ABSTRACT Post-traumatic stress disorder (PTSD) has been associated with impairments in cognitive function, including working memory, and may involve altered glutamatergic regulation in the prefrontal cortex. In this study, we used 7T functional magnetic resonance spectroscopy (fMRS) to examine dorsolateral prefrontal cortex (DLPFC) glutamate during working memory in individuals with PTSD, trauma exposure without PTSD (TE), and no trauma exposure (NT). Eighty participants (27 PTSD, 27 TE, 26 NT) underwent baseline MRS followed by fMRS during a letter n-back task. A linear mixed-effects model was used to evaluate glutamate concentrations across baseline, 0-back, 1-back, 2-back, and post-task fixation conditions. Behavioral performance was assessed using repeated-measures ANOVA for percentage correct, reaction time, and the discrimination index ( d’ ) across the 0-back, 1-back, and 2-back conditions. Glutamate differed significantly by group, condition, and the group × condition interaction. Individuals with PTSD exhibited lower glutamate than NT at baseline and during the 0-back, 1-back, and 2-back conditions. TE participants also showed lower glutamate than NT during the 1-back and 2-back conditions. Within-group analyses showed higher glutamate during the 0-back, 1-back, and 2-back conditions than at baseline in the NT group, whereas these baseline-to-task differences were limited in the PTSD and TE groups. Accuracy decreased and reaction time increased with increasing working memory load, and discrimination ( d’ ) was lower in PTSD than NT. These findings demonstrate altered DLPFC glutamate dynamics during working memory in PTSD and trauma-exposed individuals. Functional MRS provides complementary information beyond resting-state MRS by characterizing glutamatergic responses during cognitive engagement and may improve our understanding of neurochemical alterations associated with trauma and PTSD.

BACKGROUND:The double helical direction of myocardial fibers allows for left ventricular (LV) twist. This led to the hypothesis that principal strain angles determine LV functional recovery after mitral valve repair in patients with primary mitral regurgitation (PMR). METHODS:Controls (n=52) and patients with PMR with regurgitant volume defined as mild (<30 mL, n=16), moderate (30-50 mL, n=38), and severe (50 mL, n=55) had cardiac magnetic resonance with tissue tagging and 3-dimensional analysis. Fifty-four patients with PMR had presurgery and 6 (n=46), 12 (n=44), and 24 (n=25) month studies. Longitudinal, circumferential, and maximal shortening were computed along with principal strain angles (circumferential, longitudinal, and radial directions) at base, mid, and distal LV levels. RESULTS:LV ejection fraction did not differ in mild, moderate, and severe PMR with similar increases in mid LV 3-dimensional radius-to-wall thickness and decrease in LV mass to volume and sphericity index versus controls. Radial longitudinal shear strain and LV mid and distal LV circumferential and longitudinal angles increased in all groups with PMR. Postsurgery LV end-diastolic volume, LV end-diastolic mass to volume and 3-dimensional radius-to-wall thickness returned to normal at 6, 12, and 24 months; however, mid LV circumferential, longitudinal, and maximal shortening decreased below normal. LV circumferential and longitudinal angles and LV sphericity index did not change from presurgery whereas LV ejection fraction post surgery correlated with decrease in LV twist at 6, 12, and 24 months. CONCLUSIONS:Changes in principle strain angles occur early in PMR. The extent to which they persist after surgery may underlie the decrease in LV ejection fraction.

