Measurement of dynamic task related functional networks using MEG

O'Neill, George; Tewarie, Prejaas; Colclough, Giles L.; Gascoyne, Lauren E.; Hunt, Benjamin A. E.; Morris, Peter G.; Woolrich, Mark W.; Brookes, Matthew J.

doi:10.1016/j.neuroimage.2016.08.061

Cited by 101 publications

(131 citation statements)

References 91 publications

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“…Second, when estimating functional connectivity itself, most correlative or coherence based measures are highly sensitive to the number of degrees of freedom in the timecourses used to generate them. 2) In addition to the technical limitation, the well characterised dynamic nature of functional connectivity, which changes over seconds, and even milliseconds Brookes et al, 2014;Chang and Glover, 2011;Hutchison et al, 2013;G C O'Neill et al, 2015 b;O'Neill et al, 2016) must be considered, since there is a question regarding how well a short time window can capture the canonical MEG networks, if those networks are constantly changing across multiple time-scales.…”

Section: Discussionmentioning

confidence: 99%

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Optimising experimental design for MEG resting state functional connectivity measurement

et al. 2017

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The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of coregistration method and data recording duration. We show that the use of a foam head-cast, which is known to improve coregistration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.3

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Section: Discussionmentioning

confidence: 99%

“…Similarly, O'Neill et al (2016) showed that dynamic measures of functional connectivity are also variable across subjects. Although the reason for this poor reliability is not well understood, it likely results from a combination of genuine differences (Finn et al, 2015) (i.e.…”

Section: Introductionmentioning

confidence: 96%

Optimising experimental design for MEG resting state functional connectivity measurement

et al. 2017

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“…It can be used to assess the formation and dissolution of brain networks in real time as they are modulated in support of cognitive tasks (e.g. Baker et al, 2014;O'Neill et al, 2017) and this has led to its use in cutting edge neuroscience (e.g. Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Whole-Head OPM-MEG: Helmet Design and a Comparison with a Conventional System

Hill

Boto

Rea

et al. 2020

Preprint

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Magnetoencephalography (MEG) is a powerful technique for functional neuroimaging, offering a noninvasive window on brain electrophysiology. MEG systems have traditionally been based on cryogenic sensors which detect the small extracranial magnetic fields generated by synchronised current in neuronal assemblies, however such systems have fundamental limitations. In recent years quantum-enabled devices, called opticallypumped magnetometers (OPMs), have promised to lift those restrictions, offering an adaptable, motion-robust MEG device, with improved data quality, at reduced cost. However, OPM-MEG remains a nascent technology, and whilst viable systems exist, most employ small numbers of sensors sited above targeted brain regions. Here, building on previous work, we construct a wearable OPM-MEG system with 'whole-head' coverage based upon commercially available OPMs, and test its capabilities to measure alpha, beta and gamma oscillations. We design two methods for OPM mounting; a flexible (EEG-like) cap and rigid (additively-manufactured) helmet.Whilst both designs allow for high quality data to be collected, we argue that the rigid helmet offers a more robust option with significant advantages for reconstruction of field data into 3D images of changes in neuronal current. Using repeat measurements in two participants, we show signal detection for our device to be highly robust. Moreover, via application of source-space modelling, we show that, despite having 5 times fewer sensors, our system exhibits comparable performance to an established cryogenic MEG device. While significant challenges still remain, these developments provide further evidence that OPM-MEG is likely to facilitate a step change for functional neuroimaging. HIGHLIGHTS A 49-channel whole-head OPM-MEG system is constructed  System evaluated via repeat measurements of alpha, beta and gamma oscillations  Two OPM-helmet designs are contrasted, a flexible (EEG-like) cap and a rigid helmet  The rigid helmet offers significant advantages for a viable OPM-MEG device  49-channel OPM-MEG offers performance comparable to established cryogenic devices

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“…Existing methods to approach the BS allocation problem has been dominated by data-based approaches, including temporal sliding windows (Hansen et al, 2015;O'Neill et al, 2017), adaptive segmentations using clustering Khanna et al, 2015;Mheich et al, 2015), among others (O'Neill et al, 2018;Preti et al, 2017). Although the shortcomings of these approaches are well stablished (Hindriks et al, 2016;O'Neill et al, 2018), from a modelling perspective, their main limitation is that the temporal dynamics of BSs is not considered during estimation, but it is assessed a posteriori.…”

Section: Introductionmentioning

confidence: 99%

The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG

Trujillo-Barreto¹,

Araya

El-Deredy

2019

Preprint

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We consider the detection and characterisation of brain state transitions, based on ongoing Magneto and Electroencephalography (M/EEG). Here a brain state represents a specific brain dynamical regime or mode of operation, which produces a characteristic quasi-stable pattern of activity at topography, sources or network levels. These states and their transitions over time can reflect fundamental computational properties of the brain, shaping human behaviour and brain function. The Hidden Markov Model (HMM) has emerged as a useful model-based approach for uncovering the hidden dynamics of brain state transitions based on observed data. However, the Geometric distribution of state duration (dwell time) implicit in HMM places highest probability on very short durations, which makes it inappropriate for the accurate modelling of brain states in M/EEG. We propose using Hidden Semi Markov Models (HSMM), a generalisation of HMM that models the brain state duration distribution explicitly. We present a Bayesian formulation of HSMM and use the Variational Bayes framework to efficiently estimate the HSMM parameters, the number of brain states and select among alternative brain state duration distributions. We assess HSMM performance against HMM on simulated data and demonstrate that the accurate modelling of state duration is paramount for accurately and robustly modelling non-Markovian EEG brain state features. Finally, we used actual resting-state EEG data to demonstrate the approach in practice and conclude that it provides a flexible parameterised framework that permits closer interrogation of possible generative mechanisms.

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Measurement of dynamic task related functional networks using MEG

Cited by 101 publications

References 91 publications

Optimising experimental design for MEG resting state functional connectivity measurement

Optimising experimental design for MEG resting state functional connectivity measurement

Multi-Channel Whole-Head OPM-MEG: Helmet Design and a Comparison with a Conventional System

The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG

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