Large-scale EEG neural network changes in response to therapeutic TMS

Analysis of microstate features for Parkinson's disease based on reliability validation

BACKGROUND

Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution.

NEW METHOD

Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression.

RESULTS

Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS.

COMPARISON WITH EXISTING METHOD(S)

High reliability of PD microstate features obtained by our approach.

CONCLUSION

EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.

Assessment of rTMS treatment effects for methamphetamine use disorder based on EEG microstates

Microstates have been proposed as topographical maps representing large-scale resting-state networks and have recently been suggested as markers for methamphetamine use disorder (MUD). However, it is unknown whether and how they change after repetitive transcranial magnetic stimulation (rTMS) intervention. This study included a comprehensive subject population to investigate the effect of rTMS on MUD microstates. 34 patients with MUD underwent a 4-week randomized, double-blind rTMS intervention (active=17, sham=17). Two resting-state EEG recordings and VAS evaluations were conducted before and after the intervention period. Additionally, 17 healthy individuals were included as baseline controls. The modified k-means clustering method was used to calculate four microstates (MS-A∼MS-D) of EEG, and the FC network was also analyzed. The differences in microstate indicators between groups and within groups were compared. The durations of MS-A and MS-B microstates in patients with MUD were significantly lower than that in HC but showed significant improvements after rTMS intervention. Changes in microstate indicators were found to be significantly correlated with changes in craving level. Furthermore, selective modulation of the resting-state network by rTMS was observed in the FC network. The findings indicate that changes in microstates in patients with MUD are associated with craving level improvement following rTMS, suggesting they may serve as valuable evaluation markers.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function

BACKGROUND

A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography (EEG) in people with depression. However, the consistency of findings on EEG microstates in patients with depression is poor, and few studies have reported the relationship between EEG microstates, cognitive scales, and depression severity scales.

AIM

To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.

METHODS

A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. We collected information relating to demographic and clinical characteristics, as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Chinese version) and EEG.

RESULTS

Compared with the controls, the duration, occurrence, and contribution of microstate C were significantly higher [depression (DEP): Duration 84.58 ± 24.35, occurrence 3.72 ± 0.56, contribution 30.39 ± 8.59; CON: Duration 72.77 ± 10.23, occurrence 3.41 ± 0.36, contribution 24.46 ± 4.66; Duration F = 6.02, P = 0.049; Occurrence F = 6.19, P = 0.049; Contribution F = 10.82, P = 0.011] while the duration, occurrence, and contribution of microstate D were significantly lower (DEP: Duration 70.00 ± 15.92, occurrence 3.18 ± 0.71, contribution 22.48 ± 8.12; CON: Duration 85.46 ± 10.23, occurrence 3.54 ± 0.41, contribution 28.25 ± 5.85; Duration F = 19.18, P < 0.001; Occurrence F = 5.79, P = 0.050; Contribution F = 9.41, P = 0.013) in patients with depression. A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B (r = 0.405, P = 0.049).

CONCLUSION

EEG microstate, especially C and D, is a possible biomarker in depression. Patients with depression had a more frequent transition from microstate C to B, which may relate to more negative rumination and visual processing.

EEG microstates analysis after TMS in patients with subacute stroke during the resting state

To investigate whether intermittent theta burst stimulation over the cerebellum induces changes in resting-state electroencephalography microstates in patients with subacute stroke and its correlation with cognitive and emotional function. Twenty-four stroke patients and 17 healthy controls were included in this study. Patients and healthy controls were assessed at baseline, including resting-state electroencephalography and neuropsychological scales. Fifteen patients received lateral cerebellar intermittent theta burst stimulation as well as routine rehabilitation training (intermittent theta burst stimulation-RRT group), whereas 9 patients received only conventional rehabilitation training (routine rehabilitation training group). After 2 wk, baseline data were recorded again in both groups. Stroke patients exhibited reduced parameters in microstate D and increased parameters in microstate C compared with healthy controls. However, after the administration of intermittent theta burst stimulation over the lateral cerebellum, significant alterations were observed in the majority of metrics for both microstates D and C. Lateral cerebellar intermittent theta burst stimulation combined with conventional rehabilitation has a stronger tendency to improve emotional and cognitive function in patients with subacute stroke than conventional rehabilitation. The improvement of mood and cognitive function was significantly associated with microstates C and D. We identified electroencephalography microstate spatiotemporal dynamics associated with clinical improvement following a course of intermittent theta burst stimulation therapy.

