Effects of volume conductor and source configuration on simulated magnetogastrograms

Characterizing Spatial Signatures of Gastric Electrical Activity Using Biomagnetic Source Localization

BACKGROUND

The motility patterns in the gastrointestinal tract are regulated, in part, by bioelectrical events known as slow waves (SWs). Understanding temporal and spatial features of gastric SWs can help reveal the underlying causes of functional motility disorders.

OBJECTIVE

This study investigated the ability of source localization techniques to characterize the spatial signatures of SW activity using simulated and experimental magnetogastrography data.

METHODS

Two SW propagation patterns (antegrade and retrograde) with two rhythms (normogastric and bradygastric) were used to simulate magnetic fields using 4 anatomically realistic stomach and torso geometries. Source localization was performed utilizing the equivalent current dipole (ECD) and the equivalent magnetic dipole (EMD) models.

RESULTS

In the normogastric simulations when compared with the SW activity, the EMD model was capable of identifying the SW propagation in the lateral, antero-posterior, and supero-inferior axes with the median correlation coefficients of 0.66, 0.53, and 0.83, respectively, whereas the ECD model produced lower correlation scores (median: 0.52, 0.44, and 0.44). Moreover, the EMD model resulted in distinct and opposite spatial signatures for the antegrade and retrograde propagation. Similarly, when experimental data was used, the EMD model revealed antegrade-like signatures where the propagation was mostly towards the third quadrant in the supero-inferior (preprandial: 49%, postprandial: 35%) and antero-posterior (preprandial: 49%, postprandial: 50%) axes.

CONCLUSION AND SIGNIFICANCE

The EMD model was able to identify and classify the spatial signatures of SW activities, which can help to inform the interpretation of non-invasive recordings of gastric SWs as a biomarker of functional motility disorders.

Effects of magnetogastrography sensor configurations in tracking slow wave propagation

Magnetogastrography (MGG) is a non-invasive method of assessing gastric slow waves (SWs) by recording the resultant magnetic fields. MGG can capture both SW frequency and propagation, and identify SW dysrhythmias that are associated with motility disorders. However, the impact of the restricted spatial coverage and sensor density on SW propagation tracking performance is unknown. This study simulated MGG using multiple anatomically specific torso geometries and two realistic SW propagation patterns to determine the effect of different sensor configurations on tracking SW propagation. The surface current density mapping and center-of-gravity tracking methods were used to compare four magnetometer array configurations: a reference system currently used in GI research and three hypothetical higher density and coverage arrays. SW propagation patterns identified with two hypothetical arrays (with coverage over at least the anterior of the torso) correlated significantly higher with simulated realistic 3 cycle-per-minute SW activity than the reference array (p = 0.016, p = 0.005). Furthermore, results indicated that most of the magnetic fields that contribute to the performance of SW propagation tracking were located on the anterior of the torso as further increasing the coverage did not significantly increase performance. A 30% decrease in sensor spacing within the same spatial coverage of the reference array also significantly increased correlation values by approximately 0.50 when the signal-to-noise ratio was 5 dB. This study provides evidence that higher density and coverage sensor layouts will improve the utility of MGG. Further work is required to investigate optimum sensor configurations across larger anatomical variations and other SW propagation patterns.

Simulation-based Analysis of Magnetogastrography Sensor Configurations for Characterizing Gastric Slow Wave Dysrhythmias

The routine diagnosis of gastric motility disorders represents a significant problem to current clinical practice. Magnetogastrography (MGG) provides a non-invasive option for assessing gastric slow wave (SW) dysrhythmias that are associated with motility disorders. However, its ability to characterize SW propagation is impaired by the limited spatial coverage of existing superconducting quantum interference devices (SQUIDs). Recently developed optically-pumped magnetometers can potentially substitute SQUIDs and enable subject-specific MGG arrays with greater spatial coverage. This study developed simulations of gastric MGG to determine the distribution of the magnetic fields (MFs) generated by SWs above the torso, and investigated the impact of several realistic dysrhythmic patterns of propagation. The distribution of MFs was found to vary significantly for different patterns of SW propagation, with ectopic dysrhythmia displaying the greatest difference from normal. Notably, some important proportion of the MFs lay outside the coverage of an existing experimental SQUID array used in gastrointestinal research for some simulated SW propagation patterns, such as retrograde activity. Results suggest that MGG measurements should be made over the entire frontal face of the torso to capture all of the strongest MFs generated by SWs.Clinical relevance- This provides a guide for the placement of MGG sensors for the capture of both normal and dysrhythmic gastric slow wave propagation.

Characterization of Electrophysiological Propagation by Multichannel Sensors

OBJECTIVE

The propagation of electrophysiological activity measured by multichannel devices could have significant clinical implications. Gastric slow waves normally propagate along longitudinal paths that are evident in recordings of serosal potentials and transcutaneous magnetic fields. We employed a realistic model of gastric slow wave activity to simulate the transabdominal magnetogastrogram (MGG) recorded in a multichannel biomagnetometer and to determine characteristics of electrophysiological propagation from MGG measurements.

