Identification and Perceived Intensity of Facial Expressions of Emotion in Bipolar Disorder and Major Depression

Branco, Laura Damiani; Cotrena, Charles; Ponsoni, André; Salvador-Silva, Roberta; Vasconcellos, Sílvio José Lemos; Fonseca, Róchele Paz

doi:10.1093/arclin/acx080

“…This would still fit with depressed patients' reduced participation in normally rewarding activities as these tend to be social in nature. However, studies have also failed to find an influence of depression on perceived intensity of socially rewarding stimuli (Branco et al 2017, Schaefer et al 2010). An alternate possibility is that, in parallel to anxiety and threat, depression might be linked to biases in estimation of future reward value, as opposed to altered responsivity to actual outcomes.…”

Section: Sensitivity To Threat and Reward In Anxiety And Depressionmentioning

confidence: 99%

Anxiety, Depression, and Decision Making: A Computational Perspective

Bishop

¹

,

Gagné

²

2018

Annu. Rev. Neurosci.

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In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior. Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified. In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection. In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies.

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“…The well-established FACS [ 61 ], LIWC [ 59 ], and EmoVoice approach [ 60 ] should provide the technical implementation of our emotion-recognition approach with some helpful framework. This development is especially promising when the effects of emotional deficits on the social and global functioning of patients are considered [ 39 , 40 , 42 - 45 , 48 ]. Consequently, our emotion-sensitive approach is not only interesting in the context of mood-state recognition but might also increase our understanding of experienced and expressed emotions of patients with bipolar disorder.…”

Section: Discussionmentioning

confidence: 99%

“…For example, during mood episodes there are typical patterns of experienced emotions: whereas manic states are often characterized by increased happiness or anger and fear, depressive states often show patterns of elevated sadness and disgust [ 37 ]. Bipolar disorders are further associated with a generally amplified emotionality [ 38 , 39 ] and difficulties in emotion processing and regulation [ 40 - 44 ], in emotion recognition [ 45 - 47 ], and in the expression of emotions [ 48 , 49 ]. These deficits might partially be related to the current mood state of patients [ 41 , 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

“…These deficits might partially be related to the current mood state of patients [ 41 , 46 , 47 ]. Yet, they strongly affect their social and global functioning and are related to severe outcome variables such as suicidality [ 39 , 40 , 42 - 45 , 48 ]. Consequently, emotional aspects have a great impact on the patients’ everyday lives and the long-term course of bipolar disorder.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Daus

¹

,

Bloecher

²

,

Egeler

³

et al. 2020

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Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models.

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“…For example, during mood episodes there are typical patterns of experienced emotions: whereas manic states are often characterized by increased happiness or anger and fear, depressive states often show patterns of elevated sadness and disgust [37]. Bipolar disorders are further associated with a generally amplified emotionality [38,39] and difficulties in emotion processing and regulation [40][41][42][43][44], in emotion recognition [45][46][47], and in the expression of emotions [48,49]. These deficits might partially be related to the current mood state of patients [41,46,47].…”

Section: Introductionmentioning

confidence: 99%

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder (Preprint)

Daus¹,

Bloecher²,

Egeler³

et al. 2019

Preprint

0

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UNSTRUCTURED

Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models.

show abstract

Identification and Perceived Intensity of Facial Expressions of Emotion in Bipolar Disorder and Major Depression

Cited by 26 publications

References 57 publications

Anxiety, Depression, and Decision Making: A Computational Perspective

Anxiety, Depression, and Decision Making: A Computational Perspective

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder (Preprint)

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