Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and application for automated abnormality detection

The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
NeuroImage (Impact Factor: 6.36). 08/2010; 52(2):415-28. DOI: 10.1016/j.neuroimage.2010.04.238
Source: PubMed


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.

Download full-text


Available from: Kenichi Oishi
  • Source
    • "A whole brain, ROI, atlas-based analysis of diffusion-weighted data was performed using Diffeomap (Faria et al., 2010)( i m p l e m e n t e di First, affine (linear) normalization of the " JHU_MNI_single-subject " atlas (Mori et al., 2008)i s warped to individual native space. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Few studies have examined multiple measures of white matter (WM) differences in youth with familial risk for bipolar disorder (FR-BD). To investigate WM in the FR-BD group, we used three measures of WM structure and two methods of analysis. We used fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) to analyze diffusion tensor imaging (DTI) findings in 25 youth with familial risk for bipolar disorder, defined as having both a parent with BD and mood dysregulation, and 16 sex-, age-, and IQ-matched healthy controls. We conducted a whole brain voxelwise analysis using tract based spatial statistics (TBSS). Subsequently, we conducted a complementary atlas-based, region-of-interest analysis using Diffeomap to confirm results seen in TBSS. When TBSS was used, significant widespread between-group differences were found showing increased FA, increased AD, and decreased RD in the FR-BD group in the bilateral uncinate fasciculus, cingulum, cingulate, superior fronto-occipital fasciculus (SFOF), superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus, and corpus callosum. Atlas-based analysis confirmed significant between-group differences, with increased FA and decreased RD in the FR-BD group in the SLF, cingulum, and SFOF. We found significant widespread WM tract aberrations in youth with familial risk for BD using two complementary methods of DTI analysis. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    Full-text · Article · Feb 2015 · Psychiatry Research: Neuroimaging
  • Source
    • "In this analysis, multiple regions of interest are automatically defined in each individual brain by applying the anatomical parcellation previously defined in an atlas template (Faria et al., 2010; Oishi et al., 2008). The mapping between the template and each individual's brain was performed with DiffeoMap (Li, X.; Jiang, H.; and Mori, S.; Johns Hopkins University, and consisted of an initial linear transformation followed by the large deformation diffeomorphic metric mapping (Ceritoglu et al., 2009; Faria et al., 2011, 2010; Mori et al., 2008; Oishi et al., 2009, 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: A cross-culturally valid nonverbal assessment of semantic knowledge is needed. Accurately identifying impairment of object semantics is important for diagnosis of several disorders, including distinguishing semantic variant primary progressive aphasia (svPPA), a neurodegenerative condition characterised by progressive impairment in word comprehension, from logopenic and nonfluent agrammatic variants, which are not associated with impaired object semantics. However, current assessments require culturally specific knowledge.
    Full-text · Article · Sep 2014 · Aphasiology
  • Source
    • "To perform the group analysis of datasets collected from several sites, datasets were matched into Neuroimaging Informatics Technology Initiative (NIFTI) format using the transversal view and radiology convention, and were registered into the standard Montreal Neurological Institute (MNI) [Burgund et al., 2002; Faria et al, 2010; Muzik et al., 2000] brain with 3  3  3 (mm 3 ) in voxel size and 61  73  61 (axial  coronal  sagittal) in resolution. The fMRIB software Library (FSL) was used to perform the pre-processing required for obtaining the 3D activation maps [Jenkinson et al., 2002; Jenkinson and Smith, 2001; Woolrich et al., 2001]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This article describes a pattern classification algorithm for pediatric epilepsy using fMRI language-related activation maps. 122 fMRI datasets from a control group (64) and localization related epilepsy patients (58) provided by five children's hospitals were used. Each subject performed an auditory description decision task. Using the artificial data as training data, incremental Principal Component Analysis was used in order to generate the feature space while overcoming memory requirements of large datasets. The nearest-neighbor classifier (NNC) and the distance-based fuzzy classifier (DFC) were used to perform group separation into left dominant, right dominant, bilateral, and others. The results show no effect of age, age at seizure onset, seizure duration, or seizure etiology on group separation. Two sets of parameters were significant for group separation, the patient vs. control populations and handedness. Of the 122 real datasets, 90 subjects gave the same classification results across all the methods (three raters, LI, bootstrap LI, NNC, and DFC). For the remaining datasets, 18 cases for the IPCA-NNC and 21 cases for the IPCA-DFC agreed with the majority of the five classification results (three visual ratings and two LI results). Kappa values vary from 0.59 to 0.73 for NNC and 0.61 to 0.75 for DFC, which indicate good agreement between NNC or DFC with traditional methods. The proposed method as designed can serve as an alternative method to corroborate existing LI and visual rating classification methods and to resolve some of the cases near the boundaries in between categories. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
    Full-text · Article · Apr 2014 · Human Brain Mapping
Show more