title: Cortical source analysis of high-density EEG recordings in children.
abstract: EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited, because the composition and spatial configuration of head tissues changes dramatically over development. In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.
authors.affiliation: Cognitive Neuroscience & Neuropsychiatry Section, UCL Institute of Child Health; johannes.bathelt.10@ucl.ac.uk.
authors.forename: Joe
authors.initials: J
authors.lastname: Bathelt
authors.affiliation: Academic Division of Neonatology, Institute for Women's Health, University College London.
authors.forename: Helen
authors.initials: H
authors.lastname: O'Reilly
authors.affiliation: Cognitive Neuroscience & Neuropsychiatry Section, UCL Institute of Child Health.
authors.forename: Michelle
authors.initials: M
authors.lastname: de Haan
mesh_terms: D000367:Age Factors; D002540:Cerebral Cortex / Q000502:physiology*; D002648:Child; D002675:Child, Preschool; D003071:Cognition / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008297:Male; D008959:Models, Neurological