Exploring the role of in-patient magnetic resonance imaging use among admitted ischemic stroke patients in improving patient outcomes and reducing healthcare resource utilization.

Purpose: Despite the diagnostic and etiological significance of in-patient MRI in ischemic stroke (IS), its utilization is considered resource-intensive, expensive, and thus limiting feasibility and relevance. This study investigated the utilization of in-patient MRI for IS patients and its impact on patient and healthcare resource utilization outcomes. Methods: This retrospective registry-based study analyzed 1,956 IS patients admitted to Halifax's QEII Health Centre between 2015 and 2019. Firstly, temporal trends of MRI and other neuroimaging utilization were evaluated. Secondly, we categorized the cohort into two groups (MRI vs. No MRI; in addition to a non-contrast CT) and investigated adjusted differences in patient outcomes at admission, discharge, and post-discharge using logistic regression. Additionally, we analyzed healthcare resource utilization using Poisson log-linear regression. Furthermore, patient outcomes significantly associated with MRI use underwent subgroup analysis for stroke severity (mild stroke including transient ischemic attack vs. moderate and severe stroke) and any acute stage treatment (thrombolytic or thrombectomy or both vs. no treatment) subgroups, while using an age and sex-adjusted logistic regression model. Results: MRI was used in 40.5% patients; non-contrast CT in 99.3%, CT angiogram in 61.8%, and CT perfusion in 50.3%. Higher MRI utilization was associated with male sex, younger age, mild stroke, wake-up stroke, and no thrombolytic or thrombectomy treatment. MRI use was independently associated with lower in-hospital mortality (adjusted OR, 0.23; 95% CI, 0.15-0.36), lower symptomatic neurological status changes (0.64; 0.43-0.94), higher home discharge (1.32; 1.07-1.63), good functional outcomes at discharge (mRS score 0-2) (1.38; 1.11-1.72), lower 30-day stroke re-admission rates (0.48; 0.26-0.89), shorter hospital stays (regression coefficient, 0.92; 95% CI, 0.90-0.94), and reduced direct costs of hospitalization (0.90; 0.89-0.91). Subgroup analysis revealed significantly positive association of MRI use with most patient outcomes in moderate and severe strokes subgroup and non-acutely treated subgroup. Conversely, outcomes in mild strokes (including TIAs) subgroup and acute treatment subgroup were comparable regardless of MRI use. Conclusion: A substantial proportion of admitted IS patients underwent MRI, and MRI use was associated with improved patient outcomes and reduced healthcare resource utilization. Considering the multifactorial nature of IS patient outcomes, further randomized controlled trials are suggested to investigate the role of increased MRI utilization in optimizing in-patient IS management.

Impact of early MRI in ischemic strokes beyond hyper-acute stage to improve patient outcomes, enable early discharge, and realize cost savings.

BACKGROUND: Early in-patient MR Imaging may assist in identifying stroke etiology, facilitating prompt secondary prevention for ischemic strokes (IS), and potentially enhancing patient outcomes. This study explores the impact of early in patient MRI on IS patient outcomes and healthcare resource use beyond the hyper-acute stage. METHODS: In this retrospective registry-based study, 771 admitted transient ischemic attack (TIA) and IS patients at Halifax's QEII Health Centre from 2015 to 2019 underwent in-patient MRI. Cohort was categorized into two groups based on MRI timing: early (within 48 h) and late. Logistic regression and Poisson log-linear models, adjusted for age, sex, stroke severity, acute stroke protocol (ASP) activation, thrombolytic, and thrombectomy, were employed to examine in-hospital, discharge, post-discharge, and healthcare resource utilization outcomes. RESULTS: Among the cohort, 39.6 % received early in-patient MRI. ASP activation and TIA were associated with a higher likelihood of receiving early MRI. Early MRI was independently associated with a lower rate of symptomatic changes in neurological status during hospitalization (adjusted odds ratio [OR], 0.42; 95 % confidence interval [CI], 0.20-0.88), higher odds of good functional outcomes at discharge (1.55; 1.11-2.16), lower rate of non-home discharge (0.65; 0.46-0.91), shorter length of stay (regression coefficient, 0.93; 95 % CI, 0.89-0.97), and reduced direct cost of hospitalization (0.77; 0.75-0.79). CONCLUSION: Early in-patient MRI utilization in IS patients post-hyper-acute stage was independently associated with improved patient outcomes and decreased healthcare resource utilization, underscoring the potential benefits of early MRI during in-patient management of IS. Further research, including randomized controlled trials, is warranted to validate these findings.

