Pipeline for Automated Registration of Freesurfer Surfaces to the PALS-B12 Human Atlas

1. Donna Dierker1, John Harwell1, Tim Coalson1, Alan Anticevic2, Joe Cooper3, and David Van Essen1 NIAC talk 2/18/2011 Pipeline for Automated Registration of Freesurfer Surfaces to the PALS-B12 Human Atlas 1. Department of Anatomy and Neurobiology, Washington University, St. Louis, MO 2. Department of Psychology, Washington University, St. Louis, MO 3. Department of Psychiatry, Washington University, St. Louis, MO

2. Four years ago, right here, I said: “Using FreeSurfer is not sacrilegious.” Okay for segmentation Concerns about registration

3. Come back on April 8! David will talk about an improved Freesurfer "fsaverage_LR" atlas, inter-atlas transformations, and comparisons among registration algorithms.

4. Introduction Freesurfer1 is widely used for cortical segmentation and surface-based analyses of morphometric and functional data. The PALS-B12 atlas is also widely used for mapping fMRI results, stereotaxic foci, cross-species comparisons, and morphometric analyses2,3. We have substantially automated the process of bringing Freesurfer output into the PALS-B12 atlas framework: - for comparison with results from other studies - to facilitate additional analyses using Caret software Automated landmark identification (ALI) of the “Core 6” landmarks2 is a key stage.

5. INPUTS - The following software and data are fed into the pipeline: • Freesurfer subject directories (e.g., orig and aseg volumes; white, pial, and spherical surfaces) • Freesurfer software distribution (mri_info, mri_convert) • Caret software distribution (caret_command, probabilistic atlases) • Dataset downloaded from brainvis.wustl.edu (shell scripts, registration target, template scenes, etc.) OUTPUTS - The following data are output from the pipeline: • PALS-B12 registered surfaces • Maps of sulcal depth and cortical folding • ‘Deformation maps’ that allows additional data to be registered from the individual subject surface to the PALS atlas

6. Processing Automated Except for Landmark Vetting, Update Preborder script prepares Freesurfer surfaces for input to the ALI and generates the landmark borders that constrain spherical registration to Caret. Quality assurance (QA) script computes how much ALI landmarks deviate from atlas targets. This script also generates captures showing what manual correction is needed, if any. Human rater manually corrects ALI-generated borders as needed (see figure 1), using Caret's easy-to-use border update feature. After manual correction, postborder script registers the surfaces to the PALS-B12 atlas.

7. Figure 1 - Automated Landmark Identification (ALI) Lateral Medial Before Editing After Editing

8. Figure 2 - Landmark Vetting Outputs Border Captures Border Variability Report (below)

9. Results Initial results show computer-drawn landmarks compare favorably to human-drawn ones. Figure 3 shows results of a depth asymmetry test (left-right) using both computer and human drawn landmarks. Other tests show that results vary greatly across human raters. A trained rater can update over a dozen cases per hour, and all three scripts have run-times of ten minutes or less per subject. Thus, large-scale population analyses are feasible using this approach, as we are undertaking with seven ongoing projects.

10. Figure 3 – Automated Landmark Identification Performs Well in Depth Asymmetry Test! Subjects' depth maps were registered to PALS-B12 using five sets of landmarks: Computer-drawn; computer with human corrections; two trained raters; and gold standard rater. Borders encircle clusters found to be significant in paired t-tests.

11. Conclusions The Freesurfer to PALS-B12 pipeline automates an otherwise tedious and time consuming data conversion and registration process, and makes it practical to analyze large subject populations. Automation of landmark generation saves considerable time. Initial results show the computer compares favourably to humans in identifying registration landmarks. Automation of landmark generation avoids human rater bias that may otherwise occur if using different human raters across studies. A near-term objective is to incorporate these scripts as a standard option for XNAT and the Central Neuroimaging Data Archive (CNDA)4, which supports Freesurfer processing, secure data storage and user-friendly data access. Contact donna@wustl.edu for more information.

12. Who to Blame David Van Essen John Harwell Donna Dierker Tim Coalson

13. More Suspects Alan Anticevic Joe Cooper Erin Reid

14. Acknowledgments Feedback from the following early testers was greatly appreciated: Taosheng Liu, Alex Cohen, Matt Glasser, Yuning Zhang, and Jim Alexopoulos. We also thank Heather Wilkins, Andrea Lui, and Erin Reid for drawing borders. Erin Reid also provided critical feedback on the auto-landmarks. Supported by the following grants: 95177, BRAIN CIRCUITRY IN SIMPLEX AUTISM; 1R01HD05805601, NEUROBEHAVIORAL IMPAIRMENTS IN PRETERM CHILDREN; 1R21MH08231001A2, NOVEL CORTICAL LIMBIC ANALYSIS IN TWINS; and 2P30NS04805606, NINDS CENTER CORE FOR BRAIN IMAGING. References Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179-194. Van Essen, D.C. (2005) A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex. Neuroimage 28: 635-662. Van Essen, D.C. and Dierker, D. (2007) Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56: 209-225. Marcus DS, Olsen TR, Ramaratnam M, Buckner RL. (2007) The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics. Spring;5(1):11-34.