Amygdala responsiveness to emotional words is modulated by subclinical anxiety and depression

Laeger, Inga; Dobel, Christian; Dannlowski, Udo; Kugel, Harald; Grotegerd, Dominik; Kißler, Johanna; Keuper, Katharina; Eden, Annuschka; Zwitserlood, Pienie; Zwanzger, Peter

doi:10.1016/j.bbr.2012.05.036

Cited by 64 publications

(55 citation statements)

References 85 publications

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“…4 While there is broad evidence for disorder-related attentional biases (see the study by Maidenberg and colleagues ). These results are in accordance with findings of amygdala hyperactivation in patients with other psychiatric disorders, such as specific phobia 16 and major depression; 17,18 in those with high trait anxiety; [19][20][21] and after childhood maltreatment. 22,23 Altered activation patterns in prefrontal executive brain areas (i.e., prefrontal cortex [PFC]) have also been reported in patients with social phobia.…”

Section: Introductionsupporting

confidence: 90%

Have we met before? Neural correlates of emotional learning in women with social phobia

Laeger

Keuper

Heitmann

et al. 2014

J Psychiatry Neurosci

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Section: Introductionsupporting

confidence: 90%

Have we met before? Neural correlates of emotional learning in women with social phobia

Laeger

Keuper

Heitmann

et al. 2014

J Psychiatry Neurosci

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“…In comparison to healthy controls, depressed patients also demonstrate greater right amygdala activation for sad expressions (Costafreda et al, 2013;Stuhrmann et al, 2013;Suslow et al, 2010). Complementary findings have also been reported in subclinically depressed participants who, relative to never-depressed participants, show increased bilateral amygdala activation in response to negative emotional words, a neural pattern which correlates with symptom severity (Laeger et al, 2012).…”

Section: The Role Of Amygdala Function In Depressionsupporting

confidence: 69%

The ‘affect tagging and consolidation’ (ATaC) model of depression vulnerability

Harrington

Pennington

Durrant

2017

Neurobiology of Learning and Memory

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Please cite this article as: Harrington, M.O., Pennington, K., Durrant, S.J., The 'Affect Tagging and Consolidation' (ATaC) Model of Depression Vulnerability, Neurobiology of Learning and Memory (2017), doi: http://dx.doi.org/10. 1016/j.nlm.2017.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. patients, suggesting that they may be vulnerability markers of major depressive disorder (MDD), rather than mere epiphenomena of the disorder. Neuroimaging studies have revealed that depression is also associated with heightened amygdala reactivity to negative emotional stimuli, which may also be a vulnerability marker for MDD. Several models have been developed to explain the respective roles of REM sleep alterations and negatively-biased amygdala activity in the pathology of MDD, however the possible interaction between these two potential risk-factors remains uncharted. This paper reviews the roles of the amygdala and REM sleep in the encoding and consolidation of negative emotional memories, respectively. We present our 'affect tagging and consolidation' (ATaC) model, which argues that increased REM sleep density and negatively-biased amygdala activity are two separate, genetically influenced risk-factors for depression which interact to promote the development of negative memory bias -a well-known cognitive vulnerability marker for depression.Predictions of the ATaC model may motivate research aimed at improving our understanding of sleep dependent memory consolidation in depression aetiology.

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“…Thus, the gain/loss signals can be used as a measure to determine the difference between the expectancy and the actuality quantitatively. This provides a powerful quantitative tool to study emotions using numerical measures, without using any of the traditional subjective stimuli, such as facial expressions [7][8][9], emotional words [10][11][12] or sad movies [13], as the stimuli for eliciting the sad responses.…”

Section: Proportionality Hypothesis Between Emotionalmentioning

confidence: 99%

Quantitative Assessment of Sad Emotion

Tam

2015

PBS

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Abstract:Based on the disparity theory of emotion, the role of sad emotion is an internal assessment of the error-correction process to reduce the disparity between the expected and actual outcomes (loss reduction) in the reality check process. This computational theory of emotion is consistent with the psychological characteristics that sadness is an emotional response to the sense of loss (such as loss of loved ones, valuables, possessions, or achieved goals). This emotional theory of sadness also includes the emotional resolution process by accepting that nothing can be done to change the actual outcomes, and resolving the emotion by reducing the perceived loss. This self-corrective mechanism is used as an internal feedback to assess the incongruence between the expectation and the actuality, such that the perceived loss can be reduced, resolving the sad emotion in the process. Thus, sadness can serve as a motivating feedback to an individual to make a decision to reduce the loss in the emotional resolution process. The classical ultimatum game (UG) paradigm is used to elicit self-generated emotion in human subjects experimentally in response to the disparity between the proportions of money being offered to share with. Results showed that the sadness level is quantified by the sadness stimulus-response function. The level of sadness intensity is proportional to perceived loss (or inversely proportional to the perceived gain). The results also showed that there was a shifting of the baseline sadness level from a less sad level for the acceptance decision to a more sad level for the rejection decision. This shows that the sad emotion can be resolved by accepting the monetary offer in the UG paradigm, which reduces the loss compared to the decision to reject the money. These results confirmed the emotional disparity hypothesis that the level of sadness is proportional to the perceived loss, and sadness can be resolved by reducing the loss in the self-regulated internal processing of emotion. Implications on emotional intelligence are also addressed so that one of the effective skills to resolve sadness is the reduction of the perceived losses.

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Amygdala responsiveness to emotional words is modulated by subclinical anxiety and depression

Cited by 64 publications

References 85 publications

Have we met before? Neural correlates of emotional learning in women with social phobia

Have we met before? Neural correlates of emotional learning in women with social phobia

The ‘affect tagging and consolidation’ (ATaC) model of depression vulnerability

Quantitative Assessment of Sad Emotion

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