A new open-source tool for EEG source reconstruction in infants   Christian O’Reilly1,2*, Mayada Elsabbagh1 & the BASIS Team3 1 - Douglas Mental Health University Institute, McGill University 2 - Blue Brain Project, École Polytechnique Fédérale de Lausanne 3 - Birkbeck, University of London *Corresponding author: christian.oreilly@epfl.ch 1. Background & objectives 4. Results Source reconstruction of EEG is desirable. Reconstruction of EEG cortical sources can be used to associate brain activity with cortical regions. Such localization often allows investigating more insightful research questions than scalp recordings would. Source reconstruction is seldom performed in ASD. EEG source localization is rarely used in ASD studies, particularly in connectivity analyses where it has been used only once with a low-density electrode grid (Chan et al., 2015). 3. Methods Atlas from 90 infants recorded at 1 year (Li et al., 2015). Thresholding: GM ≥ WM GM > 0.25*max_intensity Probabilistic grey matter (GM) map Left hemisphere mask EEG recordings Merging WM and GM EEG cortical activity Functional connectivity matrix MRI/electrode co-registration Thresholding: GM < WM WM > 0.25*max_intensity Probabilistic white matter (WM) map Right hemisphere mask Electrode localization Cortical ribbon Tissue interfaces Figure 3. Reconstructed surfaces along with the projected regions of the Tzourio-Mazoyer et al. (2012) atlas. Functional connectivity graph 5. Conclusions Structural MRI Cortical mesh Average MRI Co-registration of the skull with standard EGI 128-channel system Take-home message An MRI template has been built for one-year-old infants for cortical source reconstruction from high-density EEG. The proposed template is available directly in Brainstorm (Matlab open-source toolbox available at http://neuroimage.usc.edu/brainstorm/). Open questions Could we benefit from ASD templates? Is the ASD population homogeneous enough for an population-averaged template to make sense? How could we benefit from both ASD and neurotypical templates to obtain more reliable source estimations without introducing a confounder? Potential improvment Use surface-based instead of volumetric MRI averaging (e.g., Macedo Rodrigues et al., 2015). Parcellation Scalp, outer skull and inner skull surfaces Figure 1. Workflow for the process of cortical source reconstruction. Propagation of volumetric parcellation to cortical ribbon by co-registering every vertex with the corresponding voxel Parcellation from (Tzourio-Mazoyer et al., 2002) [Using a 2.75 mm skull thickness (Li et al., 2015). ] Obstacles for source reconstruction of EEG in ASD Most ASD studies record EEG but not MRI. Potential alternative Use a population-averaged MRI template instead of individualized MRIs. Objectives To build a population-averaged template that can be used for EEG source reconstruction in one-year-old children when no individual MRI scans are available. 6. References Legend for software used for the different processing steps Chan et al. (2015) Neuroenhancement of Memory for Children with Autism by a Mind-Body Exercise, Front Psychol, 6(1893). Li et al., (2015) A Statistical Skull Geometry Model for Children 0-3 Years Old, PLoS One, 10(5), e0127322. Macedo Rodrigues et al. (2015) A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0–2 year age range. Front Hum Neurosci, 9(21). Shi et al. (2011) Infant Brain Atlases from Neonates to 1- and 2-Year-Olds, PLoS One, 6(4), e18746. Tzourio-Mazoyer, et al., (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, NeuroImage, 15(1), 273-289. NiBabel Figure 2. Methodology used to compute our template for one-year-old infants.