Flexible head-casts for high spatial precision MEG

Meyer, Sofie S.; Bonaiuto, James; Lim, Mark J.; Rossiter, Holly E.; Waters, Sheena; Bradbury, David; Bestmann, Sven; Brookes, Matthew J.; Callaghan, Martina F.; Weiskopf, Nikolaus; Barnes, Gareth R.

doi:10.1016/j.jneumeth.2016.11.009

Cited by 75 publications

(108 citation statements)

References 28 publications

(44 reference statements)

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“…Paradigms should be designed such that: hippocampal contrast can be maximized, for example, by having at least two types of trials that are expected to induce the same activations in all brain areas except the hippocampus. the timing of expected hippocampal activations can be reliably annotated, for example, using tasks that force recall to occur within an experimentally controlled time window. mnemonic states can be grouped (and thus averaged), for example, successful versus unsuccessful trails can be used for independently contrasting recall, encoding, and/or spatial navigation. the co‐registration of the head position in the MEG session with the individual's MRI is performed with high precision and accuracy. This can be done via digitization of the head surface and fiducials combined with the use of head position indicator coils that allow continuous monitoring of the head position (Uutela, Taulu, & Hämäläinen, ) or head casts that fix the head position during a MEG session (Meyer et al, ). …”

Section: Discussionmentioning

confidence: 99%

“…In addition to our recommendation for future studies exploring the differences between these various imaging modalities, we also would like to note the expanding field of MEG and simulation or modeling data (Attal & Schwartz, ; Balderston, Schultz, Baillet, & Helmstetter, ; Mills et al, ; Quraan et al, ; Stephen, Ranken, Aine, Weisend, & Shih, ). Currently, studies such as the recently published Meyer et al, ,b continue to elegantly demonstrate the theoretical ability of MEG to robustly detect hippocampal activity. However, the field would benefit from more work done to unite findings from simulation data with experimental data.…”

Section: Discussionmentioning

confidence: 99%

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Characterizing hippocampal dynamics with MEG: A systematic review and evidence‐based guidelines

Ruzich

Crespo-García

Dalal

et al. 2018

Human Brain Mapping

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The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro‐temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal‐to‐noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal‐to‐noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub‐structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Characterizing hippocampal dynamics with MEG: A systematic review and evidence‐based guidelines

Ruzich

Crespo-García

Dalal

et al. 2018

Human Brain Mapping

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“…Our work suggests that by using new recording techniques, namely head-casts (Meyer et al, 2017, Troebinger et al, 2014b) we have the ability to selectively study human hippocampal dynamics non-invasively.…”

Section: Discussionmentioning

confidence: 99%

Using generative models to make probabilistic statements about hippocampal engagement in MEG

et al. 2017

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Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration error and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is <3 mm and the sensor-level signal-to-noise ratio (SNR) is >−20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts.

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“…We derive a framework to compare measurement systems based on their sensitivity to perturbations in sensor geometry. Based on previous SQUID-based studies (López et al, 2012;Martínez-Vargas et al, 2016;Meyer et al, 2017;Stevenson et al, 2014;Troebinger et al, 2014), we would expect that a model of the OPM MEG data with the true geometry will have a higher evidence as approximated by the negative variational Free energy. In this study, as we wish to compare between sensor types (and the data are different precluding any direct comparison of Free energy values), we focus on the sensitivity of the Free energy to perturbations in the geometry.…”

Section: Methodsmentioning

confidence: 99%

Data‐driven model optimization for optically pumped magnetometer sensor arrays

Duque-Muñoz

Tierney

Meyer

et al. 2019

Human Brain Mapping

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Optically pumped magnetometers (OPMs) have reached sensitivity levels that make them viable portable alternatives to traditional superconducting technology for magnetoencephalography (MEG). OPMs do not require cryogenic cooling and can therefore be placed directly on the scalp surface. Unlike cryogenic systems, based on a well‐characterised fixed arrays essentially linear in applied flux, OPM devices, based on different physical principles, present new modelling challenges. Here, we outline an empirical Bayesian framework that can be used to compare between and optimise sensor arrays. We perturb the sensor geometry (via simulation) and with analytic model comparison methods estimate the true sensor geometry. The width of these perturbation curves allows us to compare different MEG systems. We test this technique using simulated and real data from SQUID and OPM recordings using head‐casts and scanner‐casts. Finally, we show that given knowledge of underlying brain anatomy, it is possible to estimate the true sensor geometry from the OPM data themselves using a model comparison framework. This implies that the requirement for accurate knowledge of the sensor positions and orientations a priori may be relaxed. As this procedure uses the cortical manifold as spatial support there is no co‐registration procedure or reliance on scalp landmarks.

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Flexible head-casts for high spatial precision MEG

Cited by 75 publications

References 28 publications

Characterizing hippocampal dynamics with MEG: A systematic review and evidence‐based guidelines

Characterizing hippocampal dynamics with MEG: A systematic review and evidence‐based guidelines

Using generative models to make probabilistic statements about hippocampal engagement in MEG

Data‐driven model optimization for optically pumped magnetometer sensor arrays

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