SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. METHODS: SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. RESULTS: We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10 × $$ 10\times $$ ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. CONCLUSION: Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

Comparison of cerebral oxygen extraction fraction using ASE and TRUST methods in patients with sickle cell disease and healthy controls.

Abnormal oxygen extraction fraction (OEF), a putative biomarker of cerebral metabolic stress, may indicate compromised oxygen delivery and ischemic vulnerability in patients with sickle cell disease (SCD). Elevated OEF was observed at the tissue level across the brain using an asymmetric spin echo (ASE) MR method, while variable global OEFs were found from the superior sagittal sinus (SSS) using a T2-relaxation-under-spin-tagging (TRUST) MRI method with different calibration models. In this study, we aimed to compare the average ASE-OEF in the SSS drainage territory and TRUST-OEF in the SSS from the same SCD patients and healthy controls. 74 participants (SCD: N = 49; controls: N = 25) underwent brain MRI. TRUST-OEF was quantified using the Lu-bovine, Bush-HbA and Li-Bush-HbS models. ASE-OEF and TRUST-OEF were significantly associated in healthy controls after controlling for hematocrit using the Lu-bovine or the Bush-HbA model. However, no association was found between ASE-OEF and TRUST-OEF in patients with SCD using either the Bush-HbA or the Li-Bush-HbS model. Plausible explanations include a discordance between spatially volume-averaged oxygenation brain tissue and flow-weighted volume-averaged oxygenation in SSS or sub-optimal calibration in SCD. Further work is needed to refine and validate non-invasive MR OEF measurements in SCD.

Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton.

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation.

CSF-Based Volumetric Imaging Biomarkers Highlight Incidence and Risk Factors for Cerebral Edema After Ischemic Stroke.

BACKGROUND: Cerebral edema has primarily been studied using midline shift or clinical deterioration as end points, which only captures the severe and delayed manifestations of a process affecting many patients with stroke. Quantitative imaging biomarkers that measure edema severity across the entire spectrum could improve its early detection, as well as identify relevant mediators of this important stroke complication. METHODS: We applied an automated image analysis pipeline to measure the displacement of cerebrospinal fluid (ΔCSF) and the ratio of lesional versus contralateral hemispheric cerebrospinal fluid (CSF) volume (CSF ratio) in a cohort of 935 patients with hemispheric stroke with follow-up computed tomography scans taken a median of 26 h (interquartile range 24-31) after stroke onset. We determined diagnostic thresholds based on comparison to those without any visible edema. We modeled baseline clinical and radiographic variables against each edema biomarker and assessed how each biomarker was associated with stroke outcome (modified Rankin Scale at 90 days). RESULTS: The displacement of CSF and CSF ratio were correlated with midline shift (r = 0.52 and - 0.74, p < 0.0001) but exhibited broader ranges. A ΔCSF of greater than 14% or a CSF ratio below 0.90 identified those with visible edema: more than half of the patients with stroke met these criteria, compared with only 14% who had midline shift at 24 h. Predictors of edema across all biomarkers included a higher National Institutes of Health Stroke Scale score, a lower Alberta Stroke Program Early CT score, and lower baseline CSF volume. A history of hypertension and diabetes (but not acute hyperglycemia) predicted greater ΔCSF but not midline shift. Both ΔCSF and a lower CSF ratio were associated with worse outcome, adjusting for age, National Institutes of Health Stroke Scale score, and Alberta Stroke Program Early CT score (odds ratio 1.7, 95% confidence interval 1.3-2.2 per 21% ΔCSF). CONCLUSIONS: Cerebral edema can be measured in a majority of patients with stroke on follow-up computed tomography using volumetric biomarkers evaluating CSF shifts, including in many without visible midline shift. Edema formation is influenced by clinical and radiographic stroke severity but also by chronic vascular risk factors and contributes to worse stroke outcomes.

White matter hyperintensity longitudinal morphometric analysis in association with Alzheimer disease.

