CFC delta-beta is related with mixed features and response to treatment in bipolar II depression

Age-related differences in delta-beta phase-amplitude coupling in autistic individuals

OBJECTIVE

We aim to investigate the relationship between the core symptoms of autism, anxiety levels, and attention deficit hyperactivity disorder (ADHD) traits, and a non-autism-specific, neurophysiological metric, the Delta-Beta phase-amplitude coupling (PAC), extracted from the resting-state EEG for autistic and non-autistic populations across three different age groups (children, adolescents, and adults).

METHODS

We analyze the eyes-open resting-state EEG of 371 individuals. We applied a phase de-biasing PAC algorithm expected to result in a more accurate PAC estimate than other PAC methodologies available in the literature.

RESULTS

In the adult group, we found a significant increase of the delta-beta PAC in the autistic subgroup who met the Autism Diagnostic Observation Schedule-2 (ADOS-2) Autism Diagnostic Interview-Revised (ADR-R) ADOS-2/ADI-R threshold compared to non-autistic individuals. The differences seem age-specific since we found no statistically significant differences in the children and adolescent populations. Moreover, we found a significant positive correlation with the restricted and repetitive behaviours score of the ADOS-2 diagnostic instrument and with ADHD hyperactivity/impulsivity in the entire autistic cohort.

CONCLUSIONS

The neurophysiological differences we found only in the autistic individuals that meet the thresholds also point out the need for future studies that look for autistic neurodiverse subgroups beyond age.

SIGNIFICANCE

The delta-beta debiasing PAC (dPAC) may potentially serve as a severity biomarker in the autistic population.

Application of graph frequency attention convolutional neural networks in depression treatment response

Depression, a prevalent global mental health disorder, necessitates precise treatment response prediction for the improvement of personalized care and patient prognosis. The Graph Convolutional Neural Networks (GCNs) have emerged as a promising technique for handling intricate signals and classification tasks owing to their end-to-end neural architecture and nonlinear processing capabilities. In this context, this article proposes a model named the Graph Frequency Attention Convolutional Neural Network (GFACNN). Primarily, the model transforms the EEG signals into graphs to depict the connections between electrodes and brain regions, while integrating a frequency attention module to accentuate brain rhythm information. The proposed approach delves into the application of graph neural networks in the classification of EEG data, aiming to evaluate the response to antidepressant treatment and discern between treatment-resistant and treatment-responsive cases. Experimental results obtained from an EEG dataset at Peking University People's Hospital demonstrate the notable performance of GFACNN in distinguishing treatment responses among depression patients, surpassing deep learning methodologies including CapsuleNet and GoogLeNet. This highlights the efficacy of graph neural networks in leveraging the connections within EEG signal data. Overall, GFACNN exhibits potential for the classification of depression EEG signals, thereby potentially aiding clinical diagnosis and treatment.

A dimensional approach to affective disorder: The relations between Scl-90 subdimensions and QEEG parameters

OBJECTIVES

QEEG reflects neuronal activity directly rather than using indirect parameters, such as blood deoxygenation and glucose utilization, as in fMRI and PET. The correlation between QEEG spectral power density and Symptom Check List-90-R may help identify biomarkers pertaining to brain function, associated with affective disorder symptoms. This study aims at determining whether there is a relation between QEEG spectral power density and Symptom Check List-90-R symptom scores in affective disorders.

METHODS

This study evaluates 363 patients who were referred for the initial application and diagnosed with affective disorders according to DSM-V, with QEEG and Scl-90-R. Spectral power density was calculated for the 18 electrodes representing brain regions.

RESULTS

Somatization scores were found to be correlated with Pz and O1 theta, O1 and O2 high beta. Whereas FP1 delta activities were correlated with anxiety, F3, F4, and Pz theta were correlated with obsession scores. Interpersonal sensitivity scores were found to be correlated with F4 delta, P3, T5, P4, T6 alpha and T5, and T6 theta activities. While depression scores were correlated with P3 and T4 delta, as well as T4 theta, there was a correlation between anger and F4, as well as T4 alpha and F8 high beta activities. Paranoia scores are correlated with FP1, F7, T6 and F8 theta, T5 and F8 delta, and O2 high beta activities.

CONCLUSIONS

According to our results, anxiety, obsession, interpersonal sensitivity, depression, anger, and paranoia are related to some spectral powers of QEEG. Delta-beta coupling seems to be a neural biomarker for affective dysregulation.

The entropy of chaotic transitions of EEG phase growth in bipolar disorder with lithium carbonate

The application of chaos measures the association of EEG signals which allows for differentiating pre and post-medicated epochs for bipolar patients. We propose a new approach on chaos necessary for proof of EEG metastability. Shannon entropies of concealed patterns of Schwarzian derivatives from absolute instantaneous frequency transformations of EEG signals after Hilbert transform are compared and found significantly statistically different between pre and post-medication periods when fitted to von Bertalanffy's functions. Schwarzian dynamics measures was compared at first baseline and then at the end of the first hour of one dose 300 mg lithium carbonate intake for the same subject in depressive patients. With an application of Schwarzian derivative on the prediction of von Bertalanffy's models, integration and segregation of phase growth orbits of neural oscillations can be understood as an influence of chaos on the mixing of frequencies. A phase growth constant parameter was performed to determine the bifurcation parameter of von Bertalanffy's model at each given non-overlapped EEG segment. Schwarzian derivative was sometimes very close positive near the origin but stayed negative for most of the number of segments. Lithium carbonate changed the chaotic invariants of the EEG Schwarzian dynamics and removed sharp boundaries in the bipolar spectrum.