Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

Bush, Keith; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

doi:10.3389/fnhum.2017.00459

Cited by 25 publications

(20 citation statements)

References 53 publications

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“…These weights likely represent the flow of information from unimodal sensory areas to the multi-or supramodal regions, described above, involved in emotional processing. In summary, the multivariate analysis revealed a large, yet restricted network of cortical and subcortical regions necessary for the accurate classification of fearful expressions, in agreement with the literature that argues for a distributed and multifaceted model of emotional processing (e.g., Bush, Inman, Hamann, Kilts, & James, 2017;Lindquist et al, 2012;Wager et al, 2015).…”

Section: Distinguishing Fear Vs Neutral Expressionssupporting

confidence: 86%

Multivariate fMRI pattern analysis of fear perception across modalities

Whitehead

Armony

2019

Eur J of Neuroscience

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The emotional expression of fear can be processed through a number of modalities, and of varying forms, however, much of the functional imaging literature has centered on investigating fear as expressed through faces. Findings point to an active involvement of the amygdala, and remain fairly consistent in other studies of unimodal fear perception; however, few studies have looked at within‐subject cross‐modal responses to fear. Thus, we approached this inquiry by testing 30 healthy young adults with fast, high‐resolution fMRI, recording the neural responses of fear perception, as expressed through faces, bodies, prosody, and vocalizations. The study was analyzed using a multivariate approach (multi‐voxel pattern analysis) and yielded a significant distinction in the responses associated with the perception of fearful vs. neutral emotions. Calculated weights highlighted areas in the amygdala and surrounding subcortical structures as contributing the greatest to the discrimination; however, a whole‐brain analysis was necessary to obtain above‐chance classification accuracy, suggesting that processing fear across modalities likely involves a broad, distributed network. Thus, our findings support a multivariate approach to studying a highly complex construct such as emotion, as it accounts for multiple voxels simultaneously and can accommodate the high subject‐level variability that oftentimes comes with studying emotion perception.

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Section: Distinguishing Fear Vs Neutral Expressionssupporting

confidence: 86%

Multivariate fMRI pattern analysis of fear perception across modalities

Whitehead

Armony

2019

Eur J of Neuroscience

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“…Image stimuli drawn from the International Affective Picture System (IAPS) (Lang et al, 2008 ), a widely-cited normed imageset that has been used in two prior MVPA-based studies of the classification of perceived affect (Baucom et al, 2012 ; Bush et al, 2017 ), were presented using two randomly interleaved formats, extrinsic (imageset A) and intrinsic (imageset B). These formats are distinguished by the instructions to either passively view (extrinsic) or actively experience (intrinsic) the affective content of the IAPS stimulus.…”

Section: Methodsmentioning

confidence: 99%

“…MVPA of fMRI response is based on the brain state hypothesis of cognitive processing: that there exists a one-to-one mapping between a brain state (i.e., a temporally succinct pattern of distributed neural activations) and the cognitive process that this state encodes. This hypothesis is particularly relevant to past MPVA-based attempts to classify brain states induced by visual stimuli according to the normed affective content of the stimuli across both discrete emotions (Saarimäki et al, 2016 ) and the independent valence and arousal properties of dimensional emotion (Baucom et al, 2012 ; Bush et al, 2017 ). The brain states induced within these studies exhibited patterns of neural activation that were distributed widely throughout the cortex and subcortex (Chang et al, 2015 ; Saarimäki et al, 2016 ; Bush et al, 2017 ) and challenged earlier hypotheses that assigned specific neuroanatomical loci to each discrete emotion (Ekman, 1999 ; Izard, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Brain States That Encode Perceived Emotion Are Reproducible but Their Classification Accuracy Is Stimulus-Dependent

Bush

Gardner

Privratsky

et al. 2018

Front. Hum. Neurosci.

