Does function fit structure? A ground truth for non-invasive neuroimaging

ABSTRACT: There are now a number of non-invasive methods to image human brain function in-vivo. However, the accuracy of these images remains unknown and can currently only be estimated through the use of invasive recordings to generate a functional ground truth. Neuronal activity follows grey matter structure and accurate estimates of neuronal activity will have stronger support from accurate generative models of anatomy. Here we introduce a general framework that, for the first time, enables the spatial distortion of a functional brain image to be estimated empirically. We use a spherical harmonic decomposition to modulate each cortical hemisphere from its original form towards progressively simpler structures, ending in an ellipsoid. Functional estimates that are not supported by the simpler cortical structures have less inherent spatial distortion. This method allows us to compare directly between magnetoencephalography (MEG) source reconstructions based upon different assumption sets without recourse to functional ground truth.

• Autores: López Hincapié, José David; Stevenson, Claire M.; Brookes, Matthew J.; Troebinger, Luzia; Mattout, Jérémie; Penny, Will D.; Morris, Peter Gordon; Hillebrand, Arjan; Henson, Richard N.; Barnes, Gareth Robert
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2014
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/41655
• Acceso en línea: https://hdl.handle.net/10495/41655
• Palabra clave: Algoritmos; Algorithms; Modelos Neurológicos; Models, Neurological; Mapeo Encefálico; Brain Mapping; Simulación por Computador; Computer Simulation; Magnetoencefalografía; Magnetoencephalography; Modelos Neurológicos; Models, Neurological; Modelos Anatómicos; Models, Anatomic; Sustancia Gris; Gray Matter; Reproducibilidad de los Resultados; Reproducibility of Results; Sensibilidad y Especificidad; Sensitivity and Specificity; https://id.nlm.nih.gov/mesh/D000465; https://id.nlm.nih.gov/mesh/D008959; https://id.nlm.nih.gov/mesh/D001931; https://id.nlm.nih.gov/mesh/D003198; https://id.nlm.nih.gov/mesh/D015225; https://id.nlm.nih.gov/mesh/D008959; https://id.nlm.nih.gov/mesh/D008953; https://id.nlm.nih.gov/mesh/D066128; https://id.nlm.nih.gov/mesh/D015203; https://id.nlm.nih.gov/mesh/D012680
• Rights: openAccess
• License: http://creativecommons.org/licenses/by/2.5/co/