Magnetoencephalography source imaging for neurological functionality characterizations
View Patent ↗Methods, systems, and devices are disclosed for implementing magnetoencephalography (MEG) source imaging. In one aspect, a method includes determining a covariance matrix based on sensor signal data in the time domain or frequency domain, the sensor signal data representing magnetic-field signals emitted by a brain of a subject and detected by MEG sensors in a sensor array surrounding the brain, defining a source grid containing source locations within the brain that generate magnetic signals, the source locations having a particular resolution, in which a number of source locations is greater than a number of sensors in the sensor array, and generating a source value of signal power for each location in the source grid by fitting the selected sensor covariance matrix, in which the covariance matrix is time-independent based on time or frequency information of the sensor signal data.
1. A method for high-resolution magnetoencephalography (MEG) source imaging, comprising:
determining a covariance matrix based on sensor signal data in the time domain, the sensor signal data representing magnetic-field signals emitted by a brain of a subject and detected by a plurality of MEG sensors in a sensor array surrounding the brain;
defining a source grid containing source locations within the brain that generate magnetic signals, the source locations having a particular resolution, wherein a number of source locations is greater than a number of sensors in the sensor array; and
generating a source value of signal power for each location in the source grid by fitting to the sensor signal data using the covariance matrix,
wherein the covariance matrix is time-independent based on time information of the sensor signal data.
2. The method of claim 1 , wherein the number of source locations is at least 30 times greater than the number of sensors in the sensor array.
3. The method of claim 2 , wherein the number of source locations include at least 10,000 voxels.
4. The method of claim 2 , wherein the number of sensors in the sensor array includes at least 250 sensors.
5. The method of claim 1 , further comprising:
producing an image including image features representing the source values at locations mapped to corresponding voxels in a magnetic resonance imaging (MRI) image of the brain.
6. A magnetoencephalography (MEG) source imaging system, comprising:
a MEG machine including MEG sensors configured to acquire magnetic field signal data including MEG sensor waveform signals from a brain of a subject, the MEG sensor signal data representing magnetic-field signals emitted by the brain of the subject; and
a processing unit including a processor configured to perform the following:
determine time-independent signal-related spatial modes from the MEG sensor waveform signals,
obtain spatial source images of the brain based on the determined time-independent signal-related spatial modes, and
determine source time-courses of the obtained spatial source images.
7. The MEG source imaging system of claim 6 , wherein the processing unit is configured to objectively remove correlated noise from the MEG sensor waveform signals.
8. The MEG source imaging system of claim 6 , wherein the processing unit is configured to obtain the spatial source images of the brain based on the determined time-independent signal-related spatial modes based at least on a source imaging map associated with each time-independent signal-related spatial mode.
9. The MEG source imaging system of claim 8 , wherein the processing unit is configured to remove bias toward grid nodes that correspond to locations in the brain that generate the MEG sensor waveform signals.
10. The MEG source imaging system of claim 8 , wherein the processing unit is configured to remove bias of the spatial source images towards coordinate axes of the spatial source images.
11. The MEG source imaging system of claim 6 , wherein the processing unit is configured to determine the source time-courses of the obtained spatial source images based at least on the following:
an inverse operator matrix constructed based on the obtained spatial source images; and
application of the constructed inverse operator matrix to the MEG sensor waveform signal.
12. The MEG source imaging system of claim 6 , wherein the processing unit is configured to determine a goodness-of-fit to the MEG sensor waveform signal.
13. The MEG source imaging system of claim 12 , wherein the processing unit is configured to determine the goodness-of-fit without calculating a predicted MEG sensor waveform signal.
14. The MEG source imaging system of claim 12 , wherein the processing unit is configured to determine the goodness-of-fit based at least on measured and predicted sensor spatial-profile matrix.
15. The MEG source imaging system of claim 7 , wherein the processing unit is configured to objectively remove the correlated noise from the MEG sensor waveform signals based at least on the following:
a mother brain noise covariance matrix estimated based on incomplete information;
a pre-whitening operator constructed based on the estimated mother brain noise covariance matrix;
a daughter pre-whitened brain noise covariance matrix formed based on application of the pre-whitening operator to daughter brain noise data;
a plot of square root of eigenvalues of the daughter pre-whitened brain noise covariance matrix;
a plot of 2nd order derivatives of the square root of the eigenvalues in the daughter pre-whitened brain noise covariance matrix;
a noise-subspace identified from the plot of 2nd order derivatives; and
associated threshold values from the plot of square root of the eigenvalues of the daughter pre-whitened brain noise covariance matrix.
16. The MEG source imaging system of claim 6 , comprising:
a magnetic resonance imaging (MRI) machine configured to acquire MRI images to obtain a source grid of the brain.
17. A tangible non-transitory storage medium embodying a computer program product comprising instructions for performing magnetoencephalography (MEG) source imaging when executed by a processing unit, the instructions including:
determining by the processing unit a covariance matrix based on sensor signal data in the time domain, the sensor signal data representing magnetic-field signals emitted by a brain of a subject and detected by a plurality of MEG sensors in a sensor array surrounding the brain;
defining by the processing unit a source grid containing source locations within the brain that generate magnetic signals, the source locations having a particular resolution, wherein a number of source locations is greater than a number of sensors in the sensor array; and
generating a source value of signal power for each location in the source grid by fitting to the sensor signal data using the covariance matrix,
wherein the covariance matrix is time-independent based on time information of the sensor signal data.
18. The tangible non-transitory storage medium embodying a computer program product comprising instructions for performing magnetoencephalography (MEG) source imaging of claim 17 , wherein the number of source locations is at least 30 times greater than the number of sensors in the sensor array.
19. The tangible non-transitory storage medium embodying a computer program product comprising instructions for performing magnetoencephalography (MEG) source imaging of claim 18 , wherein the number of source locations include at least 10,000 voxels.
20. The tangible non-transitory storage medium embodying a computer program product comprising instructions for performing magnetoencephalography (MEG) source imaging of claim 18 , wherein the number of sensors in the sensor array includes at least 250 sensors.
21. The tangible non-transitory storage medium embodying a computer program product comprising instructions for performing magnetoencephalography (MEG) source imaging of claim 17 , the instructions including:
producing by the processor an image including image features representing the source values at locations mapped to corresponding voxels in a magnetic resonance imaging (MRI) image of the brain.