130 research outputs found

    Neurodegeneration in Schizophrenia: Evidence from In Vivo

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    Although schizophrenia is primarily considered to be a neurodevelopmental disorder, there is a growing consensus that the disorder may also involve neurodegeneration. Recent research using non-invasive neuroimaging techniques, such as magnetic resonance imaging, suggests that some patients with schizophrenia show progressive losses of gray matter in the frontal and temporal lobes of the brain. The cellular mechanisms responsible for such gray matter losses are unknown, but have been hypothesized to involve abnormal increases in apoptosis

    Open access series of imaging studies: Longitudinal MRI data in nondemented and demented older adults

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    The Open Access Series of Imaging Studies (OASIS) is a series of neuroimaging data sets that is publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer‘s disease (AD). 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have ‘mild cognitive impairment’. Another 14 subjects were characterized as nondemented at the time of their initial visit (CDR 0) and were subsequently characterized as demented at a later visit (CDR > 0). The subjects were all right-handed and include both men (n=62) and women (n=88). For each scanning session, 3 or 4 individual T1-weighted MRI scans were obtained. Multiple within-session acquisitions provide extremely high contrast-to-noise making the data amenable to a wide range of analytic approaches including automated computational analysis. Automated calculation of whole brain volume is presented to demonstrate use of the data for measuring differences associated with normal aging and AD

    Large Deformation Diffeomorphic Metric Mapping Registration of Reconstructed 3D Histological Section Images and in vivo MR Images

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    Our current understanding of neuroanatomical abnormalities in neuropsychiatric diseases is based largely on magnetic resonance imaging (MRI) and post mortem histological analyses of the brain. Further advances in elucidating altered brain structure in these human conditions might emerge from combining MRI and histological methods. We propose a multistage method for registering 3D volumes reconstructed from histological sections to corresponding in vivo MRI volumes from the same subjects: (1) manual segmentation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) compartments in histological sections, (2) alignment of consecutive histological sections using 2D rigid transformation to construct a 3D histological image volume from the aligned sections, (3) registration of reconstructed 3D histological volumes to the corresponding 3D MRI volumes using 3D affine transformation, (4) intensity normalization of images via histogram matching, and (5) registration of the volumes via intensity based large deformation diffeomorphic metric (LDDMM) image matching algorithm. Here we demonstrate the utility of our method in the transfer of cytoarchitectonic information from histological sections to identify regions of interest in MRI scans of nine adult macaque brains for morphometric analyses. LDDMM improved the accuracy of the registration via decreased distances between GM/CSF surfaces after LDDMM (0.39 ± 0.13 mm) compared to distances after affine registration (0.76 ± 0.41 mm). Similarly, WM/GM distances decreased to 0.28 ± 0.16 mm after LDDMM compared to 0.54 ± 0.39 mm after affine registration. The multistage registration method may find broad application for mapping histologically based information, for example, receptor distributions, gene expression, onto MRI volumes
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