Method to detect the default mode network in fMRI data corrupted with head motion

Abstract. The Default Mode Network (DMN) is a neuronal network widely used for grading the neu- rological state in patients with disorders of consciousness (DoC). It is detected in functional magnetic resonance imaging (fMRI) when the subject is in resting state conditions. However, provided that Do...

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Autores:
Baquero Duarte, Katherine Andrea
Tipo de recurso:
Fecha de publicación:
2013
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/49705
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/49705
http://bdigital.unal.edu.co/43192/
Palabra clave:
61 Ciencias médicas; Medicina / Medicine and health
62 Ingeniería y operaciones afines / Engineering
65 Gerencia y servicios auxiliares / Management and public relations
Default Mode Network (DMN)
Functional Magnetic Resonance Imaging (fMRI)
Disorders of Consciousness (DoC)
Large head motion
Multiscale
Red Neuronal por Defecto (DMN)
Imágenes de Resonancia Magnética Funcional (fMRI)
Desordenes de Consciencia (DoC)
Movimientos de cabeza
Representación multiescala
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Abstract. The Default Mode Network (DMN) is a neuronal network widely used for grading the neu- rological state in patients with disorders of consciousness (DoC). It is detected in functional magnetic resonance imaging (fMRI) when the subject is in resting state conditions. However, provided that DoC patients present a high degree of head motion, the fMRI imaging pro- cess will end up contaminated and the DMN will be completely lost or highly blurred. This work presents a novel multiscale method that improves DMN measurement in healthy control subjects and DoC patients whose data has been perturbed by head motion. The multiscale approach consists in �nding the relevant neuronal information at each scale so that the �nal DMN measurement is improved, considering a weighted compensation of the DMN at each scale. This method was compared with two baseline fMRI data processing methods: (1) a basic pipeline without special motion artifact reduction steps; and (2) a method that includes steps that aim to remove artifacts caused by motion.