Comparison of a non-stationary voxelation-corrected cluster-size test with TFCE for group-Level MRI inference.
Hum Brain Mapp
; 38(3): 1269-1280, 2017 03.
Article
em En
| MEDLINE
| ID: mdl-27785843
ABSTRACT
Two powerful methods for statistical inference on MRI brain images have been proposed recently, a non-stationary voxelation-corrected cluster-size test (CST) based on random field theory and threshold-free cluster enhancement (TFCE) based on calculating the level of local support for a cluster, then using permutation testing for inference. Unlike other statistical approaches, these two methods do not rest on the assumptions of a uniform and high degree of spatial smoothness of the statistic image. Thus, they are strongly recommended for group-level fMRI analysis compared to other statistical methods. In this work, the non-stationary voxelation-corrected CST and TFCE methods for group-level analysis were evaluated for both stationary and non-stationary images under varying smoothness levels, degrees of freedom and signal to noise ratios. Our results suggest that, both methods provide adequate control for the number of voxel-wise statistical tests being performed during inference on fMRI data and they are both superior to current CSTs implemented in popular MRI data analysis software packages. However, TFCE is more sensitive and stable for group-level analysis of VBM data. Thus, the voxelation-corrected CST approach may confer some advantages by being computationally less demanding for fMRI data analysis than TFCE with permutation testing and by also being applicable for single-subject fMRI analyses, while the TFCE approach is advantageous for VBM data. Hum Brain Mapp 381269-1280, 2017. © 2016 Wiley Periodicals, Inc.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Encéfalo
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Mapeamento Encefálico
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Imageamento por Ressonância Magnética
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Hum Brain Mapp
Assunto da revista:
CEREBRO
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
China