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P33-21 Human cortical response to parametric passive finger movement – an MEG study
S304
Posters
P33-21 Human cortical response to parametric passive finger movement an MEG study
P33-23 Visualization of the sensitivity of the MEG sensor array based on the realistic signal generation modeling on standardized space
M. Matsuhashi1 , T. Mima1 , T. Nagamine2 , H. Shibasaki3 , H. Fukuyama1 Human Brain Research Center, Kyoto University, Kyoto, Japan, 2 Department of System Neuroscience, Sapporo Medical University, Sapporo, Japan, 3 Takeda General Hospital, Kyoto, Japan
S. Iwaki1 1 National Institute of Advanced Industrial Science and Technology (AIST), Japan
1
Objective: To clarify the cerebral mechanism of proprioceptive sensation in human Methods: Thirteen right handed healthy volunteers participated. We used a newly developed device to produce brisk but accurately controlled passive flexion of the PIP joint of the right index finger in the magnetically shielded room. The motion stimuli of varying parameters, with travel angle and time of 15/30, 15/50, 15/80, 15/120, 9/50 and 23/120 (degrees/ms), were delivered at 1 3 s random intervals while cerebral activities were recorded using 122 channel whole-head neuromagnetometer (Neuromag 122) with 0.03 300 Hz passband. The data were analyzed for the source strength of the equivalent current dipole (ECD) of averaged response and also the event related synchronization/desynchronization (ERS/ERD) as the mean power change of the left central channels. The effects of motion velocity and travel angle were tested in constant travel (15/30, 15/50, 15/80 and 15/120) and constant velocity (9/50, 15/80 and 23/120) conditions using Friedman test. Results: Three major response peaks were observed, all located in the left central area: early peak at 39±6 ms after the motion onset (mean ± s.d.), middle peak at 71±13 ms after the motion onset, and late peak at 95±28 ms after the motion offset. The ECD of these peaks were all estimated in and around the left primary sensorimotor cortex. The effect of motion velocity was significant only on the source dipole moment of the early peak (p < 0.05). The effect of travel angle was significant (p < 0.05) on the ERD of 18 24 Hz band. Conclusions: This result suggests differential physiological significance of ‘evoked response’ which reflects time-locked cortical inputs and ‘induced response’ representing local cortical oscillation in the perception of passive finger movement in the human sensorimotor cortex. P33-22 Advances in analysis of spontaneous EEG/MEG activity by independent component analysis A. Hyvarinen1 , P. Ramkumar2 , R. Hari2 1 Dept of Mathematics and Statistics, University of Helsinki, Finland, 2 Brain Research Unit, LTL, Aalto University, Espoo, Finland Objective: We develop new methods for exploratory analysis of spontaneous EEG/MEG activity. Currently, the analysis is quite difficult and existing methods need to be improved. In particular, novel methods might be able to find and analyze default-mode networks, which have been defined with fMRI but are more elusive in EEG/MEG. Methods: We use the framework of independent component analysis (ICA) which has had some success in the analysis of spontaneous activity. First, we improve the capability of ICA to analyze brain sources by introducing a new spatial variant similar to the way ICA is used with fMRI. This is possible by first computing the minimum norm solution to the inverse problem. We further combine this spatial ICA with our Fourier-domain method (Hyvarinen et al, NeuroImage, 2010). Second, we introduce a new testing method for finding components which are consistently similar over subjects. The novelty here is that we formulate a null hypothesis, under which the components are obtained as random orthogonal rotations of whitened data. Most other work has relied on heuristically chosen thresholds instead of a null hypothesis, and thus has not been able to control the false positive rate. Results: We applied the methods on MEG recordings from ten healthy subjects who were either resting or received alternating naturalistic visual, auditory, and tactile stimulation. Both temporal and spatial ICA found rhythmic narrow-band sources (networks) which were consistent across subjects. Networks with statistically significant consistency typically corresponded to early sensory areas. Some of the identified networks seem to be related to default-mode networks, especially in the spatial ICA. Conclusions: Analysis of spontaneous EEG/MEG activity is possible within the framework of independent component analysis. The spatial version of ICA provides new information compared with the temporal version of ICA. The statistical significance tests provide intuitively reasonable results.
