-
+
+
Home
+Wiki Main Page
+Several documentation files are provided with this toolbox, and are linked in this wiki. They have the extension .md (markdown), i.e. are plain text files, but can be conveniently viewed online as the github/gitlab Wiki.
+You can find them in physio/docs.
List of Documentation files:
+-
+
- [x] HOME: This page. Landing Page of Wiki. Navigation to all other files and this explanation +
- [x] FAQ: Frequently asked questions (for users) +
- [x] QUICKSTART: Example script and how to use on test data, Intro to Batch Editor GUI +
- [x] EXAMPLES: List and explanation of all examples +
- [ ] MANUAL: Reference Manual (mostly for developers) listing all functions, and rationales of the toolbox, disecting its modular structure +
- [x] README: from main physio-public repository, similar info to this page +
- [x] CHANGELOG: List of all toolbox versions and the respective release notes, i.e. major changes in functionality, bugfixes etc. +
Quickstart
+Quickstart Manual
+Purpose
+This page provides simple walk-throughs of the SPM Batch Editor GUI, the scripts to run the main examples, and the most common output plots of the PhysIO Toolbox.
+One-Page-Quickstart (with SPM)
+...of SPM Batch Editor GUI for PhysIO Toolbox
+-
+
- Copy the PhysIO Toolbox
physio/codefolder tospm/toolbox-
+
- (Optional) Rename the folder to something meaningful, e.g. PhysIO (see Figure 1). +
+ - (Re-)Start SPM (spm fmri) and the Batch editor. +
- The PhysIO Toolbox should now occur under SPM -> Tools -> TAPAS PhysIO Toolbox +
- Change directory (!) to
examples/Philips/ECG3T-folder and load an example spm_job file into the batch editor, e.g :example_spm_job_ECG3T.m
+ - Press Play! +
Readme
+TAPAS PhysIO Toolbox Version 2017
+++Copyright (C) 2012-2017 Lars Kasper kasper@biomed.ee.ethz.ch
+Translational Neuromodeling Unit (TNU)
+Institute for Biomedical Engineering
+University of Zurich and ETH Zurich
+
Copying
+The PhysIO Toolbox is free software: you can redistribute it and/or +modify it under the terms of the GNU General Public License as +published by the Free Software Foundation, either version 3 of the +License, or (at your option) any later version.
+This program is distributed in the hope that it will be useful, but +WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +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 COPYING). If not, see +http://www.gnu.org/licenses/.
+Purpose
+The PhysIO Toolbox provides model-based physiological noise correction of +fMRI data using peripheral measures of respiration and cardiac pulsation. +It incorporates noise models of cardiac/respiratory phase (RETROICOR, +Glover et al. 2000), as well as heart rate variability and respiratory +volume per time (cardiac response function, Chang et. al, 2009, respiratory +response function, Birn et al. 2006). The toolbox is usable via the SPM +batch editor, performs automatic pre-processing of noisy peripheral data +and outputs nuisance regressor files directly suitable for SPM +(multiple_regressors.txt).
+Installation
+Matlab
+-
+
- Unzip the TAPAS archive +
- Add tapas/physio/code to your matlab path +
SPM
+-
+
- Certain functionality (Batch Editor GUI, pipeline dependencies, model assessment via F-contrasts) require the installation of SPM +
- Afterwards, the PhysIO Toolbox has to be registered as an SPM toolbox by copying the
physio/codefolder tospm/toolbox/physio
+
Getting Started
+Run example_main_ECG3T.m in subdirectory Philips/ECG3T of the toolbox example repository physio-examples.
+See subdirectory physio/docs and next section of this document.
Getting Help/Documentation
+Several documentation files are provided with this toolbox. They have the extension .md (markdown), i.e. are plain text files, but can be conveniently viewed online as the github/gitlab Wiki.
+You can find them in physio/wikidocs.