Electroencephalography microstates as novel functional biomarkers for insomnia disorder

Background

Insomnia disorder (ID) is one of the most common mental disorders. Research on ID focuses on exploring its mechanism of disease, novel treatments and treatment outcome prediction. An emerging technique in this field is the use of electroencephalography (EEG) microstates, which offer a new method of EEG feature extraction that incorporates information from both temporal and spatial dimensions.

Aims

To explore the electrophysiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) for ID treatment and use baseline microstate metrics for the prediction of its efficacy.

Methods

This study included 60 patients with ID and 40 age-matched and gender-matched good sleep controls (GSC). Their resting-state EEG microstates were analysed, and the Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were collected to assess sleep quality. The 60 patients with ID were equally divided into active and sham groups to receive rTMS for 20 days to test whether rTMS had a moderating effect on abnormal microstates in patients with ID. Furthermore, in an independent group of 90 patients with ID who received rTMS treatment, patients were divided into optimal and suboptimal groups based on their median PSQI reduction rate. Baseline EEG microstates were used to build a machine-learning predictive model for the effects of rTMS treatment.

Results

The class D microstate was less frequent and contribute in patients with ID, and these abnormalities were associated with sleep onset latency as measured by PSG. Additionally, the abnormalities were partially reversed to the levels observed in the GSC group following rTMS treatment. The baseline microstate characteristics could predict the therapeutic effect of ID after 20 days of rTMS, with an accuracy of 80.13%.

Conclusions

Our study highlights the value of EEG microstates as functional biomarkers of ID and provides a new perspective for studying the neurophysiological mechanisms of ID. In addition, we predicted the therapeutic effect of rTMS on ID based on the baseline microstates of patients with ID. This finding carries great practical significance for the selection of therapeutic options for patients with ID.

The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study

BACKGROUND AND OBJECTIVES

The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks.

METHODS

We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC.

RESULTS

We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups.

DISCUSSION

Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.

The Problem and Potential of TMS' Infinite Parameter Space: A Targeted Review and Road Map Forward

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive, effective, and FDA-approved brain stimulation method. However, rTMS parameter selection remains largely unexplored, with great potential for optimization. In this review, we highlight key studies underlying next generation rTMS therapies, particularly focusing on: (1) rTMS Parameters, (2) rTMS Target Engagement, (3) rTMS Interactions with Endogenous Brain Activity, and (4) Heritable Predisposition to Brain Stimulation Treatments.

METHODS

We performed a targeted review of pre-clinical and clinical rTMS studies.

RESULTS

Current evidence suggests that rTMS pattern, intensity, frequency, train duration, intertrain interval, intersession interval, pulse and session number, pulse width, and pulse shape can alter motor excitability, long term potentiation (LTP)-like facilitation, and clinical antidepressant response. Additionally, an emerging theme is how endogenous brain state impacts rTMS response. Researchers have used resting state functional magnetic resonance imaging (rsfMRI) analyses to identify personalized rTMS targets. Electroencephalography (EEG) may measure endogenous alpha rhythms that preferentially respond to personalized stimulation frequencies, or in closed-loop EEG, may be synchronized with endogenous oscillations and even phase to optimize response. Lastly, neuroimaging and genotyping have identified individual predispositions that may underlie rTMS efficacy.

CONCLUSIONS

We envision next generation rTMS will be delivered using optimized stimulation parameters to rsfMRI-determined targets at intensities determined by energy delivered to the cortex, and frequency personalized and synchronized to endogenous alpha-rhythms. Further research is needed to define the dose-response curve of each parameter on plasticity and clinical response at the group level, to determine how these parameters interact, and to ultimately personalize these parameters.