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Volumetric localization of epileptic activity using wavelet-based synthetic aperture magnetometry
International Congress Series 1300 (2007) 697 – 700
www.ics-elsevier.com
Volumetric localization of epileptic activity using wavelet-based synthetic aperture magnetometry Jing Xiang a,⁎, Yingying Wang a , Zheng Xiao b , Christina Balioussis c , Hongmei Zhu c , Stephanie Holowka a , Roy Sharma d , Amrita Hunjan d , Hiroshi Otsubo d , Sylvester Chuang a a
MEG Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA b Department of Neurology, The First Affiliated Hospital, ChongQing University of Medical Science, ChongQing, People's Republic of China c Department of Mathematic, York University, Toronto, ON, Canada M3J 1P3 d Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada M5G 1X8
Abstract. To go beyond using magnetoencephalography (MEG) for visual identification of epileptic spikes, this study was to quantitatively estimate epileptic spectral power and volumetrically localize the neuromagnetic activity associated with epilepsy. MEG data were recorded from 16 patients with epilepsy using a whole-cortex MEG system. Focal increases of spectral power were identified using wavelet; the three-dimensional neuromagnetic distributions of the focal increases of spectral power were estimated using synthetic aperture magnetometry (SAM). SAM images and dipoles pointed to a same area in 12 patients (75% 12/16), SAM revealed focal epileptic activity but dipole modelling failed in 2 patients (12.5%, 2/16), and SAM detected more epileptic foci than dipole modelling did in 2 patients (12.5%, 2/ 16). Interestingly, spectrogram revealed focal increases of spectral power just before magnetic spikes; and SAM peaks were close to the dipoles of the initial portion of the spikes. The results suggest that waveletbased SAM analysis has the potential to localize the onset of epileptic seizures, and seems superior to dipole modelling for estimation of multiple epileptic foci. In comparison to the conventional visual identification of spike, wavelet-based SAM analysis is objective and quantitative. Thus, wavelet-based SAM analysis has the potential to be extremely useful for clinical management of epilepsy. © 2007 Elsevier B.V. All rights reserved. Keywords: Magnetoencephalography; Epilepsy; Synthetic aperture magnetometry (SAM); Wavelet; High frequency; Neuromagnetic signals
⁎ Corresponding author. Tel: +1 513 636 6303; fax: +1 513 636 1888. E-mail address: [email protected] (J. Xiang). 0531-5131/ © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2007.03.003
698
J. Xiang et al. / International Congress Series 1300 (2007) 697–700
1. Introduction Magnetoencephalography (MEG) is one of the most important advances made in the last decade in epilepsy; it has developed to the point that it has now entered routine clinical applications [1,2]. MEG can accurately localize spike sources for surgical treatment. Surgery is considered a successful therapeutic approach in patients with medically intractable epilepsy. Magnetic source imaging (MSI), defined as the integration and co-registration of functional data derived from MEG with structural magnetic resonance imaging (MRI), decreases the risk of morbidity associated with epilepsy surgery and enhances the probability of post-surgical seizure control. The objective of this study was to introduce the latest techniques into quantitative identification and volumetrical localization of epileptic activity using MEG. We specifically focused on paediatric epilepsy. 2. Materials and methods Sixteen clinical patients with epilepsy (7 female and 9 male, aged 6–18 years, with a mean age of 14 years) were retrospectively studied. Patient inclusion criteria: (1) MEG data were clean; (2) head movement during MEG recording was less than 5 mm; (3) epilepsy was diagnosed clinically and confirmed by MRI, electrocorticography (ECoG), and/or surgical outcomes. A 151-channel whole cortex CTF OMEGA system was used for MEG recordings (CTF Systems Inc., Port Coquitlam, Canada). The localization of the subject's head relative to the sensor array was measured using three small coils affixed to the nasion and pre-auricular points. Sleep deprivation was employed to provoke epileptic discharges. Data were recorded with a noise cancellation of third-order spatial gradients. The sampling rate of data acquisition was 625 Hz. Each epoch was 120 s; 15 epochs were recorded for each patient. The head position was measured before and after each epoch. To ensure accurate source localization, the movement of the patient's head during each MEG recording was limited in 5 mm. Three-dimensional MRI, acquired with a 3D-SPGR-pulse sequence, was obtained for all patients using a Signa Advantage (GE Medical Systems, Milwaukee, USA). Three fiduciary points were marked on the nasion and left and right pre-auricular points on the subject's head with MRI markers. The positions of the three fiduciary points were identical to the positions of the three coils used in MEG. MEG data were retrospectively analyzed using both wavelet-based SAM (our new method) and visual identification-based dipole modelling (the conventional method). Our new method consisted of time-frequency transform and volumetric localization. Wavelet transform was applied to all the channels. The time-frequency transform of MEG data in the study was accomplished using wavelet. An accumulated spectrogram was computed by combining all 15 spectrograms, which corresponded to 15 MEG data sets, for each patient. Focal increases of spectral power were identified in the spectrogram. The location of the focal increases of spectral power was volumetrically localized using SAM. The threedimensional epileptic region was visualized and analyzed using magnetic source locator (MSL) [3].
