Error-related brain activity in pediatric major depressive disorder: An ERP and time-frequency investigation.

BACKGROUND: The error-related negativity (ERN) reflects individual differences in error monitoring. However, findings on the ERN in adult and adolescent depression have been inconsistent. Analyzing electroencephalographic (EEG) data in both the time- and time-frequency domain can be useful to better quantify neural response to errors. The present study aimed at examining electrocortical measures of error monitoring in early adolescents with and without depression. METHOD: EEG activity was collected during an arrowhead version of the flanker task in 29 (25 females) early adolescents with depression and 34 without MDD (29 females). RESULTS: The depression group showed reduced ERN amplitude, reduced error-related theta power and increased error-related beta power compared to the control group. When all variables that related to MDD diagnosis were considered simultaneously, both theta and beta power, but not the ERN, were independently related to an increased likelihood of being diagnosed with depression. CONCLUSIONS: By examining both time-domain and separate time-frequency measures, the present study provided novel evidence on error monitoring alterations in youth depression, suggesting that depression during adolescence may be characterized by reduced error monitoring (i.e., reduced ERN and error-related theta) and post-error inhibition (i.e., greater error-related beta power). These results support that time-frequency measures might be better suited for examining error-related neural activity in MDD relative to time-domain measures.

Classifying subclinical depression using EEG spectral and connectivity measures.

Detecting depression on its early stages helps preventing the onset of severe depressive episodes. In this study, we propose an automatic classification pipeline to detect subclinical depression (i.e., dysphoria) through the electroencephalography (EEG) signal. To this aim, we recorded the EEG signals in resting condition from 26 female participants with dysphoria and 38 female controls. The EEG signals were processed to extract several spectral and functional connectivity features to feed a nonlinear Support Vector Machine (SVM) classifier embedded with a Recursive Feature Elimination (RFE) algorithm. Our recognition pipeline obtained a maximum classification accuracy of 83.91% in recognizing dysphoria patients with a combination of connectivity and spectral measures. Moreover, an accuracy of 76.11% was achieved with only the 4 most informative functional connections, suggesting a central role of cortical connectivity in the theta band for early depression recognition. The present study can facilitate the diagnosis of subclinical conditions of depression and may provide reliable indicators of depression for the clinical community.

Does inappropriate behavior hurt or stink? The interplay between neural representations of somatic experiences and moral decisions.

Embodied models suggest that moral judgments are strongly intertwined with first-hand somatic experiences, with some pointing to disgust, and others arguing for a role of pain/harm. Both disgust and pain are unpleasant, arousing experiences, with strong relevance for survival, but with distinctive sensory qualities and neural channels. Hence, it is unclear whether moral cognition interacts with sensory-specific properties of one somatic experience or with supramodal dimensions common to both. Across two experiments, participants evaluated ethical dilemmas and subsequently were exposed to disgusting (olfactory) or painful (thermal) stimulations of matched unpleasantness. We found that moral scenarios enhanced physiological and neural activity to subsequent disgust (but not pain), as further supported by an independently validated whole-brain signature of olfaction. This effect was mediated by activity in the posterior cingulate cortex triggered by dilemma judgments. Our results thus speak in favor of an association between moral cognition and sensory-specific properties of disgust.

Pain management decisions in emergency hospitals are predicted by brain activity during empathy and error monitoring.

OBJECTIVE: Pain undertreatment, or oligoanalgesia, is frequent in the emergency department (ED), with major medical, ethical, and financial implications. Across different hospitals, healthcare providers have been reported to differ considerably in the ways in which they recognise and manage pain, with some prescribing analgesics far less frequently than others. However, factors that could explain this variability remain poorly understood. Here, we used neuroscience approaches for neural signal modelling to investigate whether individual decisions in the ED could be explained in terms of brain patterns related to empathy, risk-taking, and error monitoring. METHODS: For 15 months, we monitored the pain management behaviour of 70 ED nurses at triage, and subsequently invited 33 to a neuroimaging study involving three well-established tasks probing relevant cognitive and affective dimensions. Univariate and multivariate regressions were used to predict pain management decisions from neural activity during these tasks. RESULTS: We found that the brain signal recorded when empathising with others predicted the frequency with which nurses documented pain in their patients. In addition, neural activity sensitive to errors and negative outcomes predicted the frequency with which nurses denied analgesia by registering potential side-effects. CONCLUSIONS: These results highlight the multiple processes underlying pain management, and suggest that the neural representations of others' states and one's errors play a key role in individual treatment decisions. Neuroscience models of social cognition and decision-making are a powerful tool to explain clinical behaviour and might be used to guide future educational programs to improve pain management in ED.