Alternatively, a pdf converted version of the following files is found in physio/docs/documentation.pdf
List of Documentation files:
+-
+
- README.md: this file, purpose, installation, getting started, pointer to more help +
- FAQ.md: Frequently asked questions (for users) +
- QUICKSTART.md: Example script and how to use on test data, Intro to Batch Editor GUI +
- MANUAL.md: Reference Manual (mostly for developers) listing all functions, and rationales of the toolbox, disecting its modular structure +
- WIKIMAIN.md: Landing Page of Wiki. Navigation to all other files and this explanation +
- EXAMPLES.md: List and explanation of all examples +
- CHANGELOG.md: List of all toolbox versions and the respective release notes, i.e. major changes in functionality, bugfixes etc. +
Background
+The PhysIO Toolbox provides physiological noise correction for fMRI-data +from peripheral measures (ECG/pulse oximetry, breathing belt). It is +model-based, i.e. creates nuisance regressors from the physiological +monitoring that can enter a General Linear Model (GLM) analysis, e.g. +SPM8/12. Furthermore, for scanner vendor logfiles (PHILIPS, GE, Siemens), +it provides means to statistically assess peripheral data (e.g. heart rate variability) +and recover imperfect measures (e.g. distorted R-peaks of the ECG).
+Facts about physiological noise in fMRI:
+-
+
- Physiological noise can explain 20-60 % of variance in fMRI voxel time
+series (Birn2006, Hutton2011, Harvey2008)
-
+
- Physiological noise affects a lot of brain regions (s. figure, e.g. +brainstem or OFC), especially next to CSF, arteries (Hutton2011). +
- If not accounted for, this is a key factor limiting sensitivity for effects of interest. +
+ - Physiological noise contributions increase with field strength; they +become a particular concern at and above 3 Tesla (Kasper2009, Hutton2011). +
- 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.
+Model-based correction of physiological noise:
+-
+
- Physiological noise can be decomposed into periodic time series following +heart rate and breathing cycle. +
- The Fourier expansion of cardiac and respiratory phases was introduced as +RETROICOR (RETROspective Image CORrection, Glover2000, +see also Josephs1997). +
- These Fourier Terms can enter a General Linear Model (GLM) as nuisance +regressors, analogous to movement parameters. +
- As the physiological noise regressors augment the GLM and explain +variance in the time series, they increase sensitivity in all contrasts +of interest. +
Features of this Toolbox
+Physiological Noise Modeling
+-
+
- Modeling physiological noise regressors from peripheral data
+(breathing belt, ECG, pulse oximeter)
-
+
- State of the art RETROICOR cardiac and respiratory phase expansion +
- Cardiac response function (Chang et al, 2009) and respiratory response +function (Birn et al. 2006) modelling of heart-rate variability and +respiratory volume per time influence on physiological noise +
- 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) +
+
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
+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.
+-
+
- 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)
+ - Biopac .mat-export
-
+
- assuming the following variables (as columns):
data,isi,isi_units,labels,start_sample,units
+ - See
tapas_physio_read_physlogfiles_biopac_mat.mfor details
+
+ - assuming the following variables (as columns):
- 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. +
Compatibility and Support
+-
+
- Matlab Toolbox +
- Input:
-
+
- Fully integrated to work with physiological logfiles for Philips MR systems (SCANPHYSLOG) +
- tested for General Electric (GE) log-files +
- implementation for Siemens log-files +
- also: interface for 'Custom', i.e. general heart-beat time stamps +& breathing volume time courses from other log formats +
+ - Output:
-
+
- Nuisance regressors for mass-univariate statistical analysis with SPM5,8,12 +or as text file for export to any other package +
- raw and processed physiological logfile data +
+ - Part of the TAPAS Software Collection of the Translational Neuromodeling Unit (TNU) Zurich:long term support and ongoing development +
Contributors
+-
+
- Lead Programmer:
-
+
- Lars Kasper, TNU & MR-Technology Group, IBT, University of Zurich & ETH Zurich +
+ - Project Team:
-
+
- Steffen Bollmann, Centre for Advanced Imaging, University of Queensland, Australia +
- Saskia Bollmann, Centre for Advanced Imaging, University of Queensland, Australia +
+ - Contributors:
-
+
- 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 +
+
Contact
+Send bug reports and suggestions either to +1) our mailing list: tapas@sympa.ethz.ch, or +2) as an issue on our TAPAS github account : https://github.com/translationalneuromodeling/tapas/issues
+References
+Main Toolbox Reference
+-
+
- 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)
+-
+
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).
