Artifact reduction in magnetogastrography using fast independent component analysis

Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex

Neural and cognitive deficits after mild traumatic brain injury (mTBI) are paralleled by changes in resting state functional correlation (FC) networks that mirror post-traumatic pathophysiology effects on functional outcomes. Using functional magnetic resonance images acquired both acutely and chronically after injury (∼1 week and ∼6 months post-injury, respectively), we map post-traumatic FC changes across 136 participants aged 19-79 (52 females), both within and between the brain's seven canonical FC networks: default mode, dorsal attention, frontoparietal, limbic, somatomotor, ventral attention, and visual. Significant sex-dependent FC changes are identified between (A) visual and limbic, and between (B) default mode and somatomotor networks. These changes are significantly associated with specific functional recovery patterns across all cognitive domains (p < 0.05, corrected). Changes in FC between default mode, somatomotor, and ventral attention networks, on the one hand, and both temporal and occipital regions, on the other hand, differ significantly by age group (p < 0.05, corrected), and are paralleled by significant sex differences in cognitive recovery independently of age at injury (p < 0.05, corrected). Whereas females' networks typically feature both significant (p < 0.036, corrected) and insignificant FC changes, males more often exhibit significant FC decreases between networks (e.g., between dorsal attention and limbic, visual and limbic, default-mode and somatomotor networks, p < 0.0001, corrected), all such changes being accompanied by significantly weaker recovery of cognitive function in males, particularly older ones (p < 0.05, corrected). No significant FC changes were found across 35 healthy controls aged 66-92 (20 females). Thus, male sex and older age at injury are risk factors for significant FC alterations whose patterns underlie post-traumatic cognitive deficits. This is the first study to map, systematically, how mTBI impacts FC between major human functional networks.

Acute cognitive deficits after traumatic brain injury predict Alzheimer's disease-like degradation of the human default mode network

Traumatic brain injury (TBI) and Alzheimer's disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute posttraumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory, and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, posttraumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment, and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients' chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ~6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.

Neuroimaging and Psychometric Assessment of Mild Cognitive Impairment After Traumatic Brain Injury

Traumatic brain injury (TBI) can be serious partly due to the challenges of assessing and treating its neurocognitive and affective sequelae. The effects of a single TBI may persist for years and can limit patients’ activities due to somatic complaints (headaches, vertigo, sleep disturbances, nausea, light or sound sensitivity), affective sequelae (post-traumatic depressive symptoms, anxiety, irritability, emotional instability) and mild cognitive impairment (MCI, including social cognition disturbances, attention deficits, information processing speed decreases, memory degradation and executive dysfunction). Despite a growing amount of research, study comparison and knowledge synthesis in this field are problematic due to TBI heterogeneity and factors like injury mechanism, age at or time since injury. The relative lack of standardization in neuropsychological assessment strategies for quantifying sequelae adds to these challenges, and the proper administration of neuropsychological testing relative to the relationship between TBI, MCI and neuroimaging has not been reviewed satisfactorily. Social cognition impairments after TBI (e.g., disturbed emotion recognition, theory of mind impairment, altered self-awareness) and their neuroimaging correlates have not been explored thoroughly. This review consolidates recent findings on the cognitive and affective consequences of TBI in relation to neuropsychological testing strategies, to neurobiological and neuroimaging correlates, and to patient age at and assessment time after injury. All cognitive domains recognized by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) are reviewed, including social cognition, complex attention, learning and memory, executive function, language and perceptual-motor function. Affect and effort are additionally discussed owing to their relationships to cognition and to their potentially confounding effects. Our findings highlight non-negligible cognitive and affective impairments following TBI, their gravity often increasing with injury severity. Future research should study (A) language, executive and perceptual-motor function (whose evolution post-TBI remains under-explored), (B) the effects of age at and time since injury, and (C) cognitive impairment severity as a function of injury severity. Such efforts should aim to develop and standardize batteries for cognitive subdomains—rather than only domains—with high ecological validity. Additionally, they should utilize multivariate techniques like factor analysis and related methods to clarify which cognitive subdomains or components are indeed measured by standardized tests.

Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions

Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.

PARAMETRIZATION AND CORRECTION OF ELECTROCARDIOGRAM SIGNALS USING INDEPENDENT COMPONENT ANALYSIS

Electrocardiogram (ECG) signals are largely employed as a diagnostic tool in clinical practice in order to assess the cardiac status of a specimen. Independent component analysis (ICA) of measured ECG signals yields the independent sources, provided that certain requirements are fulfilled. Properly parametrized ECG signals provide a better view of the extracted ECG signals, while reducing the amount of ECG data. Independent components (ICs) of parametrized ECG signals may also be more readily interpretable than original ECG measurements or even their ICs. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG signals for a clear interpretation of ECG data in diagnostic applications. In this work, ICA is tested on the Common Standards for Electrocardiography (CSE) database files corrupted by abrupt changes, high frequency noise, power line interference, etc. The joint approximation for diagonalization of eigen matrices (JADE) algorithm for ICA is applied to three-channel ECG, and the sources are separated as ICs. In this analysis, an extension is applied to the algorithm for further correction of the extracted components. The values of R-peak before and after application of ICA are found using quadratic spline wavelet, which facilitates the estimation of the reconstruction errors. The results indicate that, in most of the cases, the percentage reconstruction error is small at around 3%. The paper also highlights the advantages, limitations, and diagnostic feature extraction capability of ICA for clinicians and medical practitioners. Kurtosis is varied in the range of 3.0–7.0, and variance of variance (Varvar) is varied in the range of 0.2–0.5.

Multiscale observation of biological interactions of nanocarriers: from nano to macro

Microscopic observations have played a key role in recent advancements in nanotechnology-based biomedical sciences. In particular, multiscale observation is necessary to fully understand the nano-bio interfaces where a large amount of unprecedented phenomena have been reported. This review describes how to address the physicochemical and biological interactions of nanocarriers within the biological environments using microscopic tools. The imaging techniques are categorized based on the size scale of detection. For observation of the nanoscale biological interactions of nanocarriers, we discuss atomic force microscopy (AFM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). For the micro to macro-scale (in vitro and in vivo) observation, we focus on confocal laser scanning microscopy (CLSM) as well as in vivo imaging systems such as magnetic resonance imaging (MRI), superconducting quantum interference devices, and IVIS. Additionally, recently developed combined techniques such as AFM-CLSM, correlative light and electron microscopy (CLEM), and SEM spectroscopy are also discussed. In this review, we describe how each technique helps elucidate certain physicochemical and biological activities of nanocarriers such as dendrimers, polymers, liposomes, and polymeric/inorganic nanoparticles, thus providing a toolbox for bioengineers, pharmaceutical scientists, biologists, and research clinicians.

Combined method for reduction of high frequency interferences in surface electroenterogram (EEnG)

Surface electroenterogram (EEnG) recording is a novel technique for monitoring intestinal motility non-invasively. However, surface EEnG recordings are contaminated by cardiac activity, the respiratory artefact, movement artefacts and other types of interference. The goal of this work is to remove electrocardiogram (ECG) interference and movement artefacts from surface EEnG by means of a combined method of empirical mode decomposition and independent component analysis. For this purpose, 11 recording sessions were conducted on animal models. In order to quantify the effectiveness of the proposed method, several parameters were calculated from each session: signal-to-ECG interference ratio (S/I), energy over 2 Hz (EF2) which quantifies the intestinal motility index of external EEnG recording and the variation of EF2. The proposed method removes both ECG interference and movement artefacts from surface EEnG, obtaining a significantly higher S/I ratio and considerably reducing the non-physiological variation of EF2. Furthermore, after applying the combined method, the correlation coefficient between internal recording EF2 and surface recording EF2 rises significantly. The proposed method could therefore be a useful tool to reduce high frequency interference in EEnG recording and to provide more robust non-invasive intestinal motility indexes.

Biomagnetic signatures of uncoupled gastric musculature

Gastric slow waves propagate in the electrical syncytium of the healthy stomach, being generated at a rate of approximately three times per minute in a pacemaker region along the greater curvature of the antrum and propagating distally towards the pylorus. Disease states are known to alter the normal gastric slow wave. Recent studies have suggested the use of biomagnetic techniques for assessing parameters of the gastric slow wave that have potential diagnostic significance. We present a study in which the gastric syncytium was uncoupled by mechanical division as we recorded serosal electric potentials along with multichannel biomagnetic signals and cutaneous potentials. By computing the surface current density (SCD) from multichannel biomagnetic recordings, we were able to quantify gastric slow wave propagation as well as the frequency and amplitude of the slow wave and to show that these correlate well with similar parameters from serosal electrodes. We found the dominant slow wave frequency to be an unreliable indicator of gastric uncoupling as uncoupling results in the appearance of multiple slow wave sources at various frequencies in external recordings. The percentage of power distributed in specific frequency ranges exhibited significant postdivision changes. Propagation velocity determined from SCD maps was a weak indicator of uncoupling in this work; we believe that the relatively low spatial resolution of our 19-channel biomagnetometer confounds the characterization of spatial variations in slow wave propagation velocities. Nonetheless, the biomagnetic technique represents a non-invasive method for accurate determination of clinically significant parameters of the gastric slow wave.

Noninvasive detection of small bowel electrical activity from SQUID magnetometer measurements using SOBI

We report a robust method for noninvasive biomagnetic detection of small bowel electrical activity. Simultaneous Superconducting QUantum Interference Device (SQUID) magnetometer (MENG) and serosal electrode recordings were made on pig small bowel. The SOBI blind-source separation algorithm was used to separate the underlying source signals of the MENG. Comparison of identified SOBI components to the serosal recordings validated the underlying MENG sources as being enteric in origin. Non-invasive detection of small bowel electrical activity could have significant implications in a clinical setting.

Comparison of conventional filtering and independent component analysis for artifact reduction in simultaneous gastric EMG and magnetogastrography from porcines

In this study, we perform a comparative study of independent component analysis (ICA) and conventional filtering (CF) for the purpose of artifact reduction from simultaneous gastric EMG and magnetogastrography (MGG). EMG/MGG data were acquired from ten anesthetized pigs by obtaining simultaneous recordings using serosal electrodes (EMG) as well as with a superconducting quantum interference device biomagnetometer (MGG). The analysis of MGG waveforms using ICA and CF indicates that ICA is superior to the CF method in its ability to extract respiration and cardiac artifacts from MGG recordings. A signal frequency analysis of ICA- and CF-processed data was also undertaken using waterfall plots, and it was determined that the two methods produce qualitatively comparable results. Through the use of simultaneous EMG/MGG, we were able to demonstrate the accuracy and trustworthiness of our results by comparison and cross-validation within the framework of a porcine model.

Gastrointestinal arrhythmias are associated with statistically significant fluctuations in systemic information dimension

Although cardiac arrhythmias have been studied extensively, little is known about arrhythmic phenomena in the gastrointestinal (GI) system. In this study, we demonstrate for the first time that the onset of GI arrhythmias is associated with statistically significant fluctuations in the information dimension of the associated systems. We induced gastric and intestinal arrhythmias in pigs using surgical stomach division and mesenteric artery ligation, respectively. Both conditions lead to a decreased supply of blood to the GI tract, which is associated in humans with various potentially lethal conditions including chronic mesenteric ischemia, whose mortality rate is over 60%. During our experiments, we recorded simultaneous magnetocardiographic, magnetogastrographic and magnetoenterographic signals and concluded that, when GI circulation is compromised, the information dimensionality of the system fluctuates significantly. In conclusion, dimensionality may be an important diagnostic factor for the characterization of arrhythmias in the context of GI pathophysiology.

Combined method for artifact reduction in surface electroenterogram

Surface electroenterogram (EEnG) is a non-invasive method for monitoring the intestinal motility. However, surface EEnG recordings are contaminated by movement artifact, cardiac activity, respiratory artifact and other interferences. The aim of this work is to remove movement artifacts by means of a combined method of empirical mode decomposition (EMD) and independent component analysis (ICA). Four recording sessions were conducted on canine model. Surface signals from 4 different channels are decomposed using EMD. Resulting intrinsic mode functions are the inputs of ICA analysis which permits to separate and identify the activities of different origin. Signal components associated to movement artifacts are removed and the original signals are reconstructed by means of an inverse procedure. The results show that the proposed method allows extracting and cancelling movement artifacts from surface EEnG, avoiding the presence of irregular peaks in external intestinal motility indexes. Therefore, the proposed method could be useful to reduce artifacts in EEnG recording and to provide more robust non-invasive intestinal motility indicators.

Separation of gastric electrical control activity from simultaneous MGG/EGG recordings using independent component analysis

Spatiotemporal parameters of gastric electrical control activity such as its amplitude, direction and propagation velocity are physiological parameters of distinctive clinical interest due to their potential use for differentiating between the healthy and diseased states of the human stomach. Whereas their time evolution is relatively well behaved in the case of healthy subjects, significant deviations from normal have been observed in patients suffering from a number of gastric diseases such as gastroparesis and gastropathy. For this reason, monitoring ECA parameters noninvasively may offer a useful test for the presence of such diseases whose diagnosis remains problematic. Here, we describe a method for computing ECA direction and orientation from simultaneous, noninvasive magnetogastrographic (MGG) and electrogastrographic (EGG) recordings. We demonstrate how independent component analysis and standard frequency analysis methods can be used to predict the locations and orientations of gastric current dipoles from MGG/EGG data. We compare our MGG-based dipole parameters to analogous ones obtained from simultaneous EGG recordings within the experimental framework of a human model. We find that magnetic recordings are superior in their ability to portray the underlying physiology of the stomach.

Quantification of combined method for interferences reduction in multichannel surface electroenterogram

Surface electroenterogram (EEnG) is a non-invasive method for monitoring the intestinal motility. However, surface EEnG signals are contaminated by strong physiological interferences. The main interferences which affect high-frequency components of surface EEnG are cardiac activity and movement artifacts. The aim of this work is to quantify the effectiveness of a combined method based on empirical mode decomposition and independent component analysis to remove these interferences from multichannel surface EEnG. In order to do so, several parameters were calculated from five recording sessions: Signal-to-ECG interference ratio (S/I) and variation of energy over 2 Hz (EF2). The results show that the S/I of processed signals was significantly higher than that of original signals, moreover the improvement of the S/I ratio is due to the attenuation of energy associated to interference. The proposed method also allows cancelling movement artifacts from surface EEnG, reducing considerably the non-physiological variation of EF2. Furthermore after the application of the combined method, correlation coefficient between EF2 of internal recording with EF2 of surface recording is greatly higher. Therefore, the proposed method could be helpful to reduce high-frequency interferences in EEnG recording and to provide more robust non-invasive intestinal motility indicators.

Empirical mode decomposition: a method to reduce low frequency interferences from surface electroenterogram

The surface electroenterogram (EEnG) is a non-invasive method of studying myoelectrical bowel activity. However, surface EEnG recordings are contaminated by cardiac activity, respiratory and motion artifacts, and other sources of interference. The aim of this work is to remove the respiration artifact and the very low frequency components from surface EEnG by means of empirical mode decomposition (EMD). Eleven recording sessions were carried out on canine model. Several parameters were calculated before and after the application of the method: signal-to-interference ratio (S/I ratio) and the attenuation level of the signal and of interference. The results show that the S/I ratio was significantly higher after the application of the method (3.68+/-5.54 dB vs. 10.45+/-3.65 dB), the attenuation level of signal and of interference is -0.49+/-0.80 dB versus -7.26+/-5.42 dB, respectively. Therefore, EMD could be a useful aid in identifying the intestinal slow wave and in removing interferences from EEnG recordings.

