Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to m...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Guillaume, Bryan [verfasserIn]

Wang, Changqing

Poh, Joann

Shen, Mo Jun

Ong, Mei Lyn

Tan, Pei Fang

Karnani, Neerja

Meaney, Michael

Qiu, Anqi

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2018transfer abstract

Schlagwörter:

Non-parametric testing

Neonatal brain

Unknown covariates

Epigenetics

Univariate analysis

Umfang:

15

Übergeordnetes Werk:

Enthalten in: Field study of a soft X-ray aerosol neutralizer combined with electrostatic classifiers for nanoparticle size distribution measurements - Nicosia, Alessia ELSEVIER, 2017, a journal of brain function, Orlando, Fla

Übergeordnetes Werk:

volume:173 ; year:2018 ; pages:57-71 ; extent:15

Links:

Volltext

DOI / URN:

10.1016/j.neuroimage.2018.01.073

Katalog-ID:

ELV042718724

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