Facial emotion recognition and its correlation with executive functions in bipolar I patients and healthy controls

David, Denise Petresco; Soeiro-de-Souza, Márcio Gerhardt; Moreno, Ricardo Alberto; Bio, Danielle Soares

doi:10.1016/j.jad.2013.09.027

Cited by 47 publications

(33 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These scores were selected based on previous literature in BD showing important deficits in the domains of learning and memory, processing speed, decision making, set-shifting and inhibition (Arts et al, 2008; Bora et al, 2009; Goodwin et al, 2008; Robinson et al, 2006). The AGN scores were included based on previous evidence of difficulties with affect processing and facial emotion recognition in individuals with BD (David et al, 2014; Sepede et al, 2015). These variables were normalized through a Z-transform by subtracting the mean and dividing with the standard deviation.…”

Section: Methodsmentioning

confidence: 99%

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

Passos

Bauer

et al. 2016

Journal of Affective Disorders

View full text Add to dashboard Cite

Background Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC). Methods A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC. Results The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p = 0.0053. The LASSO algorithm assigned individual subjects with a probability score (0 – healthy, 1 – patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC. Limitations Our study sample was small as we only considered euthymic BD patients and demographically matched HC. Conclusion Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients.

show abstract

Section: Methodsmentioning

confidence: 99%

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

Passos

Bauer

et al. 2016

Journal of Affective Disorders

View full text Add to dashboard Cite

show abstract

“…Those positive emotions become 'efficient antidotes for the lingering effects of negative emotions' (Fredrickson, 2004). Abundant research, mostly through human experiments, empirically supports these claims (see Aspinwall, 2001;David et al, 2014;Garland et al, 2010;Reed & Aspinwall, 1998Trope & Neter, 1994). For instance, a study of US college students' emotions after the terrorist attack of September 11 showed a 60% increase of love towards their families and friends to cope with feelings of anger, fear, and sadness (Saad, 2001).…”

Section: Literature Reviewmentioning

confidence: 99%

Positive sentiments as coping mechanisms and path to resilience: the case of Qatar blockade

El-Masri

Ramsay

Ahmed

et al. 2020

Information, Communication & Society

View full text Add to dashboard Cite

Existing research on coping accentuates the role of positive emotions as defensive mechanisms to cope with stressful situations and the ensuing negative emotions. The same literature justifies the long-term effects of positive emotions that help build lasting resilience. Grounded in theories of coping and resilience, this paper (1) identifies the emotions that people actuate to cope with adversaries and (2) evaluates the resulting long-lasting adaptation and resilience. To do this, we examined the emotions felt by Qatar residents due to a land, sea, and air blockade enforced by neighbouring counties. Accordingly, we analysed 160,000 Arabic tweets originating from Qatar between June-2017 and March-2018 using a novel machine-learning algorithm termed Weighted Conditional Probability. Our algorithm achieved state-ofthe-art performance when compared with the often-used Support Vector Machine, Naïve Bayes and Deep Neural Nets algorithms. Results show that, while Qatar residents experienced an emotional roller coaster during the blockade, they used positive emotions like love and optimism to cope with adversities and accompanying emotions of fear and anger. Moreover, our analysis reveals that their adaptive resilient capacities gradually strengthened during the nine months of blockade. The study supports the renowned theory of positive emotions using an advanced methodology and a large-scale dataset. ARTICLE HISTORY

show abstract

“…However, an association between impaired FER and executive control has been found in several other serious mental disorders, including alcohol dependence (Trick et al, 2014), schizophrenia (Bortolon, Capdevielle, & Raffard, 2015), and bipolar disorders (David et al, 2014). Prefrontal dysfunction may lead to impairments in executive control and FER (Adolphs, 2002).…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition performance influences executive control impairment in Anorexia Nervosa: an exploratory study

et al. 2018

View full text Add to dashboard Cite

ObjectivesSince evidence on executive control among women with Anorexia or Bulimia Nervosa (AN/BN) are somehow inconclusive, we aimed to explore whether performance in set-shifting in AN/BN might be influenced by Facial Emotion Recognition (FER).MethodsWe randomly recruited women with a diagnosis of AN or BN, from an Eating Disorders Outpatient Clinic in Italy, as well as healthy controls (HCs). We evaluated with established tools: diagnosis (Eating Disorder Examination- EDE-17.0), executive control (Intra-Extra Dimensional Set Shift-IED) and FER (Ekman 60 Faces Test-EK-60F). Univariate distributions by diagnostic subgroups were assessed on sociodemographic and clinical variables, which were selected for subsequent multiple linear regression analyses.ResultsWomen with AN performed significantly worse than HCs on IED adjusted total errors. HCs scored significantly better than AN and BN on EK-60F fear subscale. Although IED set shifting was associated (p = 0.008) with AN, after controlling for age, EK-60F fear subscale, alexithymia and depression (i.e., clinically relevant covariates identified a priori from the literature, or associated with AN/BN at univariate level), this association could not be confirmed.ConclusionsImpaired executive control may not be a distinctive feature in women with AN, since several clinical characteristics, including fear recognition ability, are likely to have an important role. This has significant implications for relevant interventions in AN, which should aim at also improving socio-emotional processing.

show abstract

Facial emotion recognition and its correlation with executive functions in bipolar I patients and healthy controls

Cited by 47 publications

References 57 publications

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

Positive sentiments as coping mechanisms and path to resilience: the case of Qatar blockade

Facial emotion recognition performance influences executive control impairment in Anorexia Nervosa: an exploratory study

Contact Info

Product

Resources

About