Impact of brain parcellation on prediction performance in models of cognition and demographics.
Hum Brain Mapp
; 45(2): e26592, 2024 Feb 01.
Article
en En
| MEDLINE
| ID: mdl-38339892
ABSTRACT
Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Mapeo Encefálico
/
Imagen por Resonancia Magnética
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Hum Brain Mapp
Asunto de la revista:
CEREBRO
Año:
2024
Tipo del documento:
Article
País de afiliación:
Reino Unido