An automated toolchain for quantitative characterisation of structural connectome from MRI based on non-anatomical cortical parcellation.
Annu Int Conf IEEE Eng Med Biol Soc
; 2020: 5653-5656, 2020 07.
Article
en En
| MEDLINE
| ID: mdl-33019259
ABSTRACT
Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's brain or adult's brain with neuroplasticity anomalies. In this work, we design an end-to-end toolchain that processes raw MRI data and generates network metrics for brain connectivity analysis using non-anatomical equal-area parcellation. We process the structural and diffusion MRI data to generate the parcellated and segmented image, extract white matter tracks and build structural connectome and then interface it with Brain Connectivity Toolbox to extract graph theory measures.Clinical relevance An automated tool for end-to-end processing of MRI data to brain connectivity pattern extraction and its quantitative characterisation for diagnosing brain disorder.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Conectoma
/
Sustancia Blanca
Límite:
Adult
/
Humans
/
Newborn
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2020
Tipo del documento:
Article