Intracranial aneurysms in sickle cell disease are associated with hemodynamic stress and anemia.

While hemodynamic stress plays a key role in aneurysm formation outside of SCD, its role is understudied in patients with SCD. We hypothesized that tissue-based markers of hemodynamic stress are associated with aneurysm presence in a prospective SCD cohort. Children and adults with SCD, with and without aneurysms, underwent longitudinal brain MRI/MRA to assess cerebral blood flow (CBF) and oxygen extraction fraction (OEF). Baseline characteristics were recorded. In the subgroup of adults, stepwise mixed-effect logistic regression examined clinical variables, CBF, and OEF as predictors of aneurysm presence. Cumulative rates of new aneurysm formation were estimated using Kaplan-Meier analyses. Forty-three aneurysms were found in 27 of 155 patients (17%). Most aneurysms were ≤ 3 mm and in the intracranial internal carotid artery. On univariate analysis, older age (p=0.07), lower hemoglobin (p=0.002), higher CBF (p=0.03), and higher OEF (p=0.02) were associated with aneurysm presence. On multivariable analysis, age and CBF remained independently associated with aneurysm presence. Seventy-six patients (49% of enrollment) received follow-up MRAs (median 3.5 years). No aneurysm grew or ruptured, however, seven new aneurysms developed in six patients. The three-year cumulative rate of aneurysm formation was 3.5%. In 155 patients with SCD, 17% had intracranial aneurysms. Three-year aneurysm formation rate was 3.5%, although limited by small longitudinal sample size and short follow-up duration. Aneurysm presence was associated with elevated CBF in adults, as a tissue-based marker of cerebral hemodynamic stress. Future studies may examine the predictive role of CBF in aneurysm development in SCD.

Evaluating the interaction between hemorrhagic transformation and cerebral edema on functional outcome after ischemic stroke.

BACKGROUND: Hemorrhagic transformation (HT) and cerebral edema (CED) are both major complications following ischemic stroke, but few studies have evaluated their overlap. We evaluated the frequency and predictors of CED/HT overlap and whether their co-occurrence impacts functional outcome more than each in isolation. METHODS: 892 stroke patients enrolled in a prospective study had follow-up CT imaging evaluated for HT and CED; the latter was quantified using the ratio of hemispheric CSF volumes (with hemispheric CSF ratio < 0.90 used as the CED threshold). The interaction between HT and CED on functional outcome (using modified Rankin Scale at 3 months) was compared to that for each condition separately. RESULTS: Among the 275 (31%) who developed HT, 233 (85%) manifested hemispheric CSF ratio < 0.9 (CED/HT), with this overlap group representing half of the 475 with measurable CED. Higher baseline NIHSS scores and larger infarct volumes were observed in the CED/HT group compared with those with CED or HT alone. Functional outcome was worse in those with CED/HT [median mRS 3 (IQR 2-5)] than those with CED [median 2 (IQR 1-4)] or HT alone [median 1 (IQR 0-2), p < 0.0001]. Overlap of CED/HT independently predicted worse outcome [OR 1.89 (95% CI: 1.12-3.18), p = 0.02] while HT did not; however, CED/HT was no longer associated with worse outcome after adjusting for severity of CED [adjusted OR 0.35 (95% CI: 0.23, 0.51) per 0.21 lower hemispheric CSF ratio, p < 0.001]. CONCLUSIONS: Most stroke patients with HT also have measurable CED. The co-occurrence of CED and HT occurs in larger and more severe strokes and is associated with worse functional outcome, although this is driven by greater severity of stroke-related edema in those with HT.

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. NEW METHOD: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. RESULTS: Sleep states were classified with an accuracy of 84 % and Cohen's κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. COMPARISON WITH EXISTING METHOD: On a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.

This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.

Effects of Amyloid Beta (Aß) Oligomers on Blood-Brain Barrier Using a 3D Microfluidic Vasculature-on-a-Chip Model.

The disruption of the blood-brain barrier (BBB) in Alzheimer's Disease (AD) is largely influenced by amyloid beta (Aß). In this study, we developed a high-throughput microfluidic BBB model devoid of a physical membrane, featuring endothelial cells interacting with an extracellular matrix (ECM). This paper focuses on the impact of varying concentrations of Aß1-42 oligomers on BBB dysfunction by treating them in the luminal. Our findings reveal a pronounced accumulation of Aß1-42 oligomers at the BBB, resulting in the disruption of tight junctions and subsequent leakage evidenced by a barrier integrity assay. Additionally, cytotoxicity assessments indicate a concentration-dependent increase in cell death in response to Aß1-42 oligomers (LC50 ~ 1 µM). This study underscores the utility of our membrane-free vascular chip in elucidating the dysfunction induced by Aß with respect to the BBB.

