the analysis pipeline of freesurfer 2013 · pipelines 5/1/2013 3 bi/cns 184 the primate visual...

NeuronEcon Cluster

• How to login?• How to transfer files?• How to open a terminal window• Simple commands in linux• Where should we store our stuff on the cluster?• Setting up environment for free surfer (.bashrc)• Setting up matlab (keyboard)

Pipelines

5/1/2013 3Bi/CNS 184 The Primate Visual System, Shay Ohayon (c)

The Primate Visual SystemBi/CNS 184

Two Main Pipelines

• Anatomical Pipeline

RAW Structural Data Useful things for VISUALIZATIONUseful things for VISUALIZATIONAnalysis

Pipeline:Data SmoothingSegmentation

Surface Reconstruction…

Surface Reconstruction• The boundary between white and gray matter is used to define a surface

• The surface can be inflated/flattened for improve visualization

5/1/2013 Bi/CNS 184 The Primate Visual System, Shay Ohayon (c) 5

Two Main Pipelines

• Functioanl Pipeline

RAW Functional Data Useful things for INFERENCE

Useful things for INFERENCE

Analysis Pipeline:

Slice Timing CorrectionUn‐distortion

Motion CorrectionData SmoothingModel Design

Parameter Estimation

Things shared by both pipelines

• RAW data is originally saved in a file format called “DICOM”

• Each DICOM file contains a single slice– Since you acquire volumes, and each volume contains many slices, you end up with MANY files

• A first step shared by both pipelines is to convert DICOMs to something more accessable.

Unpacking

• Unpacking is the process of converting the huge amounts of dicoms into a smaller set of files, each containing a volume or a set of volumes

Know your file types

• MGH (Massachusetts General Hospital)• Used to store high resolution structural images• http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/MghFormat

• XXX.mgz is a compressed variant of MGH

• NII (a.k.a. NIFTI)– Used to store data with different meanings. Imaging data, statistical values, etc.

– Can contain time series (4D data)– XXX.nii.gz (a compress version of nii)

Unpacking

• The command line interface to unpacking is unpacksdcmdir

• You can read more about it here:• http://surfer.nmr.mgh.harvard.edu/fswiki/unpacksdcmdir

Unpack GUI

• An alternative for using the command line.• cd to /space/cns184/2013/code• Run matlab• Run the script called fMRI

Unpack GUI

• Highlight the dicom folder and then click “Unpack”

• It will unpack to– /space/cns184/2013/data/<your name>/cooked/…

Analyzing takes time…

• Be patient• You can check the status of some of the heavy computations by querying the cluster about jobs. Type “qstat” in the command line.

Unpacking Outputs

• After unpacking is done, the following folders will be created in the output folder:

• /bold/XXX/f.nii– This is where the FUNCTIONALS are stored– XXX is the RUN number

• /mri/orig/XXX/mprage.nii– This is where the structural scans are stored

• /fieldmap/XXX– Information about how to undistort the EPI images

Quick visualization of the data

• Free surfer offers a tool called tkmedit which allows you to visualize anatomical data – We will also use this tool later on to overlay the statistical maps

• For example:– Tkmedit –f …../mri/orig/002/mprage.nii

• Another small utility for displaying data is called xd– xd mprage.nii

The Functional Pipeline

RAW Functional Data Useful things for INFERENCE

Useful things for INFERENCE

Analysis Pipeline:

Slice‐timing correctionMotion Correction

Un‐distortionData SmoothingModel Design

Parameter Estimation

Slice Timing Problem


Time

Ideal World

Time

Real World

Slice Timing Correction


5

Time

3 7 2

Motion

• Within‐Modality Registration• Unless subject is anesthetized we expect small motion of the head during the scan.

• Most analysis packages try to compensate for these small motions and realign the volumes to a single reference frame such that all voxels(in time) correspond to the same physical point


Rigid Body Motion• Rigid body transformation can be represented by a rotation followed by a translation

• 3D rotation can be decomposed to a rotation about X, followed by a rotation about Y, followed by a rotation about Z

• The 3D motion, therefore, can be described by 6 parameters


Motion Correction

• There is a tool that can correct for small motions (mc‐afni2). It will output a new series of volumes (called fmc.nii), such that all of them are properly aligned.

• In addition, it will also output the values of the detected motion, as possible regressorsthat can be used in the GLM fit.– The registration values will be stored under mcextreg

Motion correction for a single run:


Motion Correction

• Small movements can be corrected automatically.

• What about large motion? Or aligning recorded sessions from multiple days?

Multi Modal Registration

• Match between the two:


Manual Registrationtkregister2 ‐‐targ static.mgz \

‐‐mov movable.mgz \‐‐reg reg.reg ‐‐regheader

Next step in the pipeline…

EPI Distortion


Un‐distortion

• EPI is sensitive to small magnetic field in homogeneities

• Distortions arise in the direction in which acquisition time between adjacent point is the greatest (i.e., phase encoding direction, which is typically along the y axis)

• Distortions along the x direction are negligible because the acquisition time between adjacent points is much less

• Correcting distortions in EPI, therefore, is reduced a 1D problem.


Undistorting EPIs

• EPI undistortion can be done with a tool called epidewarp.fsl

• This tool is a part of a different software package to analyze MR data which is called FSL

• The output of this tool will be a new set of functional volumes. The name of those will be fmcu– (to indicate that they were motion corrected and undistorted)

Spatial Smoothing

• Spatial smoothing is typically done by applying a Gaussian filter on the volume.

• This filter will “spread” (smear) the intensity at each voxel over nearby voxels.

• The width of the filter determines how strong the effect will be. It is usually expressed in millimeters as FWHM (Full width half maximum)

Why smooth the data?

• Using a filter that matches the frequency of the signal will maximize signal to noise.

• If voxels were completely uncorrelated, this will only harm the data

• However, voxels do have strong spatial correlation (anatomy is smooth)

• Furthermore, when doing analysis with several subjects, mismatch in anatomy can lead to not exactly same voxels activity

How much to smooth?

• Smoothing should match the expected spatial correlation of the data.

• A rule of thumb is to use something like 1.5‐2 times the voxel size. – In humans, where voxels are 3mm, a typical FWHM will be about 5mm

– In monkeys, where voxels are 1mm, we typically use 2mm

• In any case, you always want to look at the unsmoothed data, in addition to the smooth one.

How to smooth?

• You can use a tool called “mri_fwhm” that is part of the free surfer software package.

Spatial Normalization – The Average Brain

• MNI brain (Montreal Neurological Institute)– MNI250 / MNI305 / ICBM152– “colin27” or “T1” in SPM

• Talairach and Tournou


Brain Coordinate System• CA‐CP line (horizontal plane)

– This line passes through the superior edge of the anterior commissureand the inferior edge of the posterior commissure. It follows a path essentially parallel to the hypothalamic region.

• VCA line (vertical frontal plane)– Vertically traversing the posterior margin of the anterior commissure.

• Midline (Sagital plane)


Intermediate summary

• We started from f.nii, which was the raw signal• We then generated:

– fmc.nii (motion corrected, aligned version of the data)

– fmcu.nii (above + undistorted)– fmcus.nii (above + smoothed)

• We typically only analyze the last two types.

The easy way for preprocessing

FuncPreProc

• This is a small GUI utility that will perform many of the steps mentioned above automatically.


• The functional inference preparation step is done with a tool called mkanalysis‐sess– mkanalysis‐sess has many parameters that can be set. Some are mandatory, some are optional. Read more here: http://surfer.nmr.mgh.harvard.edu/fswiki/mkanalysis‐sess.new


• mkanalysis‐sess assumes certain files are located under certain folders

The annoying steps:

1. Setup an “analysis” folderSuppose your subject data resides in …/data/subject1Then create …/data/subject1/analyses/anal1

• You can create a folder using the mkdircommand

2. Create the following file:…/data/subject1/analyses/anal1/dfWhich contains a single line (obviously, replace the … with the full path name):…/data/

3. Create the following file:…/data/subject1/analyses/anal1/sfWhich contains a single linesubject1

4. Create the following file:…/data/subject1/subjectnameWhich contains a single linesubject1

5. Create the paradigm design file and store it under: …/data/subject1/analyses/anal1/paradigmfile

The paradigm file is a text file and has the following format:[Time ConditionNumber Duration Description]

For example:0.0 1 2.0 faces2.0 1 2.0 faces4.0 2 3.0 non‐faces7.0 2 3.0 non‐faces

6. Distribute the paradigmfile to each one of the run folders. i.e.cp paradigmfile …/data/bold/004cp paradigmfile …/data/bold/007…

7. Create the run list file that will tell mkanalysis‐sess which runs to take from the /bold folder

For example:003004005007

Putting it all together:#! /bin/csh ‐f

set AnalysisName = "anal1"set ParadigmDesignFile = "paradigmfile"set TR = 3set NumberOfConditions = 7set GammaFit = "0 8"set GammaExp = "0.3"set RunListFile = "runfile"set FuncStem = "fmcu.nii"set MaskStem = "brain_fmc.nii"

mkanalysis‐sess \‐analysis $AnalysisName \‐native \‐event‐related \‐paradigm $ParadigmDesignFile \‐TR $TR \‐nconditions $NumberOfConditions \‐gammafit $GammaFit\‐gammaexp $GammaExp \‐polyfit 2 \‐mcextreg \‐acfbins 10 \‐fsd bold \‐runlistfile $RunListFile \‐funcstem $FuncStem \‐mask $MaskStem \‐fwhm 0 \‐refeventdur $TR \‐force

The Gamma Fit:

• Describes the HRF. There are different HRFs for monkeys and humans. Furthermore, when monkeys are giving a contrast agent to enhance BOLD activity (called MION), you need to use different values as well.

• Use the following parameters:– GammaFit = "0 8“– GammaExp = "0.3“– TimeWindow = 80

MION

• VERY IMPORTANT!!!• Expect a reduction in the BOLD signal when a brain region is activated.

Summary

• mkanalysis‐sess did not analyze anything. It just setup stuff for the heavy lifting computations that will follow.

• The actual GLM fit is done by running a tool called “selxavg3‐sess”

#! /bin/csh ‐f

setenv SUBJECTS_DIR "/space/cns184/2013/groups/project1/data"

selxavg3‐sess ‐analysis anal1 ‐sf sf ‐df df ‐no‐con‐ok ‐no‐preproc

Running things on a separate machine

• make a file called “runglm” and place this code inside:

• Then write “qsub runglm”

#! /bin/csh ‐f

setenv SUBJECTS_DIR "/space/cns184/2011/groups/project1/data"

selxavg3‐sess ‐analysis anal1 ‐sf sf ‐df df ‐no‐con‐ok ‐no‐preproc

What has this computed?

• Under …/bold/anal1 many files will be generated, but the most important ones are “beta.nii”, and “rvar.nii” which contains all the betas and residuals needed to compute contrasts:

12

~T

J rank XT T

c d tc X X c

How do you compute contrasts?

• Use a tool called “mkcontrast‐sess”mkcontrast‐sess –analysis anal1 –contrast faces_objects –a 1 –a 2 –a 3 –c 4 –c 5

You will need to re‐run the selxavg3‐sess again to actually compute the contrast.

What did this generate?

• /bold/anal1/faces_objects/sig.nii– This volume contains ‐log10(p‐value)

• /bold/anal1/faces_objects/t.nii– This volume contains the T statistics

How can we visualize p‐values?

• xd sig.nii is not very helpful. You want to display this overlaid on top of the anatomical volume!

• xd …/bold/MOCOVOL/mocovol.nii ‐P sig.nii

• This will display the motion template volume overlaid with the p‐values

The alternative to the linux scripts…

• GUI Wrapper that I wrote that does everything for you (and more…)

Advanced Stuff…

• Reading volumes in matlab– Use “MRIread” function

• Converting data types/format– Use “mriconvert”

Scripts in linux

• Sometimes it is useful to generate small scripts that run the required tools one after the other.

• Here is a short example of a linux script:#! /bin/csh ‐f

echo Hello Worldset X=5echo $Xcd /space/cns184

Running a script

• Make sure that the file has the executable permission set correctly.– You can find which permissions a file or a directory have by writing “ls –la”

– To turn on the executable flag, use the chmodcommand. “chmod 775 Filename”, for example, will modify the permissions such that you will be able to run it.

the analysis pipeline of freesurfer 2013 · pipelines 5/1/2013 3 bi/cns 184 the primate visual...

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