Journal of neuroimaging : official journal of the American Society of Neuroimaging
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Differentiating idiopathic normal pressure hydrocephalus (iNPH) from neurodegenerative disorders such as progressive supranuclear palsy (PSP), Multiple System Atrophy-parkinsonian type (MSA-P), and vascular dementia (VaD) is challenging due to overlapping clinical and neuroimaging findings. This study assesses if quantitative brain stem and cerebellum metrics can aid in this differentiation. ⋯ Our study questions MRPI's diagnostic performance in distinguishing PSP from iNPH. Simpler indices such as midbrain to pons ratio and midbrain area showed similar or better accuracy. However, all these indices displayed low sensitivity despite significant differences among PSP, MSA-P, and VaD.
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Alzheimer's disease (AD) is characterized by cognitive decline and mnestic deficits. The pathophysiology of AD is not fully understood, which renders the development of accurate tools for early diagnosis and effective therapies exceedingly difficult. In this study, we investigated the use of 23Na-MRI to measure the relative sodium signal intensities (rSSIs) in CSF in patients with AD and healthy controls. ⋯ Our study provides evidence that rSSI in CSF is increased in AD patients in comparison to healthy controls. rSSI may serve as a potential marker for early detection and monitoring of disease progression. Larger, longitudinal studies are needed to confirm our findings and to investigate the association between rSSI in CSF and the severity of cognitive impairment.
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High-resolution magnetic resonance imaging (HR-MRI) can provide valuable insights into the histopathological characteristics of moyamoya disease (MMD). However, the patterns of vessel wall contrast enhancement have not been well established. We aimed to identify the contrast enhancement patterns of the vessel walls associated with acute cerebral infarction using HR-MRI in MMD. ⋯ Concentric wall enhancement was a significant predictor of acute cerebral infarction in patients with MMD.
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Review Meta Analysis
Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.
Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. ⋯ AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.
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The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. ⋯ Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.