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Titolo:
Combining independent component analysis and correlation analysis to probeinterregional connectivity in fMRI task activation datasets
Autore:
Arfanakis, K; Cordes, D; Haughton, VM; Moritz, CH; Quigley, MA; Meyerand, ME;
Indirizzi:
Univ Wisconsin, Dept Med Phys, Madison, WI 53706 USA Univ Wisconsin Madison WI USA 53706 Dept Med Phys, Madison, WI 53706 USA Univ Wisconsin, Dept Radiol, Madison, WI 53706 USA Univ Wisconsin MadisonWI USA 53706 n, Dept Radiol, Madison, WI 53706 USA
Titolo Testata:
MAGNETIC RESONANCE IMAGING
fascicolo: 8, volume: 18, anno: 2000,
pagine: 921 - 930
SICI:
0730-725X(200010)18:8<921:CICAAC>2.0.ZU;2-0
Fonte:
ISI
Lingua:
ENG
Soggetto:
FUNCTIONAL CONNECTIVITY; HUMAN BRAIN; DATA SETS; SENSORY STIMULATION; BLIND SEPARATION; MOTOR CORTEX; MRI;
Keywords:
fMRI; connectivity; ICA; activation removal; correlation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Citazioni:
32
Recensione:
Indirizzi per estratti:
Indirizzo: Arfanakis, K Univ Wisconsin, Dept Med Phys, 1530 Med Sci Ctr,1300 Univ Ave, Madison, WI53706 USA Univ Wisconsin 1530 Med Sci Ctr,1300 Univ Ave Madison WI USA 53706
Citazione:
K. Arfanakis et al., "Combining independent component analysis and correlation analysis to probeinterregional connectivity in fMRI task activation datasets", MAGN RES IM, 18(8), 2000, pp. 921-930

Abstract

A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has beenapplied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed inareas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removesactivation from the data and may allow us to study functional connectivityeven in the activated regions. (C) 2000 Elsevier Science Inc. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 04/04/20 alle ore 02:36:13