Lesion topography and microscopic white matter tract damage contribute to cognitive impairment in symptomatic carotid artery disease

Purpose: To investigate associations between neuroimaging markers of cerebrovascular disease, including lesion topography and extent and severity of strategic and global cerebral tissue injury, and cognition in carotid artery disease (CAD). Materials and Methods: All participants gave written informed consent to undergo brain magnetic resonance imaging and the Addenbrooke’s Cognitive Examination–Revised. One hundred eight patients with symptomatic CAD but no dementia were included, and a score less than 82 represented cognitive impairment. Group comparison and interrelations between global cognitive and fluency performance, lesion topography, and ultrastructural damage were assessed with voxel-based statistics. Associations between cognition, medial temporal lobe atrophy (MTA), lesion volumes, and global white matter ultrastructural damage indexed as increased mean diffusivity were tested with regression analysis by controlling for age. Diagnostic accuracy of imaging markers selected from a multivariate prediction model was tested with receiver operating characteristic analysis. Results: Cognitively impaired patients (n = 53 [49.1%], classified as having probable vascular cognitive disorder) were older than nonimpaired patients (P = .027) and had more frequent MTA (P<.001), more cortical infarctions (P = .016), and larger volumes of acute (P = .028) and chronic (P = .009) subcortical ischemic lesions. Lesion volumes did not correlate with global cognitive performance (lacunar infarctions, P = .060; acute lesions, P = .088; chronic subcortical ischemic lesions, P = .085). In contrast, cognitive performance correlated with presence of chronic ischemic lesions within the interhemispheric tracts and thalamic radiation (P< .05, false discovery rate corrected). Skeleton mean diffusivity showed the closest correlation with cognition (R2 = 0.311, P< .001) and promising diagnostic accuracy for vascular cognitive disorder (area under the curve, 0.82 [95% confidence interval: 0.75, 0.90]). Findings were confirmed in subjects with a low risk of preclinical Alzheimer disease indexed by the absence of MTA (n = 85). Conclusion: Subcortical white matter ischemic lesion locations and severity of ultrastructural tract damage contribute to cognitive impairment in symptomatic CAD, which suggests that subcortical disconnection within large-scale cognitive neural networks is a key mechanism of vascular cognitive disorder

Photonics

Contains reports on seven research projects.Air Force Rome Air Development Center (in collaboration with C.C. Leiby, Jr.)U.S. Air Force-Rome Air Development Center (Contract F19628-80-C-0077)National Science Foundation (Grant PHY79-09739)Joint Services Electronics Program (Contract DAAG29-80-C-0104)U.S. Air Force Geophysics Laboratory (Contract F19628-79-C-0082

Quantum Electronics

Contains report on ten research projects split into three sections.Joint Services Electronics Program (Contract DAAG29-78-C-0020)National Science Foundation (Grant PHY77-07156)U. S. Air Force-Office of Scientific Research (Grant AFOSR-3042)National Science Foundation (Grant ENG77-24981

Quantum Optics and Photonics

Contains reports on seven research projects.U.S. Air Force Geophysics Laboratory (Contract F19628-70-C-0082)Joint Services Electronics Program (Contract DAAG29-83-K-0003)National Science Foundation (Grant PHY82-10369)U.S. Air Force - Rome Air Development Center (in collaboration with C.C. Leiby, Jr.)U.S. Air Force - Rome Air Development Center (Contract F19628-80-C-0077)U.S. Air Force - Office of Scientific Research (Contract F49620-82-C-0091

Quantum Electronics

Contains thirteen research projects split into three sections.U.S. Air Force - Rome Air Development Center (Contract F19628-80-C-0077)National Science Foundation (Grant PHY79-09739)Joint Services Electronics Program (Contract DAAG29-78-C-0020)Joint Services Electronics Program (Contract DAAG29-80-C-0104)U.S. Air Force Geophysics Laboratory (AFSC) (Contract F19628-79-C-0082)National Science Foundation (Grant ECS79-19475)National Science Foundation (Grant DAR80-08752)National Science Foundation (Grant ENG79-09980

Quantum Electronics

Contains reports on eleven research projects.Air Force Rome Air Development Center (in collaboration with C.C. Leiby Jr)U.S. Air Force - Rome Air Development Center (Contract F19628-80-C-0077)National Science Foundation (Grant PHY79-09739)Joint Services Electronics Program (Contract DAAG29-80-C-0104)Air Force Geophysics Laboratory (Contract F 19628-79-C-0082)National Science Foundation (Grant DAR80-08752)National Science Foundation (Grant ECS79-19475)National Science Foundation (Grant ECS80-17705)National Science Foundation (Grant ENG79-09980

Quantum Electronics

Contains reports on eight research projects divided into three sections.National Science Foundation (Grant PHY79-09739)Joint Services Electronics Program (Contract DAAG29-78-C-0020)U.S. Air Force Geophysics Laboratory (AFSC) (Contract F19628-79-C-0082)National Science Foundation (Grant ENG79-09980

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)