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Introduction: Vascular cognitive impairment (VCI) is heterogeneous in brain atrophy patterns, clinical symptoms, and possibly in the factors affecting resilience to symptoms. The objective of this study was to investigate the heterogeneity of VCI by estimating different atrophy-driven subtypes and use those for identifying resilience factors in each subtype. Methods: We used cross-sectional data from the Trace-VCI cohort comprising of memory-clinic patients with vascular brain injury on MRI (n=361 all-cause dementia, n=190 MCI, and n=188 SCD). White matter hyper-intensities (WMH) were segmented, and SLF toolbox was used to refill them on the MRIs for accurate parcellation. Freesurfer volumes were used for identifying subtypes with non-negative matrix factorization. Subtype-specific pseudo-timelines of progression were estimated using a previously validated discriminative event-based model. Severity of atrophy (SA) in patients was estimated using cross-validation based on their position on the pseudo-timeline. Using linear-regression, cognition and disability (MMSE, Global deterioration scale, disability assessment for dementia) were modelled to be dependent on SA and its interactions with genetic factors, vascular markers and risk-factors, and co-pathology independently (variables in Figure-3). Resilience factors were identified by testing if the model with the interaction explains the symptoms significantly more than the one without. Results: The algorithm identified three atrophy-based VCI subtypes: Frontal, Subcortical/Temporal, and Parietal subtype. Their prevalence, vascular and clinical presentations are summarized in Figure-1. The pseudo-timelines of atrophy are shown in Figure-2. SA's interaction with education positively influenced cognition in all subtypes, but negatively influenced disability in subcortical/temporal subtype. In frontal and parietal subtypes, APOE ε4, AD co-pathology resulted in more SA without worsening symptoms. SA's interaction with smoking and microbleeds negatively influenced disability. In the subcortical/temporal subtype, SA's interaction with WMH, lacunes, infarcts negatively impacted disability. In parietal subtype, men have more cognitive resilience than women. SA's interaction with hypercholesterolemia, and smoking were significantly negative for cognition. Lacunes were associated with more SA without affecting cognition. Figure-3 summarizes the interactions for all subtypes. Discussion: We identified three atrophy-based VCI subtypes where the risk-factors have different influence on atrophy and symptoms highlighting differences in resilience. These results could aid in prognosis and in personalizing patients’ intervention strategy.

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Background and purpose: Cerebral microinfarcts (CMIs) are associated with cognitive impairment and dementia. CMIs might affect cognitive performance through disruption of cerebral networks. We investigated in memory clinic patients whether cortical CMIs are clustered in specific brain regions and if presence of cortical CMIs is associated with reduced white matter (WM) connectivity in tracts projecting to these regions.Methods:164 memory clinic patients with vascular brain injury with a mean age of 72 ± 11 years (54% male) were included. All underwent 3 tesla MRI, including a diffusion MRI and cognitive testing. Cortical CMIs were rated according to established criteria and their spatial location was marked. Diffusion imaging-based tractography was used to reconstruct WM connections and voxel based analysis (VBA) to assess integrity of WM directly below the cortex. WM connectivity and integrity were compared between patients with and without cortical CMIs for the whole brain and regions with a high CMI burden.Results:30 patients (18%) had at least 1 cortical CMI [range 1–46]. More than 70% of the cortical CMIs were located in the superior frontal, middle frontal, and pre- and postcentral brain regions (covering 16% of the cortical surface). In these high CMI burden regions, presence of cortical CMIs was not associated with WM connectivity after correction for conventional neuroimaging markers of vascular injury. WM connectivity in the whole brain and WM voxels directly underneath the cortical surface did not differ between patients with and without cortical CMIs.Conclusion:Cortical CMIs displayed a strong local clustering in highly interconnected frontal, pre- and postcentral brain regions. Nevertheless, WM connections projecting to these regions were not disproportionally impaired in patients with compared to patients without cortical CMIs. Alternative mechanisms, such as focal disturbances in cortical structure and functioning, may better explain CMI associated cognitive impairment.