http://www.cnr.it/ontology/cnr/individuo/prodotto/ID1297

BOLD signal and vessel dynamics: a hierarchical cluster analysis (Articolo in rivista)

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BOLD signal and vessel dynamics: a hierarchical cluster analysis (Articolo in rivista) (literal)

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Garreffa, G; Ken, S; Macri, MA; Giulietti, G; Giove, F; Colonnese, C; Venditti, E; De Cesare, E; Galasso, V; Maraviglia, B (2006)
BOLD signal and vessel dynamics: a hierarchical cluster analysis
in Magnetic resonance imaging
(literal)

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Garreffa, G; Ken, S; Macri, MA; Giulietti, G; Giove, F; Colonnese, C; Venditti, E; De Cesare, E; Galasso, V; Maraviglia, B (literal)

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Univ Roma La Sapienza, Dipartimento Fis, I-00185 Rome, Italy; Museo Stor Fis, I-00184 Rome, Italy; Ctr & Ric Enrico Fermi, I-00184 Rome, Italy; Ist Neurol Mediterraneo, I-86077 Pozzilli, Italy; SOFT, Ctr Ric & Sviluppo, I-00185 Rome, Italy; Univ Roma La Sapienza, Dipartimento Med Sperimentale & Patol, I-00185 Rome, Italy (literal)

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Abstract

The aim of the present study was to analyze blood oxygenation level-dependent (BOLD) signal variation during an apnea-based task in order to assess the capability of a functional magnetic resonance imaging (MRI) procedure to estimate cerebral vascular dynamic effects. We measured BOLD contrast by hierarchical cluster analysis in healthy subjects undergoing an fMRI experiment, in which the task paradigm was one phase of inspirational apnea (IA). By processing the time courses of the fMRI experiment, analysis was performed only on a subclass of all the possible signal patterns; basically, root mean square and absolute variation differences have been calculated. Considering the baseline value obtained by computing the mean value of the initial rest period as reference, particular voxels showed relative important variations during the IA task and during the recovery phase following the IA. We focused our interest on the signal response of voxels that would correspond mainly to white and gray matter regions and that also may be affected by the proximity of large venous vessels. The results are presented as maps of space-temporal distribution of time series variations with two levels of hierarchical clustering among voxels with low to high initial response. Furthermore, we have presented a clustering of the signal response delay, conducting to a partition and identification of specified brain sites. (c) 2006 Elsevier Inc. All rights reserved. (literal)

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