December 7 - Shape Applications

Overview

(12:05 - 12: 10)

• group intent - portable, easily used tools support shape applications
• results - we have produced libraries and applications which use these libraries.
• define problem domain: shapes constructed from points, edges, faces. Do not (yet) support implicit geomtries
• Tomorrow we'll talk about library - assumption, organization, and usage

• Today we'll talk about:
  • 3 appl build on the s.t. libray: viewer, ucf interpolator, shape distance
  • for each we'll describe it, demonstrate features, followed by 5 min (max) of questions )
  • pls. hold other questions to end.
• Outcome - you'll use these tools, provide feedback for improvements and future applications.

Viewer

(12:10 - 12:35)

Speaker : Roger

Purpose : simple , portable 3d shape viewer

Author : Jeff Demo:

1. Launch ShapeViewer
2. Load a UCF from the local file system for viewing
3. Change the orientation of a displayed file with the mouse
4. Change the magnification of a displayed file with the mouse and keyboard
5. Restore display size to a reasonable value
6. Change between different display modes (points, lines, surfaces)
7. Set the direction of the source lighting
8. Save what you are seeing on the screen to an output file
9. Change the color of a 3D UCF
10. Display multiple UCF's simultaneously
11. Load files using drag and drop
12. Load files through an sftp connection
13. Set colormaps for a 4D UCF
14. Save and load colormaps for a 4D UCF
15. Create and load scene files

• Questions (5 min)

Fi les to show

• locally
• remote

Notes:

• ShapeViewer Applet simpler version for web page.

Ucf Interpolate

(12:10 - 12:20 )

Speaker: Craig

Purpose: portable , flexible ucf mesh interpolation tool

features:

• variable number of interpolated points and contours
• interpolates "4d" data attribute.
• line and spline interpolation

Demo :

• show VARnc_vEw_L.ucf
• InterpolateContour -points 150 -levels 100 VARnc_vEw_L.ucf p150_l100.ucf
• display it
• "maybe low res. is better"
• InterpolateContour -points 30 -levels 30 VARnc_vEw_L.ucf p30_l30.ucf
• how about cubic spline
• InterpolateContour -points 150 -levels 100 -order 2 VARnc_vEw_L.ucf p150_l100_o2.ucf
• Questions (5 min)

Shape Distance

(12;40 - 12:50)

Speaker: Craig

Purpose: measure distance between corrosponding points

Demo:

• reference VARnc_vEw_L.ucf
• difference between surface points
• ShapeDistance -format 2 -output diff.ucf p150_l100.ucf p150_l100_o2.ucf
• display
• different metric
• ShapeDistance -format 2 -distance 2 -output taxi_diff.ucf p150_l100.ucf p150_l100_o2.ucf
• display taxi_diff

Q & A

(12:50 - 1:00)

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Shape Representation Conversions

07 October 2005

Attending:

Craig, Duygu, Yonggang

Tentative Agenda

• Review of past project notes
• Project Purpose and Vision
• Data types to support
• Conversions to perform
• Requirements
• Scheduling

Discussed

• Purpose of project: Translate between different shape representations
• definition of signed distance function (sdf).
  • sdf is equivalent to a mesh, within a specific resolution.
  • voxels with value "0" are part of mesh surface.
  • voxel absolute values are distance from nearest point of mesh surface
  • sign of voxel values (+/-) indicate whether voxel on interior or exterior of surface
• Narrow-Band (sparse) signed distance fields.
• Requirements
  • File format must be separate from internal shape representation.
  • Meshes to be converted into sdf must be oriented.
• Who will use this tool
  • Duygu
  • Yongang
  • a Math graduate student
• Possible 7 month outline
  • 2 wk: project proposal to Toga
  • 3 mo: prototype : prototype app to translate between mesh and sdf
  • 4 mo: library framework to generalize diff. types of meshes and sdf's.
  • 5 mo: releasable application
  • 5 mo: Analysis pipline using application
  • 7 mo: 2 papers using these methods (1 from mesh to sdf, 1 from sdf to mesh)
• do we need more programming support ?

Decisions

• support only triangular meshes
• Prototype will translate between a trianglar mesh and a signed distance function.

To do

• define and describe users for whom this is intended.
• define signed distance function.
• define 1 (triangular) mesh representation.
• decide if unoriented mesh's will be supported.
• decide how to define distance function .
  • coordinate system
  • volume size (dimensions)
  • voxel size (resolution)
  • conventions for sign of interior, surface and exterior.
• decide which file formats to (initially) support

Dec 8 - Shape Tool Library

Hour 1 - Library Concepts

(:05 - :10)

• Genesis
  • Library Needs
    • Portable geometry manipulation
    • Easily used
    • Easily compiled
  • Library is Not:
    • visualization toolkit
    • file format
  • Solution
    • Java
    • General data model
    • Extensible read/write archictecture
    • Documentation
    • Useful Examples

• Data Model (:10 - :15)
  • Shape definition - tinkertoy / points connected by straight (Eulerian) lines.
  • points, edges, faces, solids, attributes, children

Library Organization

(:15 - :40)

goals easy to use keep core classes small separate types of functionality into different packages extensible no dimensional assumptions (n-d vertices) excellent documentation: java doc, user guides, examples, twiki do only whats actually needed. unit testing structure (figure 2) shape package - core data model shapeio package - readers and writers shape packages other packages attribute geometry - component implementations math - math computation methods util - misc utilities

Trivial Example and important Implementation Details

(:40 - :50)

• Simple example - ShowVersion

coordinate arrays

edges are directional

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More Examples (:50 - :60)

• Example: Calculate Ucf Center of Mass Center of Ucf
• Example : Read Ucf with '4d' data attributes Ucf Quick start example 2

break (5 min)

Hour 2

(1:10 - 2:00)

Questions, Comments and Suggestions

CCB / ShapeTools

17 Sep 2007

Outline

• Portable Geometry Tools
  • CCB affords both local & remote tools
  • Scope : vector geometry
• Libraries
  • Multiple Data file types, auto-detect
  • Geometry, Math, Input/Output, Generic array
• Applications
  • CCBSoftwareToolSummary
• Documentation
  • ShapeToolsLibrary
  • ArrayStatisticsUserGuide1x0
  • ShapeViewer
  • MedialDistanceUserGuide1x0
• Demonstration
  • ShapeViewer - var. data files.
• Example Protocol
  • http://www.loni.ucla.edu/twiki/bin/view/CCB/ShapeToolsFlyWingProtocolV2_0
• Questions

December 2006 - ShapeTools LONI Update

An update to the Loni community regarding recent work and tools.

The New Tools are an alpha release: They work, however, we need your comments to ensure they are usable. The pre-release versions of the new tools will cease working at the end of January, to be replaced by the final versions.

System Requirements

• A java 1.5 runtime interpreter (all).
• Java3D v. 1.3.1 or greater (ShapeViewer, TinyShapeViewer).

New Tools

• ObjTranslation: Translates Minc Obj files into either the DX or OFF file format.
• TinyShapeViewer : A lightweight Java3D application, with minimal features, that displays shapes.
• ImageToShape - Creates a ShapeTools rectangular grid containing the data of a single 2D section of an Analyze Image file.
• ImageJAdapter - a Java library which allows ShapeTools programs access to images readable by selected ImageJ plug-ins.
• GeodesicPath - A program to compute the geodesic distance between two points on the surface of a shape
• UcfReorder - A program to correct common flaws in hippocampal contours stored as a Loni UCF.
• ConvexHull - Finds the minimal convex hull that contains a set of points.
• ComputeUcfNonLinearWarp - Computes a map taking contours in one Ucf onto the corrosponding contours in a second Ucf.
• ApplyWarp - Warps a Minc Obj shape file using the map calculated by ComputeUcfNonLinearWarp.
• PlanarUcfWarp - Computes a 2D warp between two Ucf whose contours are projected onto a plane.
• Visualize2DWarp - Generates a deformed grid illustrating the given 2D warp.

Improved Tools

• Shape Viewer : Java3D display tool. Now allows Manual selection of vertices. Selected vertex coordinates may be exported as a CSV file.
• ShapeToolsLibrary : Basic support library for ShapeTools programs. Now reads Duff and CCBBM surfaces.

Older Tools

• ShapeCurvature - A program that calculates and saves the curvature of a surface at its vertices (v1.1).
• ShapeTransform - A program that applies an AIR transform to the coordinates of a Shape's vertices.
• ShapeToDX - A program which translates a variety of Shape files into an OpenDX general format data file.
• ShapeDistance - A program to measure the distance between the vertices of two (homologous) shapes.
• Extract4DucfAttributes - A program that extracts from a Ucf file it's data attribute column.
• InterpolateContour - A program to interpolate the vertices and data attributes of Ucf format contour files.
• VFSBrowser - a File Chooser GUI that offers point-and-click access to remote and local filesystems.
• DXJava - A portable Java library that reads and writes OpenDX data files.

JenniferTom - 23 Mar 2006

Primary Auditory Cortex is located in the temporal lobe and is responsible for processing auditory stimulus (sound). It recieves input from the medial geniculate body^?.

• Approximate Location of A1 (Brodmann's Areas 41 and 42):
  

Image was released into public domain and has no copyright. Original Link

JenniferTom - 23 Mar 2006

AC-PC describes the line from the Anterior Commisure to the Posterior Commisure defines a plane of section used in the Talairach atlas and thus frequently desirable as the plane of section for MRIs. A simple graphical representation of how to find this line in the sagittal plane is provided (borrowed from Chris Rorden's Mricro page).

Back

A Flexible Tool for Automated Cortical Sulci Extraction Songfeng Zheng and Zhuowen Tu Songfeng Zheng (1), Zhuowen Tu (2), Alan Yuille (1), Ivo Dinov (2), Paul Thompson (2), Arthur Toga (2) Address: (1) Statistics Department, UCLA; (2) LONI, UCLA

Abstract:

Cortical sulci are important structures in the human brain, therefore, automatically extracting them is desirable in the medical image community. We developed a flexible software tool for automatically extracting cortical sulci. It is a learning based tool, integrating the generative information with the discriminative information. The learning algorithm is called a Probability Boosting Tree. The software was developed with C++ and has already been integrated into the SHIVA plugin. It works on MRI volumes as well as on extracted surfaces. Most sulci detection methods in the literature detect all the sulci regions, and then specify which part is central sulcus, which part is precentral and so on; but in our system, for different sulcus, we only need to use one different model because there is no parameter to tune in the system. We have tested our system on several major cortical sulci and the results are impressive. The experiments were conducted on raw volume data, extracted surface data and brains suffering from Williams Syndrome. We also provided quantitative measures of the error. The algorithm works fast: it needs about 8 hours for training on an ordinary PC and only 1 minute for testing on a typical volume of size 181X271X181 to detect one major cortical sulcus. We know the training process is offline, so it does not matter in application as long as testing is efficient.

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Back

Links for Neuroimaging Terminology

Center for Computational Biology (CCB) (UCLA)

Imaging and Unix Glossary (University of Arizona)

MR Glossary (Siemens)

Automated Image Registration (AIR) (UCLA)

Neuroscience Guide on the Internet - Resource of Web Links

Introduction to Magnetic Resonance Imaging

Whole Brain Atlas (harvard)

VTK Image Processing Book

Back

Attendees List

Name	Title	Affiliation	E-mail
David Brayford	Post Doctoral Fellow	SCI, University of Utah	dbrayford(at)sci.utah.edu
Caroline Brun	Graduate Student Researcher	Neurology, UCLA	cabrun(at)mednet.ucla.edu
Anne Berman	Principal Public Admin. Analyst	Neurology, UCLA	anne.berman(at)loni.ucla.edu
Zai Chang	Web Programmer	Neurology, UCLA	zai.chang(at)loni.ucla.edu
Martin Cole	Programmer	SCI, University of Utah	mjc(at)sci.utah.edu
Jason Corso	Post Doctoral Fellow	Radiology, UCLA	jcorso(at)gmail.com
Alexandre Cunha	Post Doctoral Fellow	Neurology, UCLA	acunha(at)loni.ucla.edu
Priscilla Deng	Administrative Assistant	Neurology, UCLA	priscilla.deng(at)loni.ucla.edu
Ivo Dinov	Assistant Professor / COO	Statistics/Neurology, UCLA	ivo.dinov(at)loni.ucla.edu
Bin Dong	Graduate Student Researcher	Mathematics, UCLA	bdong(at)math.ucla.edu
Theo Van Erp	Staff Research Associate	Psych-Clin, UCLA	vanerp(at)psych.ucla.edu
Greg Farber	Program Officer	National Institutes of Health	farberg(at)mail.nih.gov
Mala Gurbani	Lab Assistant	Neurology, UCLA	mala.gurbani(at)loni.ucla.edu
Nathan Hageman	Graduate Student Researcher	Neurology, UCLA	nhageman(at)loni.ucla.edu
John Haller	Lead Science Officer	National Institutes of Health	hallerj(at)mail.nih.gov
Liberty Hamilton	Staff Research Associate	Neurology, UCLA	liberty.hamilton(at)loni.ucla.edu
Bret Hanlon	Programmer, GSR	Cornell University	bmh35(at)cornell.edu
Hugo Hernandez-Mora	Systems Administrator	Neurology, UCLA	hugo.hernandez(at)loni.ucla.edu
Todd Holloway	Programmer Analyst	Neurology, UCLA	todd.holloway(at)loni.ucla.edu
Namshin Kim	Post Doctoral Fellow	Bio Informatics, UCLA	deepreds(at)ucla.edu
Natasha Lepore	Post Doctoral Fellow	Neurology, UCLA	nlepore(at)loni.ucla.edu
Grace Liang	Administrative Analyst	Neurology, UCLA	grace.liang(at)loni.ucla.edu
Lisa Lu	Post Doctoral Fellow	Neurology, UCLA	lisa.lu(at)loni.ucla.edu
Po Lu	Asst Clin Prof	Neurology, UCLA	plu(at)mednet.ucla.edu
Eileen Lϋders	Post Doctoral Fellow	Neurology, UCLA	eileen.lueders(at)gmx.de
Sandy Lui	Management Services officer	Neurology, UCLA	sandy.lui(at)loni.ucla.edu
Allan Mackenzie-Graham	Post Doctoral Fellow	Neurology, UCLA	amg(at)loni.ucla.edu
Rico Magsipoc	Computing Resources Manager	Neurology, UCLA	rico.magsipoc(at)loni.ucla.edu
Yu Mao	Graduate Student Researcher	Mathematics, UCLA	ymao29(at)math.ucla.edu
Katherine Narr	Assistant Professor	Neurology, UCLA	narr(at)loni.ucla.edu
Elizabeth O’Hare	Graduate Student Researcher	Neurology, UCLA	eohare(at) ucla.edu
Stott Parker	Professor	Computer Science, UCLA	stott(at)cs.ucla.edu
Vishal Patel	Graduate Student Researcher	Neurology, UCLA	vishal.patel(at)ucla.edu
Adam Pingel	Graduate Student Researcher	Computer Science, UCLA	pingel(at)cs.ucla.edu
Georges Salamon	Professor	Radiology, UCLA	gsalamon(at)mednet.ucla.ed
Craig Schwartz	Programmer Analyst	Neurology, UCLA	craig(at)loni.ucla.edu
David Shattuck	Assistant Professor	Neurology, UCLA	shattuck(at)loni.ucla.edu
Yonggang Shi	Post Doctoral Fellow	Neurology, UCLA	yshi(at)loni.ucla.edu
Bae Cheol Shin	Programmer Analyst	Neurology, UCLA	baecheol.shin(at)loni.ucla.edu
Stefano Soatto	Professor	Computer Science, UCLA	soatto(at)cs.ucla.edu
Paul Thompson	Associate Professor	Neurology, UCLA	thompson(at)loni.ucla.edu
Arthur Toga	Professor	Neurology, UCLA	toga(at)loni.ucla.edu
Duygu Tosun	Post Doctoral Fellow	Neurology, UCLA	dtosun(at)loni.ucla.edu
Zhuowen Tu	Research Scientist	Neurology, UCLA	ztu(at)stat.ucla.edu
Yalin Wang	Assistant Researcher	Mathematics, UCLA	ylwang(at)math.ucla.edu
Roger Woods	Associate Professor	Neurology, UCLA	rwoods(at)ucla.edu
Meihe Xu	Post Doctoral Fellow	Neurology, UCLA	meihe(at)loni.ucla.edu
Yuan Xu	Graduate Student Researcher	Neurology, UCLA	yuanxu(at)ucla.edu
Igor Yanovsky	Graduate Student Researcher	Mathematics, UCLA	yanovsky(at)ucla.edu
Jian Ye	Graduate Student Researcher	Mathematics, UCLA	ye(at)math.ucla.edu
Alan Yuille	Professor	Statistics, UCLA	yuille(at)stat.ucla.edu
Audrey Zhuang	Graduate Student Researcher	Neurology, UCLA	haihongz(at)seas.ucla.edu
Songfeng Zheng	Graduate Student Researcher	Statistics, UCLA	sfzheng(at)stat.ucla.edu

Back

JenniferTom - 22 Mar 2006

Affine Transformations – Mathematical transformations that effectively change the view of an image by transformations such as rotation, translation and sheer. Affine transformations are considered linear. An affine transformation is any transformation that preserves collinearity (i.e., all points lying on a line initially still lie on a line after transformation) and ratios of distances (e.g., the midpoint of a line segment remains the midpoint after transformation). While an affine transformation preserves proportions on lines, it does not necessarily preserve angles or lengths. Any triangle can be transformed into any other by an affine transformation, so all triangles are affine and, in this sense, affine is a generalization of congruent and similar.

JenniferTom - 24 Mar 2006

Loss of writing ability.

JenniferTom - 24 Mar 2006

Loss of reading ability.

Drosophila Collaboration

Presentation

Overview (5 min)

• CCB and Collaborations
• Dr. David Houle, FSU

Project Description (5 min.)

• project description (insert description of Dr. Houle's project here)
• his challenges:
  • measure morphologic phenotype efficiently
  • visualize changes in phenotype
  • study of the relationship between genotype and phenotype
• Our Challenges
  • various input data file formats
  • minimize special purpose code
  • enable him to run his own analyses on his local (Windows and Unix) machines.
  • Understanding and Communicating about the problem
• Our answers
  • translate various input formats into a common 'landmark' file format
  • split functions of (fly) image registration, warp, and warp analysis into separate tools.
  • use PERL to coordinate analysis programs (for portability, efficiency analyzing 20,000 files, and per Dr. Houle's request).
  • In person meeting with Dr. Houle for two days.

