Right frontal anxiolytic-sensitive EEG 'theta' rhythm in the stop-signal task is a theory-based anxiety disorder biomarker.

Psychiatric diagnoses currently rely on a patient's presenting symptoms or signs, lacking much-needed theory-based biomarkers. Our neuropsychological theory of anxiety, recently supported by human imaging, is founded on a longstanding, reliable, rodent 'theta' brain rhythm model of human clinical anxiolytic drug action. We have now developed a human scalp EEG homolog-goal-conflict-specific rhythmicity (GCSR), i.e., EEG rhythmicity specific to a balanced conflict between goals (e.g., approach-avoidance). Critically, GCSR is consistently reduced by different classes of anxiolytic drug and correlates with clinically-relevant trait anxiety scores (STAI-T). Here we show elevated GCSR in student volunteers divided, after testing, on their STAI-T scores into low, medium, and high (typical of clinical anxiety) groups. We then tested anxiety disorder patients (meeting diagnostic criteria) and similar controls recruited separately from the community. The patient group had higher average GCSR than their controls-with a mixture of high and low GCSR that varied with, but cut across, conventional disorder diagnosis. Consequently, GCSR scores should provide the first theoretically-based biomarker that could help diagnose, and so redefine, a psychiatric disorder.

Anxiety process "theta" biomarker in the stop signal task eliminated by a preceding relaxation test.

Anxiety disorders are currently the most prevalent psychiatric diseases in Europe and the United States, the 6th highest cause of years of life lived with disability, and so a grave and ever-increasing burden on health care resources. Categorization of specific anxiety disorders is constantly evolving, but even the new Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) manual uses symptom lists, not objective biomarkers. The DSM-5 and International Classification of Diseases (10th ed.) also aim for single diagnoses, but patients present with mixed symptoms that fit multiple diagnoses. In 1 step toward a solution to this problem, we previously reported on a human electroencephalogram anxiety process biomarker, goal-conflict-specific rhythmicity (GCSR) in a stop signal task (SST). GCSR appears homologous with rodent rhythmical slow activity, 4-12 Hz "theta" rhythmicity that, in the rat hippocampus, predicts human clinical anxiolytic action with, so far, no false positives (even with sedatives) or negatives (even with drugs ineffective in panic or depression). However, within-task stability of GCSR is too variable for test-retest. Here we tested the stability of GCSR when a simple relaxation task preceded the SST. We found that prior exposure of participants (56 female, 39 male; mean age = 21.87 years; reporting no medical or psychological treatment or any type of emotional disorder in the last 12 months) to the relaxation task appeared to almost completely eliminate GCSR. We therefore conclude that, when elicited in the stop signal task, GCSR represents a labile emotional state and should be assessed alone or as the 1st test of a series. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Removing eye blink artefacts from EEG-A single-channel physiology-based method.

BACKGROUND: EEG signals are often contaminated with artefacts, particularly with large signals generated by eye blinks. Deletion of artefact can lose valuable data. Current methods of removing the eye blink component to leave residual EEG, such as blind source component removal, require multichannel recording, are computationally intensive, and can alter the original EEG signal. NEW METHOD: Here we describe a novel single-channel method using a model based on the ballistic physiological components of the eye blink. This removes the blink component, leaving uncontaminated EEG largely unchanged. Processing time allows its use in real-time applications such as neurofeedback training. RESULTS: Blink removal had a success rate of over 90% recovered variance of original EEG when removing synthesised eye blink components. Fronto-lateral sites were poorer (∼80%) than most other sites (92-96%), with poor fronto-polar results (67%). COMPARISONS WITH EXISTING METHODS: When compared with three popular independent component analysis (ICA) methods, our method was only slightly (1%) better at frontal midline sites but significantly (>20%) better at lateral sites with an overall advantage of ∼10%. CONCLUSIONS: With few recording channels and real-time processing, our method shows clear advantages over ICA for removing eye blinks. It should be particularly suited for use in portable brain-computer-interfaces and in neurofeedback training.