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*.so
*.mexw64
*.asv
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19 changes: 19 additions & 0 deletions CHANGELOG.md
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# Changelog
TAPAS toolbox

## [3.2.0] 2019-09-dd
### Added
- HUGE: introduced object-oriented interface in addition to old interface
- HUGE: build-in unit tests
- HUGE: user manual
- PhysIO (details in tapas/PhysIO/CHANGELOG.md)
- more unit testing and integration testing for examples
- bandpass-filtering for cardiac data in preprocessing, user-defined max
heart rate for peak detection

### Fixed
- PhysIO: Bugfix RVT/HRV convolution had erroneous half-width shift
- For details on the bug, its impact and fix, see our specific [Release
Info on RVT/HRV Bugfix](https://github.com/translationalneuromodeling/tapas/issues/65)

### Changed
- HUGE: demo script reflect interface changes


## [3.1.0] 2019-03-26

### Added
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39 changes: 37 additions & 2 deletions PhysIO/CHANGELOG.md
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Expand Up @@ -4,11 +4,46 @@ RELEASE INFORMATION
Current Release
---------------

*Current version: PhysIO Toolbox Release R2019a, v7.1.0*
*Current version: PhysIO Toolbox Release R2019b, v7.2.0*

March 19, 2019
August 20, 2019


Minor Release Notes (R2019b, v7.2.0)
------------------------------------

### Added
- `requirements.txt` making dependencies on Matlab and specific toolboxes
explicit
- `max_heart_rate_bpm` now a user parameter to adjust prior on max allowed
heart rate for cardiac pulse detection (`method = 'auto_matched'`)
- bandpass-filtering of cardiac data during preprocessing now possible
(`preproc.cardiac.filter`)
- Added integration tests for all examples in `tests/integration` for both SPM
Batch Editor and Matlab-only configuration scripts. Reference data provided in
`examples/TestReferenceResults/examples`

### Changed
- Toned down and replaced irrelevant peak height and missing cardiac pulse
warnings (github issue #51)
- Updated README to include external contributors and recent findings about
impact of physiological noise for serial correlations (Bollmann2018)
- Added unit tests for convolution and moved all to `tests/unit`

### Fixed
- Corrected half-width shift of response functions for HRV and RVT regressors by
erroneous use of Matlab `conv`
- For details on the bug, its impact and fix, see our specific [Release
Info on RVT/HRV Bugfix](https://github.com/translationalneuromodeling/tapas/issues/65)
- other references: TNU gitlab issue #83, found and fixed by Sam Harrison,
TNU, see `tapas_physio_conv`)
- Bugfix `tapas_physio_init()` not working, because dependent on functions
in `utils` subfolder not in path; `utils` added to path
- `tapas_physio_review` for motion parameters (found and fixed by
Sam Harrison, TNU)
- visualization error for regressor orthogonalization (github issue #57),
when only `'RETROICOR'` set was chosen

Minor Release Notes (R2019a, v7.1.0)
------------------------------------

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135 changes: 105 additions & 30 deletions PhysIO/README.md
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TAPAS PhysIO Toolbox
====================

*Current version: Release 2019a, v7.1.0*
*Current version: Release 2019b, v7.2.0*

> Copyright (C) 2012-2019
> Lars Kasper
Expand Down Expand Up @@ -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.
Expand All @@ -98,7 +99,9 @@ pointers and templates. Before you contact us, please try the following:
[[email protected]](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
Expand Down Expand Up @@ -144,8 +147,14 @@ Facts about physiological noise in fMRI:
become a particular concern at and above 3 Tesla (Kasper2009, Hutton2011).
- In resting state fMRI, disregarding physiological noise leads to wrong
connectivity results (Birn2006).
- Uncorrected physiological noise introduces serial correlations into the residual
voxel time series, that invalidate assumptions on noise correlations (e.g., AR(1))
used in data prewhitening by all major analysis packages. This issue is particularly
aggravated at short TR (<1s), and most of its effects can be suitably addressed
by physiological noise correction (Bollmann2018)

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
Expand Down Expand Up @@ -174,30 +183,39 @@ 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
Expand All @@ -217,7 +235,9 @@ 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
Expand All @@ -229,68 +249,123 @@ 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
- Sam Harrison, TNU Zurich
- Jakob Heinzle, TNU Zurich
- Chloe Hutton, FIL London, UK (previously)
- Miriam Sebold, Charite Berlin, Germany
- External 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).


