Real-time Magnetic Resonance Imaging Research Articles

Self-Regulation of the Posterior–Frontal Brain Activity with Real-Time fMRI Neurofeedback to Influence Perceptual Discrimination

The Global Neuronal Workspace (GNW) hypothesis states that the visual percept is available to conscious awareness only if recurrent long-distance interactions among distributed brain regions activate neural circuitry extending from the posterior areas to prefrontal regions above a certain excitation threshold. To directly test this hypothesis, we trained 14 human participants to increase blood oxygenation level-dependent (BOLD) signals with real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback simultaneously in four specific regions of the occipital, temporal, insular and prefrontal parts of the brain. Specifically, we hypothesized that the up-regulation of the mean BOLD activity in the posterior–frontal brain regions lowers the perceptual threshold for visual stimuli, while down-regulation raises the threshold. Our results showed that participants could perform up-regulation (Wilcoxon test, session 1: p = 0.022; session 4: p = 0.041) of the posterior–frontal brain activity, but not down-regulation. Furthermore, the up-regulation training led to a significant reduction in the visual perceptual threshold, but no substantial change in perceptual threshold was observed after the down-regulation training. These findings show that the up-regulation of the posterior–frontal regions improves the perceptual discrimination of the stimuli. However, further questions as to whether the posterior–frontal regions can be down-regulated at all, and whether down-regulation raises the perceptual threshold, remain unanswered.

Training deep learning based dynamic MR image reconstruction using open-source natural videos

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.

Exercise MR of Skeletal Muscles, the Heart, and the Brain.

Magnetic resonance (MR) imaging (MRI) is routinely used to evaluate organ morphology and pathology in the human body at rest or in combination with pharmacological stress as an exercise surrogate. With MR during actual physical exercise, we can assess functional characteristics of tissues and organs under real-life stress conditions. This is particularly relevant in patients with limited exercise capacity or exercise intolerance, and where complaints typically present only during physical activity, such as in neuromuscular disorders, inherited metabolic diseases, and heart failure. This review describes practical and physiological aspects of exercise MR of skeletal muscles, the heart, and the brain. The acute effects of physical exercise on these organs are addressed in the light of various dynamic quantitative MR readouts, including phosphorus-31 MR spectroscopy (31P-MRS) of tissue energy metabolism, phase-contrast MRI of blood flow and muscle contraction, real-time cine MRI of cardiac performance, and arterial spin labeling MRI of muscle and brain perfusion. Exercise MR will help advancing our understanding of underlying mechanisms that contribute to exercise intolerance, which often proceed structural and anatomical changes in disease. Its potential to detect disease-driven alterations in organ function, perfusion, and metabolism under physiological stress renders exercise MR stress testing a powerful noninvasive imaging modality to aid in disease diagnosis and risk stratification. Although not yet integrated in most clinical workflows, and while some applications still require thorough validation, exercise MR has established itself as a comprehensive and versatile modality for characterizing physiology in health and disease in a noninvasive and quantitative way. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

MR-based navigation for robot-assisted endovascular procedures

AbstractThere is increasing interests in robotic and computer technologies to accurately perform endovascular intervention. One major limitation of current endovascular intervention—either manual or robot-assisted is the surgical navigation which still relies on 2D fluoroscopy. Recent research efforts are towards MRI-guided interventions to reduce ionizing radiation exposure, and to improve diagnosis, planning, navigation, and execution of endovascular interventions. We propose an MR-based navigation framework for robot-assisted endovascular procedures. The framework allows the acquisition of real-time MR images; segmentation of the vasculature and tracking of vascular instruments; and generation of MR-based guidance, both visual and haptic. The instrument tracking accuracy—a key aspect of the navigation framework—was assessed via 4 dedicated experiments with different acquisition settings, framerate, and time. The experiments showed clinically acceptable tracking accuracy in the range of 1.30–3.80 mm RMSE. We believe that this work represents a valuable first step towards MR-guided robot-assisted intervention.

Assessing osteoporosis in postmenopausal women: preliminary results using a novel lumbar spine phantom-based MRI scoring method.

