Mouth magnetoencephalography: A unique perspective on the human hippocampus

Traditional magnetoencephalographic (MEG) brain imaging scanners consist of a rigid sensor array surrounding the head; this means that they are maximally sensitive to superficial brain structures. New technology based on optical pumping means that we can now consider more flexible and creative sensor placement. Here we explored the magnetic fields generated by a model of the human hippocampus not only across scalp but also at the roof of the mouth. We found that simulated hippocampal sources gave rise to dipolar field patterns with one scalp surface field extremum at the temporal lobe and a corresponding maximum or minimum at the roof of the mouth. We then constructed a fitted dental mould to accommodate an Optically Pumped Magnetometer (OPM). We collected data using a previously validated hippocampal-dependant task to test the empirical utility of a mouth-based sensor, with an accompanying array of left and right temporal lobe OPMs. We found that the mouth sensor showed the greatest task-related theta power change. We found that this sensor had a mild effect on the reconstructed power in the hippocampus (~10% change) but that coherence images between the mouth sensor and reconstructed source images showed a global maximum in the right hippocampus. We conclude that augmenting a scalp-based MEG array with sensors in the mouth shows unique promise for both basic scientists and clinicians interested in interrogating the hippocampus.

[1]  Svenja Knappe,et al.  Magnetoencephalography of Epilepsy with a Microfabricated Atomic Magnetrode , 2014, The Journal of Neuroscience.

[2]  Emily Ruzich,et al.  Characterizing hippocampal dynamics with MEG: A systematic review and evidence‐based guidelines , 2018, Human brain mapping.

[3]  Daniel N. Barry,et al.  Imaging the human hippocampus with optically-pumped magnetoencephalography , 2019, NeuroImage.

[4]  J. Badier,et al.  Deep brain activities can be detected with magnetoencephalography , 2019, Nature Communications.

[5]  Tom Minka,et al.  Automatic Choice of Dimensionality for PCA , 2000, NIPS.

[6]  Karl J. Friston,et al.  EEG and MEG Data Analysis in SPM8 , 2011, Comput. Intell. Neurosci..

[7]  M. Kahana,et al.  Human hippocampal theta oscillations and the formation of episodic memories , 2012, Hippocampus.

[8]  Karl J. Friston,et al.  Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM , 2014, NeuroImage.

[9]  Matthew J. Brookes,et al.  A bi-planar coil system for nulling background magnetic fields in scalp mounted magnetoencephalography , 2018, NeuroImage.

[10]  P. Verschure,et al.  Coordinated representational reinstatement in the human hippocampus and lateral temporal cortex during episodic memory retrieval , 2019, Nature Communications.

[11]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[12]  Niall Holmes,et al.  Moving magnetoencephalography towards real-world applications with a wearable system , 2018, Nature.

[13]  J H Margerison,et al.  Epilepsy and the temporal lobes. A clinical, electroencephalographic and neuropathological study of the brain in epilepsy, with particular reference to the temporal lobes. , 1966, Brain : a journal of neurology.

[14]  Keith A. Johnson,et al.  Amyloid-β deposition in mild cognitive impairment is associated with increased hippocampal activity, atrophy and clinical progression. , 2015, Brain : a journal of neurology.

[15]  Mark W. Woolrich,et al.  Using generative models to make probabilistic statements about hippocampal engagement in MEG , 2017, NeuroImage.

[16]  J. Osborne,et al.  Fully integrated standalone zero field optically pumped magnetometer for biomagnetism , 2018, OPTO.

[17]  M. Walker,et al.  Hippocampal Sclerosis: Causes and Prevention , 2015, Seminars in Neurology.

[18]  G. Nolte The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. , 2003, Physics in medicine and biology.

[19]  G. Pampiglione,et al.  E.E.G. ABNORMALITIES FROM THE TEMPORAL LOBE STUDIED WITH SPHENOIDAL ELECTRODES , 1956, Journal of neurology, neurosurgery, and psychiatry.

[20]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[21]  Matthew J. Brookes,et al.  Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function , 2018, NeuroImage.

[22]  G. Buzsáki Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning , 2015, Hippocampus.

[23]  Matthew J. Brookes,et al.  On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study , 2016, PloS one.

[24]  Arne D. Ekstrom,et al.  Behavioral correlates of human hippocampal delta and theta oscillations during navigation. , 2011, Journal of neurophysiology.

[25]  Karl J. Friston,et al.  Multiple sparse priors for the M/EEG inverse problem , 2008, NeuroImage.

[26]  Matthew J. Brookes,et al.  A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers , 2017, NeuroImage.

[27]  Karl J. Friston,et al.  Canonical Source Reconstruction for MEG , 2007, Comput. Intell. Neurosci..

[28]  Matti Stenroos,et al.  Measuring MEG closer to the brain: Performance of on-scalp sensor arrays , 2016, NeuroImage.

[29]  Blake W. Johnson,et al.  Non-invasive Investigation of Human Hippocampal Rhythms Using Magnetoencephalography: A Review , 2018, Front. Neurosci..

[30]  Daniel N. Barry,et al.  The Neural Dynamics of Novel Scene Imagery , 2018, The Journal of Neuroscience.