INTRODUCTION: Vascular damage in Alzheimer's disease (AD) has shown conflicting findings particularly when analyzing longitudinal data. We introduce white matter hyperintensity (WMH) longitudinal morphometric analysis (WLMA) that quantifies WMH expansion as the distance from lesion voxels to a region of interest boundary. METHODS: WMH segmentation maps were derived from 270 longitudinal fluid-attenuated inversion recovery (FLAIR) ADNI images. WLMA was performed on five data-driven WMH patterns with distinct spatial distributions. Amyloid accumulation was evaluated with WMH expansion across the five WMH patterns. RESULTS: The preclinical group had significantly greater expansion in the posterior ventricular WM compared to controls. Amyloid significantly associated with frontal WMH expansion primarily within AD individuals. WLMA outperformed WMH volume changes for classifying AD from controls primarily in periventricular and posterior WMH. DISCUSSION: These data support the concept that localized WMH expansion continues to proliferate with amyloid accumulation throughout the entirety of the disease in distinct spatial locations.

Toward Automated Detection of Silent Cerebral Infarcts in Children and Young Adults With Sickle Cell Anemia.

BACKGROUND: Silent cerebral infarcts (SCI) in sickle cell anemia (SCA) are associated with future strokes and cognitive impairment, warranting early diagnosis and treatment. Detection of SCI, however, is limited by their small size, especially when neuroradiologists are unavailable. We hypothesized that deep learning may permit automated SCI detection in children and young adults with SCA as a tool to identify the presence and extent of SCI in clinical and research settings. METHODS: We utilized UNet-a deep learning model-for fully automated SCI segmentation. We trained and optimized UNet using brain magnetic resonance imaging from the SIT trial (Silent Infarct Transfusion). Neuroradiologists provided the ground truth for SCI diagnosis, while a vascular neurologist manually delineated SCI on fluid-attenuated inversion recovery and provided the ground truth for SCI segmentation. UNet was optimized for the highest spatial overlap between automatic and manual delineation (dice similarity coefficient). The optimized UNet was externally validated using an independent single-center prospective cohort of SCA participants. Model performance was evaluated through sensitivity and accuracy (%correct cases) for SCI diagnosis, dice similarity coefficient, intraclass correlation coefficient (metric of volumetric agreement), and Spearman correlation. RESULTS: The SIT trial (n=926; 31% with SCI; median age, 8.9 years) and external validation (n=80; 50% with SCI; age, 11.5 years) cohorts had small median lesion volumes of 0.40 and 0.25 mL, respectively. Compared with the neuroradiology diagnosis, UNet predicted SCI presence with 100% sensitivity and 74% accuracy. In magnetic resonance imaging with SCI, UNet reached a moderate spatial agreement (dice similarity coefficient, 0.48) and high volumetric agreement (intraclass correlation coefficient, 0.76; ρ=0.72; P<0.001) between automatic and manual segmentations. CONCLUSIONS: UNet, trained using a large pediatric SCA magnetic resonance imaging data set, sensitively detected small SCI in children and young adults with SCA. While additional training is needed, UNet may be integrated into the clinical workflow as a screening tool, aiding in SCI diagnosis.

Imaging biomarkers of cerebral edema automatically extracted from routine CT scans of large vessel occlusion strokes.

BACKGROUND AND PURPOSE: Volumetric and densitometric biomarkers have been proposed to better quantify cerebral edema after stroke, but their relative performance has not been rigorously evaluated. METHODS: Patients with large vessel occlusion stroke from three institutions were analyzed. An automated pipeline extracted brain, cerebrospinal fluid (CSF), and infarct volumes from serial CTs. Several biomarkers were measured: change in global CSF volume from baseline (ΔCSF); ratio of CSF volumes between hemispheres (CSF ratio); and relative density of infarct region compared with mirrored contralateral region (net water uptake [NWU]). These were compared to radiographic standards, midline shift and relative hemispheric volume (RHV) and malignant edema, defined as deterioration resulting in need for osmotic therapy, decompressive surgery, or death. RESULTS: We analyzed 255 patients with 210 baseline CTs, 255 24-hour CTs, and 81 72-hour CTs. Of these, 35 (14%) developed malignant edema and 63 (27%) midline shift. CSF metrics could be calculated for 310 (92%), while NWU could only be obtained from 193 (57%). Peak midline shift was correlated with baseline CSF ratio (ρ = -.22) and with CSF ratio and ΔCSF at 24 hours (ρ = -.55/.63) and 72 hours (ρ = -.66/.69), but not with NWU (ρ = .15/.25). Similarly, CSF ratio was correlated with RHV (ρ = -.69/-.78), while NWU was not. Adjusting for age, National Institutes of Health Stroke Scale, tissue plasminogen activator treatment, and Alberta Stroke Program Early CT Score, CSF ratio (odds ratio [OR]: 1.95 per 0.1, 95% confidence interval [CI]: 1.52-2.59) and ΔCSF at 24 hours (OR: 1.87 per 10%, 95% CI: 1.47-2.49) were associated with malignant edema. CONCLUSION: CSF volumetric biomarkers can be automatically measured from almost all routine CTs and correlate better with standard edema endpoints than net water uptake.