Acknowledgements
----------------

The PhysIO Toolbox ships with the following publicly available code from other
open source projects and gratefully acknowledges their use.

- `utils\tapas_physio_propval.m`
- `propval` function from Princeton MVPA toolbox (GPL)
a nice wrapper function to create flexible propertyName/value optional
parameters
- `utils\tapas_physio_fieldnamesr.m`
- recursive parser for field names of a structure
- Matlab file exchange, [email protected]


References
----------

### Main Toolbox Reference

Please cite the following paper in all of your publications that utilized the
PhysIO Toolbox.

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

The [FAQ](https://gitlab.ethz.ch/physio/physio-doc/wikis/FAQ#3-how-do-i-cite-physio)
contains a complete suggestion for a snippet in your methods section.

### Related Papers (Implemented noise correction algorithms and optimal parameter choices)

The following sections list papers that
- first implemented specific noise correction algorithms
- determined optimal parameter choices for these algorithms, depending on the
targeted application
- demonstrate the impact of physiological noise and the importance of its correction

It is loosely ordered by the dominant physiological noise model used in the
paper. The list is by no means complete, and we are happy to add any relevant papers
suggested to us.

#### 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).

3. Hutton, C. et al. The impact of PhysIOlogical noise correction on fMRI at 7 T.
3. Hutton, C. et al. The impact of Physiological noise correction on fMRI at 7 T.
NeuroImage 57, 101‐112 (2011).

4. Harvey, A.K. et al. Brainstem functional magnetic resonance imaging:
Disentangling signal from PhysIOlogical noise. Journal of Magnetic Resonance
Imaging 28, 1337‐1344 (2008).

5. Bollmann, S., Puckett, A.M., Cunnington, R., Barth, M., 2018.
Serial correlations in single-subject fMRI with sub-second TR.
NeuroImage 166, 152–166. https://doi.org/10.1016/j.neuroimage.2017.10.043


#### aCompCor / Noise ROIs
5. Behzadi, Y., Restom, K., Liau, J., Liu, T.T., 2007. A component based noise
6. 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
7. 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. Jo, H.J., Saad, Z.S., Simmons, W.K., Milbury, L.A., Cox, R.W., 2010.
8. 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*
- *regressor delay suggestions*

#### HRV
8. Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the
9. 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
9. Shmueli, K., van Gelderen, P., de Zwart, J.A., Horovitz, S.G., Fukunaga, M.,
10. 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*
- *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.,
11. 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
12. 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
Expand All @@ -308,4 +383,4 @@ General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program (see the file [LICENSE](LICENSE)). If not, see
<http://www.gnu.org/licenses/>.
<http://www.gnu.org/licenses/>.
13 changes: 7 additions & 6 deletions PhysIO/code/assess/tapas_physio_review.m
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hasRespData);
end


if model.movement.include
censoring = model.movement.censoring;
rp = model.movement.rp;
quality_measures = model.movement.quality_measures;
censoring = model.movement.censoring;
censoring_threshold = model.movement.censoring_threshold;
switch lower(model.movement.censoring_method)
case 'fd'
verbose.fig_handles(end+1) = tapas_physio_plot_movement_outliers_fd(rp, ...
quality_measures, censoring, movement.censoring_threshold);
verbose.fig_handles(end+1) = tapas_physio_plot_movement_outliers_fd( ...
rp, quality_measures, censoring, censoring_threshold);
case 'maxval'
verbose.fig_handles(end+1) = tapas_physio_plot_movement_outliers_maxval(rp, ...
quality_measures, censoring, movement.censoring_threshold);
verbose.fig_handles(end+1) = tapas_physio_plot_movement_outliers_maxval( ...
rp, quality_measures, censoring, censoring_threshold);
end
end

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