OBJECTIVE:Electroencephalography (EEG) microstate analysis has emerged as a tool for investigating the spatial organization and temporal dynamics of large-scale cortical networks. Its potential role in identifying risk and progression of Alzheimer's dementia (AD) remains unclear. We conducted a systematic review and meta-analysis of EEG microstate parameters in AD and mild cognitive impairment (MCI). METHODS:PubMed, PsychINFO, EMBASE, and MEDLINE were searched, identifying 30 eligible studies (16 included in meta-analysis). Random-effects models were used to pool effect sizes and 95% confidence intervals comparing microstate parameters between AD, MCI, and healthy controls. RESULTS:Sixteen studies were included in the meta-analysis. In AD vs controls, microstate A duration (g = 0.41, 95% CI [0.10, 0.72]) and microstate B duration (g = 0.48, 95% CI [0.23, 0.73]) were significantly increased. In MCI vs controls, microstate D duration was significantly decreased (g = -0.26, 95% CI [-0.48, -0.04]) and microstate A occurrence rate was increased (g = 0.40, 95% CI [0.07, 0.74]), while microstate A and B duration were not significantly different. Heterogeneity was substantial for several outcomes. CONCLUSION:Pooled evidence suggests prolonged microstate A/B duration as the most reproducible alteration in AD, with reduced microstate D duration emerging as a modest finding in MCI. However, substantial heterogeneity and possible small-study effects indicate that current evidence is best interpreted as hypothesis-generating pending standardized, longitudinal, and multimodal studies. SIGNIFICANCE:EEG microstate analysis may provide complementary information about large-scale network dysfunction in MCI and AD, but methodological limitations currently constrain clinical biomarker interpretation.

Theta burst stimulation (TBS) is a non-invasive brain stimulation technique that can modulate neural activity. The effect of TBS on regions beyond the motor cortex remains unclear. With increased interest in applying TBS to non-motor regions for research and clinical purposes, these effects must be understood and characterised. We synthesised the electrophysiological effects of a single session of TBS, as indexed by electroencephalography (EEG) and concurrent transcranial magnetic stimulation and EEG (TMS-EEG), in non-clinical participants. We reviewed 79 studies that administered either continuous TBS (cTBS) or intermittent TBS (iTBS) protocols. Broadly, cTBS suppressed and iTBS facilitated evoked response component amplitudes. Response to TBS as measured by spectral power and connectivity was much more variable. Variability increased in the presence of task stimuli. There was a large degree of heterogeneity in the research methodology across studies. Additionally, the effect of individual differences on TBS response is insufficiently investigated. Future research investigating the effects of TBS as measured by EEG must consider methodological and individual factors that may affect TBS outcomes.

Electroencephalogram (EEG) microstates, which represent quasi-stable patterns of scalp topography, are a promising tool that has the temporal resolution to study atypical spatial and temporal networks in autism spectrum disorder (ASD). While current literature suggests microstates are atypical in ASD, their clinical utility, i.e., relationship with the core behavioural characteristics of ASD, is not fully understood. The aim of this study was to examine microstate parameters in ASD, and examine the relationship between these parameters and core behavioural characteristics in ASD. We compared duration, occurrence, coverage, global explained variance percentage, global field power and spatial correlation of EEG microstates between autistic and neurotypical (NT) adults. Modified k-means cluster analysis was used on eyes-closed, resting state EEG from 30 ASD (10 females, 28.97 ± 9.34 years) and 30 age-equated NT (13 females, 29.33 ± 8.88 years) adults. Five optimal microstates, A to E, were selected to best represent the data. Five microstate maps explaining 80.44% of the NT and 78.44% of the ASD data were found. The ASD group was found to have atypical parameters of microstate A, C, D, and E. Of note, all parameters of microstate C in the ASD group were found to be significantly less than NT. While parameters of microstate D, and E were also found to significantly correlate with subscales of the Ritvo Autism Asperger Diagnostic Scale - Revised (RAADS-R), these findings did not survive a Bonferroni Correction. These findings, in combination with previous findings, highlight the potential clinical utility of EEG microstates and indicate their potential value as a neurophysiologic marker that can be further studied.

There are growing application of machine learning models to study the intricacies of non-linear and non -stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neu-robiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning ap-proaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.

An excessive long-term potentiation (LTP) or hyperplasticity was found in the motor cortex in autistic adults using transcranial magnetic stimulation. Whether dorsolateral prefrontal cortex (DLPFC), which is involved in higher-order processes, has atypical LTP in autism, is unknown. Using the combination of paired associative stimulation (PAS) and electroencephalography (EEG) (PAS-EEG), we assessed LTP in the left DLPFC in autistic adults and explored correlation between LTP and resting-state functional magnetic resonance imaging (fMRI) functional connectivity (FC) patterns. We anticipated that, compared to neurotypical (NT) controls, autistic adults will display hyperplasticity in the left DLPFC.

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large‐scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early‐to‐middle childhood (4–12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A–E) across both eyes‐closed and eyes‐open resting‐state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f‐like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes‐open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large‐scale functional brain networks during early‐to‐middle childhood, extending understanding of neural dynamics across this important period for brain development.

Cross-frequency coupling (CFC), an electrophysiologically derived measure of oscillatory coupling in the brain, is believed to play a critical role in neuronal computation, learning and communication. It has received much recent attention in the study of both health and disease. We searched for literature that studied CFC during resting state and task-related activities during electroencephalography and magnetoencephalography in psychiatric disorders. Thirty-eight studies were identified, which included attention-deficit hyperactivity disorder, Alzheimer's dementia, autism spectrum disorder, bipolar disorder, depression, obsessive compulsive disorder, social anxiety disorder and schizophrenia. The systematic review was registered with PROSPERO (ID#CRD42021224188). The current review indicates measurable differences exist between CFC in disease states vs. healthy controls. There was variance in CFC at different regions of the brain within the same psychiatric disorders, perhaps this could be explained by the mechanisms and functionality of CFC. There was heterogeneity in methodologies used, which may lead to spurious CFC analyses. Going forward, standardized methodologies need to be established and utilized in further research to understand the neuropathophysiology associated with psychiatric disorders.

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

Resting state electroencephalogram (EEG) frontal alpha asymmetry (FAA) is one candidate neural endophenotype in autism that has been linked with atypical hemispheric organization. In infants at high-risk of autism, there is evidence of atypical switching from left (rightleft) FAA, which is the opposite of what was seen in typically developing infants. In addition, atypical right FAA is observed in autistic children. Whether atypical FAA is present in autistic adults is unknown.

Electroencephalogram (EEG) microstate analysis is a promising tool to study aberrant spatial and temporal characteristics of cortical networks with excellent temporal resolution. Although efforts have been made to use microstate analysis in autism, data are sparse and too heterogeneous. We compared duration, occurrence, coverage, and global explained variance (GEV%) of EEG microstates between autistic adults and controls, and studied the relationship between these parameters and core behavioral characteristics of autism.