Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction.
Med Image Anal
; 30: 11-29, 2016 May.
Article
en En
| MEDLINE
| ID: mdl-26849421
ABSTRACT
Segregating the human cortex into distinct areas based on structural connectivity criteria is of widespread interest in neuroscience. This paper presents a groupwise connectivity-based parcellation framework for the whole cortical surface using a new high quality diffusion dataset of 79 healthy subjects. Our approach performs gyrus by gyrus to parcellate the whole human cortex. The main originality of the method is to compress for each gyrus the connectivity profiles used for the clustering without any anatomical prior information. This step takes into account the interindividual cortical and connectivity variability. To this end, we consider intersubject high density connectivity areas extracted using a surface-based watershed algorithm. A wide validation study has led to a fully automatic pipeline which is robust to variations in data preprocessing (tracking type, cortical mesh characteristics and boundaries of initial gyri), data characteristics (including number of subjects), and the main algorithmic parameters. A remarkable reproducibility is achieved in parcellation results for the whole cortex, leading to clear and stable cortical patterns. This reproducibility has been tested across non-overlapping subgroups and the validation is presented mainly on the pre- and postcentral gyri.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Interpretación de Imagen Asistida por Computador
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Corteza Cerebral
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Técnica de Sustracción
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Imagen de Difusión Tensora
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Conectoma
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Sustancia Blanca
Tipo de estudio:
Diagnostic_studies
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Evaluation_studies
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Med Image Anal
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2016
Tipo del documento:
Article