Development of Computational Methods for Neurobiological Imaging Research

1. Development of Computational Methods for Neurobiological Imaging Research Biomedical Science and Engineering Conference Measurement Science and Imaging Session March 18-19, 2009 Shaun Gleason, PhD Group Leader Image Science and Machine Vision Measurement Science and Engineering Division Oak Ridge National Laboratory

2. Outline • Background • Neuron Morphology • Neuron Migration • Research Areas • Develop algorithms to compare neuron morphology • Develop algorithms to study mechanism of neuron migration • Target applications of research • Neurological disease characterization • Neuronal interfacing

3. Guiding principle of neurobiology Structures are generated during development Structures are extremely heterogeneous Changes in structure can alter function and vice versa Neuronal Morphology: Form Equals Function Santiago Ramon y Cajal, 1900

4. Neuronal Migration: Location is Everything • Neurons travel to their final destination during development • Advances in microscopy have allowed researchers to visualize migration • Molecular mechanisms of migration can be studied using fluorescent proteins • Defects in neuronal migration severely affect function

5. 3D Image Acquisition

6. 3D Image Acquisition

7. 3D Image Processing Example: Images of retinal neurons in transgenic mouse line • Postnatal day 4 (P4) • Postnatal day 6 (P6) • (C-E) Individual neuron from P6 wild type retina • (F-H) Individual neuron from P6 mutant retina

8. Develop Algorithms to Compare Neuron Structures in 3D • Collect retinal neuron image data from wild type and mutant animals • Segment individual neurons • Develop soma extraction and neurite tracing tools • Extract features describing neuron 3D morphology • Create a searchable database of neuron images & features for classification. {f0, f1, f2, …fN-1} Database Classified Neuron: WT or KO

9. Neurites can be modeled as curvilinear structures • Curvilinear structures: “tube-like” shapes in the image/volume • Analysis of curvilinear structure: • Compute directions of principal curvature from eigenvalues of Hessian matrix • High curvature in directions perpendicular to the “tube” (v1 and v2) • Low curvature in direction of tube (v3) • First derivative vanishes v2 v1 v3 neurite segment directions of principal curvature (matrix of 2nd derivatives)

10. 2-D curvilinear centerline detection • Pixel declared centerline if Taylor approx. along principal curvature is at local maximum (nx,ny) = direction of principal curvature 2nd-order Taylor approximation along principal direction original image mesh view smoothed sub-region Pixel of interest Centerline pixel: -1/2 < t < 1/2 This pixel is not on a centerline! position along Taylor curve (t=0 is the apex) t Taylor approximation

11. 3-D neurite centerline detection by curvilinear analysis • We extend this method from 2-D to 3-D • At every voxel: • Analyze Hessian matrix • Classify the voxel as “centerline” or “not centerline” • 3D extension of 2D approach [Xiong, Zhou, Degterev, Ji, Wong, Automated Neurite Labeling and Analysis in Fluorescence Microscopy Images, Cytometry Part A 69A:494–505 (2006)] EXAMPLE: retinal neurons somas Original volume (isosurface rendering) Detected centerlines

12. 3-D soma segmentation • Use a Euclidean distance transform to locate large filled regions in the volume • Segmented somas can be used to eliminate false centerlines detected inside soma EXAMPLE: retinal neurons Original volume (isosurface rendering) Detected somas (white)

13. 4D Image Acquisition: Neuron Migration • The neuron migration machinery can be visualized for the first time • The centrosome and cytoskeleton are critical for migration • Previously, there was no way to study this machinery in large 4D datasets

14. Develop Algorithms to Study Mechanism of Neuron Migration • Collect time-series images of migrating cerebellum neurons • Enhance existing centrosome motion tracking algorithms • Add cytoskeletal characterization methods • Investigate mechanistic model of migration MOTION MODEL state uncertainty transition function measurement error

15. Centrosome tracking: detection stage • Centrosomes can be modeled as small bright spherical objects when properly labeled • Brightness of centrosomes varies widely • 3-D centrosome detection: • Project volume to 2-D • Laplacian filter + adaptive thresholding for initial detections • Roundness metric and scale detection for refined detections individual centrosomes Whole field of view

