Hand classification of fMRI ICA noise components.
Neuroimage
; 154: 188-205, 2017 07 01.
Article
en En
| MEDLINE
| ID: mdl-27989777
ABSTRACT
We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Encéfalo
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Imagen por Resonancia Magnética
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Neuroimagen Funcional
Tipo de estudio:
Guideline
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Prognostic_studies
Límite:
Adult
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Child
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Humans
Idioma:
En
Revista:
Neuroimage
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2017
Tipo del documento:
Article