Commit 30ac0f93 authored by Lars Kasper's avatar Lars Kasper

updated doc files from public

parent 67b8189f
......@@ -13,6 +13,7 @@ Unreleased
### Changed
- Toned down and replaced irrelevant peak height and missing cardiac pulse warnings (github issue #51)
- Added `requirements.txt`
Minor Release Notes (R2019a, v7.1.0)
------------------------------------
......
......@@ -76,7 +76,8 @@ Getting Started
...following the installation, you can try out an example:
1. Download the TAPAS examples via running `tapas_download_example_data()` (found in `misc`-subfolder of TAPAS)
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.
......@@ -98,7 +99,9 @@ pointers and templates. Before you contact us, please try the following:
[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, which is also an up-to-date resource to user-driven questions (since 2018).
[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
......@@ -145,7 +148,8 @@ Facts about physiological noise in fMRI:
- In resting state fMRI, disregarding physiological noise leads to wrong
connectivity results (Birn2006).
Therefore, some kind of physiological noise correction is highly recommended for every statistical fMRI analysis.
Therefore, some kind of physiological noise correction is highly recommended for
every statistical fMRI analysis.
Model-based correction of physiological noise:
- Physiological noise can be decomposed into periodic time series following
......@@ -174,30 +178,39 @@ Features of this Toolbox
- Flexible expansion orders to model different contributions of cardiac,
respiratory and interaction terms (see Harvey2008, Hutton2011)
- Data-driven noise regressors
- PCA extraction from nuisance ROIs (CSF, white matter), similar to aCompCor (Behzadi2007)
- PCA extraction from nuisance ROIs (CSF, white matter), similar to aCompCor
(Behzadi2007)
### 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
- 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 via
`tapas_physio_report_contrasts()`.
### Flexible Read-in
The toolbox is dedicated to seamless integration into a clinical research s
etting and therefore offers correction methods to recover physiological
data from imperfect peripheral measures.
The toolbox is dedicated to seamless integration into a clinical research
setting and therefore offers correction methods to recover physiological
data from imperfect peripheral measures. Read-in of the following formats is
currently supported (alphabetic order):
- Biopac `.mat` and `.txt` -export
- Brain Imaging Data Structure (BIDS)
- Custom logfiles: should contain one amplitude value per line, one logfile per
device. Sampling interval(s) are provided as a separate parameter to the toolbox.
- General Electric
- Philips SCANPHYSLOG files (all versions from release 2.6 to 5.3)
- Siemens VB (files `.ecg`, `.resp`, `.puls`)
- Siemens VD (files `*_ECG.log`, `*_RESP.log`, `*_PULS.log`)
- Siemens Human Connectome Project (preprocessed files `*Physio_log.txt`)
- Biopac .mat-export
- assuming the following variables (as columns): `data`, `isi`, `isi_units`, `labels`, `start_sample`, `units`
- See `tapas_physio_read_physlogfiles_biopac_mat.m` for details
- Custom logfiles: should contain one amplitude value per line, one logfile per device. Sampling interval(s) are provided as a separate parameter to the toolbox.
See also the
[Wiki page on Read-In](https://gitlab.ethz.ch/physio/physio-doc/wikis/MANUAL_PART_READIN)
for a more detailed list and description of the supported formats.
Compatibility
......@@ -217,7 +230,9 @@ Compatibility
or as text file for export to any other package
- raw and processed physiological logfile data
- Graphical Batch Editor interface to SPM
- Part of the TAPAS Software Collection of the Translational Neuromodeling Unit (TNU) Zurich:long term support and ongoing development
- Part of the TAPAS Software Collection of the Translational Neuromodeling Unit
(TNU) Zurich
- ensures long term support and ongoing development
Contributors
......@@ -229,12 +244,26 @@ Contributors
- Project Team:
- Steffen Bollmann, Centre for Advanced Imaging, University of Queensland, Australia
- Saskia Bollmann, Centre for Advanced Imaging, University of Queensland, Australia
- Contributors:
- Contributors (Code):
- Eduardo Aponte, TNU Zurich
- Tobias U. Hauser, FIL London, UK
- Jakob Heinzle, TNU Zurich
- Chloe Hutton, FIL London, UK (previously)
- Miriam Sebold, Charite Berlin, Germany
- TAPAS contributors listed in its [Contributor License Agreement](https://github.com/translationalneuromodeling/tapas/blob/master/Contributor-License-Agreement.md)
- Contributors (Examples):
- listed in [EXAMPLES.md](https://gitlab.ethz.ch/physio/physio-doc/wikis/EXAMPLES)
Requirements
------------
- All specific software requirements and their versions are in a separate file
in this folder, `requirements.txt`.
- In brief:
- PhysIO needs Matlab to run, and some of its toolboxes.
- Some functionality requires SPM (GUI, nuisance regression, contrast reporting,
writing residual and SNR images).
References
......@@ -245,7 +274,7 @@ References
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
Journal of Neuroscience Methods 276, 56–72. https://doi.org/10.1016/j.jneumeth.2016.10.019
### Related Papers (Implemented noise correction algorithms and optimal parameter choices)
......@@ -263,7 +292,7 @@ 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
90–101. https://doi.org/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
......@@ -288,9 +317,12 @@ NeuroImage 38, 306–320. https://doi.org/10.1016/j.neuroimage.2007.07.037
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
35, 1981–1996. https://doi.org/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
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
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