Journal of neuroimaging : official journal of the American Society of Neuroimaging
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Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics. ⋯ These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression.
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Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics. ⋯ These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression.
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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.
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Epilepsy, affecting 0.5%-1% of the global population, presents a significant challenge with 30% of patients resistant to medical treatment. Temporal lobe epilepsy, a common cause of medically refractory epilepsy, is often caused by hippocampal sclerosis (HS). HS can be divided further by subtype, as defined by the International League Against Epilepsy (ILAE). ⋯ This literature review evaluates studies on hippocampal subfields, exploring whether observable atrophy patterns from in vivo and ex vivo magnetic resonance imaging (MRI) scans correlate with histopathological examinations with manual or automated segmentation techniques. Our findings suggest only ex vivo 1.5 Tesla (T) or 3T MRI with manual segmentation or in vivo 7T MRI with manual or automated segmentations can consistently correlate subfield patterns with histopathologically derived ILAE-HS subtypes. In conclusion, manual and automated segmentation methods offer advantages and limitations in diagnosing ILAE-HS subtypes, with ongoing research crucial for refining hippocampal subfield segmentation techniques and enhancing clinical applicability.
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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.