Study Overview
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In neurological rare disease, pathological genes cause malfunction or maldevelopment of the nervous sytem, resulting in clinical symtoms
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An endophenotype (also known as intermediate phenotype) is a quantitative biological trait that is reliable in reflecting the function of a discrete biological system and is reasonably heritable, and as such is more closely related to the root cause of the disease than the broad clinical phenotype (Gottesman and Gould, 2003; Cannon and Keller, 2006; Meyer-Lindenberg and Weinberger, 2006; Tan et al., 2008).
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In general, it is difficult to predict or diagnose rare diseases using only the correlation between gene and clinical symtoms, so an approach to using neuroimaging endogenotype has recently been studied. Neuroimaging endophenotypes are quantitative indicators of brain structure or function that index genetic liability for an illness (DC Glahn et al., 2007). Many studies have been conducted to track abnormal signals in brain MRI caused by pathological gene in neurological rare diseases.
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A representative imaging endophenotype study is to extract abnormal features in MRI through quantitative analysis. Identifying diseases using the association between genotype and quantitative features in MRI is expected to reduce the probability of misdiagnosis, conduct appropriate initial treatment, and prevent unnecessary treatment due to misdiagnosis.
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Therefore we collected and analyzed MRIs of 150 patients with neurological rare diseases. Image features such as volumes for each anatomical area, white matter hyperintensity, and myelination index were extracted through the state-of-the-art algorithms for T1, T2, and FLAIR MRI images commonly taken in most patients.
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A detailed list of quantitative features in MRI is given below.
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Target MRI Features and clinical utilities
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Volumetry of brain tissue (GM, WM) and each anatomical brain area (268 areas)
Clinical Utility : Quantification of brain atrophy
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Volumetry of T2 hyperintensity in FLAIR MRI
Clinical Utility : Quantification of T2 high signal lesion in 117 gray matter and 20 white matter areas
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Voxel wise calculation of T1, T2 ratio
Clinical Utility : Quantification of the myelination in 48 white matter tracks