Reversibility of Cognitive Deficits and Functional Connectivity With Transfusion in Children With Sickle Cell Disease.

BACKGROUND AND OBJECTIVES: People with sickle cell disease (SCD) are at risk of cognitive dysfunction independent of stroke. Diminished functional connectivity in select large-scale networks and white matter integrity reflect the neurologic consequences of SCD. Because chronic transfusion therapy is neuroprotective in preventing stroke and strengthening executive function abilities in people with SCD, we hypothesized that red blood cell (RBC) transfusion facilitates the acute reversal of disruptions in functional connectivity while white matter integrity remains unaffected. METHODS: Children with SCD receiving chronic transfusion therapy underwent a brain MRI measuring white matter integrity with diffusion tensor imaging and resting-state functional connectivity within 3 days before and after transfusion of RBCs. Cognitive assessments with the NIH Toolbox were acquired after transfusion and then immediately before the following transfusion cycle. RESULTS: Sixteen children with a median age of 12.5 years were included. Global assessments of functional connectivity using homotopy (p = 0.234) or modularity (p = 0.796) did not differ with transfusion. Functional connectivity within the frontoparietal network significantly strengthened after transfusion (median intranetwork Z-score 0.21 [0.17-0.30] before transfusion, 0.29 [0.20-0.36] after transfusion, p < 0.001), while there was not a significant change seen within the sensory motor, visual, auditory, default mode, dorsal attention, or cingulo-opercular networks. Corresponding to the change within the frontoparietal network, there was a significant improvement in executive function abilities after transfusion (median executive function composite score 87.7 [81.3-90.7] before transfusion, 90.3 [84.3-93.7] after transfusion, p = 0.021). Participants with stronger connectivity in the frontoparietal network before transfusion had a significantly greater improvement in the executive function composite score with transfusion (r = 0.565, 95% CI 0.020-0.851, p = 0.044). While functional connectivity and executive abilities strengthened with transfusion, there was not a significant change in white matter integrity as assessed by fractional anisotropy and mean diffusivity within 16 white matter tracts or globally with tract-based spatial statistics. DISCUSSION: Strengthening of functional connectivity with concomitant improvement in executive function abilities with transfusion suggests that functional connectivity MRI could be used as a biomarker for acutely reversible neurocognitive injury as novel therapeutics are developed for people with SCD.

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.

Chimeric antigen receptor macrophages target and resorb amyloid plaques.

Substantial evidence suggests a role for immunotherapy in treating Alzheimer's disease (AD). While the precise pathophysiology of AD is incompletely understood, clinical trials of antibodies targeting aggregated forms of ß amyloid (Aß) have shown that reducing amyloid plaques can mitigate cognitive decline in patients with early-stage AD. Here, we describe what we believe to be a novel approach to target and degrade amyloid plaques by genetically engineering macrophages to express an Aß-targeting chimeric antigen receptor (CAR-Ms). When injected intrahippocampally, first-generation CAR-Ms have limited persistence and fail to significantly reduce plaque load, which led us to engineer next-generation CAR-Ms that secrete M-CSF and self-maintain without exogenous cytokines. Cytokine secreting "reinforced CAR-Ms" have greater survival in the brain niche and significantly reduce plaque load locally in vivo. These findings support CAR-Ms as a platform to rationally target, resorb, and degrade pathogenic material that accumulates with age, as exemplified by targeting Aß in AD.

Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data.

BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. NEW METHOD: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. RESULTS: Sleep states were classified with an accuracy of 84% and Cohen's kappa of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. COMPARISON WITH EXISTING METHOD: On a 3-hour WFCI recording, the CNN-BiLSTM achieved a kappa of 0.67, comparable to a kappa of 0.65 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep.

Intraindividual variability in post-stroke cognition and its relationship with activities of daily living and social functioning: an ecological momentary assessment approach.

INTRODUCTION: Ecological momentary assessment (EMA) is a methodological approach to studying intraindividual variation over time. This study aimed to use EMA to determine the variability of cognition in individuals with chronic stroke, identify the latent classes of cognitive variability, and examine any differences in daily activities, social functioning, and neuropsychological performance between these latent classes. METHODS: Participants (N = 202) with mild-to-moderate stroke and over 3-month post-stroke completed a study protocol, including smartphone-based EMA and two lab visits. Participants responded to five EMA surveys daily for 14 days to assess cognition. They completed patient-reported measures and neuropsychological assessments during lab visits. Using latent class analysis, we derived four indicators to quantify cognitive variability and identified latent classes among participants. We used ANOVA and Chi-square to test differences between these latent classes in daily activities, social functioning, and neuropsychological performance. RESULTS: The latent class analysis converged on a three-class model. The moderate and high variability classes demonstrated significantly greater problems in daily activities and social functioning than the low class. They had significantly higher proportions of participants with problems in daily activities and social functioning than the low class. Neuropsychological performance was not statistically different between the three classes, although a trend approaching statistically significant difference was observed in working memory and executive function domains. DISCUSSION: EMA could capture intraindividual cognitive variability in stroke survivors. It offers a new approach to understanding the impact and mechanism of post-stroke cognitive problems in daily life and identifying individuals benefiting from self-regulation interventions.