Neuroinflammation and amyloid deposition in the progression of mixed Alzheimer and vascular dementia.

BACKGROUND: Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) pathologies coexist in patients with cognitive impairment. Abnormal amyloid beta (Aß) deposition is the hallmark pathologic biomarker for AD. Neuroinflammation may be a pathophysiological mechanism in both AD and VCID. In this study, we aimed to understand the role of neuroinflammation and Aß deposition in white matter hyperintensities (WMH) progression and cognitive decline over a decade in patients with mixed AD and VCID pathologies. METHODS: Twenty-four elderly participants (median [interquartile range] age 78 [64.8, 83] years old, 14 female) were recruited from the Knight Alzheimer Disease Research Center. 11C-PK11195 standard uptake value ratio (SUVR) and 11C-PiB mean cortical binding potential (MCBP) were used to evaluate neuroinflammation and Aß deposition in-vivo, respectively. Fluid-attenuated inversion recovery MR images were acquired to obtain baseline WMH volume and its progression over 11.5 years. Composite cognitive scores (global, processing speed and memory) were computed at baseline and follow-up over 7.5 years. Multiple linear regression models evaluated the association between PET biomarkers (11C-PK11195 SUVR and 11C-PiB MCBP) and baseline WMH volume and cognitive function. Moreover, linear mixed-effects models evaluated whether PET biomarkers predicted greater WMH progression or cognitive decline over a decade. RESULTS: Fifteen participants (62.5%) had mixed AD (positive PiB) and VCID (at least one vascular risk factor) pathologies. Elevated 11C-PK11195 SUVR, but not 11C-PiB MCBP, was associated with greater baseline WMH volume and predicted greater WMH progression. Elevated 11C-PiB MCBP was associated with baseline memory and global cognition. Elevated 11C-PK11195 SUVR and elevated 11C-PiB MCBP independently predicted greater global cognition and processing speed declines. No association was found between 11C-PK11195 SUVR and 11C-PiB MCBP. CONCLUSIONS: Neuroinflammation and Aß deposition may represent two distinct pathophysiological pathways, and both independently contributed to the progression of cognitive impairment in mixed AD and VCID pathologies. Neuroinflammation, but not Aß deposition, contributed to WMH volume and progression.

Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage.

OBJECTIVE: The role of hemorrhage volume in risk of vasospasm, delayed cerebral ischemia (DCI), and poor outcomes after aneurysmal subarachnoid hemorrhage (SAH) is well established. However, the relative contribution of blood within individual compartments is unclear. We present an automated technique for measuring not only total but also volumes of blood in each major compartment after SAH. METHODS: We trained convolutional neural networks to identify compartmental blood (cisterns, sulci, and ventricles) from baseline computed tomography scans of patients with SAH. We compared automated blood volumes against traditional markers of bleeding (modified Fisher score [mFS], Hijdra sum score [HSS]) in 190 SAH patients for prediction of vasospasm, DCI, and functional status (modified Rankin Scale) at hospital discharge. RESULTS: Combined cisternal and sulcal volume was better correlated with mFS and HSS than cisternal volume alone (ρ = 0.63 vs. 0.58 and 0.75 vs. 0.70, P < 0.001). Only blood volume in combined cisternal plus sulcal compartments was independently associated with DCI (OR 1.023 per mL, 95% CI 1.002-1.048), after adjusting for clinical factors while ventricular blood volume was not. Total and specifically sulcal blood volume was strongly associated with poor outcome (OR 1.03 per mL, 1.01-1.06, P = 0.006 and OR 1.04, 1.00-1.08 for sulcal) as was HSS (OR 1.06 per point, 1.00-1.12, P = 0.04), while mFS was not (P = 0.24). CONCLUSIONS: An automated imaging algorithm can measure the volume of bleeding after SAH within individual compartments, demonstrating cisternal plus sulcal (and not ventricular) blood contributes to risk of DCI/vasospasm. Automated blood volume was independently associated with outcome, while qualitative grading was not.

Normalization of cerebral hemodynamics after hematopoietic stem cell transplant in children with sickle cell disease.

