From Pacific Standard, this is a brief review of new research around the efficacy of deep brain stimulation and transcranial magnetic stimulation for the treatment of various types of psychological distress (depression, bipolar, and so on). Their results suggest that brain networks might be used to understand why brain stimulation works and to improve this form of therapy by identifying the best places to stimulate the brain.

First up is the summary from PS, followed by the full abstract (article is paywalled).

Mysterious Resting State Networks Might Be What Allow Different Brain Therapies to Work

By Nathan Collins • October 01, 2014

FMRI scans from another study. (Photo: Public Domain)

Deep brain stimulation and similar treatments target the hubs of larger resting-state networks in the brain, researchers find.
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More and more, doctors and patients dealing with severe depression, obsessive compulsive disorder, or even Parkinson’s disease turn to techniques such as deep brain stimulation and transcranial magnetic stimulation. While those treatments have proven effective in some cases, it has been unclear why the hodgepodge of stimulation sites and techniques all seem to work. A new study suggests one possibility: the different methods each activate parts of the brain common to one of its resting state networks.

For a few decades now, neuroscientists who specialize in functional magnetic resonance imaging, or fMRI, have focused on what our brains do when we do math problems, play games, choose between politicians, and much more. But as early as the mid-1990s, researchers realized they’d been missing something: What happens when we’re not doing anything at all? With that question, they began to explore what’s called the default mode network and other resting state networks (RSNs), collections of brain regions that are active and working together specifically as we let our minds and senses wander. But no one is quite sure what exactly these networks do.

Around the same time as some were exploring RSNs, others were pioneering the next generation of brain stimulation techniques, methods somewhat less crude than early forms of electroconvulsive therapy. Some new methods are invasive—deep brain stimulation, for example, requires an electrical implant in the brain—and some aren’t. Transcranial magnetic stimulation involves a targeted magnetic pulse originating outside the brain. They have one thing in common, though: Different techniques applied in different parts of the brain often achieve the same goals.

It works that way, Michael Fox and five others argue, because of resting state networks. To figure that out, the team reviewed clinical studies that had used deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), and a third method, transcranial direct current stimulation, or tDCS, to treat 14 disorders, including anorexia, depression, and Tourette syndrome. Across all 14 diseases except for one, epilepsy, they found correlations between resting-state activity in sites where DBS was effective and in others where TMS and tDCS were effective, indicating that such sites were all part of the same resting-state network. Backing that conclusion up was the observation that there seemed to be little, if any, connection between DBS regions that worked and regions where other kinds of stimulation had failed.

“Sites effective for the same disease tend to fall within the same brain network [and] ineffective sites fall outside this network,” the authors write in Proceedings of the National Academy of Science. Researchers who study psychiatric disorders had already started thinking in network terms, and now they have an even better reason to.


Nathan Collins studied astrophysics and political science before realizing he wanted to learn about all of the science without worrying about tenure. In his second life as a freelance science writer, he’s written for Scientific American, New Scientist, and others.

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Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases

Michael D. Fox, Randy L. Buckner, Hesheng Liu, M. Mallar Chakravarty, Andres M. Lozano, and Alvaro Pascual-Leone

Edited by Michael S. Gazzaniga, University of California, Santa Barbara, CA, and approved August 28, 2014 (received for review March 17, 2014)

Significance

Brain stimulation is a powerful treatment for an increasing number of psychiatric and neurological diseases, but it is unclear why certain stimulation sites work or where in the brain is the best place to stimulate to treat a given patient or disease. We found that although different types of brain stimulation are applied in different locations, targets used to treat the same disease most often are nodes in the same brain network. These results suggest that brain networks might be used to understand why brain stimulation works and to improve therapy by identifying the best places to stimulate the brain.

Abstract

Brain stimulation, a therapy increasingly used for neurological and psychiatric disease, traditionally is divided into invasive approaches, such as deep brain stimulation (DBS), and noninvasive approaches, such as transcranial magnetic stimulation. The relationship between these approaches is unknown, therapeutic mechanisms remain unclear, and the ideal stimulation site for a given technique is often ambiguous, limiting optimization of the stimulation and its application in further disorders. In this article, we identify diseases treated with both types of stimulation, list the stimulation sites thought to be most effective in each disease, and test the hypothesis that these sites are different nodes within the same brain network as defined by resting-state functional-connectivity MRI. Sites where DBS was effective were functionally connected to sites where noninvasive brain stimulation was effective across diseases including depression, Parkinson's disease, obsessive-compulsive disorder, essential tremor, addiction, pain, minimally conscious states, and Alzheimer’s disease. A lack of functional connectivity identified sites where stimulation was ineffective, and the sign of the correlation related to whether excitatory or inhibitory noninvasive stimulation was found clinically effective. These results suggest that resting-state functional connectivity may be useful for translating therapy between stimulation modalities, optimizing treatment, and identifying new stimulation targets. More broadly, this work supports a network perspective toward understanding and treating neuropsychiatric disease, highlighting the therapeutic potential of targeted brain network modulation.

 

I've been binge watching a new series called Black Box, kind of like House, MD, but specific to neuroscience diagnostic mysteries (oh, and the main character is bipolar rather than narcissistic).

In the 5th episode (there's only 7 so far), one of the recurring characters has a brain tumor metastasize to his liver, which leaves him less than 6 months to live. He freaks out because the glioblastoma (one of the most common forms of brain tumor) had responded to treatment and he had finally reconnected with his estranged son.

The main character, a neuroscientist, suggests that maybe psilocybin will help ease his death anxiety. She repeats statistics of a study by her friend that showed 72% of terminal subjects in the study who received psilocybin had profound spiritual experiences that eased their anxiety.

And that was on ABC, not HBO or some other cable network.

A 2011 study out of UCLA showed that 11 of 12 subjects in the psilocybin study experienced reductions in Profile of Mood States (POMS) scores following administration of psilocybin. At 5 and 6 months post trial, the scores remained lower (this despite a rebound in the scores during the 2nd, 3rd, and 4th months).

You can read the study (Pilot Study of Psilocybin Treatment for Anxiety in Patients With Advanced-Stage Cancer) in JAMA Psychiatry. I posted back in 2012 on some of the research into psilocybin for cancer patients.

There are studies underway in the use of psilocybin in treating:

• addiction in treatment-resistant nicotine addicts
• anxiety in terminal cancer patients
• recall of remote autobiographical memories

All of this is background to a new study in Frontiers in Human Neuroscience that looks at both psilocybin and MDMA and how they generate changes in resting-state functional connectivity (RSFC). Between-network RSFC increased under psilocybin, suggesting reduced differentiation between networks in the altered state of consciousness. There was also decreased RSFC between visual and sensorimotor resting state networks (RSN). MDMA had a much smaller effect on between-network RSFC. The suggestion is that classic psychedelics are more likely to produce profound effects on perception.

Full Citation: 
Roseman, L, Leech, R, Feilding, A, Nutt, DJ, and Carhart-Harris, RL. (2014, May 27). The effects of psilocybin and MDMA on between-network resting state functional connectivity in healthy volunteers. Frontiers in Human Neuroscience; 8:204. doi: 10.3389/fnhum.2014.00204

The effects of psilocybin and MDMA on between-network resting state functional connectivity in healthy volunteers

Leor Roseman [1,2], Robert Leech [2], Amanda Feilding [3], David J. Nutt [1], and Robin L. Carhart-Harris [1]

1. Centre for Neuropsychopharmacology, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
2. Computational, Cognitive and Clinical Neuroscience Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
3. The Beckley Foundation, Oxford, UK

Abstract

Perturbing a system and observing the consequences is a classic scientific strategy for understanding a phenomenon. Psychedelic drugs perturb consciousness in a marked and novel way and thus are powerful tools for studying its mechanisms. In the present analysis, we measured changes in resting-state functional connectivity (RSFC) between a standard template of different independent components analysis (ICA)-derived resting state networks (RSNs) under the influence of two different psychoactive drugs, the stimulant/psychedelic hybrid, MDMA, and the classic psychedelic, psilocybin. Both were given in placebo-controlled designs and produced marked subjective effects, although reports of more profound changes in consciousness were given after psilocybin. Between-network RSFC was generally increased under psilocybin, implying that networks become less differentiated from each other in the psychedelic state. Decreased RSFC between visual and sensorimotor RSNs was also observed. MDMA had a notably less marked effect on between-network RSFC, implying that the extensive changes observed under psilocybin may be exclusive to classic psychedelic drugs and related to their especially profound effects on consciousness. The novel analytical approach applied here may be applied to other altered states of consciousness to improve our characterization of different conscious states and ultimately advance our understanding of the brain mechanisms underlying them.

Introduction

Psychedelic drugs have been used throughout history by different cultures as a means of altering consciousness. They are powerful tools for understanding the neurobiology of consciousness yet they have been underutilized by modern science, arguably due to political rather than scientific circumstances (Nutt et al., 2013). The majority of consciousness research has focused on states of reduced consciousness such as coma and sleep (Laureys, 2005). Indeed, consciousness has been defined as that which is lost during dreamless sleep (Tononi, 2004) but consciousness can also be studied in terms of changes in the mode or style of waking consciousness, such as is seen in the psychedelic state. Another popular model of consciousness describes it using two parameters: (1) wakefulness or arousal and (2) awareness (Laureys et al., 2009). It is recognized that these parameters have a mostly linear relationship; however, REM sleep and the vegetative state are considered anomalies, since the former involves greater awareness than would be predicted by wakefulness and the latter displays less (Laureys et al., 2009). The position of the psychedelic state in this model has never been considered before and it presents another interesting anomaly. There is no evidence of reduced wakefulness in the psychedelic state and although awareness is altered, it would be misleading to say that it is reduced. Indeed, the psychedelic state has been referred to as an “expansive” state of consciousness (Huxley, 1959). Thus, it is important to investigate what the neurobiological basis of this putative broadening of consciousness is.

One of the most popular theories of consciousness is the “information integration” theory of Tononi (2012). This proposes that consciousness depends on the presence of two key parameters: (1) information and (2) integration. Information is derived from information theory (Shannon and Weaver, 1949) and in the context of consciousness, refers to the potential size of the repertoire of different metastable states (Tognoli and Kelso, 2014) (or “sub-states”) the mind/brain can enter over time. Integration refers to the capacity of the mind/brain to integrate processes into a collective whole. The parameter of awareness is likely to be related to the property of information, since the greater the repertoire of sub-states the mind can enter, and the easier it can move between these, the broader consciousness will be.

