean = 0).

Participants provided written informed consent after the study had been explained to them and were paid for their time. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008 (Hartford Healthcare IRB #E-HHC-2018-0241; Yale University IRB #2000026270). The Institutional Review Boards of both Hartford Healthcare and Yale University approved all procedures.

Psychiatric symptoms and cognitive assessments

To assess socio-emotional cognition, we administered the following tests/questionnaires: (1) Bell Lysaker Emotion Recognition Task (Bryson et al. 1997), (2) Bermond-Vorst Alexithymia Questionnaire (Vorst and Bermond 2001), (3) Emotion Regulation Questionnaire (Gross and John 2003), (4), Social Responsiveness Scale, 2nd edition (Constantino and Gruber 2012), (5) Adult Self-Report (ages 18 to 59) (Achenbach and Rescorla 2003), and (6) Depression Anxiety Stress Scale-21 (Lovibond and Lovibond 1995). We estimated full-scale IQ using the Wechsler Adult Intelligence Scale (WAIS-III; Wechsler 1997), vocabulary and block design subscales. Brief descriptions of diagnostic and social cognition tests are provided in Supplement S1.

Functional MRI simulated SE task

The fMRI task consisted of naturalistic videos of actors telling stories about positive, negative, or neutral personal experiences, looking at and addressing the viewer directly and displaying emotion-related nonverbal behaviors, but without any specific task events (Prohovnik et al. 2004; Hyatt et al. 2022). The actors were seated facing and talking directly to the camera (ie the viewer). Eight actors from two ethnic groups (White and Black, 4 actors each) with two males and two females for each ethnicity, made three videos each, one for each type of content, Positive, Negative, and Neutral, for a total of 24 videos. Of those, eight emotion videos were chosen for this study based on significant valence rating change in the appropriate direction during the first 60 s of the video (see Supplement S2 for details of video selection).

In Positive videos, actors smiled frequently and spoke in a cheerful tone of voice about a personal positive memory. In Negative videos, actors spoke negatively, often while crying, about a personal memory such as a death in the family. Each video was preceded by 60 s of a fixation screen, consisting of a black background with a crosshair in the screen center, followed by the video itself and then by a second fixation screen, bringing the total fMRI run duration to 4 min and 39 s (279 s). Videos ranged in length from 3 min 3 s to 3 min 34 s. Participants underwent MRI scans on two different days, ie two MRI “visits,” which we now describe.

During the first visit, while undergoing fMRI scanning, participants viewed six videos in randomized order, two videos each of Positive, Negative, and Neutral, with ethnicity and gender of the videos also randomized and each ethnicity and gender appearing in at least one of the six videos. Participants were instructed before the task to carefully attend to each video and were notified they will be debriefed about their valence and arousal, as well as the video content, after each video. Five seconds before the video start (ie near the end of the 60 s rest period), the notification “the video is about to start … appeared on-screen until the start of the video. We only analyzed the fMRI data from the start of the video until a time point 3 min into the video.

After viewing each video, participants answered two content questions (presented verbally) to determine if they had been attentive to the video. Participants also rated their emotional valence (scale −4 to 4; from negative to positive, where 0 is neutral) and arousal level (scale 1 to 9; from calm to energized) during the video (see Supplement S3, for post-scan questionnaire example).

During the second visit, participants underwent a brief ER training prior to the MRI scan. ER training included psycho-education on the definition of reappraisal and instructions on ways to implement it. Participants were informed that for reappraisal they would be asked to “think differently about the video to change your emotional reaction to the video.” They were then trained to use two reappraisal strategies: reinterpretation and distancing with instructions for each adapted from previous research (Pitskel et al. 2014; Richey et al. 2015). Instructions for re-interpretation were to “imagine a different context for the story that would make you feel more positive.” Instructions for distancing were to “Pretend that the story in the video is fake and the person in the video is an actor who you do not know” when they are experiencing negative emotions while watching the video and “Tell yourself the story in the video is real, that the person in the video is someone you are interested in, who you really like, and who really likes you” when they feel positive emotions while watching the video. After examples were given for both positive and negative scenarios, participants practiced reappraisal on static images of Positive and Negative socially themed emotional situations selected from the International Affective Picture System (Lang et al. 2008). During the first two practice images (one each for positive and negative), participants were shown the cue “LOOK” for 2 s followed by the image for 4 s then ratings of valence and arousal. This sequence was repeated using the cue to “THINK POSITIVE” for 2 s again followed by the image for 7 s and finally ratings of valence and arousal. After each sequence, participants were asked to “Please tell me what you thought to try to make yourself feel more positive.” Additional training was provided as needed for the participant to demonstrate understanding and ability to implement both of the strategies. During the next stage of the training, participants viewed 12 images following the sequence above, except for valence and arousal ratings and without additional training between trials. Only participants who were able to apply reappraisal in at least 10 out of 12 of the images continued in the study.

