How Major Depressive Disorder Affects the Ability to Decode Multimodal Dynamic Emotional Stimuli

Scibelli, Filomena; Troncone, Alda; Likforman-Sulem, Laurence; Vinciarelli, Alessandro; Esposito, Anna

doi:10.3389/fict.2016.00016

Cited by 17 publications

(11 citation statements)

References 69 publications

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“…Indeed, MDD has been associated with other physical conditions as cardiac problems and cancer [2]. Besides physical impairments, MDD also generates social disabilities, as deficits in emotional expression recognition and in personal relationships, causing isolation and pauperization of individuals' quality of life ( [3]. The MDD symptomatology consists of core and secondary symptoms.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

et al. 2021

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“…Subsequent neuroimaging research revealed that depressed patients showed specific impairments the audiovisual integration of positive auditory stimuli (Doose-Grünefeld, Eickhoff, & Müller, 2015). Further behavioral evidence suggested that there is a general emotion processing impairment in decoding dynamic emotional audio/video stimuli (Scibelli, Troncone, Likforman-Sulem, Vinciarelli, & Esposito, 2016), and that MDD symptoms have negative effects on audiovisual integration capacity (Beatteay & Wilbiks, 2020). Recently, our group used machine learning classification models to show that MDD patients were engaged in complex functional network dysfunctions in the prefrontal cortex and limbic-subcortical areas, as well as in multisensory regions that are involved in processing audiovisual emotional stimuli (Rong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Negative bias effects during audiovisual emotional processing in major depression disorder

Shen

et al. 2021

Human Brain Mapping

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Aberrant affective neural processing and negative emotional bias are trait-marks of major depression disorders (MDDs). However, most research on biased emotional perception in depression has only focused on unimodal experimental stimuli, the neural basis of potentially biased emotional processing of multimodal inputs remains unclear. Here, we addressed this issue by implementing an audiovisual emotional task during functional MRI scanning sessions with 37 patients with MDD and 37 gender-, age-and education-matched healthy controls. Participants were asked to distinguish laughing and crying sounds while being exposed to faces with different emotional valences as background. We combined general linear model and psychophysiological interaction analyses to identify abnormal local functional activity and integrative processes during audiovisual emotional processing in MDD patients. At the local neural level, MDD patients showed increased bias activity in the ventromedial prefrontal cortex (vmPFC) while listening to negative auditory stimuli and concurrently processing visual facial expressions, along with decreased dorsolateral prefrontal cortex (dlPFC) activity in both the positive and negative visual facial conditions. At the network level, MDD exhibited significantly decreased connectivity in areas involved in automatic emotional processes and voluntary control systems during perception of negative stimuli, including the vmPFC, dlPFC, insula, as well as the subcortical regions of posterior cingulate cortex and striatum. These findings support a multimodal emotion dysregulation hypothesis for MDD by demonstrating that negative bias effects may be facilitated by the excessive ventral bottom-up negative emotional influences along with incapability in dorsal prefrontal top-down control system.

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“…The number of people living with depressive disorders and anxiety are estimated to be the 4.4% and 3,6% of the global population respectively [5]. The manifestation of such disorders is commonly accompanied, and occasionally preceded, by the deterioration of social behavior mainly exemplified by the inability to code and decode oneself and other emotions [6] and loss of motivations such apathy, reduced feelings, lack of self-esteem, reduced social contacts. There are no established cures for depression and anxiety and treatments can last for the entire life with a consistent impoverishment of patients' quality of life and substantial increase of public cares' costs.…”

Section: Introductionmentioning

confidence: 99%

Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

et al. 2021

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In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstraldomain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank. As next step, we select the best features using the Fast Correlation-Based Filtering method. Since our dataset has 129 users, then as next step, we augmented the training data by randomly selecting a percentage of the training data and adding a small random Gaussian noise to the extracted features. We then train a radial basis SVM model using the Leave-One-Out (LOO) methodology. The experimental results show an average accuracy classification improvement ranging of 15%, and an accuracy classification improvement ranging from 4% to 34% compared with baseline (state of the art) for specific emotions such as depression, anxiety, stress, and typical emotional states.

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How Major Depressive Disorder Affects the Ability to Decode Multimodal Dynamic Emotional Stimuli

Cited by 17 publications

References 69 publications

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review

Negative bias effects during audiovisual emotional processing in major depression disorder

Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

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