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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND AND PURPOSE: Early brain injury may be a more significant contributor to poor outcome after aneurysmal subarachnoid hemorrhage (aSAH) than vasospasm and delayed cerebral ischemia. However, studying this process has been hampered by lack of a means of quantifying the spectrum of injury. Global cerebral edema (GCE) is the most widely accepted manifestation of early brain injury but is currently assessed only through subjective, qualitative or semi-quantitative means. Selective sulcal volume (SSV), the CSF volume above the lateral ventricles, has been proposed as a quantitative biomarker of GCE, but is time-consuming to measure manually. Here we implement an automated algorithm to extract SSV and evaluate the age-dependent relationship of reduced SSV on early outcomes after aSAH. METHODS: We selected all adults with aSAH admitted to a single institution with imaging within 72 hours of ictus. Scans were assessed for qualitative presence of GCE. SSV was automatically segmented from serial CTs using a deep learning-based approach. Early SSV was the lowest SSV from all early scans. Modified Rankin Scale score of 4 to 6 at hospital discharge was classified as a poor outcome. RESULTS: Two hundred forty-four patients with aSAH were included. Sixty-five (27%) had GCE on admission while 24 developed it subsequently within 72 hours. Median SSV on admission was 10.7 mL but frequently decreased, with minimum early SSV being 3.0 mL (interquartile range, 0.3-11.9). Early SSV below 5 mL was highly predictive of qualitative GCE (area under receiver-operating-characteristic curve, 0.90). Reduced early SSV was an independent predictor of poor outcome, with a stronger effect in younger patients. CONCLUSIONS: Automated assessment of SSV provides an objective biomarker of GCE that can be leveraged to quantify early brain injury and dissect its impact on outcomes after aSAH. Such quantitative analysis suggests that GCE may be more impactful to younger patients with SAH.

Bulk volume susceptibility difference between deoxyhemoglobin and oxyhemoglobin for HbA and HbS: A comparative study.

PURPOSE: Sickle cell anemia is a blood disorder that alters the morphology and the oxygen affinity of the red blood cells. Cerebral oxygen extraction fraction measurements using quantitative BOLD contrast have been used for assessing inadequate oxygen delivery and the subsequent risk of ischemic stroke in sickle cell anemia. The BOLD signal in MRI studies relies on Δχdo , the bulk volume susceptibility difference between fully oxygenated and fully deoxygenated blood. Several studies have measured Δχdo for normal hemoglobin A (HbA). However, it is not known whether the value is different for sickle hemoglobin. In this study, Δχdo was measured for both HbA and sickle hemoglobin. METHODS: Six sickle cell anemia patients and 6 controls were recruited. Various blood oxygenation levels were achieved through in vivo manipulations to keep the blood close to its natural state. To account for the differences in oxygen affinity, Hill's equations were used to translate partial pressure of oxygen to oxygen saturation for HbA, sickle hemoglobin, and fetal hemoglobin (HbF) separately. The pH and PCO2 corrections were performed. Temperature and magnetic field drift were controlled for. A multivariate generalized linear mixed model with random participant effect was used. RESULTS: Assuming that Δχdo is similar for HbA and HbF and that ΔχmetHb is 5/4 of Δχdo for HbA, it was found that the Δχdo values for HbA and sickle hemoglobin were not statistically significantly different from each other. CONCLUSION: The same Δχdo value can be used for both types of hemoglobin in quantitative BOLD analysis.

Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.

PURPOSE: The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R1 , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T1 -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R1 for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging. METHODS: DL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE R2∗ , or Dixon, or T1 -MPRAGE as input. With images from 23 additional participants repeatedly scanned, the test-retest differences and within-subject coefficient of variation of standardized uptake value ratios (SUVR) were compared between PET images reconstructed using either DL-TESLA or CT for AC. RESULTS: DL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC. CONCLUSION: DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.

Functional Connectivity Decreases with Metabolic Stress in Sickle Cell Disease.

