Single-participant comprehensive atlas of the gray and WM structures
shows the JHU-DTI, JHU-DTI-MNI, and the JHU-DTI-Talairach. The superimposed anatomical outline defined by Talairach (http://ric.uthscsa.edu/new/resources/talairachdaemon/talairachdaemon.html
) (Lancaster et al., 2000
) indicates the quality of the agreement. After the normalization, the cortex was manually parcellated based on annotations in the Talairach atlas. Each GM and WM structure is identified in this participant and annotated (). shows the 3D reconstructed cortex (), the SWM (), and the DWM () of the JHU-DTI-MNI.
Fig. 2 Transformation of single-participant MPRAGE (JHU-T1w) and DTI data (JHU-DTI) into the ICBM152 space and Talairach space. (A) JHU-DTI was transformed to the ICBM152 space using an affine transformation of AIR. The transformation matrix was obtained by (more ...)
Fig. 3 (A–C) Two-dimensional views of the JHU-DTI-MNI (color-coded orientation map), in which the entire GM and the WM are parcellated by Type I WMPM. (D) JHU-DTI-MNI (FA map) overlaid with Type II WMPM, in which the cortex and the SWM was surrounded (more ...)
Fig. 4 Three-dimensional views of the cortical (A), SWM (B), and the DWM (D) parcellation of the Type I WMPM, and SWM parcellation of the Type III WMPM (C). The SWM (B and C) was parcellated to nine blade structures according to our previous publication (Oishi (more ...)
Three types of SWM definition
For the JHU-DTI-MNI, three types of the SWM definition were created, as shown in . In , the boundary between the SWM and the cortex was defined by simple FA threshold (FA>0.25) (denoted Type I hereafter). This reveals detailed SWM anatomy (), but contains the individual-specific anatomy of these highly population-variable anatomical areas. In the version shown in , the SWM is not defined and is combined with associated cortical parcellation (Type II). In , the outer boundaries of the SWM are defined by the WM probabilistic map ( and ; Type III). For this study, a WM probability higher than 0.9 was used, meaning the defined regions were highly likely be the WM, with minimum contamination from the GM and CSF. illustrates 3D views of types I and III SWM, as well as the cortical and DWM segmentations.
In , applications of these three types of the WMPMs are demonstrated. Data from a normal participant, which were not included in the atlas making, were transformed to the JHU-DTI-MNI template using LDDMM. When the Type I WMPM is overlaid, the segmentation of the DWM regions is well registered, but not for the SWM (); the segmentation sometimes misses the WM or includes the GM. This is because of the high degree of cross-participant anatomical variability, which cannot be fully removed by LDDMM. There are two approaches to deal with this issue. First, the Type II WMPM is applied to parcellate the cortex and associated SWM together (), followed by participant-by-participant delineation of the SWM (). Using an FA threshold, this can be readily performed. The second approach is to use the Type III WMPM, which automatically defines core areas of the SWM ().
Fig. 5 Normalized FA map overlaid with Type I-III WMPMs. (A) Type I WMPM cannot accurately delineate the boundary of the SWM because of excessive anatomical differences between the atlas and the participant. Yellow solid arrows indicate the WM areas, which were (more ...)
Accuracy of normalization and automated segmentation
The accuracy of normalization and automated WM segmentation, using the Type III WMPM, is demonstrated for an AD patient in . The original image () was first affine transformed () to adjust the size of the brain to ICBM space, and then subjected to various non-linear normalizations (: the atlas was non-linearly transformed to the affine-transformed patient image, or : the patient image was non-linearly transformed to the atlas). To compare the result of LDDMM with that of other non-linear normalizations, we also normalized the image using SPM5. Type III WMPM was overlaid on each image. Some of the mis-registrations found in the WM area of conventional T1-contrast-based SPM transformation (SPM-T1) could be improved by using an FA contrast-based transformation (SPM-FA) (, dotted arrows 5–7), but there were still WM mis-registrations (, solid arrows 1–4). These mis-registrations were noticeably improved by using LDDMM.
Fig. 6 Three types of non-linear normalization methods applied to an AD patient. Images are overlaid with the type III WMPM. (A) Original AD patient image; (B) ‘prepared’ AD image; (C) non-linearly transformed images. Upper row: SPM5 using T1-weighted (more ...)
The comparison of Kappa values between manual and automated WM delineation is shown in . Using the LDDMM, the average kappa values for the 11 anatomical regions were 0.70 for both NC and AD. These kappa values indicate “substantial agreement.” For the DWM areas, the kappa values were 0.76 (NC) and 0.75 (AD), with accuracy levels similar between the NC and the AD participants. The average intra- and inter-rater variability of manual delineation was 0.86 (0.89 for the DWM only) and 0.81 (0.84 for the DWM only), indicating a high level of reliability for the manual-based anatomical definition as our gold standard. We did not expect kappa values of the automated method to exceed 0.81. The FA-based SPM also achieved “substantial agreement” (kappa>0.6) for the NC, but not for the AD patients. Both for the NC and AD populations, significant improvement was observed with the LDDMM compared to the SPM5. This large improvement was observed particularly in the thin ‘line-like’ (e.g., cingulum) and ‘sheet-like’ structures (e.g., posterior thalamic radiation), which cannot be registered by affine or SPM5 (see also for visual clues). Within SPM5, employment of the FA contrast led to better registration compared to T1-weighted contrast.
Fig. 7 Result of the kappa analyses of five normal controls (NC, upper row), five Alzheimer’s disease patients (AD, middle row), and inter-rater comparison of three raters (bottom row). Abbreviations are; IFB, inferior frontal blade; PTB, parieto-temporal (more ...)
Power analysis for detecting FA reduction with this method
Atlas-based measurement applied to 21 normal participants was used for the initial estimation of power analyses. and summarize the results of power analysis to detect a 10% FA reduction and a 10% volume reduction in each WM structure defined by Type III WMPM. By using ROIEditor, the FA value and volume of each WM region was automatically measured after each image was linearly normalized to the ICBM space. Note that the measured volumes of each WM structure were normalized by the total brain size, since the images were normalized to the ICBM space by affine transformation. If volumes of the un-normalized brains are required, it is straightforward to further transform the WMPM to the native space by a reverse-affine transformation.
Number of participants required to detect a 10% FA reduction in each WM structure.
Number of participants required to detect a 10% volume reduction in each DWM structure.
With an alpha error probability less than 0.05 and power greater than 0.95, the majority of the WM structures require fewer than 20 participants to detect a 10% decrease in FA or volume. All DWM structures required fewer than 30 participants to detect a 10% FA decrease. However, for small structures, like the fornix or the medial lemniscus, a larger number of participants would be needed to detect a 10% volume reduction.