To develop a novel magnetic resonance imaging (MRI) phantom for producing F-score (for fat) and W-score (for water) and to evaluate the performance of these scores in assessing osteoporosis and related vertebral fractures. First, a real-time phantom consisting of oil and water tubes was manufactured. Then, 30 female volunteers (age: 62.3 ± 6.3years) underwent lumbar spine examination with MRI (using a novel phantom) and dual-energy X-ray absorptiometry (DXA), following ethical approval. MRI phantom-based F-score and W-score were defined by normalizing the vertebral signal intensities (SIs) by the oil and water SIs of the phantom on T1- and T2-weighted images, respectively. The diagnostic performances of the new scores for assessing osteoporosis and vertebral fractures were examined using receiver operating characteristic analysis and compared with DXA-measured areal bone mineral density (DXA-aBMD). The F-score and W-score were greater in the osteoporotic patients (3.93 and 2.29) than the non-osteoporotic subjects (3.05 and 1.79) and achieved AUC values of 0.85 and 0.74 (p < 0.05), respectively, when detecting osteoporosis. Similarly, F-score and W-score had greater values for the fracture patients (3.94 and 2.53) than the non-fracture subjects (3.14 and 1.69) and produced better AUC values (0.90 for W-score and 0.79 for F-score) compared to DXA-aBMD (AUC: 0.27, p < 0.05). In addition,the F-score and W-score had a strong correlation (r = 0.77; p < 0.001). A novel real-time lumber spine MRI phantom was developed, based upon which newly defined F-score and W-score were able to detect osteoporosis and demonstrated an improved ability over DXA-aBMD in differentiating patients with vertebral fractures.

Articulatory and acoustic differences between lyric and dramatic singing in Western classical music.

Within the realm of voice classification, singers could be sub-categorized by the weight of their repertoire, the so-called "singer's Fach." However, the opposite pole terms "lyric" and "dramatic" singing are not yet well defined by their acoustic and articulatory characteristics. Nine professional singers of different singers' Fach were asked to sing a diatonic scale on the vowel /a/, first in what the singers considered as lyric and second in what they considered as dramatic. Image recording was performed using real time magnetic resonance imaging (MRI) with 25 frames/s, and the audio signal was recorded via an optical microphone system. Analysis was performed with regard to sound pressure level (SPL), vibrato amplitude, and frequency and resonance frequencies as well as articulatory settings of the vocal tract. The analysis revealed three primary differences between dramatic and lyric singing: Dramatic singing was associated with greater SPL and greater vibrato amplitude and frequency as well as lower resonance frequencies. The higher SPL is an indication of voice source changes, and the lower resonance frequencies are probably caused by the lower larynx position. However, all these strategies showed a considerable individual variability. The singers' Fach might contribute to perceptual differences even for the same singer with regard to the respective repertoire.

Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.

Bibliometric analysis of neurofeedback research from 2000 to 2022

The application of neurofeedback is gaining increasing interest among neuroscientists as a potential neurorehabilitation approach in cases of various neuro-related functional abnormalities. Discovering the current state of research and identifying gaps in the field of neurofeedback is an essential step in planning and mapping out future research efforts. This bibliometric analysis paper aims to identify the publications and research in neurofeedback from 2000 to 2022. A comprehensive Scopus database search was conducted using the keyword "neurofeedback" and relevant publications from 2000 to 2022 were retrieved. Bibliometric analyses were performed using the Harzing's Publish or Perish and VOSviewer software programmes. The number of retrieved documents was 1835. The number of publications has shown a steadily increasing trend since 2000, with a prominent spike in publications in 2014–2015, indicating a sudden interest in neurofeedback. Among the retrieved documents, 50.3% were related to neuroscience, 23.7% related to medicine, and 13.1% related to psychology. The main contributors to this research come from the United States (24.7%), Germany (13.7%), the United Kingdom (9.4%), and Switzerland (4.9%). Based on the network visualisation of author keywords, the most frequently occurring keywords were neurofeedback, real-time functional magnetic resonance imaging (fMRI), brain-computer interface (BCI), neuromodulation, and neurofeedback training. This bibliometric analysis presents the current status, knowledge base, and future neurofeedback study directions. These findings will benefit future researchers interested in applying neurofeedback as a potential neurorehabilitation approach for a wider population.

Assessment and validation of glottic motion using cone-beam CT and real-time cine MRI

PurposeThis study aimed to assess the margin for the planning target volume (PTV) using the Van Herk formula. We then validated the proposed margin by real-time magnetic resonance imaging (MRI).MethodsAn analysis of cone-beam computed tomography (CBCT) data from early glottic cancer patients was performed to evaluate organ motion. Deformed clinical target volumes (CTV) after rigid registration were acquired using the Velocity program (Varian Medical Systems, Palo Alto, CA, USA). Systematic (Σ) and random errors (σ) were evaluated. The margin for the PTV was defined as 2.5 Σ + 0.7 σ according to the Van Herk formula. To validate this margin, we accrued healthy volunteers. Sagittal real-time cine MRI was conducted using the ViewRay system (ViewRay Inc., Oakwood Village, OH, USA). Within the obtained sagittal images, the vocal cord was delineated. The movement of the vocal cord was summed up and considered as the internal target volume (ITV). We then assessed the degree of overlap between the ITV and the PTV (vocal cord plus margins) by calculating the volume overlap ratio, represented as (ITV∩PTV)/ITV.ResultsCBCTs of 17 early glottic patients were analyzed. Σ and σ were 0.55 and 0.57 for left–right (LR), 0.70 and 0.60 for anterior–posterior (AP), and 1.84 and 1.04 for superior–inferior (SI), respectively. The calculated margin was 1.8 mm (LR), 2.2 mm (AP), and 5.3 mm (SI). Four healthy volunteers participated for validation. A margin of 3 mm (AP) and 5 mm (SI) was applied to the vocal cord as the PTV. The average volume overlap ratio between ITV and PTV was 0.92 (range 0.85–0.99) without swallowing and 0.77 (range 0.70–0.88) with swallowing.ConclusionBy evaluating organ motion by using CBCT, the margin was 1.8 (LR), 2.2 (AP), and 5.3 mm (SI). The margin acquired using CBCT fitted well in real-time cine MRI. Given that swallowing during radiotherapy can result in a substantial displacement, it is crucial to consider strategies aimed at minimizing swallowing and related motion.

Characterization of real-time cine MR imaging distortion on 0.35 T MRgRT with concentric cine imaging QA phantom

Objective. Real-time MRgRT uses 2D-cine imaging for target tracking and motion evaluation. Rotation of gantry induced B 0 off-resonance, resulting in image artifacts and imaging isocenter-shift precluding MR-guided arc therapy. Standard MRI phantoms designed for higher resolution images face challenges when low-resolution cine imaging is needed to achieve high frame rates. This work aimed to examine the spatial accuracy including geometric distortion and isocenter shift in real-time during gantry rotation on a 0.35 T MR-Linac using the concentric Cine imaging quality assurance (QA) phantom and its associated image analysis software. Approach. The Cine imaging QA phantom consists of two concentric shells of low-T1 mineral oil and a central alignment structure. The phantom was scanned on three different MRI systems; 0.55 T Siemens Free.Max, 1.5 T Philips Ingenia, and 0.35 T ViewRay MRIdian MR-Linac using 2D balanced steady-state free precession (bSSFP) imaging sequence. In addition, bSSFP cine MRI with the banding artifact correction was tested on 0.35 T ViewRay MR-Linac. Images from the MR-Linac were acquired with the Linac gantry stationary and rotating from gantry 300°→ 0° and vice versa. Three orthogonal image planes were scanned excluding the 1.5 T Philips Ingenia, where only the axial plane was scanned. The image analysis software calculated the distortion values as well as the isocenter position for each cine frame. Main results. The geometric distortion of cine imaging on MRIs and MR-Linac at gantry stationary are within 1 mm while the substantial geometric distortion of 2 and 2.2 mm were observed on 0.35 T MR-Linac while rotating the gantry clockwise (300°→ 0°) and counterclockwise 0°→ 300° respectively. The average imaging isocenter shift was 0.1 mm for both MRIs and the static gantry and imaging isocenter shift of ≤1.5 mm was observed during the gantry rotation. The imaging isocenter shift decreased by 1 ± 0.2 mm clockwise and counterclockwise with B 0 compensation. Significance. The concentric Cine imaging QA phantom and its associated software effectively demonstrate the image distortion on real-time cine imaging on regular MRIs and 0.35 T MR-Linac. The results of significant geometric distortion with a rotating gantry in the MR-Linac system require further investigation to alleviate the extent of the image distortion.

Real-time magnetic resonance imaging of velopharyngeal port posture during long pauses in naturalistic speech

Velum’s behavior as a postural substrate in naturalistic speech is not well understood and is largely based on studies of inter-utterance velopharyngeal port posture (VPP). While existing literature [e.g., Gick et al., 2004 Phonetica, 61(4); Ramanarayanan, 2013 JASA, 134(1)] describes posture variations based on rest positions, ready positions, and inter-speech pauses, a less explored aspect is the behavior of the velum during extended pauses in naturalistic speech. Addressing this gap, the present study analyzes velum posture during naturalistic speech to characterize VPP during prolonged pauses. This study draws from a corpus of real-time magnetic resonance imaging (rtMRI) videos of L1 English speakers including those engaging in unstructured speech tasks. Results based on sequences corresponding to long pauses in naturalistic speech will be reported, outlining VPP characteristics, and quantifying findings in terms of time spent in and shifts between velum positions. Implications for motor control differences in naturalistic and elicited speech data will be discussed.

