diff --git a/content/Processing_Multi_Echo_Data.md b/content/Processing_Multi_Echo_Data.md index 16089ae..cc02eec 100644 --- a/content/Processing_Multi_Echo_Data.md +++ b/content/Processing_Multi_Echo_Data.md @@ -61,6 +61,70 @@ then resulting intensity gradients and the subsequent calculation of voxelwise T An aggressive temporal filter (e.g., a 0.1Hz low pass filter) or spatial smoothing could similarly distort the relationship between the echoes at each time point. +## Pipeline-specific recommendations + +When using automated pipelines like `fMRIPrep`, it is important to carefully verify the status of the output, minimally +preprocessed data to ensure that it is in the correct format for multi-echo denoising in `tedana`. + +### fMRIPrep + +When using `fMRIPrep` to preprocess ME-EPI data, we recommend the following: +- Use the `--me-output-echos` flag to ensure that each echo is preprocessed separately, then run optimal combination +using `tedana` itself. Running `tedana` on the data that has already been optimally combined in fMRIPrep results in +distortion correction, spatial normalization, and smoothing potentially being applied before `tedana`, reducing denoising +quality. +- Ensure that `tedana` is run on unsmoothed `fmriprep` outputs. This is especially important if you are using an older +version of `fmriprep` that includes ICA-AROMA, a pipeline that requires and outputs smoothed data. + +This [page from the tedana documentation](https://tedana.readthedocs.io/en/stable/faq.html#fmriprep-versions-21-0-0) +contains a script that automatically retrieves the necessary files from `fMRIPrep` outputs and runs `tedana` on them. + +### Parallel processing on high-performance computers + +When running `tedana` on multiple subjects in parallel on a high-performance computing cluster (e.g. SLURM jobs), +ensure that jobs are run in independent nodes to avoid race conditions (and errors) when writing to the same output +directory. The following is an exemplar bash script that demonstrates running `tedana` in parallel on multiple subjects +in a SLURM job array, assuming `fmriprep` has already been run: + +``` bash +#!/bin/bash +#SBATCH --time=20:00:00 +#SBATCH --account=[PUT ALLOCATION HERE] +#SBATCH --job-name=tedana-job +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=20 +#SBATCH --mem=32G +#SBATCH --output=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/outputs/tedana-job-%j-output.txt +#SBATCH --error=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/errors/tedana-job-%j-errors.txt +#SBATCH --mail-user=[YOUR-EMAIL@INSTITUTION.EDU] +#SBATCH --mail-type=END + +echo "Job execution start: $(date)" + +# Load required software packages to start up the conda environment +module load miniconda3 +source activate /arc/project/[PUT ALLOCATION HERE]/software_envs/fmri_env + +# Choose directories +# BIDS directory +BIDS_DIR=/arc/burst/[PUT ALLOCATION HERE]/fmriprep/1_preprocessing/BIDS_datasets_temp/BIDS_dataset_jul30_24 + +# FMRIPREP directory +FMRIPREP_DIR=${BIDS_DIR}/derivatives/fmriprep +CORES=20 + +# Run the tedana script in parallel on the desired directory +python /arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/tedana/fMRIprep_to_tedana.py \ + --fmriprepDir $FMRIPREP_DIR --bidsDir $BIDS_DIR --cores $CORES + +# Exit conda environment +conda deactivate + +echo "Job termination: $(date)" +``` + +Here, `fMRIprep_to_tedana.py` is the [previously mentioned fMRIPrep to tedana python script](https://tedana.readthedocs.io/en/stable/faq.html#fmriprep-versions-21-0-0). + ```{note} We are assuming that spatial normalization and distortion correction, particularly non-linear normalization methods with higher order interpolation functions, diff --git a/docs/_sources/content/Processing_Multi_Echo_Data.ipynb b/docs/_sources/content/Processing_Multi_Echo_Data.ipynb index 93dd633..18a26a3 100644 --- a/docs/_sources/content/Processing_Multi_Echo_Data.ipynb +++ b/docs/_sources/content/Processing_Multi_Echo_Data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "53c7cbf2", + "id": "10a3df7b", "metadata": {}, "source": [ "# Processing Multi-Echo Data\n", @@ -56,6 +56,70 @@ "An aggressive temporal filter (e.g., a 0.1Hz low pass filter)\n", "or spatial smoothing could similarly distort the relationship between the echoes at each time point.