Consistent resting-state networks across healthy subjects. Export

@article{citeulike:836987, abstract = {Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3\%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.}, address = {Departments of Neurology, Physics and Medical Technology, Radiology, and Clinical Neurophysiology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; Leiden Institute for Brain and Cognition, Department of Radiology, Leiden University Medical Center, and Department of Psychology, Leiden University, Postzone C2-S, P.O. Box 9600, 2300 RC Leiden, The Netherlands.}, author = {Damoiseaux, J S S. and Rombouts, S A R B A. and Barkhof, F and Scheltens, P and Stam, C J J. and Smith, S M M. and Beckmann, C F F.}, citeulike-article-id = {836987}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0601417103}, citeulike-linkout-1 = {http://www.pnas.org/cgi/content/abstract/103/37/13848}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/16945915}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=16945915}, day = {31}, doi = {10.1073/pnas.0601417103}, issn = {0027-8424}, journal = {Proc Natl Acad Sci U S A}, keywords = {complex-network, ica, resting-state}, month = {August}, posted-at = {2006-09-11 10:15:34}, priority = {0}, title = {Consistent resting-state networks across healthy subjects.}, url = {http://dx.doi.org/10.1073/pnas.0601417103}, year = {2006} }

TY - JOUR ID - citeulike:836987 L3 - citeulike-article-id:836987 N2 - Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments. JF - Proc Natl Acad Sci U S A SN - 0027-8424 AD - Departments of Neurology, Physics and Medical Technology, Radiology, and Clinical Neurophysiology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; Leiden Institute for Brain and Cognition, Department of Radiology, Leiden University Medical Center, and Department of Psychology, Leiden University, Postzone C2-S, P.O. Box 9600, 2300 RC Leiden, The Netherlands. TI - Consistent resting-state networks across healthy subjects. KW - complex-network KW - ica KW - resting-state AU - Damoiseaux, J S AU - Rombouts, S A R B AU - Barkhof, F AU - Scheltens, P AU - Stam, C J AU - Smith, S M AU - Beckmann, C F PY - 2006/08/31/ UR - http://dx.doi.org/10.1073/pnas.0601417103 ER -