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Purpose: Magnetic resonance imaging (MRI) of the brain can provide valuable information about structural abnormalities in strabismus. The aim of this study was to evaluate the utility of MRI in this regard and to identify risk factors for abnormal MRI results in children with strabismus., Methods: A retrospective analysis of children <18 years of age presenting with strabismus, who underwent brain MRI at Pusan National University Hospital (Busan, Korea) between January 2012 and March 2017, was performed. Clinical characteristics, MRI results, and ophthalmologic findings were reviewed. Findings were classified as normal or abnormal according to MRI results. Additionally, patients were divided according to age to compare characteristics of infantile and childhood strabismus., Results: A total of 90 patients (47 [52.2%] male, 43 [47.8%] female; mean age, 2.19 ± 0.53 years) were enrolled. Of those, 64 (71.1%) presented with normal and 26 (28.9%) with abnormal MRI results. The age at presentation was lower and abnormal findings on fundus examination were more common in the abnormal MRI group ( P = .002 and P = .008, respectively). Among the patients, 46 (51.1%) had infantile strabismus and 44 (48.9%) had childhood strabismus. Global developmental delays, speech delays, and MRI abnormalities were more common in patients with infantile than in those with childhood strabismus. Ptosis and headaches were more common in patients with childhood strabismus ( P = .025, P = .025, respectively)., Conclusion: Brain MRI was helpful for accurate diagnosis and treatment of strabismus in younger children, those with abnormal findings on fundus examination, and infants with developmental, especially speech, delays.

Purpose: Obtaining brain tissue is critical to definite diagnosis and to furthering understanding of neurodegenerative diseases. The present authors have maintained the National Neuropathology Reference and Diagnostic Laboratories for Dementia in South Korea since 2016. We have built a nationwide brain bank network and are collecting brain tissues from patients with neurodegenerative diseases. We are aiming to facilitate analyses of clinic-pathological and image-pathological correlations of neurodegenerative disease and to broaden understanding thereof., Materials and Methods: We recruited participants through two routes: from memory clinics and the community. As a baseline evaluation, clinical interviews, a neurological examination, laboratory tests, neuropsychological tests, and MRI were undertaken. Some patients also underwent amyloid PET., Results: We recruited 105 participants, 70 from clinics and 35 from the community. Among them, 11 died and were autopsied. The clinical diagnoses of the autopsied patients included four with Alzheimer's disease (AD), two with subcortical vascular dementia, two with non-fluent variant primary progressive aphasia, one with leukoencephalopathy, one with frontotemporal dementia (FTD), and one with Creutzfeldt-Jakob disease (CJD). Five patients underwent amyloid PET: two with AD, one with mixed dementia, one with FTD, and one with CJD., Conclusion: The clinical and neuropathological information to be obtained from this cohort in the future will provide a deeper understanding of the neuropathological mechanisms of cognitive impairment in Asia, especially Korea., Competing Interests: The authors have no financial conflicts of interest., (© Copyright: Yonsei University College of Medicine 2018.)

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders., (Copyright © 2011 Elsevier Inc. All rights reserved.)

We introduce an automated segmentation method, extended Markov random field (eMRF), to classify 21 neuroanatomical structures of mouse brain based on three dimensional (3D) magnetic resonance images (MRI). The image data are multispectral: T2-weighted, proton density-weighted, diffusion x, y and z weighted. Earlier research (Ali, A.A., Dale, A.M., Badea, A., Johnson, G.A., 2005. Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain. NeuroImage 27 (2), 425-435) successfully explored the use of MRF for mouse brain segmentation. In this research, we study the use of information generated from support vector machine (SVM) to represent the probabilistic information. Since SVM in general has a stronger discriminative power than the Gaussian likelihood method and is able to handle nonlinear classification problems, integrating SVM into MRF improved the classification accuracy. The eMRF employs the posterior probability distribution obtained from SVM to generate a classification based on the MR intensity. Secondly, the eMRF introduces a new potential function based on location information. Third, to maximize the classification performance, the eMRF uses the contribution weights optimally determined for each of the three potential functions: observation, location and contextual functions, which are traditionally equally weighted. We use the voxel overlap percentage and volume difference percentage to evaluate the accuracy of eMRF segmentation and compare the algorithm with three other segmentation methods--mixed ratio sampling SVM (MRS-SVM), atlas-based segmentation and MRF. Validation using classification accuracy indices between automatically segmented and manually traced data shows that eMRF outperforms other methods.