How major depressive disorder affects the ability to decode multimodal dynamic emotional stimuli

Most studies investigating the processing of emotions in depressed patients reported impairments in the decoding of negative emotions. However, these studies adopted static stimuli (mostly stereotypical facial expressions corresponding to basic emotions) which do not reflect the way people experience emotions in everyday life. For this reason, this work proposes to investigate the decoding of emotional expressions in patients affected by Recurrent Major Depressive Disorder (RMDDs) using dynamic audio/video stimuli. RMDDs’ performance is compared with the performance of patients with Adjustment Disorder with Depressed Mood (ADs) and healthy (HCs) subjects. The experiments involve 27 RMDDs (16 with acute depression - RMDD-A, and 11 in a compensation phase - RMDD-C), 16 ADs and 16 HCs. The ability to decode emotional expressions is assessed through an emotion recognition task based on short audio (without video), video (without audio) and audio/video clips. The results show that AD patients are significantly less accurate than HCs in decoding fear, anger, happiness, surprise and sadness. RMDD-As with acute depression are significantly less accurate than HCs in decoding happiness, sadness and surprise. Finally, no significant differences were found between HCs and RMDD-Cs in a compensation phase. The different communication channels and the types of emotion play a significant role in limiting the decoding accuracy

Behavioral sentiment analysis of depressive states

The need to release accurate and incontrovertible diagnoses of depression has fueled the search for new methodologies to obtain more reliable measurements than the commonly adopted questionnaires. In such a context, research has sought to identify non-biased measures derived from analyses of behavioral data such as voice and language. For this purpose, sentiment analysis techniques were developed, initially based on linguistic characteristics extracted from texts and gradually becoming more and more sophisticated by adding tools for the analyses of voice and visual data (such as facial expressions and movements). This work summarizes the behavioral features accounted for detecting depressive states and sentiment analysis tools developed to extract them from text, audio, and video recordings

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review

Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field

Does My Sadness Blind Me to What is Going on Inside You? Studies on Mental State Decoding and Reasoning in Unipolar Depression and Bipolar Disorder

Zu den wesentlichen Komponenten des sozialen Funktionierens gehören sowohl die Fähigkeit, komplexe emotionale Gesichtsausdrücke zu erkennen (Dekodierung) als auch die Fähigkeit - basierend auf der Integration dieser und anderer Informationen - Rückschlüsse auf den mentalen Zustand anderer Menschen zu ziehen (Reasoning). Diese beiden Komponenten werden auch als Theory of Mind (ToM) bezeichnet. Obwohl das soziale Funktionsniveau von unipolar depressiven Patienten und bipolaren Patienten deutlich beeinträchtigt ist, gibt es bisher nur wenige Studien darüber, inwieweit die beiden genannten Komponenten der ToM bei diesen Patientengruppen beeinträchtigt sind. Die wenigen Studien, die es gibt, verwenden überwiegend Erhebungsmaterial, dessen ökologische Validität fraglich ist. Daher wurden in der vorliegenden Arbeit erstmals die Decoding- und Reasoning-Fähigkeiten von affektiven Patienten mit ökologisch validem Material untersucht. Weiterhin wurde untersucht, inwieweit mögliche Beeinträchtigungen mit dem aktuellen Zustand der Patienten (akut depressiv vs. remittiert) zusammenhängen und ob sie möglicherweise erst durch eine negative Stimmungsinduktion ausgelöst oder verstärkt werden. Darüber hinaus wurde untersucht, welchen Einfluss mögliche ToM-Defizite auf den Krankheitsverlauf bei Patienten mit affektiven Störungen haben.The essential components of social functioning include both the ability to decode complex emotional facial expressions (decoding) and the ability - based on the integration of this and other information - to draw conclusions about the mental state of other people (reasoning). These two components are also referred to as Theory of Mind (ToM). Although the social functioning level of patients with major depression and patients with bipolar disorder is significantly impaired, only a few studies have examined the extent to which the two components of ToM are impaired in these patient groups. The studies that do exist predominantly use stimulus material with questionable ecological validity. Therefore, for the first time, the work presented here investigates ToM decoding and ToM reasoning abilities of patients with unipolar depression and bipolar disorder, using highly ecologically valid material. Furthermore, we investigated to what extent such impairments are related to the current state of the patients (acutely depressed vs. remitted) and whether they are possibly first induced or intensified by a negative mood induction. In addition, the impact of possible ToM deficits on the course of illness in patients with affective disorders was investigated

The words of the body: psychophysiological patterns in dissociative narratives

Trauma has severe consequences on both psychological and somatic levels, even affecting the genetic expression and the cell\u2019s DNA repair ability. A key mechanism in the understanding of clinical disorders deriving from trauma is identified in dissociation, as a primitive defense against the fragmentation of the self originated by overwhelming experiences. The dysregulation of the interpersonal patterns due to the traumatic experience and its detrimental effects on the body are supported by influent neuroscientific models such as Damasio\u2019s somatic markers and Porges\u2019 polyvagal theory. On the basis of these premises, and supported by our previous empirical observations on 40 simulated clinical sessions, we will discuss the longitudinal process of a brief psychodynamic psychotherapy (16 sessions, weekly frequency) with a patient who suffered a relational trauma. The research design consists of the collection of self-report and projective tests, pre-post therapy and after each clinical session, in order to assess personality, empathy, clinical alliance and clinical progress, along with the verbatim analysis of the transcripts trough the Psychotherapy Process Q-Set and the Collaborative Interactions Scale. Furthermore, we collected simultaneous psychophysiological measures of the therapeutic dyad: skin conductance and hearth rate. Lastly, we employed a computerized analysis of non-verbal behaviors to assess synchrony in posture and gestures. These automated measures are able to highlight moments of affective concordance and discordance, allowing for a deep understanding of the mutual regulations between the patient and the therapist. Preliminary results showed that psychophysiological changes in dyadic synchrony, observed in body movements, skin conductance and hearth rate, occurred within sessions during the discussion of traumatic experiences, with levels of attunement that changed in both therapist and the patient depending on the quality of the emotional representation of the experience. These results go in the direction of understanding the relational process in trauma therapy, using an integrative language in which both clinical and neurophysiological knowledge may take advantage of each other

Face masks affect perception of happy faces in deaf people

The SARS-CoV-2 pandemic has led significant social repercussions and forced people to wear face masks. Recent research has demonstrated that the human ability to infer emotions from facial configurations is significantly reduced when face masks are worn. Since the mouth region is specifically crucial for deaf people who speak sign language, the current study assessed the impact of face masks on inferring emotional facial expressions in a population of adult deaf signers. A group of 34 congenitally deaf individuals and 34 normal-hearing individuals were asked to identify happiness, sadness, fear, anger, and neutral expression on static human pictures with and without facial masks presented through smartphones. For each emotion, the percentage of correct responses with and without face masks was calculated and compared between groups. Results indicated that face masks, such as those worn due to the SARS-CoV-2 pandemic, limit the ability of people to infer emotions from facial expressions. The negative impact of face masks is significantly pronounced when deaf people have to recognize low-intensity expressions of happiness. These findings are of essential importance because difficulties in recognizing emotions from facial expressions due to mask wearing may contribute to the communication challenges experienced by the deaf community during the SARS-CoV-2 pandemic, generating feelings of frustration and exclusion