J. Xiang et al. / International Congress Series 1300 (2007) 697–700
699
3. Results All the patients in this study had MEG spikes in at least 9 MEG data sets in time-domain waveform. In addition, all patients had at least one focal increase of spectral power (100%, 16/16) in frequency-domain spectrogram. We have compared magnetic spikes with corresponding spectrograms for all 15 epochs for all patients. All spikes in time-domain waveform had corresponding focal increase of spectral power. However, we found that at least one focal increase of spectral power was in a frequency range of 1–74 Hz and another in 75–312 Hz. The most important finding was that a clear focal increase of spectral power in the higher frequency band was consistently identified just before the spike. Fig. 1 shows an example of spike and pre-spike high frequency component from the same channel. Since the pre-spike high frequency component was probably the initial epileptiform activation, frequency domain 3D contour maps were used to estimate the location of the high frequency component. Based on the results of spectral analysis, SAM was applied for accurate estimation of the focal increase of spectral power, which was assuming to be epileptic areas for all patients. SAM images and the dipoles estimated with a spatiotemporal dipole model were overlapped onto individual MRI for comparison of dipole, SAM and MRI. Since dipole modelling could not reveal the epileptic foci estimated by SAM, the comparison between dipole modelling and SAM analysis could not be done for 4 patients. To trace the generator of the spike onset, we compared moving dipoles for spikes with SAM. We noted that SAM showed a focal increase of spectral power, which was close to the dipoles localized for the initial portion of the spikes and the corresponding structural
Fig. 1. Time-domain waveform, frequency-domain spectrogram and magnetic source imaging from the same data set. A focal increase of spectral power in the spectrogram (arrow A, before the select line) is clearly identifiable; a magnetic spike (arrow B, after the select line) in the waveform is visible just after the corresponding focal increase of spectral power. It indicates that the spectral abnormal occurs before the spike. In the spectrogram, the x-axis indicates time-period of spectrogram; the y-axis indicates frequency range in Hz. In the waveform, the x-axis indicates time; the y-axis indicates the amplitude. The magnetic source imaging on the right shows the SAM peak and the location of a group of moving dipoles estimated for a spike using moving dipole model. The red small area is a SAM peak (arrow SAM); the yellow small balls are dipoles (arrow Dipoles). One red small dipole indicates the location of the peak of the spike. The 3D MRI is cut to show the dipoles, SAM as well as a tumour.
