Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia.

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.

Dynamic cortical connectivity alterations associated with Major Depressive Disorder: an EEG study.

There are various depressive subtypes identified in patients with major depressive disorder (MDD). Depression with psychotic symptoms is usually known to be a severe type of depression that includes symptoms such as delusions and/or hallucinations, and remains a common condition that is often underrecognized and inadequately treated in clinical practice. Electroencephalography (EEG) biomarkers have been implicated to classify healthy and psychopathological neural signals using machine learning algorithms. In this study, we sought to identify cortical functional connectivity metrics that differentiate network manifestation of different depressive subtypes and healthy controls. We first performed replication analyses to obtain the principal functional connectivity microstates across each independent group (healthy controls, psychotic depressions and nonpsychotic depressions). Next, we examined temporal functional connectivity dynamics in each group. The results show that fundamental dynamic functional connectivity microstates are highly reproducible, both within and across participants. Based on the temporal and sequential parameters (mean duration, fractional windows and transition number) derived from dynamic functional connectivity analysis, we found inter-group differences across healthy and MDD subgroups statistically significant. These results show that the principal FC microstates dynamics are essential neural biomarkers distinctly associated with depression clinical phenotypes.Clinical relevance-Our findings suggest that a network-level feature, that may reflect the neurobiological difference between different depression subtypes, and healthy controls, and in turn may contribute towards a scalable EEG-based assisted diagnostic tool.

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology.

BACKGROUND: Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. METHODS: A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function. RESULTS: The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. CONCLUSIONS: Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

Assessing Risk for Relapse among Children with Infantile Spasms Using the Based Score after ACTH Treatment: A Retrospective Study.

INTRODUCTION: Even though adrenocorticotropic hormone (ACTH) demonstrated powerful efficacy in the initially successful treatment of infantile spasms (IS), nearly one-half of patients whose spasms were once suppressed experienced relapse. There is currently no validated method for the prediction of the risk of relapse. The Burden of Amplitudes and Epileptiform Discharges (BASED) score is an electroencephalogram (EEG) grading scale for children with infantile spasms. We sought to determine whether an association exists between the BASED score after ACTH treatment and relapse after initial response with ACTH. METHODS: Children with IS who achieved initial response after ACTH treatment were selected as the study subjects. Those who experienced relapse within 12 months after ACTH treatment were categorized as the relapse group, and those who did not were categorized as the non-relapse group. Their general clinical data and EEG data (using BASED scoring) after ACTH treatment were collected, and compared between groups. Cox proportional hazards models were fit to determine factors associated with relapse. RESULTS: A total of 64 children with IS were enrolled in the study, of which 37 (57.8%) experienced a relapse, and the median duration after ACTH treatment was 3 (1.5, 6) months. The BASED score was significantly higher in the relapse group than in the non-relapse group. Cox modeling demonstrated that BASED score was independently associated with relapse. The patients with a score greater than or equal to 3 showed a high rate (89.3%) of relapse. The relapse group had stronger, more stable EEG functional networks than the non-relapse group, and there were obvious correlations between BASED score and functional connectivity. CONCLUSION: This study suggests the BASED score after ACTH treatment has potential value as a predictor for relapse after initial response. Children with IS who have a BASED score greater than or equal to 3 after the initial response of ACTH carry a high risk of relapse within 1 year.