BrainSpace V2.1: Geometric mapping and diffusion-based software for cross-subject multimodality brain imaging informatics

Transcription

1 Brief Introduction to BrainSpace V2.1 BrainSpace V2.1: Geometric mapping and diffusion-based software for cross-subject multimodality brain imaging informatics Graphics and Imaging Laboratory Wayne State University With ever-improving imaging technologies and ever-increasing high-performance computational power, the complexity and scale of acquired imaging data have continued to grow at an explosive pace. In many scenarios, intrinsic geometric structures embedded in 3D imaging of real-world objects are very effective in mapping individual objects for interpretation of their similarity and disparity. A rigorous computational framework that tightly unifies geometric mapping, diffusion and matching is essential for integrative analysis of a variety of underlying relationships in features inherited from imaging datasets of a large number of subjects, i.e., 3D imaging informatics. Based on our continuous research effort along this direction, we have developed a novel, rigorous theoretical framework based on Riemannian geometry, geometric mapping and diffusion, and statistical analysis, which provides a basis for cross-subject imaging informatics. Specifically, our research team has designed a fundamental framework for advanced and integrated analysis of brain imaging data. It is expected that the developed, advanced informatics tools will allow the quantitative and integrative analysis of a variety of functional patterns and the relationships between anatomical and functional features in different datasets. The computational framework has the potential to be applied across multiple areas of brain research as well as in clinical diagnosis. Figure 1 shows our conformal brain model-based software system (BrainSpace v2.1) for analyzing multimodality imaging data. Figure 1. BrainSpace: The conformal brain model (Figure 1A and Figure 1B) facilitates accurate matching and registration among subjects in the canonical, spherical domain, hence supporting integrated cross-subject analysis of Positron Emission Tomography (PET) (molecular-level brain activity analysis) (Figure 1C), Diffusion Tensor Imaging (DTI) (neural fiber connectivity analysis) (Figure 1D), and Electroencephalography (EEG) (time-varying signal analysis) (Figure 1E) in computer-aided diagnosis of brain disorders. 1

2 Processing pipeline for geometry-based integrative analysis of brain imaging data. The processing pipeline for the BrainSpace software environment consists, in addition to the preprocessing programs, of the two main software components EPITOOLC and FTPTOOL, which process all imaging data in native space. Both these programs input processed data into the integrative display routine (EPIMAP), where all parameters derived from native space can be related within a 2D metric (see Figure 2). As a consequence, queries with respect to interaction between data sets derived from various modalities can be formulated within the EPIMAP environment and in turn can be fed back into native space for data analysis and extraction. Figure 2. BrainSpace pipeline. Finite cortical elements (FCE) created in EPITOOLC are tested for abnormal PET asymmetry and if the PET tracer asymmetry exceeds a predefined threshold these FCEs are marked (blue). The cortical surface is subsequently transferred into 2D space (EPIMAP) and the location of the EEG grid is displayed. Based on this data, cortical seed regions are defined and transferred into FTPTOOL as source regions for probabilistic tractography. The cortical connectivity pattern is then displayed back in EPIMAP where it can be further analyzed. In the following we present the complete BrainSpace pipeline for the integrative analysis of PET, MR, DTI and intracranial EEG data in order to provide optimal imaging clues for the guidance of resective epilepsy surgery in children with intractable epilepsy. The BrainSpace pipeline consists initially of a preprocessing step in which individual modalities are analyzed in native space. This is then followed by the processing step in which the data is interactively queried resulting in subsequent analyses in native space. This approach results in an interactive feedback loop between the integrative display and analysis in native space. Step 1 Preprocessing A. SPGR 1. tag SPGR L/R (IDL >> tag_image) 2. use MRIcro (0.65, check dilate) or BrainSuite (default parameters) to deskull SPGR image 3. create mask using BrainSuite 4. cut off cerebellum using mask image (epitoolc >> cerebellum), create smo file 5. create shrinked images (IDL >> shrink_image) at 5, 20 and 1mm 6. read shrink 1mm file in epitoolc and project 5mm shrink >> create smo1 7. create smo1.obj (OM >> Mesh) 2

3 8. read deskulled SPGR in epitoolc and project 5mm shrink >> create smo 9. create initial landmarks (epitoolc >> Geometry >> Define Midplane) 10. create surface landmarks (use MNI_landmark_def.ppt (see Figure 3) for guidance) 11. BrainMap >> Load features >> resample >> Initial Mapping 12. BrainMap >> select template file >> Load (resampled) landmarls >> Initial >> optimize 13. Decimate at level 3 (512 elements) and level 4 (2048 elements) Figure 3. Definition of the cortical landmark set using the MNI152 template brain. Only landmarks were used that can be reliably defined in different subjects. The following 9 landmarks were used: Central, pre-central and post-central sulcus, sylvian fissure, superior and inferior temporal sulcus, superior frontal sulcus, parietooccipital and transverse-occipital sulcus. B. DTI 1. check fiber tracts using DTIStudio (check gradients) >> create avw image and dpf files 2. process DTI data (FTPtool >> deterministic) >> colormap, FA, DWI 3. read in shrink5, shrink20 file >> create VEF file 4. rebin DWI files to same matrix size as SPGR 5. coregister DWI to SPGR file and create transformation matrix C. PET 1. coregister PET to SPGR 2. run PET contours (IDL >> show_contours) and use ~40% threshold 3. show_contours >> determine PET max, average and threshold (40% thrshold / avg) D. EEG 1. assign electrode location (electrodes >> output EEG.dat) based on fiducial markers 2. load electrodes in epitoolc >> visualization >> Load EEG Grid (initial guess) 3. re-define electrodes >> visualization >> Redefine electrodes >> Save file (each grid) 4. edit EEG.dat >> replace individual grids with newly defined grids 5. epitoolc >> visualization >> Load EEG data >> Save electrode flat map (EEGmap.txt) 3