Blood-Brain Barrier Dysfunction and Exposure to Head Impacts in University Football Players.

OBJECTIVE: To investigate the link between dysfunction of the blood-brain barrier (BBB) and exposure to head impacts in concussed football athletes. DESIGN: This was a prospective, observational pilot study. SETTING: Canadian university football. PARTICIPANTS: The study population consisted of 60 university football players, aged 18 to 25. Athletes who sustained a clinically diagnosed concussion over the course of a single football season were invited to undergo an assessment of BBB leakage. INDEPENDENT VARIABLES: Head impacts detected using impact-sensing helmets were the measured variables. MAIN OUTCOME MEASURES: Clinical diagnosis of concussion and BBB leakage assessed using dynamic contrast-enhanced MRI (DCE-MRI) within 1 week of concussion were the outcome measures. RESULTS: Eight athletes were diagnosed with a concussion throughout the season. These athletes sustained a significantly higher number of head impacts than nonconcussed athletes. Athletes playing in the defensive back position were significantly more likely to sustain a concussion than remain concussion free. Five of the concussed athletes underwent an assessment of BBB leakage. Logistic regression analysis indicated that region-specific BBB leakage in these 5 athletes was best predicted by impacts sustained in all games and practices leading up to the concussion-as opposed to the last preconcussion impact or the impacts sustained during the game when concussion occurred. CONCLUSIONS: These preliminary findings raise the potential for the hypothesis that repeated exposure to head impacts may contribute to the development of BBB pathology. Further research is needed to validate this hypothesis and to test whether BBB pathology plays a role in the sequela of repeated head trauma.

NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner.

BACKGROUND: The Cubresa Spark is a novel benchtop silicon-photomultiplier (SiPM)-based preclinical SPECT system. SiPMs in SPECT significantly improve resolution and reduce detector size compared to preclinical cameras with photomultiplier tubes requiring highly magnifying collimators. The NEMA NU 1 Standard for Performance Measurements of Gamma Cameras provides methods that can be readily applied or extended to characterize preclinical cameras with minor modifications. The primary objective of this study is to characterize the Spark according to the NEMA NU 1-2018 standard to gain insight into its nuclear medicine imaging capabilities. The secondary objective is to validate a GATE Monte Carlo simulation model of the Spark for use in preclinical SPECT studies. METHODS: NEMA NU 1-2018 guidelines were applied to characterize the Spark's intrinsic, system, and tomographic performance with single- and multi-pinhole collimators. Phantoms were fabricated according to NEMA specifications with deviations involving high-resolution modifications. GATE was utilized to model the detector head with the single-pinhole collimator, and NEMA measurements were employed to tune and validate the model. Single-pinhole and multi-pinhole SPECT data were reconstructed with the Software for Tomographic Image Reconstruction and HiSPECT, respectively. RESULTS: The limiting intrinsic resolution was measured as 0.85 mm owing to a high-resolution SiPM array combined with a 3 mm-thick scintillation crystal. The average limiting tomographic resolution was 1.37 mm and 1.19 mm for the single- and multi-pinhole collimators, respectively, which have magnification factors near unity at the center of rotation. The maximum observed count rate was 15,400 cps, and planar sensitivities of 34 cps/MBq and 150 cps/MBq were measured at the center of rotation for the single- and multi-pinhole collimators, respectively. All simulated tests agreed well with measurement, where the most considerable deviations were below 7%. CONCLUSIONS: NEMA NU 1-2018 standards determined that a SiPM detector mitigates the need for highly magnifying pinhole collimators while preserving detailed information in projection images. Measured and simulated NEMA results were highly comparable with differences on the order of a few percent, confirming simulation accuracy and validating the GATE model. Of the collimators initially provided with the Spark, the multi-pinhole collimator offers high resolution and sensitivity for organ-specific imaging of small animals, and the single-pinhole collimator enables high-resolution whole-body imaging of small animals.

