Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Jing Xiang, Qian Luo, Rupesh Kotecha, Abraham Korman, Fawen Zhang, Huan Luo, Hisako Fujiwara, Nat Hemasilpin, Douglas F Rose, Jing Xiang, Qian Luo, Rupesh Kotecha, Abraham Korman, Fawen Zhang, Huan Luo, Hisako Fujiwara, Nat Hemasilpin, Douglas F Rose

Abstract

Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation's memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (1~70 Hz) and high-frequency (70~200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 h by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2-3 days and used 1-2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.

Keywords: brain; high-frequency oscillations; magnetic source imaging; magnetoencephalography; multi-frequency.

Figures

Figure 1
Figure 1
Workflow for computing accumulated spectrogram (left) and the basic principle of computing accumulated spectrogram (right). Since the analysis of high-frequency MEG signals requires high-sampling rate MEG data, MEG data are digitized in a high-frequency range. To improve the performance and optimize the use of computer memory for analyzing both low- and high-frequency MEG signals, the new method can re-sample MEG data dynamically according to the analysis frequency ranges. If the data points of the recorded data are smaller than the minimum data point of wavelet transform in frequency range, the “Data Padding” function can pad some data points so as to meet the requirements of wavelet transform. The “Thresholding” indicates that a spectral value can be rejected or accepted by the accumulated spectrogram according to a threshold value. MEG data recorded are waveforms, which are divided to segments (e.g., “Waveform 1,” “Waveform N”) to minimize the use of memory for wavelet transform (“Wavelet transform”). In the new method, wavelet transform transfers each segment of waveform data to a spectrum (e.g., “Spectrum 1,” “Spectrum N”). Of note, “N” indicates the total number of segments or spectra, which can be theoretically infinitely large. The “+” indicates the process of accumulation, which add all spectra together to produce an accumulated spectrum (“Accumulated Spectrum”). The left view of the sensor distribution of our MEG system is shown on the top right.
Figure 2
Figure 2
Workflow for computing accumulated source images. The workflow includes two main computing pipelines. One computing pipeline processes MEG data with filter or wavelet transforms so as to generate multi-frequency datasets. MEG signals in multi-frequency datasets are in a set of frequency ranges. Another computing pipeline works on several tasks, which included the creation of a three-dimensional source grid (3D grid), performing forward solution by calculating lead fields, ranking the norm for each source for all sensors, and performing SVD. The node-beam lead field is completed by selecting a group of sensors which have a larger lead field norm (or weights). Of note, each location in accumulated source imaging can have multiple parameters (e.g., “Frequency Index,” “Source Strength”). Some processes are optional (red lines) and additional parameters can also be added to the workflow.
Figure 3
Figure 3
Accumulated global spectrograms in three conditions. “Magnetic Noise” was computed with MEG data recorded without subjects. “Control Subject” was computed with MEG data recorded from a healthy child. “Epilepsy Subject” was computed with MEG data recorded from a child with epilepsy between seizures (interictal). The sampling rate of all MEG recordings was 6000 Hz. An accumulated global spectrogram represents the “spatial summation” of the entire MEG sensor array accumulated spectrograms. The three spectrograms show that the epilepsy subject has elevated spectral power as compared to the control subject.
Figure 4
Figure 4
Accumulated spectrograms show the well-known alpha activity in a healthy subject and an epilepsy subject. Noticeably, healthy subject (“Healthy Subject”) has a clear activity around 8–12 Hz (alpha activity). However, the epilepsy subject (“Epilepsy Subject”) has incrased activity in 2–4 Hz (low-frequency activity). The color bar shows the color coding of spectral power.
Figure 5
Figure 5
Global accumulated spectrograms from 10 epilepsy subjects and 1 healthy subject show the main frequency components of neuromagnetic signals in 70–200 Hz in epilepsy patients. Noticeably, the activity patterns vary across patients. The color bar shows the color coding of spectral power for all the global accumulated spectrograms.
Figure 6
Figure 6
Accumulated spectral contour maps from 10 epilepsy subjects and 1 healthy subject show the spatial distributions. Noticeably, the spectral distribution varies across patients. All the contour maps have the same orientation defined by the arrows: the “L” indicates the left side of the head and the “R” indicates the right side of the head. The “F” indicates that the upper part of the contour map represents the frontal region of the head; the “B” indicates that the lower part of the contour map represents the posterior region of the head. Each small circle represents one MEG sensor. The color bar shows the color coding of spectral power for all the contour maps.
Figure 7
Figure 7
An illustration of the basic principle of accumulated source imaging. The top waveforms show MEG data at sensor levels. The bottom images show individual structural magnetic resonance image and the region of interest (ROI, blue lines) for source scanning. MEG sensor data are firstly divided into small segments (e.g., “Sensor Data Segment 1,” “Sensor Data Segment 2,” “Sensor Data Segment N”). Volumetric sources are then produced by scanning the entire ROI with each segment of sensor data. The red, yellow and white small cubes indicate the sources (or voxels) identified. For illustration purposes, a very low resolution (12 millimeter) spatial resolution was used. An accumulated source image is generated by spatially adding all volumetric sources together. Of note, only sources reach certain thresholds (in this case, 75%) are added to accumulated source images, which differentiate this accumulating process from averaging. The color bar indicates the color coding of the source strength.
Figure 8
Figure 8
Accumulated source imaging shows low frequency brain activity in 8–12 Hz (alpha) in an epilepsy subject (“Epilepsy Subject”) and a healthy subject (“Control Subject”). Alpha activity is localized to the occipital cortex in the healthy subject. However, alpha activity is overshadowed by epileptic activity in the epilepsy subject. The epileptic activity is localized to the left and right parietal cortices in the epilepsy subject, which is concordant with clinical findings.
Figure 9
Figure 9
A digital photo of intracranial recording (“ECoG”) and accumulated source imaging (“ASI”) show the concordance of the two methods. The two images are placed in the similar orientation. “Frontal” indicate the frontal cortex; “Temporal” indicates temporal lobe. The left green arrow points to the epileptic area invasively defined by intracranial recording; the right blue arrow points to the epileptic area noninvasively localized with high-frequency neuromagnetic signals. Noticeably, the areas are matched in gyrus level. The color bar shows the color coding of accumulated source imaging. The value of the source voxel is normalized T value (no unit).

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