\n", "\n", + "## Pipeline-specific recommendations\n", + "\n", + "When using automated pipelines like `fMRIPrep`, it is important to carefully verify the status of the output, minimally \n", + "preprocessed data to ensure that it is in the correct format for multi-echo denoising in `tedana`.\n", + "\n", + "### fMRIPrep\n", + "\n", + "When using `fMRIPrep` to preprocess ME-EPI data, we recommend the following:\n", + "- Use the `--me-output-echos` flag to ensure that each echo is preprocessed separately, then run optimal combination \n", + "using `tedana` itself. Running `tedana` on the data that has already been optimally combined in fMRIPrep results in\n", + "distortion correction, spatial normalization, and smoothing potentially being applied before `tedana`, reducing denoising\n", + "quality.\n", + "- Ensure that `tedana` is run on unsmoothed `fmriprep` outputs. This is especially important if you are using an older\n", + "version of `fmriprep` that includes ICA-AROMA, a pipeline that requires and outputs smoothed data.\n", + "\n", + "This [page from the tedana documentation](https://tedana.readthedocs.io/en/stable/faq.html#fmriprep-versions-21-0-0) \n", + "contains a script that automatically retrieves the necessary files from `fMRIPrep` outputs and runs `tedana` on them.\n", + "\n", + "### Parallel processing on high-performance computers\n", + "\n", + "When running `tedana` on multiple subjects in parallel on a high-performance computing cluster (e.g. SLURM jobs), \n", + "ensure that jobs are run in independent nodes to avoid race conditions (and errors) when writing to the same output \n", + "directory. The following is an exemplar bash script that demonstrates running `tedana` in parallel on multiple subjects \n", + "in a SLURM job array, assuming `fmriprep` has already been run:\n", + "\n", + "``` bash\n", + "#!/bin/bash\n", + "#SBATCH --time=20:00:00\n", + "#SBATCH --account=[PUT ALLOCATION HERE]\n", + "#SBATCH --job-name=tedana-job\n", + "#SBATCH --nodes=1\n", + "#SBATCH --ntasks-per-node=20\n", + "#SBATCH --mem=32G\n", + "#SBATCH --output=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/outputs/tedana-job-%j-output.txt\n", + "#SBATCH --error=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/errors/tedana-job-%j-errors.txt\n", + "#SBATCH --mail-user=[YOUR-EMAIL@INSTITUTION.EDU]\n", + "#SBATCH --mail-type=END\n", + "\n", + "echo \"Job execution start: $(date)\"\n", + "\n", + "# Load required software packages to start up the conda environment\n", + "module load miniconda3\n", + "source activate /arc/project/[PUT ALLOCATION HERE]/software_envs/fmri_env\n", + "\n", + "# Choose directories\n", + "# BIDS directory\n", + "BIDS_DIR=/arc/burst/[PUT ALLOCATION HERE]/fmriprep/1_preprocessing/BIDS_datasets_temp/BIDS_dataset_jul30_24\n", + "\n", + "# FMRIPREP directory\n", + "FMRIPREP_DIR=${BIDS_DIR}/derivatives/fmriprep\n", + "CORES=20\n", + "\n", + "# Run the tedana script in parallel on the desired directory\n", + "python /arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/tedana/fMRIprep_to_tedana.py \\\n", + " --fmriprepDir $FMRIPREP_DIR --bidsDir $BIDS_DIR --cores $CORES\n", + "\n", + "# Exit conda environment\n", + "conda deactivate\n", + "\n", + "echo \"Job termination: $(date)\"\n", + "```\n", + "\n", + "Here, `fMRIprep_to_tedana.py` is the [previously mentioned fMRIPrep to tedana python script](https://tedana.readthedocs.io/en/stable/faq.html#fmriprep-versions-21-0-0).