+
+Hutton, C. et al. The impact of PhysIOlogical noise correction on fMRI at 7 T. +NeuroImage 57, 101‐112 (2011).
+
+Harvey, A.K. et al. Brainstem functional magnetic resonance imaging: +Disentangling signal from PhysIOlogical noise. Journal of Magnetic Resonance +Imaging 28, 1337‐1344 (2008).
+
+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
+
+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
+
+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
+
+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
+
+
FAQ
+Frequently Asked Questions (FAQ)
+1. What is the PhysIO Toolbox?
+PhysIO is a toolbox for model-based physiological noise correction of fMRI data.
+PhysIO stands for Physiological Input/Output toolbox, which summarizes its core purpose. A quote from our paper:
+++In short, the toolbox transforms physiological input, i.e. peripheral recordings, into physiological output, i.e. regressors encoding components of physiological noise [...] A modular Matlab implementation supports command-line operation and is compatible with all major fMRI analysis packages via the export of regressor text-files. For the Statistical Parametric Mapping SPM software package in particular, PhysIO features a full integration as a Batch Editor Tool, which allows user-friendly, GUI-based setup and inclusion into existing preprocessing and modeling pipelines.
+
2. How does PhysIO differ from other toolboxes for physiological noise correction for fMRI using peripheral recordings?
+Citing from the introduction of our paper again
+>
+++Highlights
++
+- A Toolbox to integrate preprocessing of physiological data and fMRI noise modeling.
+- Robust preprocessing via iterative peak detection, shown for noisy data and patients.
+- Flexible support of peripheral data formats and noise models (RETROICOR, RVHRCOR).
+- Fully automated noise correction and performance assessment for group studies.
+- Integration in fMRI pre-processing pipelines as SPM Toolbox (Batch Editor GUI).
+
3. How do I cite PhysIO?
+The core reference for PhysIO is: The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data (http://dx.doi.org/10.1016/j.jneumeth.2016.10.019)
+Please cite this paper if you use PhysIO in your work. Moreover, this paper is also a good source for more information on PhysIO (see next question).
+A standard snippet to include in your method section could look like the following, assuming you use our specific implementation of RETROICOR, which uses Fourier expansions of different order for the estimated phases of cardiac pulsation (3rd order), respiration (4th order) and cardio-‐respiratory interactions (1st order) following (Harvey et al., 2008)
+++Correction for physiological noise was performed via RETROICOR [1,2] using Fourier +expansions of different order for the estimated phases of cardiac pulsation (3rd order), +respiration (4th order) and cardio-‐respiratory interactions (1st order) [2]: The +corresponding confound regressors were created using the Matlab PhysIO Toolbox ([4], +open source code available as part of the TAPAS software collection: +https://www.translationalneuromodeling.org/tapas).
+
-
+
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).
+
+Hutton, C. et al. The impact of PhysIOlogical noise correction on fMRI at 7 T. +NeuroImage 57, 101-‐112 (2011).
+
+Harvey, A.K. et al. Brainstem functional magnetic resonance imaging: +Disentangling signal from PhysIOlogical noise. Journal of Magnetic Resonance +Imaging 28, 1337-‐1344 (2008).