Background:The double helical direction of LV laminar sheets from endocardium to epicardium allows for wringing motion or LV twist. This provides a major component to LV wall thickening, stroke volume, and ejection fraction (EF). When this laminar sheet arrangement changes in Primary Mitral Regurgitation (PMR) and whether it reverts to normal after mitral valve repair is unknown. Methods:Normal subjects (n=55) PMR patients had cardiac magnetic resonance imaging (CMR) with tissue tagging and 3-dimensional (3-D) analysis. They were grouped as asymptomatic moderate (n=23) and severe PMR (n=25) by regurgitant volume (RV) and pre-surgery (n=54) with post-surgery follow up at six, 12, and 24 months. Amplitude and directional vector of longitudinal (Ell), circumferential (Ecc), and maximal shortening were computed along with principle strain angles (Ecc°, Ell°, and Err°) at basal, mid, and distal LV levels. Results:Asymptomatic moderate (RV 35 ± 16 ml; LVEF 62 ± 6%) and severe (RV 55 ±16 ml; LVEF 63 ± 6%) and symptomatic pre-surgery (RV 61 ± 29 ml; LVEF 63 ± 8%) had similar increases in mid LV 3-D radius to wall thickness (R/T), decrease in LV mass to volume (M/V) and sphericity index (SI) vs. normal. Radial longitudinal shear strain and mid LV Ecc° and EII° angles increased in all PMR groups, consistent with a shift in LV laminar plane direction and decreased LV SI. Post-surgery, LV end-diastolic (ED) volume, LVED M/V and 3-D R/T returned to normal within two years; however, mid LV circumferential, longitudinal, and maximal shortening decrease below normal. LV Ecc° and Ell° angles, and SI are unchanged from pre-surgery. LVEF decreased post-surgery and had a negative correlation with LV twist at six (r 2 = 0.30, p < 0.001), 12, (r 2 = 0.33, p < 0.001) and 24 months (r 2 = 0.38, p < 0.001) post-surgery. Conclusion:Early changes in Ecc° and Ell° angles, radial longitudinal shear strain, and LV spherical dilatation are consistent with a shift of LV laminar planes that persists after surgery. The extent to which this affects LV twist may underlie a heretofore explanation underlying the decrease in LVEF after surgery for PMR.

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

Aims Chronic neurohormonal activation and haemodynamic load cause derangement in the utilization of the myocardial substrate. In this study, we test the hypothesis that the primary mitral regurgitation (PMR) heart shows an altered metabolic gene profile and cardiac ultra-structure consistent with decreased fatty acid and glucose metabolism despite a left ventricular ejection fraction (LVEF) > 60%. Methods and results Metabolic gene expression in right atrial (RA), left atrial (LA), and left ventricular (LV) biopsies from donor hearts (n = 10) and from patients with moderate-to-severe PMR (n = 11) at surgery showed decreased mRNA glucose transporter type 4 (GLUT4), GLUT1, and insulin receptor substrate 2 and increased mRNA hexokinase 2, O-linked N-acetylglucosamine transferase, and O-linked N-acetylglucosaminyl transferase, rate-limiting steps in the hexosamine biosynthetic pathway. Pericardial fluid levels of neuropeptide Y were four-fold higher than simultaneous plasma, indicative of increased sympathetic drive. Quantitative transmission electron microscopy showed glycogen accumulation, glycophagy, increased lipid droplets (LDs), and mitochondrial cristae lysis. These findings are associated with increased mRNA for glycogen synthase kinase 3 beta, decreased carnitine palmitoyl transferase 2, and fatty acid synthase in PMR vs. normals. Cardiac magnetic resonance and positron emission tomography for 2-deoxy-2-[F-18]fluoro-D-glucose ([F-18]FDG) uptake showed decreased LV [F-18]FDG uptake and increased plasma haemoglobin A1C, free fatty acids, and mitochondrial damage-associated molecular patterns in a separate cohort of patients with stable moderate PMR with an LVEF > 60% (n = 8) vs. normal controls (n = 8). Conclusion The PMR heart has a global ultra-structural and metabolic gene expression pattern of decreased glucose uptake along with increased glycogen and LDs. Further studies must determine whether this presentation is an adaptation or maladaptation in the PMR heart in the clinical evaluation of PMR.

Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months’ post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks.

