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Magnetic resonance-guided focused ultrasound in combination with intravenously injected microbubbles has been shown to transiently open the blood–brain barrier, and reduce beta-amyloid and tau pathology in animal models of Alzheimer’s disease. Here, we used focused ultrasound to open the blood–brain barrier in five patients with early to moderate Alzheimer’s disease in a phase I safety trial. In all patients, the blood–brain barrier within the target volume was safely, reversibly, and repeatedly opened. Opening the blood–brain barrier did not result in serious clinical or radiographic adverse events, as well as no clinically significant worsening on cognitive scores at three months compared to baseline. Beta-amyloid levels were measured before treatment using [ 18 F]-florbetaben PET to confirm amyloid deposition at the target site. Exploratory analysis suggested no group-wise changes in amyloid post-sonication. The results of this safety and feasibility study support the continued investigation of focused ultrasound as a potential novel treatment and delivery strategy for patients with Alzheimer’s disease. Magnetic resonance-guided focused ultrasound with injected microbubbles has been used to temporarily open the blood–brain barrier (BBB) in animal models of Alzheimer's disease (AD). Here, the authors use this technology to non-invasively open the BBB in 5 patients with mild-to-moderate AD in a phase I trial, and show that the procedure is safe.

White matter hyperintensities (WMH) are prevalent. Although arteriolar disease has been implicated in their pathogenesis, venous pathology warrants consideration. We investigated relationships of WMH with histologic venous, arteriolar and white matter abnormalities and correlated findings with premortem neuroimaging. Three regions of periventricular white matter were sampled from archived autopsy brains of 24 pathologically confirmed Alzheimer disease (AD) and 18 age-matched nonAD patients. Using trichrome staining, venous collagenosis (VC) of periventricular veins (<150 µm in diameter) was scored for severity of wall thickening and occlusion; percent stenosis by collagenosis of large caliber (>200 µm) veins (laVS) was measured. Correlations were made between WMH in premortem neuroimaging and vascular and white matter pathology. We found greater VC (U(114) = 2092.5, p = 0.005 and U(114) = 2121.5, p = 0.002 for small and medium caliber veins, respectively) and greater laVS (t(110) = 3.46, p = 0.001) in patients with higher WMH scores; WMH scores correlated with VC (rs(114) = 0.27, p = 0.004) and laVS (rs(110) = 0.38, p < 0.001). By multiple linear regression analysis, the strongest predictor of WMH score was laVS (β = 0.338, p < 0.0001). VC was frequent in patients with periventricular infarcts identified on imaging. We conclude that periventricular VC is associated with WMH in both AD and nonAD patients and the potential roles of VC in WMH pathogenesis merit further study.

Medina-Rioja et al discuss frontotemporal dementia. Frontotemporal dementia should be considered in adults aged 50-75 years presenting with behavioral or language changes After Alzheimer disease, it is the second most common cause of dementia among adults younger than 65 years. Frontal and temporal lobe degeneration results in behavioral or language impairment. When frontotemporal dementia is suspected, other causes for symptoms, such as psychiatric diagnoses, should be considered. Cognitive screening with tests sensitive for executive dysfunction (e.g., Montreal Cognitive Assessment) should be used.

Neurogenic orthostatic hypotension and supine hypertension are common manifestations of cardiovascular dysautonomia in Parkinson's disease and related synucleinopathies. Because these disorders are haemodynamic opposites, improvement in one might be achieved at the expense of worsening of the other. Thus, management decisions necessitate assessment of the individual risks for patients with coexistent neurogenic orthostatic hypotension and supine hypertension. Whereas neurogenic orthostatic hypotension poses risks for falls and can be associated with cognitive impairment in the short term, chronic supine hypertension can be associated with stroke and myocardial infarction in the long term. Because few clinical trial data exist for outcomes in patients with coexistent neurogenic orthostatic hypotension and supine hypertension, clinicians need to balance, on the basis of comorbidities and disease staging, the potential immediate benefits of treatment for neurogenic orthostatic hypotension and the long-term risks of supine hypertension treatment in each patient. Future research needs to focus on ascertaining a safe degree of supine hypertension when treating neurogenic orthostatic hypotension; the effectiveness of nocturnal antihypertensive therapy in patients with coexistent neurogenic orthostatic hypotension and supine hypertension; and the prevalence, scope, and therapeutic requirements for managing neurogenic orthostatic hypotension that manifests with falls or cognitive impairment, but without postural lightheadedness or near syncope.

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI. [Display omitted] •Developed open-source benchmarking platform and metrics for robustness of DNNs.•Quantified sensitivity of DNNs to OOD data on three neuroimaging segmentation tasks.•Modern CNNs are highly susceptible to distribution shift, corruptions and artifacts.•Simple augmentation strategies improve robustness for anatomical segmentation tasks.•Vision transformers exhibit improved robustness over FCNs.

Two centuries ago in 1817, James Parkinson provided the first medical description of Parkinson’s disease, later refined by Jean-Martin Charcot in the mid-to-late 19th century to include the atypical parkinsonian variants (also termed, Parkinson-plus syndromes). Today, Parkinson’s disease represents the second most common neurodegenerative disorder with an estimated global prevalence of over 10 million. Conversely, atypical parkinsonian syndromes encompass a group of relatively heterogeneous disorders that may share some clinical features with Parkinson’s disease, but are uncommon distinct clinicopathological diseases. Decades of scientific advancements have vastly improved our understanding of these disorders, including improvements in in vivo imaging for biomarker identification. Multimodal imaging for the visualization of structural and functional brain changes is especially important, as it allows a ‘window’ into the underlying pathophysiological abnormalities. In this article, we first present an overview of the cardinal clinical and neuropathological features of, 1) synucleinopathies: Parkinson’s disease and other Lewy body spectrum disorders, as well as multiple system atrophy, and 2) tauopathies: progressive supranuclear palsy, and corticobasal degeneration. A comprehensive presentation of well-established and emerging imaging biomarkers for each disorder are then discussed. Biomarkers for the following imaging modalities are reviewed: 1) structural magnetic resonance imaging (MRI) using T1, T2, and susceptibility-weighted sequences for volumetric and voxel-based morphometric analyses, as well as MRI derived visual signatures, 2) diffusion tensor MRI for the assessment of white matter tract injury and microstructural integrity, 3) proton magnetic resonance spectroscopy for quantifying proton-containing brain metabolites, 4) single photon emission computed tomography for the evaluation of nigrostriatal integrity (as assessed by presynaptic dopamine transporters and postsynaptic dopamine D2 receptors), and cerebral perfusion, 5) positron emission tomography for gauging nigrostriatal functions, glucose metabolism, amyloid and tau molecular imaging, as well as neuroinflammation, 6) myocardial scintigraphy for dysautonomia, and 7) transcranial sonography for measuring substantia nigra and lentiform nucleus echogenicity. Imaging biomarkers, using the ‘multimodal approach’, may aid in making early, accurate and objective diagnostic decisions, highlight neuroanatomical and pathophysiological mechanisms, as well as assist in evaluating disease progression and therapeutic responses to drugs in clinical trials.

During the COVID-19 pandemic, the Canadian Medical Association and provincial health authorities advised physicians to provide virtual care to patients where possible. Virtual care, or telemedicine, has been defined as \"any interaction between patients and/or members of their circle of care, occurring remotely, using any forms of communication or information technologies, with the aim of facilitating or maximizing the quality and effectiveness of patient care.\" Such care has been increasingly used as a means of overcoming physical barriers to health care provision, particularly in the context of improving availability and accessibility in rural and remote areas. More recently, the physical distancing measures necessitated by the pandemic have created an urgent imperative to integrate virtual care into existing health care infrastructure. People with Alzheimer disease and related dementias may face unique challenges in securing access to necessary health care. These challenges may be compounded during the COVID-19 pandemic, as people who normally rely on in-person support to complete daily tasks, such as transportation to and attendance at medical appointments, may be disproportionately affected by the need for physical distancing. Moreover, a 2016 evaluation predicted that the number of people living with dementia would nearly double within 15 years.

Rhombencephalitis is an inflammatory disorder of the cerebellum, pons, and medulla oblongata that can result in abnormal cerebellar function, pyramidal weakness, proprioceptive deficits, and altered level of consciousness. Causes of rhombencephalitis include infections, inflammatory or demyelinating conditions, and paraneoplastic disorders. In immunocompromised patients, atypical infections can cause rhombencephalitis, including John Cunningham virus; this virus causes primary multifocal leukoencephalopathy (PML), which can manifest as rhombencephalitis. Patients with chronic lymphocytic leukemia are at increased risk of developing PML owing to underlying immunocompromise from the leukemia. Here, Tilley et al discuss the presence of rhombencephalitis in an 86-year-old woman with chronic lymphocytic leukemia.

The accumulation of beta amyloid in the brain has a complex and poorly understood impact on the progression of Parkinson’s disease pathology and much controversy remains regarding its role, specifically in cognitive decline symptoms. Some studies have found increased beta amyloid burden is associated with worsening cognitive impairment in Parkinson’s disease, especially in cases where dementia occurs, while other studies failed to replicate this finding. To better understand this relationship, we examined a cohort of 25 idiopathic Parkinson’s disease patients and 30 healthy controls from the Parkinson’s Progression Marker Initiative database. These participants underwent [ 18 F]Florbetaben positron emission tomography scans to quantify beta amyloid deposition in 20 cortical regions. We then analyzed this beta amyloid data alongside the longitudinal Montreal Cognitive Assessment scores across 3 years to see how participant’s baseline beta amyloid levels affected their cognitive scores prospectively. The first analysis we performed with these data was a hierarchical cluster analysis to help identify brain regions that shared similarity. We found that beta amyloid clusters differently in Parkinson’s disease patients compared to healthy controls. In the Parkinson’s disease group, increased beta amyloid burden in cluster 2 was associated with worse cognitive ability, compared to deposition in clusters 1 or 3. We also performed a stepwise linear regression where we found an adjusted R 2 of 0.495 (49.5%) in a model explaining the Parkinson’s disease group’s Montreal Cognitive Assessment score 1-year post-scan, encompassing the left gyrus rectus, the left anterior cingulate cortex, and the right parietal cortex. Taken together, these results suggest regional beta amyloid deposition alone has a moderate effect on predicting future cognitive decline in Parkinson’s disease patients. The patchwork effect of beta amyloid deposition on cognitive ability may be part of what separates cognitive impairment from cognitive sparing in Parkinson’s disease. Thus, we suggest it would be more useful to measure beta amyloid burden in specific brain regions rather than using a whole-brain global beta amyloid composite score and use this information as a tool for determining which Parkinson’s disease patients are most at risk for future cognitive decline.