Is improved access to magnetic resonance imaging imperative for optimal ischemic stroke care?

Neuroimaging, including CT and MRI, is integral to ischemic stroke (IS) treatment, management, and prevention. However, the use of MRI for IS patients is limited despite its potential to provide high-quality images that yield definitive information related to the management of IS. MRI is beneficial when the information provided by CT is insufficient for decisions related to the diagnosis, etiology, or treatment of IS. In the emergency setting, MRI can improve the diagnostic accuracy of CT-negative acute ischemic strokes (AIS) and ensure a better selection of patients for reperfusion therapies with thrombolysis and/or thrombectomy. Moreover, MR imaging may help avoid hospital admissions for patients with stroke mimics, facilitate earlier discharge, and reduce overall hospital costs. MRI in the in-patient setting can help determine stroke etiology to aid in stroke prevention management upon discharge. Furthermore, early access to MRI in IS out-patients can aid in diagnosing, risk stratifying, and determining optimal management strategies for patients with a TIA or a minor stroke. Recent technological advances, particularly low-to-mid-field MR scanners, can improve access to MRI. These MR scanners provide faster protocols, cost-effectiveness, smaller footprints, safety, and lower power requirements. In conclusion, MRI use for IS treatment, management, and prevention is imperative and justifiable, and the latest technological advancements in MR scanners hold the potential to enhance access.

Resting state functional connectivity in SLE patients and association with cognitive impairment and blood-brain barrier permeability.

OBJECTIVE: Extensive blood-brain barrier (BBB) leakage has been linked to cognitive impairment in SLE. This study aimed to examine the associations of brain functional connectivity (FC) with cognitive impairment and BBB dysfunction among patients with SLE. METHODS: Cognitive function was assessed by neuropsychological testing (n = 77). Resting-state FC (rsFC) between brain regions, measured by functional MRI (n = 78), assessed coordinated neural activation in 131 regions across five canonical brain networks. BBB permeability was measured by dynamic contrast-enhanced MRI (n = 61). Differences in rsFC were compared between SLE patients with cognitive impairment (SLE-CI) and those with normal cognition (SLE-NC), between SLE patients with and without extensive BBB leakage, and with healthy controls. RESULTS: A whole-brain rsFC comparison found significant differences in intra-network and inter-network FC in SLE-CI vs SLE-NC patients. The affected connections showed a reduced negative rsFC in SLE-CI compared with SLE-NC and healthy controls. Similarly, a reduced number of brain-wide connections was found in SLE-CI patients compared with SLE-NC (P = 0.030) and healthy controls (P = 0.006). Specific brain regions had a lower total number of brain-wide connections in association with extensive BBB leakage (P = 0.011). Causal mediation analysis revealed that 64% of the association between BBB leakage and cognitive impairment in SLE patients was mediated by alterations in FC. CONCLUSION: SLE patients with cognitive impairment had abnormalities in brain rsFC which accounted for most of the association between extensive BBB leakage and cognitive impairment.

Role of autoantibodies and blood-brain barrier leakage in cognitive impairment in systemic lupus erythematosus.

OBJECTIVE: Cognitive impairment is common in patients with SLE but the cause is unknown. The current cross-sectional study examined the association between select SLE-related autoantibodies, other serological biomarkers and extensive blood-brain barrier (BBB) leakage in patients with SLE with and without cognitive impairment. In addition, we determined whether the relationship between SLE autoantibodies, other biomarkers and cognitive impairment differed depending on the presence or absence of concurrent extensive BBB leakage. METHODS: Consecutive patients with SLE, recruited from a single academic medical centre, underwent formal neuropsychological testing for assessment of cognitive function. On the same day, BBB permeability was determined using dynamic contrast-enhanced MRI scanning. SLE autoantibodies and other serological biomarkers were measured. Regression modelling was used to determine the association between cognitive impairment, extensive BBB leakage and autoantibodies/biomarkers. RESULTS: There were 102 patients with SLE; 90% were female and 88% were Caucasian, with a mean±SD age of 48.9±13.8 years. The mean±SD SLE disease duration was 14.8±11.0 years. Impairment in one or more cognitive tests was present in 47 of 101 (47%) patients and included deficits in information processing speed (9%), attention span (21%), new learning (8%), delayed recall (15%) and executive abilities (21%). Extensive BBB leakage was present in 20 of 79 (25%) patients and was associated with cognitive impairment (15 of 20 (75%) vs 24 of 59 (41%); p=0.01) and shorter disease duration (median (IQR): 7 (8-24 years) vs 15 (2-16 years); p=0.02). No serological parameters were associated with extensive BBB leakage and there was no statistically significant association between cognitive impairment and circulating autoantibodies even after adjusting for BBB leakage. CONCLUSIONS: Extensive BBB leakage alone was associated with cognitive impairment. These findings suggest that BBB leakage is an important contributor to cognitive impairment, regardless of circulating SLE-related autoantibodies.

