Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration

Introduction Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. Methods Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. Results A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. Conclusions The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke

© 2022 The Author(s)Background: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. Aims: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. Methods: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3–12, 12–24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. Results: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3–12 months, 243/853 (28%) at 12–24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34–1.68) and multivariable (OR 1.27, 95%CI 1.10–1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. Conclusions: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.N

NETWORK-BASED LESION IMPACT SCORE IS AN INDEPENDENT PREDICTOR OF POST-STROKE COGNITIVE IMPAIRMENT

International audienceBackground: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate PSCI risk prediction remains challenging. The recently developed network impact score (see DOI:10.1161/STROKEAHA.119.025637), which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction.Aims: To determine if the network impact score is an independent predictor of PSCI at pre-specified timepoints during follow-up, and of cognitive recovery or cognitive decline.Methods: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI 24 months), cognitive recovery, and cognitive decline. Models were adjusted for age, sex, education, clinical history of stroke, infarct volume.Results: We included 2488 patients with 4941 cognitive assessments (see flowchart Figure 1; patient characteristics provided in Table 1). The lesion impact (range -3.07 to 2.46) predicted PSCI in the GEE model (odds ratio [OR] per 1-point increase: univariable 1.47 (95%CI 1.33-1.63), multivariable 1.25 (95%CI 1.12-1.40), and logistic regression models for all post-stroke intervals with comparable ORs (Table 2). The network impact score was not significantly associated with either cognitive recovery or decline. The results were unchanged after excluding patients (n=152) from the PROCRAS cohort in which the network impact score was developed.Conclusion: The network impact score is an independent predictor of PSCI at timepoints up to and beyond 24 months after ischemic stroke