Register shifts in whistling: Investigating the influence on tongue shaping and gender-related variances

The study examines whistling register changes and the influence of tongue shape. In previous work, [Kaburagi et al., 2018] used magnetic resonance imaging (MRI), finding the degree and type of restriction made by the tongue directly impacted whistle F0. [Belyk et.al., 2019] found similar results using real-time magnetic resonance imaging (rtMRI), however, did not specifically observe register shifts. This study aimed to validate whistle register shifts and identify their variations among individuals regarding tongue position and gender. We hypothesize that participants with broader whistling ranges will exhibit register shifts based on tongue shape. Measures based on ultrasound imaging of male and female participants recorded while completing whistling exercises will be included in the analysis. This methodology enables tongue movements to be visualized, offering a view of the interplay between whistling registers and tongue configurations. ImageJ analysis results of tongue positions from ultrasound imaging will be presented with relevance to whether individuals produce a shift in their vocal register while whistling, and whether these conclusions depend on gender, as in sung register. Implications will be discussed regarding potential similarities between register changes during whistling and tongue shapes associated with speech.

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice’s coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice’s coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice’s coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice’s coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future.

Assessment of the cardiac output at rest and during exercise stress using real-time cardiovascular magnetic resonance imaging in HFpEF-patients

This methodological study aimed to validate the cardiac output (CO) measured by exercise-stress real-time phase-contrast cardiovascular magnetic resonance imaging (CMR) in patients with heart failure and preserved ejection fraction (HFpEF). 68 patients with dyspnea on exertion (NYHA ≥ II) and echocardiographic signs of diastolic dysfunction underwent rest and exercise stress right heart catheterization (RHC) and CMR within 24 h. Patients were diagnosed as overt HFpEF (pulmonary capillary wedge pressure (PCWP) ≥ 15mmHg at rest), masked HFpEF (PCWP ≥ 25mmHg during exercise stress but < 15mmHg at rest) and non-cardiac dyspnea. CO was calculated using RHC as the reference standard, and in CMR by the volumetric stroke volume, conventional phase-contrast and rest and stress real-time phase-contrast imaging. At rest, the CMR based CO showed good agreement with RHC with an ICC of 0.772 for conventional phase-contrast, and 0.872 for real-time phase-contrast measurements. During exercise stress, the agreement of real-time CMR and RHC was good with an ICC of 0.805. Real-time measurements underestimated the CO at rest (Bias:0.71 L/min) and during exercise stress (Bias:1.4 L/min). Patients with overt HFpEF had a significantly lower cardiac index compared to patients with masked HFpEF and with non-cardiac dyspnea during exercise stress, but not at rest. Real-time phase-contrast CO can be assessed with good agreement with the invasive reference standard at rest and during exercise stress. While moderate underestimation of the CO needs to be considered with non-invasive testing, the CO using real-time CMR provides useful clinical information and could help to avoid unnecessary invasive procedures in HFpEF patients.

Acidic tumor microenvironment-activated MRI nanoprobes for modulation and visualization of anti-PD-L1 immunotherapy

Tumor acidity has emerged as a pivotal regulator of immune checkpoint blockade (ICB). However, acidity-based therapy presents challenges of low efficiency and lack of reliable imaging technology for assessing the immune response. Here we report a magnetic resonance imaging (MRI)-guided ICB therapy (MRGIT) strategy to modulate and visualize the efficacy of anti-PD-L1 in pancreatic ductal adenocarcinoma (PDAC). MRGIT was achieved by a pH responsive nanoprobe (APPAM@U-104) composed of ultrasmall iron oxide nanoparticles (USIONs) and a tumor pH regulator (U-104). Notably, the nanoprobes exhibited a T1 “ON” MR signal in acidic tumors but switched to a T2 “ON” MR signal in a neutralized tumor microenvironment, resulting in a switchable MR signal from T1 to T2 during real-time MRI monitoring. Moreover, the switch of MR signals can serve as an indicator for alleviating tumor immune suppression, thus guiding the timing of anti-PD-L1 therapy. Our results revealed that the MRGIT strategy can potentiate the antitumor efficacy of anti-PD-L1 against pancreatic tumors. Collectively, this strategy sensitively regulates and recognizes tumor acidosis, thus paving the way for the modulation and noninvasive monitoring of anti-PD-L1 efficacy in PDAC.