During the third phase of the training, participants practiced ER to videos similar to those used in the scanner. Participants watched up to a maximum of six emotion videos, again starting with a “LOOK” prompt. After watching the video for 45 s, the video was paused, and participants rated their valence and arousal levels. Participants then watched the remainder of each video after being prompted on-screen with the words “THINK POSITIVE” for 2 s. Participants were asked to describe the strategy they used to “think positive” and also answer two video content questions as an attention check. Only participants who were able to implement reappraisal on at least three of the six videos, with at least one of each emotion (positive, negative), continued on to the MRI session.

After ER training participants completed the fMRI task again, this time with cues to “THINK POSITIVE.” Only Positive and Negative (two each) videos, and no Neutral videos, were viewed. Sixty seconds into each video participants were cued on-screen to start using ER strategies to “Think Positive” (ie use explicit ER), with the cue lasting for 2 s. Similar to the first visit (Visit 1), after each video participants rated their valence and arousal and completed two content questions. In the second visit (Visit 2), participants were also asked to describe the ER strategy used.

Functional MRI data acquisition

We collected blood-oxygenation level (BOLD) fMRI data with a T2*-weighted echo planar imaging sequence (repetition time (TR)/echo time (TE)) = 900/35 ms, flip angle = 60°, multiband acceleration factor (AF) = 7), using a Siemens Skyra 3 Tesla scanner (Siemens, Malvern, PA) at the Olin Neuropsychiatry Research Center (ONRC; Hartford, CT). We acquired 70 contiguous axial functional slices of 2.1 mm thickness (interleaved slice order) resulting in 2.11 mm × 2.11 mm × 2.10 mm voxels. Each run consisted of 310 scans (4 min, 39 s run duration).

We also acquired T1-weighted (3D Magnetization-Prepared Rapid Acquisition Gradient Echo (MPRAGE), TR/TE/inversion time (TI) = 2,400/2.09/1,010 ms, flip angle 8°, 0.8 mm isotropic voxels) and T2-weighted (TR/TE = 3,200/564 ms, flip angle 120°, 0.8 mm isotropic voxels) structural scans and two magnitude and one phase difference field maps (TR = 731 ms, TE1 = 4.92 ms, TE2 = 7.38 ms, flip angle 50°, slice thickness 3.0 mm).

Image preprocessing and motion-artifact correction

We processed functional MRI datasets using fMRIPrep (see Supplement S4 for details). We excluded any fMRI run with more than 30% volumes with > 0.5 mm/TR head movement. We fully excluded eight participants from the study due to excess motion in all fMRI runs, resulting in the final participant count of n = 62.

For motion artifact correction following fMRI image preprocessing, we used ICA-AROMA (Automatic Removal of Motion Artifacts, 6 mm smoothing kernel, see Supplement S4 for details) and additional custom scripts for removing the mean white matter and cerebrospinal fluid signals (including first derivative, square, and derivative of the square signals). Finally, a 0.008 Hz high-pass filter was applied to remove scanner drift.

Functional connectivity multivariate pattern analyses

We analyzed voxel-to-voxel FC in our fMRI datasets using the CONN toolbox’s fc-MVPA method (Whitfield-Gabrieli and Nieto-Castanon 2012; Nieto-Castanon 2022). The results from fc-MVPA are omnibus tests, however, that do not indicate in which visit the voxel-to-voxel FC was significantly larger or smaller, and only indicate that voxel-to-voxel FC for voxels in each cluster is significantly different from voxels in the rest of the brain. Therefore, post-hoc analyses are necessary to determine the other brain regions with which the significant omnibus test cluster regions have increased or decreased voxel-to-voxel FC. We followed the fc-MVPA omnibus test results by post-hoc analysis using seed-to-voxel analysis, ie SBC (see Supplement S5 for complete details on the use of the CONN toolbox) in which the average fMRI time course from each significant fc-MVPA cluster served as seeds (ROIs) for the SBC analysis.

