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Comparison of animated spatial filtered MEG data for epileptic discharges
International Congress Series 1300 (2007) 641 – 644
www.ics-elsevier.com
Comparison of animated spatial filtered MEG data for epileptic discharges A. Hashizume a,⁎, K. Kurisu a , K. Iida a , R. Hanaya a , H. Shirozu a , H Otsubo b a
Department of Neurosurgery, Graduate School of Biomedical Sciences, Japan b Division of Neurology, Department of Pediatrics, The Hospital for Sick Children and University of Toronto, Canada
Abstract. To identify most reasonable method for epileptic discharges among various spatial filters using magnetoencephalography (MEG) data, we programmed and evaluated six spatial filters; nonadaptive spatial filters including 1) minimum L2 norm (MN); 2) weight normalized minimum norm (WMN); 3) standardized low resolution brain electromagnetic tomography (sLORETA); and adaptive spatial filters including 4) minimum variance (MV); 5) minimum variance with normalized lead field (MVW); 6) weight normalized minimum variance (WMV). MN showed current sources at superficial nodes. WMN and sLORETA estimated current sources at deep nodes. MV, WMW and WMV were influence by selection of MEG data sets, showed unexpected currents and failed to show expected currents. Our results indicated that sLORETA was most reasonable spatial filter to analyze epileptic discharge because of least influences among them. © 2007 Elsevier B.V. All rights reserved. Keywords: Magnetoencephalography; Minimum norm; Standardized low resolution electromagnetic brain tomography; Minimum variance; Epileptic discharge; Spatial filter
1. Introduction Various spatial filters are proposed to realize brain activities in the target region, using magnetoencephalography (MEG) data. There are complex of mathematical backgrounds to understand the individual spatial filtering method for clinical practice for epilepsy. We reconstructed and animated current available six spatial filtering methods; three types of
⁎ Corresponding author. Tel.: +81 82 257 5227; fax: +81 82 257 5229. E-mail address: [email protected] (A. Hashizume). 0531-5131/ © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2007.01.047
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A. Hashizume et al. / International Congress Series 1300 (2007) 641–644
non-adaptive spatial filters and other three types of adaptive spatial filters, to demonstrate the differences, their pros and cons for epileptic discharges. 2. Patients and methods We studied epileptic discharges in patients with intractable neocortical epilepsy who underwent MEG and magnetic resonance image (MRI) examinations. A whole head type neuromagnetometer, Neuromag System (Elekta-Neuromag O.Y. Helsinki, Finland) was used and spontaneous electromagnetic brain activities were recorded at 600.615 Hz sampling rate. Our developed format converting program, Fiff2MatFile.exe (freeware at http://meg.aalip.jp/ freeware/freewareE.html) enabled signal processing on MATLAB (The MathWorks, Inc, Natick, U.S.A.). From individual MRI data, 5 mm step lattice nodes were set within manually extracted cerebral voxels. Nodes within 3 cm from the spherical center were excluded. Lead field matrices were calculated based on spherical conductor model of Sarvas et al. [1]. We programmed three types of non-adaptive spatial filters; 1), minimum L2 norm (MN); 2), weight normalized minimum norm (WMN); 3), standardized low resolution brain electromagnetic tomography (sLORETA), and other three types of adaptive spatial filters;
Fig. 1. Shows reconstructed data for spikes. Nodes with highest 1% source strength are color-coded from blue to red. The left two columns are reconstructed data using planar gradiometers and the right two columns are those of magnetometer. MV does not show current sources behind the lesion (blue arrows). MVW and WMVof magnetometer show unexpected current sources at the inferior temporal region (red arrows). MN, minimum norm; WMN, weight normalized minimum norm; sLORETA, standardized low resolution brain electromagnetic tomography; MV, minimum variance; MVW, minimum variance with normalized lead field; WMV, weight normalized minimum variance. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
A. Hashizume et al. / International Congress Series 1300 (2007) 641–644
643