4 Step 2 Processing A. EPITOOLC 1. epitoolc >> load deskulled SPGR, smo1.obj, coregistered PET 2. create finite elements, histogram and save 3. load territories (lobes and BAs) 4. create AI, differencearea and stats for BA & lobes 5. write result file >> *.rst (index, PETval, diffa, AI, lobeid, baid) B. EPIMAP (first pass) 1. display epimap (IDL >> epimap), load result file, electrode flat map (EEGmap.txt) 2. define ROIs >> mirror ROI C. FTPTOOL 1. read SPGR and VEF file, load dpf and select # iterations, acceleration factor 2. read ROIs >> create probabilistic fiber tracks 3. calculate probability strength and save FEF file 4. load colormap >> define regions for major fiber tracks (arcuate, ILF, SLF) 5. save FEF map for arcuate, ILF, SLF D. EPIMAP (second pass) 1. display epimap (IDL >> epimap), load result file, EEGmap.txt, FEF file 2. calculate statistics for selected ROIs (AI, diffa, probab) Improved efficiency of data preprocessing based on curvilinear surface rendering. In order to allow a more efficient analysis of brain image data, we developed a method that provides excellent recognition of brain surface landmarks without the need to perform time-consuming deskulling of the original SPGR image volumes. The brain is initially de-skulled using readily available software tools (MRIcro or BrainSuite), however these software tools are not able to completely remove the dura mater located immediately adjacent to the cortex. In order to better visualize cortical landmarks, a curvilinear shrink operation with varying depth is performed on the surface voxels. This operation removes the outer cortex layer, thus exposing the deep folding structure of the cortex (see Figure 4). As a result, sharp border zones between white and gray matter are rendered and subsequently projected onto the original (non-shrinked) cortex, showing the location of major and minor cortical sulci. This information is subsequently employed in order to define a set of cortical landmarks used for the definition of the following (bilateral) landmark set: 1. Central sulcus 6. Superior temporal sulcus 2. Pre-central sulcus 7. Inferior temporal sulcus 3. Post-central sulcus 8. Parieto-occipital 4. Superior frontal sulcus 9. Transverse occipital sulcus 5. Sylvian fissure 4

5 Figure 4. Landmark set depicted both in native space and in the spherical domain. It can be seen that the cortical landmarks are evenly distributed on the cortical surface, thus providing ample spatial information for the conformal mapping operation. The figure also shows finite surface elements which are created in the spherical domain using a recursive parcellation scheme and subsequently transferred into native space. This landmark set was devised so that it consists of largely reproducible anatomical locations that can be recognized in brains of different subjects despite the always present physiological variation among subjects. Moreover, this landmark set covers the lateral brain surface relatively uniformly which is crucial for an accurate conformal mapping of the cortical surface (see Figure 4). New landmark definition based on the MNI152 template. We have now implemented the MNI152 atlas in order to create a landmark template for thin-plate warping of individual landmarks in the spherical domain (see Figure 5). In addition to representing the average location of cortical sulci, a further advantage of the MNI152 template is the standardized definition of cortical territories (lobes, cortical and Brodmann areas), which are subsequently used in concert with the created finite elements to automatically sample cortical data in native space. Figure 5. Cortical territories from the HarvardOxford cortical atlas overlayed on the MNI152 template. The index for each finite cortical element corresponding to a particular cortical territory was saved, allowing the automated definition of homotopic areas in different subjects. 5

6 Simplified multimodality data representation in 2D. In order to efficiently process multi-modality three-dimensional data, we transform the cortical surface into a plane using the well-established Mercator projection used in cartography. The Mercator projection constitutes a conformal mapping from 3D into 2D space under which local geometry is preserved, i.e. the angle between two straight lines is conserved. Thus, the shape of small object is preserved, however the scale of objects increases towards the poles resulting in distortions of large objects. Nevertheless, the mapping of the cortical surface into 2D space is advantageous, as both hemispheres can be assessed at the same time and the spatial relationship between functional PET abnormalities, EEG onset electrodes and cortical projection of major fiber tracts can be quantitatively assessed in a 2D metric (see Figure 6). We have applied this approach to children with epilepsy and our preliminary data indicate that connectivity strength of fiber tracts originating from the cortex underlying seizure onset electrodes is decreased, suggesting impaired function of white matter fiber tracts due to frequent seizure propagation. Figure 6. Mercator projection of the cortical surface into the 2D plane allows visual assessment of the whole cortex. Anatomical territories of the brain (frontal, parietal, temporal and occipital lobes as well as the sensorimotor cortex) are color coded. Moreover, the integrative display includes also grid electrodes (green + symbols) and connectivity strength between a cortical seed region and the remaining cortex (connectivity score coded as full circles). Finite elements which have abnormal PET asymmetry (>12%) are shown as dark blue, whereas seizure onset electrodes are coded as red + symbols. ROIs can be defined interactively (gray-lined elements) and can be tested with respect to PET values, distance to seizure onset electrodes and the connectivity score. This material is based upon work supported by the National Science Foundation under Grant Number (Principal Investigator: Jing Hua). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF) 6