1,721,161 research outputs found
Erratum: Default-mode network dysfunction and cognitive impairment in progressive MS (Neurology (2010) 74 (1252-1259))
No full textProgression Independent of Relapse Activity in Multiple Sclerosis: Closer to Solving the Pathologic Puzzle
No full textAuthor response: Gray matter volume modifications in migraine: A cross-sectional and longitudinal study
No full textUnderstanding the Pathophysiology and Magnetic Resonance Imaging of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders
Full text linkMagnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration
Role of artificial intelligence in MS clinical practice
No full textMachine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches
Age Matters in Multiple Sclerosis: Novel Insights Into Disease Progression From Clinical Onset
No full textChronic active lesions: a new MRI biomarker to monitor treatment effect in multiple sclerosis?
No full textNew approaches to lesion assessment in multiple sclerosis
No full textPurpose of review: To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their implications for clinical use and research. Recent findings: Artificial intelligence, particularly deep learning approaches, are revolutionizing MS lesion assessment and segmentation, improving accuracy, reproducibility, and efficiency. Artificial intelligence-based tools now enable automated detection not only of T2-hyperintense white matter lesions, but also of specific lesion subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, and spinal cord lesions, which hold diagnostic and prognostic value. Novel neuroimaging techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, and soma and neurite density imaging (SANDI), together with PET, are providing deeper insights into lesion pathology, better disentangling their heterogeneities and clinical relevance. Summary: Artificial intelligence-powered lesion segmentation tools hold great potential for improving fast, accurate and reproducible lesional assessment in the clinical scenario, thus improving MS diagnosis, monitoring, and treatment response assessment. Emerging neuroimaging modalities may contribute to advance the understanding MS pathophysiology, provide more specific markers of disease progression, and novel potential therapeutic targets
fMRI of the Sensorimotor System
No full textThe extensive application of fMRI to the assessment of the human sensorimotor system has disclosed a complexity that is largely beyond our original understanding. From the available data, it is accepted that this system consists of a large, and somewhat yet unknown, number of cortical and subcortical areas, with a precise location and a specialized function. In particular, a large number of regions in the frontal and parietal lobes contribute to different aspects of motor act performance. It is also evident that the properties and potentialities of this network still need to be fully elucidated by further research. Defining how the human sensorimotor system works is of the utmost importance for understanding its dysfunction in case of diseases and also to develop potential therapeutic strategies capable to enhance its functional plasticityPlasticity and reserve
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