Commit c94a17f4 authored by Lars Kasper's avatar Lars Kasper

updated doc files from public

parent dd05761f
......@@ -4,9 +4,54 @@ RELEASE INFORMATION
Current Release
---------------
PhysIO_Toolbox_R2017.3
PhysIO_Toolbox_R2018.1.3
January 24, 2018
October 30, 2018
WIP Release Notes (R2018.2.0)
-----------------------------
*not released yet*
### CHANGED
- put all functions in `code` into subfolders relating to different modules: `readin`, `sync`, `preproc`, `model`, `assess`, `utils` (gitlab-issue #58)
- updated deployment `tapas_physio_init` because of that
Bugfix Release Notes (R2018.1.3)
--------------------------------
### Changed
- fixed bug for matching of Philips SCANPHYSLOG-files (Gitlab #62), if
physlogs were acquired on different days, but similar times
Bugfix Release Notes (R2018.1.2)
--------------------------------
### Changed
- fixed bug for 3D nifti array read-in in tapas_physio_create_noise_rois_regressors (github issue #24, gitlab #52)
### Added
- BioPac txt-file reader (for single file, resp/cardiac/trigger data in different columns)
Bugfix Release Notes (R2018.1.1)
-------------------------------
### Changed
- documentation.{html,pdf} export nicer with different FAQ numbering
Major Release Notes (R2018.1)
-----------------------------
### Added
- initialization function `tapas_physio_init()` to check Matlab paths, including SPM for batch processing
- Extended motion diagnostics via Framewise displacement (Power et al., 2012)
- Outlier motion models generate 'spike' regressors from FD outliers (gitlab issue #)
- Censoring of intervals with bad physiological recordings in RETROICOR regressors (github issue #11, gitlab #36)
- Added examples of Siemens VD (Tics Format, Prisma) and Human Connectome Project (HCP) format
### Changed
- Updated read-in examples of all vendors (Siemens, Philips, GE) to latest PhysIO Toolbox version.
- Updated `README.md` to reflect changes to example download, new references
- Extended Wiki documentation, in particular examples and read-in formats
Minor Release Notes (R2017.3)
-----------------------------
......
TAPAS PhysIO Toolbox
====================
*Current version: 2017.3*
*Current version: 2018.1.3*
> Copyright (C) 2012-2018 Lars Kasper <kasper@biomed.ee.ethz.ch>
> Translational Neuromodeling Unit (TNU)
> Institute for Biomedical Engineering
> University of Zurich and ETH Zurich
> Copyright (C) 2012-2018
> Lars Kasper
> <kasper@biomed.ee.ethz.ch>
>
> Translational Neuromodeling Unit (TNU)
> Institute for Biomedical Engineering
> University of Zurich and ETH Zurich
Download
......@@ -54,7 +54,7 @@ can be found at: https://doi.org/10.1016/j.jneumeth.2016.10.019
Installation
------------
### Matlab ###
### Matlab
1. Unzip the TAPAS archive in your folder of choice
2. Open Matlab
3. Go to `/your/path/to/tapas/physio/code`
......@@ -64,8 +64,7 @@ Installation
*Note*: Step (4) executes the following steps, which you could do manually as well.
- Adds the `physio/code/` folder to your Matlab path
- Adds SPM to your Matlab path (you can enter it manually, if not found)
- Links the folder `physio/code/` to `/your/path/to/SPM/toolbox/PhysIO`,
if the PhysIO code is not already there.
- Links the folder (Linux/Max) or copies the folder (Windows) `physio/code/` to `/your/path/to/SPM/toolbox/PhysIO`, if the PhysIO code is not already found there
Only the first point is necessary for using PhysIO standalone with Matlab.
The other two points enable PhysIO's SPM integration, i.e., certain functionality
......@@ -77,10 +76,13 @@ Getting Started
...following the installation, you can try out an example:
1. Download the toolbox example repository `physio-examples` from our [website](https://www.tnu.ethz.ch/en/software/tapas/data.html)
2. Run `example_main_ECG3T.m` in subdirectory `Philips/ECG3T`
1. Download the TAPAS examples via running `tapas_download_example_data()` (found in `misc`-subfolder of TAPAS)
- The PhysIO Example files will be downloaded to `tapas/examples/<tapas-version>/PhysIO`
2. Run `philips_ecg3t_matlab_script.m` in subdirectory `Philips/ECG3T`
3. See subdirectory `physio/docs` and the next two section of this document for help.
You may try any of the examples in the other vendor folders as well.
Contact/Support
---------------
......@@ -92,11 +94,11 @@ pointers and templates. Before you contact us, please try the following:
1. A first look at the [FAQ](https://gitlab.ethz.ch/physio/physio-doc/wikis/FAQ)
(which is frequently extended) might already answer your questions.
2. A lot of questions have also been discussed on our mailinglist
2. A lot of questions (before 2018) have also been discussed on our mailinglist
[tapas@sympa.ethz.ch](https://sympa.ethz.ch/sympa/info/tapas),
which has a searchable [archive](https://sympa.ethz.ch/sympa/arc/tapas).
3. For new requests, we would like to ask you to submit them as
[issues](https://github.com/translationalneuromodeling/tapas/issues) on our github release page for TAPAS.
[issues](https://github.com/translationalneuromodeling/tapas/issues) on our github release page for TAPAS, which is also an up-to-date resource to user-driven questions (since 2018).
Documentation
......@@ -161,7 +163,7 @@ Model-based correction of physiological noise:
Features of this Toolbox
------------------------
### Physiological Noise Modeling ###
### Physiological Noise Modeling
- Modeling physiological noise regressors from peripheral data
(breathing belt, ECG, pulse oximeter)
......@@ -174,13 +176,14 @@ Features of this Toolbox
- Data-driven noise regressors
- PCA extraction from nuisance ROIs (CSF, white matter), similar to aCompCor (Behzadi2007)
### Automatization and Performance Assessment ###
### Automatization and Performance Assessment
- Automatic creation of nuisance regressors, full integration into standard
GLMs, tested for SPM8/12 ("multiple_regressors.mat")
- Integration in SPM Batch Editor: GUI for parameter input, dependencies to integrate physiological noise correction in preprocessing pipeline
- Performance Assessment: Automatic F-contrast and tSNR Map creation and display for groups of physiological noise regressors, using SPM GLM tools
### Flexible Read-in ###
### Flexible Read-in
The toolbox is dedicated to seamless integration into a clinical research s
etting and therefore offers correction methods to recover physiological
......@@ -237,10 +240,16 @@ Contributors
References
----------
### Main Toolbox Reference ###
1. Kasper, L., Bollmann, S., Diaconescu, A.O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T.U., Sebold, M., Manjaly, Z.-M., Pruessmann, K.P., Stephan, K.E., 2017. The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data. Journal of Neuroscience Methods 276, 56–72. doi:10.1016/j.jneumeth.2016.10.019
### Main Toolbox Reference
1. Kasper, L., Bollmann, S., Diaconescu, A.O., Hutton, C., Heinzle, J., Iglesias,
S., Hauser, T.U., Sebold, M., Manjaly, Z.-M., Pruessmann, K.P., Stephan, K.E., 2017.
The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.
Journal of Neuroscience Methods 276, 56–72. doi:10.1016/j.jneumeth.2016.10.019
### Related Papers (Implemented noise correction algorithms and optimal parameter choices)
### Related Papers (Implemented noise correction algorithms) ###
#### RETROICOR
2. Glover, G.H., Li, T.Q. & Ress, D. Image‐based method for retrospective correction
of PhysIOlogical motion effects in fMRI: RETROICOR. Magn Reson Med 44, 162-7 (2000).
......@@ -251,23 +260,38 @@ NeuroImage 57, 101‐112 (2011).
Disentangling signal from PhysIOlogical noise. Journal of Magnetic Resonance
Imaging 28, 1337‐1344 (2008).
#### aCompCor / Noise ROIs
5. Behzadi, Y., Restom, K., Liau, J., Liu, T.T., 2007. A component based noise
correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37,
90–101. doi:10.1016/j.neuroimage.2007.04.042
#### RVT
6. Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008. The respiration response
function: The temporal dynamics of fMRI s ignal fluctuations related to changes in
respiration. NeuroImage 40, 644–654. doi:10.1016/j.neuroimage.2007.11.059
7. Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the
7. Jo, H.J., Saad, Z.S., Simmons, W.K., Milbury, L.A., Cox, R.W., 2010.
Mapping sources of correlation in resting state FMRI, with artifact detection
and removal. NeuroImage 52, 571–582. https://doi.org/10.1016/j.neuroimage.2010.04.246
*regressor delay suggestions*
#### HRV
8. Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the
BOLD signal: The cardiac response function. NeuroImage 44, 857–869.
doi:10.1016/j.neuroimage.2008.09.029
8. Siegel, J.S., Power, J.D., Dubis, J.W., Vogel, A.C., Church, J.A., Schlaggar, B.L.,
9. Shmueli, K., van Gelderen, P., de Zwart, J.A., Horovitz, S.G., Fukunaga, M.,
Jansma, J.M., Duyn, J.H., 2007. Low-frequency fluctuations in the cardiac rate
as a source of variance in the resting-state fMRI BOLD signal.
NeuroImage 38, 306–320. https://doi.org/10.1016/j.neuroimage.2007.07.037
*regressor delay suggestions*
#### Motion (Censoring, Framewise Displacement)
10. Siegel, J.S., Power, J.D., Dubis, J.W., Vogel, A.C., Church, J.A., Schlaggar, B.L.,
Petersen, S.E., 2014. Statistical improvements in functional magnetic resonance
imaging analyses produced by censoring high-motion data points. Hum. Brain Mapp.
35, 1981–1996. doi:10.1002/hbm.22307
11. Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Copying/License
---------------
......
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