Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets Export

@article{citeulike:2667816, 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 been applied 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 in areas 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 removes activation from the data and may allow us to study functional connectivity even in the activated regions.}, author = {Arfanakis, Konstantinos and Cordes, Dietmar and Haughton, Victor M. and Moritz, Chad H. and Quigley, Michelle A. and Meyerand, Mary E.}, citeulike-article-id = {2667816}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0730-725X(00)00190-9}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6T9D-41S52BF-1/1/ee086e62f34436e58049dcfd1f8744bf}, doi = {10.1016/S0730-725X(00)00190-9}, journal = {Magnetic Resonance Imaging}, keywords = {activation, fmri, ica, resting-state}, month = {October}, number = {8}, pages = {921--930}, posted-at = {2008-04-16 16:05:37}, priority = {2}, title = {Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets}, url = {http://dx.doi.org/10.1016/S0730-725X(00)00190-9}, volume = {18}, year = {2000} }

TY - JOUR ID - citeulike:2667816 L3 - citeulike-article-id:2667816 N2 - 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 been applied 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 in areas 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 removes activation from the data and may allow us to study functional connectivity even in the activated regions. IS - 8 JF - Magnetic Resonance Imaging EP - 930 TI - Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets VL - 18 SP - 921 KW - activation KW - fmri KW - ica KW - resting-state AU - Arfanakis, Konstantinos AU - Cordes, Dietmar AU - Haughton, Victor AU - Moritz, Chad AU - Quigley, Michelle AU - Meyerand, Mary PY - 2000/10// UR - http://dx.doi.org/10.1016/S0730-725X(00)00190-9 ER -