Determinants of Leptomeningeal Collateral Status Variability in Ischemic Stroke Patients.

BACKGROUND: Collateral status is an indicator of a favorable outcome in stroke. Leptomeningeal collaterals provide alternative routes for brain perfusion following an arterial occlusion or flow-limiting stenosis. Using a large cohort of ischemic stroke patients, we examined the relative contribution of various demographic, laboratory, and clinical variables in explaining variability in collateral status. METHODS: Patients with acute ischemic stroke in the anterior circulation were enrolled in a multi-center hospital-based observational study. Intracranial occlusions and collateral status were identified and graded using multiphase computed tomography angiography. Based on the percentage of affected territory filled by collateral supply, collaterals were graded as either poor (0-49%), good (50-99%), or optimal (100%). Between-group differences in demographic, laboratory, and clinical factors were explored using ordinal regression models. Further, we explored the contribution of measured variables in explaining variance in collateral status. RESULTS: 386 patients with collateral status classified as poor (n = 64), good (n = 125), and optimal (n = 197) were included. Median time from symptom onset to CT was 120 (IQR: 78-246) minutes. In final multivariable model, male sex (OR 1.9, 95% CIs [1.2, 2.9], p = 0.005) and leukocytosis (OR 1.1, 95% CIs [1.1, 1.2], p = 0.001) were associated with poor collaterals. Measured variables only explained 44.8-53.0% of the observed between-patient variance in collaterals. CONCLUSION: Male sex and leukocytosis are associated with poorer collaterals. Nearly half of the variance in collateral flow remains unexplained and could be in part due to genetic differences.

Texture Analysis to Detect Cerebral Degeneration in Amyotrophic Lateral Sclerosis.

BACKGROUND: Evidence of cerebral degeneration is not apparent on routine brain MRI in amyotrophic lateral sclerosis (ALS). Texture analysis can detect change in images based on the statistical properties of voxel intensities. Our objective was to test the utility of texture analysis in detecting cerebral degeneration in ALS. A secondary objective was to determine whether the performance of texture analysis is dependent on image resolution. METHODS: High-resolution (0.5×0.5 mm2 in-plane) coronal T2-weighted MRI of the brain were acquired from 12 patients with ALS and 19 healthy controls on a 4.7 Tesla MRI system. Image data sets at lower resolutions were created by down-sampling to 1×1, 2×2, 3×3, and 4×4 mm2. Texture features were extracted from a slice encompassing the corticospinal tract at the different resolutions and tested for their discriminatory power and correlations with clinical measures. Subjects were also classified by visual assessment by expert reviewers. RESULTS: Texture features were different between ALS patients and healthy controls at 1×1, 2×2, and 3×3 mm2 resolutions. Texture features correlated with measures of upper motor neuron function and disability. Optimal classification performance was achieved when best-performing texture features were combined with visual assessment at 2×2 mm2 resolution (0.851 area under the curve, 83% sensitivity, 79% specificity). CONCLUSIONS: Texture analysis can detect subtle abnormalities in MRI of ALS patients. The clinical yield of the method is dependent on image resolution. Texture analysis holds promise as a potential source of neuroimaging biomarkers in ALS.

A Serpiginous Pericallosal Anterior Cerebral Artery.

The anterior cerebral artery (ACA) is a unique artery with many important variations with substantial clinical significance. Tortuous intracranial arteries usually occur in basilar, communicating, anterior, posterior cerebral arteries and in the white matter arterioles. This could happen for many reasons including but not limited to ageing, hypertension, patients with Moyamoya disease, congenital malformation, or increased flow associated with elastin degradation. While dolichoectasia of the ACA has been described even in children, to our knowledge, a serpiginous ACA without ectasia has not been reported, especially in the pediatric population.

Magnetic resonance imaging in the diagnosis of white matter signal abnormalities.

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen's kappa statistic for inter-radiologist agreement was 0.733, suggesting "good" agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.