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Cited by 180Atomic and Subatomic Physics ResearchAdvanced MRI Techniques and ApplicationsEEG and Brain-Computer Interfaces

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

A tool for functional brain imaging with lifespan compliance

Open access
Hill, Ryan M., Boto, Elena, Holmes, Niall, Hartley, Caroline, Seedat, Zelekha A., Leggett, James, Roberts, Gillian, Shah, Vishal, Tierney, Tim M., Woolrich, Mark W., Stagg, Charlotte J., Barnes, Gareth R., Bowtell, Richard, Slater, Rebeccah, Brookes, Matthew J.·2019·Nature Communications
DOI 10.1038/s41467-019-12486-xPMC PMC6831615
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Abstract

The human brain undergoes significant functional and structural changes in the first decades of life, as the foundations for human cognition are laid down. However, non-invasive imaging techniques to investigate brain function throughout neurodevelopment are limited due to growth in head-size with age and substantial head movement in young participants. Experimental designs to probe brain function are also limited by the unnatural environment typical brain imaging systems impose. However, developments in quantum technology allowed fabrication of a new generation of wearable magnetoencephalography (MEG) technology with the potential to revolutionise electrophysiological measures of brain activity. Here we demonstrate a lifespan-compliant MEG system, showing recordings of high fidelity data in toddlers, young children, teenagers and adults. We show how this system can support new types of experimental paradigm involving naturalistic learning. This work reveals a new approach to functional imaging, providing a robust platform for investigation of neurodevelopment in health and disease.

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Figures

Fig. 1 A paediatric MEG system: a Experimental setup for three participants age 2- (left), 5- (centre) and 24-years (right). OPMs, housed in a modified bike helmet, measured the MEG signal. b Time-frequency spectra from a single (synthesised gradiometer) channel. Changes in neural oscillations are shown; blue indicates a reduction in oscillatory amplitude relative to baseline; yellow indicates an increase. Note reduction in beta (13–30 Hz) and mu (8–13 Hz) amplitude. c The spatial signature of beta modulation during the period of tactile stimulation (0 s < t < 2 s) (blue overlay)
Fig. 1 A paediatric MEG system: a Experimental setup for three participants age 2- (left), 5- (centre) and 24-years (right). OPMs, housed in a modified bike helmet, measured the MEG signal. b Time-frequency spectra from a single (synthesised gradiometer) channel. Changes in neural oscillations are shown; blue indicates a reduction in oscillatory amplitude relative to baseline; yellow indicates an increase. Note reduction in beta (13–30 Hz) and mu (8–13 Hz) amplitude. c The spatial signature of beta modulation during the period of tactile stimulation (0 s < t < 2 s) (blue overlay)CC BY · PubMed Central
Fig. 2 An interactive motor paradigm: a The paradigm: a cross hair moves across the screen passing targets. The participant moves their finger in order to shoot an arrow at the target. b Measured finger movements for three different speeds of cross hair movement. The shaded area shows the average and standard deviation across trials whilst the solid line shows a single example trial. c Beta responses in a single 14-year-old participant. The upper plot shows a schematic diagram of the positions of the OPMs; the thickness of the line denotes the strength of the change in beta amplitude for all gradiometers. The lower plot shows a time-frequency spectrogram from the most anterior gradiometer pair, with time zero representing the offset of movement, measured from the recorded finger movement; all trials have been averaged
Fig. 2 An interactive motor paradigm: a The paradigm: a cross hair moves across the screen passing targets. The participant moves their finger in order to shoot an arrow at the target. b Measured finger movements for three different speeds of cross hair movement. The shaded area shows the average and standard deviation across trials whilst the solid line shows a single example trial. c Beta responses in a single 14-year-old participant. The upper plot shows a schematic diagram of the positions of the OPMs; the thickness of the line denotes the strength of the change in beta amplitude for all gradiometers. The lower plot shows a time-frequency spectrogram from the most anterior gradiometer pair, with time zero representing the offset of movement, measured from the recorded finger movement; all trials have been averagedCC BY · PubMed Central
Fig. 3 Naturalistic motor learning: a Experimental setup with a participant playing a musical instrument. Inset image shows what the participant saw on the screen. b Measured head and right hand movement showing the timing of when the ukulele was strummed. The lower trace shows the sound generated. c Schematic diagram of sensor placement; the line thickness denotes the strength of the effect in the gamma band (left) and the beta band (right). Time-frequency spectrum shows the neural oscillatory response from the gradiometer pair indicated by the dotted red line
Fig. 3 Naturalistic motor learning: a Experimental setup with a participant playing a musical instrument. Inset image shows what the participant saw on the screen. b Measured head and right hand movement showing the timing of when the ukulele was strummed. The lower trace shows the sound generated. c Schematic diagram of sensor placement; the line thickness denotes the strength of the effect in the gamma band (left) and the beta band (right). Time-frequency spectrum shows the neural oscillatory response from the gradiometer pair indicated by the dotted red lineCC BY · PubMed Central
Fig. 4 Simulated MEG helmet designs for a 2-year-old. Main figures show normalised representation of measurable signal across the brain. (Specifically a 1 nAm source dipole has been simulated at all cortical locations and we measured the Frobenius norm of the resulting sensor-level field pattern. These values were normalised by the average Frobenius norm when sensors are placed on the scalp surface—i.e. the values, which mostly scale between 0 and 1, represent signal quality relative to the bespoke helmet). Inset figures show the location of simulated MEG sensors. a Bespoke helmet. b Helmet to fit 95% of 2-year-olds. c Modified bike helmet. d Cryogenic MEG system. e Distribution of measurable signal values, across all dipole locations for each helmet type
Fig. 4 Simulated MEG helmet designs for a 2-year-old. Main figures show normalised representation of measurable signal across the brain. (Specifically a 1 nAm source dipole has been simulated at all cortical locations and we measured the Frobenius norm of the resulting sensor-level field pattern. These values were normalised by the average Frobenius norm when sensors are placed on the scalp surface—i.e. the values, which mostly scale between 0 and 1, represent signal quality relative to the bespoke helmet). Inset figures show the location of simulated MEG sensors. a Bespoke helmet. b Helmet to fit 95% of 2-year-olds. c Modified bike helmet. d Cryogenic MEG system. e Distribution of measurable signal values, across all dipole locations for each helmet typeCC BY · PubMed Central
Fig. 5 Coregistration procedure. a Schematic showing how sensor locations were determined relative to brain anatomy b Images showing the spatial signature of beta modulation; the largest peak was found in pre-central-sulcus (motor cortex) in all three subjects. c Time-frequency spectra, reconstructed using a beamformer at the location of maximum beta change (i.e. the peaks in Fig. 5b)
Fig. 5 Coregistration procedure. a Schematic showing how sensor locations were determined relative to brain anatomy b Images showing the spatial signature of beta modulation; the largest peak was found in pre-central-sulcus (motor cortex) in all three subjects. c Time-frequency spectra, reconstructed using a beamformer at the location of maximum beta change (i.e. the peaks in Fig. 5b)CC BY · PubMed Central
Fig. 6 Schematic overview of the OPM-MEG system
Fig. 6 Schematic overview of the OPM-MEG systemCC BY · PubMed Central
Fig. 7 Co-registration process. a A volumetric X-ray CT scan of the modified bicycle helmet. Three identifiable reference points on the outer helmet surface were chosen (red circles), one on the forehead, and the others above the left and right ears. These were later used to attach reflective markers visible to an IR tracking camera. b A photograph of the modified bike helmet. Note the three IR reflective markers attached to the surface. The participant also wears a pair of glasses, attached to which are a further three IR reflective markers, enabling coregistration of the helmet to the head c 3D digitisation of the participant’s head shape, defined relative to the markers on the glasses (green circles). This digitisation has been aligned to the markers on the helmet (red circles) and OPM sensors (purple markers) to provide complete coregistration of the system to brain anatomy. d Schematic of the beamformer technique used to localise the source of beta modulation
Fig. 7 Co-registration process. a A volumetric X-ray CT scan of the modified bicycle helmet. Three identifiable reference points on the outer helmet surface were chosen (red circles), one on the forehead, and the others above the left and right ears. These were later used to attach reflective markers visible to an IR tracking camera. b A photograph of the modified bike helmet. Note the three IR reflective markers attached to the surface. The participant also wears a pair of glasses, attached to which are a further three IR reflective markers, enabling coregistration of the helmet to the head c 3D digitisation of the participant’s head shape, defined relative to the markers on the glasses (green circles). This digitisation has been aligned to the markers on the helmet (red circles) and OPM sensors (purple markers) to provide complete coregistration of the system to brain anatomy. d Schematic of the beamformer technique used to localise the source of beta modulationCC BY · PubMed Central