Tools used

• See: Landmark Warp Library
• Types of transformations (2D and 3D unless stated otherwise):
  • Rigid
    • Procrustes method (3D only)
    • Dual Quaternion method (3D only)
  • Rigid with Scaling
    • Generalized Procrustes method (3D only)
  • Affine
    • linear least squares best fit
  • Non-linear spline
    • elastic pde w/ linear or hyperbolic forces (non-radial basis)
    • biharmonic equation (thin plate spline radial basis)
    • inverse multiquadratic radial basis
    • gaussian radial basis
    • wendland radial basis
• Example of use (insert sample code here)
• Describe applications created

Results obtained

Booklet entry for fly wing

Presenter

Dr. Roger Woods

Description

Landmark Registration tools, as applied to Drosophila wing image warping

To support a collaboration with Dr. David Houle, we developed a Java library to simplify creation of landmark warping applications. This was used in collaboration with Dr. Houle to create tools that compare the phenotypes of various species of Drosophila. A warp field is derived from homologous fiducial landmarks in fly wing images. Descriptive statistics of wing area change are computed across each species of fly from these fields. These tools are not limited to image data, but may be applied to any two or three dimensional arrays of scalars. The transformations estimated by this library are useful for image registration problems and shape analysis. Supported transformations include Procrustes, Generalized Procrustes, non-linear spline and affine transformations.

Image

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Shape Tools Update

Presentation

Overview (5 min)

• Mission
• Approach
  • Portable Code
  • Available Code
  • Documented

Recent Accomplishments (5 Min)

• File Formats added (UIF, VTK, Contour OBJ, ???) : File Formats List

Libraries (5 Min)

• ShapeTools library API
• DataReaderWriter API
• ImageReader API
• [[][OpenDX API]]
• LandmarkWarpTools API
• MultigridPDE API
• [[][VFSBrowser API]]

Demonstration (5 Min)

• ShapeViewer -
• Medial Axis pipeline
• Geodesic Distance Pipeline

Announce CD / Questions

Booklet entry for ShapeTools

Presenter

Craig Schwartz

Title

An update of the ShapeTools libraries and applications

Description

The ShapeTools group creates portable scientific software focussed on computational geometry data structures: things composed of points, edges and faces.

We will describe the additions to our supported file formats and applications created since the last CCB meeting, with emphasis on tools of general use to the Neuroimaging and scientific community.

A CD of the current set of ShapeTools libraries and applications will be available.

Image

Image Caption:

The ShapeViewer displaying a cortical surface (grey) and the geodesic path connecting two vertices of that surface. This path is computed with the ShapeTools application GeodesicPath, using Dijkstra's algorithm.

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Other

• New Tools
  • Applications
  • Libraries
• Old Tools
  • New file formats
  • Significant algorithms
  • ShapeViewer

Shape Tools Update

Overview

• Mission, Approach : Portable, Available, Documented Code

Accomplishments since last All Hands

• File Formats : CCBBM, Duff, FreeSurfer (binary triangle), Minc Contour OBJ, VTK (triangulated surface), RGB UIF surface
• Analysis Applications : GeodesicPath , MedialDistance, ConvexHull, FDRShape , RigidQuaternionTransform, RigidAtlas, ShapeGroupDistance
• Utility Applications : DuffOffTranslator, ExtractAttributes, UcfReorder, ObjTranslation, ShapeToImage
• ImageJ extensions (Loni file types): UIF, UCF

Application Support Libraries

• ShapeTools library API
• DataReaderWriter API
• ImageReader API
• OpenDX API
• LandmarkWarpTools API
• RegularDiscreteDataTools API

Q & A

JenniferTom - 24 Mar 2006

Alzheimer’s disease (AD) is a neurodegenerative disease and is the most common cause of dementia. It results in loss of memory and cognitive functions, a progressive inability to perform activities of daily living, and changes in personality and behavior. Alzheimer’s is diagnosed according to the Diagnostic and Statistics Manual (DSM-IV) as developing multiple cognitive deficits manifested by memory impairment and one of the following: (1) Aphasia^? (2) Apraxia^? (3) Agnosia^? (4) Disturbances in executive functioning (Grossberg).

Alzheimer’s can only be diagnosed definitively post-mortem but can be diagnosed with 85-90% accuracy by using cognitive tasks such as the MMSE and ADL and input from relatives. Other symptoms of Alzheimer’s include getting lost, an accidental fall, messy home, decline in calculating abilities, psychosis, depression and behavioral disturbances.

References: Grossberg (2005). “Diagnosis and treatment of Alzheimer’s Disease” Neurology 64: S34 - S39.

JenniferTom - 23 Mar 2006

The amygdala is important in processing emotion, fear and pleasure. An event with greater emotional relevance is thought to be remembered better due to increased amygdala activity. The amygdala is composed of several closely situated nuclei: (1) basolateral complex, (2) centromedial nucleas, and (3) cortical nucleus. It has strong hippocampal and hypothalamic connections and projects to the dorsal forebrain and the brain stem tegmentum.

A disorder that reveals some of the functions of the amygdala is Kluver-Bucy syndrome.

JenniferTom - 24 Mar 2006

ALS is a disease in which motor neurons die causing muscles to atrophy.

Interesting Research: Due to the degenerative nature of ALS, it is believed that research into stem cells^? might aid in its treatment. Stem cells have successfully been injected and incorporated into rodent spinal cords. See Press Release for more information.

Neural stem cells have also been shown to repair damage in rodents with symptoms similar to ALS and is now being tested in monkeys. See Press release for more information.

Treating ALS with stem cells has been tried in humans in Beijing. See Press Release for more information.

DuyguTosun^? - 13 Oct 2005

An Example:
object 1 class array type float rank 1 shape 3 items N data follows
[list of 3D mesh nodes]
attribute "dep" string "positions"
object 2 class array type int rank 1 shape 3 items M data folows
[list of triangle vertex ids]
attribute "ref" string "positions"
attribute "element type" string "triangles"
attribute "dep" string "connections"
object "origin" class array type float rank 1 shape 3 items 1 data follows
[x0 y0 z0]
#
object "default" field
component "positions" 1
component "connections" 2
component "origin" "origin"
end

usage:

ana2raw <inputFile> <outputFile>

The input is the file name of an ANALYZE file, excluding extension name (i.e., .hdr and .img). The output file can be given by the user.

Lobes

Lesions

Orientation

Tissue Types

For more information visit: Brief Overview of Brain Anatomy

Visual Representation of Gyri, Sulci and Various Landmarks

JenniferTom - 22 Mar 2006

In diffusion weighted imaging, movement of water molecules that is impeded in some directions more than others (e.g., movement through a tube). Compare to isotropic.

JenniferTom - 22 Mar 2006

Towards the face or front of the head. In opposition to Posterior

ApplyFlatMapWarp

Overview

This application applies a warp that is defined on a flatmap to data defined on a flatmap. In particular, the warp must be encoded as an RDD with grid size 257 x 257. The data in the warp must be the output coordinates of the warp, not the displacement. The data that is resampled must be defined on the same grid-size, but the data may be arbitrary. The data is resampled using the triangulation defined by the octahedral mesh used when flattening. In other words, the data is resampled linearly between grid points, where the plane used for interpolation is defined by the grid points that make up a triangle in the triangulated mesh representation of this surface.

Usage

Argument	Type	Required	Description	Notes
-overwrite	flag	No	Enables file overwriting
-debug	flag	No	Enables additional error reporting
-input	File	Yes	Specifies the input data structure filename.
-warp	File	Yes	Specifies the warp data structure.
-rdd	flag	No	Specifies the file type to be RDD. The default is RDD
-contour	flag	No	Specifies the file type to be Contour. The default is RDD
-point_list	flag	No	Specifies the file type to be Point List. The default is RDD
-linear	flag	No	Specifies the interpolation method to be LinearFlatmap. The default is LinearFlatmap
-bilinear	flag	No	Specifies the interpolation method to be Bilinear. The default is LinearFlatmap
-nn	flag	No	Specifies the interpolation method to be NearestNeighbor. The default is LinearFlatmap
-output	File	Yes	Specifies the output data structure filename.
-help	flag	No	Prints help and usage information.
-version	flag	No	Prints version number of this application.
-verbose	flag	No	Enables detailed status messages.
-license	flag	No	Prints license details, and exits.
-twiki	flag	No	Writes command line arguments as a TWiki format table to output and exit.
-pipeline	flag	No	Write pipeline descriptor file to output and exit.
-pipelineJar	String	No	Specify path to an application jar file, as a pipeline resource.	Example: "pipeline://localhost//home/zed/Connery.jar"
-pipelineJvm	String	No	Specify a Java Virtual Machine called by a pipeline.
-pipelineJvmOptions	String	No	Specify Java Virtual Machine options used by a pipeline.

ApplyImageWarp

Version

1.0

Overview

This project is an application that will apply a warp to a given image. The user will provide a two dimensional warp file, *.lmw. They will also provide a target and reference image, which can be any format supported by Java ImageIO^?. The reference image is used to obtain dimensions for the output image. The output image will be written in the 'png' format, however, this should be changed easily.

Restrictions, Exclusions, Limitations

The application will be compatible with all warp filetypes and image filetypes. The pixel locations (i,j) will be interpreted as their equivalent floating point values in the warp space.

Requirements

The application will have verbose and file overwrite modes. There will be a usage page and version printing. There will also be support for LONI pipeline descriptor output.

Requirements Change Procedure

All changes will be approved by all.

Requirements Changes

No changes have been made

Approach

The user will provide two images, a target and a reference. A new image is computed in the reference space which has the same dimensions as the specified reference image. The warp is applied to each point in the reference image and the new image's intensity will be the intensity at the warped location in the target image.

RACI Chart

R - Responsible person
A - Actions taken (Authorizes something, provides support, et. cetera)
C - Communicates or consults with this person
I - Inform this person

Activity	Start Wk	End Wk	RC	CS	RW	Exp Hrs	Act Hrs	Curr Exp Hr
Project definition & requiremnts	14		RA	AI	AIC	1	1	1
Write Java code	14		RA	I	AIC	3	3	3
fix problems	14		RA	AI	AIC	1
Write user guide	14		RA	A	AIC	2

Apply Landmark Warp User's Guide Version 1.0

Overview

This is an application for applying a warp to a set of points.

Description

The point and shape application have three required arguments. The first required argument is the filename of the warp file, and the second required argument is the filename of the point data. The point data may be specified by a line-delimited list of coordinates. The point data may also be specified by a ShapeTools-compatible shape, where the vertices are used for the point data. The dimension of the warp and the dimension of the point data must be equal. The third required argument is the output filename the warped point data, whose format will match the format of the second required argument. If this file exists, an error will occur; however, if the user adds the '-overwrite' flag, the file will always be written. For a usage page, use the '-usage' flag or no arguments. For verbose output, add the '-verbose' flag. The name and version of the application can be written with the '-version' flag. If there is an error executing the program, and you think a bug has occurred, the '-debug' flag will print more information for the bug report.

Examples

Consider homologous landmarks that are stored in the files 'reference.lm' and 'target.lm'.

First, the warp between the landmarks is computed and stored in the file 'warp.lmw':

java -jar ComputeLandmarkWarp.jar -ref reference.lm -tar target.lm -output warp.lmw

If we have novel points (points that are not at the landmarks), novelReference.lm, we can find their homologous points in the target space and write them to 'novelTarget.lm':

java -jar ApplyLandmarkWarp.jar -ascii -warp warp.lmw -input novelReference.lm -output novelTarget.lm

For three dimensional warps, we can apply a warp to a shape. Assume that we have derived a three dimensional warp, 'warp3D.lmw' from one of the compute applications. If we have a shape 'shapeReference.off' from the reference space, then we can find the homologous shape in the target space and write it to 'shapeTarget.off':

java -jar ApplyLandmarkWarp.jar -shape -warp warp3D.lmw -input shapeReference.off -output shapeTarget.off

If you would like to know more about the progress of the program, you can use the '-verbose' flag:

java -jar ApplyLandmarkWarp.jar -verbose -ascii -warp warp.lmw -input novelReference.lm -output novelTarget.lm

If you would like the program to overwrite files, use the '-overwrite' flag:

java -jar ApplyLandmarkWarp.jar -verbose -overwrite -ascii -warp warp.lmw -input novelReference.lm -output novelTarget.lm

Usage page

Usage: java -jar ApplyLandmarkWarp.jar [options] -warp warp.lmw -input inputFilename -output outputFilname

|   Argument   |  Type | REQ|              Description              |   Notes   |
|-overwrite    |flag   |No  |Enables file overwriting               |           |
|-debug        |flag   |No  |Enables additional error reporting     |           |
|-ascii        |flag   |No  |This flag is used to specify the       |           |
|              |       |    |input/output file format               |           |
|              |       |    |ASCII/PointList. The default is        |           |
|              |       |    |ShapeToolsVertices                     |           |
|-shape        |flag   |No  |This flag is used to specify the       |           |
|              |       |    |input/output file format               |           |
|              |       |    |ShapeToolsVertices. The default is     |           |
|              |       |    |ShapeToolsVertices                     |           |
|-input        |File   |Yes |Used to specify the input filename     |           |
|-warp         |File   |Yes |Used to specify the warp filename      |           |
|-output       |File   |Yes |Used to specify the output filename    |           |
|-help         |flag   |No  |Prints help and usage information.     |           |
|-version      |flag   |No  |Prints version number of this          |           |
|              |       |    |application.                           |           |
|-verbose      |flag   |No  |Enables detailed status messages.      |           |
|-license      |flag   |No  |Prints license details, and exits.     |           |
|-twiki        |flag   |No  |Writes command line arguments as a     |           |
|              |       |    |TWiki format table to output and exit. |           |
|-pipeline     |flag   |No  |Write pipeline descriptor file to      |           |
|              |       |    |output and exit.                       |           |
|-pipelineJar  |String |No  |Specify path to an application jar     |Example:   |
|              |       |    |file, as a pipeline resource.          |"pipeline: |
|              |       |    |                                       |//localhos |
|              |       |    |                                       |t//home/ze |
|              |       |    |                                       |d/Connery. |
|              |       |    |                                       |jar"       |
|-pipelineJvm  |String |No  |Specify a Java Virtual Machine called  |           |
|              |       |    |by a pipeline.                         |           |
|-pipelineJvmOptions
|              |String |No  |Specify Java Virtual Machine options   |           |
|              |       |    |used by a pipeline.                    |           |


DESCRIPTION:

This application applies a given warp to the a set of points.   By 
default, the points are the vertices of a ShapeTools-compatible shape.   
Alternatively, the points may be specified by an ASCII list of 
coordinates.   The coordinates of the points should be space delimited 
and the points should be line delimited.   The dimension of the warp 
must match the shape's dimension.   The output format will match the 
input format.  

ApplyPointWarp

Version

1.0

Overview

This project is an application that will compute the output of a given warp for a set of given points. The set of points will be encoded to a *.lm file. The warp will be encoded to a *.lmw type file. The output will be a *.lm type file.

Restrictions, Exclusions, Limitations

The application will be compatible with all warp filetypes. The landmarks must be specified by a landmark file, which is a file containing line-delimited point with space-delimited coordinates. The dimension of the landmarks must match the dimension of the warp file.

Requirements

The application will have verbose and file overwrite modes. There will be a usage page, and pipeline descriptor output will be supported.

Requirements Change Procedure

All changes will be approved by all.

Requirements Changes

No changes have been made

Approach

The user will provide a landmark file. The files will specify the K N-dimensional points in a simple human readable format. Each line will have the N coordinates of a point, space-delimited. There will be K lines, meaning K points.

The program will apply the warp to each of those points and write the output to another landmark file specified by the user.

RACI Chart

R - Responsible person
A - Actions taken (Authorizes something, provides support, et. cetera)
C - Communicates or consults with this person
I - Inform this person

Activity	Start Wk	End Wk	RC	CS	RW	Exp Hrs	Act Hrs	Curr Exp Hr
Project definition & requiremnts	14		RA	AI	AIC	1	1	1
Write Java code	14		RA	I	AIC	3	3	3
fix problems	14		RA	AI	AIC	1
Write user guide	14		RA	A	AIC	2

Apply Warp Applications Bug List 1.0

ArrayIO Library

Library Description

This Java utility library provides methods to read and write arrays from and to data files, hiding from the programmer the details of the particular file format used.

When complete, this library will allow a (programmer) user to read arrays from an input file, or byte stream, without having to know the particular file format.

When finished, the library will

• recognize the file format
• determine if it matches an user supplied conditions (e.g. dimensionality, file format type,..)
• provide the data values to the user program

The library does not currently automatically recognize the input file format.

Supporting Documents

• Array IO Library Changes

Supported File formats

These are the formats currently supported by the ArrayIO library

Ascii Matrix

this is a simple text file format it contains either:

• comment lines, beginning with a '#' character
• matrix row lines, containing ascii encoded numbers, separated by white space

Ascii Matrix Example:

0.926915  -0.00555646  0.00343901  8.5602
0.00537576  0.898947  0.00352094  7.22592
-0.00380805  -0.00397254  1.02016  -1.90323
0  0  0  1

RDD file format

The Regular Discrete Data format file,(RDD) version 1.0, has these sections:

Identifer String

In Version 1.0 these are "RDDF"

Data Precision Identifer

A single character, indentifying the data precision.

this character is either "f" or "d", signifying float, or double precision numbers in the data sample section of the file.

number of data dimensions

an integer defining the number of dimensions of the data values.

a description of the data samples - not yet completed

the data values - web entry not completed

Shape Tools Array IO library project definition

Overview

a small library that enables reading and writing numerical arrays to and from disk files.

Draft Requirements

• Written in Java 1.5
• support comments
• support float, and double precision
• support intermixed scientific notation and decimal notation.
• disk files are self identifying
  • array dimensions, data type
• support an application versioon number
• support a general array io version number
• support
  • Nifti files
  • support landmark warp files (?)
  • support Ryan's discrete data files
• support calls like
  • float [][] foo = readArray("myArray.file");
  • readArray(foo, "myArray.file");
• Provides means to:
  • Ensure that the data values are not used inappropriately.
  • Communicate to library clients contextual descriptions of the data (e.g. 'this is a set of samples').
• have means to require existence of a named data chunk in the input file (to assure application the the file matches the intended use).
• should have non-lossy translations between common formats
• be able to specify validity constraints on arrays before reading (e.g "must be square", "must be 4 or more columns", "must be a "myType" data file,
• be able to specify comments to be written out (where approprite)
• be able to change to numerical formatting of the values , if written to text.

Restrictions, Exclusions, Limitations

• version 1.0 only supports 2d array of scalar floats as ascii text

People Affected

Person	Initials	Notes
Craig Schwartz	CS	Programmer
Roger Woods	RW	Sponsor

Version

1.0

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig, in consultation with himself.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

• N/A

Products

This project will result in the following products:

• A Java application jar containing code that implements the requirements of this project.
• A CVS repository containing the Java source code of this library.
• Examples illustrating use of the library.
• JUnit tests that validate the external inteface of this library.