In recent years, there has been an increasing interest in human fMRI measures of resting state functional connectivity (RSFC) (Damoiseaux and Greicius, 2009). Resting state networks (RSN) can be identified using seed-based approaches (Biswal et al., 1995) and independent component analysis (ICA) (Beckmann et al., 2005). These RSNs resemble stimulus-evoked networks (Smith et al., 2009) and may be thought of as metastable sub-states making-up a particular (macro) state of consciousness (Carhart-Harris et al., 2014a). Thus, one way to describe the quality of a macro-state of consciousness may be to investigate the integrity and dynamics of its sub-states and how they interact with each other. One way this can be done is by looking at the internal stability (integrity) of an RSN, i.e., reflected in the strength of the coupling between its constituent nodes. For example, we have found decreased intra-RSN connectivity post-psilocybin with both fMRI (Carhart-Harris et al., 2012a) and magnetoencephalography (MEG) (Muthukumaraswamy et al., 2013), implying a general breakdown of the integrity or internal stability of RSNs under psilocybin.

Another way to address the behavior of a system's sub-states is to look at their relationship with each other, e.g., by measuring between-RSN functional connectivity or coupling. A frequently investigated RSN is the default mode network (DMN) (Raichle et al., 2001). The DMN is known to be more active during rest than during goal-directed cognition and its activity has been found to be “anti-correlated” or at least uncorrelated or orthogonal with activity in networks that are engaged during goal-directed cognition - referred to generically as “task positive networks” or TPNs. This anticorrelation is preserved under task free conditions (Fox et al., 2005), implying that it is an important feature of normal consciousness, perhaps accounting for the distinction between externally focused cognition and introspection (Carhart-Harris et al., 2012b). We recently found that the classic psychedelic drug psilocybin reduces the anticorrelation between DMN and a number of TPNs during resting conditions, and this was interpreted as a decrease in the natural distinction between externally-focused attention and introspection (Carhart-Harris et al., 2012b), which is relevant to the notion of “ego-boundaries,” i.e., an agent's sense of being apart from or separate to its environment. It would be a natural extension of the above analysis to address the full gamut of between-RSN FC identified by ICA rather than just focusing on just the DMN-RSN RSFC. This was the aim of the present study.

The primary focus of the present paper is the classic psychedelic state and determining its underlying neurodynamics as measured with fMRI. However, in order to understand the psychedelic state, it is useful to compare it with other states of consciousness to see how it relates to these. Thus, the present analysis focuses on the brain effects of a classic psychedelic drug, psilocybin (the active component of magic mushrooms) and compares this with the effects of the pro-serotonergic stimulant, 3–4 methylenedioxymethamphetanine, MDMA. MDMA is a potent monoamine releaser that produces an acute euphoria in most individuals but it is not considered a classic psychedelic, as psilocybin is. Direct 5-HT2AR stimulation is the defining pharmacological property of classic serotonergic psychedelics, but relative to classic psychedelics, MDMA has a far weaker affinity for the 5-HT2A receptor (Green et al., 2003). Instead, MDMA produces a more generalized, non-selective activation of monoamine receptors by increasing the concentration of their endogenous ligands in the synapse via transporter-mediated release (Green et al., 2003). The primary subjective effects of MDMA include increased positive mood, heightened sensations and prosocial sentiments and although it can produce mild visual hallucinatory phenomena, it does not alter consciousness in the same fundamental manner as classic psychedelics (Gouzoulis-Mayfrank et al., 1996).

Thus, comparing changes in RSFC under psilocybin and MDMA can enable us to isolate and identify effects that are unique to the psychedelic-induced altered state of consciousness produced by classic psychedelics such as psilocybin. Considering the previous findings of decreased intra-RSN FC and DMN-TPN anti-correlation under psilocybin (Carhart-Harris et al., 2012a,b; Muthukumaraswamy et al., 2013), we hypothesized that the normal differentiation between RSNs would be affected by psilocybin such that RSNs whose activity is usually highly correlated would show reduced RSFC under psilocybin (but not MDMA) and that networks that are normally anti-correlated would show reduced anti-correlation under psilocybin (but not MDMA). If the hypothesized effects are present under psilocybin but absent under MDMA, this will strengthen the inference that they are specifically related to psilocybin more profound effects on consciousness.

Materials and Methods

Design

Psilocybin

This is an entirely new analysis on a previously published data set (Carhart-Harris et al., 2012a,b). This was a within-subjects placebo-controlled study that was approved by a local NHS Research Ethics Committee and Research and Development department, and conducted in accordance with Good Clinical Practice guidelines. A Home Office License was obtained for storage and handling of a Schedule 1 drug. The University of Bristol sponsored the research. The research was carried out at CUBRIC, University of Cardiff.

MDMA

This is also an entirely new analysis on a previously published dataset (Carhart-Harris et al., 2014b). This was a within-subjects, double-blind, randomized, placebo-controlled study. Participants were scanned twice, 7 days apart, once after MDMA and once after placebo. The study was approved by NRES West London Research Ethics Committee, Imperial College London's Joint Compliance and Research Office (JCRO), Imperial College's Research Ethics Committee (ICREC), the Head of Imperial College's Department of Medicine, Imanova Center for Imaging Science and Imperial College London's Faculty of Medicine, and was conducted in accordance with Good Clinical Practice guidelines. A Home Office License was obtained for the storage and handling of a Schedule 1 drug and Imperial College London sponsored the research.

Participants

Psilocybin

Fifteen healthy subjects took part: 13 males and 2 females (mean age = 32, SD = 8.9). Recruitment was via word of mouth. All subjects were required to give informed consent and undergo health screens prior to enrolment. Entry criteria were: at least 21 years of age, no personal or immediate family history of a major psychiatric disorder, substance dependence, cardiovascular disease, and no history of a significant adverse response to a hallucinogenic drug. All of the subjects had used psilocybin at least once before (mean number of uses per subject = 16.4, SD = 27.2) but not within 6 weeks of the study.

MDMA

The original study sample comprised of 25 healthy participants (mean age = 34, SD = 11, 7 females) with at least 1 previous experience with MDMA. None of the participants had used MDMA for at least 7 days and other drugs for at least 48 h, and this was confirmed by a urine screen. As a conservative step to control for between-study differences in the global intensity of the subjective effects produced by the different drugs, 11 subjects who gave ratings of <50% for the intensity of MDMA's effects were excluded from the analysis. This step meant that ratings of drug effects intensity were comparable across the two studies (i.e., the mean intensity of psilocybin's subjective effects was 67 ± 19 at peak and MDMA's was 69 ± 15). An additional subject was excluded because of significant head movements (mean head motion > one voxel width). Thus, a total of 13 subjects were included in the analysis (i.e., 12 excluded). An alcohol Breathalyzer test confirmed that none of the participants had recently consumed alcohol. For the sample of 13, participants had used MDMA an average of 29 (±35) times before (range = 1–100) and the mean time since last use was 983 (±1998) days (range = 7–6570 days). Participants were screened for general health, MR-compatibility and present mental health. Screening involved routine blood tests, electrocardiogram, heart rate, blood pressure and a brief neurological exam. All subjects were deemed physically and mentally healthy at the time of study entry and none had any history of drug or alcohol dependence.

Anatomical Scans

Psilocybin

Imaging was performed on a 3T GE HDx system. Anatomical scans were performed before each functional scan. These were 3D fast spoiled gradient echo scans in an axial orientation, (1 mm isotropic voxels).

MDMA

Imaging was performed on a 3T Siemens Tim Trio (Siemens Healthcare, Erlangen, Germany) using a 32-channel phased array head coil. Anatomical reference images were acquired using the ADNI-GO recommended MPRAGE parameters (1 mm isotropic voxels).

Drug and Scanning Parameters

Psilocybin

All subjects underwent two 12-min eyes-closed resting-state blood oxygen–level dependent (BOLD) fMRI scans on 2 separate occasions at least 7 days apart: placebo (10 ml saline, 60-s intravenous injection) was given on 1 occasion and psilocybin (2 mg dissolved in 10 ml saline) on the other. Seven of the subjects received psilocybin in scan 1, and 8 received it in scan 2. Injections were given manually by a study doctor situated within the scanning suite. The 60-s infusions began exactly 6 min after the start of the 12-min scans. Subjective ratings were given post-scan using visual analog scales (VAS). The subjective effects of psilocybin were felt almost immediately after injection and were sustained for the duration of the scan.

MDMA

Two BOLD resting-state scans were performed during each functional scanning session (duration of functioning scanning = 60 min). The first resting-state BOLD scan took place 60 min after capsule ingestion and the second resting-state BOLD scan occurred 113 min after capsule ingestion. Peak subjective effects were reported 100 min post administration of MDMA, generally consistent with the plasma t-max of MDMA (Kolbrich et al., 2008). The order of MDMA and placebo administration was counterbalanced.

fMRI Data Acquisition

Psilocybin

BOLD-weighted fMRI data were acquired using a gradient echo planar imaging sequence, 3 mm isotropic voxels, TR = 3000 ms, TE = 35 ms, field-of-view = 192 mm, 90° flip angle, 53 axial slices in each TR, parallel acceleration factor = 2, 64 × 64 acquisition matrix. The psilocybin and placebo scans for this analysis were of 5 min (1 min post infusion).

MDMA

BOLD-weighted fMRI data were acquired using a gradient echo planar imaging sequence, 3 mm isotropic voxels, TR = 2000 ms, TE = 31 ms, field-of-view = 192 mm, 80° flip angle, 36 axial slices in each TR, GRAPPA acceleration = 2, bandwidth = 2298 Hz/pixel. For each condition, MDMA and placebo, two scans were used for the analysis, each one of 6 min (performed 60 min and 113 min post-capsule ingestion)

Resting State Networks (RSN)

We used RSNs that were identified in Smith et al. (2009) using ICA (Figure 1). Ten of these components were given functional labels based on their correspondence to the BrainMap database of functional imaging studies, involving task-evoked FMRI data from nearly 30,000 human subjects. These networks were: Visual-Medial Network (VisM), Visual-Lateral Network (VisL), Visual-Occipital pole Network (VisO), Auditory Network (AUD), Sensorimotor Network (SM), Default Mode Network (DMN), Executive Control Network (ECN), Left frontoparietal Network(lFP), Right frontoparietal Network (rFP) and Cerebellar network. In addition, we used three more components from Smith et al, that we named DMN2 (an anterior DMN and ECN hybrid), Dorsal Attention Network 1 and 2 (DAN1 and DAN2). Another 6 components were identified as non-neural noise (likely generated by head motion and non-neural physiological fluctuations).

FIGURE 1  

Figure 1. Non-noise resting State Networks (RSN) from Smith et al., 2009: (1) Visual–Medial (VisM), (2) Visual–Lateral (VisL), (3) Visual–Occipital pole (VisO), (4) Auditory (AUD), (5) Sensorimotor (SM), (6) Default Mode Network (DMN), (7) DMN2–A hybrid of anterior DMN and Executive Control Network, (8) Executive Control Network (ECN), (9) left Frontoparietal Network (lFP), (10) right Frontoparietal Network (rFP), (11) Dorsal Attention Network (DAN), (12) DAN2, (13) Cerebellum. Ten of these components were given functional labels based on their correspondence to the BrainMap database of functional imaging studies. (RSNs 1, 2, 3, 4, 5, 6, 8, 9, 10, 13), additional networks (7, 11, 12) were labeled by the experimenters in the current study based on the regional distribution of activity.

