Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study

IUPHAR review: Computational Psychiatry 2.0. A new tool for supporting combination therapy of psychopharmacology with neuromodulation in schizophrenia

Recent clinical trial successes in schizophrenia with non-dopaminergic agents have rejuvenated the field after a long period of unsuccesfull attempts. At the same time, non-invasive neurostimulation has been increasingly applied in other mental health disorders while a few studies have been performed in schizophrenia. The time has arrived to consider combining psychotherapy with neuromodulation. However, a systematic approach to optimize trial designs is needed. "Computational Psychiatry" has been defined as computational neuroscience modeling using biophysically and anatomically realistic representations of key brain areas based on neuroimaging data and biological knowledge. In this position paper, we will expand this concept to include modeling drug exposure and pharmacology in combination with non-invasive neuromodulation. This computational approach can be used to optimize the impact of psychotherapy and active neuromodulation. This computational platform generates a new in silico biomarker, the "information bandwidth", that might be related to clinical outcomes in schizophrenia. This is based on the assumption that the information processing capacity of the human brain can be represented by a measure of the entropy that quantifies the level of uncertainty associated with the brain processes. Previously we have shown that this readout in a computational neuroscience model of the closed cortical-striatal-thalamocortical loop is highly correlated with clinical changes in positive symptoms after antipsychotic treatment. In this paper we will present a strategy on how this expanded Computational Psychiatry approach can support optimization of clinical trial design combining neuromodulation with psychopharmacology, as well as the understanding and mitigating of the placebo response.

Resting-state EEG and MEG gamma frequencies in schizophrenia: a systematic review and exploratory power-spectrum metanalysis

The hypoactivity of parvalbumin-containing interneurons (PV-interneurons) is a pathogenetic mechanism of schizophrenia according to the glutamatergic theory, and PV-interneurons are necessary for the generation of EEG/MEG gamma-frequencies (30-100 Hz). The present study aims to a literature synthesis on resting-state gamma-frequency changes in patients with schizophrenia vs healthy controls, and to examine the relationship between these changes and severity of symptoms. A protocol was enregistered in PROSPERO and a systematic search was conducted in PubMed, PsycINFO and Cochrane Database of Systematic Reviews, following PRISMA guidelines. An exploratory metanalysis was realized. Out of 1391 records, 43 were included for a qualitative synthesis (N = 2133 [11-185], females 37.4%, age 33.9 ± 9.2). Results on power spectra were heterogeneous: in 12 studies gamma power was increased, involving the whole brain (N = 3), multiple regions (N = 6) or only frontal (N = 1), central (n = 1) and temporal (N = 1) areas; in 3 studies gamma power was reduced, involving multiple areas (N = 2) or the right temporal region (N = 1); one study revealed mixed results and 13 studies showed no differences. The meta-analysis on 4 studies (N = 211) showed non-significant differences between patients and controls and a large heterogeneity. The functional connectivity picture consists of sparse patterns of decreases and/or increases, widespread to multiple regions. Relationships emerged between gamma power and connectivity and severity of psychotic and cognitive symptoms. Theta-gamma coupling was increased in patients, with limited evidence for other changes in phase-amplitude coupling. Resting-state gamma-frequencies alterations in schizophrenia were inconsistent across studies; the heterogeneity of patients and methods could partially explain this outcome.

Functional Connectivity in Chronic Schizophrenia: An EEG Resting-State Study with Corrected Imaginary Phase-Locking

Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations.

AIM

In the present study, we explored the network dynamics of schizophrenia using a novel functional connectivity metric-corrected imaginary phase locking value (ciPLV)-which is insensitive to changes in amplitude as well as interactions at zero-phase lag. This method, which is less prone to volume conduction effects, provides a more reliable estimate of sensor-space functional network connectivity in schizophrenia.

METHODS

We employed a cross-sectional design, utilizing resting state EEG recordings from two adult groups: individuals diagnosed with chronic schizophrenia (n = 30) and a control group of healthy participants (n = 30), all aged between 18 and 55 years old.

RESULTS

Our observations revealed that schizophrenia is characterized by a prevalence of excess theta (4-8 Hz) power localized to centroparietal electrodes. This was accompanied by significant alterations in inter- and intra-hemispheric functional network connectivity patterns, mainly between frontotemporal regions within the theta band and frontoparietal regions within beta/gamma bands.

CONCLUSIONS

Our findings suggest that patients with schizophrenia demonstrate long-range electrophysiological connectivity abnormalities that are independent of spectral power (i.e., volume conduction). Overall, distinct hemispheric differences were present in frontotemporo-parietal networks in theta and beta/gamma bands. While preliminary, these alterations could be promising new candidate biomarkers of chronic schizophrenia.

