The Effect of Mild Traumatic Brain Injury on Cerebral Microbleeds in Aging

A traumatic brain injury (TBI) induces the formation of cerebral microbleeds (CMBs), which are associated with cognitive impairments, psychiatric disorders, and gait dysfunctions in patients. Elderly people frequently suffer TBIs, especially mild brain trauma (mTBI). Interestingly, aging is also an independent risk factor for the development of CMBs. However, how TBI and aging may interact to promote the development of CMBs is not well established. In order to test the hypothesis that an mTBI exacerbates the development of CMBs in the elderly, we compared the number and cerebral distribution of CMBs and assessed them by analysing susceptibility weighted (SW) MRI in young (25 ± 10 years old, n = 18) and elder (72 ± 7 years old, n = 17) patients after an mTBI and in age-matched healthy subjects (young: 25 ± 6 years old, n = 20; aged: 68 ± 5 years old, n = 23). We found significantly more CMBs in elder patients after an mTBI compared with young patients; however, we did not observe a significant difference in the number of cerebral microhemorrhages between aged and aged patients with mTBI. The majority of CMBs were found supratentorially (lobar and basal ganglion). The lobar distribution of supratentorial CMBs showed that aging enhances the formation of parietal and occipital CMBs after mTBIs. This suggests that aging and mTBIs do not synergize in the induction of the development of CMBs, and that the different distribution of mTBI-induced CMBs in aged patients may lead to specific age-related clinical characteristics of mTBIs.

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

Common Medications and Intracerebral Hemorrhage: The ARIC Study

Background Antiplatelets, anticoagulants, and statins are commonly prescribed for various indications. The associations between these medications and the risk of intracerebral hemorrhage (ICH) and cerebral microbleeds (CMBs) are unclear. Methods and Results We performed a retrospective study of the ARIC (Atherosclerosis Risk in Communities) study cohort, recruited from 4 US communities in 1987 to 1989 with follow‐up. In 2011 to 2013, a subset (N=1942) underwent brain magnetic resonance imaging with CMB evaluation. Time‐varying and any antiplatelet, anticoagulant, or statin use was evaluated at subsequent study visits in participants not on each medication at baseline. To determine the hazard of ICH and odds of CMB by medication use, logistic and Cox proportional hazard models were built, respectively, adjusting for the propensity to take the medication, concomitant use of other medications, and cognitive, genetic, and radiographic data. Of 15 719 individuals during up to 20 years of follow‐up, 130 participants experienced an ICH. The adjusted hazard of ICH was significantly lower among participants taking an antiplatelet at the most recent study visit before ICH versus nonusers (hazard ratio [HR], 0.53; 95% CI, 0.30–0.92). Statin users had a significantly lower hazard of an ICH compared with nonusers (adjusted HR, 0.13; 95% CI, 0.05–0.34). There was no association of CMB and antiplatelet, anticoagulant, or statin use in adjusted models. Conclusions In this US community‐based study, antiplatelet and statin use were associated with lower ICH hazard, whereas no association was noted between CMBs and antiplatelets, anticoagulants, and statins. Further study is needed to understand the differential roles of these medications in cerebral microhemorrhages and macrohemorrhages.

Familial Cerebral Cavernous Malformation Mimicking Cerebral Amyloid Angiopathy

A 67-year-old man was referred from ophthalmology for possible cerebral amyloid angiopathy (CAA) discovered during work-up of possible optic neuropathy. MRI (figure 1) demonstrated innumerable periventricular, brainstem, and cortical cerebral microhemorrhages (CMH). Scattered, non-specific white matter hyperintensities was seen on T2-weighted imaging without surrounding hypointense rim. He had no hypertension, and the distribution was uncharacteristic for CAA. Despite absent family history of stroke or seizure, testing for familial cerebral cavernous malformation (FCCM) identified a pathogenic mutation of KRIT1 (c.382G>T).

Longitudinal Accumulation of Cerebral Microhemorrhages in Dominantly Inherited Alzheimer Disease

Objective:To investigate the inherent clinical risks associated with the presence of cerebral microhemorrhages (CMHs) or cerebral microbleeds (CMBs) and characterize individuals at high risk for developing hemorrhagic amyloid-related imaging abnormality (ARIA-H), we evaluated longitudinally families affected by dominantly inherited Alzheimer disease (DIAD).Methods:Mutation carriers (n=310) and non-carriers (n=201) underwent neuroimaging, including gradient echo MR sequences to detect CMHs, neuropsychological, and clinical assessments. Cross-sectional and longitudinal analyses evaluated relationships between CMHs and neuroimaging and clinical marker of disease.Results:Three percent of non-carriers and eight percent of carriers developed CMHs primarily located in lobar areas. Carriers with CMHs were older, had higher diastolic blood pressure and Hachinski ischemic scores, and more clinical, cognitive, and motor impairments than those without CMH. APOE-ε4 status was not associated with the prevalence or incidence of CMHs. Prevalent or incident CMHs predicted faster change in clinical dementia rating although not composite cognitive measure, cortical thickness, hippocampal volume, or white matter lesions. Critically, the presence of two or more CMHs was associated with a significant risk for development of additional CMHs over time (8.95±10.04 per year).Conclusion:Our study highlights factors associated with the development of CMHs in individuals with DIAD. CMHs are a part of the underlying disease process in DIAD and are significantly associated with dementia. This highlights that in participants in treatment trials exposed to drugs, which carry the risk of ARIA-H as a complication, it may be challenging to separate natural incidence of CMHs from drug related CMHs.

Spectroscopic and Deep Learning-Based Approaches to Identify and Quantify Cerebral Microhemorrhages

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: 1) ratiometric analysis of RGB pixel values, 2) phasor analysis of RGB images, and 3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20x objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

Amyloid Precursor Protein Variant, E665D, Associated With Unique Clinical and Biomarker Phenotype

We describe a clinical, imaging and biomarker phenotype associated with an amyloid precursor gene (APP) E665D variant in a 45-year-old man with progressive cognitive and behavioral dysfunction. Brain MRI showed bilateral, confluent T2 hyperintensities predominantly in the anterior white matter. Amyloid imaging and CSF testing were consistent with amyloid deposition. 7 Tesla MRI revealed cerebral microhemorrhages suggestive of cerebral amyloid angiopathy (CAA). Contrary to previous reports, this case raises the possibility that the APP E665D genetic change may be pathogenic, particularly given the abnormal Alzheimer’s disease biomarkers observed in the cerebrospinal fluid, positive amyloid imaging and imaging evidence for CAA in a relatively young patient with progressive cognitive decline.

Brain magnetic resonance imaging in adult survivors of extracorporeal membrane oxygenation

Despite the common occurrence of neurologic complications in patients with extracorporeal membrane oxygenation (ECMO), data on magnetic resonance imaging (MRI) findings in adult ECMO are limited. We aimed to describe the MRI findings of patients after ECMO cannulation. Records of patients who underwent ECMO from September 2017 to June 2019 were reviewed. MRI studies were performed using multiplanar sequences consisting of T1-, T2-weighted, fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility weighted images (SWI). Of the 78 adult patients who underwent ECMO, 26 (33%) survived. Of 26, eight patients (31%) had MRI studies, with a median age of 47 years (interquartile range [IQR]: 25–57). The median ECMO support time was 8 days (IQR: 4–25) and the median time from decannulation to MRI was 12 days (IQR: 1–34). Five (63%) of eight patients had ischemic infarcts; 4 (50%) had cerebral microhemorrhage; 2 (25%) had intracranial hemorrhage; and 1 (13%) had thoracic cord ischemic infarct. There were no patients with normal MRI. All patients underwent transcranial Doppler (TCD). Four of 8 (50%) showed presence of microemboli with TCD; 3 of 4 (75%) had ischemic infarcts; and 1 of 4 (25%) had presence of multiple cerebral microhemorrhages on MRI. All ischemic infarcts had diffuse pattern of punctate to small lesions for ECMO survivors. The location of cerebral microhemorrhages included lobar ( n = 4, 100%), deep ( n = 2, 50%), and both ( n = 2, 50%). Of the MRI studies, cerebrovascular related lesions were the most frequent, with punctate ischemic infarct being the most common type that may be associated with TCD microemboli. The results of the study suggest that subclinical cerebral lesions are commonly found in patients with ECMO support. Further research is needed to understand long-term effect of these cerebral lesions.