Magnetoencephalography (MEG) has been used as a tool for investigating human brain functions non-invasively. Although MEG has excellent temporal resolution up to 1 ms, it is generally difficult to reconstruct the distribution of the neural current in the brain from the magnetic field distribution outside of the head due to the non-uniqueness of the neuromagnetic inverse problem, especially under such condition where the neural current has complex spatial distribution. Under those conditions, simple methods are required (1) to approximately visualize the source of MEG components appeared at specific sensor sets, and (2) to determine the “sensor-set of interest (SOI)” once the cortical area-ofinterest is determined either in the standardized 1 or in the subjectspecific coordinate system, especially in the clinical application. Here we present a system to visualize (i) the distribution of the sensitivity of arbitrary selected group of MEG sensors on the subject-specific cortical surface, and (ii) the distribution of MEG signal strength predicted from a realistic MEG signal generation model (forward model). The current results suggest that (a) our methods to predict MEG field distribution from a priori information about the possible “active” cortical regions obtained from standardized fMRI results is useful for determining the sensorsets of interest in the MEG studies for a specific subject under specific measurement condition, and that (b) visualization of the sensitivity of sensor groups could provide the approximate distribution of the signal sources without solving the MEG inverse problem. P33-24 Correlations between the development of cognitive functions and spontaneous MEG responses of healthy 3- to 4-year-old infants K. Nagao1,2 , Y. Yoshimura2 , G.B. Remijn2 , M. Kikuchi2 , H. Kojima2 , T. Tsubokawa2 , T. Munesue1,2 , Y. Minabe2 1 United Graduate School of Child Development, Osaka University, Kanazawa University and Hamamatsu University School of Medicine; Osaka University, Japan, 2 Kanazawa University, Japan Objective: This study examined the correlations between the development of cognitive functions and the spontaneous magnetoencephalogram (MEG) responses in 3 4 year old healthy infants. Although MEG is noninvasive and suitable to measure infant cortex, there have been no previous studies relating cognitive development and MEG data of preschool infants. Method: The cognitive functions of 22 healthy 3- to 4-year-old infants were evaluated by the Japanese adaptation of the Kaufman Assessment Battery for Children (K-ABC test). The head coil unit of the MEG system (Yokogawa Electric Co., Kanazawa, Japan) to fit the infants’ head was specially developed to measure the infants’ brain activity. The spontaneous brain response during rest and a closed eye period was measured. Results: Relative spectral power value was calculated for the MEG data. There was a positive correlation (r = 0.70) between the infants’ theta power value and their chronological age. Furthermore, the infants’ theta power value was positively correlated to their raw score on the sequential processing scale of the K-ABC (r = 0.44). By contrast, there was a negative correlation (r = 0.54) between the infants’ alpha power value and their raw score on the simultaneous processing scale of the K-ABC. Conclusions: Taken together, the results suggest that the relative spectral power of the MEG data reflected the development of the infants’ cognitive processing abilities. P33-25 Activated area of imagined movement: MEG study N. Tsuyuguchi1 , T. Uda1 , Y. Shigihara2 , K. Ohata1 Department of Neurosurgery, Osaka City Univercity Graduate School of Medicine, Osaka, Japan, 2 Department of Physiology, Osaka City University Graduate School of Medicine, Japan
1
Objective: To detect the activated area in motor imaginary using magnetoencephalography. Materials and Methods: Twenty healthy volunteers participated in this study. The subjects attempted to clench their right hand on a visual
Posters
P33-21 Human cortical response to parametric passive finger movement an MEG study
P33-23 Visualization of the sensitivity of the MEG sensor array based on the realistic signal generation modeling on standardized space
M. Matsuhashi1 , T. Mima1 , T. Nagamine2 , H. Shibasaki3 , H. Fukuyama1 Human Brain Research Center, Kyoto University, Kyoto, Japan, 2 Department of System Neuroscience, Sapporo Medical University, Sapporo, Japan, 3 Takeda General Hospital, Kyoto, Japan
S. Iwaki1 1 National Institute of Advanced Industrial Science and Technology (AIST), Japan
1
Objective: To clarify the cerebral mechanism of proprioceptive sensation in human Methods: Thirteen right handed healthy volunteers participated. We used a newly developed device to produce brisk but accurately controlled passive flexion of the PIP joint of the right index finger in the magnetically shielded room. The motion stimuli of varying parameters, with travel angle and time of 15/30, 15/50, 15/80, 15/120, 9/50 and 23/120 (degrees/ms), were delivered at 1 3 s random intervals while cerebral activities were recorded using 122 channel whole-head neuromagnetometer (Neuromag 122) with 0.03 300 Hz passband. The data were analyzed for the source strength of the equivalent current dipole (ECD) of averaged response and also the event related synchronization/desynchronization (ERS/ERD) as the mean power change of the left central channels. The effects of motion velocity and travel angle were tested in constant travel (15/30, 15/50, 15/80 and 15/120) and constant velocity (9/50, 15/80 and 23/120) conditions using Friedman test. Results: Three major response peaks were observed, all located in the left central area: early peak at 39±6 ms after the motion onset (mean ± s.d.), middle peak at 71±13 ms after the motion onset, and late peak at 95±28 ms after the motion offset. The ECD of these peaks were all estimated in and around the left primary sensorimotor cortex. The effect of motion velocity was significant only on the source dipole moment of the early peak (p < 0.05). The effect of travel angle was significant (p < 0.05) on the ERD of 18 24 Hz band. Conclusions: This result suggests differential physiological significance of ‘evoked response’ which reflects time-locked cortical inputs and ‘induced response’ representing local cortical oscillation