Preprocessing

All analyses were performed using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL, www.fmrib.ox.ac.uk/fsl) (Smith et al., 2004). We used the standard imaging preprocessing FSL pipeline that involved brain extraction (Smith, 2002), motion correction using MCFLIRT (Jenkinson et al., 2002), spatial smoothing (FWHM) of 5 mm (Smith and Brady, 1997) and a high-pass filter of 100 s. The scans were registered to the subjects' T1-weighted high-resolution (2 × 2 × 2 mm) anatomical scans and were then registered to the Montreal Neurological Institute standard brain (2 × 2 × 2 mm) (Jenkinson et al., 2002). The data was resampled into 4 mm space as part of the default processing pipeline for Melodic and was done to make the analysis more computational efficient.

Between Networks Functional Connectivity (FC)

Psilocybin

To extract time courses for each subject for each RSN and for each condition, we back-projected the components from Smith et al. (2009) into each 4D fMRI dataset using a general linear model. Specifically, we took the 20 components ICA map from Smith et al. as the set of template ICAs for the dual regression pipeline. The first step of the “dual regression” pipeline was then applied to each 4D dataset, resulting in a specific timecourse for each component for each dataset (Beckmann et al., 2009). Between-RSN coupling was presented graphically using a 13 × 13 correlation (or more strictly, regression) matrix in which the color in each square represents a beta weight or coupling strength for the corresponding RSN-RSN pair. Specifically, these weights were calculated by entering the time course for a specific RSN as a dependent variable in a general linear model, with the time course of another RSN entered as an independent variable—with this procedure repeated for each RSN pair. The mean head motion under psilocybin and its placebo condition were 0.1 ± 0.05 mm and 0.06 ± 0.015 mm, respectively (p < 0.01). Therefore, to further partial out non-neural noise confounds, six motion time courses (estimated from the motion correction) and motion outliers (estimated using the “fsl_motionoutlier” command implemented in FSL), as well as the time courses for 6 non-neural noise components were entered as confounds (some of this noise is driven by head motion). The model resulted in a parameter estimate or unstandardized beta weight (β) representing the strength of functional coupling between each RSN pair. The general linear model was estimated twice for each RSN pair: with each RSN as dependent variable in one model and as an independent variable in the second model. Since we were not looking at effective or directed connectivity (Friston et al., 2003), we created a symmetrical connectivity matrix by averaging together each subject's two β values for each RSN pair. For each RSN pair, three results were calculated: (a) group mean β value for the placebo condition; (b) group mean β value for the psilocybin condition; (c) Paired t-test (2-tail) for the difference between the mean β values of each condition (Figure 2). To correct for multiple comparisons, a false discovery rate (FDR) threshold was calculated using q = 0.05 and q = 0.1 (N = 78).

FIGURE 2  

Figure 2. Scheme of the analysis by steps. Calculating t-values for each RSN pair that represent the change in coupling strength between placebo and drug.

MDMA

The MDMA RSFC was analyzed using the same procedure described above (Figure 2). The only difference was that there were two resting state scans in the MDMA study, so β values from the two scans (performed 60 min and 113 min post-capsule ingestion) were averaged together before comparing between the placebo and drug conditions. The mean head motion under MDMA and its placebo condition were 0.083 ± 0.036 mm and 0.061 ± 0.019 mm, respectively (p = 0.047). The same procedure to control for motion in the psilocybin analysis was used for MDMA.

Results

Subjective Effects

Psilocybin

The subjective effects of psilocybin have been documented elsewhere (Carhart-Harris et al., 2011, 2012a). Briefly, the subjective effects of 2 mg psilocybin given as an intravenous injection over 60 s begin at the end of the injection period, reach a sustained peak after approximately 5 min, and subside completely after 45–60 min. Primary subjective effects include altered visual perception (e.g., hallucinated motion and geometric patterns), an altered sense of space and time, and vivified imagination. The intensity of psilocybin's global subjective effects was rated using a VAS format. The mean intensity at peak effects (5 min post-infusion) was 67% ±19.

MDMA

The subjective effects of MDMA are reported in a separate paper (Carhart-Harris et al., 2014b). At their peak, the average intensity of MDMA's global subjective effects was 69% ±15 (n = 13). There was no significant difference between intensity ratings under the two different drugs.

Between Networks FC

Psilocybin

The coupling strengths (β) for each condition can be seen graphically in the correlation matrixes in Figure 3 and numerically in the Supplementary material. For the placebo condition, see Figure 3A and Supplementary Table 1A and for the psilocybin condition see Figure 3B and Supplementary Table 1B. A paired t-test (2-tail) was done across subjects to compare the β values for each RSN pair in the drug and placebo (Figure 3C and Supplementary Table 1C). The results were corrected for multiple comparisons using FDR with q = 0.05 (resulting in a threshold of p < 0.0167) and q = 0.1 (resulting a threshold of p < 0.042). The RSN pairs that showed a significant decrease in coupling under psilocybin were: SM-VisM (p = 0.0265), SM-VisL (p = 0.0051) and SM-VisO (p = 0.0151). The RSN pairs that showed a significant increase in coupling were: VisM-lFP (p = 0.0001), VisM-DAN (p = 0.0156), VisM-rFP (p = 0.0023), VisM-DAN2 (p = 0.0002), VisM-Cerebellum (p = 0.0108), VisL-DMN (p = 0.0046), VisL-lFP (p = 0.0056), VisL-rFP (p = 0.0031), VisL-DAN2 (p = 0.0142), VisO-DAN2 (p = 0.0256), AUD-DMN (p = 0.028), AUD-ECN (p = 0.0323), AUD-lFP (p = 0.0029), AUD-rFP (p = 0.0001), AUD-DAN2 (p = 0.0005), SM-ECN (p = 0.0105), SM-lFP (p = 0.022), SM-rFP (p = 0.0026), SM-DAN2 (p = 0.034), DMN-lFP (p = 0.0029), DMN-DAN (p = 0.0058), DMN2-ECN (p = 0.0071) DMN2-lFP (p = 0.0101), DMN2-DAN (p = 0.0005), DMN2-DAN2 (p = 0.0091), ECN-lFP (p = 0.0077), ECN-rFP (p = 0.0098), lFP-DAN (p = 0.0026), rFP-DAN (p = 0.0187), and DAN-DAN2 (p = 0.0161).

FIGURE 3  

Figure 3. Between networks resting state functional connectivity results. Within each matrix, each colored square represents coupling between corresponding RSN pairs with the color of the square denoting the coupling strength (A,B,D,E) or change in coupling strength (C,F) between the RSN pairs (blue, negative coupling or a decrease in coupling; red, positive coupling or an increase in coupling). The six images are: (A) Group mean of β values for the placebo of psilocybin condition. (B) Group mean of β values for the psilocybin condition. (C) Paired t-test (2-tail) for the difference between the mean β values of psilocybin and placebo. (D) Group mean of β values for the placebo of MDMA condition. (E) Group mean of β values for the MDMA condition. (F) Paired t-test (2-tail) for the difference between the mean β values of MDMA and placebo. The networks from Smith et al., (2009) are: (1) Visual—Medial (VisM), (2) Visual—Lateral (VisL), (3) Visual—Occipital pole (VisO), (4) Auditory (AUD), (5) Sensorimotor (SM), (6) Default Mode Network (DMN), (7) DMN2—A hybrid of anterior DMN and Executive Control Network, (8) Executive Control Network (ECN), (9) left Frontoparietal Network (lFP), (10) right Frontoparietal Network (rFP), (11) Dorsal Attention Network (DAN), (12) DAN2, (13) Cerebellum. FDR correction for multiple comparison (N = 78) was applied on the t-tests: *0.05 < q < 0.1. **q < 0.05.

MDMA

The same analysis as above was repeated for the MDMA condition using a q of 0.05, resulting in a threshold of p < 0.0006 and q = 0.1, resulting in a threshold of p < 0.0012. Only one RSN pair showed a significant change in coupling under MDMA, i.e., increased coupling between the DMN2-ECN (p = 0.0001).

Differences in Movement

Both drugs showed significant, yet relatively modest, increased head motion between conditions. The mean head motion under psilocybin and its placebo condition were 0.1 ± 0.05 mm and 0.06 ± 0.015 mm, respectively (p < 0.01). The mean head motion under MDMA and its placebo condition were 0.083 ± 0.036 mm and 0.061 ± 0.019 mm, respectively (p = 0.047). Power et al. (2012) suggest that head motion can change the results of RSFC, therefore, in the regression analysis, we added several motion confounds: six motion time courses, motion outliers [similar to the procedure of scrubbing within regression (spike regression) mentioned by Yan et al. (2013) and Satterthwaite et al. (2013)] and time courses of RSNs that were driven by motion. However, it still remains possible that the increased movement under the drugs may have caused the changes in RSFC. Hence, we investigated if there was a relationship between the change in estimated motion (mean framewise displacement) between placebo and drug and the change in coupling strength (for pairs of RSNs that showed significant differences in coupling). For most of the RSN pairs no relationship was found (p < 0.05). However, under psilocybin, there were significant correlations with motion in the following RSN pairs: VisM-SM (p = 0.002), VisL-SM (p = 0.001), VisO-SM (p = 0.02), VisL-DMN (p = 0.03), VisM-rFN (p = 0.048), VisL-rFN (p = 0.01), VisO-DAN2, DMN-lFN (p = 0.001), DMN-DAN (p = 0.01). For that reason, the significant results of these RSN pairs should be approached with caution.

Discussion

To our knowledge, this is the first analysis to test the effects of different pharmacological agents using a standard ICA-derived template of RSNs to construct between-network functional connectivity matrixes for different drug states. This approach may have wider application, enabling researchers to determine connectivity “fingerprints” for characterizing different states of consciousness, i.e., not only those induced by pharmacological agents but sleep states and even pathological states. This will enable informed comparisons to be made between different states, potentially allowing us to categorize different states based on their connectivity profiles. Functional connectivity matrixes have been used before to differentiate between pathology states such as schizophrenia and bipolar disorder (Mamah et al., 2013) and here we suggest that they could be used more broadly to characterize states of consciousness, including those induced by psychoactive drugs.

