Frontal localisation of a theory-based anxiety disorder biomarker - Goal conflict specific rhythmicity.

Purpose: Anxiety disorders are a major global issue. Diagnosis via symptoms, not biological causes, delivers poor treatment outcomes. Our frontal EEG biomarker, Goal Conflict Specific Rhythmicity (GCSR; 4-12 Hz), developed from our long-standing detailed neuropsychological theory of anxiety processes, is reduced by all chemical types of selective anxiolytic and is high in cases across a range of currently diagnosed anxiety disorders.

Is lack of goal-conflict-specific rhythmicity a biomarker for treatment resistance in generalised anxiety but not social anxiety or major depression?

Background: Anxiety and depression cause major detriment to the patient, family, and society - particularly in treatment-resistant (TR) cases, which are highly prevalent. TR prevalence may be due to current diagnoses being based not on biological measures but on symptom lists that suffer from clinical subjectivity, variation in symptom presentation, and comorbidity.

Aims: Goal-conflict-specific rhythmicity (GCSR) measured using the Stop-Signal Task (SST) may provide the first neural biomarker for an anxiety process and disorder.

Midfrontal theta reactivity to conflict and error are linked to externalizing and internalizing respectively.

Dimensional psychopathology scores measure symptom severity; cutting across disorder categories. Their clinical utility is high given comorbidity, but their neural basis is unclear. We used scalp electroencephalography (EEG) to concurrently assess neural activity across internalizing and externalizing traits.

Alpha wave asymmetry is associated with only one component of melancholia, and in different directions across brain regions.

Alpha wave asymmetry inconsistently correlates with Major Depressive Disorder (MDD). One possible reason for this inconsistency is the heterogeneity of MDD, leading to study of depressive 'subtypes', one of which is Melancholia. To investigate the correlation between Melancholia and alpha-wave asymmetry, 100 community participants (44 males, 56 females; aged at least 18 yr) completed the Zung self-rated Depression Scale, and underwent 3 min of eyes closed EEG recording from 24 scalp sites.

Network analysis of frontal lobe alpha asymmetry confirms the neurophysiological basis of four subtypes of depressive behavior.

Introduction: Although depression is widespread carries a major disease burden, current treatments remain non-universally effective, arguably due to the heterogeneity of depression, and leading to the consideration of depressive "subtypes" or "depressive behavior subtypes." One such model of depressive behavior (DB) subtypes was investigated for its associations with frontal lobe asymmetry (FLA), using a different data analytic procedure than in previous research in this field.

Methods: 100 community volunteers (54 males, 46 females) aged between 18 yr.

EEG frontal lobe asymmetry as a function of sex, depression severity, and depression subtype.

To investigate possible contributors to the inconsistent association between frontal lobe asymmetry (FLA) and depression, EEG data were collected across five frontal sites, and examined for their associations with four subtypes of depression (Depressed mood, Anhedonia, Cognitive depression, Somatic depression). One hundred community volunteers (54 males, 46 females) aged at least 18 yr completed standardized scales for depression and anxiety, and gave EEG data under Eyes Open and Eyes Closed conditions. Results indicated that, although there was no significant correlation between the differences in EEG power across each of the five pairs of frontal sites and total depression scores, there were several meaningful correlations (accounting for at least 10% of the variance) between specific EEG site differences data and each of the four depression subtypes.

Right Frontal Theta: Is It a Response Biomarker for Ketamine's Therapeutic Action in Anxiety Disorders?

Anxiety disorders are the most prevalent mental disorders in the world, creating huge economic burdens on health systems and impairing the quality of life for those affected. Recently, ketamine has emerged as an effective anxiolytic even in cases resistant to conventional treatments (TR); but its therapeutic mechanism is unknown. Previous data suggest that ketamine anxiety therapy is mediated by reduced right frontal electroencephalogram (EEG) theta power measured during relaxation.

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.

Laterality of an EEG anxiety disorder biomarker largely follows handedness.

Anxiety disorders are the most common mental disorders impacting people worldwide. Using an auditory Stop Signal Task (SST), we have developed an anxiety disorder biomarker (goal-conflict specific rhythmicity/GCSR) that occurs at the right frontal site F8 in right-handed participants. Here, we compare its laterality in left-handers (n = 26) versus demographically-matched right-handers (n = 26) between the ages of 18-30.

Exploring approach motivation: Correlating self-report, frontal asymmetry, and performance in the Effort Expenditure for Rewards Task.

Frontal EEG asymmetry has been investigated as a physiological metric of approach motivation, with higher left frontal activity (LFA) suggested to reflect approach motivation. However, correlations between LFA and traditional metrics of approach motivation (e.g.

Higuchi's fractal dimension, but not frontal or posterior alpha asymmetry, predicts PID-5 anxiousness more than depressivity.

Depression is a major cause of health disability. EEG measures may provide one or more economical biomarkers for the diagnosis of depression. Here we compared frontal alpha asymmetry (FAA), posterior alpha asymmetry (PAA), and Higuchi's fractal dimension (HFD) for their capacity to predict PID-5 depressivity and for the specificity of these predictions relative to PID-5 anxiousness.

Human anxiety-specific "theta" occurs with selective stopping and localizes to right inferior frontal gyrus.

Anxiety disorders have high prevalence and generate major disability. But they have poor treatment targeting because psychiatry lacks diagnostic biomarkers. Right frontal goal-conflict-specific-rhythmicity (GCSR) in the simple stop signal task appears homologous to hippocampal "theta" as an anxiety-process biomarker but is weak and transient.

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 (5th ed.; ) manual uses symptom lists, not objective biomarkers.

Ketamine Effects on EEG during Therapy of Treatment-Resistant Generalized Anxiety and Social Anxiety.

Background: Ketamine is swiftly effective in a range of neurotic disorders that are resistant to conventional antidepressant and anxiolytic drugs. The neural basis for its therapeutic action is unknown. Here we report the effects of ketamine on the EEG of patients with treatment-resistant generalized anxiety and social anxiety disorders.

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.