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Metadata

Authors
Hill, R. M., Boto, E., Holmes, N., Hartley, C., Seedat, Z. A., Leggett, J., Roberts, G., Shah, V., Tierney, T. M., Woolrich, M. W., Stagg, C. J., Barnes, G. R., Bowtell, R., Slater, R., & Brookes, M. J.
Affiliations
  • University of Nottingham (GB)
  • John Radcliffe Hospital (GB)
  • QuSpin (United States) (US)
  • Wellcome Centre for Human Neuroimaging (GB)
  • Warneford Hospital (GB)
Corresponding author
Brookes, Matthew J.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-11-05
Volume
10
Issue
1
Pages
4785-4785
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-12486-x
PMC
PMC6831615
OpenAlex
W2969234156
Cited by
180
References
35
Citations by year
2026: 12 · 2025: 35 · 2024: 31 · 2023: 31 · 2022: 31 · 2021: 25 · 2020: 15
FWCI
7.80
Type
journal article
Language
en
Update notices
correction (2019-12-04)
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (publishedVersion, other-oa)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Wellcome Trust (203257/Z/16/Z, 203257/B/16/Z)
Topics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Atomic and Subatomic Physics Research
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
quantum technologies, functional neuroimaging, toddlers, children, adolescents, head movement, head growth, naturalistic learning, non-invasive brain imaging
MeSH terms
Adolescent, Adult, Brain, Brain Mapping, Child, Child, Preschool, Cognition, Electrophysiology, Female, Humans, Infant, Male, Magnetoencephalography, Young Adult

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