Biology subjects

Woolrich, M.

Publications and source records attributed to Woolrich, M..

3 recordsLinked to original sources

bioRxiv2018

Transient spectral events in resting state MEG predict individual time-frequency task responses

Even in response to apparently simple tasks such as hand moving, human brain activity shows remarkable inter-subject variability. Presumably, this variability reflects genuine behavioural or functional variability. Recently, spatial variability of resting-state features in fMRI - specifically connectivity - has been shown to explain (spatial) task-response variability. Such a link, however, is still missing for M/EEG data and its spectrally rich structure. At the same time, it has recently been shown that task responses in M/EEG can be well represented using transient spectral events bursting at fast time scales. Here, we show that individual differences in the spatio-spectral structure of M/EEG task responses, can, to a reasonable degree, be predicted from individual differences in transient spectral events identified at rest. In a MEG dataset of diverse task conditions (including motor responses, working memory and language comprehension tasks) and resting-state sessions for each subject (n = 89), we used Hidden-Markov-Modelling to identify transient spectral events as a feature set to learn the mapping of space-time-frequency content from rest to task. Resulting trial-averaged, subject-specific task-response predictions were then compared with the actual task responses in left-out subjects. All task conditions were predicted significantly above chance. Furthermore, we observed a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. These findings support the idea that subject-specific transient spectral events in resting-state neural activity are linked to, and predictive of, subject-specific trial-averaged task responses in a wide range of experimental conditions.

neuroscience↗

bioRxiv2018

Spontaneous network activity accounts for variability in stimulus-induced gamma responses

Gamma range activity in human visual cortex is believed to play a major role in cognitive functions, such as selective attention. Although recent studies have revealed substantial variability in gamma activity, its origins are still unclear.\n\nWe investigated whether variability in stimulus-induced gamma activity is related to the spontaneous dynamics of resting-state networks using Hidden Markov Modelling. The magnetoencephalogram (MEG) of 15 healthy participants was recorded at rest and while they were performing a task involving a visual stimulus inducing strong, narrow-band gamma activity. Brain states were inferred from the tasks baseline periods and from resting-state recordings, respectively.\n\nOur results show how network states are related to the amplitude of stimulus-induced gamma responses. Across trials, we found an association between the amplitude of gamma responses and the brain state occurring immediately prior to stimulus presentation. Strong gamma responses followed a state characterized by prominent delta/theta oscillations in parieto-occipital regions and comparably weak alpha activity. Across subjects, the overall probability of visiting this state in the baseline period, i.e. the individual preference for this state, correlated positively with the amplitude of the trial-averaged gamma response. Remarkably, this relationship persisted when states were inferred from resting-state recordings rather than the tasks baseline.\n\nIn summary, both within- and across-subject variability in stimulus-induced gamma activity can in part be explained by the ongoing dynamics of whole-brain network states. Fast, pre-stimulus modulations of brain states account for differences between trials while stable, individual state preferences account for differences between subjects.

neuroscience↗

bioRxiv2016

Single or Multi-Frequency Generators in On-going Brain Activity: a Mechanistic Whole-Brain Model following empirical MEG evidences

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.\n\nConsidering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

neuroscience↗