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Abstract. We construct a computational framework for automatic central nervous system (CNS) disease discrimination using high resolution Magnetic Resonance Images (MRI) of human brains. More than 3000 MR image features are extracted, forming a high dimensional coarseto-fine hierarchical image description that quantifies brain asymmetry, texture and statistical properties in corresponding local regions of the brain. Discriminative image feature subspaces are computed, evaluated and selected automatically. Our initial experimental results show 100% and 90% separability between chronicle schizophrenia (SZ) and first episode SZ versus their respective matched controls. Under the same computational framework, we also find higher than 95% separability among Alzheimer's Disease, mild cognitive impairment patients, and their matched controls. An average of 88% classification success rate is achieved using leave-one-out cross validation on five different well-chosen patient-control image sets of sizes from 15 to 27 subjects per disease class.

The purpose of this study was to utilize a novel imaging biomarker to assess the associations between physical activity (PA), body mass index (BMI), and brain structure in normal aging, mild cognitive impairment (MCI) and Alzheimer's dementia (AD). We studied 963 participants (mean age: 74.1 ± 4.4) from the multi-site Cardiovascular Health Study including healthy controls (n=724), AD (n=104), and MCI (n=135). Volumetric brain images were processed using tensor-based morphometry for analyzing regional brain volumes. We regressed the local brain tissue volume on reported PA and computed BMI, and performed conjunction analyses using both variables. Covariates included age, sex and study site. PA was independently associated with greater whole brain and regional brain volumes, and reduced ventricular dilation. People with higher BMI had lower whole brain and regional brain volumes. A PA-BMI conjunction analysis showed brain preservation with PA and volume loss with increased BMI in overlapping brain regions. In one of the largest voxel-based cross-sectional studies to date, PA and lower BMI may be beneficial to the brain across the spectrum of aging and neurodegeneration.

This paper describes a robust algorithm for reliable ideal Midsagittal Plane extraction (iMSP) from 3D neuroimages. The algorithm makes no assumptions about initial orientation of a given 3D brain image and works reliably on neuroimages of normal brains as well as brains with significant pathologies. Presented technique is truly three-dimensional since we treat each neuroimage as a three-dimensional volume rather than a set of two-dimensional slices. We use an edgebased approach which employs cross-correlation to extract iMSP. Proposed algorithm was quantitatively evaluated on a variety of real and artificial neuroimages. We find that our algorithm is able to extract iMSP from neuroimages with arbitrary initial orientations, large asymmetries, and low signal to noise ratio. We also demonstrate that presented algorithm can increase robustness of existing neuroimage registration algorithms, be it rigid, affine or less restricted deformable registration. Our algorithm was implemented using Insight Toolkit(ITK).

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We propose a computational framework for learning predictive image features as "biomarkers" for Alzheimer's Disease discrimination using high-resolution Magnetic Resonance (MR) brain images. We focus on the exploration of a very large (> 500 million) feature space derived extensively from the deformation and tensor elds. In such a huge space, our computational tool supports an automatic search for discriminative feature subspaces and the corresponding anatomical regions in human brains, which can be used to discriminate previously unseen, individual structural MR images from Alzheimer's Disease (AD) and normal control (CTL) subjects. Our aggressive leave-ten-out cross-validations on 40 subjects demonstrate higher than 90% sensitivity and speci city. In addition, we demonstrate intriguing anatomical locations as automatically discovered "biomarkers" and the spatial distributions of 20 Mild cognitive impairment (MCI) subjects in the discriminative feature space automatically learned for AD and CTL separations. Our results illustrate a truly complementary effort of human and computers for early diagnosis of AD from MR images.

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