Neural dynamics during emotional video engagement relate to anxiety

Resting state EEG source derived salience network theta connectivity mediates anxiety in community dwelling individuals reporting childhood trauma

Childhood trauma (CT) has been consistently linked with etiology of anxiety and depression. Finding biomarkers that mediate the relationship between CT and psychopathology is important and electroencephalography (EEG) can be a useful and cost-effective tool serving this purpose. Hence, in the current research we investigated resting state EEG biomarkers associated with CT and how these may link to psychopathology of anxiety and depression in adults. A total of 324 community recruited participants (age range 15-93 years) completed standard self-report scales of CT, anxiety and depression, and also underwent an eyes-closed resting state EEG recording session. Based on several functional neuroimaging studies, which have shown that connectivity in the salience network with major nodes in the anterior cingulate cortex (ACC) and insula is modulated by CT, we derived salience network connectivity measures from resting state EEG source imaging. Also given that theta band (3-7 Hz) neural oscillations have been shown to have an ACC source, we specifically focused on theta band salience network connectivity. The results showed significant positive correlation between CT and both anxiety and depression. We also found that theta band salience network connectivity, but not network activity, had a significant inverse relationship with CT and specifically mediated the relationship between CT and anxiety, but not depression. Interrogating the subcomponents of CT, theta connectivity in the salience network mediated the relationship between anxiety and both emotional abuse and physical neglect. These results showcase the utility of a resting state EEG source imaging-based biomarker in understanding the mechanistic associations between CT and psychopathology in community dwelling individuals.

Neural activity during inhibitory control predicts suicidal ideation with machine learning

Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.

Design and Implementation of a Brief Digital Mindfulness and Compassion Training App for Health Care Professionals: Cluster Randomized Controlled Trial

BACKGROUND

Several studies show that intense work schedules make health care professionals particularly vulnerable to emotional exhaustion and burnout.

OBJECTIVE

In this scenario, promoting self-compassion and mindfulness may be beneficial for well-being. Notably, scalable, digital app-based methods may have the potential to enhance self-compassion and mindfulness in health care professionals.

METHODS

In this study, we designed and implemented a scalable, digital app-based, brief mindfulness and compassion training program called "WellMind" for health care professionals. A total of 22 adult participants completed up to 60 sessions of WellMind training, 5-10 minutes in duration each, over 3 months. Participants completed behavioral assessments measuring self-compassion and mindfulness at baseline (preintervention), 3 months (postintervention), and 6 months (follow-up). In order to control for practice effects on the repeat assessments and calculate effect sizes, we also studied a no-contact control group of 21 health care professionals who only completed the repeated assessments but were not provided any training. Additionally, we evaluated pre- and postintervention neural activity in core brain networks using electroencephalography source imaging as an objective neurophysiological training outcome.

RESULTS

Findings showed a post- versus preintervention increase in self-compassion (Cohen d=0.57; P=.007) and state-mindfulness (d=0.52; P=.02) only in the WellMind training group, with improvements in self-compassion sustained at follow-up (d=0.8; P=.01). Additionally, WellMind training durations correlated with the magnitude of improvement in self-compassion across human participants (ρ=0.52; P=.01). Training-related neurophysiological results revealed plasticity specific to the default mode network (DMN) that is implicated in mind-wandering and rumination, with DMN network suppression selectively observed at the postintervention time point in the WellMind group (d=-0.87; P=.03). We also found that improvement in self-compassion was directly related to the extent of DMN suppression (ρ=-0.368; P=.04).

CONCLUSIONS

Overall, promising behavioral and neurophysiological findings from this first study demonstrate the benefits of brief digital mindfulness and compassion training for health care professionals and compel the scale-up of the digital intervention.

TRIAL REGISTRATION

Trial Registration: International Standard Randomized Controlled Trial Number Registry ISRCTN94766568, https://www.isrctn.com/ISRCTN94766568.