Novel Tools for (Functional) Magnetic Resonance Image Analysis Development and Implementation in the Scientific and Statistical Computing Core Robert W Cox and a cast of several

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Novel Tools for (Functional) Magnetic Resonance
Image AnalysisDevelopment and Implementation in
the Scientific and Statistical Computing
CoreRobert W Coxand a cast of several
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Experiment design
Raw data
MR-scanner
Scanner Subject Stimulus Delivery
Reconstruction Distortion correction
BOLD EPI
Anatomy
Function
BOLD signal
Statistical models Inference
Func. Anat.
Group analysis

• Co-registration

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Scientific Statistical Computing Core

• Develop and implement new methodologies to meet
  user needs
• Consult with IRP users/groups regarding
• Experimental design
• Processing methods and tools
• Statistical inferences
• Conduct classes on designing and processing FMRI
  experiments
• Answer FMRI / MRI questions on message board
• Distribute maintain our open-source software
  tools
• Facilitate cross-talk between different FMRI
  tools
• AFNI, FSL, FMRIstat, FreeSurfer, Caret, SPM,

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AFNI SUMA

• AFNI collection of programs for FMRI analysis
• Visualization
• 2D, 3D, time-series, cortical surface (SUMA)
• Time Series Analysis
• Linear nonlinear regression
• Statistics on 3D Image Collections
• 1-5 way ANOVA non-parametrics SEM
• Data editing tools
• Spatial and temporal filtering
• 3D image registration
• Clustering ROI drawing Atlas-based ROIs

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The AFNI / SSCC Philosophy

• Enable users to stay close to their data
• Save intermediate results
• Look at images and data in connected ways
• User controls processing steps and parameters
• Everyone has an opinion
• Special problems need special solutions
• Efficient (fast) implementations
• Things that are easy and fast to do will get done
  more often
• Help the users
• Until our patience runs out

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Next Set of Slides

• Features Added to AFNI and SUMA in Response to
  User Requests and / or Problems / Complaints
• (at least in part)

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Feature Atlases

• Problem Navigating in a complicated folded up 3D
  object (i.e., the brain) with few easily
  recognized landmarks
• Solution Coordinate-based brain atlases
• Accepting the 5-10 mm uncertainty of brain
  coordinates
• Atlas 1 Talairach-Tournoux atlas
• As parsed by Peter Foxs group at UT San Antonio
• Atlas 2 Cytoarchitectonic atlases from Karl
  Zilles group at Forschungszentrum Jülich
• 10 brains being sliced diced stained
  scanned
• About 40 complete at this time
• Where Am I? Jump To Colorization ROIs
• Plans keep up with Zilles Animal atlases?

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Example Where Am I?
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Feature Skull Stripping

• Problem other skull stripping software (e.g.,
  BET in FSL) didnt work reliably enough
• Solution was to re-visit problem from scratch,
  and build on BETs surface growing algorithm
• Then add new features special knowledge about
  where the eyes are likely to be 3D edges etc.
• Then test it on the hard cases from NIH (ab)users
• Extra feature extend it to monkey images
• Plans continue testing and improvements

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Feature De-Spiking

• Problem occasional big spikes in echo planar
  images gathered for functional MRI
• Problem eventually traced to gradient coil
• In the meantime can studies be saved?
• Wrecks the standard time series analysis

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Feature Amplitude Modulated FMRI

• Situation Each stimulus event comes with an
  auxiliary parameter
• May be measured (GSR, reaction time, ) or may be
  determined by experimenter
• Want to determine if FMRI response magnitude is
  proportional to this auxiliary parameter
• Solution was to add amplitude modulated
  regressors to AFNIs 3dDeconvolve program
• Two regressors per condition
• First is each stimulus response identical
• Second is each stimulus response proportional to
  auxiliary parameter for that stimulus
• Plans 2-3 params/event event-wise amplitudes

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Feature Nonlinear Regression Models

• Pharmacological models for time series analysis
• AFNIs nonlinear regression program 3dNLfim
• Michaelis-Menton dynamics for BOLD FMRI with
  psychoactive drugs (e.g., ethanol)
• Dynamic Contrast Enhanced MRI for quantifying Gd
  contrast leakage through blood-brain barrier

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Feature Smart Blurring

• FMRI time series datasets are often smoothed
  (blurred) in space to
• Reduce noise (by averaging)
• Increase intra-subject activation blob overlap
• Blurring brain non-brain signals together is
  silly
• When combining data from different scanners
  (i.e., multi-center studies), image smoothness
  varies
• Should blur images until they have the same level
  of smoothness so that inter-scanner combinations
  make statistical sense
• Developed a method for blurring inside a mask
  that stops when image noise reaches specified
  level of smoothness

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Feature Structural Equation Modeling

• SEM is a form of connectivity analysis
• Input correlations between activated ROIs
• Regions where the activations fluctuate in
  strength together will be more highly correlated
• Input connectivity diagram between ROIs
• Output strength of connections
• Can also search for better fitting connections

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Feature All-in-One Analysis Program

• Common complaint AFNI is tooooooo hard to use
• Analysis of single subject data involves several
  steps, each instantiated in separate programs
• Registration, smoothing, normalizing, model
  analysis
• Solution is a program afni_proc.py that will run
  all these programs in a coherent sequence
• Intermediate results are saved to make it
  possible to track backwards when results are
  confusing
• This script is not intended to let the user avoid
  understanding the data analysis process!

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Feature Diffusion Tensor Analysis

• Goal Compute the Diffusion Tensor (etc.) from
  Diffusion Weighted image collections
• Problem 1 loglinear method is inaccurate in
  highly anisotropic locations (the cool places to
  be)
• Problem 2 published nonlinear solution methods
  not available in open-source software
• Solution was to create and implement an efficient
  robust nonlinear method for finding the diffusion
  tensor D in each voxel
• Also, a optional nonlinear image smoother (2D and
  3D) to reduce noise in homogenous areas
• Our code now incorporated into DTI Query, an
  open-source tractography program from Stanford

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Feature Inter-Modality Registration

• Goal Efficiently align 3D volumes acquired with
  different imaging contrasts
• Solution is a general program using
  histogram-based measurements of image matching
  (e.g., mutual information)
• This one is still very much a work-in-progress
• Works pretty well on simple cases (e.g.,
  whole-brain to whole-brain)
• Dealing with partial-brain to whole-brain and
  with brain images that have holes in them is less
  reliable right now
• Also want to add non-affine warping capabilities

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Example Inter-Modality Registration
Skull Stripped MRI
masked CT
CT overlaid on MRI in color - unaligned
CT overlaid on MRI in color - aligned
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Feature Analysis of Mn Contrast MRI

• Mn is an MRI contrast agent and a calcium analog
• Goal time-dependent in vivo tract tracing in
  monkeys
• Problems abound
• Like FMRI, signal changes are small
• Other artifacts from day-to-day scanning are
  larger
• Simple image subtraction isnt reliable
• Next 3 slides some data and results

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Mn Data Different Days
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Mn Data Subtract t-Test
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Mn Data Cleverer t-Test
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Next Set of Slides

• Features Added to AFNI and SUMA in Response to
  Our Own Crazy Thoughts
• (mostly)

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Realtime FMRI
Functional activation Motion estimation in
realtime
AFNI
Dimon
Feedback Receiver
MR Scanner, Image Files
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Surface-Based Analyses

• Create cortical surface models, project 3D data
  to these surfaces, analyze in that space
• Respects geometry and topology of cortex
• Most AFNI statistical tools now work with image
  data defined over surfaces as well as over 3D
  volumes

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Visualization Links Between Modes
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NIfTINeuroimaging Informatics Technology
Initiative

• Goal facilitate inter-operability of FMRI data
  analysis software
• First fruit NIfTI-1.1 standard for storing
  datasets defined over 3D volumes (plus time axis)
• Works with AFNI, FSL, SPM, BrainVoyager,
• Agreement is not a one-time thing
• Ongoing process is needed to deal with
  compatibility, extensions, new ideas along the
  same line,
• Efforts underway
• NIfTI-G standard for storing cortical surface
  models (and associated data)
• NIfTI-W standard for storing
• non-affine spatial warps

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Closely Linked Communication

• Programs talk to each other (esp. AFNI SUMA)
• Exchange data
• Issue commands - you can script many parts of
  the AFNI SUMA graphical interfaces

AFNI
SUMA
3dSkullStrip
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Developer-friendliness
Realtime physiological monitoring using
AFNI Jerzy Bodurka, FIM/LBC/NIMH
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Brain State Classification

• Train Support Vector Machine (SVM) classifier on
  a collection of pre-categorized 3D brain images
• e.g., looking at house and looking at face
• Classifies new 3D images into the categories

From Stephen LaConte Emory, transitioning to Rice
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Penultimate Slide

• Much of our most fruitful and satisfying work
  comes from close and ongoing interactions with
  investigators that have interesting problems
• Derived from studies that are pushing the
  envelope of deriving information from MRI
• We are here to provide solutions to problems (of
  image analysis)
• Your current short-term problems (lots of these!)
• Your actual longer-term problems
• What we think your future needs will be

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Ultimate Slide
The Team