No abstract

Increasing evidence recognizes Alzheimer's disease (AD) as a multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. The recently updated AD Research Framework put forth by the National Institute on Aging-Alzheimer's Association describes a biomarker-based pathologic definition of AD focused on amyloid, tau, and neuronal injury. In response to this article, here we first discussed evidence that vascular dysfunction is an important early event in AD pathophysiology. Next, we examined various imaging sequences that could be easily implemented to evaluate different types of vascular dysfunction associated

Compared to women, men may be more susceptible to greater volumes of VRS, particularly in the white matter. RESULTS support the hypothesis that VRS in the white matter may be more related to AD-related vascular pathology compared to VRS found in the basal ganglia. Future work using this novel VRS segmentation tool will examine its potential utility as an imaging biomarker of vascular rather than parenchymal amyloid.

Perivascular Spaces (PVS) are a feature of Small Vessel Disease (SVD), and are an important part of the brain’s circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. We used ordered logit models and visual rating scales as alternative ground truth for Frangi filter parameter optimization and evaluation. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N = 20) and patients who previously had mild to moderate stroke (N = 48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated well with neuroradiological assessments (Spearman’s ρ = 0.74, p < 0.001), supporting the potential of our proposed method.

Although the brain lacks conventional lymphatic vessels found in peripheral tissue, evidence suggests that the space surrounding the vasculature serves a similar role in the clearance of fluid and metabolic waste from the brain. With aging, neurodegeneration, and cerebrovascular disease, these microscopic perivascular spaces can become enlarged, allowing for visualization and quantification on structural MRI. The purpose of this review is to: (i) describe some of the recent pre-clinical findings from basic science that shed light on the potential neurophysiological mechanisms driving glymphatic and perivascular waste clearance, (ii) review some of the pathobiological etiologies that may lead to MRI-visible enlarged perivascular spaces (ePVS), (iii) describe the possible clinical implications of ePVS, (iv) evaluate existing qualitative and quantitative techniques used for measuring ePVS burden, and (v) propose future avenues of research that may improve our understanding of this potential clinical neuroimaging biomarker for fluid and metabolic waste clearance dysfunction in neurodegenerative and neurovascular diseases.

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.

The representation of autobiographical memory is distributed over a network of brain structures, with the medial temporal lobe (MTL) at its epicenter. Some believe that, over time, all memories become independent of their MTL component ("consolidation theories"). Others have suggested that this is true only of semantic memory, while episodic aspects of autobiographical memories are dependent on the MTL for as long as they exist, such as multiple trace theory (MTT). In the present study, the volumes of 28 brain regions, including the MTL, and their relation to autobiographical memory were investigated in a group of patients with Alzheimer's disease with varying degrees of retrograde memory loss as assessed by the Autobiographical Memory Interview (AMI). We used the multivariate analysis method of partial least squares (PLS) to assess patterns of atrophy that can lead to retrograde amnesia. We found that different aspects of autobiographical memory were associated with different patterns of tissue loss. Personal semantics were related to a pattern of bilateral anterior and posterior lateral temporal cortex degeneration, more pronounced on the left, as well as right frontal degeneration. Autobiographical event memory ("episodic") was associated with combined atrophy in bilateral MTL and anterior lateral temporal neocortex, more pronounced on the right. This pattern was invariant for memories from childhood, early adulthood, and recent memories, in line with the predictions of MTT, suggesting that MTL tissue is crucial for retrieval of episodic memories regardless of their age.

These preliminary results suggest that sleep may play a role in perivascular clearance in ischemic brain disease, and invite future research into the potential relevance of Virchow-Robin spaces as an imaging biomarker for nocturnal metabolite clearance.