METHODS

Using MGG simulations of slow wave sources in a realistic abdomen (both superficial and deep sources) and in a horizontally-layered volume conductor, we compared two analytic methods (second-order blind identification, SOBI and surface current density, SCD) that allow quantitative characterization of slow wave propagation. We also evaluated the performance of the methods with simulated experimental noise. The methods were also validated in an experimental animal model.

RESULTS

Mean square errors in position estimates were within 2 cm of the correct position, and average propagation velocities within 2 mm/s of the actual velocities. SOBI propagation analysis outperformed the SCD method for dipoles in the superficial and horizontal layer models with and without additive noise. The SCD method gave better estimates for deep sources, but did not handle additive noise as well as SOBI.

CONCLUSION

SOBI-MGG and SCD-MGG were used to quantify slow wave propagation in a realistic abdomen model of gastric electrical activity.

SIGNIFICANCE

These methods could be generalized to any propagating electrophysiological activity detected by multichannel sensor arrays.

Reconstruction of normal and abnormal gastric electrical sources using a potential based inverse method

The use of cutaneous recordings to non-invasively characterize gastric slow waves has had limited clinical acceptance, primarily due to the uncertainty in relating the recorded signal to the underlying gastric slow waves. In this study we aim to distinguish and quantitatively reconstruct different slow wave patterns using an inverse algorithm. Slow wave patterns corresponding to normal, retrograde and uncoupled activity at different frequencies were imposed on a stomach surface model. Gaussian noise (10% peak-to-peak) was added to cutaneous potentials and the Greensite-Tikhonov inverse method was used to reconstruct the potentials on the stomach. The effectiveness of the number or location of electrodes on the accuracy of the inverse solutions was investigated using four different electrode configurations. Results showed the reconstructed solutions were able to reliably distinguish the different slow wave patterns and waves with lower frequency were better correlated to the known solution than those with higher. The use of up to 228 electrodes improved the accuracy of the inverse solutions. However, the use of 120 electrodes concentrated around the stomach was able to achieve similar results. The most efficient electrode configuration for our model involved 120 electrodes with an inter-electrode distance of 32 mm.

Reconstruction of multiple gastric electrical wave fronts using potential-based inverse methods

One approach for non-invasively characterizing gastric electrical activity, commonly used in the field of electrocardiography, involves solving an inverse problem whereby electrical potentials on the stomach surface are directly reconstructed from dense potential measurements on the skin surface. To investigate this problem, an anatomically realistic torso model and an electrical stomach model were used to simulate potentials on stomach and skin surfaces arising from normal gastric electrical activity. The effectiveness of the Greensite-Tikhonov or the Tikhonov inverse methods were compared under the presence of 10% Gaussian noise with either 84 or 204 body surface electrodes. The stability and accuracy of the Greensite-Tikhonov method were further investigated by introducing varying levels of Gaussian signal noise or by increasing or decreasing the size of the stomach by 10%. Results showed that the reconstructed solutions were able to represent the presence of propagating multiple wave fronts and the Greensite-Tikhonov method with 204 electrodes performed best (correlation coefficients of activation time: 90%; pacemaker localization error: 3 cm). The Greensite-Tikhonov method was stable with Gaussian noise levels up to 20% and 10% change in stomach size. The use of 204 rather than 84 body surface electrodes improved the performance; however, for all investigated cases, the Greensite-Tikhonov method outperformed the Tikhonov method.

Influence of body parameters on gastric bioelectric and biomagnetic fields in a realistic volume conductor

Electrogastrograms (EGG) and magnetogastrograms (MGG) provide two complementary methods for non-invasively recording electric or magnetic fields resulting from gastric electrical slow wave activity. It is known that EGG signals are relatively weak and difficult to reliably record while magnetic fields are, in theory, less attenuated by the low-conductivity fat layers present in the body. In this paper, we quantified the effects of fat thickness and conductivity values on resultant magnetic and electric fields using anatomically realistic torso models and trains of dipole sources reflecting recent experimental results. The results showed that when the fat conductivity was increased, there was minimal change in both potential and magnetic fields. However, when the fat conductivity was reduced, the magnetic fields were largely unchanged, but electric potentials had a significant change in patterns and amplitudes. When the thickness of the fat layer was increased by 30 mm, the amplitude of the magnetic fields decreased 10% more than potentials but magnetic field patterns were changed about four times less than potentials. The ability to localize the underlying sources from the magnetic fields using a surface current density measure was altered by less than 2 mm when the fat layer was increased by 30 mm. In summary, results confirm that MGG provides a favorable method over EGG when there are uncertain levels of fat thickness or conductivity.