\n", + "\n", "```{note}\n", "We are assuming that spatial normalization and distortion correction,\n", "particularly non-linear normalization methods with higher order interpolation functions,\n", diff --git a/docs/_sources/content/Processing_Multi_Echo_Data.md b/docs/_sources/content/Processing_Multi_Echo_Data.md index 16089ae..cc02eec 100644 --- a/docs/_sources/content/Processing_Multi_Echo_Data.md +++ b/docs/_sources/content/Processing_Multi_Echo_Data.md @@ -61,6 +61,70 @@ then resulting intensity gradients and the subsequent calculation of voxelwise T An aggressive temporal filter (e.g., a 0.1Hz low pass filter) or spatial smoothing could similarly distort the relationship between the echoes at each time point. +## Pipeline-specific recommendations + +When using automated pipelines like `fMRIPrep`, it is important to carefully verify the status of the output, minimally +preprocessed data to ensure that it is in the correct format for multi-echo denoising in `tedana`. + +### fMRIPrep + +When using `fMRIPrep` to preprocess ME-EPI data, we recommend the following: +- Use the `--me-output-echos` flag to ensure that each echo is preprocessed separately, then run optimal combination +using `tedana` itself. Running `tedana` on the data that has already been optimally combined in fMRIPrep results in +distortion correction, spatial normalization, and smoothing potentially being applied before `tedana`, reducing denoising +quality. +- Ensure that `tedana` is run on unsmoothed `fmriprep` outputs. This is especially important if you are using an older +version of `fmriprep` that includes ICA-AROMA, a pipeline that requires and outputs smoothed data. + +This [page from the tedana documentation](https://tedana.readthedocs.io/en/stable/faq.html#fmriprep-versions-21-0-0) +contains a script that automatically retrieves the necessary files from `fMRIPrep` outputs and runs `tedana` on them. + +### Parallel processing on high-performance computers + +When running `tedana` on multiple subjects in parallel on a high-performance computing cluster (e.g. SLURM jobs), +ensure that jobs are run in independent nodes to avoid race conditions (and errors) when writing to the same output +directory. The following is an exemplar bash script that demonstrates running `tedana` in parallel on multiple subjects +in a SLURM job array, assuming `fmriprep` has already been run: + +``` bash +#!/bin/bash +#SBATCH --time=20:00:00 +#SBATCH --account=[PUT ALLOCATION HERE] +#SBATCH --job-name=tedana-job +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=20 +#SBATCH --mem=32G +#SBATCH --output=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/outputs/tedana-job-%j-output.txt +#SBATCH --error=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/errors/tedana-job-%j-errors.txt +#SBATCH --mail-user=[YOUR-EMAIL@INSTITUTION.EDU] +#SBATCH --mail-type=END + +echo "Job execution start: $(date)" + +# Load required software packages to start up the conda environment +module load miniconda3 +source activate /arc/project/[PUT ALLOCATION HERE]/software_envs/fmri_env + +# Choose directories +# BIDS directory +BIDS_DIR=/arc/burst/[PUT ALLOCATION HERE]/fmriprep/1_preprocessing/BIDS_datasets_temp/BIDS_dataset_jul30_24 + +# FMRIPREP directory +FMRIPREP_DIR=${BIDS_DIR}/derivatives/fmriprep +CORES=20 + +# Run the tedana script in parallel on the desired directory +python /arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/tedana/fMRIprep_to_tedana.py \ + --fmriprepDir $FMRIPREP_DIR --bidsDir $BIDS_DIR --cores $CORES + +# Exit conda environment +conda deactivate + +echo "Job termination: $(date)" +``` + +Here, `fMRIprep_to_tedana.py` is the [previously mentioned fMRIPrep to tedana python script](https://tedana.readthedocs.io/en/stable/faq.html#fmriprep-versions-21-0-0). + ```{note} We are assuming that spatial normalization and distortion correction, particularly non-linear normalization methods with higher order interpolation functions, diff --git a/docs/content/Processing_Multi_Echo_Data.html b/docs/content/Processing_Multi_Echo_Data.html index d249a06..a13b8e7 100644 --- a/docs/content/Processing_Multi_Echo_Data.html +++ b/docs/content/Processing_Multi_Echo_Data.html @@ -459,6 +459,11 @@

