TY - JOUR
T1 - Sparse cortical current density imaging in motor potentials induced by finger movement
AU - Ding, Lei
AU - Ni, Ying
AU - Sweeney, John
AU - He, Bin
PY - 2011/6
Y1 - 2011/6
N2 - Predominant components in electro- or magneto-encephalography (EEG/MEG) are scalp projections of synchronized neuronal electrical activity distributed over cortical structures. Reconstruction of cortical sources underlying EEG/MEG can thus be achieved with the use of the cortical current density (CCD) model. We have developed a sparse electromagnetic source imaging method based on the CCD model, named as the variation-based cortical current density (VB-SCCD) algorithm, and have shown that it has much enhanced performance in reconstructing extended cortical sources in simulations (Ding 2009 Phys. Med. Biol. 54 2683-97). The present study aims to evaluate the performance of VB-SCCD, for the first time, using experimental data obtained from six participants. The results indicate that the VB-SCCD algorithm is able to successfully reveal spatially distributed cortical sources behind motor potentials induced by visually cued repetitive finger movements, and their dynamic patterns, with millisecond resolution. These findings of motor sources and cortical systems are supported by the physiological knowledge of motor control and evidence from various neuroimaging studies with similar experiments. Furthermore, our present results indicate the improvement of cortical source resolvability of VB-SCCD, as compared with two other classical algorithms. The proposed solver embedded in VB-SCCD is able to handle large-scale computational problems, which makes the use of high-density CCD models possible and, thus, reduces model misspecifications. The present results suggest that VB-SCCD provides high resolution source reconstruction capability and is a promising tool for studying complicated dynamic systems of brain activity for basic neuroscience and clinical neuropsychiatric research.
AB - Predominant components in electro- or magneto-encephalography (EEG/MEG) are scalp projections of synchronized neuronal electrical activity distributed over cortical structures. Reconstruction of cortical sources underlying EEG/MEG can thus be achieved with the use of the cortical current density (CCD) model. We have developed a sparse electromagnetic source imaging method based on the CCD model, named as the variation-based cortical current density (VB-SCCD) algorithm, and have shown that it has much enhanced performance in reconstructing extended cortical sources in simulations (Ding 2009 Phys. Med. Biol. 54 2683-97). The present study aims to evaluate the performance of VB-SCCD, for the first time, using experimental data obtained from six participants. The results indicate that the VB-SCCD algorithm is able to successfully reveal spatially distributed cortical sources behind motor potentials induced by visually cued repetitive finger movements, and their dynamic patterns, with millisecond resolution. These findings of motor sources and cortical systems are supported by the physiological knowledge of motor control and evidence from various neuroimaging studies with similar experiments. Furthermore, our present results indicate the improvement of cortical source resolvability of VB-SCCD, as compared with two other classical algorithms. The proposed solver embedded in VB-SCCD is able to handle large-scale computational problems, which makes the use of high-density CCD models possible and, thus, reduces model misspecifications. The present results suggest that VB-SCCD provides high resolution source reconstruction capability and is a promising tool for studying complicated dynamic systems of brain activity for basic neuroscience and clinical neuropsychiatric research.
UR - http://www.scopus.com/inward/record.url?scp=79957941276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79957941276&partnerID=8YFLogxK
U2 - 10.1088/1741-2560/8/3/036008
DO - 10.1088/1741-2560/8/3/036008
M3 - Article
C2 - 21478573
AN - SCOPUS:79957941276
SN - 1741-2560
VL - 8
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 3
M1 - 036008
ER -