Probably the most striking result of the present study was the marked increases in between-network RSFC under psilocybin. These increases were evident for heteromodal networks, both in terms of increased unimodal-heteromodal (e.g., AUD-rFP) and heteromodal-heteromodal network RSFC (e.g., lFP-ECN). Based on previous analyses (Carhart-Harris et al., 2012b), we had predicted that RSN pairs with weak or negative RSFC at baseline would show increased coupling post-psilocybin, and this was found (e.g., DMN-VisL). However, the increases in between-network RSFC were more fundamental than this, being evident for RSN pairs that were already positively coupled at baseline (e.g., DMN2-ECN). The increase in correlated brain activity across normally distinct brain networks was particularly true for heteromodal RSNs, where the distribution of 5-HT2A receptors is known to be highest (Erritzoe et al., 2010) and 5-HT2A receptor stimulation is linked to desynchronous cortical activity (Riba et al., 2002; Wood et al., 2012; Muthukumaraswamy et al., 2013) and network disintegration (Muthukumaraswamy et al., 2013; Carhart-Harris et al., 2014a).

The pattern of increased between-network RSFC under psilocybin did not apply universally for the whole of the brain. Decreased RSFC was observed between the three visual RSNs and the sensorimotor network [these networks are known to be highly connected (Wise et al., 1997; Van Den Heuvel et al., 2008)], and there was a general trend toward decreased unimodal-unimodal network RSFC (e.g., VisM-AUD and SM-AUD showed decreased RSFC under psilocybin but this failed to survive FDR correction, see Supplementary Table 1). However these decreases can also be explained by the changes in head motion between conditions and further work is required to test whether these decreases in sensory RSN RSFC under psilocybin relate to the drug's characteristic perceptual/hallucinatory effects.

Previous neuroimaging studies with psychedelics have so far failed to reveal a simple and compelling explanation for their characteristic hallucinogenic effects (Vollenweider et al., 1997; Carhart-Harris et al., 2012a; Muthukumaraswamy et al., 2013) (but see De Araujo et al., 2012) and so drug-induced visual hallucinations remain poorly understood. Under normal conditions, activity in the visual cortex is driven by and thus anchored to visual input. Moreover, activity in other networks (e.g., the DMN), concerned with other distinct functions (e.g., introspection), is often weakly or inversely coupled to visual activity (e.g., see the pale and blue colored squares for the visual-RSN pairs in Figures 3A,D). Thus, increased communication between the visual system and systems that are usually reserved for distinct functions may lead to erroneous perceptual associations. For example, increased DMN-visual network RSFC, may relate to an increased influence of imagination (mediated by the DMN) on visual perception (mediated by the visual networks). A similar process may occur in situations of sensory deprivation where sensory processing becomes decoupled from sensory stimulation, allowing the system to “free-wheel” with the potential for the spontaneous emergence of internally-generated percepts. Decreased cross-modality RSFC and increased unimodal to heteromodal network RSFC may be a common characteristic of such states but future studies are required to test this. For example, comparisons between the present results and changes in RSFC in the meditative state could inform these speculations.

Given reports of synesthesia-like experiences under psychedelics (e.g., participants reported that the noise of the MR scanner influenced the rate and content of their closed eye visual hallucinations Carhart-Harris et al., 2012a and see also Luke and Terhune, 2013) one may have predicted increased cross-modality communication under psilocybin rather than the decreased coupling that was observed here. However, it has yet to be determined whether synesthesia-like experiences in drug-induced altered states of consciousness are qualitatively and mechanistically related to synesthesia experienced outside of this context and it is also worth noting that increased visual to heteromodal cortical functional connectivity has been found in color-grapheme synesthesia (Dovern et al., 2012; Sinke et al., 2012) as well as in the present study.

Taking a dynamical systems theory approach to the present results, RSNs can be conceived of as “attractors,” i.e., patterns of activity into which the brain tends to gravitate for short periods of time (Deco et al., 2009; Hellyer et al., 2014). A macro-state of consciousness (such as normal waking, deep sleep or the psychedelic state) may, therefore, be graphically represented as an “attractor landscape” in which the depth of “basins of attraction” (valleys in an otherwise flat 2D-plane) reflect the stability of particular RSNs or metastable “sub-states,” i.e., more long lasting sub-states will have deep basins of attraction and unstable sub-states will have shallow ones. A recent paper (Kanamaru et al., 2013) has described brain function in these terms, suggesting that the shape of attractors depends on selective attention. In this particular model, high levels of acetylcholine activating muscarinic receptors were found to produce an attractor landscape with more stable sub-states. Relating this to the present results, the increased RSFC observed between different RSNs could be interpreted as a flattening of the attractor landscape, in which the basins of attraction are shallower, implying that the global system will move more easily between different metastable sub-states. A flattened (but not flat) attractor landscape would be consistent with increased “information” in the sense of the “information-integration” theory of consciousness (Tononi, 2012) since greater movement between metastable sub-states would imply that a larger number of these sub-states (or a broader “repertoire”) can be entered over a given time. At a critical flatness, the size of the repertoire of metastable states will be maximal but if the landscape is too flat, information will be reduced because attractors will become too unstable. This scenario is referred to as “super-criticality” (Chialvo, 2010), and if taken to the extreme, an entirely flat landscape would imply that the system has no metastable states, or just one entirely disordered one. Future studies are required to determine whether the psychedelic state is “critical” or “super-critical” in this sense (Tagliazucchi et al., 2012; Carhart-Harris et al., 2014a). Another way these results could be perceived however, is that increased between-RSN RSFC under psilocybin is representative of a “sub-critical” system, i.e., one that is more globally synchronous and therefore ordered; however, that there were also decreases in between-RSN RSFC under psilocybin, does not support this view. We intend to follow-up this matter in order to test our hypothesis that it is specifically the ease of transition (or transition probability) between RSNs/metastable sub-states that is facilitated under the drug.

In contrast to the marked changes in between-network RSFC observed with psilocybin, only one RSN-pair showed a significant change in RSFC under MDMA, i.e., increased ECN-DMN2 RSFC (Figure 3F). This result is difficult to interpret in isolation; however, it is worth noting that ECN-DMN2 RSFC was also significantly increased under psilocybin (Figure 3C). MDMA is not considered a classic psychedelic, although like psilocybin, its subjective effects are known to be significantly mediated by serotonergic mechanisms (Liechti and Vollenweider, 2001; Van Wel et al., 2011). Thus, increased ECN-DMN2 RSFC may relate to a shared aspect of these drugs' subjective effects, such as their propensity to alter mood and cognition (Carhart-Harris et al., 2014b). Pre-treatment studies with selective receptor antagonists would help to inform these matters.

There is an important caveat to be addressed about the present analysis. It should be noted that the two studies from which the data was derived employed quite different methodologies (e.g., intravenous administration of psilocybin vs. oral administration of MDMA, different MR scanners and different study samples). Thus, it would be problematic to attempt to make inferences based entirely on a comparison of their relative RSFC profiles. This analysis was not intended to be a formal comparison of the brain effects of MDMA and psilocybin and if this was the intention, then a standardized methodology would need to be employed. Rather, the present analysis has focused on understanding the neural correlates of the psychedelic state as produced by the classic psychedelic, psilocybin, and the finding that MDMA had a less marked effects on between-network RSFC has merely served to emphasize that the psychedelic state rests on a particularly profound disturbance of brain function. This does not imply that MDMA's own subjective effects are unimportant or that they do not involve some (albeit more subtle) changes in between-network RSFC.

The significant change in head movement under psilocybin implies that some of the results should be interpreted with caution, in particular the decreases in coupling strength. We have used multiple ways to model motion as a possible confound but for a subset of the RSN pairs, the changes with drug correlate with the differences in mean motion. These significant correlations do not necessarily mean that motion is responsible for these changes, since intensity of drug is likely to be associated with increased movement, meaning that disambiguating the two effects is problematic for some RSN pairs. In support of this, we found a marginally significant correlation between changes in motion and changes in the subjective intensity rating (r = 0.382, p = 0.08). Future work restricting head motion in the scanner and with larger samples is necessary to be able to demonstrate that changes in these RSN pairs that correlate with motion reflect genuine brain activity or not.

In conclusion, this new analysis has used between-network functional connectivity to investigate the effects of two distinct serotonergic compounds on spontaneous brain function. It was found that psilocybin produced marked changes in between-network RSFC, generally in the direction of increased coupling between RSNs, with an additional decrease in coupling between visual and sensorimotor networks. MDMA had a notably less marked effect on between-network RSFC implying that psilocybin's more profound effects on global brain function (at least as determined by this measure) may explain its more profound effects on consciousness. The analytic methods used in this study, i.e., using ICA templates to determine functional connectivity matrixes for different drug states, may have wider application, enabling researchers to more objectively describe and potentially categorize different states of consciousness.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

These studies received financial and intellectual support from the Beckley Foundation. We would like to thank the reviewers for their useful comments on previous versions of this manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnhum.2014.00204/abstract

References available at the Frontiers site.

Does social fear (of being judged or evaluated) look different in the brain than physical fear (of being hurt or attacked)? The study of the similarities and differences is based on the psychological construction approach that views as emotions as situated, i.e., defined by the situation.

Based on this model, the researchers speculated that socially situated fear situations would have a high correlation with the physical fear scenarios in how the brain responds, as well as there being some differences. According to the authors:

We hypothesized that distributed neural patterns would underlie immersion in social evaluation and physical danger situations, with shared activity patterns across both situations in multiple sensory modalities and in circuitry involved in integrating salient sensory information, and with unique activity patterns for each situation type in coordinated large-scale networks that reflect situated responding. More specifically, we predicted that networks underlying the social inference and mentalizing involved in responding to a social threat (in regions that make up the “default mode” network) would be reliably more active during social evaluation situations. In contrast, networks underlying the visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations. The results supported these hypotheses.

Interesting stuff.

Full Citation: 
Wilson-Mendenhall, CD, Barrett, LF, and Barsalou, LW. (2013, Nov 26). Situating emotional experience. Frontiers in Human Neuroscience; 7:764. doi: 10.3389/fnhum.2013.00764

Situating emotional experience

Christine D. Wilson-Mendenhall [1], Lisa Feldman Barrett [1] and Lawrence W. Barsalou [2]

1. Department of Psychology, Northeastern University, Boston, MA, USA
2. Department of Psychology, Emory University, Atlanta, GA, USA

Psychological construction approaches to emotion suggest that emotional experience is situated and dynamic. Fear, for example, is typically studied in a physical danger context (e.g., threatening snake), but in the real world, it often occurs in social contexts, especially those involving social evaluation (e.g., public speaking). Understanding situated emotional experience is critical because adaptive responding is guided by situational context (e.g., inferring the intention of another in a social evaluation situation vs. monitoring the environment in a physical danger situation). In an fMRI study, we assessed situated emotional experience using a newly developed paradigm in which participants vividly imagine different scenarios from a first-person perspective, in this case scenarios involving either social evaluation or physical danger. We hypothesized that distributed neural patterns would underlie immersion in social evaluation and physical danger situations, with shared activity patterns across both situations in multiple sensory modalities and in circuitry involved in integrating salient sensory information, and with unique activity patterns for each situation type in coordinated large-scale networks that reflect situated responding. More specifically, we predicted that networks underlying the social inference and mentalizing involved in responding to a social threat (in regions that make up the “default mode” network) would be reliably more active during social evaluation situations. In contrast, networks underlying the visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations. The results supported these hypotheses. In line with emerging psychological construction approaches, the findings suggest that coordinated brain networks offer a systematic way to interpret the distributed patterns that underlie the diverse situational contexts characterizing emotional life.