Contents

  • 1. Estimate motion correction parameters from one echo and apply those parameters to all echoes
  • 2. Perform slice timing correction and motion correction before multi-echo denoising
  • 3. Perform distortion correction, spatial normalization, smoothing, and any rescaling or filtering after denoising
    • +
  • Pipeline-specific recommendations +
    • @@ -512,6 +517,68 @@

    3. Perform distortion correction, spatial normalization, smoothing, and any then resulting intensity gradients and the subsequent calculation of voxelwise T2* values will be distorted or incorrect. An aggressive temporal filter (e.g., a 0.1Hz low pass filter) or spatial smoothing could similarly distort the relationship between the echoes at each time point.

    + +
    +

    Pipeline-specific recommendations#

    +

    When using automated pipelines like fMRIPrep, it is important to carefully verify the status of the output, minimally +preprocessed data to ensure that it is in the correct format for multi-echo denoising in tedana.

    +
    +

    fMRIPrep#

    +

    When using fMRIPrep to preprocess ME-EPI data, we recommend the following:

    +
      +

    • Use the --me-output-echos flag to ensure that each echo is preprocessed separately, then run optimal combination +using tedana itself. Running tedana on the data that has already been optimally combined in fMRIPrep results in +distortion correction, spatial normalization, and smoothing potentially being applied before tedana, reducing denoising +quality.

      • +

    • Ensure that tedana is run on unsmoothed fmriprep outputs. This is especially important if you are using an older +version of fmriprep that includes ICA-AROMA, a pipeline that requires and outputs smoothed data.

      • +
    +

    This page from the tedana documentation +contains a script that automatically retrieves the necessary files from fMRIPrep outputs and runs tedana on them.

    +
    +
    +

    Parallel processing on high-performance computers#

    +

    When running tedana on multiple subjects in parallel on a high-performance computing cluster (e.g. SLURM jobs), +ensure that jobs are run in independent nodes to avoid race conditions (and errors) when writing to the same output +directory. The following is an exemplar bash script that demonstrates running tedana in parallel on multiple subjects +in a SLURM job array, assuming fmriprep has already been run:

    +
    #!/bin/bash
+#SBATCH --time=20:00:00
+#SBATCH --account=[PUT ALLOCATION HERE]
+#SBATCH --job-name=tedana-job
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=20
+#SBATCH --mem=32G
+#SBATCH --output=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/outputs/tedana-job-%j-output.txt
+#SBATCH --error=/arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/errors/tedana-job-%j-errors.txt
+#SBATCH --mail-user=[YOUR-EMAIL@INSTITUTION.EDU]
+#SBATCH --mail-type=END
+
+echo "Job execution start: $(date)"
+
+# Load required software packages to start up the conda environment
+module load miniconda3
+source activate /arc/project/[PUT ALLOCATION HERE]/software_envs/fmri_env
+
+# Choose directories
+# BIDS directory
+BIDS_DIR=/arc/burst/[PUT ALLOCATION HERE]/fmriprep/1_preprocessing/BIDS_datasets_temp/BIDS_dataset_jul30_24
+
+# FMRIPREP directory
+FMRIPREP_DIR=${BIDS_DIR}/derivatives/fmriprep
+CORES=20
+
+# Run the tedana script in parallel on the desired directory
+python /arc/burst/[PUT ALLOCATION HERE]/fmri_env_scripts/tedana/fMRIprep_to_tedana.py \
+ --fmriprepDir $FMRIPREP_DIR --bidsDir $BIDS_DIR --cores $CORES
+
+# Exit conda environment
+conda deactivate
+
+echo "Job termination: $(date)"
+
    +
    +

    Here, fMRIprep_to_tedana.py is the previously mentioned fMRIPrep to tedana python script.

    Note

    We are assuming that spatial normalization and distortion correction, @@ -524,6 +591,7 @@

    3. Perform distortion correction, spatial normalization, smoothing, and any research to better understand how these steps can be applied before denoising.

    +