Electroanatomical mapping of the stomach with simultaneous biomagnetic measurements

Gastric motility is coordinated by bioelectric slow waves (SWs) and dysrhythmic SW activity has been linked with motility disorders. Magnetogastrography (MGG) is the non-invasive measurement of the biomagnetic fields generated by SWs. Dysrhythmia identification using MGG is currently challenging because source models are not well developed and the impact of anatomical variation is not well understood. A novel method for the quantitative spatial co-registration of serosal SW potentials, MGG, and geometric models of anatomical structures was developed and performed on two anesthetized pigs to verify feasibility. Electrode arrays were localized using electromagnetic transmitting coils. Coil localization error for the volume where the stomach is normally located under the sensor array was assessed in a benchtop experiment, and mean error was 4.2±2.3mm and 3.6±3.3° for a coil orientation parallel to the sensor array and 6.2±5.7mm and 4.5±7.0° for a perpendicular coil orientation. Stomach geometries were reconstructed by fitting a generic stomach to up to 19 localization coils, and SW activation maps were mapped onto the reconstructed geometries using the registered positions of 128 electrodes. Normal proximal-to-distal and ectopic SW propagation patterns were recorded from the serosa and compared against the simultaneous MGG measurements. Correlations between the center-of-gravity of normalized MGG and the mean position of SW activity on the serosa were 0.36 and 0.85 for the ectopic and normal propagation patterns along the proximal-distal stomach axis, respectively. This study presents the first feasible method for the spatial co-registration of MGG, serosal SW measurements, and subject-specific anatomy. This is a significant advancement because these data enable the development and validation of novel non-invasive gastric source characterization methods.

Lowered Delta Activity in Post-COVID-19 Patients with Fatigue and Cognitive Impairment

In post-COVID-19 syndrome (PCS), neurocognitive symptoms and fatigue are often associated with alterations in electroencephalographic (EEG) activity. The present study investigates the brain source activity at rest in PCS patients (PCS-pts) perceiving cognitive deficits and fatigue. A total of 18 PCS-pts and 18 healthy controls (HCs) were enrolled. A Montreal Cognitive Assessment (MoCA), Perceived Cognitive Difficulties Scale (PDCS) and Fatigue Severity Scale (FSS) were administered for assessing the symptoms' severity. Brain activity at rest, both with open (OE) and closed eyes (CE), was recorded by high-density EEG (Hd-EEG) and localized by source estimation. Compared to HCs, PCS-pts exhibited worse performance in executive functions, language and memory, and reported higher levels of fatigue. At resting OE state, PCS-pts showed lower delta source activity over brain regions known to be associated with executive processes, and these changes were negatively associated with PDCS scores. Consistent with recent literature data, our findings could indicate a dysfunction in the neuronal networks involved in executive functions in PCS-pts complaining of fatigue and cognitive impairment.

Stomach Geometry Reconstruction Using Serosal Transmitting Coils and Magnetic Source Localization

OBJECTIVE

Bioelectric slow waves (SWs) are a key regulator of gastrointestinal motility, and disordered SW activity has been linked to motility disorders. There is currently a lack of practical options for the acquisition of the 3D stomach geometry during research studies when medical imaging is challenging. Accurately recording the geometry of the stomach and co-registering electrode and sensor positions would provide context for in-vivo studies and aid the development of non-invasive methods of gastric SW assessment.

METHODS

A stomach geometry reconstruction method based on the localization of transmitting coils placed on the gastric serosa was developed. The positions and orientations of the coils, which represented boundary points and surface-normal vectors, were estimated using a magnetic source localization algorithm. Coil localization results were then used to generate surface models. The reconstruction method was evaluated against four 3D-printed anatomically realistic human stomach models and applied in a proof of concept in-vivo pig study.

RESULTS

Over ten repeated reconstructions, average Hausdorff distance and average surface-normal vector error values were 4.7 ±0.2 mm and 18.7 ±0.7° for the whole stomach, and 3.6 ±0.2 mm and 14.6 ±0.6° for the corpus. Furthermore, mean intra-array localization error was 1.4 ±1.1 mm for the benchtop experiment and 1.7 ±1.6 mm in-vivo.

CONCLUSION AND SIGNIFICANCE

Results demonstrated that the proposed reconstruction method is accurate and feasible. The stomach models generated by this method, when co-registered with electrode and sensor positions, could enable the investigation and validation of novel inverse analysis techniques.

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel-Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.

Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection

Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects' data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.

EEG Network Analysis in Epilepsy with Generalized Tonic–Clonic Seizures Alone

Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic–clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.

Anatomically Constrained Gastric Slow Wave Localization using Biomagnetic Data

Detection of dysrhythmic gastric slow wave (SW) activity could have significant clinical utility because dysrhyth-mias have been linked to gastric motility disorders. The elec-trogastrogram (EGG) and magnetogastrogram (MGG) enable the non-invasive assessment of SW activity, but most analysis methods can only resolve frequency and velocity. Improved characterization of dysrhythmic propagation patterns from non-invasive measurements is important for the diagnosis of motility disorders and could allow early treatment stratification. In this study, we demonstrate the use of a penalized linear regression framework to localize SW events on the longitudinal stomach axis using simulated MGG data. Priors relating to spatial sparsity, the organization of wavefronts into complete circumferential rings, and the local distribution of depolar-ization and repolarization phases were used to constrain the inverse solution. This method was applied to MGG computed for a single wavefront case and a multiple wavefront case that were constructed from simulated 3 cycle-per-minute normal SW activity. Propagation patterns along the longitudinal stomach axis were identifiable from reconstructed SW activity for both cases. Localization error was 5.7 ± 0.1 mm and 7.7 ± 0.1 mm for each respective case within the distal stomach when the signal-to-noise ratio was 10 dB. Results indicate that penalized linear regression can successfully localize SW events provided the 3D geometry of the stomach and torso were acquired. Clinical Relevance- This method could help to improve the efficiency and accuracy of diagnosing gastric motility disorders from non-invasive measurements.