Luke Tait

The paper examines beta-frequency oscillations in mice during novel environments, finding increased beta burst rates and connectivity in the retrosplenial cortex.

This study identifies biomarkers for predicting mild cognitive impairment conversion to Alzheimer's dementia using EEG, neuropsychological tests, and mathematical models.

The paper investigates whether Alzheimer's disease EEG changes are due to periodic or aperiodic signal alterations, finding they are purely periodic.

The paper introduces +microstate, a MATLAB toolbox for brain microstate analysis in EEG/MEG data, and demonstrates its use with example analyses.

This study generalizes EEG microstate analysis to MEG source data, identifying cortical microstates and their functional connectivity, showing they are modulated by auditory stimuli.

This study uses EEG and computational modeling to show that AD brains are more prone to seizures due to altered large-scale network structure and increased cortical excitability.

This study evaluates six source reconstruction algorithms for resting-state MEG, finding that eLORETA provides zero localization error, high spatial resolution, and superior sensor-level activity prediction.

The paper shows that individual variability in brain connectivity is consistent across modalities and linked to microstructural properties.

This study uses EEG microstate complexity and spectral measures to improve early diagnosis of Alzheimer's disease with high sensitivity and specificity.