Impact of Sleep Disorders and Disturbed Sleep on Brain Health: A Scientific Statement From the American Heart Association.

Accumulating evidence supports a link between sleep disorders, disturbed sleep, and adverse brain health, ranging from stroke to subclinical cerebrovascular disease to cognitive outcomes, including the development of Alzheimer disease and Alzheimer disease-related dementias. Sleep disorders such as sleep-disordered breathing (eg, obstructive sleep apnea), and other sleep disturbances, as well, some of which are also considered sleep disorders (eg, insomnia, sleep fragmentation, circadian rhythm disorders, and extreme sleep duration), have been associated with adverse brain health. Understanding the causal role of sleep disorders and disturbances in the development of adverse brain health is complicated by the common development of sleep disorders among individuals with neurodegenerative disease. In addition to the role of sleep disorders in stroke and cerebrovascular injury, mechanistic hypotheses linking sleep with brain health and biomarker data (blood-based, cerebrospinal fluid-based, and imaging) suggest direct links to Alzheimer disease-specific pathology. These potential mechanisms and the increasing understanding of the "glymphatic system," and the recognition of the importance of sleep in poststroke recovery, as well, support a biological basis for the indirect (through the worsening of vascular disease) and direct (through specific effects on neuropathology) connections between sleep disorders and brain health. Given promising evidence for the benefits of treatment and prevention, sleep disorders and disturbances represent potential targets for early treatment that may improve brain health more broadly. In this scientific statement, we discuss the evidence supporting an association between sleep disorders and disturbances and poor brain health ranging from stroke to dementia and opportunities for prevention and early treatment.

Analysis of brain edema in RHAPSODY.

BACKGROUND: Cerebral edema is a secondary complication of acute ischemic stroke, but its time course and imaging markers are not fully understood. Recently, net water uptake (NWU) has been proposed as a novel marker of edema. AIMS: Studying the RHAPSODY trial cohort, we sought to characterize the time course of edema and test the hypothesis that NWU provides distinct information when added to traditional markers of cerebral edema after stroke by examining its association with other markers. METHODS: A total of 65 patients had measurable supratentorial ischemic lesions. Patients underwent head computed tomography (CT), brain magnetic resonance imaging (MRI) scans, or both at the baseline visit and after 2, 7, 30, and 90 days following enrollment. CT and MRI scans were used to measure four imaging markers of edema: midline shift (MLS), hemisphere volume ratio (HVR), cerebrospinal fluid (CSF) volume, and NWU using semi-quantitative threshold analysis. Trajectories of the markers were summarized, as available. Correlations of the markers of edema were computed and the markers compared by clinical outcome. Regression models were used to examine the effect of 3K3A-activated protein C (APC) treatment. RESULTS: Two measures of mass effect, MLS and HVR, could be measured on all imaging modalities, and had values available across all time points. Accordingly, mass effect reached a maximum level by day 7, normalized by day 30, and then reversed by day 90 for both measures. In the first 2 days after stroke, the change in CSF volume was associated with MLS (ρ = -0.57, p = 0.0001) and HVR (ρ = -0.66, p < 0.0001). In contrast, the change in NWU was not associated with the other imaging markers (all p ⩾ 0.49). While being directionally consistent, we did not observe a difference in the edema markers by clinical outcome. In addition, baseline stroke volume was associated with all markers (MLS (p < 0.001), HVR (p < 0.001), change in CSF volume (p = 0.003)) with the exception of NWU (p = 0.5). Exploratory analysis did not reveal a difference in cerebral edema markers by treatment arm. CONCLUSIONS: Existing cerebral edema imaging markers potentially describe two distinct processes, including lesional water concentration (i.e. NWU) and mass effect (MLS, HVR, and CSF volume). These two types of imaging markers may represent distinct aspects of cerebral edema, which could be useful for future trials targeting this process.

Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study.

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

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.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis.

OBJECTIVE: To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs. DESIGN: An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition. SETTING: Home and Community. PARTICIPANTS: 202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke). INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances. RESULTS: Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition. CONCLUSIONS: The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

Harmonic components of photoplethysmography and pathological patterns: A cross-sectional study.

