Naoaki Tanaka1, Christos Papadelis2, Eleonora Tamilia2, Joseph R Madsen3, Phillip L Pearl4, Steven M Stufflebeam1. 1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, U.S.A. 2. Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A. 3. Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A. 4. Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A.
Abstract
INTRODUCTION: This study evaluates magnetoencephalographic (MEG) spike population as compared with intracranial electroencephalographic (IEEG) spikes using a quantitative method based on distributed source analysis. METHODS: We retrospectively studied eight patients with medically intractable epilepsy who had an MEG and subsequent IEEG monitoring. Fifty MEG spikes were analyzed in each patient using minimum norm estimate. For individual spikes, each vertex in the source space was considered activated when its source amplitude at the peak latency was higher than a threshold, which was set at 50% of the maximum amplitude over all vertices. We mapped the total count of activation at each vertex. We also analyzed 50 IEEG spikes in the same manner over the intracranial electrodes and created the activation count map. The location of the electrodes was obtained in the MEG source space by coregistering postimplantation computed tomography to MRI. We estimated the MEG- and IEEG-active regions associated with the spike populations using the vertices/electrodes with a count over 25. RESULTS: The activation count maps of MEG spikes demonstrated the localization associated with the spike population by variable count values at each vertex. The MEG-active region overlapped with 65 to 85% of the IEEG-active region in our patient group. CONCLUSIONS: Mapping the MEG spike population is valid for demonstrating the trend of spikes clustering in patients with epilepsy. In addition, comparison of MEG and IEEG spikes quantitatively may be informative for understanding their relationship.
INTRODUCTION: This study evaluates magnetoencephalographic (MEG) spike population as compared with intracranial electroencephalographic (IEEG) spikes using a quantitative method based on distributed source analysis. METHODS: We retrospectively studied eight patients with medically intractable epilepsy who had an MEG and subsequent IEEG monitoring. Fifty MEG spikes were analyzed in each patient using minimum norm estimate. For individual spikes, each vertex in the source space was considered activated when its source amplitude at the peak latency was higher than a threshold, which was set at 50% of the maximum amplitude over all vertices. We mapped the total count of activation at each vertex. We also analyzed 50 IEEG spikes in the same manner over the intracranial electrodes and created the activation count map. The location of the electrodes was obtained in the MEG source space by coregistering postimplantation computed tomography to MRI. We estimated the MEG- and IEEG-active regions associated with the spike populations using the vertices/electrodes with a count over 25. RESULTS: The activation count maps of MEG spikes demonstrated the localization associated with the spike population by variable count values at each vertex. The MEG-active region overlapped with 65 to 85% of the IEEG-active region in our patient group. CONCLUSIONS: Mapping the MEG spike population is valid for demonstrating the trend of spikes clustering in patients with epilepsy. In addition, comparison of MEG and IEEG spikes quantitatively may be informative for understanding their relationship.
Authors: N Tanaka; H Liu; C Reinsberger; J R Madsen; B F Bourgeois; B A Dworetzky; M S Hämäläinen; S M Stufflebeam Journal: AJNR Am J Neuroradiol Date: 2012-08-09 Impact factor: 3.825
Authors: Naoaki Tanaka; Jurriaan M Peters; Anna K Prohl; Shigetoshi Takaya; Joseph R Madsen; Blaise F Bourgeois; Barbara A Dworetzky; Matti S Hämäläinen; Steven M Stufflebeam Journal: Epilepsy Res Date: 2013-11-18 Impact factor: 3.045
Authors: Naoaki Tanaka; Matti S Hämäläinen; Seppo P Ahlfors; Hesheng Liu; Joseph R Madsen; Blaise F Bourgeois; Jong Woo Lee; Barbara A Dworetzky; John W Belliveau; Steven M Stufflebeam Journal: Neuroimage Date: 2009-12-16 Impact factor: 6.556
Authors: S Knake; E Halgren; H Shiraishi; K Hara; H M Hamer; P E Grant; V A Carr; D Foxe; S Camposano; E Busa; T Witzel; M S Hämäläinen; S P Ahlfors; E B Bromfield; P M Black; B F Bourgeois; A J Cole; G R Cosgrove; B A Dworetzky; J R Madsen; P G Larsson; D L Schomer; E A Thiele; A M Dale; B R Rosen; S M Stufflebeam Journal: Epilepsy Res Date: 2006-03-03 Impact factor: 3.045
Authors: Eleonora Tamilia; Michel AlHilani; Naoaki Tanaka; Melissa Tsuboyama; Jurriaan M Peters; P Ellen Grant; Joseph R Madsen; Steven M Stufflebeam; Phillip L Pearl; Christos Papadelis Journal: Clin Neurophysiol Date: 2019-01-31 Impact factor: 3.708