+
+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
+
+
If you use respiratory‐volume-per time (RVT), heart-‐rate +variability (HRV), noise ROIs or 12/24 regressor motion modeling, also include the +respective references:
+-
+
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
+
+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 +PhysIO Toolbox | Citing this work 20
+
+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
+
+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
+
+
4. Where do I find more documentation for PhysIO?
+-
+
- The paper describing its structure, objective and modules +
- README.md in the main folder when downloading
-
+
- For help on installation and getting started +
+ - Quickstart
-
+
- PDF (or markdown .md file) +
- Tutorial matlab-scripts +
+ - Reference Manual (for developers) +
5. I am using FSL, AFNI, BrainVoyager, etc., for my fMRI analyses. Do I need SPM for PhysIO to work?
+No, the basic functionality of PhysIO, i.e. creating nuisance regressors for your GLM analysis, is available in plain Matlab. The following extra functionality related to automatizing and assessing noise correction, require the installation of SPM:
+-
+
- GUI (SPM Batch Editor) +
- Pipeline dependencies (automatic input of realignment parameters, feed-in of multiple regressors file to GLM) +
- Model assessment via F-tests and automatic F-map/tSNR report +
- Noise-ROIs model (read-in of nifti files via SPM) +
6. I am using device X for physiological recordings. Does PhysIO support the physiological logfile format Y?
+Currently, PhysIO natively supports the following physiological logfile types:
+-
+
- 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)
+ - Biopac .mat-export
-
+
- assuming the following variables (as columns):
data,isi,isi_units,labels,start_sample,units
+ - See
tapas_physio_read_physlogfiles_biopac_mat.mfor details
+
+ - assuming the following variables (as columns):
Furthermore, physiological recordings can be entered via a custom data format, i.e., providing one text file per device. The files should contain one amplitude value per line. The corresponding sampling interval(s) are provided as a separate parameter in the toolbox.
+If your favourite logfile format is not supported, please contact the developers. We try everything to accomodate the read-in flexibility of the toolbox to your needs.
+7. I am running the toolbox for a lot of subjects / on a remote server without graphics. Can I somehow reproduce the output figures relevant to assess the data quality?
+Yes you can, using the toolbox function tapas_physio_review. This function takes the physio-structure as an input argument, which is per default saved as physio.mat in the specified output folder of your batch job.
8. How do I interpret the various output plots of the toolbox?
+Have a look at our publication: The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data (http://dx.doi.org/10.1016/j.jneumeth.2016.10.019)
+The figures there give a good overview of the toolbox output figures.
+9. I want to access subject's physiological measures, e.g. heart rate or respiratory volume (per time), before they enter the regressors. Where can I do that?
+All intermediate data processing steps (e.g. filtering, cropping) of the peripheral data, including the computation of physiologically meaningful time courses, such as heart rate and respiratory volume, are saved in the substructure ons_secs ("onsets in seconds) of the physio-structure mentioned in question 7. This structure is typically saved in a file physio.mat.
physio.ons_secs then contains the different time courses, cropped to the acquisition window synchronized to your fMRI scan (the same values before synchronization/cropping, is found in physio.ons_secs.raw). Here are the most important ones:
-
+
ons_secs.t= []; % time vector corresponding to c and r
+ons_secs.c= []; % raw cardiac waveform (ECG or PPU)
+ons_secs.r= []; % raw respiration amplitude time course
+ons_secs.cpulse= []; % onset times of cardiac pulse events (e.g. R-peaks)
+ons_secs.fr= []; % filtered respiration amplitude time series
+ons_secs.c_sample_phase= []; % phase in heart-cycle when each slice of each volume was acquired
+ons_secs.r_sample_phase= []; % phase in respiratory cycle when each slice of each volume was acquired
+ons_secs.hr= []; % [nScans,1] estimated heart rate at each scan
+ons_secs.rvt= []; % [nScans,1] estimated respiratory volume per time at each scan
+ons_secs.c_outliers_high= []; % onset of too long heart beats
+ons_secs.c_outliers_low= []; % onsets of too short heart beats
+ons_secs.r_hist= []; % histogram of breathing amplitudes
+
For a detailed list of all properties and their documentation, read the source code of tapas_physio_new.m