Biomagnetic investigation of injury currents in rabbit intestinal smooth muscle during mesenteric ischemia and reperfusion

A noninvasive, sensitive, and specific method of detecting mesenteric ischemia would be of great use in reducing the morbidity and mortality with which it is associated. Acute lesions in polarized electrically coupled tissues lead to injury currents driven by the transmembrane resting potential gradient. These injury currents are an effective indicator of pathophysiology. The presence of near-DC injury currents in rabbit intestinal smooth muscle has already been demonstrated using a Superconducting quantum interference device (SQUID), and the aim of this study was to evaluate the effect of arterial reperfusion upon these currents. We exteriorized the small bowel of 14 New Zealand white rabbits and placed a remotely operated vascular occluder around the distal most artery supplying a 3-in segment of the jejunum. Experiments were conducted in three groups, i.e., control (n=3), ischemia (n=6), and reperfusion following ischemia (n=5). The subject's position was modulated in and out of the biological field detection range of a SQUID magnetometer using a lift constructed of nonmagnetic material. The changes in magnetic field amplitude were 9.3 and 31.01% for the control and ischemia groups, respectively. The reperfusion group first exhibited a decrease of 17.35% from the pre-ischemic to the ischemic period, followed by an increase of 13.88% of the ischemic value after reestablishing perfusion. In conclusion, injury currents in GI smooth muscle that appear during ischemia are reduced to near-pre-ischemic levels during reperfusion.

Magnetogastrography (MGG) reproducibility assessments of gastric emptying on healthy subjects

The aim of this study was to evaluate the half-time gastric emptying reproducibility measured by magnetogastrography (MGG). Seven healthy subjects were subjected to a magnetic pulse of 32 mT for 17 ms, seven times in 90 min. The procedure was repeated one and two weeks later. Assessments of the gastric emptying were carried out for each one of the measurements and statistical analyses of ANOVA and Bland-Altman were performed for every group of data. A mean of 21.7 +/- 3.3 min was measured for the half-time of gastric emptying for yoghurt. Reproducibility estimation was above 85%. Therefore, magnetogastrography seems to be a reliable technique to be implemented in routine clinical trials.

An integrative software package for gastrointestinal biomagnetic data acquisition and analysis using SQUID magnetometers

The study of bioelectric and biomagnetic activity in the human gastrointestinal (GI) tract is of great interest in clinical research due to the proven possibility to detect pathological conditions thereof from electric and magnetic field recordings. The magnetogastrogram (MGG) and magnetoenterogram (MENG) can be recorded using superconducting quantum interference device (SQUID) magnetometers, which are the most sensitive magnetic flux-to-voltage converters currently available. To address the urgent need for powerful acquisition and analysis software tools faced by many researchers and clinicians in this important area of investigation, an integrative and modular computer program was developed for the acquisition, processing and analysis of GI SQUID signals. In addition to a robust hardware implementation for efficient data acquisition, a number of signal processing and analysis modules were developed to serve in a variety of both clinical procedures and scientific investigations. Implemented software features include data processing and visualization, waterfall plots of signal frequency spectra as well as spatial maps of GI signal frequencies. Moreover, a software tool providing powerful 3D visualizations of GI signals was created using realistic models of the human torso and internal organs.

Adaptive filtering of ECG interference on surface EEnGs based on signal averaging

An external electroenterogram (EEnG) is the recording of the small bowel myoelectrical signal using contact electrodes placed on the abdominal surface. It is a weak signal affected by possible movements and by the interferences of respiration and, principally, of the cardiac signal. In this paper an adaptive filtering technique was proposed to identify and subsequently cancel ECG interference on canine surface EEnGs by means of a signal averaging process time-locked with the R-wave. Twelve recording sessions were carried out on six conscious dogs in the fasting state. The adaptive filtering technique used increases the signal-to-interference ratio of the raw surface EEnG from 16.7 +/- 6.5 dB up to 31.9 +/- 4.0 dB. In addition to removing ECG interference, this technique has been proven to respect intestinal SB activity, i.e. the EEnG component associated with bowel contractions, despite the fact that they overlap in the frequency domain. In this way, more robust non-invasive intestinal motility indicators can be obtained with correlation coefficients of 0.68 +/- 0.09 with internal intestinal activity. The method proposed here may also be applied to other biological recordings affected by cardiac interference and could be a very helpful tool for future applications of non-invasive recordings of gastrointestinal signals.