A graph-based brain parcellation method extracting sparse networks

Nicolas Honnorat, Harini Eavani, Theodore D. Satterthwaite, Christos Davatzikos

Producción científica: Conference contribution

2 Citas (Scopus)

Resumen

FMRI is a powerful tool for assessing the functioning of the brain. The analysis of resting-state fMRI allows to describe the functional relationship between the cortical areas. Since most connectivity analysis methods suffer from the curse of dimensionality, the cortex needs to be first partitioned into regions of coherent activation patterns. Once the signals of these regions of interest have been extracted, estimating a sparse approximation of the inverse of their correlation matrix is a classical way to robustly describe their functional interactions. In this paper, we address both objectives with a novel parcellation method based on Markov Random Fields that favors the extraction of sparse networks of regions. Our method relies on state of the art rsfMRI models, naturally adapts the number of parcels to the data and is guaranteed to provide connected regions due to the use of shape priors. The second contribution of this paper resides in two novel sparsity enforcing potentials. Our approach is validated with a publicly available dataset.

Idioma originalEnglish (US)
Título de la publicación alojadaProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
Páginas157-160
Número de páginas4
DOI
EstadoPublished - 2013
Publicado de forma externa
Evento2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 - Philadelphia, PA, United States
Duración: jun 22 2013jun 24 2013

Serie de la publicación

NombreProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013

Other

Other2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
País/TerritorioUnited States
CiudadPhiladelphia, PA
Período6/22/136/24/13

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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