OBJECTIVE: Children with sickle cell disease (SCD) experience cognitive deficits even when unaffected by stroke. Using functional connectivity magnetic resonance imaging (MRI) as a potential biomarker of cognitive function, we tested our hypothesis that children with SCD would have decreased functional connectivity, and that children experiencing the greatest metabolic stress, indicated by elevated oxygen extraction fraction, would have the lowest connectivity. METHODS: We prospectively obtained brain MRIs and cognitive testing in healthy controls and children with SCD. RESULTS: We analyzed data from 60 participants (20 controls and 40 with sickle cell disease). There was no difference in global cognition or cognitive subdomains between cohorts. However, we found decreased functional connectivity within the sensory-motor, lateral sensory-motor, auditory, salience, and subcortical networks in participants with SCD compared with controls. Further, as white matter oxygen extraction fraction increased, connectivity within the visual (p = 0.008, parameter estimate = -0.760 [95% CI = -1.297, -0.224]), default mode (p = 0.012, parameter estimate = -0.417 [95% CI = -0.731, -0.104]), and cingulo-opercular (p = 0.009, parameter estimate = -0.883 [95% CI = -1.517, -0.250]) networks decreased. INTERPRETATION: We conclude that there is diminished functional connectivity within these anatomically contiguous networks in children with SCD compared with controls, even when differences are not seen with cognitive testing. Increased white matter oxygen extraction fraction was associated with decreased connectivity in select networks. These data suggest that elevated oxygen extraction fraction and disrupted functional connectivity are potentially presymptomatic neuroimaging biomarkers for cognitive decline in SCD. ANN NEUROL 2020;88:995-1008.

Hydroxyurea reduces cerebral metabolic stress in patients with sickle cell anemia.

Chronic transfusion therapy (CTT) prevents stroke in selected patients with sickle cell anemia (SCA). We have shown that CTT mitigates signatures of cerebral metabolic stress, reflected by elevated oxygen extraction fraction (OEF), which likely drives stroke risk reduction. The region of highest OEF falls within the border zone, where cerebral blood flow (CBF) nadirs; OEF in this region was reduced after CTT. The neuroprotective efficacy of hydroxyurea (HU) remains unclear. To test our hypothesis that patients receiving HU therapy have lower cerebral metabolic stress compared with patients not receiving disease-modifying therapy, we prospectively obtained brain magnetic resonance imaging scans with voxel-wise measurements of CBF and OEF in 84 participants with SCA who were grouped by therapy: no disease-modifying therapy, HU, or CTT. There was no difference in whole-brain CBF among the 3 cohorts (P = .148). However, whole-brain OEF was significantly different (P < .001): participants without disease-modifying therapy had the highest OEF (median 42.9% [interquartile range (IQR) 39.1%-49.1%]), followed by HU treatment (median 40.7% [IQR 34.9%-43.6%]), whereas CTT treatment had the lowest values (median 35.3% [IQR 32.2%-38.9%]). Moreover, the percentage of white matter at highest risk for ischemia, defined by OEF greater than 40% and 42.5%, was lower in the HU cohort compared with the untreated cohort (P = .025 and P = .034 respectively), but higher compared with the CTT cohort (P = .018 and P = .029 respectively). We conclude that HU may offer neuroprotection by mitigating cerebral metabolic stress in patients with SCA, but not to the same degree as CTT.

Reduction in Cerebrospinal Fluid Volume as an Early Quantitative Biomarker of Cerebral Edema After Ischemic Stroke.

Background and Purpose- Cerebral edema (CED) develops in the hours to days after stroke; the resulting increase in brain volume may lead to midline shift (MLS) and neurological deterioration. The time course and implications of edema formation are not well characterized across the spectrum of stroke. We analyzed displacement of cerebrospinal fluid (ΔCSF) as a dynamic quantitative imaging biomarker of edema formation. Methods- We selected subjects enrolled in a stroke cohort study who presented within 6 hours of onset and had baseline and ≥1 follow-up brain computed tomography scans available. We applied a neural network-based algorithm to quantify hemispheric CSF volume at each imaging time point and modeled CSF trajectory over time (using a piecewise linear mixed-effects model). We evaluated ΔCSF within the first 24 hours as an early biomarker of CED (defined as developing MLS on computed tomography beyond 24 hours) and poor outcome (modified Rankin Scale score, 3-6). Results- We had serial imaging in 738 subjects with stroke, of whom 91 (13%) developed CED with MLS. Age did not differ (69 versus 70 years), but baseline National Institutes of Health Stroke Scale was higher (16 versus 7) and baseline CSF volume lower (132 versus 161 mL, both P<0.001) in those with CED. ΔCSF was faster in those developing MLS, with the majority seen by 24 hours (36% versus 11% or 2.4 versus 0.8 mL/h; P<0.0001). Risk of CED almost doubled for every 10% ΔCSF within 24 hours (odds ratio, 1.76 [95% CI, 1.46-2.14]), adjusting for age, glucose, and National Institutes of Health Stroke Scale. Risk of neurological deterioration (1.6-point increase in National Institutes of Health Stroke Scale at 24 hours) and poor outcome (adjusted odds ratio, 1.34 [95% CI, 1.15-1.56]) was also greater for every 10% increase in ΔCSF. Conclusions- CSF volumetrics provides quantitative evaluation of early edema formation. ΔCSF from baseline to 24-hour computed tomography is a promising early biomarker for the development of MLS and worse neurological outcome.