10. What is the order of the regressor columns in the multiple regressors file?
+This depends on the physiological models (and their order) specified in the model-submodule of physio (or in the batch editor). The general order is outlined in Fig. 7A of the http://dx.doi.org/10.1016/j.jneumeth.2016.10.019. The []-brackets indicate the number of regressors:
-
+
- RETROICOR cardiac regressors [2 x nOrderCardiac] +
- RETROICOR respiratory regressors [2 x nOrderRespiratory] +
- RETROICOR cardXResp interaction regressors [4 x nOrderCardiacXRespiratory] +
- HRV [nDelaysHRV] +
- RVT [nDelaysRVT] +
- Noise ROIs (PCA signatures and mean of each region) [nNoiseROIs x (nComponents+1)] +
- Other (included other text file) [nColumnsOtherFile] +
- Motion [6 or 12 or 24, depending on motion model] +
If any of the models was not specified, the number of regressors is reduced accordingly.
+11. I cannot find the answer to my question in the FAQ. Whom do I ask for help?
+Subscribe to our TAPAS mailing list by clicking Subscribe on the left side of this website
+Afterwards you can send e-mails with your questions to tapas@sympa.ethz.ch. Both, core developers of PhysIO and experienced users are receiving your e-mail and are eager to help.
The mailing list also has a searchable archive (click Archive on the left of above-mentioned website), where you might already find the answer to your question.
+Examples
+Example Datasets for PhysIO
+The following datasets are available and can be downloaded with the toolbox in a specific physio-examples repository, which can be cloned from git@tnurepository.ethz.ch:physio/physio-examples.git.
Besides the raw physiological logfiles, each example contains example scripts to run physio as
+-
+
- SPM job (
*spm_job.mat)
+ - editable SPM job (
*spm_job.m)
+ - plain matlab script (
*matlab_script.m)
+
Philips
+ECG 3T
+Courtesy of Sandra Iglesias, Translational Neuromodeling Unit, ETH & +University of Zurich
+4-electrode ECG and breathing belt, Philips 3T Achieva scanner
+Description: Standard example; shows how to use volume counting either +from beginning or end of run to synchronize physiological logfile with +acquisition onsets of fMRI scans.
+ECG 7T
+Courtesy of Zina-Mary Manjaly, University Hospital Zurich
+4-electrode ECG and breathing belt, Philips 7T Achieva scanner
+Description: The ECG data for ultra-high field data is typically much +noisier than at 3 Tesla. Therefore, R-wave peaks are frequently missed +by prospective trigger detection and not marked correctly in the +logfile. This example shows how to select typical R-wave-peaks manually +and let the algorithm find the heartbeat events.
+PPU 3T
+Courtesy of Diana Wotruba, University and University Hospital of Zurich
+PPU (finger plethysmograph) and breathing belt, Philips 3T Achieva +scanner
+Description: Similar to ECG3T, but a plethysmograph instead of an ECG +was used to monitor the cardiac pulsation. Example shows how to extract +heart and breathing rate.
+General Electric
+PPU 3T
+Courtesy of Steffen Bollmann, Kinderspital Zurich and ETH Zurich
+PPU (finger plethysmograph) and breathing belt, General Electric 3T +scanner
+Description: Similar to PPU, but acquired on a GE system with two +separate output logfiles for pulse oximetry and breathing amplitude, +sampled with 40 Hz. The quality of the signal is particularly +challenging, stemming from a patient population.
+Siemens
+ECG 3T
+Courtesy of Miriam Sebold, Charite Berlin, and Quentin Huys, TNU Zurich
+4-electrode ECG data, Siemens 3T scanner
+Description: Similar to ECG 3T, but acquired on a Siemens system with only one logfile for ECG data. The quality of the signal is challenging, stemming from a patient population.
+Changelog/Versions
+RELEASE INFORMATION
+Current Release
+PhysIO_Toolbox_R2017.1
+February 19, 2017
+Minor Release Notes (R2017.1)
+-
+
- Substantially improved Siemens interface, both for VB/VD and 3T/7T releases
-
+
- several bugfixes +
- based on extensive user feedback from Berlin and Brisbane +
+ - New functionality tapas_physio_overlay_contrasts.m to display non-physio +contrasts automatically as well +
Major Release Notes (r904 / R2016.1)
+-
+
- Software version for accepted PhysIO Toolbox Paper: doi:10.1016/j.jneumeth.2016.10.019 +
- Tested and expanded versions of examples +
- Improved stability by bugfixes and compatibility to Matlab R2016 +
- Slice-wise regressor creation +
- Detection of constant physiological time series (detachment, clipping) +
- Refactoring of report_contrasts and compute_tsnr_gains as standalone functionality +
- Improved Read-in capabilities (Siemens respiration data, BioPac .mat) +
- Migration from svn (r904) to git (tnurepository) for version control +
Major Release Notes (r835)
+-
+
- Software version for Toolbox Paper submission +
- Noise ROIs modeling +
- Extended motion models (24 parameters, Volterra expansion) +
- HRV/RVT models with optional multiple delay regressors +
- Report_contrasts with automatic contrast generation for all regressor groups +
- compute_tsnr_gains for individual physiological regressor groups +
- consistent module naming (scan_timing, preproc) +
- Visualisation improvement (color schemes, legends) +
Minor Release Notes (r666)
+-
+
- Compatibility tested for SPM12, small bugfixes Batch Dependencies +
- Cleaner Batch Interface with grouped sub-menus (cfg_choice) +
- new model: 'none' to just read out physiological raw data and preprocess, +without noise modelling +
- Philips: Scan-timing via gradient log now automatized (gradient_log_auto) +
- Siemens: Tics-Logfile read-in (proprietary, needs Siemens-agreement) +
- All peak detections (cardiac/respiratory) now via auto_matched algorithm +
- Adapt plots/saving for Matlab R2014b +
Major Release Notes (r534)
+-
+
- Read-in of Siemens plain text log files; new example dataset for Siemens +
- Speed up and debugging of auto-detection method for noisy cardiac data => new method thresh.cardiac.initial_cpulse_select.method = ???auto_matched??? +
- Error handling for temporary breathing belt failures (Eduardo Aponte, TNU Zurich) +
- slice-wise regressors can be created by setting sqpar.onset_slice to a index vector of slices +
Major Release Notes (r497)
+-
+
- SPM matlabbatch GUI implemented (Call via Batch -> SPM -> Tools -> TAPAS PhysIO Toolbox) +
- improved, automatic heartbeat detection for noisy ECG now standard for ECG and Pulse oximetry (courtesy of Steffen Bollmann) +
- QuickStart-Manual and PhysIO-Background presentation expanded/updated +
- job .m/.mat-files created for all example datasets +
- bugfixes cpulse-initial-select method-handling (auto/manual/load) +
Major Release Notes (r429)
+-
+
- Cardiac and Respiratory response function regressors integrated in workflow (heart rate and breathing volume computation) +
- Handling of Cardiac and Respiratory Logfiles only +
- expanded documentation (Quickstart.pdf and Handbook.pdf) +
- read-in of custom log files, e.g. for BrainVoyager peripheral data +
- more informative plots and commenting (especially in tapas_physio_new). +
Minor Release Notes (r354)
+-
+
- computation of heart and breathing rate in Philips/PPU/main_PPU.m +
- prefix of functions with tapas_* +
Major Release Notes (r241)
+-
+
- complete modularization of reading/preprocessing/regressor creation for peripheral physiological data +
- manual selection of missed heartbeats in ECG/pulse oximetry (courtesy of Jakob Heinzle) +
- support for logfiles from GE scanners (courtesy of Steffen Bollmann, KiSpi Zuerich) +
- improved detection of pulse oximetry peaks (courtesy of Steffen Bollmann) +
- improved documentation +
- consistent function names (prefixed by "physio_") +
NOTE: Your main_ECG/PPU.m etc. scripts from previous versions (<=r159) will not work with this one any more. Please adapt one of the example scripts for your needs (~5 min of work). The main benefit of this version is a complete new variable structure that is more sustainable and makes the code more readable.
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