Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

Faces are one of the key ways that we obtain social information about others. They allow people to identify individuals, understand conversational cues, and make judgements about others’ mental states. When the COVID-19 pandemic hit the United States, widespread mask-wearing practices were implemented, causing a shift in the way Americans typically interact. This introduction of masks into social exchanges posed a potential challenge—how would people make these important inferences about others when a large source of information was no longer available? We conducted two studies that investigated the impact of mask exposure on emotion perception. In particular, we measured how participants used facial landmarks (visual cues) and the expressed valence and arousal (affective cues), to make similarity judgements about pairs of emotion faces. Study 1 found that in August 2020, participants with higher levels of mask exposure used cues from the eyes to a greater extent when judging emotion similarity than participants with less mask exposure. Study 2 measured participants’ emotion perception in both April and September 2020 –before and after widespread mask adoption—in the same group of participants to examine changes in the use of facial cues over time. Results revealed an overall increase in the use of visual cues from April to September. Further, as mask exposure increased, people with the most social interaction showed the largest increase in the use of visual facial cues. These results provide evidence that a shift has occurred in how people process faces such that the more people are interacting with others that are wearing masks, the more they have learned to focus on visual cues from the eye area of the face.

Depression has been associated with poor performance following errors, but the clinical implications, response to treatment and neurobiological mechanisms of this post-error behavioral adjustment abnormality remain unclear. To fill this gap in knowledge, we tested depressed patients in a partial hospital setting before and after treatment (cognitive behavior therapy combined with medication) using a flanker task. To evaluate the translational relevance of this metric in rodents, we performed a secondary analysis on existing data from rats tested in the 5-choice serial reaction time task after treatment with corticotropin-releasing factor (CRF), a stress peptide that produces depressive-like signs in rodent models relevant to depression. In addition, to examine the effect of treatment on post-error behavior in rodents, we examined a second cohort of rodents treated with JDTic, a kappa-opioid receptor antagonist that produces antidepressant-like effects in laboratory animals. In depressed patients, baseline post-error accuracy was lower than post-correct accuracy, and, as expected, post-error accuracy improved with treatment. Moreover, baseline post-error accuracy predicted attentional control and rumination (but not depressive symptoms) after treatment. In rats, CRF significantly degraded post-error accuracy, but not post-correct accuracy, and this effect was attenuated by JDTic. Our findings demonstrate deficits in post-error accuracy in depressed patients, as well as a rodent model relevant to depression. These deficits respond to intervention in both species. Although post-error behavior predicted treatment-related changes in attentional control and rumination, a relationship to depressive symptoms remains to be demonstrated.

Faces are one of the key ways that we obtain social information about others. They allow people to identify individuals, understand conversational cues, and make judgements about other’s mental states. When the COVID-19 pandemic hit the United States, widespread mask-wearing practices were implemented, causing a shift in the way Americans typically interact. This introduction of masks into social exchanges posed a potential challenge – how would people make these important inferences about others when a large source of information was no longer available? We conducted two studies that investigated the impact of mask exposure on emotion perception. In particular, we measured how participants used facial landmarks (visual cues) and the expressed valence and arousal (affective cues), to make similarity judgements about pairs of emotion faces. Study 1 found that participants with higher levels of mask exposure used cues from the eyes to a greater extent when judging emotion similarity than participants with less mask exposure. Study 2 measured participants’ emotion perception in both April and September 2020 – before and after widespread mask adoption – in the same group of participants to examine changes in the use of facial cues over time. Results revealed an overall increase in the use of visual cues from April to September. Further, as mask exposure increased, people with the most social interaction showed the largest increase in the use of visual facial cues. These results provide evidence that a shift has occurred in how people process faces such that the more people are interacting with others that are wearing masks, the more they have learned to focus on visual cues from the eye area of the face.