Author Correction: Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG

Choi, Kang-Min; Jeongyoun, Kim; Kim, Yong-Wook; Han, Jung Won; Im, Chang‐Hwan; Lee, Seung Hwan

doi:10.1038/s41598-021-03736-4

Cited by 9 publications

(2 citation statements)

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“…In this study, we used EEG data measured in resting state during which default mode network (DMN) or salience network is more active than the task period ( Choi et al, 2021 ), and thus DMN might be usefully used for PTSD diagnosis. We investigated the ROIs selected when the maximum classification accuracy was obtained using delta PSD features, and found that 16 of 18 DMN ROIs were selected together with 2 other ROIs, i.e., entorhinal and para hippocampal areas.…”

Section: Discussionmentioning

confidence: 99%

Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder

Shim

Lee

et al. 2022

Front. Neuroinform.

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Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis.

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Section: Discussionmentioning

confidence: 99%

Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder

Shim

Lee

et al. 2022

Front. Neuroinform.

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“…Furthermore, multi-frequency band resting-state functional connectivity analyses highlighted reduced resting long-range functional alpha band connectivity in posterior areas, and condition-dependent increased beta band connectivity (106). Graph-theoretical analysis also showed altered topological structure during rest, in the alpha and beta bands (106,107), with hypo-clustering in the low-alpha band and hypo/hyper-clustering in low-alpha and high-beta bands, respectively (107).…”

Section: Introductionmentioning

confidence: 97%

The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder

et al. 2023

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We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.

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Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

Auer,

Goldthorpe,

Peach

et al. 2024

Human Brain Mapping

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The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non‐reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi‐modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain‐specific electrophysiological mechanisms underlying memory performance and ageing.Highlights We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross‐modal interpretation of EEG findings.

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Author Correction: Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG

Cited by 9 publications

References 0 publications

Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder

Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder

The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

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