4), minimum variance (MV), 5), minimum variance with normalized lead (MVW); 6), weight normalized minimum variance (WMV). Equations of these filters were listed in Sekihara et al. [2]. Neuromag System has 204 planar gradiometers and 102 magnetometers [3] and spatial filters of gradiometers and magnetometers were programmed separately. Euclidean norm of orthogonal tangential reconstructed currents at each node was calculated and highest 1% nodes were used for evaluation. We made a volume rendered brain surface images [4] and the other reconstructed images with sources 3 cm below the surface. Strength of current density was color-coded (nAm). We compared original MEG waveforms and reconstructed MEG images of both surface and depth for the six spatial filtering methods. We created movies of 1024 × 768 pix 24 frame/s for each spatial filter. 3. Results Figs. 1 and 2 show MEG waveforms and reconstructed data using six spatial filtering methods for epileptic discharges. Fig. 1 shows reconstructed data for spike discharges. The left six images represent reconstructed data using gradiometer, and the right six images represent reconstructed data using planar magnetometer. Fig. 2 shows reconstructed data for non-spike discharges. Table 1 describes a list of affects for both non-adaptive and adaptive spatial filters. We found; 1) Among non-adaptive spatial filters, MN estimated current sources at shallow nodes close to sensors. WMN and sLORETA estimated current sources at deeper nodes;
Fig. 2. Shows reconstructed data at non discharge with low amplitude. MV, MVW, and WMV of planar gradiometer show unexpected current sources (blue zones, red arrow). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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A. Hashizume et al. / International Congress Series 1300 (2007) 641–644
Table 1 Pros and cons of spatial filters
Influenced by set nodes Influenced by MEG data set Corresponded to MEG waveforms Unexpected currents Missing expected currents Location bias [2]
Non-adaptive spatial filters
Adaptive spatial filters
MN
WMN
sLORETA
MV
MVW
WMV
Yes No Yes No No Yes
Yes No Yes No⁎ No Yes
Yes No Yes No⁎ No No
No Yes No Yes Yes Yes
No Yes No Yes Yes No
No Yes No Yes Yes NA
⁎Estimating deep currents, NA not assessed.
2) Among adaptive spatial filters, MV estimated current sources at deep nodes. MVW and WMV estimated current sources at shallow nodes; 3) Selection of time period of epileptic discharges influenced spatial distribution of current sources for MV, MVW and WMV; 4) Adaptive spatial filters of MV, MVW, and WMV estimated unexpected current sources from some low amplitude magnetic fields; 5) Adaptive spatial filters of MV, MVW, and WMV were unable to estimate expected current sources for some spike discharges; 6) There were different distribution of reconstructed current sources between planar gradiometers and magnetometers. We presented all findings in Biomag 2006 at Vancouver. This proceeding does show only 4–6 findings because of limited pages. 4. Discussion Non-adaptive spatial filters had variance matrices of lead field matrices in their equations. It indicated that setting nodes influenced estimating current sources. For instance lattice nodes or cortical mesh nodes, whether including cerebellum or not, influenced the results. We chose simple lattice nodes because of difficulty to establish proper mesh model in the brain with lesion. Adaptive spatial filters had variance matrices of targeted MEG data set in their equations. It indicated that selection of MEG data influenced estimating current sources. For instance selection of sensors and of time period influenced the results. It should be noted that selected time period altered the results of adaptive spatial filters for spontaneous brain activities with epileptic discharges. Sekihara et al. reported that MN, WMN and MV had location bias in which location of current sources altered their accuracies [2]. Our results indicated that sLORETA was the most reliable spatial filters for epileptic discharges because of least influences among the six spatial filters. References [1] J. Sarvas, Basic mathematical and electromagnetic inverse problem, Phys. Med. Biol. 32 (1987) 11–22. [2] K. Sekihara, M. Sahani, S.S. Nagarajan, Location bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction, Neuroimage 25 (2005) 1056–1067. [3] User's Manual: System Hardware, Neuromagsystem™ NM20456a 13.7.1999, Neuromag (1999). [4] A. Hashizume, et al., Development of magnetoencephalography–magnetic resonance imaging integration software—technical note, Neurol. Med.-Chir. (Tokyo) 42 (2002) 455–457.