Statistical analyses

We performed second (group) level statistics on fc-MVPA and SBC analyses, with both visits included, using the CONN toolbox (general linear modeling, followed by multiple comparison correction using threshold free cluster enhancement, TFCE; see Supplement S5 for details).

We should note that any post-hoc analyses (SBC) of the ROIs from the main fc-MVPA result are typically considered biased or “nonindependent” due to use of ROIs that were “selected for showing the effect of interest” (Kriegeskorte et al. 2010; Nieto-Castanon 2022). An independent sample would need to be obtained to confirm these post-hoc analyses. These post-hoc analyses are nonetheless informative in that they show which voxels in the brain, at least for our sample, had significantly different FC with the voxels/ROIs from the main fc-MPVA analysis.

For the second-level analysis of fc-MVPA, we performed a 2 × 2 factorial general linear model (GLM) analysis which included the within-subjects factor “Emotion,” with two levels, Positive and Negative (includes all Positive and Negative fMRI runs from both visits), and the within-subjects factor “Regulation,” with two levels, Passive (ie all Positive and Negative fMRI runs from the first visit, before participant ER training) and Active (ie all Positive and Negative fMRI runs from the second visit, with ER training actively applied). Finally, we examined the main effect of Emotion, the main effect of Regulation and their interaction.

Results

Main effect of emotion

Figure 1 shows the TFCE-scaled fc-MVPA results for the Main effect of Emotion (P < 0.01 family-wise error (FWE)), including post-hoc SBC analyses of the 10 significant clusters, while Table 2 describes the 10 clusters that showed differentiation in FC patterns for the main effect of Emotion.

Fig. 1.

Fig. 1

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Main effect of Emotion, fc-MVPA results (P < 0.01 FWE TFCE) showing the 10 significant clusters (color coded), along with SBC post-hoc results for each of the 10 clusters (TFCE scaled). A single average SBC map is shown for areas E01, 03, 05, and 07 due to their juxtaposition and similar post-hoc maps (see text and Supplement S6 and Supplementary Fig. S1).

Table 2.

Anatomical locations of the 10 significant fc-MVPA clusters for the main effect of emotion (TFCE P < 0.01 FWE).

Cluster MNI of peak Anatomical location Size in voxels TFCE value
E01 +58 −10 +2 R: Heschl’s gyrus, planum temporale, superior temporal gyrus 180 467.36
E02 +32 +64 −2 R: Frontal pole 134 414.90
E03 −40 −30 +6 L: Planum temporale 114 406.09
E04 +38 +38 +42 R: Middle frontal gyrus 196 393.16
E05 −54 −18 +2 L: Planum temporale 40 384.69
E06 −8 +28 +30 L: dACC 74 382.15
E07 +68 −24 +2 R: Superior temporal gyrus 43 376.65
E08 −36 +48 +12 L: Frontal pole 46 367.35
E09 +32 +48 +22 R: Frontal pole 39 354.94
E10 +26 +42 +28 R: Middle frontal gyrus, R: Frontal pole 36 352.37
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MNI, Montreal Neurological Institute; TFCE, threshold-free cluster enhancement; L, left; R, right; dACC, dorsal anterior cingulate cortex.

The 10 significant clusters were located in bilateral primary and secondary auditory cortex, including the superior temporal gyrus, Heschl’s gyrus, and planum temporale, and in bilateral prefrontal regions, including the lateral superior and middle frontal gyri (four clusters in the right hemisphere, one in the left hemisphere) and dorsal anterior cingulate cortex (dACC) (left hemisphere only). Note that post-hoc SBC analysis (Positive—Negative contrast) showed four clusters in bilateral primary and secondary auditory cortex (E01, E03, E05, E07) that had very similar patterns of emotion valence-dependent differences in FC, indicating greater FC, during Positive (vs. Negative) videos, with occipital lobe (ie visual cortex), medial prefrontal cortex, and left insula, while showing greater FC during Negative (vs. Positive) videos, with adjacent brain regions (ie auditory cortex). Therefore, only a single average TFCE map for clusters E01, E03, E05, and E07 is displayed in Fig. 1, while the four individual SBC post-hoc results for these clusters are shown in Supplement S6 and Supplementary Fig. S1.

