Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Seven-tesla magnetic resonance imaging offers detailed 3-dimensional visualization of PVS, their morphological features, and their related perforating arteries. This may offer new opportunities to study the role of PVS in ageing and cerebral small vessel disease.

Retinal microvascular changes can be visualized noninvasively and have been associated with cognitive decline and brain changes in relation to aging and vascular disease. We systematically reviewed studies, published between 1990 and November 2012, on the association between retinal microvascular changes and dementia, cognitive functioning, and brain imaging abnormalities, in the context of aging and vascular risk factors. In cross-sectional studies (k=26), retinal microvascular changes were associated with the presence of dementia (range of odds ratios (ORs) 1.17;5.57), with modest decrements in cognitive functioning in nondemented people (effect sizes -0.25;0.03), and with brain imaging abnormalities, including atrophy and vascular lesions (ORs 0.94;2.95). Longitudinal studies were more sparse (k=9) and showed no consistent associations between retinal microvascular changes and dementia or cognitive dysfunctioning 3 to 15 years later (ORs and hazard ratios 0.77;1.55). However, there were indications of prospective associations with brain imaging abnormalities ((ORs) 0.81;3.19). In conclusion, particularly in cross-sectional studies there is a correlation between retinal microvascular changes and dementia, cognitive impairment, and brain imaging abnormalities. Associations are strongest for more severe retinal microvascular abnormalities. Retinal microvascular abnormalities may offer an important window on the brain for etiological studies.

In neuroimaging studies, pathologies can present themselves as abnormal intensity patterns. Thus, solutions for detecting abnormal intensities are currently under investigation. As each patient is unique, an unbiased and biologically plausible model of pathological data would have to be able to adapt to the subject's individual presentation. Such a model would provide the means for a better understanding of the underlying biological processes and improve one's ability to define pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge. Its application on simulated and clinical data demonstrated the ability to detect abnormal intensity clusters, resulting in a competitive to improved behavior in white matter lesion segmentation when compared to three other freely-available automated methods.

Perivascular spaces are an emerging marker of small vessel disease. Perivascular spaces in the centrum semiovale have been associated with cerebral amyloid angiopathy. However, a direct topographical relationship between dilated perivascular spaces and cerebral amyloid angiopathy severity has not been established. We examined this association using post-mortem magnetic resonance imaging in five cases with evidence of cerebral amyloid angiopathy pathology. Juxtacortical perivascular spaces dilation was evaluated on T2 images and related to cerebral amyloid angiopathy severity in overlying cortical areas on 34 tissue sections stained for Amyloid β. Degree of perivascular spaces dilation was significantly associated with cerebral amyloid angiopathy severity (odds ratio = 3.3, 95% confidence interval 1.3-7.9, p = 0.011). Thus, dilated juxtacortical perivascular spaces are a promising neuroimaging marker of cerebral amyloid angiopathy severity.