Managed by UT-Battelle for the Department of Energy Development of Computational Methods for Neurobiological Imaging Research Shaun Gleason, PhD Group

Managed by UT-Battellefor the Department of Energy

Development of Computational Methods for Neurobiological Imaging Research

Shaun Gleason, PhD

Group Leader

Image Science and Machine Vision

Measurement Science and Engineering Division

Oak Ridge National Laboratory

Biomedical Science and Engineering Conference

Measurement Science and Imaging Session

March 18-19, 2009

2 Managed by UT-Battellefor the Department of Energy

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


Neuronal Morphology: Form Equals Function

Guiding principle of neurobiology

Structures are generated during development

Structures are extremely heterogeneous

Changes in structure can alter function and vice versa

Santiago Ramon y Cajal, 1900


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


3D Image Acquisition


3D Image Acquisition


3D Image Processing

(A) Postnatal day 4 (P4)

(B) Postnatal day 6 (P6)

(C-E) Individual neuron from P6 wild type retina

(F-H) Individual neuron from P6 mutant retina

Example: Images of retinal neurons in transgenic mouse line


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


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

v1

v2

v3

neurite segment directions of principal curvature

zzyzxz

yzyyxy

xzxyxx

lll

lll

lll

H (matrix of 2nd derivatives)


2-D curvilinear centerline detection

Pixel declared centerline if Taylor approx. along principal curvature is at local maximum

yyyxyyxxxx

yyxx

lnlnnln

lnlnt

22

2/1

2

2 4 6 8 10 12 14

2

4

6

8

10

12

14

(nx,ny) = direction of principal curvature

smoothed sub-region

Pixel of interest

2nd-order Taylor approximation along principal direction

mesh view

Taylor approximationt

Centerline pixel: -1/2 < t < 1/2

This pixel is not on a centerline!

position along Taylor curve (t=0 is the apex)

original image


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)]

Original volume (isosurface rendering)

Detected centerlines

EXAMPLE: retinal neurons

somas


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

Original volume (isosurface rendering)

Detected somas (white)

EXAMPLE: retinal neurons


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


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

iii

iii

vHxy

GuFxx 1

F ix

iy

G

1ix ivmeasurement error

transition function

state uncertainty

MOTION MODEL


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 centrosomesWhole field of view


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

Centrosome tracking: linking stage

?

detections and tracks from previous frames

detections in current frame

1-D ILLUSTRATION 3-D RESULTANT TRACKS


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:

1. Specificity of centrosome detection

2. Tracking proximal centrosomes

3. Characterizing the cytoskelton


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)


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


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


We are investigating the application ofnanostructured materials as a multimodaltissue interface for neural prostheses.

Physical:Quasi 3-Dimensionalcell and tissue scaffolding

10 m

Electrical:Electroanalytical Probes/Actuators

Genetic Level:Localized modulation of tissue response via geneticlevel manipulation

Fluidic:Localized modulation of tissue viareagent delivery.


Collaboration with the Morrison Lab at Columbiahas demonstrated these arrays may be repeatedly used for

whole tissue electrophysiological recording....

1 M TTX

1 M TTX

E38

E38

0

2

4

6

8

10

12

14

0 50 100 150 200 250 300 350 400Time (sec)

Fir

ing

Ra

te (

Hz

)

E01 E40

DG

CA3

CA1

100 ms

100 V

E01 E40

DG

CA3

CA1

E03 E04

E39 E40

200 ms

200 V

50 M BIC

50 M BIC

50 M BIC

50 M BIC

Yu Z, et al, J Neurotrauma 24 (7), 2007, Yu Z, et al. Nanoletters, 7 (8), 2007.


… and stimulus,

slope

amplitude

-0.05

0.05

0.15

0.25

0.35

0 20 40 60 80 100 120

Intensity of Stimuli (A)

Slo

pe

of

Re

sp

on

se

(V

/ms

ec

)

E32

E36

E40

VACNFStimulus

0

400

800

1200

1600

2000

0 20 40 60 80 100 120

Intensity of Stimuli ( A)

Am

plit

ude

of R

espo

nse

( V

pp)

E32

E36

E40

Yu Z, et al. Nanoletters, 7 (8), 2007.

using the commercial MCSmicroelectrode array platform.

E40

E01

DG

CA3

CA1

B

Managed by UT-Battellefor the Department of Energy

Neurotechnology, the application of electronics andengineering to the human nervous system, is one of the most rapidly advancing fields of translational medicine.

Image credit: NIH Medical Arts

Image credit: Second Sight Medical

Image credit: St Jude Medical, Inc.

Nanostructured electrode systems based on vertically aligned nanofiber arrays (VACNFs) enable many exciting paths forward for advanced neuronal prostheses.


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


Acknowledgements

ORNLRyan Kerekes, PhD, MSSERichard Ward, PhD, CSEDBarbara Beckerman, MBA, CSEDM. Nance Ericson, PhD, MSSETim McKnight, MSSE

St. Jude Children’s Research HospitalMichael Dyer, PhD, SJCRHDavid Solecki, PhD, SJCRHStanislav Zakharenko, MD, PhD, SJCRH

Columbia UniversityBarclay Morrison, PhD

Documents

Managed by UT-Battelle for the Department of Energy Development of Computational Methods for Neurobiological Imaging Research Shaun Gleason, PhD Group