Kazi Akhter

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.

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.

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.

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.

Quantitative analysis of brain pathology based on MRI and brain atlases—Applications for cerebral palsy

We have developed a new method to provide a comprehensive quantitative analysis of brain anatomy in cerebral palsy patients, which makes use of two techniques: diffusion tensor imaging and automated 3D whole brain segmentation based on our brain atlas and a nonlinear normalization technique (large-deformation diffeomorphic metric mapping). This method was applied to 13 patients and normal controls. The reliability of the automated segmentation revealed close agreement with the manual segmentation. We illustrate some potential applications for individual characterization and group comparison. This technique also provides a framework for determining the impact of various neuroanatomic features on brain functions.

Mapping of Functional Areas in the Human Cortex Based on Connectivity through Association Fibers

In the human brain, different regions of the cortex communicate via white matter tracts. Investigation of this connectivity is essential for understanding brain function. It has been shown that trajectories of white matter fiber bundles can be estimated based on orientational information that is obtained from diffusion tensor imaging (DTI). By extrapolating this information, cortical regions associated with a specific white matter tract can be estimated. In this study, we created population-averaged cortical maps of brain connectivity for 4 major association fiber tracts, the corticospinal tract (CST), and commissural fibers. It is shown that these 4 association fibers interconnect all 4 lobes of the hemispheres. Cortical regions that were assigned based on association with the CST and the superior longitudinal fasciculus (SLF) agreed with locations of their known (CST: motor) or putative (SLF: language) functions. The proposed approach can potentially be used for quantitative assessment of the effect of white matter abnormalities on associated cortical regions.

Multi-Modal MRI Analysis with Disease-Specific Spatial Filtering: Initial Testing to Predict Mild Cognitive Impairment Patients Who Convert to Alzheimer’s Disease

Background: Alterations of the gray and white matter have been identified in Alzheimer’s disease (AD) by structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). However, whether the combination of these modalities could increase the diagnostic performance is unknown. Methods: Participants included 19 AD patients, 22 amnestic mild cognitive impairment (aMCI) patients, and 22 cognitively normal elderly (NC). The aMCI group was further divided into an “aMCI-converter” group (converted to AD dementia within 3 years), and an “aMCI-stable” group who did not convert in this time period. A T1-weighted image, a T2 map, and a DTI of each participant were normalized, and voxel-based comparisons between AD and NC groups were performed. Regions-of-interest, which defined the areas with significant differences between AD and NC, were created for each modality and named “disease-specific spatial filters” (DSF). Linear discriminant analysis was used to optimize the combination of multiple MRI measurements extracted by DSF to effectively differentiate AD from NC. The resultant DSF and the discriminant function were applied to the aMCI group to investigate the power to differentiate the aMCI-converters from the aMCI-stable patients. Results: The multi-modal approach with AD-specific filters led to a predictive model with an area under the receiver operating characteristic curve (AUC) of 0.93, in differentiating aMCI-converters from aMCI-stable patients. This AUC was better than that of a single-contrast-based approach, such as T1-based morphometry or diffusion anisotropy analysis. Conclusion: The multi-modal approach has the potential to increase the value of MRI in predicting conversion from aMCI to AD.

Fractional anisotropy in individuals with schizophrenia and their nonpsychotic siblings

Fractional anisotropy (FA) was examined in a priori selected fiber tracts in individuals with schizophrenia (n=25) and their non-psychotic siblings (n=29) versus controls (n=35). FA was reduced in a portion of the fornix in individuals with schizophrenia (although this did not survive correction for the number of tracts investigated). FA in the siblings did not differ from that in controls in any of the investigated tracts.

Erratum to Quantitative analysis of brain pathology based on MRI and brain atlases-Applications for cerebral palsy [NeuroImage, 54, 3, (2011), 1854-1861]

a The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA b Department of Radiology, University of Campinas, Campinas, SP, Brazil c Division of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD, USA d F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA e Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA