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

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Abstract

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.

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Authors and Affiliations

  1. Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000, Bern 60, Switzerland

    Kay Jann, Ariane Orosz, Thomas Dierks & Andrea Federspiel

  2. Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA

    Kay Jann & Danny J. J. Wang

  3. Department of Diagnostic and Interventional Neuroradiology, Inselspital and University of Bern, Bern, Switzerland

    Roland Wiest

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  1. Kay Jann
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  2. Ariane Orosz
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  3. Thomas Dierks
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  4. Danny J. J. Wang
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  5. Roland Wiest
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  6. Andrea Federspiel
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Correspondence to Kay Jann.

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Jann, K., Orosz, A., Dierks, T. et al. Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches. Brain Topogr 26, 569–580 (2013). https://doi.org/10.1007/s10548-013-0280-3

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  • DOI: https://doi.org/10.1007/s10548-013-0280-3

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