Cargando…
A Bayesian spatial model for neuroimaging data based on biologically informed basis functions
The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are cha...
Autores principales: | Huertas, Ismael, Oldehinkel, Marianne, van Oort, Erik S.B., Garcia-Solis, David, Mir, Pablo, Beckmann, Christian F., Marquand, Andre F. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5692833/ https://www.ncbi.nlm.nih.gov/pubmed/28782681 http://dx.doi.org/10.1016/j.neuroimage.2017.08.009 |
Ejemplares similares
-
Bayesian multi-task learning for decoding multi-subject neuroimaging data
por: Marquand, Andre F., et al.
Publicado: (2014) -
Mapping dopaminergic projections in the human brain with resting-state fMRI
por: Oldehinkel, Marianne, et al.
Publicado: (2022) -
Warped Bayesian Linear Regression for Normative Modelling of Big Data
por: Fraza, Charlotte J., et al.
Publicado: (2021) -
Accommodating Site Variation In Neuroimaging Data Using Normative And Hierarchical Bayesian Models
por: Bayer, Johanna M. M., et al.
Publicado: (2022) -
Striatal connectopic maps link to functional domains across psychiatric disorders
por: Mulders, Peter C. R., et al.
Publicado: (2022)