This repository contains the code for Big Linear Models for Neuroimaging cluster and local usage.

To use the BLM-py code, fsl 5.0.10 or greater must be installed and fslpython must be configured correctly. Alternatively the following python packages must be installed:

numpy>=1.14.0
nibabel>=2.2.1
yaml
pandas
subprocess

(This code may work with older versions of numpy and nibabel but caution is advised as these versions have not been tested).

If running BLM-py on a cluster, fsl_sub must also be configured correctly.

To run BLM-py first specify your design using blm_config.yml and then run the job in parallel by following the below guidelines.

The regression model for BLM must be specified in blm_config.yml. Below is a complete list of possible inputs to this file.

The following fields are mandatory:

• Y_files: Text file containing a list of response variable images in NIFTI format.

• X: CSV file of the design matrix (no column header, no ID row).

• outdir: Path to the output directory.

• contrasts: Contrast vectors to be tested. They should be listed as c1,c2,... etc and each contrast should contain the fields:

  • name: A name for the contrast. i.e. AwesomelyNamedContrast1.
  • vector: A vector for the contrast. This contrast must be one dimensional for a T test and two dimensional for an F test. For example; [1, 0, 0] (T contrast) or [[1, 0, 0],[0,1,0]] (F contrast).

  At least one contrast must be given, see Examples for an example of how to specify contrasts.

The following fields are optional:

• MAXMEM: This is the maximum amount of memory (in bits) that the BLM code is allowed to work with. How this should be set depends on your machine capabilities; the default value however matches the SPM default of 2^32 (note this must be in python notation i.e. 2**32).
• data_mask_files: A text file containing a list of masks to be applied to the Y_files.
  • The number of masks must be equal to the number of Y_files as each mask is applied to the corresponding entry Y_files. E.g. The first mask listed for data_mask_files will be applied to the first nifti in Y_files, the second mask in data_mask_files will be applied to the second nifti in Y_files and so on.
• Missingness: This field allows the user to mask the image based on how many studies had recorded values for each voxel. This can be specified in 3 ways.
  • MinPercent: The percentage of studies present at a voxel necessary for that voxel to be included in the final analysis mask. For example, if this is set to 0.1 then any voxel with recorded values for at least 10% of studies will be kept in the analysis.
  • MinN: The number of studies present at a voxel necessary for that voxel to be included in the final analysis mask. For example, if this is set to 20 then any voxel with recorded values for at least 20 studies will be kept in the analysis.
• analysis_mask: A mask to be applied during analysis.
• OutputCovB: If set to True this will output between beta covariance maps. For studies with a large number of paramters this may not be desirable as, for example, 30 analysis paramters will create 30x30=900 between beta covariance maps. By default this is set to True.
• data_mask_thresh: Any voxel with value below this threshold will be treated as missing data. (By default, no such thresholding is done, i.e. data_mask_thresh is essentially -infinity).
• minlog: Any -inf values in the -log10(p) maps will be converted to the value of minlog. Currently, a default value of -323.3062153431158 is used as this is the most negative value which was seen during testing before -inf was encountered (see this thread for more details).

Below are some example blm_config.yml files.

Example 1: A minimal configuration.

Y_files: /path/to/data/Y.txt
X: /path/to/data/X.csv
outdir: /path/to/output/directory/
contrasts:
  - c1:
      name: Tcontrast1
      vector: [1, 0, 1, 0, 1]

Example 2: A configuration with multiple optional fields.

MAXMEM: 2**32
Y_files: /path/to/data/Y.txt
data_mask_files: /path/to/data/M_.txt
data_mask_thresh: 0.1
X: /path/to/data/X.csv
outdir: /path/to/output/directory/
contrasts:
  - c1:
      name: Tcontrast1
      vector: [1, 0, 0]
  - c2:
      name: Tcontrast2
      vector: [0, 1, 0]
  - c3:
      name: Tcontrast3
      vector: [0, 0, 1]
  - c4:
      name: Fcontrast1
      vector: [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
Missingness:
  MinPercent: 0.10
  MinN: 15
analysis_mask: /path/to/data/MNI152_T1_2mm_brain_mask.nii.gz

An analysis can either be run in parallel on a computing cluster using BLM.

To run an analysis in parallel, log into the cluster you wish to run it on and ensure that fsl and fsl_sub are in the environment. On the rescomp cluster this can be done like so:

module add fsl
module add fsl_sub

Ensure you are in the BLM-py directory and once you are happy with the analysis you have specified in blm_config.yml, run the following command:

bash ./blm_cluster.sh

After running this you will see text printed to the commandline telling you the analysis is being set up and the jobs are being submitted. For large analyses or small cluster this may take a minute or two as several jobs may be submitted to the cluster. Once you can access the command line again, you can use qstat to see the jobs which have been submitted. You will typically see jobs of the following form:

• setup: This will be working out the number of batches/blocks the analysis needs to be split into.
• batch*: There may be several jobs with names of this format. These are the "chunks" the analysis has been split into. These are run in parallel to one another and typically don't take very long.
• results: This code is combining the output of each batch to obtain statistical analyses. This will run once all batch* jobs have been completed. Please note this code has been streamlined for large numbers of subjects but not large number of parameters; therefore this job may take some time for large numbers of parameters.

Below is a full list of NIFTI files output after a BLM analysis.

The maps are given the same ordering as the inputs. For example, in blm_vox_con, the 0th volume corresponds to the 1st contrast, the 1st volume corresponds to the 2nd contrast and so on. For covariances, the ordering is of a similar form with covariance between beta 1 and beta 1 (variance of beta 1) being the 0th volume, covariance between beta 1 and beta 2 being the 1st volume and so on. In addition, a copy of the design is saved in the output directory as inputs.yml. It is recommended that this be kept for data provenance purposes.

Note: All the below tests require access to test data. To ask access, please ask @TomMaullin directly.

To generate test cases:

bash ./generate_test_cases.sh $outdir $datadir

(Where $datadir is a data directory containg all data needed for analyses test_cfg01.yml, test_cfg02.yml,... test_cfg10.yml and $outdir is the desired output directory)

To check the logs:

bash ./check_logs.sh

To verify the test cases against ground truth:

bash ./verify_test_cases.sh $GTDIR

(Where $GTDIR is a directory containing ground truth data from a previous run, i.e. inside $GTDIR are the folders test_cfg01.yml, test_cfg02.yml, ... etc).

To generate test cases:

bash ./generate_test_cases_fsl.sh $outdir $datadir

(Where $datadir is a data directory containg all data needed for designs fsltest_cfg01.yml, fsltest_cfg02.yml and fsltest_cfg03.yml and $outdir is the desired output directory)

To check the logs:

bash ./check_logs.sh

To verify the test cases against ground truth:

bash ./verify_test_cases_against_fsl.sh