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J. Xiang et al. / International Congress Series 1300 (2007) 697–700
abnormality. Fig. 1 shows an example of the correlation of the moving dipoles from a single spike and corresponding SAM. 4. Discussion The present results suggest that wavelet-based SAM analysis has the potential to localize the spike onset. The comparison of waveform and spectrogram has demonstrated that a focal increase of spectral power appeared just before the spike (Fig. 1). The comparison of moving dipoles of spikes revealed that the peak of SAM close to the dipoles localized to the early portion of the spike (Fig. 1). In addition, dipoles were found to be around an SAM peak in five patients. All those phenomena strongly indicate that conventional dipole-based source localization is sensitive to spread signals, but wavelet-based SAM seems sensitive to the initial signals generated by the epileptogenic zone or seizure onset. It is well known that for an interictal spike to be equal in amplitude to the background brain signals, there must be several square centimeters of cortex activated [2]. As the focal increases of spectral power were clearly identifiable just before the spikes, we consider that wavelet-based SAM is sensitive to the rapid change of frequency, particularly, high frequency. Therefore, the current results indicate that wavelet-based SAM analysis can detect the subtle initial epileptic activity or seizure onset. To our knowledge, this subtle initial epileptic activity is commonly ignorable in visual identification of spike and has low goodness-of-fit in dipole modelling. Therefore, waveletbased SAM analysis has the potential to provide much more useful information than dipole modelling for estimation of initial epileptogenic activity or seizure onset. The present results have also demonstrated that the epileptic brain is associated with high frequency oscillation in 200–300 Hz (see Fig. 1). It implies that conventional spike is just a small portion of the electroneurophysiological abnormalities associated with the epileptic brain. The frequency range (bandwidth) of clinical EEG systems is usually 0.5 to 100 Hz. Four categories of frequencies are used most frequently in clinical practices: Delta (0.5 b 4 Hz), Theta (4–8 Hz), Alpha (8–13 Hz) and Beta (13–30 Hz). An epileptic spike is defined as being 14–70 ms in duration (14–70 Hz) and an epileptic sharp wave has a duration of 70–200 ms (5–14 Hz). Noticeably, the MEG/EEG signals that have been typically analyzed in the past are limited to this low frequency range (b100 Hz). Our results indicate that the epileptic brain generates high-frequency neuromagnetic signals (Fig. 1). High frequency neuromagnetic signals may provide a new window for identification of brain abnormality in epilepsy. Based on current results, it would be interesting to compare SAM images with intracranial recording. A prospective study with intracranial recordings and surgical outcomes will enable us to further verify our wavelet-based SAM technology for localizing epileptogenic zone for epilepsy surgery. References [1] G.L. Barkley, C. Baumgartner, MEG and EEG in epilepsy, J. Clin. Neurophysiol. 20 (2003) 163–178. [2] J. Vrba, S.E. Robinson, Signal processing in magnetoencephalography, Methods 25 (2001) 249–271. [3] J. Xiang, et al., Volumetric localization of somatosensory cortex in children using synthetic aperture magnetometry, Pediatr. Radiol. 33 (2003) 321–327.
www.ics-elsevier.com
Volumetric localization of epileptic activity using wavelet-based synthetic aperture magnetometry Jing Xiang a,⁎, Yingying Wang a , Zheng Xiao b , Christina Balioussis c , Hongmei Zhu c , Stephanie Holowka a , Roy Sharma d , Amrita Hunjan d , Hiroshi Otsubo d , Sylvester Chuang a a
MEG Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA b Department of Neurology, The First Affiliated Hospital, ChongQing University of Medical Science, ChongQing, People's Republic of China c Department of Mathematic, York University, Toronto, ON, Canada M3J 1P3 d Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada M5G 1X8
Abstract. To go beyond using magnetoencephalography (MEG) for visual identification of epileptic spikes, this study was to quantitatively estimate epileptic spectral power and volumetrically localize the neuromagnetic activity associated with epilepsy. MEG data were recorded from 16 patients with epilepsy using a whole-cortex MEG system. Focal increases of spectral power were identified using wavelet; the three-dimensional neuromagnetic distributions of the focal increases of spectral power were estimated using synthetic aperture magnetometry (SAM). SAM images and dipoles pointed to a same area in 12 patients (75% 12/16), SAM revealed focal epileptic activity but dipole modelling failed in 2 patients (12.5%, 2/16), and SAM detected more epileptic foci than dipole modelling did in 2 patients (12.5%, 2/ 16). Interestingly, spectrogram revealed focal increases of spectral power just before magnetic spikes; and SAM peaks were close to the dipoles of the initial portion of the spikes. The results suggest that waveletbased SAM analysis has the potential to localize the onset of epileptic seizures, and seems superior to dipole modelling for estimation of multiple epileptic foci. In comparison to the conventional visual identification of spike, wavelet-based SAM analysis is objective and quantitative. Thus, wavelet-based SAM analysis has the potential to be extremely useful for clinical management of epilepsy. © 2007 Elsevier B.V. All rights reserved. Keywords: Magnetoencephalography; Epilepsy; Synthetic aperture magnetometry (SAM); Wavelet; High frequency; Neuromagnetic signals
⁎ Corresponding author. Tel: +1 513 636 6303; fax: +1 513 636 1888. E-mail address: [email protected] (J. Xiang). 0531-5131/ © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2007.03.003
698
J. Xiang et al. / International Congress Series 1300 (2007) 697–700