Top-down threat bias in pain perception is predicted by intrinsic structural and functional connections of the brain.

Top-down processes such as expectations play a key role in pain perception. In specific contexts, inferred threat of impending pain can affect perceived pain more than the noxious intensity. This biasing effect of top-down threats can affect some individuals more strongly than others due to differences in fear of pain. The specific characteristics of intrinsic brain characteristics that mediate the effects of top-down threat bias are mainly unknown. In this study, we examined whether threat bias is associated with structural and functional brain connectivity. The variability in the top-down bias was mapped to the microstructure of white matter in diffusion weighted images (DWI) using MRTrix3. Mean functional connectivity of five canonical resting state networks was tested for association with bias scores and with the identified DWI metrics. We found that the fiber density of the splenium of the corpus callosum was significantly low in individuals with high top-down threat bias (FWE corrected with 5000 permutations, p < 0.05). The mean functional connectivity within the language/memory and between language/memory and default mode networks predicted the bias scores. Functional connectivity within language memory networks predicted the splenium fiber density, higher pain catastrophizing and lower mindful awareness. Probabilistic tractography showed that the identified region in the splenium connected several sensory regions and high-order parietal regions between the two hemispheres, indicating the splenium's role in sensory integration. These findings demonstrate that individuals who show more change in pain with changes in the threat of receiving a stronger noxious stimulus have lower structural connectivity in the pathway necessary for integrating top-down cue information with bottom-up sensory information. Conversely, systems involved in memory recall, semantic and self-referential processing are more strongly connected in people with top-down threat bias.

Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images.

PURPOSE: Multiparametric MRI (mp-MRI) is a widely used tool for diagnosing and staging prostate cancer. The purpose of this study was to evaluate whether transfer learning, unsupervised pre-training and test-time augmentation significantly improved the performance of a convolutional neural network (CNN) for pixel-by-pixel prediction of cancer vs. non-cancer using mp-MRI datasets. METHODS: 154 subjects undergoing mp-MRI were prospectively recruited, 16 of whom subsequently underwent radical prostatectomy. Logistic regression, random forest and CNN models were trained on mp-MRI data using histopathology as the gold standard. Transfer learning, unsupervised pre-training and test-time augmentation were used to boost CNN performance. Models were evaluated using Dice score and area under the receiver operating curve (AUROC) with leave-one-subject-out cross validation. Permutation feature importance testing was performed to evaluate the relative value of each MR contrast to CNN model performance. Statistical significance (p<0.05) was determined using the paired Wilcoxon signed rank test with Benjamini-Hochberg correction for multiple comparisons. RESULTS: Baseline CNN outperformed logistic regression and random forest models. Transfer learning and unsupervised pre-training did not significantly improve CNN performance over baseline; however, test-time augmentation resulted in significantly higher Dice scores over both baseline CNN and CNN plus either of transfer learning or unsupervised pre-training. The best performing model was CNN with transfer learning and test-time augmentation (Dice score of 0.59 and AUROC of 0.93). The most important contrast was apparent diffusion coefficient (ADC), followed by Ktrans and T2, although each contributed significantly to classifier performance. CONCLUSIONS: The addition of transfer learning and test-time augmentation resulted in significant improvement in CNN segmentation performance in a small set of prostate cancer mp-MRI data. Results suggest that these techniques may be more broadly useful for the optimization of deep learning algorithms applied to the problem of semantic segmentation in biomedical image datasets. However, further work is needed to improve the generalizability of the specific model presented herein.

