Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician

Abstract

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

Keywords: Magnetoencephalography; clinical; connectivity; epilepsy; neurophysiology.

Figure 1.

Electrophysiological basis of MEG signals and example of dipole and beamformer source localization results. (a) Shows an action potential generated from a neuron along with a corresponding electric and magnetic field that are produced by the movement of negative charge along the outer membrane of the axon. A magnified simplified view of the measurement of electric (post-synaptic potentials) and magnetic (intracellular current) fields generated. The two EEG electrodes measure the difference in voltage from two groups of neurons along the neuronal axon, while the MEG coil measures unreferenced magnetic flux produced tangentially from a single group of neurons. This can lead to differences in field strength between MEG and EEG in both gyri and sulci, for this type of activity. Figure created using BioRender software. (b) Example of CTF’s dipole source algorithm interface. The observed waveform with overlaid sensors can be seen at the bottom, with the red vertical line indicating the selected time point. The top three views show the computed dipole location. Information regarding the exact time point, goodness-of-fit/error, and dipole coordinates are also provided. Topographic maps looking down on the head with the plot for the acquired data at the top, the reconstructed data from the forward solution in the middle, and the difference at the bottom. Example of the source localization applied using a beamforming algorithm.

Figure 2.

Contemporary magnetoencephalography platforms. (a) Photograph of the CTF MEG system at the Hospital for Sick Children (Toronto, ON, Canada). (b) Photograph of the pediatric BabyMEG system at the Boston Children’s Hospital (Boston, MA, USA). (c) Photograph of the BabySQUID system at the Boston Children’s Hospital (Waltham, MA, USA).

Figure 3.

Identifying the irritative zone (Example 1). A pediatric patient (younger than 10 years of age) with intractable epilepsy. Spontaneous data were collected with the whole-head pediatric MEG system at Boston Children’s Hospital while the patient was awake. Data were bandpass filtered between 4 and 70 Hz with a notch filter at 60 Hz, bad channels and segments of the data with motion artifacts were removed from the analysis. Data was analyzed with the Brainstorm software. T1-weighted MRI data of the patient were processed through the Freesurfer software and registered to the patient’s brain for source modeling to be performed using the Boundary Element Method. Equivalent current dipoles were thresholded at 70%. No ictal activity was observed in the data. Most of the interictal spikes had a tight cluster of dipoles localized in the right parietal lobe where the center of the cluster surrounds the intraparietal sulcus extending both to the superior and inferior parietal lobule. The anterior aspect of the intraparietal sulcus had a suspected focal cortical dysplasia. There were also scattered dipoles localized to the right frontal lobe, primarily in the parasagittal region.

Figure 4.

Identifying the irritative zone (Example 2). A pediatric patient (younger than 5 years of age) with tuberous sclerosis complex, developmental delay, infantile spasms, refractory complex partial seizures, and status epilepticus. Simultaneous spontaneous data during natural sleep were collected using the whole-head pediatric MEG system at Boston Children’s Hospital. Data were bandpass filtered between 4 and 60 Hz with a notch filter at 60 Hz, bad channels and data segments were removed from the analysis. The data was analyzed with the Brainstorm software. Patient’s T1-weighted MRI data was processed with the Freesurfer software and co-registered to the patient’s brain for source modeling to be performed by the Boundary Element Method. Equivalent current dipoles were thresholded at 85%. No ictal activity was observed. Interictal activity was observed over the right hemisphere where dipole localization involved the inferior right precentral gyrus, adjacent anterior inferior right parietal lobe, posterior right insula, and posterior aspect of the right superior temporal gyrus. There is a tuber in the precentral gyrus just superior to the dipole cluster in the right inferior precentral gyrus.

Figure 5.

Mapping of eloquent cortices. Example of typical averaged waveforms with overlaid sensors (first column) for somatosensory, auditory, visual, and language paradigms. The thin vertical lines (for sensory, auditory, and visual) indicate the time point of interest for the dipole fit, and the yellow box (for language) indicates the time points for a moving dipole fit. The middle column shows topographic plots at the selected time points. The projection by convention is looking down on the head with the nose at the top and the ears at the sides. The last column shows the dipole location obtained from the source reconstruction algorithm, and then overlaid onto individual MRIs after co-registration with fiducials.

Figure 6.

Auditory evoked fields. Auditory evoked fields generated by the auditory presentation of beep sounds (∼220 epochs) from the left hemispheres of a typically developing (TD) child (aged between 7 and 12 years old) and a child with an autism spectrum disorder (ASD) (aged between 7 and 12 years old). Data were collected with the BabySQUID system, at Boston Children’s Hospital, Waltham. BabySQUID (Tristan Technologies, Inc., San Diego, CA, USA) is a pediatric MEG system (143) (BCH, Waltham), with a partial head coverage sensor array (coverage area ∼265 cm²) consisting of 76-axial gradiometers (10 mm pickup coil diameter, 30 mm baseline, and 12–14 mm coil center-to-coil center spacing, gap of 7–10 mm between each coil and scalp). During the recording, children lay on the BabySQUID bed, resting their head on the helmet. Minimum norm estimate (in pico ampere meter) solutions overlaid on the participant’s own cortical surface for the TD participant and on an age matched template brain for the child with ASD at the peak of M100 which was delayed in the child with ASD compared to the TD child as shown in previous studies [103].