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The brain state hypothesis of image-induced affect processing, which posits that a one-to-one mapping exists between each image stimulus and its induced functional magnetic resonance imaging (fMRI)-derived neural activation pattern (i.e., brain state), has recently received support from several multivariate pattern analysis (MVPA) studies. Critically, however, classification accuracy differences across these studies, which largely share experimental designs and analyses, suggest that there exist one or more unaccounted sources of variance within MVPA studies of affect processing. To explore this possibility, we directly demonstrated strong inter-study correlations between image-induced affective brain states acquired 4 years apart on the same MRI scanner using near-identical methodology with studies differing only by the specific image stimuli and subjects. We subsequently developed a plausible explanation for inter-study differences in affective valence and arousal classification accuracies based on the spatial distribution of the perceived affective properties of the stimuli. Controlling for this distribution improved valence classification accuracy from 56% to 85% and arousal classification accuracy from 61% to 78%, which mirrored the full range of classification accuracy across studies within the existing literature. Finally, we validated the predictive fidelity of our image-related brain states according to an independent measurement, autonomic arousal, captured via skin conductance response (SCR). Brain states significantly but weakly (r = 0.08) predicted the SCRs that accompanied individual image stimulations. More importantly, the effect size of brain state predictions of SCR increased more than threefold (r = 0.25) when the stimulus set was restricted to those images having group-level significantly classifiable arousal properties.

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“…With the advancement of neuroscience, contemporary emotion theories have increasingly advocated the idea that emotions are not subserved by a single region of brain but rather involve synergy among multiple brain regions and multiple functional networks (Bush, Inman, Hamann, Kilts, & James, 2017;Kragel & LaBar, 2016;Pessoa, 2017). In studies of anxiety, there is a great deal of evidence supporting that attentional bias (Barry, Vervliet, & Hermans, 2015;Goodwin, Yiend, & Hirsch, 2017;Heeren, Mogoase, Philippot, & McNally, 2015;Pergamin-Hight, Naim, Bakermans-Kranenburg, van, & Bar-Haim, 2015), memory (Bannerman et al, 2014;Mitte, 2008), somatosensory functions or interoception (Anderson & Hope, 2009;Gupta, 2013;Paulus & Stein, 2010;Stern, 2014;Yoris et al, 2015), and emotional regulation strategies (Aldao, Nolen-Hoeksema, & Schweizer, 2010;Amstadter, 2008;Cisler, Olatunji, Feldner, & Forsyth, 2010) are supported by systems that span multiple functional networks.…”

Section: Multivariate Method Task-state Network and Distributed Rmentioning

confidence: 99%

Brain connection pattern under interoceptive attention state predict interoceptive intensity and subjective anxiety feeling

Shi

Wei

et al. 2018

Human Brain Mapping

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Interoception involves the processing of a variety of different types of information ascending from the body. Accumulating evidence has indicated that interoception plays a fundamental role in cognitive and emotional processes, such as anxiety, but how different functional connectivity patterns contribute to emotions and visceral feelings during an interoceptive attention state is still unclear. In the present study, an interoceptive attention task was performed during functional magnetic resonance imaging of healthy subjects, and the participants' subjective ratings of the intensity of interoception and feelings of anxiety were recorded. Several network nodes were selected, based on previous studies, to construct task‐dependent functional connectivity patterns, which were processed by support vector regression to predict the corresponding feeling scores. The results showed that for interoception, the cingulo‐opercular task control network provided the greatest contribution, whereas the most important feature for anxiety was the connections between the sensorimotor area (SSM) and the salience network (SN). There existed four overlapping connections between the two predictions: two negative connections between the default mode network (DMN) and the SSM, one negative connection between the DMN and the SN, and one positive connection between the ventral attention network and the SN; this overlap might suggest common bodily attention processing that is involved in both interoception and anxiety. This study remediates the lack of network‐level biomarkers of interoception and provides a reference at the level of the brain for further understanding anxiety from an interoceptive perspective.

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Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

Cited by 25 publications

References 53 publications

Multivariate fMRI pattern analysis of fear perception across modalities

Multivariate fMRI pattern analysis of fear perception across modalities

Brain States That Encode Perceived Emotion Are Reproducible but Their Classification Accuracy Is Stimulus-Dependent

Brain connection pattern under interoceptive attention state predict interoceptive intensity and subjective anxiety feeling

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