Real-time functional magnetic resonance imaging: methods and applications.

@article{citeulike:2831600, abstract = {Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment.}, address = {Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG London, UK. n.weiskopf@fil.ion.ucl.ac.uk}, author = {Weiskopf, N. and Sitaram, R. and Josephs, O. and Veit, R. and Scharnowski, F. and Goebel, R. and Birbaumer, N. and Deichmann, R. and Mathiak, K. }, citeulike-article-id = {2831600}, doi = {10.1016/j.mri.2007.02.007}, issn = {0730-725X}, journal = {Magnetic resonance imaging}, keywords = {social}, month = {July}, number = {6}, pages = {989--1003}, posted-at = {2008-05-25 21:14:47}, priority = {2}, title = {Real-time functional magnetic resonance imaging: methods and applications.}, url = {http://dx.doi.org/10.1016/j.mri.2007.02.007}, volume = {25}, year = {2007} }

TY - JOUR ID - citeulike:2831600 L3 - citeulike-article-id:2831600 TI - Real-time functional magnetic resonance imaging: methods and applications. JF - Magnetic resonance imaging VL - 25 IS - 6 SP - 989 EP - 1003 AD - Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG London, UK. n.weiskopf@fil.ion.ucl.ac.uk SN - 0730-725X N2 - Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment. KW - social AU - Weiskopf, N AU - Sitaram, R AU - Josephs, O AU - Veit, R AU - Scharnowski, F AU - Goebel, R AU - Birbaumer, N AU - Deichmann, R AU - Mathiak, K PY - 2007/07// UR - http://dx.doi.org/10.1016/j.mri.2007.02.007 ER -