While it is well known that the brain undergoes rapid developmental changes from birth to early childhood, remarkably little is understood about the relationship between changes in brain size and composition and normal cognitive development. Yet we now know that several potentially debilitating disorders, among them the Autism Spectral Disorders, Attention Deficit Hyperactivity Disorder and Schizophrenia, are a consequence of delays or abnormalities in brain development. In children, the study of normal cognitive and brain development is best accomplished using non-invasive techniques that are not overly restrictive of movement and do not require ionizing radiation. Of available techniques, electroencephalography (EEG), particularly with the advent of high density sensor arrays, provides the ability to assess cognitive function safely and non-invasively. However, to provide functional localization of cognitively important brain regions and networks requires an accurate model of head tissue geometry and conductivity, particularly in the first years of life, when skull and brain change rapidly in composition and size. This Phase I project will create age-group head models based on measured conductivity values of skull and brain for five age groups to determine when changes in head shape, size and composition significantly impact the ability to accurately localize seizure activity. By introducing advanced computational resources for creating patient-specific head models, we will allow to optimize the use of non-invasive dense-array EEG to elucidate the developmental trajectory of neural networks underlying cognition in normal children.
The product innovation proposed in this project will create age-group pediatric head models for neuroimaging . By introducing advanced computational resources for creating age-specific child head models, this technology will provide clinicians and researchers with a tool for optimal use of non-invasive high density EEG in localizing seizure activities and elucidating the developmental trajectory of neural networks underlying cognition in normal children.
