Magnetic Source Imaging and Infant MEG: Current Trends and Technical Advances

Kao, Chieh; Zhang, Yang

doi:10.3390/brainsci9080181

Cited by 10 publications

(7 citation statements)

References 115 publications

(167 reference statements)

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“…Source estimation has not been frequently reported in the infant EEG literature, with the notable exception of a few research groups that used volumetric approaches such as CDR with realistic head models built from MRIs of head-size-matched individuals ( Xie and Richards, 2017 ) or from age-matched MRI averages ( Lunghi et al, 2019 ), or using ECD with a four-shell ellipsoidal head model ( Ortiz-Mantilla et al, 2019 ). Source imaging is more frequently used in magnetoencephalography (MEG) ( Kao and Zhang, 2019 ), but it generally relies on over simplistic spherical models to overcome the absence of realistic head models ( Imada et al, 2006 ; Kuhl et al, 2014 ) or it uses custom-built subject-specific head models that are not reusable by the research community ( Ramírez et al, 2017 ; Travis et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Structural templates for imaging EEG cortical sources in infants

et al. 2021

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Electroencephalographic (EEG) source reconstruction is a powerful approach that allows anatomical localization of electrophysiological brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, we introduce a new series of 13 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and boundary element method (BEM) segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. Source reconstruction with these templates is demonstrated and validated using 100 high-density EEG recordings from 7-month-old infants. Hopefully, these templates will support future studies on EEG-based neuroimaging and functional connectivity in healthy infants as well as in clinical pediatric populations.

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Section: Introductionmentioning

confidence: 99%

Structural templates for imaging EEG cortical sources in infants

et al. 2021

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“…In the past few decades, neuroimaging has been widely used to study brain diseases [7][8][9]. Neuroimaging technology provides anatomical and functional images of the brain, such as Positron Emission Computed Tomography (PECT), Structural Magnetic Resonance Imaging (SMRI), Diffusion Magnetic Resonance Imaging (DMRI), Functional Magnetic Resonance Imaging (FMRI), Electroencephalogram (EEG), and Magnetoencephalography (MEG) [10][11][12]. Among them, SMRI is often used for the characterization and prediction of AD due to its relatively low cost and good imaging quality.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Based Multi-Model Learning for MCI Diagnosis

2020

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Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).

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“…Since neuroimage recording requires children to stay still for a long period of time in a noisy or closed environment, the requirements for children are exacting and difficult to meet [37]. FMRI studies on young children are usually conducted during natural sleep to conduct resting-state network-related studies; therefore, the ability to directly investigate brain function during tasks is limited (such as facial recognition or language processing) [38]. Near-infrared (NIR) imaging technology, similar to functional magnetic resonance imaging technology, is a noninvasive method for measuring the absorption of near-infrared light through the skull, allowing researchers to speculate on the proxy of nerve activation, which is attributed to the relative changes in oxygenated and deoxygenated hemoglobin concentrations in blood vessels in the cortical structure [39]- [40].…”

Section: Why Meg For Ndds Research?mentioning

confidence: 99%

Whole-Head Magnetoencephalogram and Its Application in Developmental Communication Disorders Research: A Review

Liang

et al. 2021

IEEE Access

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The human brain undergoes tremendous changes during the first decade of life. The susceptibility of the immature central nervous system to factors such as adverse environments and genetic factors is attributed to its plasticity. Neurodevelopmental disorders (NDDs) occur once the immature brain veers off the typical developmental trajectory. However, little is known about the neurophysiological traits of atypically developing children because it is challenging for them to remain still during recording. Magnetoencephalography (MEG) is a noninvasive neuroimaging technique with high-spatiotemporal resolution. An ultralow magnetic field produced by neural electrical activity can be detected using a highly sensitive wholehead magnetometer array. Current cryogenic SQUID-MEG combined with individual MRI has shown great potential in reflecting neural activity under resting states and during tasks. MEG instrumentation for children is expected to be an effective tool for investigating brain dynamics, which provides neuropsychological evidence for atypical development in communication ability. In this paper, the advantages of pediatric MEG in neurodevelopmental disorder studies are discussed first and compared with those of several noninvasive functional imaging modalities. Existing commercial pediatric MEG systems are summarized based on their respective characteristics and parameters. State-of-the-art newly emerging sensing techniques based on spinexchange relaxation-free (SERF) along with their application in detecting infants' brain responses are introduced. Recent discoveries on MEG biomarkers in children with NDDs are then summarized concerning disorders that have comorbidities in communication, such as specific language impairment (SLI), autism spectrum disorders (ASD), dyslexia and stuttering. Progress on multimodality research and total-field wearable SERF magnetometers offers insight into the neuropsychological substrates of communication disorders.

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Magnetic Source Imaging and Infant MEG: Current Trends and Technical Advances

Cited by 10 publications

References 115 publications

Structural templates for imaging EEG cortical sources in infants

Structural templates for imaging EEG cortical sources in infants

Multi-View Based Multi-Model Learning for MCI Diagnosis

Whole-Head Magnetoencephalogram and Its Application in Developmental Communication Disorders Research: A Review

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