Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration.
Stat Med
; 41(11): 2005-2024, 2022 05 20.
Article
em En
| MEDLINE
| ID: mdl-35118686
ABSTRACT
Functional magnetic resonance imaging (fMRI) is a non-invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting-state, that is, resting-state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high-resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high-dimensional functional data relying on depth-based notions that enable computationally efficient 2-dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Mapeamento Encefálico
/
Imageamento por Ressonância Magnética
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Suíça