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods- Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results- Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8-43) for hemorrhage and 12 mL (interquartile range, 5-30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85-0.93) and 0.54 (0.39-0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions- We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.

Red cell exchange transfusions lower cerebral blood flow and oxygen extraction fraction in pediatric sickle cell anemia.

Blood transfusions are the mainstay of stroke prevention in pediatric sickle cell anemia (SCA), but the physiology conferring this benefit is unclear. Cerebral blood flow (CBF) and oxygen extraction fraction (OEF) are elevated in SCA, likely compensating for reduced arterial oxygen content (CaO2). We hypothesized that exchange transfusions would decrease CBF and OEF by increasing CaO2, thereby relieving cerebral oxygen metabolic stress. Twenty-one children with SCA receiving chronic transfusion therapy (CTT) underwent magnetic resonance imaging before and after exchange transfusions. Arterial spin labeling and asymmetric spin echo sequences measured CBF and OEF, respectively, which were compared pre- and posttransfusion. Volumes of tissue with OEF above successive thresholds (36%, 38%, and 40%), as a metric of regional metabolic stress, were compared pre- and posttransfusion. Transfusions increased hemoglobin (Hb; from 9.1 to 10.3 g/dL; P < .001) and decreased Hb S (from 39.7% to 24.3%; P < .001). Transfusions reduced CBF (from 88 to 82.4 mL/100 g per minute; P = .004) and OEF (from 34.4% to 31.2%; P < .001). At all thresholds, transfusions reduced the volume of peak OEF found in the deep white matter, a location at high infarct risk in SCA (P < .001). Reduction of elevated CBF and OEF, both globally and regionally, suggests that CTT mitigates infarct risk in pediatric SCA by relieving cerebral metabolic stress at patient- and tissue-specific levels.

Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke.

INTRODUCTION: Malignant cerebral edema develops in a small subset of patients with hemispheric strokes, precipitating deterioration and death if decompressive hemicraniectomy (DHC) is not performed in a timely manner. Predicting which stroke patients will develop malignant edema is imprecise based on clinical data alone. Head computed tomography (CT) imaging is often performed at baseline and 24-h. We determined the incremental value of incorporating imaging-derived features from serial CTs to enhance prediction of malignant edema. METHODS: We identified hemispheric stroke patients at three sites with NIHSS ≥ 7 who had baseline as well as 24-h clinical and CT imaging data. We extracted quantitative imaging features from baseline and follow-up CTs, including CSF volume, intracranial reserve (CSF/cranial volume), as well as midline shift (MLS) and infarct-related hypodensity volume. Potentially lethal malignant edema was defined as requiring DHC or dying with MLS over 5-mm. We built machine-learning models using logistic regression first with baseline data and then adding 24-h data including reduction in CSF volume (ΔCSF). Model performance was evaluated with cross-validation using metrics of recall (sensitivity), precision (predictive value), as well as area under receiver-operating-characteristic and precision-recall curves (AUROC, AUPRC). RESULTS: Twenty of 361 patients (6%) died or underwent DHC. Baseline clinical variables alone had recall of 60% with low precision (7%), AUROC 0.59, AUPRC 0.15. Adding baseline intracranial reserve improved recall to 80% and AUROC to 0.82 but precision remained only 16% (AUPRC 0.28). Incorporating ΔCSF improved AUPRC to 0.53 (AUROC 0.91) while all imaging features further improved prediction (recall 90%, precision 38%, AUROC 0.96, AUPRC 0.66). CONCLUSION: Incorporating quantitative CT-based imaging features from baseline and 24-h CT enhances identification of patients with malignant edema needing DHC. Further refinements and external validation of such imaging-based machine-learning models are required.