Fulltext

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

Jann, Kay ; Orosz, Ariane ; Dierks, Thomas ; Wang, Danny ; Wiest, Roland ; Federspiel, Andrea

In: Brain Topography, 2013, vol. 26, no. 4, p. 569-580

Add to personal list
    Title
    • Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches
    Author
    • Jann, Kay. Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000, Bern 60, Switzerland
    • Orosz, Ariane. Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000, Bern 60, Switzerland
    • Dierks, Thomas. Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000, Bern 60, Switzerland
    • Wang, Danny. Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA
    • Wiest, Roland. Department of Diagnostic and Interventional Neuroradiology, Inselspital and University of Bern, Bern, Switzerland
    • Federspiel, Andrea. Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000, Bern 60, Switzerland
    Document Type
    • Postprint
    Language
    • English
    Published in
    • Brain Topography, 2013, vol. 26, no. 4, p. 569-580. Springer US; http://www.springer-ny.com
    Other Version
    OAI-PMH Identifier
    • oai:doc.rero.ch:309554
    Summary
    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