Fear-induced brain activations distinguish anxious and trauma-exposed brains

Brain activation during fear extinction recall in unmedicated patients with obsessive-compulsive disorder

Specific brain activation patterns during fear conditioning and the recall of previously extinguished fear responses have been associated with obsessive-compulsive disorder (OCD). However, further replication studies are necessary. We measured skin-conductance response and blood oxygenation level-dependent responses in unmedicated adult patients with OCD (n = 27) and healthy participants (n = 22) submitted to a two-day fear-conditioning experiment comprising fear conditioning, extinction (day 1) and extinction recall (day 2). During conditioning, groups differed regarding the skin conductance reactivity to the aversive stimulus (shock) and regarding the activation of the right opercular cortex, insular cortex, putamen, and lingual gyrus in response to conditioned stimuli. During extinction recall, patients with OCD had higher responses to stimuli and smaller differences between responses to conditioned and neutral stimuli. For the entire sample, the higher the response delta between conditioned and neutral stimuli, the greater the dACC activation for the same contrast during early extinction recall. While activation of the dACC predicted the average difference between responses to stimuli for the entire sample, groups did not differ regarding the activation of the dACC during extinction recall. Larger unmedicated samples might be necessary to replicate the previous findings reported in patients with OCD.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Objective

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls.

Methods

This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve.

Results

The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction.

Conclusions

Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

A Multivoxel Pattern Analysis of Anhedonia During Fear Extinction: Implications for Safety Learning

BACKGROUND

Pavlovian learning processes are central to the etiology and treatment of anxiety disorders. Anhedonia and related perturbations in reward processes have been implicated in Pavlovian learning. Associations between anhedonia symptoms and neural indices of Pavlovian learning can inform transdiagnostic associations among depressive and anxiety disorders.

METHODS

Participants ages 18 to 19 years (67% female) completed a fear extinction (n = 254) and recall (n = 249) paradigm during functional magnetic resonance imaging. Symptom dimensions of general distress (common to anxiety and depression), fears (more specific to anxiety), and anhedonia-apprehension (more specific to depression) were evaluated. We trained whole-brain multivoxel pattern decoders for anhedonia-apprehension during extinction and extinction recall and tested the decoders' ability to predict anhedonia-apprehension in an external validation sample. Specificity analyses examined effects covarying for general distress and fears. Decoding was repeated within canonical brain networks to highlight candidate neurocircuitry underlying whole-brain effects.

RESULTS

Whole-brain decoder training succeeded during both tasks. Prediction of anhedonia-apprehension in the external validation sample was successful for extinction (R² = 0.047; r = 0.276, p = .002) but not extinction recall (R² < 0.001, r = -0.063, p = .492). The extinction decoder remained significantly associated with anhedonia-apprehension covarying for fears and general distress (t₁₂₁ = 3.209, p = .002). Exploratory results highlighted activity in the cognitive control, default mode, limbic, salience, and visual networks related to these effects.

CONCLUSIONS

Results suggest that patterns of brain activity during extinction, particularly in the cognitive control, default mode, limbic, salience, and visual networks, can be predictive of anhedonia symptoms. Future research should examine associations between anhedonia and extinction, including studies of exposure therapy or positive affect treatments among anhedonic individuals.