This study aimed to examine whether the 3 harmonic components (HCs) of photoplethysmography (PTG) - total harmonic distortion (THD), harmonic power (HP), and normalized harmonic amplitude (HA) - have aging effects and may serve as an arterial stiffness marker and examine the relationship between HCs and clinical severity of pathological patterns. This study had a retrospective chart review design, and electronic medical records of 173 female patients (age: 38.57 ± 11.64 years) were reviewed. Patients were asked to complete the phlegm, blood stasis (BS), and food retention (FR) pattern questionnaires and underwent PTG and the second derivative of PTG measurements. THD, HP, and HA data were extracted till the 12th HCs from the raw PTG data. THD and HA had an aging effect (ß: -0.179 to -0.278) and were related to b/a (r: -02.76 to -0.455) and d/a (r: 0.265-0.360) of the second derivative of PTG. In the younger group (≤33 years), HP and HA were positively correlated with phlegm, BS, and FR patterns (r: 0.257-0.370), while HP was positively correlated with the FR pattern (r: 0.278-0.315) in the middle age group (34-45 years). In the older group (≥46 years), HP and HA were positively or negatively correlated with the phlegm pattern (r: ±0.263 to ±0.440). HCs may serve as an arterial stiffness marker, and may be partially related to phlegm, BS, and FR patterns. Aging effect needs to be considered when utilizing HCs as an indicator of phlegm, BS, and FR patterns.

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.

Chimeric Antigen Receptor Macrophages Target and Resorb Amyloid Plaques in a Mouse Model of Alzheimer's Disease.

Substantial evidence suggests a role for immunotherapy in treating Alzheimer's disease (AD). Several monoclonal antibodies targeting aggregated forms of beta amyloid (Aß), have been shown to reduce amyloid plaques and in some cases, mitigate cognitive decline in early-stage AD patients. We sought to determine if genetically engineered macrophages could improve the targeting and degradation of amyloid plaques. Chimeric antigen receptor macrophages (CAR-Ms), which show promise as a cancer treatment, are an appealing strategy to enhance target recognition and phagocytosis of amyloid plaques in AD. We genetically engineered macrophages to express a CAR containing the anti-amyloid antibody aducanumab as the external domain and the Fc receptor signaling domain internally. CAR-Ms recognize and degrade Aß in vitro and on APP/PS1 brain slices ex vivo; however, when injected intrahippocampally, these first-generation CAR-Ms have limited persistence and fail to reduce plaque load. We overcame this limitation by creating CAR-Ms that secrete M-CSF and self-maintain without exogenous cytokines. These CAR-Ms have greater survival in the brain niche, and significantly reduce plaque load locally in vivo. These proof-of-principle studies demonstrate that CAR-Ms, previously only applied to cancer, may be utilized to target and degrade unwanted materials, such as amyloid plaques in the brains of AD mice.

A Critical Role for ERO1α in Arterial Thrombosis and Ischemic Stroke.

BACKGROUND: Platelet adhesion and aggregation play a crucial role in arterial thrombosis and ischemic stroke. Here, we identify platelet ERO1α (endoplasmic reticulum oxidoreductase 1α) as a novel regulator of Ca2+ signaling and a potential pharmacological target for treating thrombotic diseases. METHODS: Intravital microscopy, animal disease models, and a wide range of cell biological studies were utilized to demonstrate the pathophysiological role of ERO1α in arteriolar and arterial thrombosis and to prove the importance of platelet ERO1α in platelet activation and aggregation. Mass spectrometry, electron microscopy, and biochemical studies were used to investigate the molecular mechanism. We used novel blocking antibodies and small-molecule inhibitors to study whether ERO1α can be targeted to attenuate thrombotic conditions. RESULTS: Megakaryocyte-specific or global deletion of Ero1α in mice similarly reduced platelet thrombus formation in arteriolar and arterial thrombosis without affecting tail bleeding times and blood loss following vascular injury. We observed that platelet ERO1α localized exclusively in the dense tubular system and promoted Ca2+ mobilization, platelet activation, and aggregation. Platelet ERO1α directly interacted with STIM1 (stromal interaction molecule 1) and SERCA2 (sarco/endoplasmic reticulum Ca2+-ATPase 2) and regulated their functions. Such interactions were impaired in mutant STIM1-Cys49/56Ser and mutant SERCA2-Cys875/887Ser. We found that ERO1α modified an allosteric Cys49-Cys56 disulfide bond in STIM1 and a Cys875-Cys887 disulfide bond in SERCA2, contributing to Ca2+ store content and increasing cytosolic Ca2+ levels during platelet activation. Inhibition of Ero1α with small-molecule inhibitors but not blocking antibodies attenuated arteriolar and arterial thrombosis and reduced infarct volume following focal brain ischemia in mice. CONCLUSIONS: Our results suggest that ERO1α acts as a thiol oxidase for Ca2+ signaling molecules, STIM1 and SERCA2, and enhances cytosolic Ca2+ levels, promoting platelet activation and aggregation. Our study provides evidence that ERO1α may be a potential target to reduce thrombotic events.