Real-time cardiovascular magnetic resonance imaging for non-invasive characterisation of heart failure with preserved ejection fraction: final outcomes of the HFpEF stress trial

BackgroundThe diagnosis of heart failure with preserved ejection fraction (HFpEF) remains challenging. Recently, the HFpEF Stress Trial demonstrated feasibility and accuracy of non-invasive cardiovascular magnetic resonance (CMR) real-time (RT) exercise-stress atrial function imaging for early identification of HFpEF. However, no outcome data have yet been presented.MethodsThe HFpEF Stress Trial (DZHK-17) prospectively recruited 75 patients with dyspnea on exertion and echocardiographic preserved EF and signs of diastolic dysfunction (E/eʹ > 8). 68 patients entered the final study cohort and were characterized as HFpEF (n = 34) or non-cardiac dyspnea (n = 34) according to pulmonary capillary wedge pressure (HFpEF: PCWP rest: ≥ 15 mmHg stress: ≥ 25 mmHg). These patients were contacted by telephone and hospital charts were reviewed. The clinical endpoint was cardiovascular events (CVE).ResultsFollow-up was performed after 48 months; 1 patient was lost to follow-up. HFpEF patients were more frequently compared to non-cardiac dyspnea (15 vs. 8, p = 0.059). Hospitalised patients during follow-up had higher H2FPEF scores (5 vs. 3, p < 0.001), and impaired left atrial (LA) function at rest (p ≤ 0.002) and stress (p ≤ 0.006). Impairment of CMR-derived atrial function parameters at rest and during exercise-stress (p ≤ 0.003) was associated with increased likelihood for CVE. CMR-Feature Tracking LA Es/Ee (p = 0.016/0.017) and RT-CMR derived LA long axis strain (p = 0.003) were predictors of CVE independent of the presence of atrial fibrillation.ConclusionsLeft atrial function emerged as the strongest predictor for 4-year outcome in the HFpEF Stress Trial. A combination of rest and exercise-stress LA function quantification allows accurate diagnostic and prognostic stratification in HFpEF.Clinicaltrials.gov: NCT03260621.Graphical abstract

A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression.

Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.

Real-time speech MRI datasets with corresponding articulator ground-truth segmentations

The use of real-time magnetic resonance imaging (rt-MRI) of speech is increasing in clinical practice and speech science research. Analysis of such images often requires segmentation of articulators and the vocal tract, and the community is turning to deep-learning-based methods to perform this segmentation. While there are publicly available rt-MRI datasets of speech, these do not include ground-truth (GT) segmentations, a key requirement for the development of deep-learning-based segmentation methods. To begin to address this barrier, this work presents rt-MRI speech datasets of five healthy adult volunteers with corresponding GT segmentations and velopharyngeal closure patterns. The images were acquired using standard clinical MRI scanners, coils and sequences to facilitate acquisition of similar images in other centres. The datasets include manually created GT segmentations of six anatomical features including the tongue, soft palate and vocal tract. In addition, this work makes code and instructions to implement a current state-of-the-art deep-learning-based method to segment rt-MRI speech datasets publicly available, thus providing the community and others with a starting point for developing such methods.

Dynamic changes of vocal tract dimensions with sound pressure level during messa di vocea).

The messa di voce (MdV), which consists of a continuous crescendo and subsequent decrescendo on one pitch is one of the more difficult exercises of the technical repertoire of Western classical singing. With rising lung pressure, regulatory adjustments both on the level of the glottis and the vocal tract are required to keep the pitch stable. The dynamic changes of vocal tract dimensions with the bidirectional variation of sound pressure level (SPL) during MdV were analyzed by two-dimensional real-time magnetic resonance imaging (25 frames/s) and synchronous audio recordings in 12 professional singer subjects. Close associations in the respective articulatory kinetics were found between SPL and lip opening, jaw opening, pharynx width, uvula elevation, and vertical larynx position. However, changes in vocal tract dimensions during plateaus of SPL suggest that perceived loudness could have been varied beyond the dimension of SPL. Further multimodal investigation, including the analysis of sound spectra, is needed for a better understanding of the role of vocal tract resonances in the control of vocal loudness in human phonation.