www.ics-elsevier.com
Comparison of animated spatial filtered MEG data for epileptic discharges A. Hashizume a,⁎, K. Kurisu a , K. Iida a , R. Hanaya a , H. Shirozu a , H Otsubo b a
Department of Neurosurgery, Graduate School of Biomedical Sciences, Japan b Division of Neurology, Department of Pediatrics, The Hospital for Sick Children and University of Toronto, Canada
Abstract. To identify most reasonable method for epileptic discharges among various spatial filters using magnetoencephalography (MEG) data, we programmed and evaluated six spatial filters; nonadaptive spatial filters including 1) minimum L2 norm (MN); 2) weight normalized minimum norm (WMN); 3) standardized low resolution brain electromagnetic tomography (sLORETA); and adaptive spatial filters including 4) minimum variance (MV); 5) minimum variance with normalized lead field (MVW); 6) weight normalized minimum variance (WMV). MN showed current sources at superficial nodes. WMN and sLORETA estimated current sources at deep nodes. MV, WMW and WMV were influence by selection of MEG data sets, showed unexpected currents and failed to show expected currents. Our results indicated that sLORETA was most reasonable spatial filter to analyze epileptic discharge because of least influences among them. © 2007 Elsevier B.V. All rights reserved. Keywords: Magnetoencephalography; Minimum norm; Standardized low resolution electromagnetic brain tomography; Minimum variance; Epileptic discharge; Spatial filter
1. Introduction Various spatial filters are proposed to realize brain activities in the target region, using magnetoencephalography (MEG) data. There are complex of mathematical backgrounds to understand the individual spatial filtering method for clinical practice for epilepsy. We reconstructed and animated current available six spatial filtering methods; three types of
⁎ Corresponding author. Tel.: +81 82 257 5227; fax: +81 82 257 5229. E-mail address: [email protected] (A. Hashizume). 0531-5131/ © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2007.01.047
642
A. Hashizume et al. / International Congress Series 1300 (2007) 641–644
non-adaptive spatial filters and other three types of adaptive spatial filters, to demonstrate the differences, their pros and cons for epileptic discharges. 2. Patients and methods We studied epileptic discharges in patients with intractable neocortical epilepsy who underwent MEG and magnetic resonance image (MRI) examinations. A whole head type neuromagnetometer, Neuromag System (Elekta-Neuromag O.Y. Helsinki, Finland) was used and spontaneous electromagnetic brain activities were recorded at 600.615 Hz sampling rate. Our developed format converting program, Fiff2MatFile.exe (freeware at http://meg.aalip.jp/ freeware/freewareE.html) enabled signal processing on MATLAB (The MathWorks, Inc, Natick, U.S.A.). From individual MRI data, 5 mm step lattice nodes were set within manually extracted cerebral voxels. Nodes within 3 cm from the spherical center were excluded. Lead field matrices were calculated based on spherical conductor model of Sarvas et al. [1]. We programmed three types of non-adaptive spatial filters; 1), minimum L2 norm (MN); 2), weight normalized minimum norm (WMN); 3), standardized low resolution brain electromagnetic tomography (sLORETA), and other three types of adaptive spatial filters;
Fig. 1. Shows reconstructed data for spikes. Nodes with highest 1% source strength are color-coded from blue to red. The left two columns are reconstructed data using planar gradiometers and the right two columns are those of magnetometer. MV does not show current sources behind the lesion (blue arrows). MVW and WMVof magnetometer show unexpected current sources at the inferior temporal region (red arrows). MN, minimum norm; WMN, weight normalized minimum norm; sLORETA, standardized low resolution brain electromagnetic tomography; MV, minimum variance; MVW, minimum variance with normalized lead field; WMV, weight normalized minimum variance. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
A. Hashizume et al. / International Congress Series 1300 (2007) 641–644
643
