Kenichi Oishi

Stereotaxic White Matter Atlas Based on Diffusion Tensor Imaging in an ICBM Template

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.

Dominant-negative DISC1 transgenic mice display schizophrenia-associated phenotypes detected by measures translatable to humans

Here, we report generation and characterization of Disrupted-In-Schizophrenia-1 (DISC1) genetically engineered mice as a potential model for major mental illnesses, such as schizophrenia. DISC1 is a promising genetic risk factor for major mental illnesses. In this transgenic model, a dominant-negative form of DISC1 (DN-DISC1) is expressed under the αCaMKII promoter. In vivo MRI of the DN-DISC1 mice detected enlarged lateral ventricles particularly on the left side, suggesting a link to the asymmetrical change in anatomy found in brains of patients with schizophrenia. Furthermore, selective reduction in the immunoreactivity of parvalbumin in the cortex, a marker for an interneuron deficit that may underlie cortical asynchrony, is observed in the DN-DISC1 mice. These results suggest that these transgenic mice may be used as a model for schizophrenia. DN-DISC1 mice also display several behavioral abnormalities, including hyperactivity, disturbance in sensorimotor gating and olfactory-associated behavior, and an anhedonia/depression-like deficit.

Human Brain White Matter Atlas: Identification and Assignment of Common Anatomical Structures in Superficial White Matter

Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.

Maternal infection leads to abnormal gene regulation and brain atrophy in mouse offspring: implications for genesis of neurodevelopmental disorders.

Prenatal viral infection has been associated with development of schizophrenia and autism. Our laboratory has previously shown that viral infection causes deleterious effects on brain structure and function in mouse offspring following late first trimester (E9) administration of influenza virus. We hypothesized that late second trimester infection (E18) in mice may lead to a different pattern of brain gene expression and structural defects in the developing offspring. C57BL6J mice were infected on E18 with a sublethal dose of human influenza virus or sham-infected using vehicle solution. Male offsping of the infected mice were collected at P0, P14, P35 and P56, their brains removed and prefrontal cortex, hippocampus and cerebellum dissected and flash frozen. Microarray, qRT-PCR, DTI and MRI scanning, western blotting and neurochemical analysis were performed to detect differences in gene expression and brain atrophy. Expression of several genes associated with schizophrenia or autism including Sema3a, Trfr2 and Vldlr were found to be altered as were protein levels of Foxp2. E18 infection of C57BL6J mice with a sublethal dose of human influenza virus led to significant gene alterations in frontal, hippocampal and cerebellar cortices of developing mouse progeny. Brain imaging revealed significant atrophy in several brain areas and white matter thinning in corpus callosum. Finally, neurochemical analysis revealed significantly altered levels of serotonin (P14, P35), 5-Hydroxyindoleacetic acid (P14) and taurine (P35). We propose that maternal infection in mouse provides an heuristic animal model for studying the environmental contributions to genesis of schizophrenia and autism, two important examples of neurodevelopmental disorders.

Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively analyze the status of the white matter anatomy in a tract-specific manner in many types of diseases. This approach, however, involves subjective judgment in the tract-editing process to extract only the tracts of interest. This process, usually performed by manual delineation of regions of interest, is also time-consuming, and certain tracts, especially the short cortico-cortical association fibers, are difficult to reconstruct. In this paper, we propose an automated approach for reconstruction of a large number of white matter tracts. In this approach, existing anatomical knowledge about tract trajectories (called the Template ROI Set or TRS) were stored in our DTI-based brain atlas with 130 three-dimensional anatomical segmentations, which were warped non-linearly to individual DTI data. We examined the degree of matching with manual results for selected fibers. We established 30 TRSs to reconstruct 30 prominent and previously well-described fibers. In addition, TRSs were developed to delineate 29 short association fibers that were found in all normal subjects examined in this paper (N=20). Probabilistic maps of the 59 tract trajectories were created from the normal subjects and were incorporated into our image analysis tool for automated tract-specific quantification.