1. Introduction Magnetoencephalography (MEG) is one of the most important advances made in the last decade in epilepsy; it has developed to the point that it has now entered routine clinical applications [1,2]. MEG can accurately localize spike sources for surgical treatment. Surgery is considered a successful therapeutic approach in patients with medically intractable epilepsy. Magnetic source imaging (MSI), defined as the integration and co-registration of functional data derived from MEG with structural magnetic resonance imaging (MRI), decreases the risk of morbidity associated with epilepsy surgery and enhances the probability of post-surgical seizure control. The objective of this study was to introduce the latest techniques into quantitative identification and volumetrical localization of epileptic activity using MEG. We specifically focused on paediatric epilepsy. 2. Materials and methods Sixteen clinical patients with epilepsy (7 female and 9 male, aged 6–18 years, with a mean age of 14 years) were retrospectively studied. Patient inclusion criteria: (1) MEG data were clean; (2) head movement during MEG recording was less than 5 mm; (3) epilepsy was diagnosed clinically and confirmed by MRI, electrocorticography (ECoG), and/or surgical outcomes. A 151-channel whole cortex CTF OMEGA system was used for MEG recordings (CTF Systems Inc., Port Coquitlam, Canada). The localization of the subject's head relative to the sensor array was measured using three small coils affixed to the nasion and pre-auricular points. Sleep deprivation was employed to provoke epileptic discharges. Data were recorded with a noise cancellation of third-order spatial gradients. The sampling rate of data acquisition was 625 Hz. Each epoch was 120 s; 15 epochs were recorded for each patient. The head position was measured before and after each epoch. To ensure accurate source localization, the movement of the patient's head during each MEG recording was limited in 5 mm. Three-dimensional MRI, acquired with a 3D-SPGR-pulse sequence, was obtained for all patients using a Signa Advantage (GE Medical Systems, Milwaukee, USA). Three fiduciary points were marked on the nasion and left and right pre-auricular points on the subject's head with MRI markers. The positions of the three fiduciary points were identical to the positions of the three coils used in MEG. MEG data were retrospectively analyzed using both wavelet-based SAM (our new method) and visual identification-based dipole modelling (the conventional method). Our new method consisted of time-frequency transform and volumetric localization. Wavelet transform was applied to all the channels. The time-frequency transform of MEG data in the study was accomplished using wavelet. An accumulated spectrogram was computed by combining all 15 spectrograms, which corresponded to 15 MEG data sets, for each patient. Focal increases of spectral power were identified in the spectrogram. The location of the focal increases of spectral power was volumetrically localized using SAM. The threedimensional epileptic region was visualized and analyzed using magnetic source locator (MSL) [3].
J. Xiang et al. / International Congress Series 1300 (2007) 697–700
699
3. Results All the patients in this study had MEG spikes in at least 9 MEG data sets in time-domain waveform. In addition, all patients had at least one focal increase of spectral power (100%, 16/16) in frequency-domain spectrogram. We have compared magnetic spikes with corresponding spectrograms for all 15 epochs for all patients. All spikes in time-domain waveform had corresponding focal increase of spectral power. However, we found that at least one focal increase of spectral power was in a frequency range of 1–74 Hz and another in 75–312 Hz. The most important finding was that a clear focal increase of spectral power in the higher frequency band was consistently identified just before the spike. Fig. 1 shows an example of spike and pre-spike high frequency component from the same channel. Since the pre-spike high frequency component was probably the initial epileptiform activation, frequency domain 3D contour maps were used to estimate the location of the high frequency component. Based on the results of spectral analysis, SAM was applied for accurate estimation of the focal increase of spectral power, which was assuming to be epileptic areas for all patients. SAM images and the dipoles estimated with a spatiotemporal dipole model were overlapped onto individual MRI for comparison of dipole, SAM and MRI. Since dipole modelling could not reveal the epileptic foci estimated by SAM, the comparison between dipole modelling and SAM analysis could not be done for 4 patients. To trace the generator of the spike onset, we compared moving dipoles for spikes with SAM. We noted that SAM showed a focal increase of spectral power, which was close to the dipoles localized for the initial portion of the spikes and the corresponding structural
Fig. 1. Time-domain waveform, frequency-domain spectrogram and magnetic source imaging from the same data set. A focal increase of spectral power in the spectrogram (arrow A, before the select line) is clearly identifiable; a magnetic spike (arrow B, after the select line) in the waveform is visible just after the corresponding focal increase of spectral power. It indicates that the spectral abnormal occurs before the spike. In the spectrogram, the x-axis indicates time-period of spectrogram; the y-axis indicates frequency range in Hz. In the waveform, the x-axis indicates time; the y-axis indicates the amplitude. The magnetic source imaging on the right shows the SAM peak and the location of a group of moving dipoles estimated for a spike using moving dipole model. The red small area is a SAM peak (arrow SAM); the yellow small balls are dipoles (arrow Dipoles). One red small dipole indicates the location of the peak of the spike. The 3D MRI is cut to show the dipoles, SAM as well as a tumour.
