This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to "background" or "artifact." The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.

Metabolic dysfunction and microvascular abnormality may contribute to the pathogenesis of schizophrenia. Most previous studies of cerebral perfusion in schizophrenia measured total cerebral blood volume (CBV) and cerebral blood flow (CBF) in the brain, which reflect the ensemble signal from the arteriolar, capillary, and venular compartments of the microvasculature. As the arterioles are the most actively regulated blood vessels among these compartments, they may be the most sensitive component of the microvasculature to metabolic disturbances. In this study, we adopted the inflow-based vascular-space-occupancy (iVASO) MRI approach to investigate alterations in the volume of small arterial (pial) and arteriolar vessels (arteriolar cerebral blood volume [CBVa]) in the brain of schizophrenia patients. The iVASO approach was extended to 3-dimensional (3D) whole brain coverage, and CBVa was measured in the brains of 12 schizophrenia patients and 12 matched controls at ultra-high magnetic field (7T). Significant reduction in grey matter (GM) CBVa was found in multiple areas across the whole brain in patients (relative changes of 14%-51% and effect sizes of 0.7-2.3). GM CBVa values in several regions in the temporal cortex showed significant negative correlations with disease duration in patients. GM CBVa increase was also found in a few brain regions. Our results imply that microvascular abnormality may play a role in schizophrenia, and suggest GM CBVa as a potential marker for the disease. Further investigation is needed to elucidate whether such effects are due to primary vascular impairment or secondary to other causes, such as metabolic dysfunction.

The amygdala has attracted considerable research interest because of its potential involvement in various neuropsychiatric disorders. Recently, attempts have been made using magnetic resonance imaging (MRI) to evaluate the integrity of the axonal connections to and from the amygdala under pathological conditions. Although amygdalar pathways have been studied extensively in animal models, anatomical references for the human brain are limited to histology-based resources from a small number of slice locations, orientations and annotations. In the present study, we performed high-resolution (250 μm) MRI of postmortem human brains followed by serial histology sectioning. The histology data were used to identify amygdalar pathways, and the anatomical delineation of the assigned structures was extended into 3D using the MRI data. We were able to define the detailed anatomy of the stria terminalis and amygdalofugal pathway, as well as the anatomy of the nearby basal forebrain areas, including the substantia innominata. The present results will help us understand in detail the white matter structures associated with the amygdala, and will serve as an anatomical reference for the design of in vivo MRI studies and interpretation of their data.

This paper examines the problem of diffeomorphic image mapping in the presence of differing image intensity profiles and missing data. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections with 1 micron in plane resolution. Multiple stains, as well as damaged, folded, or missing tissue are common in this situation. We overcome these challenges by introducing two new concepts. Cross modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity, together with pose and deformation parameters. Missing data is accommodated via a multiple atlas selection procedure where several atlases may be of homogeneous intensity and correspond to "background" or "artifact". The two concepts are combined within an Expectation Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively, and polynomial coefficients are computed in closed form. We show results for 3D reconstruction of digital pathology and MRI in standard atlas coordinates. In conjunction with convolutional neural networks, we quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen. Author summaryOur work in Alzheimer's disease (AD) is attempting to connect histopathology at autopsy and longitudinal clinical magnetic resonance imaging (MRI), combining the strengths of each modality in a common coordinate system. We are bridging this gap by January 4, 2019 1/19 using post mortem high resolution MRI to reconstruct digital pathology in 3D. This image registration problem is challenging because it combines images from different modalities in the presence of missing tissue and artifacts. We overcome this challenge by developing a new registration technique that simultaneously classifies each pixel as "good data" / "missing tissue" / "artifact", learns a contrast transformation between modalities, and computes deformation parameters. We name this technique "(D)eformable (R)egistration and (I)ntensity (T)ransformation with (M)issing (D)ata", pronounced as "Dr. It, M.D.". In conjunction with convolutional neural networks, we use this technique to map the three dimensional distribution of tau tangles in the medial temporal lobe of an AD postmortem specimen. 1 High throughput neuroinformatics is emerging in neuroscience [1, 2]. Atlas based image 2 analysis plays a key role, as it enables information encoded by millions of independent 3 voxel measurements to be reconstructed in ontologies of the roughly 100 evolutionarily 4 stable structures. At the 1 millimeter scale there are many atlases, including Tailarach 5 coordinates [3], Montreal Neurological Institute (MNI) [4], and Mori's diffusion tensor 6 imaging (DTI) white matter atlases [5], which define the locations of neuroanatomical 7 structures as well as important structural and functional properties such as volume, 8 shape, blood oxygen-level dependent (BOLD) signals, etc. At micron and m...

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