Respiratory-related Signal Research Articles

Improving laminar fMRI specificity by reducing macrovascular bias revealed by respiration effects

Abstract Functional MRI (fMRI) time series are inherently susceptible to the influence of respiratory variations. While many studies treat respiration as a source of noise in fMRI, this study employs natural respiratory variations during high resolution (0.8mm) fMRI at 7T to formulate a respiration effect related map and then use this map to reduce macrovascular bias for a more laminar-specific fMRI measurement. Our results indicate that respiratory related signal changes are modulated by breath phase (breathing in/out or in the transition between breath in and out) during fMRI acquisition, with distinct patterns across various brain regions. We demonstrate that respiration maps generated from normal fMRI runs, such as task-oriented sessions, closely resemble those from deep-breath and breath-hold experiments. These maps show a significant correlation with the macro-vasculature automatically segmented based on SWI and QSM images. Most crucially, by removing voxels most responsive to respiratory variations, we can refine high-resolution fMRI measurements to be more layer-specific, improving the accuracy of laminar fMRI analysis.

Post-COVID breathlessness: a mathematical model of respiratory processing in the brain.

Breathlessness is among the most common post-COVID symptoms. In a considerable number of patients, severe breathlessness cannot be explained by peripheral organ impairment. Recent concepts have described how such persistent breathlessness could arise from dysfunctional processing of respiratory information in the brain. In this paper, we present a first quantitative and testable mathematical model of how processing of respiratory-related signals could lead to breathlessness perception. The model is based on recent theories that the brain holds an adaptive and dynamic internal representation of a respiratory state that is based on previous experiences and comprises gas exchange between environment, lung and tissue cells. Perceived breathlessness reflects the brain's estimate of this respiratory state signaling a potentially hazardous disequilibrium in gas exchange. The internal respiratory state evolves from the respiratory state of the last breath, is updated by a sensory measurement of CO2 concentration, and is dependent on the current activity context. To evaluate our model and thus test the assumed mechanism, we used data from an ongoing rebreathing experiment investigating breathlessness in patients with post-COVID without peripheral organ dysfunction (N = 5) and healthy control participants without complaints after COVID-19 (N = 5). Although the observed breathlessness patterns varied extensively between individual participants in the rebreathing experiment, our model shows good performance in replicating these individual, heterogeneous time courses. The model assumes the same underlying processes in the central nervous system in all individuals, i.e., also between patients and healthy control participants, and we hypothesize that differences in breathlessness are explained by different weighting and thus influence of these processes on the final percept. Our model could thus be applied in future studies to provide insight into where in the processing cascade of respiratory signals a deficit is located that leads to (post-COVID) breathlessness. A potential clinical application could be, e.g., the monitoring of effects of pulmonary rehabilitation on respiratory processing in the brain to improve the therapeutic strategies.

Automated Evaluation of Respiratory Signals to Provide Insight Into Respiratory Drive

The diaphragm muscle is the primary inspiratory muscle in mammals and is highly active throughout life. The repetitive activation of the diaphragm muscle (and of other muscles driven by central pattern generator activity) presents an opportunity to analyze these physiological data on a per‐event basis. Here we highlight the development and implementation of the Automated Evaluation of Respiratory Signals (AERS) algorithm to detect individual respiratory events across a range of motor behaviors. Because different respiratory‐related signals provide different types of information, the AERS algorithm is designed to be robust regardless of the signal type—e.g., diaphragm electromyography (EMG) and transdiaphragmatic pressure (Pdi). Individual respiratory event data obtained from adult rats were assessed using the AERS algorithm. This analysis revealed diaphragm root‐mean‐square (RMS) EMG and Pdi were lowest during eupnea, increased slightly in response to hypoxia‐hypercapnia (10% O2 ‐ 5% CO2), and increased approximately 3‐fold in response to airway occlusion compared to the eupneic baseline. The coefficient of variation (CV) within subjects was less than 10% across all behaviors for both RMS EMG and Pdi. By contrast, the CV was approximately 30% across subjects. Respiratory rate was increased by ~20 breaths/min during hypoxia‐hypercapnia compared to eupnea, as expected based on previous work from our lab and others. The respiratory rate CV within subjects was less than 10% during eupnea and hypoxia‐hypercapnia; during airway occlusion, the CV was ~17%, likely owing to the small number of respiratory events (2‐5) analyzed for this behavior per animal. For eupnea and hypoxia‐hypercapnia, the CV across subjects was ~20%, whereas during airway occlusion it was ~25%. Overall, our findings validate the mean values across various measures of respiratory motor output compared to standard subject means approach. However, per‐event analyses greatly enhance statistical power, allowing detection of physiologically relevant measures by taking within‐subject variability into account. Additionally, the AERS analyses show that various measures of respiratory variability as well as time‐frequency analyses can provide deeper insights into respiratory drive when accounting for individual respiratory events. These analyses can be effectively applied to a variety of models of respiratory motor unit loss or dysfunction (e.g., spinal cord injury and aging), allowing the extraction of large amounts of data from respiratory signals for more comprehensive assessments. Our findings suggest that the AERS approach may be particularly beneficial in reducing animal numbers in certain types of studies, for the detection of physiological changes reflected in respiratory signals with interventions causing gain or loss of function, and for analyses of respiratory variability, where breath‐to‐breath dynamics are important.

Respiratory Activity during Exercise: A Feasibility Study on Transition Point Estimation Using Impedance Pneumography

The current diagnostic procedures for assessing physiological response to exercise comprise blood lactates measurements, ergospirometry, and electrocardiography. The first is not continuous, the second requires specialized equipment distorting natural breathing, and the last is indirect. Therefore, we decided to perform the feasibility study with impedance pneumography as an alternative technique. We attempted to determine points in respiratory-related signals, acquired during stress test conditions, that suggest a transition similar to the gas exchange threshold. In addition, we analyzed whether or not respiratory activity reaches steady states during graded exercise. Forty-four students (35 females), practicing sports on different levels, performed a graded exercise test until exhaustion on cycloergometer. Eventually, the results from 34 of them were used. The data were acquired with Pneumonitor 2. The signals demonstrated that the steady state phenomenon is not as evident as for heart rate. The results indicated respiratory rate approaches show the transition point at the earliest (more than 6 min before the end of the exercise test on average), and the tidal volume ones at the latest (less than 5 min). A combination gave intermediate findings. The results showed the impedance pneumography appears reasonable for the transition point estimation, but this should be further studied with the reference.

A simple but useful way to assess fMRI scan qualities

This short “how to” article describes a plot I find useful for assessing fMRI data quality. I discuss the reasoning behind the plot and how it is constructed. I create the plot in scans from several publicly available datasets to illustrate different kinds of fMRI signal variance, ranging from thermal noise to motion artifacts to respiratory-related signals. I also show how the plot can be used to understand the variance removed during denoising. Code to make the plot is provided with the article, and supplemental movies show plots for hundreds of additional subjects.

The influence of physiological noise correction on test-retest reliability of resting-state functional connectivity.

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

QUANTIFICATION OF RESPIRATORY SINUS ARRHYTHMIA WITH HIGH-FRAMERATE ELECTRICAL IMPEDANCE TOMOGRAPHY

Respiratory Sinus Arrhythmia, the variation in the heart rate synchronized with the breathing cycle, forms an interconnection between cardiac-related and respiratory-related signals. It can be used by itself for diagnostic purposes, or by exploiting the redundancies it creates, for example by extracting respiratory rate from an electrocardiogram (ECG). To perform quantitative analysis and patient specific modeling, however, simultaneous information about ventilation as well as cardiac activity needs to be recorded and analyzed. The recent advent of medically approved Electrical Impedance Tomography (EIT) devices capable of recording up to 50 frames per second facilitates the application of this technology. This paper presents the automated selection of a cardiac-related signal from EIT data and quantitative analysis of this signal. It is demonstrated that beat-to-beat intervals can be extracted with a median absolute error below 20 ms. A comparison between ECG and EIT data shows a variation in peak delay time that requires further analysis. Finally, the known coupling of heart rate variability and tidal volume can be shown and quantified using global impedance as a surrogate for tidal volume.

Extraction of cardiac and respiration signals in electrical impedance tomography based on independent component analysis

Abstract Electrical Impedance Tomography (EIT) has successive wide range in impedance imaging, but still it is difficult to extract cardiac-related conductivity changes and respiratory-related conductivity changes in spontaneous breathing subjects. Quite a few methods are attempted to extract these two signals such as electrocardiogram gated averaging, frequency domain filtering and principal component analysis. However, such methods are not able to take apart these components properly or put some effort in real time imaging and have their own limitations. The purpose of this paper is to introduce a new method in the EIT clinical application field, Independent Component Analysis (ICA) to extract cardiac and respiratory related signals in electrical impedance tomography. Independent component analysis has been introduced to use in electrical impedance tomography but this is the first attempt ever to implement this method to separate these two signals and image those independent conductivity distribution of respiration and cardiac changes independently. Data has been collected from a spontaneous breathing subject. Filtration technique has been used to remove random noise and multi level spatial ICA has been applied to obtain independent component signals which has been later used in reconstruction algorithm for imaging.

Esophageal Acid Exposure Alters the Functional Connectivity of the Deglutition Network

Background & Aim: Early studies using functional MRI (fMRI) have demonstrated that volitional swallow is associated with activation of a number of cortical areas (middle cingulate gyrus, ventrolateral prefrontal cortex, ventrolateral sensory/motor cortices andmiddle insula), which collectively represent the “cortical swallowing network” (CSN). Although the activation of these regions has been reported in the literature, the reproducibility of these activations has not been systematically studied. The aim of our study is to investigate the reproducibility of the CSN across different fMRI sessions. Methods: We studied 16 young right-handed healthy volunteers utilizing a standard paradigm driven fMRI protocol and analyzed with an event-related analysis technique. Echo-planar images were obtained during two identical nine minute scans across different sessions while twenty-one dry swallows were performed by visual cues in random intervals. Physiologic circulatory and respiratory-related signal changes were removed retrospectively. Functional images were then aligned, registered (12 degrees of freedom), and mapped stereotaxically to the standard coordinate system. Generalized linear model was then used to remove undesirable signal changes correlated with motion, white matter and cerebrospinal fluid. Finally, the second order group random mixed-effect multilevel analysis was performed to identify active cortical regions. Clusters with p<0.01 corrected for multiple comparisons were considered significant. The number of activated voxels (3.75x3.75x4 mm3) along with their average and peak percent signal change within each cluster was reported and compared between the two study sessions in order to determine the correlation co-efficient. Results: All subjects showed significant and clustered activity within the deglutition network during both scans. Activity within the right precuneus and supplementary area were not reproducible. Overall, the percent fMRI signal change and the number of activated voxels were similar between the two studies with correlation co-efficients r=0.88 and r=0.86, respectively (p<0.0001). Activity within the right precuneus and supplementary area were not reproducible. Within the remaining 20 regions of interest (ROI), the overlap of activity maps across both sessions showed that the center of the activated clusters was within 6.3±4.8 mm (less than two voxels). The difference in maximum activated voxel activity was 9.7±5.9 mm. Conclusions: fMRI activity maps as well as percent signal change and number of voxels activated by volitional swallow in the CSN are reproducible between studies.

Participation of the Default Mode Network in the Cortical Control of Volitional Swallowing

Background & Aim: Early studies using functional MRI (fMRI) have demonstrated that volitional swallow is associated with activation of a number of cortical areas (middle cingulate gyrus, ventrolateral prefrontal cortex, ventrolateral sensory/motor cortices andmiddle insula), which collectively represent the “cortical swallowing network” (CSN). Although the activation of these regions has been reported in the literature, the reproducibility of these activations has not been systematically studied. The aim of our study is to investigate the reproducibility of the CSN across different fMRI sessions. Methods: We studied 16 young right-handed healthy volunteers utilizing a standard paradigm driven fMRI protocol and analyzed with an event-related analysis technique. Echo-planar images were obtained during two identical nine minute scans across different sessions while twenty-one dry swallows were performed by visual cues in random intervals. Physiologic circulatory and respiratory-related signal changes were removed retrospectively. Functional images were then aligned, registered (12 degrees of freedom), and mapped stereotaxically to the standard coordinate system. Generalized linear model was then used to remove undesirable signal changes correlated with motion, white matter and cerebrospinal fluid. Finally, the second order group random mixed-effect multilevel analysis was performed to identify active cortical regions. Clusters with p<0.01 corrected for multiple comparisons were considered significant. The number of activated voxels (3.75x3.75x4 mm3) along with their average and peak percent signal change within each cluster was reported and compared between the two study sessions in order to determine the correlation co-efficient. Results: All subjects showed significant and clustered activity within the deglutition network during both scans. Activity within the right precuneus and supplementary area were not reproducible. Overall, the percent fMRI signal change and the number of activated voxels were similar between the two studies with correlation co-efficients r=0.88 and r=0.86, respectively (p<0.0001). Activity within the right precuneus and supplementary area were not reproducible. Within the remaining 20 regions of interest (ROI), the overlap of activity maps across both sessions showed that the center of the activated clusters was within 6.3±4.8 mm (less than two voxels). The difference in maximum activated voxel activity was 9.7±5.9 mm. Conclusions: fMRI activity maps as well as percent signal change and number of voxels activated by volitional swallow in the CSN are reproducible between studies.

Reliability and Reproducibility of FMRI Activity in the Cortical Swallowing Network

Background & Aim: Early studies using functional MRI (fMRI) have demonstrated that volitional swallow is associated with activation of a number of cortical areas (middle cingulate gyrus, ventrolateral prefrontal cortex, ventrolateral sensory/motor cortices andmiddle insula), which collectively represent the “cortical swallowing network” (CSN). Although the activation of these regions has been reported in the literature, the reproducibility of these activations has not been systematically studied. The aim of our study is to investigate the reproducibility of the CSN across different fMRI sessions. Methods: We studied 16 young right-handed healthy volunteers utilizing a standard paradigm driven fMRI protocol and analyzed with an event-related analysis technique. Echo-planar images were obtained during two identical nine minute scans across different sessions while twenty-one dry swallows were performed by visual cues in random intervals. Physiologic circulatory and respiratory-related signal changes were removed retrospectively. Functional images were then aligned, registered (12 degrees of freedom), and mapped stereotaxically to the standard coordinate system. Generalized linear model was then used to remove undesirable signal changes correlated with motion, white matter and cerebrospinal fluid. Finally, the second order group random mixed-effect multilevel analysis was performed to identify active cortical regions. Clusters with p<0.01 corrected for multiple comparisons were considered significant. The number of activated voxels (3.75x3.75x4 mm3) along with their average and peak percent signal change within each cluster was reported and compared between the two study sessions in order to determine the correlation co-efficient. Results: All subjects showed significant and clustered activity within the deglutition network during both scans. Activity within the right precuneus and supplementary area were not reproducible. Overall, the percent fMRI signal change and the number of activated voxels were similar between the two studies with correlation co-efficients r=0.88 and r=0.86, respectively (p<0.0001). Activity within the right precuneus and supplementary area were not reproducible. Within the remaining 20 regions of interest (ROI), the overlap of activity maps across both sessions showed that the center of the activated clusters was within 6.3±4.8 mm (less than two voxels). The difference in maximum activated voxel activity was 9.7±5.9 mm. Conclusions: fMRI activity maps as well as percent signal change and number of voxels activated by volitional swallow in the CSN are reproducible between studies.

Does Biofeedback Therapy Modulate Anorectal (Gut)-Brain Axis in Patients With Dyssynergic Defecation?

Background & Aim: Early studies using functional MRI (fMRI) have demonstrated that volitional swallow is associated with activation of a number of cortical areas (middle cingulate gyrus, ventrolateral prefrontal cortex, ventrolateral sensory/motor cortices andmiddle insula), which collectively represent the “cortical swallowing network” (CSN). Although the activation of these regions has been reported in the literature, the reproducibility of these activations has not been systematically studied. The aim of our study is to investigate the reproducibility of the CSN across different fMRI sessions. Methods: We studied 16 young right-handed healthy volunteers utilizing a standard paradigm driven fMRI protocol and analyzed with an event-related analysis technique. Echo-planar images were obtained during two identical nine minute scans across different sessions while twenty-one dry swallows were performed by visual cues in random intervals. Physiologic circulatory and respiratory-related signal changes were removed retrospectively. Functional images were then aligned, registered (12 degrees of freedom), and mapped stereotaxically to the standard coordinate system. Generalized linear model was then used to remove undesirable signal changes correlated with motion, white matter and cerebrospinal fluid. Finally, the second order group random mixed-effect multilevel analysis was performed to identify active cortical regions. Clusters with p<0.01 corrected for multiple comparisons were considered significant. The number of activated voxels (3.75x3.75x4 mm3) along with their average and peak percent signal change within each cluster was reported and compared between the two study sessions in order to determine the correlation co-efficient. Results: All subjects showed significant and clustered activity within the deglutition network during both scans. Activity within the right precuneus and supplementary area were not reproducible. Overall, the percent fMRI signal change and the number of activated voxels were similar between the two studies with correlation co-efficients r=0.88 and r=0.86, respectively (p<0.0001). Activity within the right precuneus and supplementary area were not reproducible. Within the remaining 20 regions of interest (ROI), the overlap of activity maps across both sessions showed that the center of the activated clusters was within 6.3±4.8 mm (less than two voxels). The difference in maximum activated voxel activity was 9.7±5.9 mm. Conclusions: fMRI activity maps as well as percent signal change and number of voxels activated by volitional swallow in the CSN are reproducible between studies.