Stressed, depressed, and rank obsessed: Individual differences in compassion and neuroticism predispose towards rank-based depressive symptomatology

Emotional stimulated speech-based assisted early diagnosis of depressive disorders using personality-enhanced deep learning

BACKGROUND

Early diagnosis of depression is crucial, and speech-based early diagnosis of depression is promising, but insufficient data and lack of theoretical support make it difficult to be applied. Therefore, it is valuable to combine psychiatric theories, collect speech recognition data for depression, and develop a practicable recognition method for depression.

METHODS

In this study, 24 patients with major depressive disorders (MDDs) and 36 healthy controls (HCs) were recruited to participate in a multi-task speech experiment. Descriptive statistics and tests of variance were used to analyze subjects' personality and speech changes. Subsequently, the speech task with the most depressive cues was explored using the Bidirectional Long - Short Term Memory (Bi-LSTM) algorithm, on which a personality-assisted multitasking deep model, i.e., multi-task attentional temporal convolutional network model (TCN-MTA).

RESULTS

Statistical analyses of speech duration showed that the fable reading, neutral stimulus, and negative stimulus tasks had significant differences on subjects' speech duration, and the negative stimulus task had significant differences between the depressed and control groups (p < 0.001, 0.03, 0.04). Notably, the Big Five personality emotional stability scores were significantly different between the depressed and control groups (0.03). Depression was best identified using Bi-LSTM in negative (Youden index = 0.44) and positive stimulus speech (Youden index = 0.42). Further, the specificity of 0.72 and sensitivity of 0.87 for recognizing depression in negative stimulus speech using our proposed TCN-MTA outperforms existing methods.

LIMITATIONS

The sample size enrolled in this study is higher than the minimum sample size calculated through G-Power 3.1, but the sample size in this study is still small.

CONCLUSION

The proposed deep learning-based personality-assisted multitasking method could accurately recognize major depression, which demonstrated the potential of the method based on the fusion of specialized theories and artificial intelligence.

Dificuldades no engajamento às medidas contra a COVID-19

Como motivação pró-social, a compaixão tende a promover maior envolvimento em mentalidades de cuidado e ações de senso coletivo, capacidades prejudicadas pelos medos da compaixão. O estudo objetivou investigar o papel dos medos da compaixão em fatores envolvidos no engajamento às medidas de distanciamento social. 284 adultos (idade média = 36,47) responderam as Escalas de Medos da Compaixão, Escala de Dificuldades de Regulação Emocional e um questionário sobre engajamento aos protocolos contra a COVID-19. Um modelo de path analysis explicou 32% da variância na adesão aos protocolos por duas vias: o medo de ser compassivo por outros diminui o engajamento por motivações pró-sociais; enquanto os medos de expressar compaixão por si e outros predizem maiores dificuldades de regulação emocional, diminuindo a aderência por exaustão emocional. Oportunizar o desenvolvimento da compaixão pode contribuir para um enfrentamento mais saudável às dificuldades emocionais suscitadas pela pandemia e no envolvimento em ações coletivas.

Compassion protects against vital exhaustion and negative emotionality

We investigated (i) the predictive relationships of compassion with negative emotionality (a marker of susceptibility to stress) and vital exhaustion (a marker of chronic stress response) and (ii) the effect of compassion on the developmental courses of negative emotionality and vital exhaustion over a follow-up from early adulthood to middle age. We used the prospective Young Finns data (n = 1031-1495, aged 20-50). Compassion was evaluated in 1997, 2001, and 2012; and vital exhaustion and negative emotionality in 2001, 2007, and 2012. The predictive paths from compassion to vital exhaustion and negative emotionality were stronger than vice versa: high compassion predicted lower vital exhaustion and lower negative emotionality. The effect of high compassion on lower vital exhaustion and lower negative emotionality was evident from early adulthood to middle age. Overall, high compassion appears to protect against dimensions of stress from early adulthood to middle age, whereas this study found no evidence that dimensions of stress could reduce disposition to feel compassion for others' distress over a long-term follow-up.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11031-021-09878-2.

COVID-19 Lockdown and Mental Health in a Sample Population in Spain: The Role of Self-Compassion

Previous data support that mental health is affected during pandemic and lockdown situations. Yet, little is known about the positive factors that protect mental health during a lockdown. This study analyzed mental health status-particularly emotional problems-and the role of several sociodemographic and clinical variables; it also explored whether there is a positive relationship between self-compassion and better mental health status. A cross-sectional study was carried out in Spain with the participation of 917 fluent Spanish-speaking residents in a survey conducted approximately midway through the COVID-19 lockdown. The survey tested for anxiety, depression, and stress using the Depression Anxiety Stress Scale-21 (DASS-21), the Self-Compassion Scale (SCS) to measure self-compassion values, and the Perceived Vulnerability to Disease Questionnaire (PVDQ) to assess the degree of risk perceived by participants. Around 30% of the individuals surveyed (recruited by snowball sampling) showed clinically significant levels of anxiety, depression, and stress. The variables most frequently associated with anxiety, depression, and stress were low levels of self-compassion, age, gender, previous physical symptoms, a previous mental disorder, being a student, and perceived vulnerability to disease. We discuss the hypothetical protective role against anxiety, depression, and stress of certain skills such as self-compassion and the possibility that increasing self-compassion may be used to promote better mental health in similar situations.

Attachment styles modulate neural markers of threat and imagery when engaging in self-criticism

Attachment styles hold important downstream consequences for mental health through their contribution to the emergence of self-criticism. To date, no work has extended our understanding of the influence of attachment styles on self-criticism at a neurobiological level. Herein we investigate the relationship between self-reported attachment styles and neural markers of self-criticism using fMRI. A correlation network analysis revealed lingual gyrus activation during self-criticism, a marker of visual mental imagery, correlated with amygdala activity (threat response). It also identified that secure attachment positively correlated with lingual gyrus activation, whilst avoidant attachment was negatively correlated with lingual gyrus activation. Further, at greater levels of amygdala response, more securely attached individuals showed greater lingual gyrus activation, and more avoidantly attached individuals showed less lingual gyrus activation. Our data provide the first evidence that attachment mechanisms may modulate threat responses and mental imagery when engaging in self-criticism, which have important clinical and broader social implications.

Neural and Self-Report Markers of Reassurance: A Generalized Additive Modelling Approach

Research has shown that engaging in self-reassurance, a compassionately motivated cognitive relating style, can down-regulate neural markers of threat and pain. Whilst important, the relationship between neural and self-report markers of reassurance are largely unknown. Here we analyzed previously published fMRI data which measured neural responses when participants engaged in self-reassurance toward a mistake, setback, or failure. Within the present paper, we identified correlations between regions of interest extracted during self-reassurance with fMRI and self-report data. Using generalized additive modelling, we show that participants with greater inadequate forms of self-criticism exhibited greater neural activation within the medial prefrontal cortex (MPFC) and anterior insula (AI). Furthermore, a relationship between greater fears of expressing compassion to the self and neural activation within the MPFC returned non-significant after correction for multiple comparisons. No significant relationships were observed between brain activation and hated and reassuring forms of self-criticism. Our results identify preliminary evidence for neural activity during self-reassurance as correlated with self-report markers, and we outline a method for modelling neural and self-report data which can be applied to future studies in compassion science, particularly with a clinical sample.