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J. Xiang et al. / International Congress Series 1300 (2007) 697–700
abnormality. Fig. 1 shows an example of the correlation of the moving dipoles from a single spike and corresponding SAM. 4. Discussion The present results suggest that wavelet-based SAM analysis has the potential to localize the spike onset. The comparison of waveform and spectrogram has demonstrated that a focal increase of spectral power appeared just before the spike (Fig. 1). The comparison of moving dipoles of spikes revealed that the peak of SAM close to the dipoles localized to the early portion of the spike (Fig. 1). In addition, dipoles were found to be around an SAM peak in five patients. All those phenomena strongly indicate that conventional dipole-based source localization is sensitive to spread signals, but wavelet-based SAM seems sensitive to the initial signals generated by the epileptogenic zone or seizure onset. It is well known that for an interictal spike to be equal in amplitude to the background brain signals, there must be several square centimeters of cortex activated [2]. As the focal increases of spectral power were clearly identifiable just before the spikes, we consider that wavelet-based SAM is sensitive to the rapid change of frequency, particularly, high frequency. Therefore, the current results indicate that wavelet-based SAM analysis can detect the subtle initial epileptic activity or seizure onset. To our knowledge, this subtle initial epileptic activity is commonly ignorable in visual identification of spike and has low goodness-of-fit in dipole modelling. Therefore, waveletbased SAM analysis has the potential to provide much more useful information than dipole modelling for estimation of initial epileptogenic activity or seizure onset. The present results have also demonstrated that the epileptic brain is associated with high frequency oscillation in 200–300 Hz (see Fig. 1). It implies that conventional spike is just a small portion of the electroneurophysiological abnormalities associated with the epileptic brain. The frequency range (bandwidth) of clinical EEG systems is usually 0.5 to 100 Hz. Four categories of frequencies are used most frequently in clinical practices: Delta (0.5 b 4 Hz), Theta (4–8 Hz), Alpha (8–13 Hz) and Beta (13–30 Hz). An epileptic spike is defined as being 14–70 ms in duration (14–70 Hz) and an epileptic sharp wave has a duration of 70–200 ms (5–14 Hz). Noticeably, the MEG/EEG signals that have been typically analyzed in the past are limited to this low frequency range (b100 Hz). Our results indicate that the epileptic brain generates high-frequency neuromagnetic signals (Fig. 1). High frequency neuromagnetic signals may provide a new window for identification of brain abnormality in epilepsy. Based on current results, it would be interesting to compare SAM images with intracranial recording. A prospective study with intracranial recordings and surgical outcomes will enable us to further verify our wavelet-based SAM technology for localizing epileptogenic zone for epilepsy surgery. References [1] G.L. Barkley, C. Baumgartner, MEG and EEG in epilepsy, J. Clin. Neurophysiol. 20 (2003) 163–178. [2] J. Vrba, S.E. Robinson, Signal processing in magnetoencephalography, Methods 25 (2001) 249–271. [3] J. Xiang, et al., Volumetric localization of somatosensory cortex in children using synthetic aperture magnetometry, Pediatr. Radiol. 33 (2003) 321–327.