Post-hoc SBC analyses of the six remaining clusters, all in prefrontal cortex (E02, E04, E08, E09, and E10 in DLFPC, E06 in left dACC) showed, in general, greater FC during Negative (vs. Positive) videos, with bilateral temporal lobes, fusiform gyrus, ventromedial prefrontal cortex (PFC), and posterior cingulate cortex (PCC), while indicating greater FC, during Positive (vs. Negative) videos, with dACC (E02, E04, E09), and the R02 (E02, E04) and R01 (E02 only) clusters (see main effect of regulation).

Main effect of regulation

Figure 2 and Table 3 show the TFCE-scaled fc-MVPA results for the Main effect of Regulation (P < 0.01 FWE), including post-hoc SBC analyses of the four significant clusters. Here, the significant fc-MVPA clusters are located mostly in posterior brain regions, with two small clusters in the left supramarginal (R03) and angular (R04) gyri, and two large clusters (R01 to 02) in medial posterior regions. Cluster R01 includes, or is adjacent to, the PCC, and follows the splenium of the corpus callosum. Cluster R02 includes mostly precuneus, but also cuneus, and borders the parieto-occipital sulcus. Figure 2, panel B, depicts cluster R01 and R02 overlaid on a sagittal slice (x = −6) to show the anatomical location of these two clusters in more detail.

Fig. 2.

Fig. 2

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Panel (A) presents fc-MVPA results (P < 0.01 FWE TFCE) showing the four significant clusters for the main effect of Regulation, along with SBC post-hoc results for each of the four clusters (TFCE scaled). Panel (B) shows a sagittal slice (at x = −6 Montreal Neurological Institute [MNI]) depicting in greater detail the location of fc-MVPA clusters R01 and R02. Panel (C) presents a coronal slice (at y = 0 MNI) showing R02 post-hoc map in more detail, with amygdala and posterior insula circled.

Table 3.

Anatomical locations of the four significant fc-MVPA clusters (TFCE P < 0.01 FWE) for the main effect of Regulation.

Cluster MNI of peak Anatomical location Size in voxels TFCE value
R01 −08 −70 +28 L/R: Precuneus, cuneus, and supracalcarine cortex 2,115 907.78
R02 −04 −44 +20 L/R: Posterior cingulate cortex 682 842.82
R03 −62 −30 +36 L: Supramarginal gyrus 301 492.75
R04 −42 −60 +50 L: Angular gyrus, superior parietal lobe 178 472.32
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Post-hoc SBC analyses for the R01 to 02 clusters for the Passive (vs. Active) regulation contrast indicated mostly greater FC with other regions of the brain, although during Active (vs. Passive) regulation, R02 had greater FC with a small region in left visual cortex. The regions with greater FC, during Passive (vs. Active) regulation, are sensorimotor regions, supramarginal gyrus, and posterior insula. For cluster R02, the bilateral amygdala is also a region with which it has significantly greater FC for Passive (vs. Active) regulation. However, because the four-view mosaic images on the semi-inflated brain do not show subcortical regions well, amygdala, as well as posterior insula, are shown in a coronal slice view (at y = 0) (see Fig. 2, panel C).

Interaction of the factors emotion and regulation

There were no significant findings (P < 0.05 FWE TFCE) in any region of the brain for fc-MVPA 2 × 2 interaction of the factor Emotion with the factor of Regulation.

Discussion

In this study, we aimed to delineate the neural networks subserving emotion processing and explicit ER in a cohort of TD adults (n = 62) during viewing of naturalistic emotional stimuli. We performed data-driven voxel-to-voxel FC analysis using fc-MVPA on fMRI data from an emotion video task that included actors telling positive, negative, and neutral stories to the camera, as if they were telling their personal story to the participants. Therefore, in addition to needing to understand the story being told, participants also had to integrate social cues such as facial expression and tone of voice to fully appraise the emotional content of the story. During the first visit fMRI session, participants were instructed to view each video and then report their valence and arousal following the video. The second visit was similar to the first, but immediately prior to the fMRI session, participants were given a brief education and training on ER reappraisal strategies, emphasizing reinterpretation and distancing (but not excluding other strategies participants might have used). We analyzed the main effect of Emotion (ie Positive, Negative, and Neutral valences), across both visits, and the main effect of Regulation (ie Passive viewing vs. Active ER), between the two visits, as well as their interaction. We found widespread differences in FC that depended on both video emotional valence and the explicit application of an ER strategy during video viewing. Importantly, the interaction of the two factors was not significant for fc-MVPA, suggesting that application of explicit ER elicits FC patterns that are generally independent of video emotional content. We discuss these results below in detail.

