A tool for functional brain imaging with lifespan compliance

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.

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. 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. 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. 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. 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. 6

Schematic overview of the OPM-MEG system

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