16. Centrosome tracking: linking stage • Apply joint probabilistic data association filter (JPDAF) tracking algorithm [Bar-Shalom and Fortmann, Tracking and Data Association. New York, NY: Academic, 1988] • Tracks multiple objects simultaneously using multi-hypothesis analysis • Use a Newtonian state-space motion model allowing for random acceleration of centrosomes in x, y, and z directions 1-D ILLUSTRATION 3-D RESULTANT TRACKS ? detections and tracks from previous frames detections in current frame

17. Cerebellar Neuron Migration: Recent Progress • ORNL developed a centrosome tracking algorithm • Achieved throughput increase and added new dimension (from 2D manual to 3D automatic) • Results closely agree with St. Jude manual results and are being published in Neuron [Govek E, Trivedi N, Kerekes RA, Gleason SS, Hatten ME and Solecki DJ. "Par6α regulated Myosin II motors drive the coordinated movement of centrosome and soma during glial-guided neuronal migration" Neuron (In revision)]. • Challenges remain: • Specificity of centrosome detection • Tracking proximal centrosomes • Characterizing the cytoskelton

18. Average motion of all centrosomes shows remarkable similarity between St. Jude and ORNL results Fully automated ORNL approach (2 minutes run time) Semi-manual St. Jude approach (~2 weeks effort)

19. Future focus of tracking moving to dual-and tri-labelled neuronal images • Red FP labeled cytoplasmic material, green FP labeled cytoskeleton (actin) Frame 27 Frame 51

20. Applications of morphological and migration research are numerous • Neurological disease characterization and treatment: • Alzheimer’s, Parkinson’s, schizophrenia, epilepsy, cancer of the nervous system, retinal disorders, autism, etc. • Neurotechnology, the application of electronics and engineering to the human nervous system (neuronal interfacing) • We need to understand how neurons respond at the cellular level to probes used for • Neuronal prostheses • Neuronal stimulation (e.g. deep brain) B. Beckerman_ LDRD08

21. 10 mm We are investigating the application of nanostructured materials as a multimodal tissue interface for neural prostheses. Genetic Level: Localized modulation of tissue response via genetic level manipulation Physical: Quasi 3-Dimensional cell and tissue scaffolding Fluidic: Localized modulation of tissue via reagent delivery. Electrical: Electroanalytical Probes/Actuators

22. Collaboration with the Morrison Lab at Columbia has demonstrated these arrays may be repeatedly used for whole tissue electrophysiological recording.... DG CA1 DG CA1 E01 E40 E40 E01 CA3 CA3 E38 E03 E04 1 mM TTX 100 mV 50 mM BIC 50 mM BIC 100 ms E39 E40 E38 200 mV 1 mM TTX 50 mM BIC 50 mM BIC 200 ms Yu Z, et al, J Neurotrauma 24 (7), 2007, Yu Z, et al. Nanoletters, 7 (8), 2007.

23. 2000 ) pp B V 1600 m E40 CA3 E01 CA1 1200 DG Amplitude of Response ( 800 VACNF Stimulus E32 400 E36 E40 0 0 20 40 60 80 100 120 mA Intensity of Stimuli ( ) slope amplitude … and stimulus, using the commercial MCS microelectrode array platform. Yu Z, et al. Nanoletters, 7 (8), 2007.

24. Neurotechnology, the application of electronics and engineering to the human nervous system, is one of the most rapidly advancing fields of translational medicine. Image credit: Second Sight Medical Image credit: St Jude Medical, Inc. Image credit: NIH Medical Arts Nanostructured electrode systems based on vertically aligned nanofiber arrays (VACNFs) enable many exciting paths forward for advanced neuronal prostheses.

25. Summary & Conclusions • Neurobiologists need tools to help them analyze complex neurobiological processes systematically and efficiently. • Methods are being developed to address: • Morphological-based neuron classification • Characterization of neuron migration • Result will be a foundation upon which more sophisticated and powerful tools can be built. • Methods will enable new ways to conduct research in various neurobiological fields, e.g.: • Alzheimer’s, Parkinson’s, schizophrenia, epilepsy, cancer of the nervous system, retinal disorders, autism • Neurotechnology

26. Acknowledgements • ORNL • Ryan Kerekes, PhD, MSSE • Richard Ward, PhD, CSED • Barbara Beckerman, MBA, CSED • M. Nance Ericson, PhD, MSSE • Tim McKnight, MSSE • St. Jude Children’s Research Hospital • Michael Dyer, PhD, SJCRH • David Solecki, PhD, SJCRH • Stanislav Zakharenko, MD, PhD, SJCRH • Columbia University • Barclay Morrison, PhD