Portraying emotions at their unfolding: A multilayered approach for probing dynamics of neural networks

Raz, Gal; Winetraub, Yonatan; Jacob, Yael; Kinreich, Sivan; Maron‐Katz, Adi; Shaham, Galit; Podlipsky, Ilana; Gilam, Gadi; Soreq, Eyal; Hendler, Talma

doi:10.1016/j.neuroimage.2011.12.084

Cited by 91 publications

(109 citation statements)

References 61 publications

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“…1) (Raz et al, 2014;Raz et al, 2012). The NCI is a sliding-window estimator of the connectivity between brain networks' nodes derived from functional magnetic resonance imaging (fMRI).…”

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confidence: 99%

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences

Raz

Touroutoglou

Wilson-Mendenhall

et al. 2016

Cogn Affect Behav Neurosci

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Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., Bsadness,^Bhappiness^), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems. Cogn Affect Behav Neurosci (2016) 16:709-723 DOI 10.3758/s13415-016-0425-4 Gal Raz and Alexandra Touroutoglou contributed equally to this work and share first authorship. (Barrett, 2006(Barrett, , 2012Barrett & Satpute, 2013;Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012;Touroutoglou, Lindquist, Dickerson, & Barrett, 2015;Wilson-Mendenhall, Barrett, & Barsalou, 2015). Situating recent meta-analytic evidence on brain basis of emotion within a growing systems neuroscience literature reveals that the regions involved in emotion are distributed across multiple, anatomically constrained Bresting-state^networks that contribute to many psychological phenomena (Barrett & Satpute, 2013;Kober et al., 2008;. Specific regions that had initially been shown to be more active for one emotion category than for others in meta-analytic data (e.g., more active for fear than for sadness or disgust; Fusar-Poli et al., 2009;Vytal & Hamann, 2010) were not replicated in a more comprehensive metaanalysis , but instead operate in large-scale networks that are not specific to any given emotion category (Touroutoglou et al., 2015), or even to the domain of emotion (Anderson, 2015). These networks support more domain-general functions, such as executive function, affiliation, salience detection, and so forth, and thus contribute to constructing emotional experiences (as well as other kinds of experiences; Barrett & Satpute, 2013;. When regional or network patterns emerge for one emotion category versus another, this is because that category of emotional experiences tends to draw more on certain domain-general func...

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“…1) (Raz et al, 2014;Raz et al, 2012). The NCI is a sliding-window estimator of the connectivity between brain networks' nodes derived from functional magnetic resonance imaging (fMRI).…”

mentioning

confidence: 99%

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences

Raz

Touroutoglou

Wilson-Mendenhall

et al. 2016

Cogn Affect Behav Neurosci

Self Cite

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“…Most commonly, the latter has been addressed by looking for correlations between the extracted indexes of connectivity and some behavioral measure ([e.g. Raz et al, 2012; Thompson et al, 2013; see also Krishnan et al, 2011] for a review of Partial Least Squares [PLS] methods, including behavior‐PLS and task‐PLS that can test for relationships between inter‐regional patterns of activity and behavior/task).…”

Section: Discussionmentioning

confidence: 99%

“…The connectivity indexes were correlated with behavioral and parasympathetic measures related to the subjects' emotional state, on a window‐by‐window basis. The results showed significant correlations between the connectivity of the limbic system with the prefrontal cortex and the sadness ratings, demonstrating the functional relevance of the dynamic connectivity changes [Raz et al, 2012; see also Baldassarre et al, 2012 and van den Heuvel and Sporns, 2013] (who demonstrated the task‐relevance of resting‐state networks using inter‐individual differences, rather than within‐scan time‐resolved measures).…”

Section: Introductionmentioning

confidence: 98%

“…More recently, there has been an increasing interest in the study of brain functioning using more complex and realistic stimuli, such as videos and movies [e.g., Bartels and Zeki, 2004; Fiser et al, 2004; Hasson et al, 2008; Kayser et al, 2004; Nishimoto et al, 2011; Wolf et al, 2010; see also Peelen and Kastner, 2014]. Unlike traditional paradigms, in these cases there is little a priori knowledge about the timing/occurrence of the relevant events and, hence, the construction of “predictors” for data‐fitting is challenging [but see Bartels et al, 2008; Bordier et al, 2013; Lahnakoski et al, 2012a; Ogawa et al, 2013; Raz et al, 2012]. Indeed, most studies using naturalistic stimuli sought to identify patterns of activity based only on the data structure [i.e., data‐driven approaches, e.g., independent component analyses, Bartels and Zeki, 2005; Lahnakoski et al, 2012b; for review see Calhoun and Pearlson, 2012; intersubject correlation analyses, Hasson et al, 2004; cluster analyses, Heller et al, 2006].…”

Section: Introductionmentioning

confidence: 99%

“…For example, using short time‐windows, Thompson et al showed that the level of (anti‐)correlation between “the default mode network” and the “task positive network” in a 12.5 s window centered just before the target onset predicted the speed of target detection on a trial‐by‐trial basis [Thompson et al, 2013]. Recently, Raz et al used 30 s sliding‐windows to extract dynamic patterns of inter‐regional connectivity during movie watching [Raz et al, 2012]. The connectivity indexes were correlated with behavioral and parasympathetic measures related to the subjects' emotional state, on a window‐by‐window basis.…”

Section: Introductionmentioning

confidence: 99%

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Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

Bordier

Macaluso

2015

Human Brain Mapping

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Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time‐series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time‐series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task‐dependent changes of connectivity). We present a fully data‐driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time‐windows was used to identify if/when any area showed stimulus/task‐related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time‐series (stimulus/task‐related networks). Finally, for each network, a second clustering step grouped together all the time‐windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task‐related activity at specific time‐points during the fMRI time‐series. We label these configurations: “brain modes” (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli. Hum Brain Mapp 36:3404–3425, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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The neural networks of subjectively evaluated emotional conflicts

et al. 2016

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Previous work on the neural underpinnings of emotional conflict processing has largely focused on designs that instruct participants to ignore a distracter which conflicts with a target. In contrast, this study investigated the noninstructed experience and evaluation of an emotional conflict, where positive or negative cues can be subjectively prioritized. To this end, healthy participants freely watched short film scenes that evoked emotional conflicts while their BOLD responses were measured. Participants' individual ratings of conflict and valence perception during the film scenes were collected immediately afterwards, and the individual ratings were regressed against the BOLD data. Our analyses revealed that (a) amygdala and medial prefrontal cortex were significantly involved in prioritizing positive or negative cues, but not in subjective evaluations of conflict per se, and (b) superior temporal sulcus (STS) and inferior parietal lobule (IPL), which have been implicated in social cognition and emotion control, were involved in both prioritizing positive or negative cues and subjectively evaluating conflict, and may thus constitute "hubs" or "switches" in emotional conflict processing. Psychophysiological interaction (PPI) analyses further revealed stronger functional connectivity between IPL and ventral prefrontal-medial parietal areas in prioritizing negative cues, and stronger connectivity between STS and dorsal-rostral prefrontal-medial parietal areas in prioritizing positive cues. In sum, our results suggest that IPL and STS are important in the subjective evaluation of complex conflicts and influence valence prioritization via prefrontal and parietal control centers. Hum Brain Mapp 37:2234-2246, 2016. © 2016 Wiley Periodicals, Inc.

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Portraying emotions at their unfolding: A multilayered approach for probing dynamics of neural networks

Cited by 91 publications

References 61 publications

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

The neural networks of subjectively evaluated emotional conflicts

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