Journal of neuroimaging : official journal of the American Society of Neuroimaging
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The diagnosis of Dementia with Lewy Bodies (DLB) is challenging due to various clinical presentations and clinical and neuropathological features that overlap with Alzheimer's disease (AD). The use of 18 F-Fluorodeoxyglucose-PET (18 F-FDG-PET) can be limited due to similar patterns in DLB and AD. However, metabolism in the posterior cingulate cortex is known to be relatively preserved in DLB and visual assessment of the "cingulate island sign" became a helpful tool in the analysis of 18F-FDG-PET. The aim of this study was the evaluation of visual and semiquantitative 18F-FDG-PET analyses in the diagnosis of DLB and the differentiation to AD as well as its relation to other dementia biomarkers. ⋯ Semiquantitative 18F-FDG-PET imaging and especially the use of an optimized cingulate island ratio are valuable tools to differentiate between DLB and AD.
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The purpose was to explore the effects of transcutaneous trigeminal nerve stimulation (TNS) on neurochemical concentrations (brainstem, anterior cingulate cortex [ACC], dorsolateral prefrontal cortex [DLPFC], ventromedial prefrontal cortex [VMPFC], and the posterior cingulate cortex [PCC]) using ultrahigh-field magnetic resonance spectroscopy. ⋯ These data demonstrate that a single session of unilateral TNS slightly decreased tCr concentrations in the DLPFC region.
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There are a few studies regarding intracranial findings in neonates with Noonan syndrome (NS); however, there are no quantitative analyses in a pediatric population. The aim of this study was to find characteristic intracranial abnormalities and to quantitatively analyze the posterior fossa and cranium base in children with NS. ⋯ Children with NS had characteristic callosal and tentorial findings and neuroimaging findings similar to other RASopathies. This study also shows that a small posterior fossa and flattening of the cranial base are present in children with NS, which may aid in diagnosis.
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Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. ⋯ The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.
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Autoimmune encephalitis is a category of autoantibody-mediated neurological disorders that often presents a diagnostic challenge due to its variable clinical and imaging findings. The purpose of this image-based review is to provide an overview of the major subtypes of autoimmune encephalitis and their associated autoantibodies, discuss their characteristic clinical and imaging features, and highlight several disease processes that may mimic imaging findings of autoimmune encephalitis. A literature search on autoimmune encephalitis was performed and publications from neuroradiology, neurology, and nuclear medicine literature were included. Cases from our institutional database that best exemplify major imaging features were presented.