Introduction

Darwin’s The Expression of the Emotions in Man and Animals is often used to motivate emotion research that focuses on identifying the biological signatures for five or so emotion categories (Ekman, 2009; Hess and Thibault, 2009). Interestingly, though, the evolution paradigm shift initiated by Darwin and other scientists heavily emphasized variability: species are biopopulations in which individuals within a population are unique and in which individual variation within a species is meaningfully tied to variation in the environment (and they are not physical types defined by essential features; Barrett, 2013). In other words, an individual organism is best understood by the situational context in which it operates. It is not a great leap, then, to hypothesize that “situatedness” is also a basic principle by which the human mind operates, during emotions and during many other mental phenomena (Barrett, 2013).

Situated approaches to the mind typically view the brain as a coordinated system designed to use information captured during prior situations (and stored in memory) to flexibly interpret and infer what is happening in the current situation – dynamically shaping moment-to-moment responding in the form of perceiving, coordinating action, regulating the body, and organizing thoughts (Glenberg, 1997; Barsalou, 2003, 2009; Aydede and Robbins, 2009; Mesquita et al., 2010; Barrett, 2013). “Cognitive” research domains (e.g., episodic and semantic memory, visual object recognition, language comprehension) are increasingly adopting a situated view of the mind (for empirical reviews, see Zwaan and Radvansky, 1998; Barsalou, 2003; Bar, 2004; Yeh and Barsalou, 2006; Mesquita et al., 2010). In contrast, emotion research largely remains entrenched in a “stimulus-response” reflexive approach to brain function, which typically views the brain as reacting to the demands of the environment, often in a simple, stereotyped way (cf. Raichle, 2010). Traditional “basic” emotion views often assume that an event (i.e., a stimulus) triggers one of several stereotyped responses in the brain and body that can be classified as either fear, disgust, anger, sadness, happiness, etc. (for a review of basic emotion models, see Tracy and Randles, 2011). Decades of research have revealed substantial variability in the neural, physiological, and behavioral patterns associated with these emotion categories (cf. Barrett, 2006; Lindquist et al., 2012). Whereas basic emotion approaches now focus on trying to identify primitive “core” (and often narrowly defined) instances of these emotions, alternative theoretical approaches to emotion, such as psychological construction, propose taking a situated approach to explaining the variability that exists in the experiences people refer to using words like fear, disgust, anger, sadness, happiness (and using many other emotion terms; Barrett, 2009b, 2013).

In the psychological construction view that we have developed, emotions are not fundamentally different from other kinds of brain states (Barrett, 2009a, 2012; Wilson-Mendenhall et al., 2011). During emotional experiences and during other kinds of experiences, the brain is using prior experience to dynamically interpret ongoing neural activity, which guides an individual’s responding in the situation. We refer to this process, which often occurs without awareness (i.e., it is a fundamental process for making sense of one’s relation to the world at any given moment), as situated conceptualization. The term situated takes on a broad meaning in our view, referring to the distributed neural activity across the modal systems of the brain involved in constructing situations, not just to perception of the external environment or to what might be considered the background. More specifically, situated neural activity reflects the dynamic actions that individuals engage in, and the events, internal bodily sensations, and mentalizing that they experience, as well as the perceptions of the external environmental setting and the physical entities and individuals it contains (Wilson-Mendenhall et al., 2011).

Emotions, like other classes of mental experiences, operate in this situation-specific way because rich, cross-modal knowledge is critical for interpreting, inferring, and responding when similar situations occur in the future. On this view, situational knowledge develops for emotion categories like fear, anger, etc., as it does for other abstract categories of experiences (e.g., situations that involve the abstract categories gossip, modesty, or ambition). Experiences categorized as fear, for example, can occur when delivering a speech to a respected audience or when losing control while driving a car. A situated, psychological construction perspective suggests that it is more adaptive to respond differently in these situations, guided by knowledge of the situation, than to respond in a stereotyped way. Whereas responding in the social speech situation involves inferring what audience members are thinking, responding in the physical car situation involves rapid action and attention to the environment. Stereotyped responding in the form of preparing the body to flee or fight does not address the immediate threat present in either of these situations. A psychological construction approach highlights the importance of studying the situations commonly categorized as emotions like fear or anger, not because these situations merely describe emotions, but because emotions would not exist without them.

A significant challenge in taking a situated approach to studying emotional experience is maintaining a balance between the rich, multimodal nature of situated experiences and experimental control. Immersion in emotional situations through vividly imagined imagery is recognized as a powerful emotion induction method for evoking physiological responses (Lang et al., 1980; Lench et al., 2011). Imagery paradigms were initially developed to study situations thought to be central to various forms of psychopathology (Lang, 1979; Pitman et al., 1987), and remain a focus in clinical psychology (for a review, see Holmes and Mathews, 2010). In contrast, a small proportion of neuroimaging studies investigating emotion in typical populations use these methods. Figure 1 illustrates the methods used across 397 studies in a database constructed for neuroimaging meta-analyses of affect and emotion (Kober et al., 2008; Lindquist et al., 2012)1. Visual methods dominate (70% of studies), with the majority of these studies using faces (42% of visual methods) and pictures (36% of visual methods) like the International Affective Picture System (IAPS; Lang et al., 2008). In contrast, only 6% of studies have used imagery methods2. Imagery methods appear to be used more frequently when studying complex socio-emotional experiences that would be difficult to induce with an unfamiliar face or picture and that are often clinically oriented, including angry rumination (Denson et al., 2009), personal anxiety (Bystritsky et al., 2001), competition and aggression (Rauch et al., 1999; Pietrini et al., 2000), social rejection and insult (Kim et al., 2008; Kross et al., 2011), romantic love (Aron et al., 2005), moral disgust (Moll et al., 2005; Schaich Borg et al., 2008), and empathy (Perry et al., 2012).

FIGURE 1


FIGURE 1. Methods used to study emotion and affect. Visual methods typically involved viewing faces, pictures, films, words, sentences, and/or bodies. Auditory methods typically involved listening to voices, sounds, music, words, and/or sentences. Imagery methods typically involved generating imagery using personal memories, sentences, faces, and/or pictures (and are described further in the main text). Recall methods typically involved recalling personal events, words, films, or pictures. Tactile methods involved touch or thermal stimulation, olfaction methods involved smelling odors, and taste methods involved tasting food. Multiple modalities refers to studies that involved two or more of the aforementioned methods in the same study, with visual and auditory methods being the most frequent combination.

Imagery-based neuroimaging studies of emotional experience typically take one of two approaches. The most frequent approach is to draw on the personal experiences of the participant, cueing specific, vivid memories in the scanner. Often participants’ personal narratives are scripted and vividly imagined (guided by the experimenter) outside the scanner, and then a version of this script is used to induce these memory-based emotional experiences during neuroimaging (e.g., Bystritsky et al., 2001; Marci et al., 2007; Gillihan et al., 2010). Less often, a specific visual stimulus is potent enough to easily evoke personal, emotional imagery in the scanner (e.g., face of a romantic partner; Aron et al., 2005; Kross et al., 2011). The second approach is to present standard prompts (e.g., a sentence) that participants use to generate imagery underlying emotional experiences (e.g., Colibazzi et al., 2010; Costa et al., 2010). A key strength of the first approach is that emotional experiences are tightly tied to situated, real-life memories, whereas a key strength of the second approach is the experimental control afforded by presenting the same prompts to all participants. In both cases, though, the situational context of the emotional experiences is typically lost, either because the situational details are specific to the individual (and thus lost in group-level analyses) or because standard prompts are not designed to cultivate and/or systematically manipulate the situational context of the emotional experience.

Building on the strengths of existing imagery-based approaches, we developed a neuroimaging procedure that would allow us to examine participants’ immersion in rich, situated emotional experiences while maximizing experimental control and rigor. In our paradigm, participants first received training outside the scanner on how to immerse themselves in richly detailed, full paragraph-long versions of emotional scenarios from a first-person perspective. The scenarios reflected two ecologically important situation types in which emotional experiences are often grounded: social evaluation and physical danger. Every scenario was constructed using written templates to induce a social evaluation emotional experience or a physical danger emotional experience (see Table 1 for examples). Participants listened to audio recordings of the scenarios, which facilitated immersion by allowing participants to close their eyes. In the scanner, participants were prompted with shorter, core (audio) versions of the scenarios in the scanner, so that a statistically powerful neuroimaging design could be implemented.

TABLE 1


TABLE 1. Examples of physical danger and social evaluation scenarios used in the experiment.

We hypothesized that immersion across both social evaluation and physical danger situations would be characterized by distributed neural patterns across multiple sensory modalities and across regions involved in detecting and integrating salient sensory information. Much previous research has demonstrated neural overlap between sensorimotor perception/action and sensorimotor imagery (for a review, see Kosslyn et al., 2001). If our scenario immersion method induces richly situated emotional experiences, then the vivid mental imagery generated should be grounded in brain regions underlying sensory perception and action. Perhaps surprisingly, studies using imagery paradigms to investigate emotional experiences do not typically examine sensorimotor activity, because the goal is often to isolate a category of experience (e.g., anger, disgust) or other “emotion” components. In contrast, our approach is designed to examine the distributed neural patterns that underlie emotional experiences.

Our second, primary hypothesis was motivated by a situated approach to studying the varieties of emotional experience. We hypothesized that unique activity patterns for each situation type would occur in coordinated large-scale networks that reflect situated responding. Whereas networks underlying the social inference and mentalizing involved in responding to a social threat (in regions that make up the “default mode” network) would be reliably more active during social evaluation situations (for reviews of default mode network functions, see Buckner et al., 2008; Barrett and Satpute, 2013)3, networks underlying the visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations (for reviews of attention networks, see Chun et al., 2011; Petersen and Posner, 2012; Posner, 2012). These large-scale, distributed networks largely consist of heteromodal regions that engage in the multimodal integration necessary for coordinated interpretation and responding (Sepulcre et al., 2012; Spreng et al., 2013).

As a further test of our second hypothesis, we examined whether participants’ trial-by-trial ratings of immersion during the training session correlated with neural activity, across social evaluation scenarios and across physical danger scenarios. If emotional experience is situated, then feeling immersed in a situation should be realized by neural circuitry that underlies engaging in the specific situation. Whereas immersion in social evaluation situations should occur when affect is grounded in mentalizing about others, immersion in physical danger situations should occur when affect is grounded in taking action in the environment.

