Brain Topography

, Volume 26, Issue 4, pp 569–580 | Cite as

Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches

  • Kay Jann
  • Ariane Orosz
  • Thomas Dierks
  • Danny J. J. Wang
  • Roland Wiest
  • Andrea Federspiel
Original Paper


Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test–retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.


Functional connectivity Independent component analysis Seed based analysis Arterial spin labeling Network quantification Cerebral blood flow 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Kay Jann
    • 1
    • 2
  • Ariane Orosz
    • 1
  • Thomas Dierks
    • 1
  • Danny J. J. Wang
    • 2
  • Roland Wiest
    • 3
  • Andrea Federspiel
    • 1
  1. 1.Department of Psychiatric NeurophysiologyUniversity Hospital of Psychiatry, University of BernBern 60Switzerland
  2. 2.Department of NeurologyAhmanson-Lovelace Brain Mapping Center, UCLALos AngelesUSA
  3. 3.Department of Diagnostic and Interventional NeuroradiologyInselspital and University of BernBernSwitzerland

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