4), minimum variance (MV), 5), minimum variance with normalized lead (MVW); 6), weight normalized minimum variance (WMV). Equations of these filters were listed in Sekihara et al. [2]. Neuromag System has 204 planar gradiometers and 102 magnetometers [3] and spatial filters of gradiometers and magnetometers were programmed separately. Euclidean norm of orthogonal tangential reconstructed currents at each node was calculated and highest 1% nodes were used for evaluation. We made a volume rendered brain surface images [4] and the other reconstructed images with sources 3 cm below the surface. Strength of current density was color-coded (nAm). We compared original MEG waveforms and reconstructed MEG images of both surface and depth for the six spatial filtering methods. We created movies of 1024 × 768 pix 24 frame/s for each spatial filter. 3. Results Figs. 1 and 2 show MEG waveforms and reconstructed data using six spatial filtering methods for epileptic discharges. Fig. 1 shows reconstructed data for spike discharges. The left six images represent reconstructed data using gradiometer, and the right six images represent reconstructed data using planar magnetometer. Fig. 2 shows reconstructed data for non-spike discharges. Table 1 describes a list of affects for both non-adaptive and adaptive spatial filters. We found; 1) Among non-adaptive spatial filters, MN estimated current sources at shallow nodes close to sensors. WMN and sLORETA estimated current sources at deeper nodes;
Fig. 2. Shows reconstructed data at non discharge with low amplitude. MV, MVW, and WMV of planar gradiometer show unexpected current sources (blue zones, red arrow). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
644
A. Hashizume et al. / International Congress Series 1300 (2007) 641–644
Table 1 Pros and cons of spatial filters
Influenced by set nodes Influenced by MEG data set Corresponded to MEG waveforms Unexpected currents Missing expected currents Location bias [2]
Non-adaptive spatial filters
Adaptive spatial filters
MN
WMN
sLORETA
MV
MVW
WMV
Yes No Yes No No Yes
Yes No Yes No⁎ No Yes
Yes No Yes No⁎ No No
No Yes No Yes Yes Yes
No Yes No Yes Yes No
No Yes No Yes Yes NA
⁎Estimating deep currents, NA not assessed.
2) Among adaptive spatial filters, MV estimated current sources at deep nodes. MVW and WMV estimated current sources at shallow nodes; 3) Selection of time period of epileptic discharges influenced spatial distribution of current sources for MV, MVW and WMV; 4) Adaptive spatial filters of MV, MVW, and WMV estimated unexpected current sources from some low amplitude magnetic fields; 5) Adaptive spatial filters of MV, MVW, and WMV were unable to estimate expected current sources for some spike discharges; 6) There were different distribution of reconstructed current sources between planar gradiometers and magnetometers. We presented all findings in Biomag 2006 at Vancouver. This proceeding does show only 4–6 findings because of limited pages. 4. Discussion Non-adaptive spatial filters had variance matrices of lead field matrices in their equations. It indicated that setting nodes influenced estimating current sources. For instance lattice nodes or cortical mesh nodes, whether including cerebellum or not, influenced the results. We chose simple lattice nodes because of difficulty to establish proper mesh model in the brain with lesion. Adaptive spatial filters had variance matrices of targeted MEG data set in their equations. It indicated that selection of MEG data influenced estimating current sources. For instance selection of sensors and of time period influenced the results. It should be noted that selected time period altered the results of adaptive spatial filters for spontaneous brain activities with epileptic discharges. Sekihara et al. reported that MN, WMN and MV had location bias in which location of current sources altered their accuracies [2]. Our results indicated that sLORETA was the most reliable spatial filters for epileptic discharges because of least influences among the six spatial filters. References [1] J. Sarvas, Basic mathematical and electromagnetic inverse problem, Phys. Med. Biol. 32 (1987) 11–22. [2] K. Sekihara, M. Sahani, S.S. Nagarajan, Location bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction, Neuroimage 25 (2005) 1056–1067. [3] User's Manual: System Hardware, Neuromagsystem™ NM20456a 13.7.1999, Neuromag (1999). [4] A. Hashizume, et al., Development of magnetoencephalography–magnetic resonance imaging integration software—technical note, Neurol. Med.-Chir. (Tokyo) 42 (2002) 455–457.