UNASSIGNED: Efficiently and accurately detecting cerebral microbleeds (CMBs) is crucial for diagnosing dementia, stroke, and traumatic brain injury. Manual CMB detection, however, is time-consuming and error-prone. This study evaluates a novel artificial intelligence (AI) software designed for the automated detection of CMBs using susceptibility weighted imaging (SWI).
UNASSIGNED: The SWI data from 265 patients, 206 of whom had a history of stroke and others of whom presented a variety of other medical histories, including hypertension, diabetes, hyperlipidemia, cerebral hemorrhage, intracerebral vascular malformations, tumors, and inflammation, collected between January 2015 and December 2018, were analyzed. Two independent radiologists initially reviewed the images to identify and count the number of CMBs. Subsequently, the images were processed using an automatic CMB detection software. The generated reports were then reviewed by the radiologists. A final consensus between the two radiologists, obtained after a second review of the images, was used to compare results obtained from the initial manual detection and those of the automatic CMB detection software. The differences of detection sensitivity and precision for patients with or without CMBs and for individual CMBs between the radiologist and the automatic CMB detection software were compared using Pearson chi-squared tests.
UNASSIGNED: A total of 1,738 CMBs were detected among 148 patients (71.4±10.7 years, 100 males) from the analyzed SWI data. While the radiologists identified 139 cases with CMBs, the automatic CMB detection software detected 145 cases. Nevertheless, there was no statistical difference in the sensitivity and specificity of the automatic CMB detection software compared to manual detection in determining patients with CMBs (P=0.656 and P=0.212, chi-square test). However, the radiologist identified 93 patients without CMBs, while the automatic CMB detection software detected 121 patients without CMBs, exhibiting a statistically significant difference (P=0.016, chi-square test). In terms of individual CMBs, the radiologists found 1,284, whereas the automatic CMB detection software detected 1,677 CMBs. The detection sensitivity for human versus the automatic CMB detection software were 75.5% and 96.5% respectively (P<0.001, chi-square test), while the precision rates were 92.2% and 86.0% (P<0.001, chi-square test), respectively. Notably, the radiologists were more likely to overlook CMBs when the number of CMBs was high (above 30).
UNASSIGNED: The automatic CMB detection software proved to be an effective tool for the detection and quantification of CMBs. It demonstrated higher sensitivity than the radiologists, especially in detecting minuscule CMBs and in cases with high CMB prevalence.

The purpose of this study is to the relationship between peripheral apolipoproteins and cerebral small vessel disease (CSVD) imaging markers.
We reviewed the data of a population that above 40 years old with CSVD, while free of known dementia or acute stroke. We evaluated CSVD imaging markers, including white matter hyperintensities (WMHs), enlarged perivascular spaces (EPVS), lacunas, microbleeds by MRI scans, and measured peripheral apolipoproteins.
After adjusting for age, sex and vascular risk factors,1) apoB and apoB/apoA-1 were related to grade of EPVS in basal ganglia(apoB:r=0.196,p<0.001;apoB/apoA-1:r=0.208,p<0.001), apoE was related to grade of EPVS in centrum semiovale (r=0.125,p=0.040); 2) apoB(OR=1.739, 95%CI=1.357-2.061, p<0.001), apoB/apoA-1(OR=1.116, 95%CI=1.037-1.761, p=0.005) and apoE(OR=1.287, 95%CI=1.036-1.599, p=0.023) were independent factors of presence of severer EPVS in basal ganglia, apoE was an independent factor of presence of severer EPVS in centrum semiovale (OR=1.235, 95%CI=1.021-1.494, p=0.029).
Our findings demonstrated peripheral apolipoproteins, including apoB, apoB/apoA-1, and apoE, were independent factor for EPVS in CSVD.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