Multi-Contrast Human Neonatal Brain Atlas: Application to Normal Neonate Development Analysis

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37-53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders.

Atlas-Based Analysis of Neurodevelopment from Infancy to Adulthood Using Diffusion Tensor Imaging and Applications for Automated Abnormality Detection

Quantification of normal brain maturation is a crucial step in understanding developmental abnormalities in brain anatomy and function. The aim of this study was to develop atlas-based tools for time-dependent quantitative image analysis, and to characterize the anatomical changes that occur from 2years of age to adulthood. We used large deformation diffeomorphic metric mapping to register diffusion tensor images of normal participants into the common coordinates and used a pre-segmented atlas to segment the entire brain into 176 structures. Both voxel- and atlas-based analyses reported a structure that showed distinctive changes in terms of its volume and diffusivity measures. In the white matter, fractional anisotropy (FA) linearly increased with age in logarithmic scale, while diffusivity indices, such as apparent diffusion coefficient (ADC), and axial and radial diffusivity, decreased at a different rate in several regions. The average, variability, and the time course of each measured parameter are incorporated into the atlas, which can be used for automated detection of developmental abnormalities. As a demonstration of future application studies, the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated.

Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging.

Diffusion tensor imaging (DTI) can reveal detailed white matter anatomy and has the potential to detect abnormalities in specific white matter structures. Such detection and quantification are, however, not straightforward. The voxel-based analysis after image normalization is one of the most widely used methods for quantitative image analyses. To apply this approach to DTI, it is important to examine if structures in the white matter are well registered among subjects, which would be highly dependent on employed algorithms for normalization. In this paper, we evaluate the accuracy of normalization of DTI data using a highly elastic transformation algorithm, called large deformation diffeomorphic metric mapping. After simulation-based validation of the algorithm, DTI data from normal subjects were used to measure the registration accuracy. To examine the impact of morphological abnormalities on the accuracy, the algorithm was also tested using data from Alzheimers disease (AD) patients with severe brain atrophy. The accuracy level was measured by using manual landmark-based white matter matching and surface-based brain and ventricle matching as gold standard. To improve the accuracy level, cascading and multi-contrast approaches were developed. The accuracy level for the white matter was 1.88+/-0.55 and 2.19+/-0.84 mm for the measured locations in the controls and patients, respectively.

Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease

The fornix is the predominant outflow tract of the hippocampus, a brain region known to be affected early in the course of Alzheimers disease (AD). The aims of the present study were to: (1) examine the cross‐sectional relationship between fornix diffusion tensor imaging (DTI) measurements (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity, and radial diffusivity), hippocampal volume, and memory performance, and (2) compare fornix DTI measures with hippocampal volumes as predictors of progression and transition from amnestic mild cognitive impairment to AD dementia.

White matter atlases based on diffusion tensor imaging

Purpose of reviewDiffusion tensor imaging (DTI) has a unique capability to delineate axonal tracts within the white matter, which has not been possible with previous noninvasive imaging techniques. In the past 10 years, we have witnessed a large increase in the use of DTI-based studies and a score of new anatomical knowledge and image analysis tools have been introduced in recent years. This review will provide an overview of the recent advancements in DTI-based studies and new image analysis tools. Recent findingsDTI provided new dimensions for the characterization of white matter anatomy. This characterization of the white matter can be roughly divided into two categories. First, the white matter can be parcellated into constituent white matter tracts, based on pixel-by-pixel orientation and anisotropy information. Second, the DTI information can be extrapolated to obtain three-dimensional connectivity information. Based on these capabilities of DTI, many new image analysis tools are being developed to investigate the status of the white matter. SummaryIn the past, the white matter has often been treated as one compartment. With DTI and recently developed analysis tools, we can investigate the status of intra-white matter structures and deepen our understanding of white matter structures and their abnormalities under pathological conditions.