High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer’s disease (AD) using high-dimensional...

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Detalles Bibliográficos
Autores principales: Dipasquale, Ottavia, Griffanti, Ludovica, Clerici, Mario, Nemni, Raffaello, Baselli, Giuseppe, Baglio, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315015/
https://www.ncbi.nlm.nih.gov/pubmed/25691865
http://dx.doi.org/10.3389/fnhum.2015.00043

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315015/
https://www.ncbi.nlm.nih.gov/pubmed/25691865
http://dx.doi.org/10.3389/fnhum.2015.00043

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