Draft Format

• Description Block
• Comment Block
• Data Block

CCB ArrayStatistics

Project Components

• Documentation
  • ArrayStatistics User's Guide v 1.0
  • Changes Log
  • A list of Frequently Asked Questions.
• Feedback
  • Project Bug List (v1.0)
• Download Links
  • Download
• Feature Wish List

Revised 3 August 2007

ArrayStatistics Bugs

ArrayStatistics Change Log

ArrayStatistics FAQ

ArrayStatistics Feature Wish List

• Output precision specification
• ASCII delimiter specification

Array Statistics Project Definition

Restrictions, Exclusions, Limitations

People Affected

Person	Initials	Notes
Ryan Cabeen	RC
Craig Schwartz	CS
Roger Woods	RW

Version

1.0 draft

Description

This program will read a collection of array data and calculate the mean and variance of the collection.

Input formats

The data samples may be in any of the following formats. A default format is assumed. If this is incorrect the input file format must be specified by the user via the appropriate command line argument.

• an ASCII file with space-delimited and line-delimited floating point values.
• a LONI FPIC UIF file.

Input Modes

The input data filenames may be specified in the following ways. A default input mode is assumed.

• the filenames as command line arguments
• the filename of an ascii list of input data filenames

Output Formats

The output file formats will be the same as the input file format.

Requirements

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig and Ryan.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

Command Line Arguments

Note: this list is provisional

the set of command line arguments will be

• -fileList filePath : a text file containing list of paths to the input files. If not present, files will be read from the command line.
• -claList : the input filenames will be parsed from the command line arguments
• -uif : the input files will be read as UIFs
• -ascii : the input files will be read as ASCII arrays
• -var fileName : the name of the output file to hold the calculated sample variance (optional).
• -mean fileName : the name of the output file to hold the calculated sample mean (optional).
• -verbose : enables runtime status messages.
• -usage : prints a summary of how to run this program.
• -version : prints the program version number.
• -license : prints license status, if appropriate.
• -delimiter char - sets the sample value delimiter.

The output file format will match the input file format.

Run-time errors

• if the input file formats do not agree
• if the input file dimensions disagree
• if no output statistic is selected.

Products

This project will result in the following products:

• A Java application jar that will create a file storing the Jacobian determinant.
• A Loni Pipeline descriptor definition.
• A CVS repository containing the Java source code of this application.
• Examples illustrating use of the application.
• Documentation describing the algorithms and limitations of the calculations.

Lessons Learned

ArrayStatistics Application User's Guide Version 1.0

Overview

This is an application for computing statistics of data in a simple ASCII array format. Given a collection of similarly formatted data, this application can compute either the mean, the variance, or both. The default format is an ASCII format with space and newline delimiting. Alternatively, the user can specify the format to be UIF/FPIC format. The output format will always match the input format. When specifying the collection of data, the user may pass the filenames as command line arguments, or they may specify a list of filenames.

All computations are done in 64-bit floating-point precision, regardless of the input data precision. There are no constraints on the format; however, all of the input files must have a consistent format. There may be an arbitrary number of rows and columns of data.

Examples

Consider two data files, 'a.txt' and 'b.txt':

$ cat a.txt 
1.2 6.3
6.5 7.8
0.4 4.2

$ cat b.txt 
0.5 1.2
5.4 9.1
3.1 0.9

The mean of these files is computed with the following call:

java -jar ArrayStatistics.jar a.txt b.txt -outputMean mean.txt

which creates the file 'mean.txt':

$ cat mean.txt 
0.85 3.75
5.95 8.45
1.75 2.5500000000000003

Similarly, the variance of the data is computed with the following call:

java -jar ArrayStatistics.jar a.txt b.txt -outputVar var.txt

which creates the file 'var.txt':

$ cat var.txt 
0.12249999999999998 6.5024999999999995
0.3024999999999998 0.4224999999999999
1.8225000000000002 2.7225

If both the mean and variance are needed, it is more efficient to compute them simultaneously with the following call:

java -jar ArrayStatistics.jar a.txt b.txt -outputMean mean.txt -outputVar var.txt

For reference, here is a call that verbosely computes the variance of a set of FPICs that are listed in a file 'dataFilenames.txt', writing the output to 'var.uif':

java -jar ArrayStatistics.jar -verbose -uif -inputFileList dataFilenames.txt -outputVar var.uif

CCB ArrayToImage

Project Components

• Documentation
  • ArrayToImage User's Guide v 1.0
  • Changes Log
  • A list of Frequently Asked Questions.
• Feedback
  • Project Bug List (v1.0)
• Download Links
  • Download
• Feature Wish List

Revised 3 August 2007

ArrayToImage Bug List

ArrayToImage Change Log

ArrayToImage FAQ

ArrayToImage Feature Wish List

• Input data delimiter specification
• More floating point image output formats
• Color map specification for non-floating point image output

ArrayToImage Project Definition

Restrictions, Exclusions, Limitations

People Affected

Person	Initials	Notes
Ryan Cabeen	RC
Craig Schwartz	CS
Roger Woods	RW

Version

1.0 draft

Description

This program will read an array, created by the ArrayStatistics program, and create a FPIC image from the data values. This program will optionally read an existing FPIC file (presumed to be grayscale) and color the the gray values according to the data samples in the array.

Input formats

(see ArrayStatistics output)

Output Formats

• FPIC

Requirements

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig and Ryan.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

• 7/30 - write color mapped files to java ImageIO classs (PNG,TIFF,GIF).

Command Line Arguments

Note: this list is provisional

the set of command line arguments will be

• -numx integer - number of samples in x dimension of the created image.
• -numy integer - number of samples in y dimension of the created image.
• -array file - the file containing the array data samples.
• -image file - the image to be overlaid with the data samples.
• -colormap mapSpecification - an optional parameter to select the colormap used maybe...
• -output file - the name of the output image file.
• -verbose
• -usage
• -version
• -license

Products

This project will result in the following products:

• A Java application jar that will create a file storing the Jacobian determinant.
• A Loni Pipeline descriptor thingy.
• A CVS repository containing the Java source code of this application.
• Examples illustrating use of the application.
• Documentation describing the algorithms and limitations of the calculations.

Lessons Learned

ArrayImage Application User's Guide Version 1.0

Overview

This is an application for converting data encoded in a floating point array format. The primary action of this application is to create a UIF/FPIC from an ASCII array file. The ASCII array file is expected to have column elements that are space-delimited and rows that are line-delimited. By default, all data is interpreted to have 64-bit floating point precision.

If the data is encodes the Jacobian determinant, this application has an output mode which creates a visualization where values increasingly greater than one are mapped to increasingly brighter reds and decreasingly less than one values are mapped to increasingly brighter blues. Values near one will map to black which, by definition, has no brightness.

Examples

Consider a data file, 'data.txt':

$ cat data.txt 
1.2 6.3
6.5 7.8
0.4 4.2

The a UIF/FPIC named 'data.uif' is created from this data with the following call:

java -jar ArrayToImage.jar -input data.txt -outputUif data.uif

All 3D and/or interactive designers we hire undergo an intensive review process, which includes a portfolio review, phone interview, reference review and on-site interview. As such, please include in your application the following information:

• contact information
• upload of (or link to) a recent resume
• upload of (or link to) a portfolio which demonstrates your creative thinking, execution ability and a good eye for detail.
• names and contact information for 3 references

MichaelJPan - 24 Mar 2005

Back

Attendees List

Name	Title	Affiliation	E-mail
Anne Berman	Principal Public Admin. Analyst	Neurology, UCLA	anne.berman(at)loni.ucla.edu
David Brayford	Post Doctoral Fellow	SCI, University of Utah	dbrayford(at)sci.utah.edu
Caroline Brun	Graduate Student Researcher	Neurology, UCLA	cabrun(at)mednet.ucla.edu
Zai Chang	Web Programmer	Neurology, UCLA	zai.chang(at)loni.ucla.edu
Martin Cole	Programmer	SCI, University of Utah	mjc(at)sci.utah.edu
Jason Corso	Post Doctoral Fellow	Radiology, UCLA	jcorso(at)gmail.com
Alexandre Cunha	Post Doctoral Fellow	Neurology, UCLA	acunha(at)loni.ucla.edu
Priscilla Deng	Administrative Assistant	Neurology, UCLA	priscilla.deng(at)loni.ucla.edu
Ivo Dinov	Assistant Professor / COO	Statistics/Neurology, UCLA	ivo.dinov(at)loni.ucla.edu
Bin Dong	Graduate Student Researcher	Mathematics, UCLA	bdong(at)math.ucla.edu
Theo Van Erp	Staff Research Associate	Psych-Clin, UCLA	vanerp(at)psych.ucla.edu
Greg Farber	Program Officer	National Institutes of Health	farberg(at)mail.nih.gov
Mala Gurbani	Lab Assistant	Neurology, UCLA	mala.gurbani(at)loni.ucla.edu
Nathan Hageman	Graduate Student Researcher	Neurology, UCLA	nhageman(at)loni.ucla.edu
John Haller	Lead Science Officer	National Institutes of Health	hallerj(at)mail.nih.gov
Liberty Hamilton	Staff Research Associate	Neurology, UCLA	liberty.hamilton(at)loni.ucla.edu
Bret Hanlon	Programmer, GSR	Cornell University	bmh35(at)cornell.edu
Hugo Hernandez-Mora	Systems Administrator	Neurology, UCLA	hugo.hernandez(at)loni.ucla.edu
Todd Holloway	Programmer Analyst	Neurology, UCLA	todd.holloway(at)loni.ucla.edu
Namshin Kim	Post Doctoral Fellow	Bio Informatics, UCLA	deepreds(at)ucla.edu
Natasha Lepore	Post Doctoral Fellow	Neurology, UCLA	nlepore(at)loni.ucla.edu
Grace Liang	Administrative Analyst	Neurology, UCLA	grace.liang(at)loni.ucla.edu
Lisa Lu	Post Doctoral Fellow	Neurology, UCLA	lisa.lu(at)loni.ucla.edu
Po Lu	Asst Clin Prof	Neurology, UCLA	plu(at)mednet.ucla.edu
Eileen Lϋders	Post Doctoral Fellow	Neurology, UCLA	eileen.lueders(at)gmx.de
Sandy Lui	Management Services officer	Neurology, UCLA	sandy.lui(at)loni.ucla.edu
Allan Mackenzie-Graham	Post Doctoral Fellow	Neurology, UCLA	amg(at)loni.ucla.edu
Rico Magsipoc	Computing Resources Manager	Neurology, UCLA	rico.magsipoc(at)loni.ucla.edu
Yu Mao	Graduate Student Researcher	Mathematics, UCLA	ymao29(at)math.ucla.edu
Katherine Narr	Assistant Professor	Neurology, UCLA	narr(at)loni.ucla.edu
Elizabeth O’Hare	Graduate Student Researcher	Neurology, UCLA	eohare(at) ucla.edu
Stott Parker	Professor	Computer Science, UCLA	stott(at)cs.ucla.edu
Vishal Patel	Graduate Student Researcher	Neurology, UCLA	vishal.patel(at)ucla.edu
Adam Pingel	Graduate Student Researcher	Computer Science, UCLA	pingel(at)cs.ucla.edu
Georges Salamon	Professor	Radiology, UCLA	gsalamon(at)mednet.ucla.ed
Craig Schwartz	Programmer Analyst	Neurology, UCLA	craig(at)loni.ucla.edu
David Shattuck	Assistant Professor	Neurology, UCLA	shattuck(at)loni.ucla.edu
Yonggang Shi	Post Doctoral Fellow	Neurology, UCLA	yshi(at)loni.ucla.edu
Bae Cheol Shin	Programmer Analyst	Neurology, UCLA	baecheol.shin(at)loni.ucla.edu
Stefano Soatto	Professor	Computer Science, UCLA	soatto(at)cs.ucla.edu
Paul Thompson	Associate Professor	Neurology, UCLA	thompson(at)loni.ucla.edu
Arthur Toga	Professor	Neurology, UCLA	toga(at)loni.ucla.edu
Duygu Tosun	Post Doctoral Fellow	Neurology, UCLA	dtosun(at)loni.ucla.edu
Zhuowen Tu	Research Scientist	Neurology, UCLA	ztu(at)stat.ucla.edu
Yalin Wang	Assistant Researcher	Mathematics, UCLA	ylwang(at)math.ucla.edu
Roger Woods	Associate Professor	Neurology, UCLA	rwoods(at)ucla.edu
Meihe Xu	Post Doctoral Fellow	Neurology, UCLA	meihe(at)loni.ucla.edu
Yuan Xu	Graduate Student Researcher	Neurology, UCLA	yuanxu(at)ucla.edu
Igor Yanovsky	Graduate Student Researcher	Mathematics, UCLA	yanovsky(at)ucla.edu
Jian Ye	Graduate Student Researcher	Mathematics, UCLA	ye(at)math.ucla.edu
Alan Yuille	Professor	Statistics, UCLA	yuille(at)stat.ucla.edu
Audrey Zhuang	Graduate Student Researcher	Neurology, UCLA	haihongz(at)seas.ucla.edu
Songfeng Zheng	Graduate Student Researcher	Statistics, UCLA	sfzheng(at)stat.ucla.edu

---

Back

JenniferTom - 24 Mar 2006

ADHD is an psychiatric disorder diagnosed in children. The symptoms (summarized from the criteria for ADHD on the CDC website) include inattention (not attentive to detail, easily distracted, forgetful), hyperactivity (excessively chatty and can not sit still), and impulsivity (can not wait one's turn, interrupts others).

CCB AttrStatistic

Statistically analyze 4d attribute values of data files between one file and the other file. This analysis can also be done between one file and many other files.

Project Components

• Documentation
  • AttrStatistic User's Guide v 1.0
  • Examples using project version 1.0^?
  • Changes Log^?
  • A list of Frequently Asked Questions^?.
• Feedback
  • Project Bug List (v1.0)^?
• Download Links
  • Download v 1.0^?
• Feature Wish List^?

Revised 26 April 2007

AttrStatistic Project Definition

Overview

Restrictions, Exclusions, Limitations

N/A

People Affected

Person	Initials	Notes
Bae Cheol Shin	BC
Craig Schwartz	CS
Roger Woods	RW

Version

1.0

Requirements

• This program will be written using Java 1.5
• write the results in a new output file whose name is specified by the user.
• A text command line interface (not a Gui) is used.
• Command line parameters will specify
  • the input file.
  • the name of the output file that will be created

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig and Bae Cheol Shin.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

• N/A

Products

This project will result in the following products:

• A Java application jar that will create a shape file represents convex hull of the original surface mesh.
• A CVS repository containing the Java source code of this application.
• Examples illustrating use of the application.

Overview

Statistical analysis of coordinates and 4d attribute values of shapes among data files are needed.

Command Line AverageFiles Usage

The command line follows this pattern: "java {optional java arguments} -jar {optional command line arguments} input_file output_file". The "optional command line arguments" are described by the table in the next section, and by the illustrative examples that follow. The "optional java arguments' control the behavior of the Java virtual machine used to run the application and, with one exception, are seldom used. That exception is the memory allocation argument, -Xmx{number}, which asks your computer's operating system to give more memory to the Java virtual machine that it ordinarily gets. This is used when the data to be manipulated exceed the amount of memory. If you run the program and see an error with text resembling

"Out of Memory"

then you will probably want to add the memory argument. To increase the amount of JVM memory, pass an argument to Your java an argument:

java -Xmx512M -jar AttrStatistic.jar input_file1 input_file2 input_file3

this sets the ammount of memory to 512MB. Note: the -Xmx512M is the argument that requests additional memory.

Command line arguments

The program is controlled by use of command line arguments. The complete table of command line arguments follows. The "Default" values listed are the values used by the program if the corrosponding command line argument is not specified.

Not all arguments need be specified by a user; generally, an argument is used only to override an unwanted default behavior of the program.

Argument	Default	Description	Notes
-version	n/a	Prints a version string to the console output and exits normally.
-help	n/a	Prints usage string to the console output and exits normally.
-verbose	false	Enables detailed status messages during calculations.
-examples	n/a	Prints examples of common usage
-list		Select input files from a list of file in a given text file.
-directory		Select input files from all readable files in a given directory.
-prefix	'output_'	Specify output file prefix
-suffix		Specify output file suffix
-ref	n/a	Specify reference input file	This argument is required.
-attrname	n/a	Selects name of attribute for input files and reference file.	If not specified the first scalar data attribute found is used.
-outputattrname	n/a	Selects name of output data attribute	If not specified input data attribute will be used.

Examples of common usage

In these examples we assume that the ConvexHull application is saved in a file named AverageFiles.jar.

Ex 1) Print a usage summary to the console output.

Use this command: java -jar AverageFiles.jar -help

Ex 2) Read input file from input1.ucf, input2.ucf, input3.ucf, and input4.ucf. Calculate average of coordinates of all files. Average of values fromthe first data attribute will be also calculated. The result shape will be written to output file, outputFileName.

Use this command: java -jar AverageFiles.jar -output outputFileName input1.ucf input2.ucf input3.ucf input4.ucf

---

Detailed Description of the program

Common Runtime Errors:

No input or output file

if both input file and output file are not provided the application will exit.

Not appropriate input file

if input file doesn't exist, or cannot be read the application wlll exit.

Not appropriate output file

if it is not possible to write the output file then the application will exit.

Not appropriate output type

if input file type doesn't match output type the application will exit.

Not appropriate shape

if shape has no face set or the face set has no faces the application will exit.

Data formats supported

Any file formats that support per vertex attributes and are readable by the ShapeTools library may be read by this program, however, it is possible that a valid file may not contain a closed mesh surface. Loni Ucf files are a particular example of this. Loni Ucf files may in some circumstances be explicitly interpolated to form a surface mesh. Please see the shape tools InterpolateContour application for more details.

The following output formats are currently supported (4 December 2006)

Abbreviated Name	Description
TXT	Ascii text files
DX	OpenDX ascii general format data files

---

System requirements

• Platform - Platform independent
• Operating System - Any OS that has Java 1.4.2 or greater.

Restrictions, Exclusions, Limitations

• Duplicate vertices are not allowed.
• Surfaces with holes are not allowed.
• Faces that vertices originally came from are assumed to be not distorted.

Acknowledgements

This program is a product of the CCB Shape Tools program.

CCB AttributesStatistic

Statistically analyze 4d attribute values of data files between one file and the other file. This analysis can also be done between one file and many other files.

Project Components

• Documentation
  • AttributesStatistic User's Guide v 1.0^?
  • Examples using project version 1.0^?
  • Changes Log^?
  • A list of Frequently Asked Questions^?.
• Feedback
  • Project Bug List (v1.0)^?
• Download Links
  • Download v 1.0^?
• Feature Wish List^?

Revised 26 April 2007

AttributesStatistic Project Definition

Overview

Restrictions, Exclusions, Limitations

N/A

People Affected

Person	Initials	Notes
Bae Cheol Shin	BC
Craig Schwartz	CS
Roger Woods	RW

Version

1.0

Requirements

• This program will be written using Java 1.5
• write the results in a new output file whose name is specified by the user.
• A text command line interface (not a Gui) is used.
• Command line parameters will specify
  • the input file.
  • the name of the output file that will be created

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig and Bae Cheol Shin.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

• N/A

Products

This project will result in the following products:

• A Java application jar that will create a shape file represents convex hull of the original surface mesh.
• A CVS repository containing the Java source code of this application.
• Examples illustrating use of the application.

JenniferTom - 24 Mar 2006

Autism is a neurodevelopmental disorder generally detected around two to four years of age. Symptoms from the Wikipedia Autism Entry include abnormal social interaction, communication ability, patterns of interests, and patterns of behavior.

Interesting Research: As part of the Autism Genome project, researchers are trying to determine the genetic basis behind the heritable predisposition of Autism via Affymetrix 500K genotyping chips. For more information visit Press release.

Recent MRI research done on infants zero to two years of age showed that autistic infants have an abnormal increase in head circumference size (a predictor of brain size) relative to normal infants at around one year. By the end of the second year, an autistic infant has experienced much greater brain growth relative to normal. From two to four years, there is an increase in gray and white matter in the frontal and temporal lobes and the development of an abnormal ratio of gray to white matter in the cerebellum. Between five and twelve years of age, the autistic brain no longer shows a significant size difference from the normal (Hazlett).

References: Hazlett, HC (2005). “Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years.” Archives of General Psychiatry. Dec;62(12):1366-76.

Courchesne, E. (2004). “The autistic brain: birth through adulthood.” Current Opinions in Neurology. Aug;17(4):489-96.

CCB Automated Volume Parsing Mapping File

Automated Volume Parsing Tool

The following table contains the mapping between the image intensities and ROI names in the output volume of the Brain Parser tool.

Index	Volume_Intensity	ROI_Name
0	0	Background
1	21	L_superior_frontal_gyrus
2	24	R_middle_frontal_gyrus
3	50	R_precuneus
4	181	cerebellum
5	47	L_angular_gyrus
6	122	R_cingulate_gyrus
7	83	L_middle_temporal_gyrus
8	90	R_lingual_gyrus
9	81	L_superior_temporal_gyrus
10	91	L_fusiform_gyrus
11	44	R_superior_parietal_gyrus
12	66	R_inferior_occipital_gyrus
13	87	L_parahippocampal_gyrus
14	162	R_caudate
15	85	L_inferior_temporal_gyrus
16	182	brainstem
17	43	L_superior_parietal_gyrus
18	28	R_precentral_gyrus
19	23	L_middle_frontal_gyrus
20	89	L_lingual_gyrus
21	41	L_postcentral_gyrus
22	86	R_inferior_temporal_gyrus
23	163	L_putamen
24	26	R_inferior_frontal_gyrus
25	102	R_insular_cortex
26	25	L_inferior_frontal_gyrus
27	46	R_supramarginal_gyrus
28	34	R_gyrus_rectus
29	65	L_inferior_occipital_gyrus
30	164	R_putamen
31	61	L_superior_occipital_gyrus
32	30	R_middle_orbitofrontal_gyrus
33	42	R_postcentral_gyrus
34	27	L_precentral_gyrus
35	32	R_lateral_orbitofrontal_gyrus
36	121	L_cingulate_gyrus
37	31	L_lateral_orbitofrontal_gyrus
38	92	R_fusiform_gyrus
39	45	L_supramarginal_gyrus
40	88	R_parahippocampal_gyrus
41	22	R_superior_frontal_gyrus
42	29	L_middle_orbitofrontal_gyrus
43	68	R_cuneus
44	62	R_superior_occipital_gyrus
45	33	L_gyrus_rectus
46	48	R_angular_gyrus
47	64	R_middle_occipital_gyrus
48	84	R_middle_temporal_gyrus
49	49	L_precuneus
50	67	L_cuneus
51	161	L_caudate
52	165	L_hippocampus
53	166	R_hippocampus
54	82	R_superior_temporal_gyrus
55	63	L_middle_occipital_gyrus
56	101	L_insular_cortex

LindaCapetillo^? - 22 Feb 2005

Back

Automatic Brain Sulcal Detection Tool & Conformal Spherical Analysis Tool Yalin Wang Lab of Neuro Imaging, Neurology Department Mathematics Department, UCLA Phone: (310)206-7334 Web: http://www.math.ucla.edu/~ylwang

Abstract:

We developed two software tools, Automatic Brain Sulcal Detection Tool and Conformal Spherical Analysis Tool. The goal of the tools is to provide some convenient research tools to analyze anatomical surfaces with conformal technology. Specifically, automatic brain sulcal detection tool detects brain sulcal landmark on cortical surfaces by solving a partial differential equation and a variational problem. Conformal spherical analysis tool is aimed to analysis the frequency domain of brain cortical and hippocampal surface shape. Both software tools were demonstrated in CCB Software Development Meeting on CCB All Hands Meeting. Among them, conformal spherical analysis tool won “the best visualization software” award in CCB software competition. The figure below demonstrates some results of conformal spherical analysis tool.

Back

The Automated Wing Measurement system.

1. Knock out fly and suck the wing into the narrow slit of the wing grabber:

2. Two points at the base of the wing are marked by the handler

3. Semi-automated software fits series of clamped quadratic B-splines:

4. The landmarks are placed on the intersections between the splines or at the intersection between the spline and the imaginary line at the base of the wing:

Automatic Statistical Shape Analysis

Project Description

Summary

This project will create an automated tool to peform a statistical analysis of the differences between populations of hippocampi. This analysis differs from the medial analysis protocol now in use in the following ways:

• it's based on surfaces (shapes).
• it's "true 3d" (does not depend on 2d planar approxmations of UCF contours).
• it does not depend on the assumption that you can reasonably fit a line to the (Hippocampus) shape.
• it will be cross-platform (e.g. Java, and C++ binaries for Sun,Mac,Windows and selected Linux variations).
• source code will be available.

This tool will be composed of several computer programs, applied sequentially, whose final result will be a an atlas created from the analyzed hippocampus surfaces. Each vertex of this atlas will have an associated p-value indicating the statistical significance of a selected metric used to characterize the difference between an individual hippocampus and the group atlas.

In this proposal, we will use the Euclidean distance as the difference metric, and a Students T-Test as the statistical method. Our modular approach to the computations (e.g. via a distinct computer program) means that other metrics and statistics may easily substituted, as they become available.

Expected Implementation

We expect to implement this as

• a set of Java programs to create the atlas and statistics (Craig's group provides this)
• a C++ program to perform the Direct Mapping of Hippocampus (DMH) (from Yongang).
• a Pipeline to tie it all together.
• web pages documenting all the parts, and how they fit together
• Various LONI software download package containing all the above.

Personnel and Resources

• Yongang Shi - DMH coding, DMH documentation
• Craig Schwartz - ShapeTools coding, project coordination, download packaging, documentation
• Ryan Cabeen - ShapeTools coding.
• BaeCheol Shin - ShapeTools coding.
• Ivo Dinov - coordinates with test researchers.
• Nancy Sicotte - test researcher

Schedule

• 15 June - Draft Project Definition
• 21 June - Final Project Definition
• 20 July - Project Approval
• 22 October - Beta release to friendly, external testers
• 19 November - Final release

Products

• Tools
  • Shape Matching software - Direct Mapping of Hippocampus (DMH), in these forms:
    • Windows Compiled binary, as LONI software download package
    • Sun compiled binary , as LONI software download package
    • Macintosh compiled binary , as LONI software download package
    • Ubuntu Linux compiled binary , as LONI software download package
    • Source code, as a LONI software download package
    • Source code, in a CCB source code archive (SVN).
  • Shape optimal rigid transform calculation software (java), as LONI software download package.
  • Shape transform software (java), as LONI software download package.
  • Shape difference metric calculation tool (java), as LONI software download package.
  • Shape averaging software (java), as LONI software download package.
  • Shape t-test calculation (java), as LONI software download package.
  • A Loni Pipeline to perform the analysis, configured to use the LONI Pipeline Server to perform the calculations.
• Documentation
  • Web pages for all tools
  • A user manual ,in PDF form, including
    • a description of how to use the plpeline.
    • illustrations of the various stages of the analysis.
• Sample Analyses
  • A sample data set, divided into two populations.
  • The results for running the sample data set
  • A pipeline configured to analyze the sample data set
  • Instructions on unpacking this sample data set and running the pipeline analysis.

Analysis Procedure

Data: a group of shapes from controls (G_Ctrl) and a group of shapes from patients (G_Pat):
 1) Determine the Atlas from the control group (G_Ctrl)
   a) pick one shape from the control group.
   b) rigidly align other shapes to this one
   c) compute average of (b) and the ref. shape -> AVG0
   d) align all shapes of the control group to AVG0 -> G1
   e) compute average of G1 -> AVG1
   f) measure change between || AVG1 - AVG0 ||
   g) repeat (d),(e),(f) until (f) < tolerance
   h) final AVG_N is the "AtlasG0"
   
 2) at each point in each control shape (G_Ctrl):
    a) find difference vector from the shape to the Atlas (dilation)
    b) project this vector onto the atlas surface normal at that point
    c) append the magnitude of this projection to a list for this vertex
 3) do (2) except for disease shapes G_Pat)
 4) calculate a statistical test (T-Test, initally)  comparing the two lists for significance (p-value)
 5) assign this P-value to a copy of the atlas (p<0.05 is probably significant).

Tasks

• modify pipeline to use direct hippocampal mapping program (via pipeline server)
• Get sample data from NS
• Define and write atlas creation application
• Debug RigidAtlas program

Accomplishments

• Add 'ascii" matrix support to ShapeTransform (8/15).
• Rigid transform calculation software
• Shape transform software (updated 9/10).
• Shape difference metric calculation tool (updated 9/4/).
• Shape averaging software.
• RigidAtlas beta program done (9/15).

Automated Statistical Shape Analysis Planning Notes

this page contains rough notes used to define the automated statistical shape analysis. These have been revised and edited to form the formal project proposal

Summary, Meeting of 15 June 2007

We outlined a short sequence of analysis programs (starting with Yongang's work) that we believe will provide a general means to create a 3D shape-based atlas of shapes (e.g. vector geometry objects). The product of the analysis will be an atlas of the input shapes and a set of p-values representing the significance of some statistic that measure the difference of a shape in the original population from the atlas.

Initally, this difference measurement will be Euclidean distance, and the p-value based on a standard t-test. If it all works out, we can later add other mesurements and statistical tests.

We expect it will be used like this:

Researcher:
a) identifies two popluations - a test group , and a control group
b) generates shape ROI of , say, a hippocampus for these,
c) turns these into the input to Yongang's program, which is now (i think) minc OBJ files.
d) calls our analysis with the two groups of OBJ files as input
e) get's the output atlas with the p-values
f) publishes, with the attendant fame and glory.

This differs from what we presently do by a) it's based on shapes
b) it's "true 3d" (does not depend on 2d planar approxmations of UCF contours).
c) it does not depend on the assumption that you can reasonably fit a line to the shape (this is the core assumption of the medial core analysis of the hippocampus that is used now).
d) it will be cross-platform (e.g. Java, and C++ binaries for Sun,Mac,Windows and selected Linux).
e) source code will be available.

We expect to implement this as

• a set of Java programs to create the atlas and statistics (Craig's group provides this)
• a C++ program to the the nonlinear matching (please correct my description) (from Yongang)
• a Pipline to tie it all together - for LONI afilliates to use
• a script (probably PERL) to tie it all together, for -non LONI afilliates
• web pages documenting all the parts, and how they fit together
• A loni software download package containing all the above.

As we are all busy already, we'll need to fit the work to be done into what we are already doing, and so we plan to present a timeline to Dr. Toga of

• now - plan the project
• sep 30 - beta relase of everything to LONI afilliates for a month of testing
• oct 30 - final public release.

Raw Meeting Notes, 15 June 2007

process:
  
   for each shape in a group of shapes (G0):
 1)  Find Atlas (G0)

   a) pick one shape from the group G0.
   b) rigid align other shapes to this one
   c) compute average of (b) and the ref. shape -> AVG0
   d) align all members of G0 to AVG0 -> G1
   e) compute average of G1 -> AVG1
   f) measure change between || AVG1 - AVG0 ||
   g) repeat (d),(e),(f) until (f) < tolerance
   h) final AVG_N is the "AtlasG0"
   
 2) at each point in each control shape (SC) in G0:
    a) find difference vector between SC and Atlas (dilation)
    b) project this vector onto the atlas surface normal at that point
    c) append the magnitude of this projection to a list for this vertex
 3) do (2) except for disease shapes (DC)
 4) calulate a statistical test (T-Test, initally)  comparing the two lists for significance (p-value)
 5) assign this P-value to a copy of the atlas (p<0.05 is probably significant).



This method differs from existing tools because
   * it is completely general (no assumption that a medial line is a good fit)
   * it is totally 3d


Other Statistical Tests:
   * R-rank test ? 
   * distance should be euclidean distance
   * other metrics to measure ...

Questions:
   * is RigidQuaternionTransform equivalent to Yongang's matlab code (his Matlab code is well known, and "optimum").

Analysis Procedure

Data: a group of shapes from controls (G_Ctrl) and a group of shapes from patients (G_Pat): 1) Determine the Atlas from the control group (G_Ctrl)

a)	pick one shape from the control group.
b)	rigidly align other shapes to this one	RigidQuaternionTransform ShapeTransform
c)	compute average of (b) and the ref. shape -> AVG0	ShapeAverage
d)	align all shapes of the control group to AVG0 -> G1	RigidQuaternionTransform
e)	compute average of G1 -> AVG1
f)	measure change between dist(AVG1, AVG0)
g)	repeat (d),(e),(f) until (f) < tolerance
h)	final AVG_N is the "AtlasG0"

2) at each point in each control shape (G_Ctrl): a) find difference vector from the shape to the Atlas (dilation) b) project this vector onto the atlas surface normal at that point c) append the magnitude of this projection to a list for this vertex 3) do (2) except for disease shapes G_Pat) 4) calculate a statistical test (T-Test, initally) comparing the two lists for significance (p-value) 5) assign this P-value to a copy of the atlas (p<0.05 is probably significant).

 2) at each point in each control shape (G_Ctrl):
    a) find difference vector from the shape to the Atlas (dilation)
    b) project this vector onto the atlas surface normal at that point
    c) append the magnitude of this projection to a list for this vertex
 3) do (2) except for disease shapes G_Pat)
 4) calculate a statistical test (T-Test, initally)  comparing the two lists for significance (p-value)
 5) assign this P-value to a copy of the atlas (p<0.05 is probably significant).

AverageFiles Project Definition

Overview

An application program that calculates averages of coordinates of input files as well as avertage of 4d attribute values.

Restrictions, Exclusions, Limitations

N/A

People Affected

Person	Initials	Notes
Bae Cheol Shin	BC
Craig Schwartz	CS
Roger Woods	RW

Version

1.0

Requirements

• This program will be written using Java 1.5
• write the results in a new output file whose name is specified by the user.
• A text command line interface (not a Gui) is used.
• Command line parameters will specify
  • the input files.
  • the name of the output file that will be created.
• Attribute name can be provided, but not requried.

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig and Bae Cheol Shin.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

• N/A

Products

This project will result in the following products:

• A Java application jar that will create a shape file represents convex hull of the original surface mesh.
• A CVS repository containing the Java source code of this application.
• Examples illustrating use of the application.

Overview

Average values of surface input files are need.

Command Line AverageFiles Usage

The command line follows this pattern: "java {optional java arguments} -jar {optional command line arguments} input_file output_file". The "optional command line arguments" are described by the table in the next section, and by the illustrative examples that follow. The "optional java arguments' control the behavior of the Java virtual machine used to run the application and, with one exception, are seldom used. That exception is the memory allocation argument, -Xmx{number}, which asks your computer's operating system to give more memory to the Java virtual machine that it ordinarily gets. This is used when the data to be manipulated exceed the amount of memory. If you run the program and see an error with text resembling

"Out of Memory"

then you will probably want to add the memory argument. To increase the amount of JVM memory, pass an argument to Your java an argument:

java -Xmx512M -jar AverageFiles.jar -output output input_file1 input_file2 input_file3

this sets the ammount of memory to 512MB. Note: the -Xmx512M is the argument that requests additional memory.

Command line arguments

The program is controlled by use of command line arguments. The complete table of command line arguments follows. The "Default" values listed are the values used by the program if the corrosponding command line argument is not specified.

Not all arguments need be specified by a user; generally, an argument is used only to override an unwanted default behavior of the program.

Argument	Default	Description	Notes
-version	n/a	prints a version string to the console output and exits normally.
-help	n/a	prints usage string to the console output and exits normally.
-verbose	false	enables detailed status messages during calculations.
-examples	n/a	prints examples of common usage
-list		Select input files from a list of file in a given text file.
-directory		Select input files from all readable files in a given directory.
-output	n/a	specify output file path	This argument is required.
-attrname	n/a	selects name of input data attribute.	If not specified the first scalar data attribute found is used.

Examples of common usage

In these examples we assume that the ConvexHull application is saved in a file named AverageFiles.jar.

Ex 1) Print a usage summary to the console output.

Use this command: java -jar AverageFiles.jar -help

Ex 2) Read input file from input1.ucf, input2.ucf, input3.ucf, and input4.ucf. Calculate average of coordinates of all files. Average of values fromthe first data attribute will be also calculated. The result shape will be written to output file, outputFileName.

Use this command: java -jar AverageFiles.jar -output outputFileName input1.ucf input2.ucf input3.ucf input4.ucf

---

Detailed Description of the program

• Common Runtime Errors:

No input or output file

if both input file and output file are not provided the application will exit.

Not appropriate input file

if input file doesn't exist, or cannot be read the application wlll exit.

Not appropriate output file

if it is not possible to write the output file then the application will exit.

Not appropriate output type

if input file type doesn't match output type the application will exit.

Not appropriate shape

if shape has no face set or the face set has no faces the application will exit.

• Data formats supported

Any mesh files supported by the ShapeTools library may be read by this program, however, it is possible that a valid file may not contain a closed mesh surface. Loni Ucf files are a particular example of this. Loni Ucf files may in some circumstances be explicitly interpolated to form a surface mesh. Please see the shape tools InterpolateContour application for more details.

The following input formats are currently supported (4 December 2006)

Abbreviated Name	Descripoint	Notes
Ucf	Loni Universal Contour Format files.	Not a mesh format, however, adjacent polyline contours with equal numbers of vertices may be interpreted as a square grid mesh.
OBJ	Montreal Neurological Institute surface mesh Object files.	Do not allow arbitrary data values at mesh vertices. Does support color and vertex normals
OFF	Geomview surface mesh files.	Do not allow arbitrary data values at mesh vertices. Does support color and vertex normals
DX	OpenDX general format data files (using "positions" and "connections" to define the mesh).	supports arbitrary data components at all mesh vertices

The following output formats are currently supported (4 December 2006)

Abbreviated Name	Descripoint
TXT	Ascii text files
DX	OpenDX ascii general format data files

---

System requirements

• Platform - Platform independent
• Operating System - Any OS that has Java 1.4.2 or greater.

Restrictions, Exclusions, Limitations

• Duplicate vertices are not allowed.
• Surfaces with holes are not allowed.
• Faces that vertices originally came from are assumed to be not distorted.

Acknowledgements

This program is a product of the CCB Shape Tools program.

CCB AverageUcf

Calculates average values of coordinates and 4d attribute values of given data files.

Project Components

• Documentation
  • AverageUcf User's Guide v 1.0
  • Examples using project version 1.0^?
  • Changes Log^?
  • A list of Frequently Asked Questions^?.
• Feedback
  • Project Bug List (v1.0)^?
• Download Links
  • Download v 1.0^?
• Feature Wish List^?

Revised 26 April 2007

AverageUcf Project Definition

Overview

Restrictions, Exclusions, Limitations

N/A

People Affected

Person	Initials	Notes
Bae Cheol Shin	BC
Craig Schwartz	CS
Roger Woods	RW

Version

1.0

Requirements

• This program will be written using Java 1.5
• write the results in a new output file whose name is specified by the user.
• A text command line interface (not a Gui) is used.
• Command line parameters will specify
  • the input file.
  • the name of the output file that will be created

Requirements Change Procedure

• Changes to be mutually agreed upon by Craig and Bae Cheol Shin.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

• N/A

Products

This project will result in the following products:

• A Java application jar that will create a shape file represents convex hull of the original surface mesh.
• A CVS repository containing the Java source code of this application.
• Examples illustrating use of the application.

Overview

Command Line AverageUcf Usage

The command line follows this pattern: "java {optional java arguments} -jar {optional command line arguments} input_file output_file". The "optional command line arguments" are described by the table in the next section, and by the illustrative examples that follow. The "optional java arguments' control the behavior of the Java virtual machine used to run the application and, with one exception, are seldom used. That exception is the memory allocation argument, -Xmx{number}, which asks your computer's operating system to give more memory to the Java virtual machine that it ordinarily gets. This is used when the data to be manipulated exceed the amount of memory. If you run the program and see an error with text resembling

"Out of Memory"

then you will probably want to add the memory argument. To increase the amount of JVM memory, pass an argument to Your java an argument:

java -Xmx512M -jar ShapeCurvature.jar input_file output_file

this sets the ammount of memory to 512MB. Note: the -Xmx512M is the argument that requests additional memory.

Command line arguments

The program is controlled by use of command line arguments. The complete table of command line arguments follows. The "Default" values listed are the values used by the program if the corrosponding command line argument is not specified.

Not all arguments need be specified by a user; generally, an argument is used only to override an unwanted default behavior of the program.

Argument	Default	Description	Notes
-version	n/a	prints a version string to the console output and exits normally.
-help	n/a	prints usage string to the console output and exits normally.
-out	outputFile	specify output file name.
-verbose	false	enables detailed status messages during calculations.
-examples	n/a	prints examples of common usage

Examples of common usage

In these examples we assume that the AverageUcf application is saved in a file named AverageUcf.jar.

Ex 1) Print a usage summary to the console output.

Use this command: java -jar AverageUcf.jar -help

Ex 2) Read input file from input1.ucf, input2.ucf, and input3.ucf. .

Use this command: java -jar AverageUcf.jar -out outputFileName input1.ucf input2.ucf input3.ucf

---

Detailed Description of the program

• Common Runtime Errors:

No input or output file

if both input file and output file are not provided the application will exit.

Not appropriate input file

if input file doesn't exist, or cannot be read the application wlll exit.

Not appropriate output file

if it is not possible to write the output file then the application will exit.

Not appropriate output type

if input file type doesn't match output type the application will exit.

Not appropriate shape

if shape has no face set or the face set has no faces the application will exit.

• Data formats supported

Any mesh files supported by the ShapeTools library may be read by this program, however, it is possible that a valid file may not contain a closed mesh surface. Loni Ucf files are a particular example of this. Loni Ucf files may in some circumstances be explicitly interpolated to form a surface mesh. Please see the shape tools InterpolateContour application for more details.

The following input formats are currently supported (4 December 2006)

Abbreviated Name	Descripoint	Notes
Ucf	Loni Universal Contour Format files.	Not a mesh format, however, adjacent polyline contours with equal numbers of vertices may be interpreted as a square grid mesh.
OBJ	Montreal Neurological Institute surface mesh Object files.	Do not allow arbitrary data values at mesh vertices. Does support color and vertex normals
OFF	Geomview surface mesh files.	Do not allow arbitrary data values at mesh vertices. Does support color and vertex normals
DX	OpenDX general format data files (using "positions" and "connections" to define the mesh).	supports arbitrary data components at all mesh vertices

The following output formats are currently supported (4 December 2006)

Abbreviated Name	Descripoint
TXT	Ascii text files
DX	OpenDX ascii general format data files

---

System requirements

• Platform - Platform independent
• Operating System - Any OS that has Java 1.4.2 or greater.

Restrictions, Exclusions, Limitations

• Duplicate vertices are not allowed.
• Surfaces with holes are not allowed.
• Faces that vertices originally came from are assumed to be not distorted.

Acknowledgements

This program is a product of the CCB Shape Tools program.

Award Recipients

Best New Development: PHOTO: 244 Roger Woods (ShapeViewer/ShapeTool)

Best New Development: PHOTO: 243 Jason Corso (Statistical Learning for Volumetric Parcellation)

PHOTO: 247 Zhuowen Tu (Automated Volumetric Tessellation using Statistical Learning)

PHOTO: 253 Yalin Wang (Conformal Technology Tool)

PHOTO: 251 Duygu Tosun (Representation & Segmentation of Cortical Regions)

Most Novel Approach: PHOTO: 255 Yonggang Shi (Direct Cortical Mapping)

Back

Award Categories and Recipients

Best Software Tool Roger Woods & Craig Schwartz ShapeViewer/ShapeTool

Best New Development Jason Corso Statistical Learning for Volumetric Parcellation

Best New Development Zhuowen Tu Automated Volumetric Tessellation using Statistical Learning

Best Visualization Tool Yalin Wang Conformal Technology Tool

Best Shape Extraction Tool Duygu Tosun Representation & Segmentation of Cortical Regions

Most Novel Approach Yonggang Shi Direct Cortical Mapping

---

Back

JenniferTom - 23 Mar 2006

Axial (also known as transverse and horizontal) descrbes a plane, slice or section made by cutting the body or part of it at right angles to the long axis. If the body or part is upright, the cut would be parallel to the horizon.

JenniferTom - 24 Mar 2006

Balint's syndrome iscaused by bilateral damage to the posterior parietal cortex and results in optic ataxia^?, optic apraxia^?, and simultanagnosia^?.

For more information visit source at: Wikipedia Balint's Syndrome entry

Jump to HOME

JenniferTom - 24 Mar 2006

The basal ganglia are subcortical telencephalic nuclei that integrate signals from the cerebellum and cortex for motor execution. It includes the striatum (putamen^?, caudate nucleus, nucleus accumbens), globus pallidus, subthalamic nucleus and substantia nigra and has strong connections with the motor thamalus and cortex.

References: E.R. Kandel, Schwartz J.H., and Jessel T.M., editors. Principles of Neural Science. McGraw Hill, New York, 4th edition, 2000

These are links to some of the Neuro Imaging resources located at UCLA.

Laboratory of Neuroimaging LONI website

Center for Computational Biology CCB website

International Consortium for Brain Mapping ICBM website

JenniferTom - 24 Mar 2006

Was noted in a patient who underwent removal of both hippocampi to control seizures. This patient known as HM lost the ability to form new memories.

References: Hyman, B (1997) "H.M.'s Medial Temporal Lobe Lesion: Findings From Magnetic Resonance Imaging." The Journal of Neuroscience. 17(10)3964-3979.

Jump to HOME

JenniferTom - 24 Mar 2006

Bipolar disorder can be difficult to diagnose and detect. People with bipolar disorder swing between periods of mania and depression. Symptoms of mania summarized from the NIH website include increased energy, irritability and aggressive behavior. Symptoms of depression summarized from the NIH website include decreased energy, feelings of sadness and hopelessness.

Interesting Research: MRI has also been used recently to show differences in cortical thickness in bipolar versus normals. Bipolar subjects have significantly decreased cortical thickness in the multiple prefrontal cortices (Lyoo).

References: Lyoo, IK (2006). “Regional cerebral cortical thinning in bipolar disorder.” Bipolar Disord. Feb;8(1):65-74.

Blend Flatmap

Description

BlendFlatmap is an application which combines the flattened map of a surface and it's dual mapping into a single flattened two dimensional mapped surface. Each flattened surface is expected to have been created by the FlattenSurface program, or to be a transformation (See ApplyFlatMapWarp) of such a grid.

Project Components

• Documentation
  • BlendFlatmap User's Guide v 1.0
  • A list of Frequently Asked Questions.
  • Changes since version 1.0
  • Frequently Asked Questions
• Feedback
  • Project Bug List (v1.0)
  • Feature Wish List

See Also

• InflateSurface

none reported

no changes have been made

Frequently Asked Questions - BlendFlatmap

Table of Contetnts

None thus far

none requested

BlendFlatmap Application User's Guide Version 1.0

Overview

Ths program blends the results of two warps, where each of the two results comes from a different tessellation of a three dimensional surface. One of these tessellations is called the "dual", the other, the "non-dual".

The two tessellations overlap, and the areas where they redundantly describe the surface represented will 'blended" by this program, as described in detail below.

Data Types

BlendFlatmap supports these 2d data types

• RDD grid (Regular Discrete Data - .rdd files)
• UIF files (four are needed to encode the integer and fractional portions both dual, and non-dual flat maps.)

Typically, a flattened surface and its dual are blended after each component have warped. The blended output is then transformed back into a 3 dimensional surface.

Command Line Usage

The command line follows this pattern:

"java {optional java arguments} -jar {optional command line arguments} BlendFlatmap.jar -inputDual input_file1 -inputNonDual input_file2 -output output_file".

Command line arguments

The program is controlled by use of command line arguments. The complete table of command line arguments follows. The "Default" values listed are the values used by the program if the corresponding command line argument is not specified.

Not all arguments need be specified by a user; generally, an argument is used only to override an unwanted default behavior of the program.

Argument	Type	Required	Description	Notes
-examples	flag	No	Prints examples of common usage.
-uif	flag	No	Enable to use UIF blender.
-rotation	flag	No	Allow use of rotation map for non-dual part of data.
-overwrite	flag	No	Allow overwriting the output file.
-output	File	Yes	Specify output file name.
-inputDual	File	Yes	Input files containing the dual data.
-inputNonDual	File	Yes	Input files containing the non dual data.
-help	flag	No	Prints help and usage information.
-version	flag	No	Prints version number of this application.
-verbose	flag	No	Enables detailed status messages.
-license	flag	No	Prints license details, and exits.
-twiki	flag	No	Writes command line arguments as a TWiki format table to output and exit.
-pipeline	flag	No	Write pipeline descriptor file to output and exit.
-pipelineJar	String	No	Specify path to an application jar file, as a pipeline resource.	Example: "pipeline://localhost//home/zed/Connery.jar"
-pipelineJvm	String	No	Specify a Java Virtual Machine called by a pipeline.
-pipelineJvmOptions	String	No	Specify Java Virtual Machine options used by a pipeline.

Examples

In these examples:

• The non-dual surface file is named 'surfaceA_flatmap.rdd'.
• The dual surface file is named 'surfaceA_dual_flatmap.rdd'.

Blend a warped surface, saved in the RDD file format:

The output file is named 'blended_surface_A_BD_flatmap.rdd'

java -jar BlendSurface.jar -inputNonDual surfaceA_flatmap.rdd -inputDual surfaceB_dual_flatmap.rdd -output blended_surface_A_BD_flatmap.rdd

Blending Algorithm

How dual and non-dual flattened surface maps are blended

	The rectangle (left) is split into 8 triangles of equal area. The values inside the diamond come from the non-dual flatmap.; values outside the diamond come from the dual flatmap. Values on the diamond are a weighted sum of nearby points in both Dual and Non-Dual images. The weighting table is shown below.
The original NonDual image:	The original Dual image:
The Dual image's top left and bottom right corners are flipped about the diagnonal running from bottom right to top left; the top right and bottom left corners are translated (sliding) to the opposite corner.	The original non-dual image is rotated 90 degrees clockwise before being copied to the blended image. This rotation is applied only when "-rotation" command is used.
Dual top left corner transformation	Dual top right transformation
Dual bottom left transformation	Dual bottom right transformation
	The resulting blended image with the case when the rotation flag is set. The 8 quadrants of the original image are copied, after the transformations described, into the blended images. pixel values near the diamond boundary are averaged from nearby dual and non-dual values.

On and near the boundary (diamond) ,the blended values are summed from the non-dual and dual image contribute to make a smooth transition between the two. In the following table, a number (P) is the proportion contributed by the non-dual map; the dual map contributes 1-P.

Blending weights for the diagonal bordering the bottom left corner of the blended image.

Sample Results

These images, SurfaceA and SurfaceBDual, are three dimensional (inflated) renderings of the dual and non-dual flattened surface.

SurfaceA

SurfaceBDual

SurfaceA and SurfaceB, blended into one surface.

Surface A (Non-Dual, in red) and the blended surface.

Surface B (Dual, red) and the blended surface (white)


When all three images are displayed, area, which is in red, shows where blending occured. Blended Result - SurfaceA, SurfaceBDaul, and Blended image displayed

System requirements

• Platform - Platform independent
• Operating System - Any OS that has Java 1.5 or greater.

Notes

Acknowledgements

This program is a product of the CCB Shape Tools program.

Public web page of BlendFlatmap^?

BlendFlatmap Project Definition ( v1x01 )

Overview

Ths program blends the results of two warps, where each of the two results comes from a different tessellation of a three dimensional surface. One of these tessellations is called the "dual", the other, the "non-dual".

The two tessellations overlap, and the areas where they redundantly describe the surface represented will 'blended" by this program, as described in detail below.

Both input and output data formats for this version of this program are the [[][RDD]] data format

Restrictions, Exclusions, Limitations

• only the RDD file format is used
• java 1.5 is expected.
• only a 257x257 grid is supported.
• both dual and non dual surface must be homologous.

People Affected

Person	Initials	Role	Notes
Bae Cheol	BC	programmer
Craig Schwartz	CS	project supervisor and programmer
Roger Woods	RW	project sponsor

Version

1.0

Requirements

• Java 1.5 is used to implement this program

Command line Options

Usage:

Option

Argument

default

description

• input files
• output file name
• verbose flag
• -pipeline
• -twiki
• if not arguments provided, print usage() statemsent

Requirements Change Procedure

• Changes to be mutually agreed upon by __ and __.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

3/6/2008 changes

• split -input file file2 into -inputNonDual file -inputDual file
• disable automatic 90 rotation of dual
• add flag to rotation dual 90 degress "-rotateDual"

Products

This project will result in the following products:

• An application program that does what we said.
• junit test cases
• a pipleline wrapper (.pipe) file for the GRID build
• A Twiki user web page describing in detail, with a picture! the blend algorithm.

Typical Usage

java -jar BlendFlatmap in.rdd inDual.rdd out.rdd

Lessons Learned

(filled out after the post-project review)

• lesson 1

no bugs have been reported

no changes have been made

no questions have been asked

no features have been requested

CCB BooleanListsThreshold

Project Components

• Documentation
  • BooleanListsThreshold User's Guide v 1.0
  • Changes Log
  • A list of Frequently Asked Questions.
• Feedback
  • Project Bug List (v1.0)
  • Feature Wish List

Software Download

Download

Revised 9 April 2008

no bugs yet

no changes yet

no questions yet

no wishes yet

Public web page of BooleanListsThreshold

BooleanListsThreshold Project Definition ( v1x0 )

Overview

This application will take a collection of boolean lists and a percentage as input and produce another boolean list as output, where the i-th element of the output list is true if the given percentage of the i-th input list elements are true.

The input lists may be specified by filenames on the the command line or by a list of filenames on the command line.

The percentage is expressed as a value from zero to a hundred.

The program expects the lists to be the same size, and use the same boolean encoding. The boolean encoding is the choice of symbols used to encode true and false. In this program, any two distinct strings may encode the boolean values, and user-specified symbols are allowed. In each list, the symbols must be line delimited.

The output list will be the same length as the input lists. The boolean symbols in the output list will match the input lists.

For a given list index, the result will be true if the percentage of trues is strictly greater than the threshold.

Restrictions, Exclusions, Limitations

• The input lists must be the same length
• The boolean symbols must be distinct
• The input lists must contain no symbols besides the boolean symbols and new-lines.
• The percentage must be from the interval [0,100]

People Affected

Person	Initials	Role	Notes
Bae Cheol	BC	programmer
Ryan Cabeen	RC	advisor
Craig Schwartz	CS	advisor
Roger Woods	RW	project sponsor

Version

1.0

Requirements

• Java 1.5 is used to implement this program
• The program will report an error if any of the assumptions are not met

Requirements Change Procedure

• Changes to be mutually agreed upon by RC , CS and BC.
• Changes and their date will be entered in a 'requirements change' list.

Requirements Changes

Products

This project will result in the following products:

• An application program that will perform the specified function
• a pipleline wrapper (.pipe) file for the GRID build
• A Twiki user web page describing the programs algorithms and use

Typical Usage

Suppose we have three boolean lists A.txt, B.txt and C.txt with the boolean encoding (true, false) = ("T", "F")

$ cat A.txt
T
F
T

$ cat B.txt
T
F
F

$ cat C.txt
T
T
T

We can run the program with the given call:

$ java -jar BooleanListsThreshold.jar -true "T" -false "F" -files A.txt B.txt C.txt -threshold 50 -output result.txt;

This would produce the result:

$ cat result.txt
T
F
T

Lessons Learned

(filled out after the post-project review)

• lesson 1

BooleanListsThreshold Application User's Guide Version 1.0

Overview

This application performs a boolean "AND" operation on a group of text files that contain a list of boolean values (true or false). The separate files are treated as if they defined columns in table of boolean values. The output of this program is a text file containing one line for each line of the input files; The value of each row in this output file is the boolean AND of all the input value of that particular row, subject to a threshold of the percentage of True values. If the percentage of True input row values exceeds the threshold, then the output row value is True.

The default threshold is zero, which causes all input rows to evaluate as True.

for example, if a particular input row has 7 true and 3 false values, then

• the strict AND will result in this row being evaluated as False
• a threshold of 30 % will result in this row being evaluated as False
• a threshold of 25 % will result in this row evaluating as True

The text strings interpreted to mean "true" and "false" may be specified when the program is run, in case the input text is not composed solely of "T" or "F"

Input lists must have only types of text:

• a False value
• a True value
• a line feed.

The presence of any other text (in version 1.0) is considered to be an error, and will cause the program to terminate.

Input files may be explicitly listed, on the command line, or enumerated in a text file containing one file name per line, or both.

Command Line Arguments

Argument	Type	Required	Description	Notes
-verbose	flag	No	Enable verbose statement.
-filelist	File	No	Accept boolean lists from a file containing file paths that have boolean lists.
-files	flag	No	Accept boolean list from following file paths.
-true	String	No	Set string that encode true the boolean value.
-false	String	No	Set string that encode false the boolean value.
-threshold	Double	No	Set threshold value.
-output	File	Yes	Specify output file path.
-overwrite	flag	No	Enables overwriting the output file, if it exists.
-examples	flag	No	Prints examples of common usage.
-help	flag	No	Prints help and usage information.
-version	flag	No	Prints version number of this application.
-verbose	flag	No	Enables detailed status messages.
-license	flag	No	Prints license details, and exits.
-twiki	flag	No	Writes command line arguments as a TWiki format table to output and exit.
-pipeline	flag	No	Write pipeline descriptor file to output and exit.
-pipelineJar	String	No	Specify path to an application jar file, as a pipeline resource.	Example: "pipeline://localhost//home/zed/Connery.jar"
-pipelineJvm	String	No	Specify a Java Virtual Machine called by a pipeline.
-pipelineJvmOptions	String	No	Specify Java Virtual Machine options used by a pipeline.

Examples

The values in the four test input files used here are:

file1	file2	file3	file4
T	T	T	T
T	F	T	F
F	F	T	F

Filter input files with a threshold of 50 %

• the input files are listed in file named "data/list.txt"

java -jar distribution/BooleanListsThreshold.jar -filelist data/list.txt -output data/out.txt -threshold 50

produces this output:

T

F

F

Filter input files with a threshold of 10 %

• the input files are listed in file named "data/list.txt"

java -jar distribution/BooleanListsThreshold.jar -filelist data/list.txt -output data/out.txt -threshold 10

produces this output:

T

T

T

BrainSuite

BrainSuite is a collection of computer programs that operate on human brain scans. please send email to dshattuck@loni.ucla.edu for more details.

JenniferTom - 24 Mar 2006

Broca's aphasia results in loss of speech while preserving comprehension.

JenniferTom - 24 Mar 2006

Broca's Area is located in the frontal lobe of the brain and has strong connections to Wernickes area. Both are involved in the processing of language.

Related Syndromes:

Brocas Aphasia

• Location of Broca's Area:
  

Image was released into public domain and has no copyright. Original Link

CCB Visitors & Projects: Logistics: Accounts

• Account management for all visitors (opening, suspending, closing, disc space): Rico Magsipoc
• Computer hardware, email, etc.:Rico Magsipoc

CCB Visitors & Projects: Logistics: Administrative

• Space, desk, travel, payroll, introduction (email and in person): Grace Liang

CCB Visitors & Projects: Logistics: Projects

• Project Design, Outline and Management: Ivo Dinov

CCBBM Programming Notes

This TWiki topic provides notes on use of CCBBM files (BM).

Overview

The CCBBM format uses ASCII text to represent arbitrary triangulated meshes with associated metadata.

A CCBBM file contains:

• The vertices of a set of polygons which collectively define a surface in a three dimensional space.
• The faces of the surface, defined by the indices of the vertices that compose the polygons.
• The edges of the surface (these are not used in the ShapeTools Library)
• Attributes of those vertices or faces.
• Any number of comments
• A magic string on the first line of the file

Format Specification

In the CCBBM format, each line is either white space, a comment, a vertex, a face, and edge or a mixture of one and a comment. Comments are specified by a '#', and the rest of the line following this will not be considered.

The format has the following constraints:

• the first line contains the comment: "# CCBBM 1.0"
• all faces must refer to vectices defined somewhere in the file
• all indices must be natural numbers
• all or none of the vertices or faces must have an attribute type

Vertex and face specifications are of the form:

Vertex index x y z

Face index vertexA vertexB vertexC

where x, y, and z are floats. The order of the vertex and faces does not matter, as long as the faces refer to defined vertices.

Attributes are appended to the end of the vertex or face specification. They are defined as such:

Vertex 3 1.3 4.3 7.7 {attribute1=(value1 value2 value3) attribute2=(value4 value5 value6)}

where attribute1,2 are strings and value1,2,3,4,5,6 are floats. Attributes are specified in braces and have the form $name=$values. =$value=s can either be space-delimited floats in parenthesis or a single float. Recall that attributes must be consistent. All vertices would have to specify the same types of attributes otherwise an error occurs. Two attributes are the same type if they have the same string and dimension.

Here is a simple example file, which specifies four vertices with one attribute each and two faces with two attribute each:

# CCBBM 1.0
# A comment

Vertex 2 1.4 2.6 7.8 {width=4.6}
Vertex 1 7.2 7.3 5.4 {width=6.7}
Vertex 3 6.1 6.1 3.1 {width=2.2}
Vertex 4 1.1 2.1 5.1 {width=4.2}
Face 1 3 2 1 {normal=(.1 .02 .8) weight=(.8)}
Face 2 4 3 2 {normal=(.01 .4 .7) weight=(.4)}

How a CCBBM is represented as a ShapeTools Shape

Overview

ShapeTools reads data from CCBBM files into a Shape.

The CCBBM vertices are stored in the Shape's PointSet.

The CCBBM faces are stored in the Shape's FaceSet with an implementation CGALFaceSet.

The vertex/face attributes are stored in FloatTupleArrayAttributes in an AttributeSet of the corresponding PointSet or FaceSet of the Shape.

The name of the CCBBM attribute in the file is used as the ID of the ShapeTools attribute that encodes it. If color or normals are used, the appropriate common attribute is used.

The ShapeTools Data Model

The basic data model used in ShapeTools is:

• Each point has a unique index.
• Points are stored in PointSet.
• A directed line from one point to another is an Edge.
• Each Edge has a unique index.
• Edges are stored in an EdgeSet.
• An ordered set of Edges comprise a Face (polygon).
• Each Face of a Shape has a unique index number.
• Faces are stored in a FaceSet.
• An ordered set of Faces comprise a Solid
• Each Solid has a unique number.
• Solids are stored in a SolidSet (not yet implemented - v1.1.0).

• Each SolidSet (will) refer to a FaceSet
• Each FaceSet refers to an EdgeSet
• Each EdgeSet refers to a PointSet
• A Shape is not required to contain any of PointSets, EdgeSets , FaceSets, or SolidSets, except as noted above.
• Shapes, PointSets, EdgeSets, FaceSets and SolidSets all contain a attributes, stored in an AttributeSet.

Reading a CCBBM file using ShapeTools

The easiest way to to read a CCBBM is to use the ShapeTools ShapeToolsReader.read() method. This method will determine which ShapeReader class can read the data file. This frees a ShapeTools programmer from having to know the type of the input file. Here is how to do so:

import edu.ucla.loni.ccb.shape.About;
import edu.ucla.loni.ccb.shape.Shape;
import edu.ucla.loni.ccb.shapeio.util.ShapeToolsReader;  
import java.io.IOException;

public class Example 
{
  // construct an Example
  public Example ()
    {
      // check version of library
      if (About.getMajorVersionNumber() != 1) {
        System.err.println("Incompatible ShapeTools library.");
       System.exit(1);
      }
    }

  /** 
   * Read a CCBBM file into a shape.
   * 
   * @param ccbbmFilename name of a CCBBM file.
   *
   * @returns a Shape read from a CCBBM.
   * throws IOException if unable to read the CCBBM.
  */
  public Shape read(String ccbbmFilename)
    {
      ShapeToolsReader reader = new ShapeToolsReader();
      Shape ccbbmShape = reader.readShape(ccbbmFilename);
      return ccbbmShape;
    }
}

An alternative (and more direct approach) is to use the method in the edu.ucla.loni.ccb.shapeio.ccbbm.BmIO class.

The call

Shape ccbbmShape = BmIO.read(String filename)

will read a shape from the file located at the given filename. An IOException will be thrown if it cannot be read for some reason.

Getting data from a CCBBM Shape

Getting vertex coordinates

Vertex coordinates (points) are stored in the Shape's PointSet.

import edu.ucla.loni.ccb.shape.Shape;
import edu.ucla.loni.ccb.shape.geometry.IPointSet;

IPointSet pointSet = shape.getVertices();

Note: Points of any dimension are supported by ShapeTools. If you need to know the number of dimensions of a point use

int numDimensions = pointSet.getPointDimensions(); this will always be 3 for a CCBBM file

To get the coordinates of a single point Note: this method allocates a new float[] for each point's coordinates so obtained.

// index of desired point
int index = 23;
float [] coordinateTuple = pointSet.getPoint(index);

You'll often want the coordinates of the points as an array of floats. To minimize memory use ShapeTools returns such requests as a group of arrays in which each array contains all point coordinates of a specific spatial dimension.

float [][] coordinates = pointSet.getPoints();
// print coordinates of all points

for (int pointIndex = 0; pointIndex < pointSet.size(); pointIndex++) {
 System.out.print("Coords of point #" + pointIndex + ": [");
 for (int dim = 0; dim< coordinates.length; dim ++) { 
   System.out.print(coordinates[dim][pointIndex] + " ");
  }
 System.out.println("]");
}

Getting triangle faces from a CCBBM Shape

The vertices of a particular face may be obtained from the FaceSet

 // get number of the points of all faces in a Shape:
 IFaceSet faceSet = shape.getFaces();
 for (int faceIndex =0; faceIndex < faceSet.size(); faceIndex++) {
   int [] verticesInAFace = getVertexIndices(int faceIndex)
 }

The edges of a particular face may be obtained from the FaceSet

 // get number of the points of all faces in a Shape:
 IFaceSet faceSet = shape.getFaces();
 for (int faceIndex =0; faceIndex < faceSet.size(); faceIndex++) {
   int [] edgesInFace  = getEdgeIndices(int faceIndex)
 }

If the order of faces retrieved from a FaceSet does not matter you can use a FaceSetIterator

IFaceSet faces;
FaceSetIterator iter = new FaceSetIterator(faces);

while (iter.hasNext()) {
  Face thisFace = (Face)iter.next();
}

Getting attribute data from a CCBBM Shape

Face attributes are stored in the AttributeSet of the FaceSet, and likewise, the vertex attributes are stored in the AttributeSet of the PointSet.

Each ShapeTools attribute has a string that identifies it. If you need attribute data from a CCBBM shape, you need to know the name used in the file. This will be the ID of the ShapeTools attribute.

For example, if a file specified a vertex attribute as such:

Vertex 25 1.5 4.3 5.6 {curvature=(1.3 5.3 2.2)}

then the shape would have a PointSet attribute with an ID "curvature". The attribute will be a FloatTupleArrayAttribute. Here is a call to get the attribute:

FloatTupleArrayAttribute attribute = shape.getVertices().getAttributeSet().getAttribute("curvature");

You could then check the dimensions of the attribute with the following calls:

int numberOfAttribute = attribute.size();
int dimension = attribute.getFloat(0).length;

You could get the values of the N-th attribute (the N-th vertex's) as a float[]:

float[] value = attribute.getFloat(N);

There are several special attributes used. First is the format attribute. This is a StringAttribute in the AttributeSet of the Shape. This stores the version of the file read. Additionally, there are two "common attributes", color and normals. If there are CCBBM attributes with the name "rgb" or "normal", these are register as common attributes of the FaceSet. Here is a call to get the attribute associated with the common color attribute (the same applies for normals):

String colorAttributeName = aShape.getFaces().getCommonAttributeName(ShapeComponent.COLOR_ATTRIBUTE);

if(colorAttributeName != null)
  FloatTupleArrayAttribute = aShape.getFaces().getAttributeSet().getAttribute(colorAttributeName);

CCB Driving Biological Projects (DBPs)

Currently we do not accept applications for formal CCB DBP projects. The next call for DBP applications will be announced in 2008/2009. Interested investigators may still contact us to propose or discuss prospective formal or informal collaborations with the CCB.

CCB DBP Request for Applications (RFA) for new DBPs (August 01, 2010 - July 31, 2012)

Current Driving Biological Projects (2007-2010)

• DBP 1: Identifying Age Related Atrophy Using Levelset Registration of Embedded Maps (PI Christopher Wyatt)
• DBP 2: Developmental origin of phenotypic variation in Drosophila melanogaster (PI David Houle)
• DBP 3: Mapping Brain Changes in HIV/AIDS (PI Paul Thompson)
• DBP 4

Former Driving Biological Projects (2004-2007)

• Brain Development and Language (PI Elizabeth Sowell)
• Spatial and Temporal Changes in Alzheimer's Disease (PI Paul Thompson)
• Multiple Sclerosis (PI Rhonda Voskuhl)
• Genetic Effects and Structural Changes in Schizophrenia (PI Tyrone Cannon)

2007 CCB DBP Request for DBP Applications (August 01, 2007 - July 31, 2010)

CCB 2007 New/Old DBP Report (06/05/2007)

RFA Dissemination Plan

• Emailing twice (one month apart) from the Dean's office to SOM Everyone
• Posting on LONI News web
• Posting on Main CCB Page and "CCB News"
• Posting on IPAM's web News
• Posting on UCLA College's, SOM web pages and www.research.ucla.edu
• 1-page Flier (SVG group)
• Posting Flier at UCLA
• Sending Flier (and may be email) to Targeted Major Hospitals/Brain Imaging Centers
• Email to CCB All
• OHBM mailing list

CCB DBP New DBP Applications (August 01, 2007 - July 31, 2009)

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CCB New Driving Biological Projects (2007 DBPs)

Developmental origin of phenotypic variation in Drosophila melanogaster

• PI: David Houle
• Abstract: This project's overall goal is to help close the gap between the increasing ability to rapidly sequence the DNA, the genotype, of an organism, and the relative lack of techniques to measure the even more complex physical form of the organism, its phenotype. The measurement of complex, high-dimensional phenotypes is an essential complement to the genomic level data. We call the integration of such phenotypic data with genetics phenomics. This project exploits an existing system for automated wing measurement, and will develop techniques for the rapid measurement of other phenotypes, including whole flies. From a biological point of view, the overall aim of this project is to understand the genetic causes of phenotypic variation in the wings and other aspects of external morphology of Drosophila melanogaster. The proposed work will test the usefulness of a phenomic approach for the Drosophila wing.

• Specific Aims: The overall aim of this project is to understand the genetic causes of phenotypic variation in the wings and other aspects of external morphology of Drosophila melanogaster. More broadly, we wish to develop and validate an approach to the study of the relationship between genotype and phenotype that we call phenomic. The key idea is that the measurement of complex, high-dimensional phenotypes is an essential complement to the genomic level data that is increasingly easy to obtain. Many of the important problems in biology require us to determine the relationship between the genotype and the phenotype; we will not make progress on this until we treat the measurement of the phenotype as an important task. In the proposed work we wish to test the usefulness of this approach for the Drosophila wing, and to develop capabilities to rapidly measure other aspects of external morphology. Our specific aims are summed up in three questions:
  • Aim 1: What genetic changes can cause variation in wing shape? We will develop a “dictionary” of genetic effects on wing form by systematically manipulating gene expression at genetic loci hypothesized to be involved in wing development. The PI’s semi-automated system for measuring wing phenotypes will be used in conjunction with the genetic resources of D. melanogaster to rapidly characterize the phenotypic effects at a wide range of genes. We will engineer novel variants in a few key genes to characterize the sensitivity of the phenotype to perturbations in gene expression with known timing and magnitude. Once these data are in hand, we will then summarize and model these data to answer the following questions: Are all of the genes in a particular developmental pathway producing a similar set of transformations of form when mutated? Are some kinds of form transformations easier to obtain by perturbation than others? Is there a limited set of possible form transformations? These effects will then be used to build predictive models of the connections between developmental pathways and phenotypic variation.
  • Aim 2:What are the genetic causes of natural variation in wing shape? We will apply the phenomic approach to the genetic basis of natural variation in wing shape in D. melanogaster using the dictionary of genetic effects and the resulting models generated in Specific Aim 1. We will characterize the quantitative genetic variation in wing shape in a natural population of D. melanogaster, then attempt to infer its possible causes based on the correspondence of the bserved variation with the dictionary. These inferences will be tested using QTL mapping and quantitative complementation. Both exploratory and confirmatory (structural equation) models will be fit to the data. Finally, we will apply this approach to the PI’s existing data sets on the quantitative genetic variation in wing form in populations of three Drosophila species.
  • Aim 3: How do genetic changes that cause variation in wing shape affect other body parts? Pleiotropy, or the effects of a single genetic variant on multiple traits, is an important feature of genetic systems, with profound implications for evolution. It is difficult to study because of the sheer number of possible phenotypic effects that can be involved. We will develop and generalize methods to measure variation in Drosophila eggs, and to whole flies. A detailed quantitative atlas of Drosophila morphology will first be generated, then used to map 2-D images onto the atlas, allowing quantitative information on 3-D form to be extracted while minimizing human involvement. The pleiotropic effects of the variants characterized in Aim 1 will then be characterized for their pleiotropic effects.

CCB New Driving Biological Projects (2007 DBPs)

Mapping Brain Changes in HIV/AIDS

• PI: Paul Thompson
• Abstract: This 3-year project will provide enormous insight into HIV/AIDS. Knowledge of how HIV affects the brain is sorely lacking. Working with members of the UCLA Center for Computational Biology (CCB), we will develop a multidimensional, computational atlas of HIV/AIDS, including the most powerful tools to track HIV disease progression in the brain. We will investigate how specific factors affect the rate of brain degeneration, such as anti-retroviral therapy, duration of illness, age and gender, and viral load. We will relate brain changes over time to immune system measures, such as CD4+ T-cell counts. This effort will create the computational tools to study how deficits in HIV patients emerge and spread in the living brain. We will test novel tools for large-scale analysis and mapping of brain change (Aims 1 and 2), novel tools to correlate these brain changes with immune deterioration and imminent clinical decline (Aim 3), and methods to gauge and optimize the power of these tools for large-scale drug trials (Aims 4 and 5). Our new computational tools will map 3D dynamic changes in serial brain MRI scans. We will link detected brain changes to serum measures (T-cell decline or recovery) and to treatment parameters (e.g., whether the patient is using HAART therapy or not). We will relate changes to clinical data from detailed neuropsychological exams. We will examine 100 AIDS patients with a broad range of disease severity, and 100 healthy controls matched for HIV-associated risk factors (see Experimental Design). The project will be very efficient as these scans have already been collected - they are stored in the LONI/CCB database, and urgently await analysis.
• Specific Aims:
  • Aim 1 (Map Disease Progression in the Cortex): We will map the 3D profile of cortical thickness in 100 HIV/AIDS patients and 100 matched controls, scanned twice with high-resolution MRI at a 2-year interval. We will create average maps of gray matter thickness for each subject group at each time point, and analyze changes statistically to localize regions with significant changes. We will answer the following questions: How do cortical brain deficits progress in HIV/AIDS? Are some brain systems protected, and to what degree? Is gray matter in the motor cortices selectively damaged? Hypothesis: In HIV/AIDS, cortical deficits will progress from ensorimotor and adjacent association cortices, to pervade the entire cortex late in the illness.
  • Aim 2 (Map Rates of Structural Brain Change Over Time): Using 3D MRI data acquired longitudinally from 100 HIV/AIDS patients and 100 matched controls over a 2-year interval, we will determine a 3D map of the mean rate of atrophy in each subject group. We will create 3D tatistical maps to answer the following questions: what is the pattern of brain change (volume loss) over time? Which brain regions change the most? Can tensor-based morphometry (TBM), based on fluid image registration techniques developed at the CCB, identify systematic changes in a group of patients? How do changes in deep gray matter structures – the caudate, putamen, and hippocampus – correlate with the cortical changes mapped in Aim 1? Hypothesis: Deep nuclei will show greatest deficits early in the illness, as they have the highest viral load. We expect significant longitudinal changes in the supplementary motor cortex, corpus callosum and ventricles, based on our cross-sectional studies. Over time, white matter atrophy will spread radially outwards to the cortex as CD4+ T-cell counts and neuropsychological function decline.
  • Aim 3 (Links to Declining Cognition, Immune Function, and Viral Load): We will correlate the brain changes mapped in Aim 2 and 3 with clinical data measured at the time of the baseline and follow-up scans on neuropsychiatric test performance, CD4+ T-cell counts, viral load, duration of illness, and medication status. We will answer the following questions. Are brain changes mapped with MRI linked with declining levels of CD4+ T-cells, the commonest biomarker of HIV disease progression? Does brain change depend on whether the patient is on HAART therapy? Are brain changes correlated with viral load, measured over a 2- year interval in patients? In which specific brain regions are these processes linked? Do deficits, detected in the initial MRI scan, predict future cognitive decline, ascertained by tests of psychomotor speed, executive function, and memory performance? Hypothesis: Dynamic rates of tissue atrophy are expected to link with Tcell counts, but not with viral load, which can fluctuate dramatically over relatively short intervals.
  • Aim 4 (Compute Power Estimates for Drug Trials): Using the neuroimaging measures from Aims 1 and 2, we will compute how many subjects would be needed in a drug trial to detect a 20%, 10% or 5% slowing of disease progression, over a 2-year follow-up interval. We will also determine how much better the automated technique is (in statistical power) than conventional morphometry, where regions are hand-traced on brain images and their volumes assessed? Hypothesis: We expect the cortical maps in Aim 1 to afford greater power per subject than the fluid maps of brain change in Aim 2, but the fluid maps should provide higher throughput and greater automation, i.e. greater power per unit processing time in a drug trial.
  • Aim 5 (Algorithm Optimization): We will assess whether two alternative choices of algorithm parameters can further increase our power to detect the HIV effects on the brain. Using false discovery rate (FDR) plots to compare detection power, we will assess the influence of the image registration “cost function” (Jensen-Renyi divergence versus Mutual Information) for recovering brain changes mapped in Aims 2 and 3. We expect that the Jensen-Rényi divergence will outperform Mutual Information, which is the current standard in the field. Second, for detecting brain changes in HIV/AIDS, we will compare Lie group methods - which analyze the full tensor-valued information - versus standard univariate statistical methods. We hypothesize that the Lie group methods will outperform standard statistics; our preliminary data support this hypothesis and detect HIV effects with greater power; Chiang et al., 2006, Lepore et al., 2006, in Appendix). This final step will optimize the processing sequence for use in drug trials.

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CCB New Driving Biological Projects (2007 DBPs)

Identifying Age Related Atrophy Using Levelset Registration of Embedded Maps

• PI: Christopher Wyatt
• Abstract: Structural brain changes that occur during the process of normal aging are of great interest to neuroscientists investigating age-related changes in cognitive function. A variety of techniques currently exist to identify group differences in brain anatomy with the greatest focus having been placed on cortical anatomy. Although cortical anatomy is of considerable significance, the underlying white matter tracts are at great risk in the aged brain. Patchy white matter lesions referred to as leukoaraiosis (LA) have long been identified radiologically as T2-weighted hyperintensities in older adults. Techniques such as voxel-based morphometry (VBM), deformation-based morphometry (DBM), or tissue thickness measurements allow for quantitative comparisons of white matter in a regionally specific manner. However, VBM is dependent upon segmentation algorithms to measure tissue density, a task difficult in images displaying the diffuse contrast changes associated with LA. Similarly, DBM is dependent on registration algorithms to establish a spatial correspondence between images. Unfortunately, current image registration methods do not perform effectively in regions where tissue contrast has changed, such as in leukoaraiosis. The goal of this Driving Biologic Project (DBP) is to evaluate the relationship between changes in cognitive function and cerebral white matter in older adults. Since current VBM and DBM methods cannot be applied to subjects with leukoaraiosis, the project will expand current image registration techniques developed by the Center for Computational Biology (CCB) at UCLA to accommodate topological changes in the images.
• Specific Aims:
  • Aim 1: Develop a deformable registration method for images represented as embedded maps, employing the level-set framework. We will develop a deformable registration approach representing each image as an embedded map. This registration framework will simultaneously account for changes in intensity and morphology between image pairs.
  • Aim 2: Identify the relationship between executive cognitive functions and age-related changes in cerebral white matter using the level-set based registration of embedded maps approach. To evaluate white matter integrity, quantitative T1 and T2 maps will be registered using the embedded maps algorithm developed in Aim 1. We will identify white matter intensity and volume changes (i.e. leukoaraiosis) in a regionally specific manner and correlate these changes with cognitive function.

CCB Driving Biological Projects (DBPs) - Request for Applications

• What?
  Request for Applications (RFA) for new DBPs (August 01, 2007 - July 31, 2009)
  Center for Computational Biology (CCB)
  David Geffen School of Medicine
  University of California, Los Angeles
  

• Program Summary
  The CCB was established to develop, implement and test computational biology methods that are applicable across spatial scales and biological systems. Our objective is to help elucidate characteristics and relationships that would otherwise be impossible to detect and measure. CCB provides a number of advanced computational tools, research infrastructure and resources to the biomedical community.
  
  This request for applications (RFA) intends to solicit outstanding proposals that will provide new, exciting and challenging biomedical problems and applications that will serve as driving forces for the development of novel mathematical models and computational tools within the CCB.

• Eligibility
  
  • This search for new DBPs is nationwide. However, UCLA investigators are encouraged to apply.
  • Any investigator with a PI status is eligible
  • Project must be aligned with the overreaching goals of CCB
  • Project must provide a driving motivational biological problem

• Important Notes
  • • NIH/NCBC RFA (http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html).
    • CCB DBP Wiki page: http://www.loni.ucla.edu/twiki/bin/view/CCB/CCBDBPS.
    • All current CCB DBPs are for 3 years.
    • CCB is recruiting up to 4 additional DBPs.
    • Successful applications will be funded directly by the CCB
    • Annual Total Costs cannot exceed $180,000
    • NIH R01 proposal format must be used for all applications. PHS 398 Form
    • Applicants should have a CCB liaison from the CCB principals.

• How to Apply?
  
  • Pre-proposal: Each potential applicant must first review in detail the aims and challenges addressed by the CCB investigators. Then the applicant will normally contact one CCB Principal Investigator 6-8 weeks before the initial pre-proposal is due. This Principal Investigator must be carefully chosen so that he is an expert in the chosen field, interested and willing to serve as a local CCB liaison for this proposal.
    
    Once initial contact with the liaison is established you need to beging formalizing your research proposal in close collaboration with your CCB liaison. Finally, you need to submit your pre-proposal and full proposal to CCB by the deadlines listed in the Timeline section. With your initial inquiry, please submit two additional documents: a complete biosketch and other support (including pending and proposed).
    
    
  • Full proposal submissions are by invitation only and based on the pre-proposals. These must include:
    • What to submit? Like a regular PHS 398 grant application http://grants.nih.gov/grants/funding/phs398/phs398.html, we need a complete proposal, including Face Page, Form Page 2, Table of Contents, Budget Forms, Budget Justification, Biosketches, Resources, Research Plan, Checklist, Targeted/Planned Enrollment Table & Inclusion Enrollment Report if there're human subjects involved in the project, and publications for the appendix. In addition, we need your Statement of Work & a copy of your institution F & A Costs Agreement for the subcontract set-up at UCLA.
      If there're human subjects or animal subjects involved in the project, please work on your human or animal protocol ASAP, list our CCB primary funding source, Grant #U54 RR021813 in your protocol. Once you obtain the institutional approval, please send all the related documents to us, as we'll have to get the approval from UCLA as well.
    • Send the final proposal directly to CCB.
    • Please send us three hard copies of the full proposal (NIH-1; CCB office-1; UCLA Contract and Grant Office-1, for the subaward set-up). The institutional signature is needed for the Face Page.
    • UCLA should be listed as the sponsor, not NIH, like a regular subcontract submission, the primary site (i.e. UCLA) is your sponsor, not NIH.
• Timeline:

May 31, 2006	August 30, 2006	November 30, 2006	February 28, 2007	August 01, 2007
Submission of 3-page preproposals	Submission deadline for full proposals	First round of selections – top 8 proposals	Final CCB DBP Selection and Announcement of 3-4 new DBPs	Start Date for new DBPs

• The criteria for selection will be based on:
  • Scientific merit
  • Relevance of the proposal to the aims of the CCB
  • Approach & Innovation
  • PI & Environment
  • Plans to obtain support for the DBPs research after July 31, 2009.
    

• DBP application review committee includes the CCB Principals with feedback from the CCB NIH Science/Program Officers.

• For More Information contact

Arthur W. Toga, Ph.D.
Center for Computational Biology
Laboratory of Neuro Imaging
David Geffen School of Medicine at UCLA
635 Charles Young Drive South, Suite 225
Los Angeles, CA 90095-7334. USA
http://www.ccb.ucla.edu
Tel (310) 206-2101
Fax (310) 206-5518
Email toga@loni.ucla.edu

CCB Driving Biological Projects (DBPs) - Request for Applications

• What?
  Request for Applications (RFA) for new DBPs (September 01, 2010 - August 31, 2012)
  Center for Computational Biology (CCB)
  David Geffen School of Medicine
  University of California, Los Angeles
  

• Program Summary
  The CCB was established to develop, implement and test computational biology methods that are applicable across spatial scales and biological systems. Our objective is to help elucidate characteristics and relationships that would otherwise be impossible to detect and measure. CCB provides a number of advanced computational tools, research infrastructure and resources to the biomedical community.
  
  This request for applications (RFA) intends to solicit outstanding proposals that will provide new, exciting and challenging biomedical problems and applications that will serve as driving forces for the development of novel mathematical models and computational tools within the CCB.

• Eligibility
  
  • This search for new DBPs is nationwide.
  • Any investigator with a PI status is eligible
  • Project must be aligned with the overreaching goals of CCB
  • Project must provide a driving motivational biological problem for developing novel computational techniques within the CCB.

• Important Notes
  • NIH/NCBC Collaborations (separate from this CCB DBP call)
  • CCB DBP Wiki page.
  • The 2007-2010 CCB DBPs were for 3 years, new CCB DBP projects will be for 2 years!
  • CCB is recruiting up to 4 additional DBPs.
  • Successful applications will be funded directly by the CCB
  • Annual Total Costs cannot exceed $200,000
  • NIH R01 proposal format must be used for all applications. PHS 398 Form
  • Applicants should have a CCB liaison from the CCB principals.
  • There are absolutely no guarantees for funding, as these applications are contingent upon NIH NCBC/CCB grant continuation.

• How to Apply?
  
  • Pre-proposal: Each potential applicant must first review in detail the aims and challenges addressed by the CCB investigators. Then the applicant will normally contact one CCB Principal Investigator 2-4 weeks before the initial pre-proposal is due. The Principal Investigator must be carefully selected with expertise in the applicant's chosen field, and be interested and willing to serve as a local CCB liaison for this proposal.
    
    Once initial contact with the liaison is established you need to begin formalizing your research proposal in close collaboration with your CCB liaison. Finally, you need to submit your pre-proposal and full proposal to CCB by the deadlines listed in the Timeline section. With your initial inquiry, please submit two additional documents: a complete biosketch and other support (including pending and proposed).
    
    
  • Full proposal submissions are by invitation only and based on the evaluation of the pre-proposals. These must include:
    • What to submit? Like a standard PHS 398 grant application, we need a complete proposal, including Face Page, Form Page 2, Table of Contents, Budget Forms, Budget Justification, Biosketches, Resources, Research Plan, Checklist, Targeted/Planned Enrollment Table & Inclusion Enrollment Report if there are human subjects involved in the project, and publications for the appendix. In addition, we need your Statement of Work & a copy of your institution F & A Costs Agreement for the subcontract set-up at UCLA.
      If there are human subjects or animal subjects involved in the project, please work on your human or animal protocol ASAP, list our CCB primary funding source, Grant #U54 RR021813 in your protocol. Once you obtain the institutional approval, please send all the related documents to us, as we'll have to get the approval from UCLA as well.
    • Send the final proposal directly to CCB or via email to .
    • Please send us three hard copies of the full proposal (NIH-1; CCB office-1; UCLA Contract and Grant Office-1, for the subaward set-up). The institutional signature is needed for the Face Page.
    • UCLA should be listed as the sponsor, not NIH, like a regular subcontract submission, the primary site (i.e. UCLA) is your sponsor, not NIH.
• Timeline:

December 10, 2009	December 25, 2009	January 01, 2010	September 01, 2010 (approx.)
Submission of 3-page preproposals	(By invitation only!) Submission deadline for full proposals	Final CCB DBP Selection and Announcement of 3-4 new DBPs	Start Date for new DBPs

• The criteria for selection will will include:
  • Scientific merit
  • Relevance of the proposal to the aims of the CCB
  • Approach & Innovation
  • PI & Environment
  • Plans to obtain support for the DBPs research after August 31, 2012.
    

• DBP application review committee includes the CCB Principals with feedback from the CCB NIH Science/Program Officers.

• For More Information contact

Arthur W. Toga, Ph.D.
Center for Computational Biology
Laboratory of Neuro Imaging
David Geffen School of Medicine at UCLA
635 Charles Young Drive South, Suite 225
Los Angeles, CA 90095-7334. USA
http://www.ccb.ucla.edu
Tel (310) 206-2101
Fax (310) 206-5518
Email toga@loni.ucla.edu

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CCB Courses, Training and Education

2009

• 2009 NS IDP Visit/Training (Part I, 02/23/09; Part II, 03/09/09, 2:00pm-2:30pm).
• NYLF visit to CCB/LONI hosted by Ivo Dinov, Thu, 06/25/09, 10:00-11:00AM
• Upcoming SW/Tools Training Events
  • ISBI'2009 Shape Training Workshop and CCB/NAMIC Shape Analysis Training Workshop at ISBI 2009.
    • CCB ISBI'09 Organization Pages
  • Neuroimaging Solutions: A Hands-on Training Course for Data Processing, Analysis & Interpretation using the LONI Pipeline; OHBM 2009 (June 18 - 23, 2009 San Francisco, CA).
    • Pipeline 2009 OHBM Workshop Flier
  • Discriminative Learning for Medical Imaging: MICCAI 2009 Tutorial (Thursday, September 24, 2009, 9.00AM – 5.30PM).
• CCB Summer Graduate Student Research Fellowships (Summer 2009)
  • 2009 CCB Summer Fellows Presentation (09/30/09)
• Biomedical Engineering CM 102/202 Students LONI/CCB Tour (Fri Nov. 20, 2009)

2008

• NS IDP Visit/Training (Part I, 02/04/2008; Part II, 03/03/2008).
• CSUMS Training (04/25/08).
• STEM-PLEDGE: STEM-PLEDGE visit to CCB/LONI hosted by Ivo Dinov, Fri, 05/30/07, 10:30-10:50AM
• CCB/IPAM 2008 MBI Workshop
• NYLF: NYLF visit to CCB/LONI hosted by Ivo Dinov, Thu, 06/26/08, 10:00-11:00AM
• SIAM Imaging Science meeting. A CCB-sponsored mini-simposium organized by Luminita Vese and Ivo Dinov, July 07-11, 2008, San Diego, CA.
• CCB/Math-IGERT Summer graduate student research fellowships (Summer 2008)

2007

• NS IDP Visit/Training (Part I, 02/05/2007)
• Kansas University ADNI Group Visit (02/06-07/07)
• NS IDP Visit/Training (Part II, 03/05/2007)
• Young Presidents Organization (03/07/07)
• St. Paul 6th Grade Students' Visit (Mon., Mar. 26 2007, 11:00 - 12:00 Noon)
• Buckley Students' Visit (Tue., April 24, 2007, 11:00 - 12:00 Noon)
• CCB/IPAM 2007 Workshop on Random Shapes
• NYLF: NYLF visit to CCB/LONI hosted by Ivo Dinov, Wed, 06/27/07, 11:00-12:00 Noon

2006:

• CCB Winter 2006 Computational Neuroimaging and Brain Mapping Course (Jan-March 2006)
• NS IDP Visit/Training (03/20/2006)
• NS Visit (April 14, 2006)
• CCB/SIAM Symposium on Brain Segmentation (May 2006)
• NYLF Visit to CCB/LONI. Details of the NYLF visit to CCB/LONI (June 2006)
• CCB/SIAM Symposium on Mathematics for Brain Imaging (July 2006)
• CCB/NCBC Dissemination Event (NIH, July 2006)

2005:

• ISMB2005 Alternative Splicing Special Interest Group meeting (June 23-24, 2005)

CCB / Math-VIGRE Summer graduate student research fellowships -- Summer 2008

Together with Math/VIGRE support, CCB is sponsoring annual Summer Research Fellowships for graduate students. In 2008, the following 4 projects were selected that address some of the main mathematical and computational challenges tackled by CCB researchers.

Quantitative Comparisons of Narrow-Band and Mesh-based Algorithms for Computing the Laplace-Beltrami Spectrum

• GSR: Wenhua Gao
• Summary: In this project we will perform quantitative comparisons of a narrow-band approach of computing the Laplace-Beltrami spectrum, which we developed recently, with the conventional algorithm using triangular meshes. Because the narrow-band approach is based on regular grids, we believe it will perform better numerically. More specifically, we will first study the conditional number of the eigenvalue problem that is formed by the two different approaches. After that, we will study the robustness of the two different approaches with respect to small perturbations to the geometry of the surface.

Graph matching techniques for the analysis cortical folding patterns

• GSR: Alvin Wong
• Summary: The folding pattern of the cortical surfaces is an intriguing and important topic in analyzing brain morphometry. Based on previous work of computing Hamilton-Hacobi skeletons on cortical surfaces, we investigate the analysis of cortical folding patterns via graph matching. We will first study the construction of attributed graphs based on the skeletal representation of the sucal/gyral regions. After that, graph matching algorithm will be designed to extract the major sulci/gyri from the cortical surface.

Application of eigenfunctions of Laplacian_Beltrami operator to shape analysis

• GSR: Rongjie Lai
• Summary: This project will study how to use eigenfunctions of Laplacian-Beltrami operator on shapes to study shapes themselves. Mathematically, all of eigenfunctions of a given shape form a basis of the L^2 function space of the given shape, and if we know the L^2 function space of a given shapes, we should know the information of the given shape. Moreover, the big advantage of eigenfunctions is that all of them are intrinsic, i.e. rotation and translation invariant. We already know how to get the eigenfunctions of a given shape -- our goal this summer is to find out how to use these eigenfunctions to study the intrinsic geometric information of the shape.

Spherical deconvolution techniques for diffusion image reconstruction

• GSR: Wenye Ma
• Summary: With the promise of resolving crossing fibers in the white matter of human brains, high angular resolution diffusion imaging (HARDI) is becoming an important research topic in brain mapping. In this project, we will study spherical deconvolution techniques to reconstruct fiber orientation density (FOD) functions from noisy and sparse HARDI data. More specifically, we will investigate and extend L1 regularization techniques to the problem of spherical deconvolution and compare with existing algorithms in terms of resolution and efficiency.
• Private HARDI Project Page

Final Presentations/Reports

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CCB Summer Graduate Student Research Fellowships -- Summer 2009 -- Allan Garcia Project Page

Brain Representation and Registration Using a Tetrahedral Mesh and Elastic Deformations

• Summary: MRI data will be represented in three dimensions as a tetrahedral mesh which will allow for physical deformation from one brain into another. The tetrahedron can be made of variable size so that near the boundary, or other areas of interest, a high level of detail can be maintained while interior areas can be made up of larger tetrahedron to allow for greater computational speed. The brain registration can make use of existing LONI software that divides the brain into a few dozen regions. The elastic deformation should enforce that each region in one brain is mapped into the same region in another brain. In the end, this should give a reasonable correspondence between every point in one brain and every point in another brain.

CCB Summer Graduate Student Research Fellowships -- Summer 2009

Together with Math/VIGRE support, CCB is sponsoring annual Summer Research Fellowships for graduate students. In 2009, the following 2 projects were selected that address some of the main mathematical and computational challenges tackled by CCB researchers.

Brain Representation and Registration Using a Tetrahedral Mesh and Elastic Deformations

• GSR: Carl Lederman
• Summary: MRI data will be represented in three dimensions as a tetrahedral mesh which will allow for physical deformation from one brain into another. The tetrahedron can be made of variable size so that near the boundary, or other areas of interest, a high level of detail can be maintained while interior areas can be made up of larger tetrahedron to allow for greater computational speed. The brain registration can make use of existing LONI software that divides the brain into a few dozen regions. The elastic deformation should enforce that each region in one brain is mapped into the same region in another brain. In the end, this should give a reasonable correspondence between every point in one brain and every point in another brain. (PDF)

Multi-scale Chan-Vese on cortical surfaces for robust sulci/gyri segmentation

• GSR: Rongjie Lai
• Summary: In cortical surface analysis, detecting sulci and gyri is an important problem. People usually use level set function representation or develop a spherical parametrization to segment the mean curvature of a given cortical surface. We are trying to do the segmentation on surface itself without introducing any other additional data and generalizing the split Bregman iteration on surfaces to speed up the computation. Furthermore, by applying the laplacian smoothing , tracking the segmentation results and comparing with the cortical group we known, we can identify the main sulci/gyci of the given cortical surface. (PDF)

Reports

Carl Lederman

• Project Page

Rongjie Lai

• Project Page

Presentations (09/29/09)

• 2009 CCB Summer Fellows Presentation

CCB Summer Student Research Fellowships -- Summer 2009

Kevin Chan

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Brain Representation and Registration Using a Tetrahedral Mesh and Elastic Deformations

MRI data will be represented in three dimensions as a tetrahedral mesh which will allow for physical deformation from one brain into another. The tetrahedron can be made of variable size so that near the boundary, or other areas of interest, a high level of detail can be maintained while interior areas can be made up of larger tetrahedron to allow for greater computational speed. The brain registration can make use of existing LONI software that divides the brain into a few dozen regions. The elastic deformation should enforce that each region in one brain is mapped into the same region in another brain. In the end, this should give a reasonable correspondence between every point in one brain and every point in another brain.

The registration of the tetrahedral mesh will be analyzed using a variety of metrics including displacement, Jacobian determinant of the mapping, volumetric strain, and average stretch. Additional metrics can be included later to better quantify change. The registration algorithm will be applied to a population of AD, MCI, and NC subjects, and the outputs will be analyzed using the previously mentioned metrics. A multivariate Hotelling's T² Test will be applied to test differences between different populations (AD vs MCI, MCI vs NC, AD vs NC), and tetrahedra with p-values less than a certain threshold will be isolated and displayed.

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Current Status

Currently, all the steps in the workflow have been implemented, and we have preliminary results from a set of data from ADNI. However, each step still needs improvement and fixing. There are a number of things that need to be worked on, including:

1. Registration using the L² distance minimization. This method results in inverted tetrahedra.

2. Registration using the gradient vector flow. This method breaks down away from the boundaries.

3. Tetrahedral Mesh Generation Code. This has some bugs in the function set_face_class_array which result in segmentation faults.

4. Generation of a template mesh.

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Additional Information

Brain Mesh Pictures
Tetrahedral Mesh Workflow Diagram
Tetrahedral Mesh Process (Word Document)

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CCB Summer Graduate Student Research Fellowships -- Summer 2009 -- Rongjie Lai Project Page

Multi-scale Chan-Vese on cortical surfaces for robust sulci/gyri segmentation

• GSR: Rongjie Lai
• Summary: In cortical surface analysis, detecting sulci and gyri is an important problem. People usually use level set function representation or develop a spherical parametrization to segment the mean curvature of a given cortical surface. We are trying to do the segmentation on surface itself without introducing any other additional data and generalizing the split Bregman iteration on surfaces to speed up the computation. Furthermore, by applying the laplacian smoothing , tracking the segmentation results and comparing with the cortical group we known, we can identify the main sulci/gyci of the given cortical surface. (PDF)

CCB Summer Graduate Student Research Fellowships -- Summer 2009 -- Carl Lederman Project Page

Brain Representation and Registration Using a Tetrahedral Mesh and Elastic Deformations

• GSR: Carl Lederman
• Summary: MRI data will be represented in three dimensions as a tetrahedral mesh which will allow for physical deformation from one brain into another. The tetrahedron can be made of variable size so that near the boundary, or other areas of interest, a high level of detail can be maintained while interior areas can be made up of larger tetrahedron to allow for greater computational speed. The brain registration can make use of existing LONI software that divides the brain into a few dozen regions. The elastic deformation should enforce that each region in one brain is mapped into the same region in another brain. In the end, this should give a reasonable correspondence between every point in one brain and every point in another brain. (PDF)
• People: JackVanHorn, AnandJoshi, Kevin Chan's Page, Allan Garcia's page and IvoDinov

Suggested Outline for PPT Demos

• Title: Tetrahedral Representation of Volumetric Brain Imaging Data
• Motivation: Lattice/grid-based volumetric vs. shape-based analyses;
• Some literature review of prior attempts/successes/tools
• Tetrahedral Representation schemes - examples
• Analysis of Tetrahedral Representations using morphometrics - examples
• Analysis protocol outline
• Conclusions and future work

Suggested Outline of Technical Report

• Title ...
• Abstract ...
• Introduction - problem, status-quo, mathematical/computational/neuroscientific challenges, available tools
• Methods - describe the mathematical details/approach
• results/Applications - apply the Tetrahedral Representation to analyze a study with 2+ cohorts (e.g., ADNI data)
• Conclusions
• Acknowledgments
• References

CCB 2009 Biomedical Engineering CM 102/202 Visit to CCB/LONI

Logistics

• Host: IvoDinov
• Date: Fri., 11/20/2009
• Time: 2:00PM - 2:50PM
• Place: CCB/LONI, DIVE, 635 Young Dr. South, NRB 225, UCLA, Los Angeles, CA 90095-7334
• Tel: 310-206-2101
• Fax: USA + 310-206-5518
• URL: http://www.loni.ucla.edu/twiki/bin/view/CCB/CCBEducation_2009_BME_CM202

Agenda

• IvoDinov: Math, Computers & the Brain
  • Welcome
  • Computational Math (PPT)
    • Digital data representation: Demo Police Siren Audio Frequencies, Parametric Modeling, Wavelets, Tensors, Pipeline
    • Mathematical Modeling
    • Statistical Analysis
  • Atlasing: An atlas of the brain allows us to define its spatial characteristics. Where is a given structure; relative to what other features; what are its shape and characteristics and how do we refer to it? Play movies
  • Alzheimer's Disease: Investigating structural, functional, metabolic and pathological changes triggered by Alzheimer's disease. Play movies
  • Pipeline Demo using the LONI Pipeline environment. See also the Try It Now online web-service.
  • Interactive Hyperbolic Map of CCB/LONI Research Activities
• Vishal Patel: Diffusion Tensor Imaging
  • Slides
• Two group rotations:
  • LONI IT (DavidHasson / JonathanTrout): Hardware & Data Center
  • LONI Reel Animation: DIVE

Materials

• CCB Brochure (Brain facts) (and their answers)
• Pinky & the Brain's neuroanatomy song
  • Lyrics

Verse 1: 

Neocortex, frontal lobe, 
Brain stem, brain stem, 
Hippocampus, neural node, 
Right hemisphere, 
Pons and cortex visual, 
Brain stem, brain stem, 
Sylvian fissure, pineal, 
Left hemisphere, 
Cerebellum left, cerebellum right, 
Synapse, hypothalamus, 
Striatum, dendrite. 

Verse 2: 

Axon fibres, matter gray, 
Brain stem, brain stem, 
Central tegmental pathway, 
Temporal lobe, 
White ---?--- matter, forebrain, skull, 
Brain stem, brain stem, 
Central fissure, cord spinal, 
Par-ie-tal, 
Pia mater, meningeal vein, 
Medulla oblongata and lobe limbic, 
Microelectrodes, 
The brain! 

CCB: ISMB2005 Alternative Splicing Special Interest Group meeting, June 23-24, 2005, Detroid, Michigan, USA

• General Conference Site: http://biolinfo.org/as-sig/2005/
• Presenters Here is the list of accepted speakers for AS-SIG 2005 (in alphabetic order):
  1. Volker Brendel, Iowa State University, USA
  2. Tom Cooper, Baylor College of Medicine, USA
  3. Xiang-Dong Fu, UCSD, USA
  4. Michael Hiller, University of Jena, Germany
  5. Viive Howell, University of Michigan, USA
  6. Katherina Kechris, UCSF, USA
  7. Roscoe Klinck, University of Sherbrooke, Canada
  8. Chris Lee, UCLA, USA
  9. Ji-Ann Lee, UCLA, USA
  10. Dick Madden, University of Sherbrooke, Canada
  11. Wojciech Makalowski, Pennsylvania State University, USA
  12. Stephen Mount, University of Maryland, USA
  13. Sandy Pan, University of Toronto, Canada
  14. Shoba Ranganathan, Macquarie University, Australia
  15. Ronen Shemesh, Compugen, Israel
  16. Stefan Stamm, University Erlangen-Nurenberg, Germany
  17. Alphonse Thanaraj, EBI, UK
  18. Yi Xing, UCLA, USA
  19. Gene Yeo, Salk Institute, USA
  20. Xiang Zhang, Columbia University, USA
  21. Mihaela Zavolan, University of Basel, Switzerland

   

• About the Workshop

The organizers of AS-SIG would like to invite you to participate in the first ISMB Special Interest Group meeting on Alternative Splicing, on June 23-24, 2005 at Detroit, Michigan, USA. This workshop is scheduled immediately before ISMB2005, June 25-29, 2005 and is jointly sponsored by the RNA Society and ISCB.

AS-SIG follows on from the highly successful Alternative Splicing SIG meeting held at the Pacific Symposium of Biocomputing, 2004.

• Background and Aims

Alternative splicing generates multiple products from a single gene and is a major mechanism responsible for diversity in the transcriptome of higher organisms.

This is an exciting time in the field of alternative splicing, combining new discoveries from genomics, bioinformatics and molecular biology.  Long considered to be an interesting but less common form of regulation, alternative splicing has emerged as a ubiquitous mechanism of regulation, thanks to genome analysis of human and other higher organisms.  Whereas the Human Genome Project has produced a net result of 25,000 – 30,000 genes, alternative splicing evidently produces over 100,000 distinct transcript forms.  Identifying, quantifying and analyzing the regulation, function and evolution of these forms constitutes a “Human Transcriptome Project”, and will require as remarkable and as concerted an effort as the Human Genome Project.  Above all, it will require close collaboration between bioinformaticists and experimentalists, to build a community of shared tools, databases, nomenclature and standards that permit everyone to contribute what they do best, while benefiting from what everyone else has done.  The AS-SIG aims to establish a permanent forum for bioinformaticists and experimentalists to come together in this field.

AS-SIG will address the latest results and questions in this exciting field, and to bring together bioinformaticists and experimentalists, focusing on questions that demand their collaborative inputs.  

Besides oral and poster presentation sessions, the workshop will address the issue of data standards to pave the way for the international efforts directed towards the human transcriptome analysis.

The purpose of this SIG is to cover the latest results and questions in this exciting field, and to bring together bioinformaticists and experimentalists, focusing on questions that demand their collaboration.The SIG will include studies of alternative splicing both in human and other organisms, and will consist of two days of talks (approximately 20 minutes each), and a poster session.

• The meeting sessions will follows; four major thematic areas:

1. Technologies: new experimental approaches for high-throughput discovery, quantification, and functional analysis of alternative splice forms in both mRNA and protein.  These include splice-variant measurement technologies such as microarrays; laboratory protocols/assays; validation techniques; and novel instrumentation platforms.


2. Biology: Biological mechanisms of splicing and regulation; biological functions such as the impact of splice variants on protein structure and biological pathways; phenomena such as nonsense-mediated decay and disease associations.


3. Bioinformatics: algorithms and analysis of alternative splicing, including topics such as analysis of alternative splicing evidence, products, and functional impact; comparative genomics; alternative splicing regulation; and data-mining.
   


4. Databases, Standards, and Community Building for the “Human Transcriptome Project”: our research community is in effect building a catalog of the 100,000+ transcript forms found in human (and other transcriptomes), including their precise structures, products, regulation, functional and experimental annotation.  This massive project, comparable in scale to the Human Genome Project, requires the efforts of the entire community, as well as agreed standards for sharing data, i.e. a common database, data standards, and shared tools.  What data and tool standards do we need?  What nomenclature must we agree on?  How can we unify our efforts via a common database?  How can we integrate data from genomics, bioinformatics and traditional experiments?  How can we enable community annotation, curation and validation?

• Venue, Registration and Accommodation

The meeting will be held at the Detroit Marriott at the Renaissance Center, Detroit, Michigan, USA.

AS-SIG registration will be along with ISMB2005 registration.

ISMB2005 conference rates have been extended to cover pre-conference days required to attend AS-SIG.  For more information on hotels offering special rates, please see ISMB2005 Housing. Hotel reservation will have to be made along with the SIG registration.

• Organizers:
  • Professor Chistopher Lee, Chair, University of California Los Angeles, USA
  • Prof. Shoba Ranganathan, Co-Chair, Macquarie University, Sydney, Australia
  • Professor Stefan Stamm, Co-Chair, University of Erlangen-Nuremberg, Germany
  • Dr. Hui Wang, Co-Chair, Affymetrix Inc., USA

CCB Courses, Training and Education - Winter 2006 Computational Brain Mapping and Neuroimaging COurse

• Course Details
• Student Evaluations, Comments and Feedback

CCB Courses, Training and Education - Winter 2006 Computational Brain Mapping and Neuroimaging Course

Student Evaluation, Comments and Feedback about the course and the isntructors

• Summary of Student Evaluations by Instructors

Instructor	Date	Consider all materials, readings, activities, and presentations for this session. Did this session provide you with important information that was useful to your development as a researcher?	Does the instructor have command of the subject? (e.g., present materials in an analytic way, contrasts various points of view, discusses current developments, and relates topics to other areas of knowledge)	Does instructor present material in a clear and organized manner? (e.g., states objectives, summarizes major points, and provides emphasis)	Is instructor sensitive to the response of the class? (e.g., encourages participation, welcomes questions and discussions, responds to participants issues)	Does the instructor enjoy teaching? (e.g., is enthusiastic about subject, stimulates and maintains interest, exciting, and has self-confidence)	What is your overall evaluation of the instructor? Consider clarity of presentations, preparation, fairness, helpfulness, availability, dedication, etc.	Total Average
Allan MacKenzie?-Graham	1/17/2006	3.8	4.1	4	4.5	4.4	4.2	4.2
Arthur Toga	1/10/2006	4.6	4.9	4.8	4.8	4.9	4.8	4.8
	2/9/2006	4.9	5	4.9	5	5	4.9	5.0
David Shattuck	2/14/2006	3.7	4.2	3.8	4.1	4	4	4.0
	2/23/2006	4.5	4.6	4.4	4.5	4	4.1	4.4
	3/2/2006	4.4	4.7	4.3	4.3	3.9	4.1	4.3
Elizabeth Sowell	1/26/2006	4.2	4.6	4.4	4.6	4.6	4.4	4.5
	2/2/2006	4.2	4.4	4.2	4.6	4.6	4.4	4.4
George Bartzokis	3/14/2006	4.5	5	4.8	4.8	4.8	4.8	4.8
Ivo D. Dinov	1/12/2006	4	4.1	3.9	4.5	4.7	4.2	4.2
	1/19/2006	4.2	4.4	3.8	4.4	4.4	4.2	4.2
	1/24/2006	4.3	4.5	4.3	4.6	4.6	4.4	4.5
	1/31/2006	4	4	4	4.5	4.5	4.2	4.2
	3/7/2006	4.6	4.6	4.4	4.8	5	4.4	4.6
Liana Apostolova	2/7/2006	4.6	4.7	4.6	4.6	4.7	4.7	4.7
Michael Pan	3/9/2006	4.1	4.1	3.9	4.1	4	4	4.0
Susan Bookheimer	3/16/2006	4.8	5	4.8	5	5	4.8	4.9

• Legend: Very Poor (1); Below Average (2); Average (3); Above Average (4); Excellent (5)

• Randomly Selected Comments Regarding the Course Content & Material:
  • Writing on the board can be bigger, otherwise, it’s hard to see from the back. * Material is useful. Clear and fluid.
  • Very nice cutting edge images.
  • It was a good amount of material for one lecture. These were difficult mathematical concepts, so maybe more animations could be used to help explain them.
  • Very useful, but complicated concepts.
  • Very useful since this material isn’t really presented anywhere else in the curriculum. Strives to fully answer all questions.
  • Useful, overview, sculpted to class’s apparent interest.

• Randomly Selected Comments Regarding the Instructors:
  • [Instructor is] Very knowledgeable in many aspects. The atlasing section was interesting.
  • The material in this first lecture was very interesting.
  • Instructor engages the class well.
  • [Instructor] is very familiar and comfortable with material she presented.
  • Good. Jumped around a bit so hard to follow but a good/interesting speaker.