Differences in Facial Expression Recognition Between Unipolar and Bipolar Depression

Relationship between low mood and micro-expression processing: evidence of negative bias in interpreting fleeting facial expressions

Depression affects the recognition of emotion in facial expressions by reducing the detection accuracy and adding a bias towards negativity. However, no study has examined associations between depression and the recognition of microfacial expressions (fleeting facial cues of emotions in people's faces). Thus, we investigated associations between low mood and micro-expression processing using video stimuli of micro-expressions. We examined whether (i) individuals with low mood had trouble recognizing emotions, (ii) were more likely to perceive happy facial expressions as neutral and neutral facial expressions as sad, and (iii) recognized sad emotional expressions better than control subjects (n = 349). We found that participants with low mood showed poorer performance when judging emotions in faces (p = 0.03). Furthermore, there was a specific deficit among them in recognizing happiness. Lastly, participants with low moods were more likely to perceive neutral faces as sad (p = 0.042). However, no evidence was found that individuals with low moods confused happy faces as neutral or were better than the control group at recognizing sad faces. Our results show that mood affects the perception of emotions in facial expressions, which has the potential to negatively affect interpersonal interactions and ultimately quality of life.

Neurocognition across bipolar disorder phases compared to healthy subjects

INTRODUCTION

Bipolar disorder (BD) is associated with cognitive abnormalities that may persist during euthymia and are linked to poor occupational performance. The cognitive differences between phases of BD are not well known. Therefore, a cross-sectional study with a relatively large population was conducted to evaluate the differences among BD phases in a wide range of neurocognitive parameters.

METHODS

Neuropsychological profile of 169 patients with a diagnosis of BD in manic, depressive, mixed, and euthymic phases between the ages of 18 and 70 years were compared to 45 healthy individuals' between ages of 24 and 69 years. The working memory (digit-span backward test), face recognition, executive functions (verbal fluency and Stroop test), face recognition, and visual and verbal memory (immediate and delayed recall) were evaluated. For BD subgroup analyses, we used the Kruskal-Wallis (KW) test. Then, for the comparison of BD versus healthy individuals, we used the Mann-Whitney U (MWU) test.

RESULTS

Analyses based on non-parametric tests showed impairments in BD for all tests. There were no significant differences between phases.

CONCLUSION

Cognitive performance in patients with BD appears to be mostly unrelated to the phase of the disorder, implying that cognitive dysfunction in BD is present even during remission.

Differences in facial emotion recognition between bipolar disorder and other clinical populations: A systematic review and meta-analysis

Facial emotion (or expression) recognition (FER) is a domain of affective cognition impaired across various psychiatric conditions, including bipolar disorder (BD). We conducted a systematic review and meta-analysis searching for eligible articles published from inception to April 26, 2023, in PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO to examine whether and to what extent FER would differ between people with BD and those with other mental disorders. Thirty-three studies comparing 1506 BD patients with 1973 clinical controls were included in the present systematic review, and twenty-six of them were analyzed in random-effects meta-analyses exploring the discrepancies in discriminating or identifying emotional stimuli at a general and specific level. Individuals with BD were more accurate in identifying each type of emotion during a FER task compared to individuals diagnosed with schizophrenia (SCZ) (SMD = 0.27; p-value = 0.006), with specific differences in the perception of anger (SMD = 0.46; p-value = 1.19e-06), fear (SMD = 0.38; p-value = 8.2e-04), and sadness (SMD = 0.33; p-value = 0.026). In contrast, BD patients were less accurate than individuals with major depressive disorder (MDD) in identifying each type of emotion (SMD = -0.24; p-value = 0.014), but these differences were more specific for sad emotional stimuli (SMD = -0.31; p-value = 0.009). No significant differences were observed when BD was compared with children and adolescents diagnosed with attention-deficit/hyperactivity disorder. FER emerges as a potential integrative instrument for guiding diagnosis by enabling discrimination between BD and SCZ or MDD. Enhancing the standardization of adopted tasks could further enhance the accuracy of this tool, leveraging FER potential as a therapeutic target.

A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences

Considering the increasing aging of the population, multi-device monitoring of the activities of daily living (ADL) of older people becomes crucial to support independent living and early detection of symptoms of mental illnesses, such as depression and Alzheimer's disease. Anomalies can anticipate the diagnosis of these pathologies in the patient's normal behavior, such as reduced hygiene, changes in sleep habits, and fewer social interactions. These abnormalities are often subtle and hard to detect. Especially using non-intrusive monitoring devices might cause anomaly detectors to generate false alarms or ignore relevant clues. This limitation may hinder their usage by caregivers. Furthermore, the notion of abnormality here is context and patient-dependent, thus requiring untrained approaches. To reduce these problems, we propose a self-supervised model for multi-sensor time series signals based on Hyperbolic uncertainty for Anomaly Detection, which we dub HypAD. HypAD estimates uncertainty end-to-end, thanks to hyperbolic neural networks, and integrates it into the "classic" notion of reconstruction loss in anomaly detection. Based on hyperbolic uncertainty, HypAD introduces the principle of a detectable anomaly. HypAD assesses whether it is sure about the input signal and fails to reconstruct it because it is anomalous or whether the high reconstruction loss is due to the model uncertainty, e.g., a complex but regular signal (cf. this parallels the residual model error upon training). The proposed solution has been incorporated into an end-to-end ADL monitoring system for elderly patients in retirement homes, developed within a funded project leveraging an interdisciplinary consortium of computer scientists, engineers, and geriatricians. Healthcare professionals were involved in the design and verification process to foster trust in the system. In addition, the system has been equipped with explainability features.

Working memory updating in individuals with bipolar and unipolar depression: fMRI study

Understanding neurobiological characteristics of cognitive dysfunction in distinct psychiatric disorders remains challenging. In this secondary data analysis, we examined neurobiological differences in brain response during working memory updating among individuals with bipolar disorder (BD), those with unipolar depression (UD), and healthy controls (HC). Individuals between 18-45 years of age with BD (n = 100), UD (n = 109), and HC (n = 172) were scanned using fMRI while performing 0-back (easy) and 2-back (difficult) tasks with letters as the stimuli and happy, fearful, or neutral faces as distractors. The 2(n-back) × 3(groups) × 3(distractors) ANCOVA examined reaction time (RT), accuracy, and brain activation during the task. HC showed more accurate and faster responses than individuals with BD and UD. Difficulty-related activation in the prefrontal, posterior parietal, paracingulate cortices, striatal, lateral occipital, precuneus, and thalamic regions differed among groups. Individuals with BD showed significantly lower difficulty-related activation differences in the left lateral occipital and the right paracingulate cortices than those with UD. In individuals with BD, greater difficulty-related worsening in accuracy was associated with smaller activity changes in the right precuneus, while greater difficulty-related slowing in RT was associated with smaller activity changes in the prefrontal, frontal opercular, paracingulate, posterior parietal, and lateral occipital cortices. Measures of current depression and mania did not correlate with the difficulty-related brain activation differences in either group. Our findings suggest that the alterations in the working memory circuitry may be a trait characteristic of reduced working memory capacity in mood disorders. Aberrant patterns of activation in the left lateral occipital and paracingulate cortices may be specific to BD.