New Therapeutics in Alzheimer’s Disease longitudinal cohort study (NTAD): study protocol by Lanskey, JH, Kocagoncu, E, Quinn, AJ, Cheng, Y-J, Karadag, M, Pitt, J, Lowe, S, Perkinton, M, Raymont, V, Singh, KD, Woolrich, M, Nobre, AC, Henson, RN, Rowe, JB

“…Measurements of the clinical, cognitive and imaging data (magnetoencephalography, electroencephalography and MRI) of all participants will be taken at baseline. …”

Dynamic state allocation for MEG source reconstruction. by Woolrich, M, Baker, A, Luckhoo, H, Mohseni, H, Barnes, G, Brookes, M, Rezek, I

“…Here, we present a novel adaptive time-varying approach to source reconstruction that can be applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. The method is underpinned by a Hidden Markov Model (HMM), which infers the points in time when particular states re-occur in the sensor space data. …”

Testing sensory evidence against mnemonic templates by Myers, N, Rohenkohl, G, Wyart, V, Woolrich, M, Nobre, A, Stokes, M

“…Here we recorded magneto- and electroencephalography during a visual target-detection task, and used pattern analyses to decode template, stimulus, and decision-variable representation. …”

Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep by Stevner, A, Vidaurre, D, Cabral, J, Rapuano, K, Nielsen, S, Tagliazucchi, E, Laufs, H, Vuust, P, Deco, G, Woolrich, M, Van Someren, E, Kringelbach, M

“…The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. …”

A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates by Hunyadi, B, Woolrich, M, Quinn, A, Vidaurre, D, De Vos, M

“…In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. …”

Dynamics of large-scale electrophysiological networks: a technical review by O'Neill, G, Tewarie, P, Vidaurre, D, Liuzzi, L, Woolrich, M, Brookes, M

“…In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. …”

Interpretable many-class decoding for MEG by Csaky, R, van Es, MWJ, Parker Jones, O, Woolrich, M

“…Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. …”

Spatiotemporally resolved multivariate pattern analysis for M/EEG by Higgins, CJ, Vidaurre, D, Kolling, N, Liu, Y, Behrens, T, Woolrich, M

“…An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. While electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. …”

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales by Coquelet, N, De Tiège, X, Roshchupkina, L, Peigneux, P, Goldman, S, Woolrich, M, Wens, V

“…State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. …”

Inferring brain network dynamics of MEG and EEG in healthy aging and Alzheimer’s disease by Cho, S

“…Amidst the ongoing quest to discover novel biomarkers with enhanced clinical utility, this thesis aims to elucidate the prospect of static and dynamic changes in brain network features, derived from electroencephalography (EEG) and magnetoencephalography (MEG), to explicate the earliest changes in the brain caused by AD.…”

Decoding non-invasive brain activity with novel deep-learning approaches by Csaky, R

“…<p>This thesis delves into the world of non-invasive electrophysiological brain signals like electroencephalography (EEG) and magnetoencephalography (MEG), focusing on modelling and decoding such data. …”

Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov Model by Zhang, S, Cao, C, Quinn, A, Vivekananda, U, Zhan, S, Liu, W, Sun, B, Woolrich, M, Lu, Q, Litvak, V

“…<br><strong>Background<br></strong> Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. …”