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Higher-Level Gait Disorder (HLGD) is a type of gait disorder estimated to affect up to 6% of the older population. By definition, its symptoms originate from the higher-level nervous system, yet its association with brain morphology remains unclear. This study hypothesizes that there are patterns in brain morphology linked to HLGD. For the first time in the literature, this work investigates whether deep learning, in the form of convolutional neural networks, can capture patterns in magnetic resonance images to identify individuals affected by HLGD. To handle this new classification task, we propose setting up an ensemble of models. This leverages the benefits of combining classifiers instead of determining which network is the most suitable, developing a new architecture, or customizing an existing one. We introduce a computationally cost-effective search algorithm to find the optimal ensemble by leveraging a cost function of both traditional performance scores and the diversity among the models. Using a unique dataset from a large population-based cohort (VESPR), the ensemble identified by our algorithm demonstrated superior performance compared to single networks, other ensemble fusion techniques, and the best linear radiological measure. This emphasizes the importance of implementing diversity into the cost function. Furthermore, the results indicate significant morphological differences in brain structure between HLGD-affected individuals and controls, motivating research about which areas the networks base their classifications on, to get a better understanding of the pathophysiology of HLGD.

Over the last decade, it has become evident that cerebrospinal fluid (CSF) plays a pivotal role in brain solute clearance through perivascular pathways and interactions between the brain and meningeal lymphatic vessels. Whereas most of this fundamental knowledge was gained from rodent models, human brain clearance imaging has provided important insights into the human system and highlighted the existence of important interspecies differences. Current gold standard techniques for human brain clearance imaging involve the injection of gadolinium-based contrast agents and monitoring their distribution and clearance over a period from a few hours up to 2 days. With both intrathecal and intravenous injections being used, which each have their own specific routes of distribution and thus clearance of contrast agent, a clear understanding of the kinetics associated with both approaches, and especially the differences between them, is needed to properly interpret the results. Because it is known that intrathecally injected contrast agent reaches the blood, albeit in small concentrations, and that similarly some of the intravenously injected agent can be detected in CSF, both pathways are connected and will, in theory, reach the same compartments. However, because of clear differences in relative enhancement patterns, both injection approaches will result in varying sensitivities for assessment of different subparts of the brain clearance system. In this opinion review article, the "EU Joint Programme – Neurodegenerative Disease Research (JPND)" consortium on human brain clearance imaging provides an overview of contrast agent pharmacokinetics in vivo following intrathecal and intravenous injections and what typical concentrations and concentration–time curves should be expected. This can be the basis for optimizing and interpreting contrast-enhanced MRI for brain clearance imaging. Furthermore, this can shed light on how molecules may exchange between blood, brain, and CSF.