High-dimensional ica analysis detects within-network functional connectivity damage of default-mode and sensory-motor networks in alzheimer’s disease

Ottavia Dipasquale, Ludovica Griffanti, Mario Clerici, Raffaello Nemni, Giuseppe Baselli, Francesca Baglio

Research output: Contribution to journalArticlepeer-review

Abstract

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 ICA. For this reason, we performed both lowand high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN.

Original languageEnglish
Article number43
JournalFrontiers in Human Neuroscience
Volume9
Issue numberFEB
DOIs
Publication statusPublished - Feb 3 2015

Keywords

  • Alzheimer’s disease
  • Default-mode network
  • Functional connectivity
  • Group independent component analysis
  • Resting-state fMRI
  • Sensory-motor network

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Neurology
  • Biological Psychiatry
  • Behavioral Neuroscience
  • Neuropsychology and Physiological Psychology

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