The brain regions showing significantly different FC patterns between positive and negative videos (independently of ER) included bilateral auditory cortex, and prefrontal cortex, mostly right dorsolateral prefrontal cortex (DLPFC), but also left DLPFC and dACC. This result demonstrates differentiation between positive and negative valence processing by our task, as expected. Analysis of the first visit only, comparing positive, negative, and neutral video viewing, further confirms these results (see Supplement S6 and Supplementary Fig. S2). More specifically, we found reduced FC between the DLPFC (E04, E09, and E10), frontal pole (E02), and dACC (E06) clusters for the negative stimuli as apparent in the post-hoc tests. Additional changes were delineated with regions not highlighted in the main effect maps, including frontal pole and clusters in PCC, precuneus, lateral temporal regions, and occipital primary visual cortices.

Notably, while dACC involvement in emotion processing is well-documented (Etkin et al. 2011; Comte et al. 2016), the other frontal regions significant for the main effect of emotion, such as DLPFC, are more often associated with ER (Ochsner et al. 2012; Underwood et al. 2021). This seeming discrepancy might be explained by the nature of our stimuli, which might implicitly evoke ER even in the first scan of passive viewing. While the vast majority of previous studies have used static emotionally charged static pictures, we used naturalistic videos of individuals telling emotional (and neutral) personal stories that included multiple emotional cues, and thus might explain greater evocation of emotional arousal, and therefore implicit ER, than that obtained with static pictures. Because negative stimuli require more ER than positive stimuli, it is possible that FC differences in the areas showing significant main effect of emotion are not purely due to low-level emotional processes but are also attributed to higher level, implicit ER processes.

Lastly, bilateral auditory cortex, including Heschl’s gyrus and planum temporale, during positive (vs. negative) video viewing, showed greater FC with the visual cortex, but reduced FC with surrounding auditory processing regions and mPFC. An explanation as to why these auditory regions show a significant main effect of emotion is beyond the scope of this study and should be the subject of further investigation.

The main effect of regulation showed significant changes in FC in posterior regions, including two lateral regions, left supramarginal (R03) and angular (R04) gyri, and two medial regions, R01 and R02, overlapping, and adjacent to, precuneus and PCC, respectively. These regions have reduced FC, during active ER versus passive viewing, with sensorimotor regions, supramarginal gyri, posterior insula, and amygdala. This finding suggests that explicit ER, mostly using either reinterpretation or distancing as reappraisal strategies, involves reducing FC with regions associated with auditory, emotion, and arousal processes. We further found increased FC during explicit ER between left angular gyrus (R04) and PCC and precuneus (R01 and R02, respectively).

Notably, while FC differences in brain regions for active ER versus passive viewing in parietal regions, such as supramarginal and angular gyri (R03 and R04, respectively) have been reported before (Etkin et al. 2015; Morawetz et al. 2016a, 2022), the two medial posterior regions, precuneus (R01) and PCC (R02), appear less commonly in the ER literature. The dorsal PCC, however, is a region that was described in a recent review as recruited during the reappraisal strategy of “distancing” (Denny et al. 2023). These regions are often considered as “hubs” associated with the default mode network (DMN), but we note the cluster locations do not entirely spatially coincide with the traditional PCC/precuneus DMN regions. Nevertheless, as regions that overlap, and are in juxtaposition to the DMN, it is possible that they play a supporting role in functions associated with DMN.

