Task-evoked dynamic network analysis through hidden Markov modelling by Quinn, A, Vidaurre, D, Abeysuriya, R, Becker, R, Nobre, A, Woolrich, M

“…Here, we present one potential solution using Hidden Markov Models (HMMs), which are able to identify brain states characterised by engaging distinct functional networks that reoccur over time. …”

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

“…Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. …”

Mapping interictal activity in epilepsy using a hidden Markov model: a magnetoencephalography study by Seedat, Z, Rier, L, Gascoyne, L, Cook, H, Woolrich, M, Quinn, A, Roberts, T, Furlong, P, Armstrong, C, St Pier, K, Mullinger, K, Marsh, E, Brookes, M, Gaetz, W

“…Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. …”

The role of transient spectral ‘bursts’ in functional connectivity: A magnetoencephalography study by Seedat, ZA, Quinn, A, Vidaurre, D, Liuzzi, L, Gascoyne, LE, Hunt, BAE, O'Neill, GC, Pakenham, DO, Mullinger, KJ, Morris, PG, Woolrich, MW, Brookes, MJ

“…We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. …”

Discovering dynamic brain networks from big data in rest and task by Vidaurre, D, Abeysuriya, R, Becker, R, Quinn, A, Alfaro-Almagro, F, Smith, S, Woolrich, M

“…Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. …”

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. …”

Mixtures of large-scale dynamic functional brain network modes by Gohil, C, Roberts, E, Timms, R, Skates, A, Higgins, C, Quinn, A, Pervaiz, U, van Amersfoort, J, Notin, P, Gal, Y, Adaszewski, S, Woolrich, M

“…Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. …”

Balance between competing spectral states in Subthalamic nucleus is linked to motor impairment in Parkinson’s Disease by Khawaldeh, S, Tinkhauser, G, Torrecillos, F, He, S, Foltynie, T, Limousin, P, Oswal, A, Quinn, A, Vidaurre, D, Tan, H, Litvak, V, Kuhn, A, Woolrich, M, Brown, P

“…LFPs were analysed using Hidden Markov Modelling to identify transient spectral states with frequencies under 40Hz. …”