Resting-State Electroencephalographic Signatures Predict Treatment Efficacy of tACS for Refractory Auditory Hallucinations in Schizophrenic Patients

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage. We divided 17 schizophrenic patients with refractory auditory hallucinations into responsive(RE) and non-responsive(NR) groups based on their auditory hallucination symptom reduction rates after one month of tACS treatment. The pre-treatment resting-state electroencephalogram(rsEEG) was recorded and then computed absolute power spectral density (PSD), Hjorth parameters (HPs, Hjorth activity (HA), Hjorth mobility (HM), and Hjorth complexity (HC) included) from different frequency bands to portray the brain oscillations. The results demonstrated that statistically significant differences localized within the high gamma frequency bands of the right brain hemisphere. Immediately, we input the significant dissociable features into popular machine learning algorithms, the Cascade Forward Neural Network achieved the best recognition accuracy of 93.87%. These findings preliminarily imply that high gamma oscillations in the right brain hemisphere may be the main influencing factor leading to different responses to tACS treatment, and incorporating rsEEG signatures could improve personalized decisions for integrating tACS in clinical treatment.

Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral-Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals

BACKGROUND/OBJECTIVES

The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five distinct states, including one control group and four categories of psychological disorders.

METHODS

Our investigation will utilize algorithms based on Granger causality and local graph structures to improve classification accuracy. Feature extraction from connectivity matrices was performed using local structure graphs. The extracted features were subsequently classified employing K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes classifiers.

RESULTS

The KNN classifier demonstrated the highest accuracy in the gamma band for the depression category, achieving an accuracy of 89.36%, a sensitivity of 89.57%, an F1 score of 94.30%, and a precision of 99.90%. Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band.

CONCLUSIONS

The proposed methodology provides a novel approach for analyzing EEG signals and holds potential applications in the classification of psychological disorders.

Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current diagnostic approaches, including genetic screening, face challenges in identifying secondary cases due to symptom overlap with other disorders. This study introduces a novel PKD recognition method utilizing a resting-state electroencephalogram (EEG) functional connectivity matrix and a deep learning architecture (AT-1CBL). Resting-state EEG data from 44 PKD patients and 44 healthy controls (HCs) were collected using a 128-channel EEG system. Functional connectivity matrices were computed and transformed into graph data to examine brain network property differences between PKD patients and controls through graph theory. Source localization was conducted to explore neural circuit differences in patients. The AT-1CBL model, integrating 1D-CNN and Bi-LSTM with attentional mechanisms, achieved a classification accuracy of 93.77% on phase lag index (PLI) features in the Theta band. Graph theoretic analysis revealed significant phase synchronization impairments in the Theta band of the functional brain network in PKD patients, particularly in the distribution of weak connections compared to HCs. Source localization analyses indicated greater differences in functional connectivity in sensorimotor regions and the frontal-limbic system in PKD patients, suggesting abnormalities in motor integration related to clinical symptoms. This study highlights the potential of deep learning models based on EEG functional connectivity for accurate and cost-effective PKD diagnosis, supporting the development of portable EEG devices for clinical monitoring and diagnosis. However, the limited dataset size may affect generalizability, and further exploration of multimodal data integration and advanced deep learning architectures is necessary to enhance the robustness of PKD diagnostic models.

Resting State EEG Analysis for Schizophrenia: from Alpha-Rhythm Reduction to Microstates Assessment

Background: due to the emergence of new technologies for analyzing of EEG signal, many new researches in this field have appeared in recent years, including those investigating EEG parameters of schizophrenia. The aim: this publication provides an overview of actual studies on the possibilities of using the assessment of resting state EEG recordings in the diagnostics and prognosis of schizophrenia course. Material and methods: publications were selected in eLibrary, PubMed, Google Scholar and CNKI databases using the keywords: “psychosis”, “schizophrenia”, “EEG”, “resting state”. Methodologically, the atricle is a narrative literature review. Thirty-three sources were selected for analysis. Discussion and conclusion: according to the data available to present date qualitive and quantitative assessment of resting EEGs cannot be used for the instrumental diagnosis of schizophrenia because the most commonly detected increase in the proportion of slow-wave activity is seen in a several disorders. However, some quantitative spectral estimates of resting state EEG could be used to identify poor prognosis response to antipsychotic therapy, as well as for objective assessment of the dynamics of the mental state. Estimation of the power of slow resting EEG rhythms and other methods of assessing the connectivity of different neural networks could be considered as potential markers of the presence of a specific endophenotype. Modern digital technologies, including machine learning and artificial intelligence algorithms, make it possible to identify resting EEG of the schizophrenic patients from healthy controls with accuracy, sensitivity and specificity more than 95%. EEG microstates assessment, which can be used to assess the functioning of large neuronal ensembles, are one of the methods for detecting the endophenotype of schizophrenia.