Children with sickle cell disease (SCD) demonstrate cerebral hemodynamic stress and are at high risk of strokes. We hypothesized that curative hematopoietic stem cell transplant (HSCT) normalizes cerebral hemodynamics in children with SCD compared with pre-transplant baseline. Whole-brain cerebral blood flow (CBF) and oxygen extraction fraction (OEF) were measured by magnetic resonance imaging 1 to 3 months before and 12 to 24 months after HSCT in 10 children with SCD. Three children had prior overt strokes, 5 children had prior silent strokes, and 1 child had abnormal transcranial Doppler ultrasound velocities. CBF and OEF of HSCT recipients were compared with non-SCD control participants and with SCD participants receiving chronic red blood cell transfusion therapy (CRTT) before and after a scheduled transfusion. Seven participants received matched sibling donor HSCT, and 3 participants received 8 out of 8 matched unrelated donor HSCT. All received reduced-intensity preparation and maintained engraftment, free of hemolytic anemia and SCD symptoms. Pre-transplant, CBF (93.5 mL/100 g/min) and OEF (36.8%) were elevated compared with non-SCD control participants, declining significantly 1 to 2 years after HSCT (CBF, 72.7 mL/100 g per minute; P = .004; OEF, 27.0%; P = .002), with post-HSCT CBF and OEF similar to non-SCD control participants. Furthermore, HSCT recipients demonstrated greater reduction in CBF (-19.4 mL/100 g/min) and OEF (-8.1%) after HSCT than children with SCD receiving CRTT after a scheduled transfusion (CBF, -0.9 mL/100 g/min; P = .024; OEF, -3.3%; P = .001). Curative HSCT normalizes whole-brain hemodynamics in children with SCD. This restoration of cerebral oxygen reserve may explain stroke protection after HSCT in this high-risk patient population.

The moderating effect of appearance on the impact of performance rankings in the live streaming market.

The live streaming market is becoming increasingly competitive, and relative performance information regarding broadcasters is available to all participants in the live streaming industry, exacerbating the level of competition. Using data from 42,166 live streams by 293 broadcasters, we investigated two benefits of relative performance information in relation to the live streaming business and how these effects vary when the broadcaster appears competent, trustworthy, likable, or attractive. On the basis of economic and social comparison theory, as well as insights from the herd behavior and beauty premium literature, we predicted and found that relative performance information can improve live streaming performance by either increasing broadcaster effort (the effort-eliciting effect) or encouraging viewer participation (the informational effect), with these effects being stronger when broadcasters look more competent, trustworthy, likable, or attractive. The findings of this study contribute to the live streaming literature by demonstrating that providing relative performance information in the live streaming business can yield both effort-eliciting and informational benefits.

Association of Data-Driven White Matter Hyperintensity Spatial Signatures With Distinct Cerebral Small Vessel Disease Etiologies.

OBJECTIVES: Topographical distribution of white matter hyperintensities (WMH) are hypothesized to vary by cerebrovascular risk factors. We used an unbiased pattern discovery approach to identify distinct WMH spatial patterns and investigate their association with different WMH etiologies. METHODS: We performed a cross-sectional study on participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify spatially distinct WMH distribution patterns using voxel-based spectral clustering analysis of aligned WMH probability maps. We included all participants from the ADNI Grand Opportunity/ADNI 2 study with available baseline 2D-FLAIR MRI scans, without prior history of stroke or presence of infarction on imaging. We evaluated the associations of these WMH spatial patterns with vascular risk factors, amyloid-ß PET, and imaging biomarkers of cerebral amyloid angiopathy (CAA), characterizing different forms of cerebral small vessel disease (CSVD) using multivariable regression. We also used linear regression models to investigate whether WMH spatial distribution influenced cognitive impairment. RESULTS: We analyzed MRI scans of 1,046 ADNI participants with mixed vascular and amyloid-related risk factors (mean age 72.9, 47.7% female, 31.4% hypertensive, 48.3% with abnormal amyloid PET). We observed unbiased partitioning of WMH into five unique spatial patterns: deep frontal, periventricular, juxtacortical, parietal, and posterior. Juxtacortical WMH were independently associated with probable CAA, deep frontal WMH were associated with risk factors for arteriolosclerosis (hypertension and diabetes), and parietal WMH were associated with brain amyloid accumulation, consistent with an Alzheimer's disease (AD) phenotype. Juxtacortical, deep frontal, and parietal WMH spatial patterns were associated with cognitive impairment. Periventricular and posterior WMH spatial patterns were unrelated to any disease phenotype or cognitive decline. DISCUSSION: Data-driven WMH spatial patterns reflect discrete underlying etiologies including arteriolosclerosis, CAA, AD, and normal aging. Global measures of WMH volume may miss important spatial distinctions. WMH spatial signatures may serve as etiology-specific imaging markers, helping to resolve WMH heterogeneity, identify the dominant underlying pathological process, and improve prediction of clinical-relevant trajectories that influence cognitive decline.

Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke.

Quantifying the extent and evolution of cerebral edema developing after stroke is an important but challenging goal. Lesional net water uptake (NWU) is a promising CT-based biomarker of edema, but its measurement requires manually delineating infarcted tissue and mirrored regions in the contralateral hemisphere. We implement an imaging pipeline capable of automatically segmenting the infarct region and calculating NWU from both baseline and follow-up CTs of large-vessel occlusion (LVO) patients. Infarct core is extracted from CT perfusion images using a deconvolution algorithm while infarcts on follow-up CTs were segmented from non-contrast CT (NCCT) using a deep-learning algorithm. These infarct masks were flipped along the brain midline to generate mirrored regions in the contralateral hemisphere of NCCT; NWU was calculated as one minus the ratio of densities between regions, removing voxels segmented as CSF and with HU outside thresholds of 20-80 (normal hemisphere and baseline CT) and 0-40 (infarct region on follow-up). Automated results were compared with those obtained using manually-drawn infarcts and an ASPECTS region-of-interest based method that samples densities within the infarct and normal hemisphere, using intraclass correlation coefficient (ρ). This was tested on serial CTs from 55 patients with anterior circulation LVO (including 66 follow-up CTs). Baseline NWU using automated core was 4.3% (IQR 2.6-7.3) and correlated with manual measurement (ρ = 0.80, p < 0.0001) and ASPECTS (r = -0.60, p = 0.0001). Automatically segmented infarct volumes (median 110-ml) correlated to manually-drawn volumes (ρ = 0.96, p < 0.0001) with median Dice similarity coefficient of 0.83 (IQR 0.72-0.90). Automated NWU was 24.6% (IQR 20-27) and highly correlated to NWU from manually-drawn infarcts (ρ = 0.98) and the sampling-based method (ρ = 0.68, both p < 0.0001). We conclude that this automated imaging pipeline is able to accurately quantify region of infarction and NWU from serial CTs and could be leveraged to study the evolution and impact of edema in large cohorts of stroke patients.

Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB).

PURPOSE: CT is routinely used to detect cranial abnormalities in pediatric patients with head trauma or craniosynostosis. This study aimed to develop a deep learning method to synthesize pseudo-CT (pCT) images for MR high-resolution pediatric cranial bone imaging to eliminating ionizing radiation from CT. METHODS: 3D golden-angle stack-of-stars MRI were obtained from 44 pediatric participants. Two patch-based residual UNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures/sutures, or air (NetBA) to synthesize pCT. A third residual UNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCTNetWH ) in the brain area and NetBA (pCTNetBA ) in the nonbrain area were combined to generate pCTCom . A manual processing method using inverted MR images was also employed for comparison. RESULTS: pCTCom (68.01 ± 14.83 HU) had significantly smaller mean absolute errors (MAEs) than pCTNetWH (82.58 ± 16.98 HU, P < 0.0001) and pCTNetBA (91.32 ± 17.2 HU, P < 0.0001) in the whole head. Within cranial bone, the MAE of pCTCom (227.92 ± 46.88 HU) was significantly lower than pCTNetWH (287.85 ± 59.46 HU, P < 0.0001) but similar to pCTNetBA (230.20 ± 46.17 HU). Dice similarity coefficient of the segmented bone was significantly higher in pCTCom (0.90 ± 0.02) than in pCTNetWH (0.86 ± 0.04, P < 0.0001), pCTNetBA (0.88 ± 0.03, P < 0.0001), and inverted MR (0.71 ± 0.09, P < 0.0001). Dice similarity coefficient from pCTCom demonstrated significantly reduced age dependence than inverted MRI. Furthermore, pCTCom provided excellent suture and fracture visibility comparable to CT. CONCLUSION: MR high-resolution pediatric cranial bone imaging may facilitate the clinical translation of a radiation-free MR cranial bone imaging method for pediatric patients.