Materials and Methods

Participants

Twenty right-handed, native-English speakers from the Emory community, ranging in age from 20 to 33 (10 female), participated in the experiment. Six additional participants were dropped due to problems with audio equipment (three participants) or excessive head motion in the scanner. Participants had no history of psychiatric illness and were not currently taking any psychotropic medication. They received $100 in compensation, along with anatomical images of their brain.

Materials

A full and core form of each scenario was constructed, the latter being a subset of the former (see Table 1). The full form served to provide a rich, detailed, and affectively compelling scenario. The core form served to minimize presentation time in the scanner, so that the number of necessary trials could be completed in the time available. Each full and core scenario described an emotional situation from a first-person perspective, such that the participant could immerse him- or herself in it. As described shortly, participants practiced enriching the core form of the scenario during the training sessions using details from the full form, so that they would be prepared to immerse in the rich situational detail of the full forms during the scanning session when they received the core forms.

Both situation types were designed so the threat described could be experienced as any number of high arousal, negative emotions like fear or anger (and participants’ ratings of the ease of experiencing negative emotions in the two situation types validated this approach; see Wilson-Mendenhall et al., 2011 for details). In social evaluation situations, another person put the immersed participant in a socially threatening situation that involved damage to his or her social reputation/ego. In physical danger situations, the immersed participant put him- or herself in a physically threatening situation that involved impending or actual bodily harm.

Templates were used to systematically construct different scenarios in each situation type (social evaluation and physical danger). Table 1 provides examples of the social evaluation and physical danger scenarios. Each template for the full scenarios specified a sequence of six sentences: three primary sentences (Pi) also used in the related core scenario, and three secondary sentences (Si) not used in the core scenario that provided additional relevant detail. The two sentences in each core scenario were created using P1 as the first sentence and a conjunction of P2A and P2C as the second sentence.

For the social evaluation scenarios, the template specified the following six sentences in order: P1 described a setting and activity performed by the immersed participant in the setting, along with relevant personal attributes; S1 provided auditory detail about the setting; P2A described an action (A) of the immersed participant; P2C described the consequence (C) of that action; S2 described another person’s action in response to the consequence; S3 described the participant’s resulting internal bodily experience. The templates for the physical danger scenarios were similar, except that S1 provided visual detail about the setting (instead of auditory), S2 described the participant’s action in response to the consequence (instead of another person’s action), and S3 described the participant’s resulting external somatosensory experience (on the body surface).

A broad range of real-world situations served as the content of the experimental situations. The physical danger scenarios were drawn from situations that involved vehicles, pedestrians, water, eating, wildlife, fire, power tools, and theft. The social evaluation scenarios were drawn from situations that involved friends, family, neighbors, love, work, classes, public events, and service.

During the training sessions and the critical scan session, 30 social evaluation scenarios and 30 physical danger scenarios were presented. An additional three scenarios of each type were included in the training sessions so participants could practice the scanner task prior to the scan session.

Imaging Design

The event-related neuroimaging design involved two critical events: (1) immersing in an emotional scenario (either a social evaluation or physical danger scenario) and (2) experiencing the immersed state in one of four ways upon hearing an auditory categorization cue (as emotional: fearful or angry, or as another active state: planning or observing). We will refer to the first event as “immersion” and the second event as “categorization.” Because all neural patterns described here reflect activity during the first immersion event, we focus on this element of the design (for the categorization results and related methodological details, please see Wilson-Mendenhall et al., 2011). This design afforded a unique opportunity to examine the situations in which emotions emerge before the emotional state was explicitly categorized. As will be described later, the participant could not predict which categorization cue would follow the scenario, so the immersion period reflects situated activity that is not tied to a specific emotion category.

In order to separate neural activity during the immersion events from neural activity during the categorization events, we implemented a catch trial design (Ollinger et al., 2001a, b). Participants received 240 complete trials that each contained a social evaluation scenario or a physical danger scenario followed immediately by one of the four categorization cues. Participants also received 120 partial “catch” trials containing only a scenario (with no subsequent categorization cue), which enabled separation of the first scenario immersion event from the second categorization event. The partial trials constituted 33% of the total trials, a proportion in the recommended range for an effective catch trial design. Each of the 30 social evaluation scenarios and the 30 physical danger scenarios was followed once by each categorization cue, for a total of 240 complete trials (60 scenarios followed by 4 categorizations). Each of the 60 scenarios also occurred twice as a partial trial, for a total of 120 catch trials.

During each of 10 fMRI runs, participants received 24 complete trials and 12 partial trials. The complete and partial trials were intermixed with no-sound baseline periods that ranged from 0 to 12 s in increments of 3 s (average 4.5 s) in a pseudo-random order optimized by optseq2 (Greve, 2002). On a given trial, participants could not predict whether a complete or partial trial was coming, a necessary condition for an effective catch trial design (Ollinger et al., 2001a, b). Participants also could not predict the type of situation or the categorization cue they would hear. Across trials in a run, social evaluation and physical danger situations each occurred 18 times, and each of the 4 categorization cues (anger, fear, observe, plan) occurred 6 times, equally often with social evaluation and physical danger scenarios. A given scenario was never repeated within a run.

Procedure

The experiment contained two training sessions and an fMRI scan session. The first training session occurred 24–48 h before the second training session, followed immediately by the scan. During the training sessions, participants were encouraged to immerse themselves in all scenarios from a first-person perspective, to imagine the scenario in as much vivid detail as possible, and to construct mental imagery as if the scenario events were actually happening to them. The relation of the full to the core scenarios was also described, and participants were encouraged to reinstate the full scenario whenever they heard a core scenario.

During the first training session, participants listened over computer headphones to the full versions of the 66 scenarios that they would later receive on the practice trials and in the critical scan 24–48 h later, with the social evaluation and physical danger scenarios randomly intermixed. After hearing each full scenario, participants provided three judgments about familiarity and prior experiences, prompted by questions and response scales on the screen. After taking a break, participants listened to the 66 core versions of the scenarios, again over computer headphones and randomly intermixed. While listening to each core scenario, participants were instructed to reinstate the full version that they listened to earlier, immersing themselves fully into the respective scenario as it became enriched and developed from memory. After hearing each core scenario over the headphones, participants rated the vividness of the imagery that they experienced while immersed in the scenario. This task encouraged the participants to develop rich imagery upon hearing the core version. A detailed account of the first training session can be found in Wilson-Mendenhall et al. (2011).

During the second training session directly before the scan, participants first listened to the 66 full scenarios to be used in the practice and critical scans, and rated how much they were able to immerse themselves in each scenario, again hearing the scenarios over computer headphones and in a random order. After listening to each full scenario, the computer script presented the question, “How much did you experience ‘being there’ in the situation?” Participants responded on the computer keyboard, using a 1–7 scale, where one meant not experiencing being there in the situation at all, four meant experiencing being there a moderate amount, and seven meant experiencing being there very much, as if it was actually happening to them. The full scenarios were presented again at this point to ensure that participants were reacquainted with all the details before hearing the core versions later in the scanner. This first phase of the second training session lasted about an hour.

Participants were then instructed on the task that they would perform in the scanner and performed a run of practice trials. During the practice and during the scans, audio events were presented and responses collected using E-prime software (Schneider et al., 2002). On each complete trial, participants were told to immerse in the core version of a scenario as they listened to it, and that they would receive one of four words (anger, fear, observe, plan) afterward. The participant’s task was to judge how easy it was to experience what the word described in the context of the situation. The core scenario was presented auditorily at the onset of a 9 s period, lasting no more than 8 s. The word was then presented auditorily at the onset of a 3 s period, and participants responded as soon as ready. To make their judgments, participants pressed one of three buttons on a button box for not easy, somewhat easy, and very easy. During the practice trials, participants used an E-Prime button box to practice making responses. In the scanner, participants used a Current Designs fiber optic button box designed for high magnetic field environments. Participants were also told that there would be partial trials containing scenarios and no word cues, and that they were not to respond on these trials.

At the beginning of the practice trials, participants heard the same short instruction that they would hear before every run in the scanner: “Please close your eyes. Listen to each scenario and experience being there vividly. If a word follows, rate how easy it was to have that experience in the situation.” Participants performed a practice run equal in length to the runs that they would perform in the scanner. Following the practice run, the experimenter and the participant walked 5 min across campus to the scanner. Once settled safely and comfortably in the scanner, an initial anatomical scan was performed, followed by the 10 critical functional runs, and finally a second anatomical scan. Prior to beginning each functional run, participants heard the same short instruction from the practice run over noise-muffling headphones. Participants took a short break between each of the 8 min 3 s runs. Total time in the scanner was a little over 1.5 h.

Image Acquisition

The neuroimaging data were collected in the Biomedical Imaging Technology Center at Emory University on a research-dedicated 3T Siemens Trio scanner. In each functional run, 163 T2*-weighted echo planar image volumes depicting BOLD contrast were collected using a Siemens 12-channel head coil and parallel imaging with an iPAT acceleration factor of 2. Each volume was collected using a scan sequence that had the following parameters: 56 contiguous 2 mm slices in the axial plane, interleaved slice acquisition, TR = 3000 ms, TE = 30 ms, flip angle = 90°, bandwidth = 2442 Hz/Px, FOV = 220 mm, matrix = 64, voxel size = 3.44 mm × 3.44 mm × 2 mm. This scanning sequence was selected after testing a variety of sequences for susceptibility artifacts in orbitofrontal cortex, amygdala, and the temporal poles. We selected this sequence not only because it minimized susceptibility artifacts by using thin slices and parallel imaging, but also because using 3.44 mm in the X–Y dimensions yielded a voxel volume large enough to produce a satisfactory temporal signal-to-noise ratio. In each of the two anatomical runs, 176 T1-weighted volumes were collected using a high resolution MPRAGE scan sequence that had the following parameters: 192 contiguous slices in the sagittal plane, single-shot acquisition, TR = 2300 ms, TE = 4 ms, flip angle = 8°, FOV = 256 mm, matrix = 256, bandwidth = 130 Hz/Px, voxel size = 1 mm × 1 mm × 1 mm.

Image Preprocessing and Analysis

Image preprocessing and statistical analysis were conducted in AFNI (Cox, 1996). The first anatomical scan was registered to the second, and the average of the two scans computed to create a single high-quality anatomical scan. Initial preprocessing of the functional data included slice time correction and motion correction in which all volumes were registered spatially to a volume within the last functional run. A volume in the last run was selected as the registration base because it was collected closest in time to the second anatomical scan, which facilitated later alignment of the functional and anatomical data. The functional data were then smoothed using an isotropic 6 mm full-width half-maximum Gaussian kernel. Voxels outside the brain were removed from further analysis at this point, as were high-variability low-intensity voxels likely to be shifting in and out of the brain due to minor head motion. Finally, the signal intensities in each volume were divided by the mean signal value for the respective run and multiplied by 100 to produce percent signal change from the run mean. All later analyses were performed on these percent signal change data.

The averaged anatomical scan was corrected for non-uniformity in image intensity, skull-stripped, and then aligned with the functional data. The resulting aligned anatomical dataset was warped to Talairach space using an automated procedure employing the TT_N27 template (also known as the Colin brain, an averaged dataset from one person scanned 27 times).

Regression analyses were performed on each individual’s preprocessed functional data using a canonical, fixed-shape Gamma function to model the hemodynamic response. In the first regression analysis, betas were estimated using the event onsets for 10 conditions: 2 situation immersion conditions (social, physical) and 8 categorization conditions that resulted from crossing the situation with the categorization cue (social-anger, physical-anger, social-fear, physical-fear, social-observe, physical-observe, social-plan, physical-plan). Again, we only present results for the two situation immersion conditions here (see Wilson-Mendenhall et al., 2011 for the categorization results). The two situation immersion conditions were modeled by creating regressors that included scenario immersion events from both the complete trials and the partial trials. Including scenario immersion events from both trial types in one regressor made it possible to mathematically separate the situation immersion conditions from the subsequent categorization conditions (Ollinger et al., 2001a, b). Because scenario immersion events were 9 s in duration, the Gamma function was convolved with a boxcar function for the entire duration to model the situation immersion conditions. Six regressors obtained from volume registration during preprocessing were also included to remove any residual signal changes correlated with movement (translation in the X, Y, and Z planes; rotation around the X, Y, and Z axes). Scanner drift was removed by finding the best-fitting polynomial function correlated with time in the preprocessed time course data.

At the group level, the betas resulting from the each individual’s regression analysis were then entered into a second-level, random-effects ANOVA. Two key analyses were computed at this level of analysis using a voxel-wise threshold of p < 0.005 in conjunction with the 41-voxel extent threshold determined by AFNI ClustSim to produce an overall corrected threshold of p < 0.05. In the first analysis (that assessed our first hypothesis), we extracted clusters that were more active during immersion in social evaluation situations than in the no-sound baseline and clusters that were more active during immersion in physical danger situations than in the no-sound baseline (using the voxel-wise and extent thresholds specified above). We then entered the results of these two contrasts (social evaluation > baseline; physical danger > baseline) into a conjunction analysis to determine clusters shared by the two situation types (i.e., overlapping regions of activity). In the second analysis (that assessed our second hypothesis), we computed a standard contrast to directly compare immersion during social evaluation situations to immersion during physical danger situations using t tests (social evaluation > physical danger; physical danger > social evaluation).

A second individual-level regression was computed to examine the relationship between neural activity and the scenario immersion ratings collected during the training session just prior to the scan session, providing an additional test of our second hypothesis. This regression model paralleled the first regression model with the following exceptions. In this regression analysis, each participant’s “being there” ratings were specified trial-by-trial for each scenario in the social evaluation immersion condition and in the physical danger immersion condition. For the two situation immersion conditions (social evaluation and physical danger), both the onset times and ratings were then entered into the regression using the amplitude modulation option in AFNI. This option specified two regressors for each situation immersion condition, which were used to detect: (1) voxels in which activity was correlated with the ratings (also known as a parametric regressor); (2) voxels in which activity was constant for the condition and was not correlated with the ratings.

At the group level, each participant’s betas produced from the first parametric regressor for each situation immersion condition (i.e., indicating the strength of the correlation between neural activity and “being there” immersion ratings) were next entered into a second-level analysis. In this analysis, the critical statistic for each condition was a t test indicating if the mean across individuals differed significantly from zero (zero indicating no correlation between neural activity and the ratings). In these analyses, a slightly smaller cluster size of 15 contiguous voxels was used in conjunction with the voxel-wise threshold of p < 0.005.

In summary, this analysis is examining whether scenarios rated as easier to immerse in during the training are associated with greater neural activity in any region of the brain (the individual-level analysis), and whether this relationship between immersion ratings and neural activity is consistent across participants (group-level analysis). We computed this analysis separately for social evaluation and for physical danger situation types to test our hypothesis. This analysis is not examining between-subject individual differences in immersion (i.e., whether participants who generally experience greater immersion across all scenarios also show greater neural activity in specific regions), which is a different question that is not of interest here.

Results

Common Neural Activity during Immersion Across Situations

Our first hypothesis was that neural activity during both situations would be reliably greater than baseline across multiple sensory modalities and across regions involved in detecting and integrating salient sensory information (see Table 2 for the baseline contrasts). As shown in Figure 2A, neural activity was reliably greater than baseline in bilateral primary somatomotor and visual cortex, as well as premotor cortex, SMA, and extrastriate visual cortex, suggesting that participants easily immersed in the situations. The self-reported rating data from the training session confirmed that participants found the social evaluation and physical danger situations relatively easy to immerse in (see Figure 2B), with no significant differences in “being there” ratings between situation types [repeated measures t test; t(19) = 1.64, p > 0.05]. Because participants listened to the scenarios with their eyes closed and because participants did not make responses while immersing in the scenarios, it is significant that these sensorimotor regions were significantly more active than the no-sound baseline. As would be expected with an auditory, language-based immersion procedure, we observed activity in bilateral auditory cortex and in superior temporal and inferior frontal regions associated with language processing, with more extensive activity in the left frontal regions.

FIGURE 2


FIGURE 2. (A) shared neural activity during social evaluation and physical danger situations in sensorimotor cortex (revealed by the conjunction analysis in which each situation was compared to the “no sound” baseline) (B) self- reported immersion ratings from the training session (error bars depict SEM across participant condition means) (C) shared neural activity revealed by the conjunction analysis in the amygdala and hippocampus.

TABLE 2


TABLE 2. Social evaluation > baseline and physical danger > baseline contrasts.

Consistent with the hypothesis that immersion would also generally involve selection, encoding, and integration of salient sensory and other information, we observed activity in bilateral hippocampus and in right amygdala (see Figure 2C). Extensive evidence implicates the hippocampus in mnemonic functions (Squire and Zola-Morgan, 1991; Tulving, 2002; Squire, 2004), especially the integration and binding of the multimodal information involved in constructing (and reconstructing) situated memories (Addis and McAndrews, 2006; Kroes and Fernandez, 2012). More recent evidence establishes a central role for this structure in simulating future, imagined situations (Addis et al., 2007; Hassabis et al., 2007; Schacter et al., 2007, 2012), which is similar in nature to our immersion paradigm, and which requires similar integration and binding of concepts established in memory (from prior experience). The amygdala plays a central role in emotional experiences by efficiently integrating multisensory information to direct attention and guide encoding (Costafreda et al., 2008; Bliss-Moreau et al., 2011; Klasen et al., 2012; Lindquist et al., 2012), especially during situations that involve threat (Adolphs, 2008; Miskovic and Schmidt, 2012). As we will see, no differences emerged in the amygdala or in the hippocampus during the social evaluation and physical danger situations, suggesting these structures played a similar role in both types of experiences.

Unique Neural Patterns Emerge for Social Evaluation and Physical Danger Situations

Our second hypothesis was that networks underlying the social inference and mentalizing involved in responding to a social threat would be reliably more active during social evaluation situations, whereas networks underlying visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations. As Table 3, together with Figures 3–5, illustrate, the neural patterns that emerged when we compared social evaluation situations to physical danger situations are consistent with these predictions. Figure 3 shows these results on representative 2D slices, with regions showing reliably greater activity during social evaluation in orange, and regions showing reliably greater activity during physical danger in green. Figures 4 and 5 display these maps projected onto the surface of the brain4, and directly compare the maps from this study with the large-scale networks that have been defined using resting state connectivity techniques across large samples (Yeo et al., 2011).

FIGURE 3


FIGURE 3. Social evaluation vs. physical danger contrast, with regions reliably more active during social evaluation in orange and regions reliably more active during physical danger in green.

FIGURE 4


FIGURE 4. Comparison of the social evaluation map from this study with the default mode network defined by Yeo et al. (2011)

FIGURE 5


FIGURE 5. Comparison of the physical danger map from this study with the attention networks defined by Yeo et al. (2011)

Heightened activity in the default mode network during social evaluation

As displayed in Figure 3 and Table 3, robust activity was observed during immersion in social evaluation situations (vs. physical danger situations) in midline medial prefrontal and posterior cingulate regions, as well as lateral temporal regions, in which activity spanned from the temporal pole to the posterior superior temporal sulcus/temporoparietal junction bilaterally, and on the left, extended in to inferior frontal gyrus. This pattern of activity maps onto a network that is often referred to as the “default mode” network (Gusnard and Raichle, 2001; Raichle et al., 2001; Buckner et al., 2008). Figure 4 illustrates the overlap between the default mode network and the pattern of neural activity that underlies immersing in social evaluation situations here (Yeo et al., 2011). The default mode network has been implicated in mentalizing and social inference (i.e., inferring what others’ are thinking/feeling and how they will act), as well as other socially motivated tasks, including autobiographical memory retrieval, envisioning the future, and moral reasoning (for reviews, see Buckner et al., 2008; Van Overwalle and Baetens, 2009; Barrett and Satpute, 2013). Consistent with the idea of situated emotional experience, participants engaged in the social inference and mentalizing that would be adaptive in responding to a social threat when immersed in social evaluation situations.

TABLE 3


TABLE 3. Brain regions that emerged in the social evaluation vs. physical danger contrast.

Heightened activity in fronto-parietal attention networks during physical danger

Figure 3 and Table 3 show the fronto-parietal patterns of activity observed during immersion in physical danger situations (vs. social evaluation situations). In addition to lateral frontal and parietal regions (including bilateral middle frontal gyrus, bilateral inferior frontal gyrus extending into pars orbitalis, bilateral inferior parietal lobule, and bilateral superior parietal/precuneus), neural activity was also reliably greater in right anterior insula, mid cingulate cortex, and bilateral premotor cortex during immersion in physical danger situations. Figure 5 illustrates the overlap between this pattern of activity and three networks that have been implicated in attention5 (Chun et al., 2011; Petersen and Posner, 2012; Posner, 2012). The most significant overlap was observed in the lateral fronto-parietal executive network and the dorsal attention network. These networks are thought to allocate attentional resources to prioritize specific sensory inputs (what is often referred to as “orienting” to the external environment) and to guide flexible shifts in behavior (Dosenbach et al., 2007; Petersen and Posner, 2012). The operations they carry out are critical for maintaining a vigilant state (Tang et al., 2012), which is important during threat. Less overlap was evident in the ventral attention network that is thought to interrupt top-down operations through bottom-up “salience” detection (Corbetta et al., 2008), although robust activity was observed in the mid cingulate regions shown in Figure 5 that support the action monitoring that occurs, especially, in situations involving physical pain (Morecraft and Van Hoesen, 1992; Vogt, 2005). Taken together, this pattern of results suggests, strikingly, that immersion in the physical danger situations (from a first-person perspective with eyes closed) engaged attention networks that are studied almost exclusively using external visual cues. Consistent with the idea of situated emotional experience, participants engaged in the monitoring of the environment and preparation for flexible action that would be adaptive in action to a physical threat when immersed in physical danger situations.

Immersion ratings correlate with activity in different regions during social evaluation vs. physical danger situations

To provide another test of our second hypothesis, we examined whether self-reported immersion ratings of “being there” in the situation (from the training session) were associated with brain activity during the two situation types. If emotional experience is situated, then feeling immersed in a situation should be realized by neural circuitry that underlies engaging in the specific situation. Whereas immersion in social evaluation situations should occur when affect is grounded in mentalizing about others, immersion in physical danger situations should occur when affect is grounded in taking action in the environment. The results displayed in Figure 6 support this prediction.

FIGURE 6

FIGURE 6. Regions in which neural activity was significantly correlated with participants’ “being there” ratings of immersion collected during the training session just prior to scanning, for the social evaluation situations and for the physical danger situations.

During social evaluation situations, participants’ immersion ratings correlated with activity in anterior medial prefrontal cortex (frontal pole area; peak voxel -6 51 0; 23 voxels) and in superior temporal gyrus/sulcus (peak voxel -47 -49 14; 24 voxels; see Figure 6). As described above, these regions are part of the default mode network and are central to social perception and mentalizing (Allison et al., 2000; Buckner et al., 2008; Adolphs, 2009; Van Overwalle, 2009). The anterior, frontal pole region of medial prefrontal cortex is considered the anterior hub of the default mode network (Andrews-Hanna et al., 2010) that integrates affective information from the body with social event knowledge (including inferences about others’ thoughts) originating in ventral and dorsal aspects of medial prefrontal cortex, respectively (Mitchell et al., 2005; Krueger et al., 2009). This integration may underlie the experience of “personal significance” (Andrews-Hanna et al., 2010) that appears important for immersing in social evaluation situations.

In contrast, during physical danger situations, participants’ immersion ratings correlated with activity in dorsal anterior cingulate/mid cingulate (extending into SMA; peak -1 17 40; 40 voxels) and in left inferior parietal cortex (peak -36 -46 39; 15 voxels; see Figure 6). The robust cluster of activity that emerged in the cingulate is part of the ventral attention “salience” network, and it is anterior to the mid cingulate activity observed in the initial whole-brain contrasts reported above. Because this region has been implicated across studies of emotion, pain, and cognitive control, and because it is anatomically positioned at the intersection of insular-limbic and fronto-parietal sub-networks within the attention system, it may play an especially important role in specifying goal-directed action based on affective signals originating in the body (Shackman et al., 2011; Touroutoglou et al., 2012). This integration may underlie the experience of action-oriented agency (Craig, 2009) that appears important for immersing in physical danger situations. The significant correlation with activity in left inferior parietal cortex, which supports planning action in egocentric space (e.g., Fogassi and Luppino, 2005), further suggests that immersion in physical danger situations is driven by preparing to act in the environment.

Discussion

Our novel scenario immersion paradigm revealed robust patterns of neural activity when participants immersed themselves in social evaluation scenarios and in physical danger scenarios. Consistent with participants’ high self-reported immersion ratings, neural activity across multiple sensory regions, and across limbic regions involved in the multisensory integration underlying the selection, encoding, and interpretation that influences what is salient and remembered (e.g., amygdala, hippocampus), occurred during both situation types. In addition to this shared activity, distributed patterns unique to each situation type reflected situated responding, with regions involved in mentalizing and social cognition more active during social evaluation and with regions involved in attention and action planning more active during physical danger.

Taken together, these findings suggest that our method produced vivid, engaging experiences during neuroimaging scans and that it could be used to study a variety of emotional experiences. One reason this immersion paradigm may be so powerful is that people often find themselves immersed in imagined situations in day-to-day life. Large-scale experience sampling studies have revealed that people spend much of their time imagining experiences that are unrelated to the external world around them (e.g., Killingsworth and Gilbert, 2010). An important direction for future research will be to understand if, consistent with other imagery-based paradigms, physiological changes occur during our scenario immersion paradigm and if these physiological changes are associated with subjective experiences of immersion.

The scenarios we developed for this study represent a small subset of the situations that people experience in real life (see also Wilson-Mendenhall et al., 2013). Because emotional experiences vary tremendously, it is adaptive to develop situated knowledge that guides inference and responding when similar situations arise in the future (Barsalou, 2003, 2008, 2009; Barrett, 2013). Here, we focused on immersion in emotion-inducing situations before they were explicitly categorized as an emotion (or another state). From our perspective, the situation plays a critical role in the emergence of an emotion, and it should not be considered a separate phenomenon from it (Barrett, 2009b, 2012; Wilson-Mendenhall et al., 2011). For example, it would be impossible to experience fear upon delivering a public speech without inferring others’ thoughts. Instead of viewing mentalizing as a “cold” cognitive process that interacts with a primitive “hot” emotion, we view mentalizing as an essential part of the situation in which the emotion emerges. Likewise, it would be impossible to experience fear upon getting lost in the woods without focusing attention on the environment (in other words, if one was instead lost in internal thought while traversing the same environment, it is unlikely that this fear would occur). We propose that it will be more productive to study emotional experience as dynamic situated conceptualizations that the brain continually generates to interpret one’s current state (based on prior experience), as opposed to temporally constrained cognition-emotion frameworks that often strip away much of the dynamically changing situated context. A situated approach also offers new insights into studying dynamic emotion regulation and dysregulation (Barrett et al., in press).

Network approaches to brain function provide functional frameworks for interpreting the distributed patterns that characterize situated experiences (Cabral et al., 2011; Deco et al., 2011; Lindquist and Barrett, 2012; Barrett and Satpute, 2013). As shown in Figures 4 and 5, the patterns unique to each situation type in this study can be differentiated by the anatomically constrained resting state networks6 identified in previous work (Raichle et al., 2001; Fox et al., 2005; Vincent et al., 2006; Dosenbach et al., 2007; Fair et al., 2007; Seeley et al., 2007; Yeo et al., 2011; Touroutoglou et al., 2012). Whereas the neural patterns underlying social threat situations primarily map onto the default mode network that supports social inference and mentalizing, the neural patterns underlying physical threat situations primarily map onto attention networks underlying monitoring of the environment and action planning. The neural pattern unique to each situation type reflects adaptive, situated responding. Furthermore, regions traditionally associated with emotion diverged in line with these networks (e.g., ventromedial prefrontal cortex as part of the default mode network; lateral orbitofrontal cortex and cingulate regions as part of the attention networks). Interestingly, these regions appear to be central to immersion in each type of situation, with the anterior medial prefrontal cortex (which is often considered part of ventromedial prefrontal cortex) associated with immersion during social evaluation situations and dorsal anterior cingulate associated with immersion during physical danger situations. These results suggest, strikingly, that the brain realizes immersion differently depending on the situation.

Resting state networks provide a starting point for examining how networks underlie situated experiences, but recent evidence suggests that coordination between regions in these networks dynamically changes during different psychological states (e.g., van Marle et al., 2010; Raz et al., 2012; Wang et al., 2012). In this study, for example, the neural patterns underlying physical danger experiences recruited various aspects of several different attention networks. Attention is primarily studied using simple visual detection tasks that examine external stimuli vs. internal goal dichotomies. Recent reviews emphasize the need for research that examines how attention systems operate during experiences guided by memory (e.g., Hutchinson and Turk-Browne, 2012), which arguably constitute much of our experience. Because inferior parietal cortex and cingulate regions figured prominently in the pattern observed across the attention networks in this study, this particular configuration may reflect the attention operations involved in coordinating bodily actions in space. It is also important to consider that these patterns reflect relative differences between the social and physical threat situations. As we showed initially, the situation types also share patterns of activity that contribute to the overall pattern of situated activity. In our view, it is useful to think about situated neural activity as dynamically changing patterns that are distributed across structurally and functionally distinct networks (see also Barrett and Satpute, 2013). Even within a structurally defined network, different distributed patterns of neural activity may reflect unique functional motifs that underlie different experiences and behaviors (Sporns and Kotter, 2004).

In closing, a psychological construction approach to studying situated emotion motivates different questions than traditional approaches to studying emotion. It invites shifting research agendas from defining five or so emotion categories to studying the rich situations that characterize emotional experiences.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Preparation of this manuscript was supported by an NIH Director’s Pioneer Award DPI OD003312 to Lisa Feldman Barrett at Northeastern University with a sub-contract to Lawrence Barsalou at Emory University. We thank A. Satpute and K. Lindquist for the meta-analysis codes indicating study methods/tasks.

Footnotes

^ This meta-analytic database has recently been updated to include articles through 2011. The proportions reported here reflect the updated database.
^ Lindquist etal. (2012) distinguished between “emotion perception” (defined as perception of emotion in others) and “emotion experience” (defined as experience of emotion in oneself) in their meta-analysis. When restricting our analysis of study methods to studies that involved emotion experience (as coded in the database), the use of imagery methods was still minimal (10% of 233 studies). Although emotional imagery is typically thought of as an induction of emotion experience, it seems likely that imagined situations, especially if they are social in nature, involve dynamic emotion perception as well.
^ There is substantial evidence that default mode network (DMN) regions are active during tasks that involve social inference and mentalizing (for reviews, see Barrett and Satpute, 2013; Buckner and Carroll, 2007; Van Overwalle and Baetens, 2009) and that the DMN is disrupted in disorders involving social deficits (for reviews, see Menon, 2011; Whitfield-Gabrieli and Ford, 2012). Recent work has directly demonstrated that neural activity during social/mentalizing tasks occurs in the DMN as it is defined using resting state analyses (e.g., Andrews-Hanna etal., 2010) and that resting state connectivity in the DMN predicts individual differences in social processing (e.g., Yang etal., 2012).
^ It is important to note that each individual’s data were not analyzed on the surface. We are using a standardized (Talairach) surface space for illustration of the group results in comparison to the resting state network maps from a large sample that have been made freely available (Yeo etal., 2011).
^ These networks are sometimes referred to by different names, and can take somewhat different forms depending on the methods used to define them (with core nodes remaining the same). Because the network maps we present here are taken from Yeo etal. (2011), we use their terminology. They note (and thus so do we) that the ventral attention network, especially, is similar to what has been described as the salience network (Seeley etal., 2007) and the cingulo-opercular network (Dosenbach etal., 2007).
^ The term “resting state” is often misinterpreted to mean the resting brain. It should not be assumed that the brain is actually “at rest” during these scans, but simply that there is no externally orienting task.

References are available at the Frontiers site.