Forskning ved Københavns Universitet - Københavns Universitet

Forside

A method for independent component graph analysis of resting-state fMRI

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Dokumenter

  • Demetrius Ribeiro de Paula
  • Erik Ziegler
  • Pubuditha M. Abeyasinghe
  • Tushar K. Das
  • Carlo Cavaliere
  • Marco Aiello
  • Lizette Heine
  • Carol di Perri
  • Athena Demertzi
  • Quentin Noirhomme
  • Vanessa Charland-Verville
  • Audrey Vanhaudenhuyse
  • Stender, Johan
  • Francisco Gomez
  • Jean-Flory L. Tshibanda
  • Steven Laureys
  • Adrian M. Owen
  • Andrea Soddu
Introduction
Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.

Objective
Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.

Methods
First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network “neuronal” properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network.

Results
Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness.

Conclusions
This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.
OriginalsprogEngelsk
Artikelnummere00626
TidsskriftBrain and Behavior
Vol/bind7
Udgave nummer3
Antal sider12
ISSN2162-3279
DOI
StatusUdgivet - 2017

Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk


Ingen data tilgængelig

ID: 182582675