Background: 2020 American College of Cardiology/American Heart Association (ACC/AHA) Guidelines state that the ideal time for mitral valve surgery in primary mitral regurgitation (PMR) is when the LV approaches but has not yet reached echocardiographic LV ejection fraction (EF) < 60% or LV end-systolic dimension (ESD) > 40 mm. However, it is difficult to know the imminent risk of crossing this threshold when the surgical outcome is less optimal. Objective: Using machine learning and statistical models, we have shown that cardiac magnetic resonance (CMR) LV sphericity index (SI) and LV mid circumferential strain rate (SRcirc) added to LVEF and LVESD predict LVEF < 50% after mitral valve surgery. Here we test the hypothesis that these CMR features predict LVEF < 60% in asymptomatic PMR patients at 18 months. Methods: 33 asymptomatic PMR patients with moderate to severe mitral regurgitation had CMR with tissue tagging at baseline and every 6 months for 18 months. Two types of models were employed to predict LVEF < 60% at 18 months: a model using CMR features at a single time point (e.g., baseline) and a model utilizing repeated measurements over time. Results: CMR LVEF decreased below 60% in 13 patients over 18 months. LVEF varied over time with an inverse relation to mean arterial pressure and mean end-systolic wall stress. Random Forest models utilizing LV SI, LV mid SRcirc, LVESD, and LVEF at a single time point (baseline) had a predictive accuracy of 64%. LV SI, LV mid SRcirc, LVESD and LVEF at baseline, 6, and 12 months achieved a higher predictive accuracy of 79%, improved sensitivity from 57% to 85% than baseline alone and identified a threshold of CMR LVEF 63%-64% signaling LVEF < 60%. Conclusion: The variability of LVEF due to blood pressure dependence may require a longitudinal study that incorporates LVEF, LVESD, SRcirc at multiple time points to identify the threshold at which LVEF is at risk for decline to less than 60%.

Of the 38 million people affected by Diabetes Mellitus (DM) in the United States, up to 50% develop Diabetic Peripheral Neuropathy (DPN) leading to an increased risk of pain, amputation, and morbidity. Standard interventions involve intensive pharmaceuticals to treat evident symptoms as opposed to the damaged underlying neural pathways. DPN creates a feedback loop where chronic burning, tingling, numbness, and pain lead to a reduction in physical activity and vascular flow with an increase in peripheral edema. Here, we present the design and implementation of a non-pharmaceutical wearable treatment device to provide nerve stimulation to the foot and shank with the aim to improve blood flow to the neurovasculature. This device, the Garment Application of Intelligent Non-invasive Stimulation Boot (GAINS-Boot), was applied to nine participants for eight 45 minute sessions over the span of two weeks. Vascular blood flow was recorded via a phase-contrast MRI to assess macro blood flow before and after the intervention. A survey was completed to analyze self-reported symptoms and pain relief analyzed pre- and post-treatment for participants. We hypothesize that the GAINS-Boot tri-modal (heat, pressure, and vibration) intervention will aid the underlying mechanism of DPN through increased vascularization, improved nerve health, and ultimately reduce DPN symptoms through an increased blood flow. In this pilot study, we observed an increase in blood flow in participants with more severe symptoms, and qualitative feedback suggests promise for this non-pharmacological approach.

Deep neural networks (DNN) are increasingly being used in neuroimaging research for the diagnosis of brain disorders and understanding of human brain. Despite their impressive performance, their usage in medical applications will be limited unless there is more transparency on how these algorithms arrive at their decisions. We address this issue in the current report. A DNN classifier was trained to discriminate between healthy subjects and those with posttraumatic stress disorder (PTSD) using brain connectivity obtained from functional magnetic resonance imaging data. The classifier provided 90% accuracy. Brain connectivity features important for classification were generated for a pool of test subjects and permutation testing was used to identify significantly discriminative connections. Such heatmaps of significant paths were generated from 10 different interpretability algorithms based on variants of layer-wise relevance and gradient attribution methods. Since different interpretability algorithms make different assumptions about the data and model, their explanations had both commonalities and differences. Therefore, we developed a consensus across interpretability methods, which aligned well with the existing knowledge about brain alterations underlying PTSD. The confident identification of more than 20 regions, acknowledged for their relevance to PTSD in prior studies,was achieved with a voting score exceeding 8 and a family-wise correction threshold below 0.05. Our work illustrates how robustness and physiological plausibility of explanations can be achieved in interpreting classifications obtained from DNNs in diagnostic neuroimaging applications by evaluating convergence across methods. This will be crucial for trust in AI-based medical diagnostics in the future.