Interestingly, a recent study showed that the DMN was the primary functional network showing significant neural responses in participants watching an evolving, naturalistic movie story (Yang et al. 2023). That study is part of a growing trend in neuroscience where naturalistic tasks are used in functional imaging, known as “naturalistic” neuroscience (Song et al. 2021; Finn et al. 2022; Yang et al. 2023). Therefore, because our fMRI task involves watching and listening to a naturalistic video of an actor telling an emotional story, engagement of the DMN might be incorporated into reappraisal-based ER strategies. Due to emotion-related task demands while watching the video, participants might engage in any number of DMN-related processes, such as social cognition or mentalizing (Hyatt et al. 2015, 2020), autobiographical memory recall (Philippi et al. 2015; Menon 2023; Yamaguchi and Jitsuishi 2023), or self-referential processing and mind-wandering (Utevsky et al. 2014; Sezer et al. 2022), to reappraise the depicted SE situation.

Studies have suggested that the precuneus and PCC are considered domain-general regions, also known as multiple-demand (MD) regions (Assem et al. 2022), in that they are activated during multiple cognitive demands. Acting as MD regions, the precuneus and PCC integrate information arising from a number of domain-specific regions, such as the sensory (ie visual and auditory) networks. Furthermore, the precuneus and PCC each contain multiple subregions, with the subregions having only partly overlapping functionality.

The posterior part of the precuneus, largely corresponding to our cluster R01, has been shown to have a different structural connectivity (SC) with the rest of the brain than other parts of the precuneus (Yamaguchi and Jitsuishi 2023). In particular, the most posterior precuneus region, POS2, as described by the Human Connectome Project Multimodal Parcellation, and overlaps cluster R01 here, has strong SC with the medial temporal lobe (MTL) and visual networks. A transcranial magnetic stimulation (TMS) study using continuous theta burst stimulation showed that POS2 is involved in autobiographical memory retrieval (Hebscher et al. 2020). This TMS study finding together with tractography studies linking POS2 to the MTL, might suggest that cluster R01 participates in autobiographical memory recall, and such memory recall might be engaged by study participants when using certain reappraisal strategies, especially distancing.

Meanwhile, the most dorsal (and anterior) part of the PCC (ie dorsal PCC, or dPCC), which approximately corresponds to our cluster R02 (Fig. 2), has been shown to have strong FC with not only the DMN, but also “task-positive” networks involved in cognitive control located in frontal and parietal regions (Leech and Sharp 2014). In their review of the PCC, Leech and Sharp described the dPCC as having a “transitional” pattern of connectivity, in that it has significant FC with not only the DMN, but also the frontoparietal control network (ie a cognitive control network), parts of the dorsal attention, sensorimotor, and salience networks. Conversely, the ventral PCC, a traditional hub of the DMN, has strong FC mostly within the DMN, and is mostly involved in supporting internally directed thought (Leech and Sharp 2014). Because of its high connectivity with paralimbic and limbic structures, it is the ventral PCC that engages in autobiographical memory recall, similar to the posterior precuneus (cluster R01). In contrast, dPCC (R02) appears to be engaged in directing attention, “controlling the balance between an internal and external attentional focus” (Leech and Sharp 2014). The authors also note that “when subjects are waiting for an external cue to action and broad vigilance is required,” activity in the dPCC is high. Lastly, our findings show that dPCC (R02) has reduced FC with amygdala during active ER versus passive viewing (see Fig. 2, bottom right). This finding suggests that the putative role of the dPCC in balancing internal and external attention might have a similar role during ER to that of the dACC, a salience network hub associated with attention control, emotion processing, regulation, and fear extinction (Ochsner et al. 2012; Pico-Perez et al. 2019). In support of this premise, the post-hoc analyses for both E02 (Fig. 1) and R03 (Fig. 2) show that R02 and dACC share identical significant FC differences with cluster E04, and along with R01, clusters E02 and R03.

The involvement of DMN posterior regions in ER is further supported by studies of the neurobiology of mindfulness as suggested by a recent review (Sezer et al. 2022), as it relies on ER among several other related processes, such as self-awareness. Relevant to our results, it has been shown that higher trait mindfulness is associated with decreased FC between precuneus (in a cluster overlapping with R01) and regions in both the salience network, including insula, and the central executive network, including supramarginal and angular gyri (Bilevicius et al. 2018). Our results for the main effect of Regulation and post-hoc analyses (Fig. 2) are largely in accord with these findings, as explicit ER is associated with decreased FC between R01, R03, insula, and amygdala. We note, however, that the FC between precuneus (cluster R01) and angular gyrus (Fig. 2, post-hoc map for R04) is reversed in polarity from the effect reported by Bilevicius and colleagues (Bilevicius et al. 2018). Although we found convincing evidence that the posterior precuneus and dorsal PCC are involved in ER during naturalistic emotion video viewing, the disparate roles of the posterior precuneus (R01) and dorsal PCC (R02) in ER during naturalistic emotional tasks should be the focus of further investigation.

