AnalyzeFMRI: an R package to perform statistical analysis on fMRI C - - PowerPoint PPT Presentation

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AnalyzeFMRI: an R package to perform statistical analysis on fMRI C - - PowerPoint PPT Presentation

Mar 30, 2024 19 likes •504 views

AnalyzeFMRI: an R package to perform statistical analysis on fMRI C ecile Bordier, Michel Dojat, Pierre Lafaye de Micheaux use R 2009 July 9th, 2009 AAAAAA AAAAAA Package R/C : AnalyzeFMRI 2001 J.Marchini 2007 AnalyzeFMRI extension

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AnalyzeFMRI: an R package to perform statistical analysis on fMRI

C´ ecile Bordier, Michel Dojat, Pierre Lafaye de Micheaux

use R 2009

July 9th, 2009 AAAAAA AAAAAA

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SLIDE 2

Package R/C : AnalyzeFMRI

◮ 2001 J.Marchini ◮ 2007 AnalyzeFMRI extension

Processing and analysis of large structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI) datasets

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MRI & functional MRI

Non invasive procedure

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MRI & functional MRI

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MRI & functional MRI

Anatomical

[linewidth=2pt, arrowsize=10pt]-¿(-0

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MRI & functional MRI

Anatomical Functional

  • r EPI
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SLIDE 7

MRI & functional MRI

Anatomical Functional

  • r EPI

hrf

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Example of Experiment

Paradigm

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Example of Experiment

Paradigm

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Example of Experiment

Paradigm Expected Signal

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Example of Experiment

Paradigm Expected Signal Image Acquisition

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Example of Experiment

Paradigm Expected Signal Image Acquisition Correlation between voxels and paradigm

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Problem & Solution Problem: Each voxel is a mix of several original signals:

  • ccular movement, heart rate, respiratory

cycle, noise... Solutions: GLM : General Linear Model : Linear modelisation of

the hemodynamic response during the paradigm

ICA: Independent Component Analysis : exploratory

method

Is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence

  • f the non-Gaussian source signals
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SLIDE 14

Problem & Solution Problem: Each voxel is a mix of several original signals:

  • ccular movement, heart rate, respiratory

cycle, noise... Solutions: GLM : General Linear Model : Linear modelisation of

the hemodynamic response during the paradigm

ICA: Independent Component Analysis : exploratory

method

Is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence

  • f the non-Gaussian source signals
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SLIDE 15

Problem & Solution Problem: Each voxel is a mix of several original signals:

  • ccular movement, heart rate, respiratory

cycle, noise... Solutions: GLM : General Linear Model : Linear modelisation of

the hemodynamic response during the paradigm

ICA: Independent Component Analysis : exploratory

method

Is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence

  • f the non-Gaussian source signals
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SLIDE 16

Problem & Solution Problem: Each voxel is a mix of several original signals:

  • ccular movement, heart rate, respiratory

cycle, noise... Solutions: GLM : General Linear Model : Linear modelisation of

the hemodynamic response during the paradigm

ICA: Independent Component Analysis : exploratory

method

Is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence

  • f the non-Gaussian source signals
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SLIDE 17

Spatial ICA

◮ Spatial decomposition

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Spatial ICA

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Temporal ICA

◮ Temporal decomposition

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Temporal ICA

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Temporal ICA

”...Note that TICA is typically much more computationally demanding than SICA for functional MRI applications because

  • f a higher spatial than temporal dimension and can grow

quickly beyond practical feasibility. Thus a covariance matrix

  • n the order of N2 (where N is the number of spatial voxels of

interests) must be calculated. A combination of increased hardware capacity as well as more advanced methods for calculating and storing the covariance matrix may provide a solution in the future ...”

Calhoun, Human Brain Mapping, 2001

Volume= 128X128X30 voxels and Time= 240 volumes Spatial ICA: covariance matrix = 2402 = 57600 Temporal ICA: covariance matrix ≈ 5000002 = 25 ∗ 1011

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Temporal ICA

”...Note that TICA is typically much more computationally demanding than SICA for functional MRI applications because

  • f a higher spatial than temporal dimension and can grow

quickly beyond practical feasibility. Thus a covariance matrix

  • n the order of N2 (where N is the number of spatial voxels of

interests) must be calculated. A combination of increased hardware capacity as well as more advanced methods for calculating and storing the covariance matrix may provide a solution in the future ...”

Calhoun, Human Brain Mapping, 2001

Not available in other software like FSL

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Temporal ICA

”...Note that TICA is typically much more computationally demanding than SICA for functional MRI applications because

  • f a higher spatial than temporal dimension and can grow

quickly beyond practical feasibility. Thus a covariance matrix

  • n the order of N2 (where N is the number of spatial voxels of

interests) must be calculated. A combination of increased hardware capacity as well as more advanced methods for calculating and storing the covariance matrix may provide a solution in the future ...”

Calhoun, Human Brain Mapping, 2001

Not available in other software like FSL Possible with the singular value decomposition (svd)

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Simulation: sine wave with various frequency

2 4 6

+Gaussian noise S/N=10%

1 3 5

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Spatial ICA simulation results

Poor Results

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Temporal ICA simulation results

Better Results

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

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Real fMRI data with the AnalyzeFMRI package

◮ Experimental Protocol

...

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Real fMRI data results with the AnalyzeFMRI package

◮ Original

+hrf

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Real fMRI data results with the AnalyzeFMRI package

◮ Original

+hrf

◮ Spatial ICA signal result

cor= -0.52

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SLIDE 36

Real fMRI data results with the AnalyzeFMRI package

◮ Original

+hrf

◮ Spatial ICA signal result

cor= -0.52

◮ Temporal ICA signal result

cor= 0.44

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Comparison results

Spatial ICA results Temporal ICA results Results obtained with spm general linear model

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AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

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SLIDE 39

AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
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AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti
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AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

Read, write, modify metada

More than 40 parameters: orientations, size, subject informations...

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AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

Read, write, modify metada Read, write, convert 3D and/to 4D

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SLIDE 43

AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

Read, write, modify metada Read, write, convert 3D and/to 4D Display nifti volume

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SLIDE 44

AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

◮ ICA

  • Existing : Spatial ICA
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SLIDE 45

AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

◮ ICA

  • Existing : Spatial ICA
  • New : Temporal ICA
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SLIDE 46

AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

◮ ICA

  • Existing : Spatial ICA
  • New : Temporal ICA

Submission to CRAN very soon!

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SLIDE 47

AnalyzeFMRI Package

Updates :

◮ Image Format in the package :

  • Existing : Analyze
  • New : nifti

◮ ICA

  • Existing : Spatial ICA
  • New : Temporal ICA

Submission to CRAN very soon! Maintainers: Pierre Lafaye de Micheaux Maintainers: C´ ecile Bordier

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