Journal of neuroimaging : official journal of the American Society of Neuroimaging
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Vascular malformations (VMs) of the central nervous system (CNS) include a wide range of pathological conditions related to intra and extracranial vessel abnormalities. Although some VMs show typical neuroimaging features, other VMs share and overlap pathological and neuroimaging features that hinder an accurate differentiation between them. Hence, it is not uncommon to misclassify different types of VMs under the general heading of arteriovenous malformations. ⋯ Beyond MR images, new insights using 3D printed models are being incorporated as part of the armamentarium for a noninvasive evaluation of VMs. In this paper, we briefly review the pathophysiology of CNS VMs, focusing on the MRI findings that may be helpful to differentiate them. We discuss the role of each conventional and advanced MRI sequence for VMs assessment and provide some insights about the value of structured reports of 3D printing to evaluate VMs.
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Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine. ⋯ DeepnCCA (https://github.com/plattenmichael/DeepnCCA/) is an openly available tool that can provide fast and accurate corpus callosum measurements applicable to large MS cohorts, potentially suitable for monitoring disease progression and therapy response.
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Cognitive impairment is a critical health problem in the elderly population. Research has shown that patients with mild cognitive impairment (MCI) may develop dementia in later years. Therefore, early identification of MCI could allow for interventions to help delay the progression of this devastating disease. Our objective in this study was to detect the early presence of MCI in elderly patients via neuroimaging and dual-task performance. ⋯ This study highlighted the potential of dual-tasking and MRI morphometric changes as a simple and accurate tool for early detection of cognitive impairment among community-dwelling older adults. The strong interaction effects of cognitive group on UEF dual-task score suggest higher association between atrophy of these brain structures and compromised dual-task performance among the MCI group.
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Fatigue is the common symptom in patients with multiple sclerosis (MS), yet its pathophysiological mechanism is poorly understood. We investigated the metabolic changes in fatigue in a group of relapsing-remitting MS (RRMS) patients using MR two-dimensional localized correlated spectroscopy (2D L-COSY). ⋯ Our results suggest that fatigue in MS is strongly correlated with an imbalance in neurometabolites but not structural brain measurements.
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Dementia with Lewy bodies (DLB) is the second most prevalent cause of degenerative dementia next to Alzheimer's disease (AD). Though current DLB diagnostic criteria employ several indicative biomarkers, relative preservation of the medial temporal lobe as revealed by structural MRI suffers from low sensitivity and specificity, making them unreliable as sole supporting biomarkers. In this study, we investigated how a deep learning approach would be able to differentiate DLB from AD with structural MRI data. ⋯ Our results confirmed that the deep learning method with gray matter images can detect fine differences between DLB and AD that may be underestimated by the conventional method.