Vascular cognitive impairment is an umbrella term for cognitive dysfunction associated with and presumed to be caused by vascular brain damage. Autopsy studies have identified microinfarcts as an important neuropathological correlate of vascular cognitive impairment that escapes detection by conventional magnetic resonance imaging (MRI). As a frame of reference for future highresolution MRI studies, we systematically reviewed the literature on neuropathological studies on cerebral microinfarcts in the context of vascular disease, vascular risk factors, cognitive decline and dementia. We identified 32 original patient studies involving 10,515 people. The overall picture is that microinfarcts are common, particularly in patients with vascular dementia (weighted average 62%), Alzheimer's disease (43%), and demented patients with both Alzheimer-type and cerebrovascular pathology (33%) compared with nondemented older individuals (24%). In many patients, multiple microinfarcts were detected. Microinfarcts are described as minute foci with neuronal loss, gliosis, pallor, or more cystic lesions. They are found in all brain regions, possibly more so in the cerebral cortex, particularly in watershed areas. Reported sizes vary from 50 lm to a few mm, which is within the detection limit of current high-resolution MRI. Detection of these lesions in vivo would have a high potential for future pathophysiological studies in vascular cognitive impairment.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness.Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners.The challenge remains open for future submissions and provides a public platform for method evaluation.

OBJECTIVETo examine whether type 2 diabetes is associated with microstructural abnormalities in specific cerebral white matter tracts and to relate these microstructural abnormalities to cognitive functioning.RESEARCH DESIGN AND METHODSThirty-five nondemented older individuals with type 2 diabetes (mean age 71 ± 5 years) and 35 age-, sex-, and education-matched control subjects underwent a 3 Tesla diffusion-weighted MRI scan and a detailed cognitive assessment. Tractography was performed to reconstruct several white matter tracts. Diffusion tensor imaging measures, including fractional anisotropy (FA) and mean diffusivity (MD), were compared between groups and related to cognitive performance.RESULTSMD was significantly increased in all tracts in both hemispheres in patients compared with control subjects (P < 0.05), reflecting microstructural white matter abnormalities in the diabetes group. Increased MD was associated with slowing of information-processing speed and worse memory performance in the diabetes but not in the control group after adjustment for age, sex, and estimated IQ (group × MD interaction, all P < 0.05). These associations were independent of total white matter hyperintensity load and presence of cerebral infarcts.CONCLUSIONSIndividuals with type 2 diabetes showed microstructural abnormalities in various white matter pathways. These abnormalities were related to worse cognitive functioning.