Although our study was conducted with a nonclinical sample, the results of posterior medial regions involved in active ER more than passive viewing for both positive and negative stimuli, a result potentially corresponding with passive viewing of negative stimuli, might have implications for psychotherapies emphasizing ER. Traditionally these therapies have focused on negative stimuli, but recently it has been suggested that upregulating positive stimuli can be therapeutic as well (Zaharia et al. 2021; van Kleef et al. 2022). Our results might outline the mechanism of this effect. Further research in clinical populations characterized by emotion dysregulation, such as anxiety disorders and ASD (Gross 2013; Mazefsky et al. 2013), could further strengthen this theory.

Study limitations

Limitations to our study include a modest sample size and a relatively wide age range (18 to 40). Also, SBC post-hoc analyses might be biased, due to a circular analysis (Nieto-Castanon 2022), so the analysis should be repeated using an independent sample to provide generalizability. In addition, we cannot rule out order effects, due to participants having already been exposed to the emotion video task during Visit 1 (albeit to different videos) potentially introducing effects such as familiarity, practice, and boredom, which potentially contributed to differences in FC between the two visits.

Conclusions

While our results replicate previous results delineating the neural networks underlying emotion perception and regulation, they also suggest novel directions. First, we showed the involvement of posterior medial regions, precuneus, and dPCC, both known default-mode related regions, in explicit ER, suggesting a critical role for these regions in ER during naturalistic socio-emotional processing. In addition, the involvement in the main effect of Emotion of the dorsolateral prefrontal cortex and left dACC, brain regions known to “support control systems that modulate activity” during explicit ER (Ochsner et al. 2012), strongly suggest that participants engage in implicit ER during negative emotional video viewing. From a clinical perspective, our results suggest that the posterior DMN regions subserving mindfulness or similar introspective SE cognitive processes, might be directly related to successful explicit ER, and therefore potential targets for interventions for treatment.

Supplementary Material

YALE_EMOTION_ER_STUDY_-_Online_Supplement_FINAL_bhaf118
yale_emotion_er_study_-_online_supplement_final_bhaf118.docx (6.6MB, docx)

Contributor Information

Christopher J Hyatt, Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States.

Bruce E Wexler, Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, United States.

Gretchen J Diefenbach, Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, United States; Anxiety Disorders Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States.

Gabriel S Dichter, Department of Psychiatry, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States.

Carla A Mazefsky, Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15213, United States.

Lavinia C Uscatescu, Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States; Department of Psychology, Virginia Tech, 109 Williams Hall, Blacksburg, VA 24061, United States.

Julie Wolf, Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06519, United States.

Robert A Sahl, Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States; Mary W. Parker Autism Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States.

Brian Pittman, Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, United States.

Godfrey D Pearlson, Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States; Departments of Psychiatry and Neuroscience, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, United States.

Michal Assaf, Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Avenue, Hartford, CT 06106, United States; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, United States.

Author contributions

Christopher Hyatt (Data curation, Formal analysis, Methodology, Writing—original draft, Writing—review & editing), Bruce Wexler (Conceptualization, Resources, Writing—review & editing), Gretchen J. Diefenbach (Conceptualization, Resources, Writing—review & editing), Gabriel Dichter (Resources, Writing—review & editing), Carla A. Mazefsky (Resources, Writing—review & editing), Lavinia C. Uscatescu (Data curation, Writing—review & editing), Julie Wolf (Data curation, Resources, Writing—review & editing), Robert A. Sahl (Data curation, Methodology, Writing—review & editing), Brian Pittman (Methodology, Software, Writing—review & editing), Godfrey Pearlson (Supervision, Writing—review & editing), and Michal Assaf (Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing—original draft, Writing—review & editing)

Funding

National Institutes of Health (grants R01MH119069 and R01MH132044).

Conflict of interest statement: The authors have declared that there are no competing interests in relation to the subject of this study.

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