Background: Left ventricular (LV) diastolic function is a key determinant of cardiac output and impairments of diastolic function can lead to heart failure. Assessment of diastolic function is challenging due to several factors, including the load dependence of ventricular filling. We developed a method using cardiovascular magnetic resonance (CMR) imaging to model the untwisting motion of the LV as a viscoelastic damped oscillator to derive myocardial torsional modulus (mu) and frictional damping characteristics, and hypothesized that the torsional modulus would correlate with invasive measures of LV stiffness. Methods: Twenty-two participants who underwent invasive left heart catheterization (LHC) and CMR for the evaluation of chest pain were evaluated. mu and damping constants were determined by solving a system of equations using CMRmeasured LV geometrical and angular displacement data during diastole. Time constant of pressure decay tau and chamber stiffness j3 were measured from invasive LHC and CMR-derived volume data as comparison metrics of diastolic function. Results: mu was correlated with chamber stiffness constant j3 and time constant of pressure decay tau, derived from invasive measurement (R = 0.78, p < 0.001, and R = 0.51, p = 0.014, respectively). was also correlated with pre-A-wave diastolic pressure (0.67, p = 0.001). Conclusion: We propose a new method to objectively evaluate diastolic relaxation properties of the LV. This method may have promise to replace invasive, catheter-based assessment of diastolic function.

In the recent years, there is an upsurge of Artificial Intelligent (AI) systems. These systems, along with efficient performance and predictability also need to incorporate the power of explainability and interpretability. This can significantly aid clinical decision support by providing explainable predictions to assist clinicians. Explainability generally involves uncovering of key input features important for classification. However, characterizing the uncertainty underlying the decisions of the AI system is an important aspect needed for interpreting the decisions. This is especially important in clinical decision support systems given considerations of medical ethics such as nonmaleficence and beneficence. In this study, we develop methods for characterizing the decision certainty of Machine Learning (ML) based clinical decision support systems. As an illustrative example, we introduce a framework for ML based posttraumatic stress disorder (PTSD) diagnostic classification which classifies the subjects into pure and mixed classes. Accordingly, a clinician can have very high confidence (≥ 95% probability) about the diagnosis of a subject in a pure PTSD or combat control class. Remaining sample points for which the AI classification tool does not have very high confidence (< 95% probability) are grouped into a mixed class. Such a scheme will address ethical considerations of nonmaleficence and beneficence since the clinicians can use the AI system to identify those subjects whose diagnosis has very high degree of confidence (and proceed treatment accordingly), and refer those in the uncertain/mixed group to further tests. This is a novel approach, in contrast to existing framework which aim to maximize classification.

We set out to measure phenylalanine in the human brain using magnetic resonance spectroscopy (MRS) in a ultra-high field 7T MRI scanner. Phenylalanine is a precursor to Norepinephrine, a neurotransmitter important for attention and arousal. Depletion in norepinephrine, especially in the locus coeruleus, has been implicated as an etiological factor in Alzheimer’s disease. Therefore, being able to noninvasively measure phenylalanine in vivo in humans has a multitude of translational applications. Using phantom experiments, we first validate and optimize the MRS techniques used for observing phenylalanine in the brain at 7T. However, we failed to detect phenylalanine in human volunteers (N=15). In order to understand the reasons for this failure, we performed experiments in a cat model with external phenylalanine injections to determine the amount of phenylalanine required for it to be detected in vivo in the brain. This threshold was found to be 3.4 mM. This indicated that phenylalanine concentrations in both healthy and AD patients, that too in a small region such as the locus coeruleus, will likely not meet this threshold. Therefore, we conclude that even with state-of-the-art technologies and 7T MRI, it is not possible to detect phenylalanine in the human brain in vivo under natural conditions.