Silent Infarcts, White Matter Integrity, and Oxygen Metabolic Stress in Young Adults With and Without Sickle Cell Trait.

BACKGROUND: Individuals with sickle cell anemia have heightened risk of stroke and cognitive dysfunction. Given its high prevalence globally, whether sickle cell trait (SCT) is a risk factor for neurological injury has been of interest; however, data have been limited. We hypothesized that young, healthy adults with SCT would show normal cerebrovascular structure and hemodynamic function. METHODS: As a case-control study, young adults with (N=25, cases) and without SCT (N=24, controls) underwent brain magnetic resonance imaging to quantify brain volume, microstructural integrity (fractional anisotropy), silent cerebral infarcts (SCI), intracranial stenosis, and aneurysms. Pseudocontinuous arterial spin labeling and asymmetric spin echo sequences measured cerebral blood flow and oxygen extraction fraction, respectively, from which cerebral metabolic oxygen demand was calculated. Imaging metrics were compared between SCT cases and controls. SCI volume was correlated with baseline characteristics. RESULTS: Compared with controls, adults with SCT demonstrated similar normalized brain volumes (SCT 0.80 versus control 0.81, P=0.41), white matter fractional anisotropy (SCT 0.41 versus control 0.43, P=0.37), cerebral blood flow (SCT 62.04 versus control, 61.16 mL/min/100 g, P=0.67), oxygen extraction fraction (SCT 0.27 versus control 0.27, P=0.31), and cerebral metabolic oxygen demand (SCT 2.71 versus control 2.70 mL/min/100 g, P=0.96). One per cohort had an intracranial aneurysm. None had intracranial stenosis. The SCT cases and controls showed similar prevalence and volume of SCIs; however, in the subset of participants with SCIs, the SCT cases had greater SCI volume versus controls (0.29 versus 0.07 mL, P=0.008). Of baseline characteristics, creatinine was mildly elevated in the SCT cohort (0.9 versus 0.8 mg/dL, P=0.053) and correlated with SCI volume (ρ=0.49, P=0.032). In the SCT cohort, SCI distribution was similar to that of young adults with sickle cell anemia. CONCLUSIONS: Adults with SCT showed normal cerebrovascular structure and hemodynamic function. These findings suggest that healthy individuals with SCT are unlikely to be at increased risk for early or accelerated ischemic brain injury.

Impact of national culture on the severity of the COVID-19 pandemic.

This study examines how cultural differences can affect the transmission of COVID-19 in different countries. From a sample of 92 countries, we used cross-country data based on Hofstede's cultural dimensions to investigate the impact of culture on COVID-19 transmission. We found a significant impact of culture on the spread of COVID-19. Specifically, this study reveals that individualism, masculinity, and uncertainty avoidance have a positive impact on confirmed COVID-19 cases. The relationships between cultural differences and the total number of COVID-19 deaths were also positive. This study provides valuable insights into the influences that national culture could have on the effectiveness of responses to a similar global pandemic situation in the future.

Cerebral oxygen metabolic stress is increased in children with sickle cell anemia compared to anemic controls.

Patients with sickle cell anemia (SCA) experience cerebral metabolic stress with an increase in oxygen extraction fraction (OEF) to compensate for reduced oxygen carrying capacity due to anemia. It remains unclear if anemia alone drives this metabolic stress. Using MRI, we collected voxel-wise OEF measurements to test our hypothesis that OEF would be elevated in anemic controls without SCA (AC) compared to healthy controls (HC), but OEF would be even higher in SCA compared to AC. Brain MRIs (N = 159) were obtained in 120 participants (34 HC, 27 AC, 59 SCA). While hemoglobin was lower in AC versus HC (p < 0.001), hemoglobin was not different between AC and SCA cohorts (p = 0.459). Whole brain OEF was higher in AC compared to HC (p < 0.001), but lower compared to SCA (p = 0.001). Whole brain OEF remained significantly higher in SCA compared to HC (p = 0.001) while there was no longer a difference between AC versus HC (p = 0.935) in a multivariate model controlling for age and hemoglobin. OEF peaked within the border zone regions of the brain in both SCA and AC cohorts, but the volume of white matter with regionally elevated OEF in AC was smaller (1.8%) than SCA (58.0%). While infarcts colocalized within regions of elevated OEF, more SCA participants had infarcts than AC (p < 0.001). We conclude that children with SCA experience elevated OEF compared to AC and HC after controlling for the impact of anemia, suggesting that there are other pathophysiologic factors besides anemia contributing to cerebral metabolic stress in children with SCA.