Mapping of fatty acid composition with free-breathing MR spectroscopic imaging and compressed sensing.

Nonalcoholic fatty liver disease (NAFLD) is a growing health problem, and a major challenge in NAFLD management is identifying which patients are at risk of progression to more serious disease. Simple measurements of liver fat content are not strong predictors of clinical outcome, but biomarkers related to fatty acid composition (ie, saturated vs. unsaturated fat) may be more effective. MR spectroscopic imaging (MRSI) methods allow spatially resolved, whole-liver measurements of chemical composition but are traditionally limited by slow acquisition times. In this work we present an accelerated MRSI acquisition based on spin echo single point imaging (SE-SPI), which, using appropriate sampling and compressed sensing reconstruction, allows free-breathing acquisition in a mouse model of fatty liver disease. After validating the technique's performance in oil/water phantoms, we imaged mice that had received a normal diet or a methionine and choline deficient (MCD) diet, some of which also received supplemental injections of iron to mimic hepatic iron overload. SE-SPI was more resistant to the line-broadening effects of iron than single-voxel spectroscopy measurements, and was consistently able to measure the amplitudes of low-intensity spectral peaks that are important to characterizing fatty acid composition. In particular, in the mice receiving the MCD diet, SE-SPI showed a significant decrease in a metric associated with unsaturated fat, which is consistent with the literature. This or other related metrics may therefore offer more a specific biomarker of liver health than fat content alone. This preclinical study is an important precursor to clinical testing of the proposed method. MR-based quantification of fatty acid composition may allow for improved characterization of non-alcoholic fatty liver disease. A spectroscopic imaging method with appropriate sampling strategy allows whole-liver mapping of fat composition metrics in a free-breathing mouse model. Changes in metrics like the surrogate unsaturation index (UIs) are visible in mice receiving a diet which induces fat accumulation in the liver, as compared to a normal diet; such metrics may prove useful in future clinical studies of liver disease.

Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study.

We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.

Blood-brain barrier leakage in systemic lupus erythematosus is associated with gray matter loss and cognitive impairment.

OBJECTIVES: To examine the association between blood-brain barrier (BBB) integrity, brain volume and cognitive dysfunction in adult patients with systemic lupus erythematosus (SLE). METHODS: A total of 65 ambulatory patients with SLE and 9 healthy controls underwent dynamic contrast-enhanced MRI scanning, for quantitative assessment of BBB permeability. Volumetric data were extracted using the VolBrain pipeline. Global cognitive function was evaluated using a screening battery consisting of tasks falling into five broad cognitive domains, and was compared between patients with normal versus extensive BBB leakage. RESULTS: Patients with SLE had significantly higher levels of BBB leakage compared with controls (p=0.04). Extensive BBB leakage (affecting over >9% of brain volume) was identified only in patients with SLE (16/65; 24.6%), who also had smaller right and left cerebral grey matter volumes compared with controls (p=0.04). Extensive BBB leakage was associated with lower global cognitive scores (p=0.02), and with the presence of impairment on one or more cognitive tasks (p=0.01). CONCLUSION: Our findings provide evidence for a link between extensive BBB leakage and changes in both brain structure and cognitive function in patients with SLE. Future studies should investigate the mechanisms underlying BBB-mediated cognitive impairment, validate the diagnostic utility of BBB imaging, and determine the potential of targeting the BBB as a therapeutic strategy in patients with SLE.

Slow blood-to-brain transport underlies enduring barrier dysfunction in American football players.

Repetitive mild traumatic brain injury in American football players has garnered increasing public attention following reports of chronic traumatic encephalopathy, a progressive tauopathy. While the mechanisms underlying repetitive mild traumatic brain injury-induced neurodegeneration are unknown and antemortem diagnostic tests are not available, neuropathology studies suggest a pathogenic role for microvascular injury, specifically blood-brain barrier dysfunction. Thus, our main objective was to demonstrate the effectiveness of a modified dynamic contrast-enhanced MRI approach we have developed to detect impairments in brain microvascular function. To this end, we scanned 42 adult male amateur American football players and a control group comprising 27 athletes practicing a non-contact sport and 26 non-athletes. MRI scans were also performed in 51 patients with brain pathologies involving the blood-brain barrier, namely malignant brain tumours, ischaemic stroke and haemorrhagic traumatic contusion. Based on data from prolonged scans, we generated maps that visualized the permeability value for each brain voxel. Our permeability maps revealed an increase in slow blood-to-brain transport in a subset of amateur American football players, but not in sex- and age-matched controls. The increase in permeability was region specific (white matter, midbrain peduncles, red nucleus, temporal cortex) and correlated with changes in white matter, which were confirmed by diffusion tensor imaging. Additionally, increased permeability persisted for months, as seen in players who were scanned both on- and off-season. Examination of patients with brain pathologies revealed that slow tracer accumulation characterizes areas surrounding the core of injury, which frequently shows fast blood-to-brain transport. Next, we verified our method in two rodent models: rats and mice subjected to repeated mild closed-head impact injury, and rats with vascular injury inflicted by photothrombosis. In both models, slow blood-to-brain transport was observed, which correlated with neuropathological changes. Lastly, computational simulations and direct imaging of the transport of Evans blue-albumin complex in brains of rats subjected to recurrent seizures or focal cerebrovascular injury suggest that increased cellular transport underlies the observed slow blood-to-brain transport. Taken together, our findings suggest dynamic contrast-enhanced-MRI can be used to diagnose specific microvascular pathology after traumatic brain injury and other brain pathologies.

Variability and bias between magnetoencephalography systems in localization of the primary visual cortex.

OBJECTIVES: When using MEG for pre-surgical mapping it is critically important that reliable estimates of functional locations, such as the primary visual cortex (V1) can be provided. Several different models of MEG systems exist, each with varying software and hardware configurations, and it is not currently known how the system type contributes to variability in V1 localization. PATIENTS AND METHODS: In this study, participants underwent MEG sessions using two different systems (Vector View and CTF) during which they were presented with a repeating grating stimulus to the lower-left visual quadrant to generate a visual evoked field (VEF). The location, amplitude and latency of the VEF source was compared between systems for each participant. RESULTS: No significant differences were found in latency and amplitude between systems, however, a significant bias in the latero-medial position of the localization was present. The median inter-system Euclidian distance between V1 localization across participants was 10.5 mm. CONCLUSIONS: Overall, our results indicate that mapping of V1 can be reliably reproduced within approximately one centimetre by different MEG systems. SIGNIFICANCE: This result provides knowledge of the useful limits on the reliability of localization which can be taken into consideration in clinical practice.

Threat Prediction from Schemas as a Source of Bias in Pain Perception.

Our sensory impressions of pain are generally thought to represent the noxious properties of an agent but can be influenced by the predicted level of threat. Predictions can be sourced from higher-order cognitive processes, such as schemas, but the extent to which schemas can influence pain perception relative to bottom-up sensory inputs and the underlying neural underpinnings of such a phenomenon are unclear. Here, we investigate how threat predictions generated from learning a cognitive schema lead to inaccurate sensory impressions of the pain stimulus. Healthy male and female participants first detected a linear association between cue values and stimulus intensity and rated pain to reflect the linear schema when compared with uncued heat stimuli. The effect of bias on pain ratings was reduced when prediction errors (PEs) increased, but pain perception was only partially updated when measured against stepped increases in PEs. Cognitive, striatal, and sensory regions graded their responses to changes in predicted threat despite the PEs (p < 0.05, corrected). Individuals with more catastrophic thinking about pain and with low mindfulness were significantly more reliant on the schema than on the sensory evidence from the pain stimulus. These behavioral differences mapped to variability in responses of the striatum and ventromedial prefrontal cortex. Thus, this study demonstrates a significant role of higher-order schemas in pain perception and indicates that pain perception is biased more toward predictions and less toward nociceptive inputs in individuals who report less mindfulness and more fear of pain.SIGNIFICANCE STATEMENT This study demonstrates that threat predictions generated from cognitive schemas continue to influence pain perception despite increasing prediction errors arising in pain pathways. Individuals first formed a cognitive schema of linearity in the relationship between the cued threat value and the stimulus intensity. Subsequently, the linearity was reduced gradually, and participants partially updated their evaluations of pain in relation to the stepped increases in prediction errors. Individuals who continued to rate pain based more on the predicted threat than on changes in nociceptive inputs reported high pain catastrophizing and less mindful-awareness scores. These two affects mapped to activity in the ventral and dorsal striatum, respectively. These findings direct us to a significant role of top-down processes in pain perception.

Comparison of Objective Image Quality Metrics to Expert Radiologists' Scoring of Diagnostic Quality of MR Images.

Image quality metrics (IQMs) such as root mean square error (RMSE) and structural similarity index (SSIM) are commonly used in the evaluation and optimization of accelerated magnetic resonance imaging (MRI) acquisition and reconstruction strategies. However, it is unknown how well these indices relate to a radiologist's perception of diagnostic image quality. In this study, we compare the image quality scores of five radiologists with the RMSE, SSIM, and other potentially useful IQMs: peak signal to noise ratio (PSNR) multi-scale SSIM (MSSSIM), information-weighted SSIM (IWSSIM), gradient magnitude similarity deviation (GMSD), feature similarity index (FSIM), high dynamic range visible difference predictor (HDRVDP), noise quality metric (NQM), and visual information fidelity (VIF). The comparison uses a database of MR images of the brain and abdomen that have been retrospectively degraded by noise, blurring, undersampling, motion, and wavelet compression for a total of 414 degraded images. A total of 1017 subjective scores were assigned by five radiologists. IQM performance was measured via the Spearman rank order correlation coefficient (SROCC) and statistically significant differences in the residuals of the IQM scores and radiologists' scores were tested. When considering SROCC calculated from combining scores from all radiologists across all image types, RMSE and SSIM had lower SROCC than six of the other IQMs included in the study (VIF, FSIM, NQM, GMSD, IWSSIM, and HDRVDP). In no case did SSIM have a higher SROCC or significantly smaller residuals than RMSE. These results should be considered when choosing an IQM in future imaging studies.

Blood-brain barrier imaging as a potential biomarker for bipolar disorder progression.

Bipolar disorder affects approximately 2% of the population and is typically characterized by recurrent episodes of mania and depression. While some patients achieve remission using mood-stabilizing treatments, a significant proportion of patients show progressive changes in symptomatology over time. Bipolar progression is diverse in nature and may include a treatment-resistant increase in the frequency and severity of episodes, worse psychiatric and functional outcomes, and a greater risk of suicide. The mechanisms underlying bipolar disorder progression remain poorly understood and there are currently no biomarkers for identifying patients at risk. The objective of this study was to explore the potential of blood-brain barrier (BBB) imaging as such a biomarker, by acquiring the first imaging data of BBB leakage in bipolar patients, and evaluating the potential association between BBB dysfunction and bipolar symptoms. To this end, a cohort of 36 bipolar patients was recruited through the Mood Disorders Clinic (Nova Scotia Health Authority, Canada). All patients, along with 14 control subjects (matched for sex, age and metabolic status), underwent contrast-enhanced dynamic MRI scanning for quantitative assessment of BBB leakage as well as clinical and psychiatric evaluations. Outlier analysis has identified a group of 10 subjects with significantly higher percentages of brain volume with BBB leakage (labeled the "extensive BBB leakage" group). This group consisted exclusively of bipolar patients, while the "normal BBB leakage" group included the entire control cohort and the remaining 26 bipolar subjects. Among the bipolar cohort, patients with extensive BBB leakage were found to have more severe depression and anxiety, and a more chronic course of illness. Furthermore, all bipolar patients within this group were also found to have co-morbid insulin resistance, suggesting that insulin resistance may increase the risk of BBB dysfunction in bipolar patients. Our findings demonstrate a clear link between BBB leakage and greater psychiatric morbidity in bipolar patients and highlight the potential of BBB imaging as a mechanism-based biomarker for bipolar disorder progression.

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detection of PCa will use an engineered scalar image called Ktrans to summarize the information in the DCE time-series images. This work proposes a new model that amalgamates the U-net and the convGRU neural network architectures for the purpose of interpreting DCE time-series in a temporal and spatial basis for segmenting PCa in MR images. Ultimately, experiments show that the proposed model using the DCE time-series images can outperform a baseline U-net segmentation model using Ktrans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model.