Background: It is theorized that the lack of a synovial lining after anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) contributes to slow ligamentization and possible graft failure. Whether graft maturation and incorporation can be improved with the use of a scaffold requires investigation. Purpose: To evaluate the safety and efficacy of wrapping an ACL autograft with an amnion collagen matrix and injecting bone marrow aspirate concentrate (BMAC), quantify the cellular content of the BMAC samples, and assess 2-year postoperative patient-reported outcomes. Study Design: Randomized controlled trial; Level of evidence, 2. Methods: A total of 40 patients aged 18 to 35 years who were scheduled to undergo ACLR were enrolled in a prospective single-blinded randomized controlled trial with 2 arms based on graft type: bone–patellar tendon–bone (BTB; n = 20) or hamstring (HS; n = 20). Participants in each arm were randomized into a control group who underwent standard ACLR or an intervention group who had their grafts wrapped with an amnion collagen matrix during graft preparation, after which BMAC was injected under the wrap layers after implantation. Postoperative magnetic resonance imaging (MRI) mapping/processing yielded mean T2* relaxation time and graft volume values at 3, 6, 9, and 12 months. Participants completed the Single Assessment Numeric Evaluation Score, Knee injury and Osteoarthritis Outcome Score, and pain visual analog scale. Statistical linear mixed-effects models were used to quantify the effects over time and the differences between the control and intervention groups. Adverse events were also recorded. Results: No significant differences were found at any time point between the intervention and control groups for BTB T2* (95% CI, –1.89 to 0.63; P = .31), BTB graft volume (95% CI, –606 to 876.1; P = .71), HS T2* (95% CI, –2.17 to 0.39; P = .162), or HS graft volume (95% CI, –11,141.1 to 351.5; P = .28). No significant differences were observed between the intervention and control groups of either graft type on any patient-reported outcome measure. No adverse events were reported after a 2-year follow-up. Conclusion: In this pilot study, wrapping a graft with an amnion collagen matrix and injecting BMAC appeared safe. MRI T2* values and graft volume of the augmented ACL graft were not significantly different from that of controls, suggesting that the intervention did not result in improved graft maturation. Registration: NCT03294759 (ClinicalTrials.gov identifier).

MRI is a valuable diagnostic tool to investigate spinal cord (SC) pathology. SC MRI can benefit from the increased signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at ultra-high fields such as 7 T. However, SC MRI acquisitions with routine Cartesian readouts are prone to image artifacts caused by physiological motion. MRI acquisition techniques with non-Cartesian readouts such as rosette can help reduce motion artifacts. The purpose of this study was to demonstrate the feasibility of high-resolution SC imaging using rosette trajectory with magnetization transfer preparation (MT-prep) and compressed sensing (CS) at 7 T. Five healthy volunteers participated in the study. Images acquired with rosette readouts demonstrated reduced motion artifacts compared to the standard Cartesian readouts. The combination of multi-echo rosette-readout images improved the CNR by approximately 50% between the gray matter (GM) and white matter (WM) compared to single-echo images. MT-prep images showed excellent contrast between the GM and WM with magnetization transfer ratio (MTR) and cerebrospinal fluid normalized MT signal (MTCSF) = 0.12 ± 0.017 and 0.74 ± 0.013, respectively, for the GM; and 0.18 ± 0.011 and 0.58 ± 0.009, respectively, for the WM. Under-sampled acquisition using rosette readout with CS reconstruction demonstrated up to 6 times faster scans with comparable image quality as the fully-sampled acquisition.

Knowledge of the timing of cardiac valve opening and closing is important in cardiac physiology. The relationship between valve motion and electrocardiogram (ECG) is often assumed, however is not clearly defined. Here we investigate the accuracy of cardiac valve timing estimated using only the ECG, compared to Doppler echocardiography (DE) flow imaging as the gold standard. DE was obtained in 37 patients with simultaneous ECG recording. ECG was digitally processed and identifiable features (QRS, T, P waves) were examined as potential reference points to determine opening and closure of aortic and mitral valves, as compared to DE outflow and inflow measurement. Timing offset of the cardiac valves opening and closure between ECG features and DE was measured from derivation set (n = 19). The obtained mean offset in combination with the ECG features model was then evaluated on a validation set (n = 18). Using the same approach, additional measurement was also done for the right sided valves. From the derivation set, we found a fixed offset of 22 ± 9 ms, 2 ± 13 ms, 90 ± 26 ms, and − 2 ± − 27 ms when comparing S to aortic valve opening, Tend to aortic valve closure, Tend to mitral valve opening, and R to mitral valve closure respectively. Application of this model to the validation set showed good estimation of aortic and mitral valve opening and closure timing value, with low model absolute error (median of the mean absolute error of the four events = 19 ms compared to the gold standard DE measurement). For the right-sided (tricuspid and pulmonic) valves in our patient set, there was considerably higher median of the mean absolute error of 42 ms for the model. ECG features can be used to estimate aortic and mitral valve timings with good accuracy as compared to DE, allowing useful hemodynamic information to be derived from this easily available test.

Background:Class I echocardiographic guidelines in primary mitral regurgitation (PMR) risks left ventricular ejection fraction (LVEF) < 50% after mitral valve surgery even with pre-surgical LVEF > 60%. There are no models predicting LVEF < 50% after surgery in the complex interplay of increased preload and facilitated ejection in PMR using cardiac magnetic resonance (CMR). Objective:Use regression and machine learning models to identify a combination of CMR LV remodeling and function parameters that predict LVEF < 50% after mitral valve surgery. Methods:CMR with tissue tagging was performed in 51 pre-surgery PMR patients (median CMR LVEF 64%), 49 asymptomatic (median CMR LVEF 63%), and age-matched controls (median CMR LVEF 64%). To predict post-surgery LVEF < 50%, least absolute shrinkage and selection operator (LASSO), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed and validated in pre-surgery PMR patients. Recursive feature elimination and LASSO reduced the number of features and model complexity. Data was split and tested 100 times and models were evaluated via stratified cross validation to avoid overfitting. The final RF model was tested in asymptomatic PMR patients to predict post-surgical LVEF < 50% if they had gone to mitral valve surgery. Results:Thirteen pre-surgery PMR had LVEF < 50% after mitral valve surgery. In addition to LVEF (P = 0.005) and LVESD (P = 0.13), LV sphericity index (P = 0.047) and LV mid systolic circumferential strain rate (P = 0.024) were predictors of post-surgery LVEF < 50%. Using these four parameters, logistic regression achieved 77.92% classification accuracy while RF improved the accuracy to 86.17%. This final RF model was applied to asymptomatic PMR and predicted 14 (28.57%) out of 49 would have post-surgery LVEF < 50% if they had mitral valve surgery. Conclusions:These preliminary findings call for a longitudinal study to determine whether LV sphericity index and circumferential strain rate, or other combination of parameters, accurately predict post-surgical LVEF in PMR.

Purpose: To determine the blood pressure independent effects of spironolactone on left atrial (LA) size and function in patients with resistant hypertension (RHTN). Methods: Patients with RHTN (N=36, 55±7 years) were prospectively recruited. Spironolactone was initiated at 25 mg/day and increased to 50 mg/day after four weeks. Other antihypertensives were withdrawn to maintain constant blood pressure. Cardiac magnetic resonance imaging was performed at baseline and after six months of spironolactone treatment and changes in LA functional metrics were assessed (Figure 1). The statistical analysis was conducted using two-sided tests of significance. Results: LA size and function parameters improved from baseline to month-six: LA volumes indexed to body surface area (LAVI) were reduced (LAVI max 41.4±12 vs 33.2±9.7 ml/m 2 ; LAVI pre-A 32.6±9.8 vs 25.6±8.1 ml/m 2 ; LAVI min 18.5 [13.9 - 24.8] vs 14.1 [10.9 - 19.2] ml/m 2 ; all p < 0.05); left atrioventricular coupling index was reduced (28.2±11.5 vs 22.7±9.2 %, p < 0.05); LA emptying fractions (LAEF) were increased (total LAEF 52.4 [48.7 - 60.3] vs 55.9 [50.3 - 61.1] %; active LAEF 40.2±8.6 vs 43.1±7.8 %, both p < 0.05). There was a substantial increase in reservoir strain (29.1 ± 8.5 % vs 30.9 ± 5.5 %, p = 0.068) whereas active strain was significantly increased from baseline (16.3 ± 4.1 % vs 17.8 ± 4.2 %, p < 0.05). Changes in passive strain and strain rates were not statistically significant (p > 0.1). The effect of spironolactone was similar in patients with high (N = 18) and normal (N = 18) aldosterone status (defined by plasma renin activity and 24-hour urine aldosterone). Conclusion: Treatment of RHTN with spironolactone is associated with improvements in LA size and function regardless of whether aldosterone levels were normal or high. This study suggests the need for larger prospective studies examining effects of mineralocorticoid receptor antagonists on atrial function and atrioventricular coupling.

Cardiac T2-mapping is ideal for assessing myocardial edema resulting from events, such as, acute myocardial infarction, myocarditis and tako-tsubo cardiomyopathy. While many T2mapping sequences exist, most have design shortcuts that degrade their consistency, accuracy and prognostic value. T2maps captured at end-systole (ES) have maximum wall thickness and fewest artifacts and 4-point T2 curve-fits provide good T2 estimates. We present an Adiabatic T2-Prep Mapping sequence that produces accurate 4-point T2maps at ES within a 22-second breath hold at 3T. Pretesting on phantoms and in vivo validation on healthy human volunteers presented superior results.

Magnetic resonance spectroscopic imaging (MRSI) provides information about the spatial distribution of metabolites in the brain. These metabolite maps can be valuable in diagnosing central nervous system pathology. However, MRSI generally suffers from a long acquisition time, poor spatial resolution, and a low metabolite signal-to-noise ratio (SNR). Ultrahigh field strengths (≥ 7 T) can benefit MRSI with an improved SNR and allow high-resolution metabolic mapping. Non-Cartesian spatial-spectral encoding techniques, such as rosette spectroscopic imaging, can efficiently sample spatial and temporal domains, which significantly reduces the imaging time and enables high-resolution metabolic mapping in a clinically relevant scan time. In the current study, high-resolution (in-plane resolution of 2 × 2 mm2 ) mapping of proton (1 H) metabolites in the human brain at 7 T, is demonstrated. Five healthy subjects participated in the study. Using a time-efficient rosette trajectory and short TR/TE free induction decay MRSI, high-resolution maps of 1 H metabolites were obtained in a clinically relevant imaging time (6 min). Suppression of the water signal was achieved with an optimized water suppression enhanced through T1 effects approach and lipid removal was performed using L2 -regularization in the postprocessing. Spatial distributions of N-acetyl-aspartate, total choline, creatine, N-acetyl-aspartyl glutamate, myo-inositol, and glutamate were generated with Cramer-Rao lower bounds of less than 20%.