UNASSIGNED: Cognitive decline in acromegaly has gained increasing attention. Cerebral microbleeds (CMBs) as radiographic markers for microvascular injury have been linked to various types of cognitive decline. However, the association between CMB formation and acromegaly has not yet been quantified. This study is designed to investigate the prevalence and the radiographic patterns of CMBs and the association between cognitive function and acromegaly-related CMBs in growth hormone (GH)-secreting pituitary adenoma, which is characterized by acromegaly.
UNASSIGNED: In a cohort of 55 patients with GH-secreting pituitary adenoma (acromegaly) and 70 healthy control (HC) patients, we determined the presence of CMBs using a 3.0-T MRI scanner. The numbers, locations, and grades of CMBs were determined via susceptibility-weighted imaging (SWI) and the Microbleed Anatomical Rating Scale. Obstructive sleep apnea (OSA) was assessed using the criteria of the American Academy of Sleep Medicine (AASM) Scoring Manual Version 2.2. The Montreal Cognitive Assessment (MoCA) was used to assess cognitive performance within 3 days of admission. The association between CMBs and cognitive function as well as clinical characteristics was explored.
UNASSIGNED: The incidence of CMBs was 29.1%, whereas that of OSA was 65.5% in acromegaly. There was a statistically significant difference in the prevalence of CMBs between subjects with and without acromegaly (29.1% and 5.3%, respectively) (p < 0.01). The age of acromegaly patients with CMBs was much younger compared with HCs with CMBs. Compared with HCs, a significant cognitive decline and the occurrence of OSA were demonstrated in patients with acromegaly (p < 0.01). Binary logistic regression analysis adjusted for age, education, and body mass index (BMI) revealed that CMB was an independent risk factor for cognitive impairment in patients with acromegaly (OR = 3.19, 95% CI 1.51-6.76, p = 0.002). Furthermore, in the logistic regression models adjusted for age, BMI, diabetes, and hypertension, OSA was independently associated with the occurrence of CMBs in patients with acromegaly (OR = 13.34, 95% CI 3.09-57.51, p = 0.001).
UNASSIGNED: A significant increase of CMBs was demonstrated in patients with acromegaly, which may be a result of OSA in acromegaly. The present study indicated that increasing CMBs are responsible for cognitive decline in patients with acromegaly.

BACKGROUND: Gait disturbances may appear prior to cognitive dysfunction in the early stage of silent cerebrovascular disease (SCD). Subtle changes in gait characteristics may provide an early warning of later cognitive decline. Our team has proposed a vision-based artificial intelligent gait analyzer for the rapid detection of spatiotemporal parameters and walking pattern based on videos of the Timed Up and Go (TUG) test. The primary objective of this study is to investigate the relationship between gait features assessed by our artificial intelligent gait analyzer and cognitive function changes in patients with SCD.
METHODS: This will be a multicenter prospective cohort study involving a total of 14 hospitals from Shanghai and Guizhou. One thousand and six hundred patients with SCD aged 60-85 years will be consecutively recruited. Eligible patients will undergo the intelligent gait assessment and neuropsychological evaluation at baseline and at 1-year follow-up. The intelligent gait analyzer will divide participant into normal gait group and abnormal gait group according to their walking performance in the TUG videos at baseline. All participants will be naturally observed during 1-year follow-up period. Primary outcome are the changes in Mini-Mental State Examination (MMSE) score. Secondary outcomes include the changes in intelligent gait spatiotemporal parameters (step length, gait speed, step frequency, step width, standing up time, and turning back time), the changes in scores on other neuropsychological tests (Montreal Cognitive Assessment, the Stroop Color Word Test, and Digit Span Test), falls events, and cerebrovascular events. We hypothesize that both groups will show a decline in MMSE score, but the decrease of MMSE score in the abnormal gait group will be more significant.
CONCLUSIONS: This study will be the first to explore the relationship between gait features assessed by an artificial intelligent gait analyzer and cognitive decline in patients with SCD. It will demonstrate whether subtle gait abnormalities detected by the artificial intelligent gait analyzer can act as a cognitive-related marker for patients with SCD.
BACKGROUND: This trial was registered at ClinicalTrials.gov ( NCT04456348 ; 2 July 2020).

OBJECTIVE: Established visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)-based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls.
METHODS: A prospective study including 80 individuals (46 females, mean age 73.4 ± 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers.
RESULTS: AD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%.
CONCLUSIONS: The AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.
CONCLUSIONS: • AD-RAI score at baseline was associated with Corsi score (visuospatial memory) decline. • BrainAGE score was associated with Trail Making B (executive functions) decrease. • AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

BACKGROUND: Autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a cerebrovascular disease closely related to the NOTCH3 gene. More than 200 mutations in this gene have been reported to be associated with this disease.
METHODS: The NOTCH3 gene from CADASIL patient was screened for mutations by whole-exome sequencing (WES). PCR amplification and direct Sanger sequencing were used to verify the suspicious gene mutation sites detected by WES.
RESULTS: We performed second-generation sequencing on a sample of the patient\'s genome and found a heterozygous deletion-insertion mutation c.512_605delinsA in exon 4 of NOTCH3, which resulted in amino acid changes p.G171_A202delinsE. This variation was confirmed by the direct Sanger sequencing. It may be rated as a CADASIL clinical variation.
CONCLUSIONS: Discovery of this mutation site provides an important theoretical basis for specific gene-based diagnosis and treatment of CADASIL.

Few studies have focused on the connection between glymphatic dysfunction and cerebral small vessel disease (CSVD), partially due to the lack of non-invasive methods to measure glymphatic function. We established modified index for diffusion tensor image analysis along the perivascular space (mALPS-index), which was calculated on diffusion tensor image (DTI), compared it with the classical detection of glymphatic clearance function calculated on Glymphatic MRI after intrathecal administration of gadolinium (study 1), and analyzed the relationship between CSVD imaging markers and mALPS-index in CSVD patients from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734) (study 2). Among 39 patients included in study 1, mALPS-index were significantly related to glymphatic clearance function calculated on Glymphatic MRI ( r  = -0.772~-0.844, p < 0.001). A total of 330 CSVD patients were included in study 2. Severer periventricular and deep white matter hyperintensities (β = -0.332, p < 0.001; β = -0.293, p < 0.001), number of lacunas (β = -0.215, p < 0.001), number of microbleeds (β = -0.152, p = 0.005), and severer enlarged perivascular spaces in basal ganglia (β = -0.223, p < 0.001) were related to mALPS-index. Our results indicated that non-invasive mALPS-index might represent glymphatic clearance function, which could be applied in clinic in future. Glymphatic clearance function might play a role in the development of CSVD.

Ischemic stroke is a common clinical feature of neurosyphilis, but its accompanying cognitive decline is often overlooked. The mechanisms of cognitive impairment in neurosyphilis presenting with ischemic stroke are not fully understood. Cerebral small vessel disease (CSVD) was recently shown to predict post-stroke cognitive decline. Therefore, this study aims to validate the correlation between CSVD and cognitive impairment in neurosyphilis presenting with ischemic stroke.
We enrolled 179 neurosyphilis patients diagnosed as acute ischemic stroke and performed a 12-month cognitive assessment follow-up. CSVD burden was evaluated by neuroimaging markers, including white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), and perivascular spaces (PVS). We performed multivariate logistic regression analysis to determine the association between cognitive decline and total CSVD burden score in neurosyphilis patients.
The neurosyphilis participants had a significantly higher total CSVD score and lower cognitive function score compared with the syphilis-uninfected patients. Acute cognitive impairment was associated with total CSVD score, extensive microbleeds, and Grade 3 WMHs. After 12-month follow-up, the poor prognosis of post-stroke cognitive impairment was associated with a higher burden of CSVD and extensive microbleeds.
Cerebral small vessel disease loads in neurosyphilis patients presenting with ischemic stroke are independently associated with acute cognitive impairment and have a prospective value for post-stroke cognitive outcomes.

To analyze the incidence and risk factors of microbleeds lesions and to use thromboelastography (TEG) to evaluate the relationship between perioperative platelet function and microbleed events in patients with unruptured intracranial aneurysms (UIAs) undergoing Stent-Assisted Coil (SAC) embolization.
We retrospectively enrolled 261 patients with UIAs undergoing SAC embolization between November 2017 and October 2019. All patients received unanimous antiplatelet protocol (aspirin 300 mg and clopidogrel 300 mg). Platelet function was evaluated by TEG, and magnetic resonance susceptibility-weighted imaging (SWI) was performed for microbleeds detection before and after surgery. Univariate and multivariate logistic regression analyses were used to identify potential risk factors for microbleeds following embolization.
Microbleed lesions were identified in 122 of 261 patients (46.7%). Most of the microbleeds were asymptomatic, except for 22 patients complaining slight headaches, and 3 patients who developed cerebral hemorrhage after discharge. Among the clinical characters, female, previous intracerebral hemorrhage (ICH) history and TEG parameters variation (higher reaction time (R) and lower maximum amplitude of adenosine diphosphate (MAADP)) were associated with microbleeds occurrence. Subsequent multivariate analysis indicated that gender, hemorrhage history, R, and MAADP were still independent risk factors of microbleeds. The R-value (>7.6 min) and MAADP (<29.2 mm) were predictive values, yielding areas under the receiver operating curve (ROC) of 0.76 (95% CI 0.70 to 0.82) and 0.89 (95% CI 0.86 to 0.93), respectively.
The incidence of microbleeds may be high in UIA patients treated with SAC and dual antiplatelet therapy. Lesions occurred more frequently in female patients and patients with ICH history. Among the TEG parameters, the R-value and MAADP were predictors for microbleed events.