Accelerating Prediction of Malignant Cerebral Edema After Ischemic Stroke with Automated Image Analysis and Explainable Neural Networks.

BACKGROUND: Malignant cerebral edema is a devastating complication of stroke, resulting in deterioration and death if hemicraniectomy is not performed prior to herniation. Current approaches for predicting this relatively rare complication often require advanced imaging and still suffer from suboptimal performance. We performed a pilot study to evaluate whether neural networks incorporating data extracted from routine computed tomography (CT) imaging could enhance prediction of edema in a large diverse stroke cohort. METHODS: An automated imaging pipeline retrospectively extracted volumetric data, including cerebrospinal fluid (CSF) volumes and the hemispheric CSF volume ratio, from baseline and 24 h CT scans performed in participants of an international stroke cohort study. Fully connected and long short-term memory (LSTM) neural networks were trained using serial clinical and imaging data to predict those who would require hemicraniectomy or die with midline shift. The performance of these models was tested, in comparison with regression models and the Enhanced Detection of Edema in Malignant Anterior Circulation Stroke (EDEMA) score, using cross-validation to construct precision-recall curves. RESULTS: Twenty of 598 patients developed malignant edema (12 required surgery, 8 died). The regression model provided 95% recall but only 32% precision (area under the precision-recall curve [AUPRC] 0.74), similar to the EDEMA score (precision 28%, AUPRC 0.66). The fully connected network did not perform better (precision 33%, AUPRC 0.71), but the LSTM model provided 100% recall and 87% precision (AUPRC 0.97) in the overall cohort and the subgroup with a National Institutes of Health Stroke Scale (NIHSS) score ≥ 8 (p = 0.0001 vs. regression and fully connected models). Features providing the most predictive importance were the hemispheric CSF ratio and NIHSS score measured at 24 h. CONCLUSIONS: An LSTM neural network incorporating volumetric data extracted from routine CT scans identified all cases of malignant cerebral edema by 24 h after stroke, with significantly fewer false positives than a fully connected neural network, regression model, and the validated EDEMA score. This preliminary work requires prospective validation but provides proof of principle that a deep learning framework could assist in selecting patients for surgery prior to deterioration.

Oxygen Metabolic Stress and White Matter Injury in Patients With Cerebral Small Vessel Disease.

BACKGROUND: Chronic hypoxia-ischemia is a putative mechanism underlying the development of white matter hyperintensities (WMH) and microstructural disruption in cerebral small vessel disease. WMH fall primarily within deep white matter (WM) watershed regions. We hypothesized that elevated oxygen extraction fraction (OEF), a signature of hypoxia-ischemia, would be detected in the watershed where WMH density is highest. We further hypothesized that OEF would be elevated in regions immediately surrounding WMH, at the leading edge of growth. METHODS: In this cross-sectional study conducted from 2016 to 2019 at an academic medical center in St Louis, MO, participants (age >50) with a range of cerebrovascular risk factors underwent brain magnetic resonance imaging using pseudocontinuous arterial spin labeling, asymmetric spin echo, fluid-attenuated inversion recovery and diffusion tensor imaging to measure cerebral blood flow (CBF), OEF, WMH, and WM integrity, respectively. We defined the physiologic watershed as a region where CBF was below the 10th percentile of mean WM CBF in a young healthy cohort. We conducted linear regression to evaluate the relationship between CBF and OEF with structural and microstructural WM injury defined by fluid-attenuated inversion recovery WMH and diffusion tensor imaging, respectively. We conducted ANOVA to determine if OEF was increased in proximity to WMH lesions. RESULTS: In a cohort of 42 participants (age 50-80), the physiologic watershed region spatially overlapped with regions of highest WMH lesion density. As CBF decreased and OEF increased, WMH density increased. Elevated watershed OEF was associated with greater WMH burden and microstructural disruption, after adjusting for vascular risk factors. In contrast, WM and watershed CBF were not associated with WMH burden or microstructural disruption. Moreover, OEF progressively increased while CBF decreased, in concentric contours approaching WMH lesions. CONCLUSIONS: Chronic hypoxia-ischemia in the watershed region may contribute to cerebral small vessel disease pathogenesis and development of WMH. Watershed OEF may hold promise as an imaging biomarker to identify individuals at risk for cerebral small vessel disease progression.

The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research.

Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University's clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes.