http://www.mrc-cbu.cam.ac.uk/~rhodri/aa/aa_topbar.gif

aa release 1.1 Rhodri Cusack, MRC CBU, Cambridge rhodri.cusack [at] mrc-cbu.cam.ac.uk

Acknowledgements: Thank you to Matthew Brett, Jessica Grahn, Daniel Mitchell, Rik Henson & Matt Davis, who have contributed to the code and this manual.

CONTENTS

Table Of Contents

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Summary

The system automates the analysis of neuroimaging data. This version has been configured to work on data from the Wolfson Brain Imaging Centre, reading Bruker data and analysing it with SPM. It is written in Matlab.

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Main Features

• Automatic. Almost completely automatic analysis, currently up to the end of normalisation.

• Flexible control. Users that are new to neuroimaging don’t get bombarded with options, but are directed to what is essential, while all other settings take sensible defaults. More experienced users can easily change a range of settings, and advanced users can add or replace modules to the system to change any behaviour

• Restartable. If the users stops the script, or if it crashes (e.g., because of a machine fault, or because the right data aren’t there) then when restarted it will begin at the stage where it left off.

• Modular. The system is modular at two levels, to make it easy to switch in or out relevant parts.

• Automatic good citizen behaviour. Field maps and structurals are automatically detected. Structurals are copied to /imaging/local/structurals/cbu automatically by default.

• Easy to maintain. The code components are stored in a central repository (/cbu/imagers/spm/aa ?) which means that new components can be made available, or existing ones changed in new releases

• Record keeping. Unlike SPM used from the GUI, the system records exactly which parameters were used, and allows easy recreation of a dataset from the raw data at a later date.

• Simple interface for new modules. It is easy for Matlab programmers to write new modules and incorporate them into the processing stream.

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System overview

An overview of the different types of components is shown in this table:

= =The most important of these by far from the user’s point of view is the “User script”. The next section describes this. Anchor(s)

User script

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4. Overview

This is the master script, which controls the analysis. Most studies can be analysed with this alone.

There are 6 sections to the user script:

• ||<style="vertical-align: top;">

  Section ||<style="vertical-align: top;">Probability you need to change it and when ||

An example, which will process 3 subjects each with 4 sessions up to and including normalisation is below:

An example, which will process 3 subjects each with 4 sessions up to and including normalisation is below:

% Automatic analysis

% User master script

% Rhodri Cusack MRC CBU Cambridge 2005

% (1) RESET ALL PARAMETERS

aap=[];

% (2) ANALYSIS RECIPE

% General auto TR recipe; slice order Siemens product sequence default

aap=aarecipe_general(aap);

% (3) MODIFY STANDARD RECIPE MODULE SELECTION HERE IF YOU'D LIKE

• % do this here

% (4) GET ALL THE PARAMETERS FOR THIS RECIPE

aap=aa_init(aap);

% (5) DEFINE STUDY SPECIFIC PARAMETERS

aap.options.aa_minver=1.0; % will only work on aa version 1.0 or above

• % The study directory

aap.acq_details.root = '/home/rhodri/pvs/cbu';

• % Add subjects and session numbers for EPI data

aap=aas_addsubject(aap,'*CBU050011/*',[5 11]);

aap=aas_addsubject(aap,'*CBU050012/*',[4 10]);

• % Condition names for each session, must be same for all subjects

aap.acq_details.sessions={'mystery1','mystery2'};

• % Number of dummy scans at the start of each session

aap.acq_details.numdummies=18;

% (6) SET ANY OTHER PARAMETERS YOU WOULD LIKE TO BE DIFFERENT FROM THE DEFAULTS

% (7) SET ANY SPM DEFAULTS IF NEEDED

aap.spm.defaults.normalise.write.vox=[3 3 3];

% (8) DO PROCESSING

aa_doprocessing(aap);

The four sections you are mostly likely to need to change (1, 2, 4 and 5) are now discussed in turn.

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4. Choosing the recipe

These are chosen with the line

e.g., aap=aarecipe_general(aap);

An example recipe is shown below. Descriptions of the parameter sets are in the next section. A full list of recipes is in the “Reference” section.

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4. Modify parameter set selection

A recipe comprises a description of the parameter sets that should be included. Once you’ve chosen a recipe you may override some of the different parameter sets. You would do this in stage 2 of the user script (see Error! Reference source not found.) with a line like

(NB: no ‘.m’ on end)

There are 5 different types of parameter sets. A full list of the available parameter sets is given in the “Reference” section.

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4. Setting your acquisition parameters

You always need to do this, to tell the system which files to analyse. The essential lines with explanation and notes are these:

Too make it easy to see the correspondence between subject and session number, I now recommend subject names are added with the aas_addsubject command:

aap=aas_addsubject(aap, ‘*CBU050011\*’,[5 11]);

The full syntax of this command is in Error! Reference source not found., but the first string specifies the raw data for this subject (you may use wildcards) and the numbers in square brackets the series numbers for the EPI data. There should be as mank of these are there are blocks, as specified in aap.acq_details.sessions and described above.

By default, MPRAGE structural and GRE_FIELDMAPPING fieldmap scans are automatically detected.

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4. Set specific parameters that you wish to differ from the defaults

Once you’ve set up the parameters using a recipe and any different parameter sets you’d like, there may still be one or two parameters that you need to change. You do this at this stage. For example, in the sample script, if my data were no longer in /mridata/cbu I would add the line:

aap.directory_conventions.rawdatadir='/imaging/rhodri/newsimultaneous';

A full list of parameters with descriptions is given in section Error! Reference source not found..

Set any SPM defaults

Any SPM defaults can be changed. You do this using lines like the following:

The aap.spm.defaults structure gets copied into the GLOBAL “defaults” variable before running SPM functions.

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Everyday tasks

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5. Getting ready to use AA

Once you’ve started a Matlab/SPM session, type

at the Matlab prompt to add the automatic analysis paths

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5. Running an analysis

Once you’ve made your user script, just type its name to run it. When it starts to run, it does some checks, and so you may see some warnings. These give you advanced notice that your script may have problems at some stage

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5. Restarting an analysis

To restart an analysis, just type the script name again. It will start from where it left off.

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5. How do I force an analysis to start all over again?

Change to the study directory and delete the “done_aamod_studyinit” flag

Example

Type at UNIX prompt. When you run an analysis again it will redo the realignment stage and all stages after it for this subject.

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5. How do I force an analysis to start from a particular stage?

To track how far through the analysis it has got, the system writes small files that start with “done_...” and end with the module name. Where a module has to be executed once per study, the done_ file will be in the study directory. Where it has to be executed once per subject, it will be in the subject directory. Where it has to be done for every session, it will be in the individual session directories. If you delete one of these flags, then this stage and all others after it will be re-run. Note it isn’t always completely obvious whether a module is once-per-subject (e.g., realignment) or once-per-session (e.g., slice timing). See the Reference section 0 to check.

Example

Type at UNIX prompt. When you run an analysis again it will redo the realignment stage and all stages after it for this subject.

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Pitfalls - beware!

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6. Check your data

Although the processing is automated, this doesn’t mean that things will never go wrong. Your data may have a problem – scanning glitches or excessive subject movement, for example – or there maybe something wrong with the analysis. Just as you would when doing it by hand, you should check your data at various stages.

Diagnostic JPEGs

One way of checking your data is to check through the images dumped in the analysed study, subject and block directories. These files begin with “diagnostic_aamod” end have a “.jpg” suffix. These are described in the table below.

Current output is all graphical, in the form of JPEG files. These may be viewed using a Windows graphics viewer, or with xv on unix. Examples are below

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6. Changing the aa_user script

Be careful about changing the aa_user script after you’ve run part of an analysis. The problem with doing this is that if the changes you make would have affected the parts that have already been completed, you won’t be able to use the script to exactly recreate your data in future.

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