in the perception of passive finger movement in the human sensorimotor cortex. P33-22 Advances in analysis of spontaneous EEG/MEG activity by independent component analysis A. Hyvarinen1 , P. Ramkumar2 , R. Hari2 1 Dept of Mathematics and Statistics, University of Helsinki, Finland, 2 Brain Research Unit, LTL, Aalto University, Espoo, Finland Objective: We develop new methods for exploratory analysis of spontaneous EEG/MEG activity. Currently, the analysis is quite difficult and existing methods need to be improved. In particular, novel methods might be able to find and analyze default-mode networks, which have been defined with fMRI but are more elusive in EEG/MEG. Methods: We use the framework of independent component analysis (ICA) which has had some success in the analysis of spontaneous activity. First, we improve the capability of ICA to analyze brain sources by introducing a new spatial variant similar to the way ICA is used with fMRI. This is possible by first computing the minimum norm solution to the inverse problem. We further combine this spatial ICA with our Fourier-domain method (Hyvarinen et al, NeuroImage, 2010). Second, we introduce a new testing method for finding components which are consistently similar over subjects. The novelty here is that we formulate a null hypothesis, under which the components are obtained as random orthogonal rotations of whitened data. Most other work has relied on heuristically chosen thresholds instead of a null hypothesis, and thus has not been able to control the false positive rate. Results: We applied the methods on MEG recordings from ten healthy subjects who were either resting or received alternating naturalistic visual, auditory, and tactile stimulation. Both temporal and spatial ICA found rhythmic narrow-band sources (networks) which were consistent across subjects. Networks with statistically significant consistency typically corresponded to early sensory areas. Some of the identified networks seem to be related to default-mode networks, especially in the spatial ICA. Conclusions: Analysis of spontaneous EEG/MEG activity is possible within the framework of independent component analysis. The spatial version of ICA provides new information compared with the temporal version of ICA. The statistical significance tests provide intuitively reasonable results.
Magnetoencephalography (MEG) has been used as a tool for investigating human brain functions non-invasively. Although MEG has excellent temporal resolution up to 1 ms, it is generally difficult to reconstruct the distribution of the neural current in the brain from the magnetic field distribution outside of the head due to the non-uniqueness of the neuromagnetic inverse problem, especially under such condition where the neural current has complex spatial distribution. Under those conditions, simple methods are required (1) to approximately visualize the source of MEG components appeared at specific sensor sets, and (2) to determine the “sensor-set of interest (SOI)” once the cortical area-ofinterest is determined either in the standardized 1 or in the subjectspecific coordinate system, especially in the clinical application. Here we present a system to visualize (i) the distribution of the sensitivity of arbitrary selected group of MEG sensors on the subject-specific cortical surface, and (ii) the distribution of MEG signal strength predicted from a realistic MEG signal generation model (forward model). The current results suggest that (a) our methods to predict MEG field distribution from a priori information about the possible “active” cortical regions obtained from standardized fMRI results is useful for determining the sensorsets of interest in the MEG studies for a specific subject under specific measurement condition, and that (b) visualization of the sensitivity of sensor groups could provide the approximate distribution of the signal sources without solving the MEG inverse problem. P33-24 Correlations between the development of cognitive functions and spontaneous MEG responses of healthy 3- to 4-year-old infants K. Nagao1,2 , Y. Yoshimura2 , G.B. Remijn2 , M. Kikuchi2 , H. Kojima2 , T. Tsubokawa2 , T. Munesue1,2 , Y. Minabe2 1 United Graduate School of Child Development, Osaka University, Kanazawa University and Hamamatsu University School of Medicine; Osaka University, Japan, 2 Kanazawa University, Japan Objective: This study examined the correlations between the development of cognitive functions and the spontaneous magnetoencephalogram (MEG) responses in 3 4 year old healthy infants. Although MEG is noninvasive and suitable to measure infant cortex, there have been no previous studies relating cognitive development and MEG data of preschool infants. Method: The cognitive functions of 22 healthy 3- to 4-year-old infants were evaluated by the Japanese adaptation of the Kaufman Assessment Battery for Children (K-ABC test). The head coil unit of the MEG system (Yokogawa Electric Co., Kanazawa, Japan) to fit the infants’ head was specially developed to measure the infants’ brain activity. The spontaneous brain response during rest and a closed eye period was measured. Results: Relative spectral power value was calculated for the MEG data. There was a positive correlation (r = 0.70) between the infants’ theta power value and their chronological age. Furthermore, the infants’ theta power value was positively correlated to their raw score on the sequential processing scale of the K-ABC (r = 0.44). By contrast, there was a negative correlation (r = 0.54) between the infants’ alpha power value and their raw score on the simultaneous processing scale of the K-ABC. Conclusions: Taken together, the results suggest that the relative spectral power of the MEG data reflected the development of the infants’ cognitive processing abilities. P33-25 Activated area of imagined movement: MEG study N. Tsuyuguchi1 , T. Uda1 , Y. Shigihara2 , K. Ohata1 Department of Neurosurgery, Osaka City Univercity Graduate School of Medicine, Osaka, Japan, 2 Department of Physiology, Osaka City University Graduate School of Medicine, Japan
1
Objective: To detect the activated area in motor imaginary using magnetoencephalography. Materials and Methods: Twenty healthy volunteers participated in this study. The subjects attempted to clench their right hand on a visual