- Before installing TA Toolbox, check that Matlab is correctly installed
- Download the zip
- Extract the archive
- Add the toolbox to Matlab path
The iCAPs Toolbox is released and maintained on the c4science.ch git infrastructure:
A nightly build (zip) can also be downloaded from here (approx. 2GB)
About innovation-driven Co-Activation Patterns (iCAPs)
Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art sparsity-pursuing deconvolution, termed "Total Activation" (TA), we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs' maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial and is consistent with their behavioral profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, iCAPs show evidence for two DMN-related subnetworks consisting the posterior cingulate cortex that differentially interact with the attention network. This methodology demonstrates how fMRI resting state carries complex interactions between large-scale functional networks, a property that can be approached by decomposing the data into spatially and temporally overlapping building blocks using iCAPs.
Using iCAPs to reveal functional networks
- F. I. Karahanoglu, D. Van De Ville, "Transient Brain Activity Disentangles fMRI Resting-State Dynamics in Terms of Spatially and Temporally Overlapping Networks", Nature Communications, vol. 6, art. 7751, 2015.
- D. Van De Ville, F. I. Karahanoglu, "Resting-State Neuroimaging Unravels Functional Organization in the Brain", SPIE Newsroom, August 15, 2016.
Total activation deconvolution
- F. I. Karahanoglu, I. Bayram, D. Van De Ville, "A Signal Processing Approach to Generalized 1-D Total Variation", IEEE Transactions on Signal Processing, vol. 59(11), pp. 5265-5274, 2011.
- F. I. Karahanoglu, C. Caballero-Gaudes, C., Lazeyras, F., and Van De Ville, "Total Activation: FMRI Deconvolution Through Spatio-Temporal Regularization", Neuroimage, vol. 73, pp. 121-134, 2013.
- Y. Farouj, F. I. Karahanoglu, D. Van De Ville, "Regularized Spatiotemporal Deconvolution of fMRI Data Using Gray-Matter Constrained Total Variation", Proc. 14th IEEE Int. Symp. Biomed. Imaging From Nano to Macro, pp. 472-475, 2017.
- D. Zöller, T.A. Bolton, F. I. Karahanoglu, S. Eliez, M. Schaer, D. Van De Ville, "Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatiotemporal Regression", IEEE Transactions on Medical Imaging, in press.
More background about dynamic functional connectivity
- M. G. Preti, T. Bolton, D. Van De Ville, "The Dynamic Functional Connectome: State-of-the-Art and Perspectives", NeuroImage, vol. 160, pp. 41-54, 2017.
- F. I. Karahanoglu, D. Van De Ville, "Dynamics of Large-Scale fMRI Networks: Deconstruct Brain Activity to Build Better Models of Brain Function", Current Opinion in Biomedical Engineering, vol. 3, pp. 28-36, 2017.
The following source code was released earlier, but it is not maintained anymore.
Sparsity-driven deconvolution of fMRI timecourses
The Total Activation toolbox is implemented by F. Isik Karahanoglu. The main contribution of TA is to combine generalized total-variation regularization (in time) with structured sparsity (in space) for recovering fMRI activity-inducing signals without specification of the paradigm. The Matlab code can be downloaded here.
Generalized total variation
Total Activation is based on the 'generalized total variation' framework that has been proposed in the IEEE Transactions on Signal Processing (2011) paper. The Matlab code allows to regenerate Figure 2.
The following software should be available on your system: (necessary tools are included in the package)
- Matlab (The Mathworks, USA)
- SPM2, SPM5, or SPM8 (University College London, UK)
- NYU CBI NIfTI Matlab tool
Here we demonstrate the use of TA toolbox. We show how to make the basic settings for running TA algorithm. A simple synthetic dataset (Neuroimage paper) is included in the toolbox (test_data/phantom1db).
1. Data Preprocessing
Generally the functional data is preprocessed (realignment, smoothing, etc.) before TA. Normalization of the functional data into a common space (MNI/Talairach) is not necessary, however, the atlas and the functional data have to be matched. We use atlas wrapper to convert the structural atlas to functional space (from IBASPM and Jonas Richiardi preprocessing pipeline). Atlas should be a volume whose intensity consists of postive numbers inside the atlas and zero otherwise.
2. Running TA
The algorithm is run through a main (main.m) function in the toolbox. First, the toolbox should be added into the matlab path. Then, to start the process user needs to input the data path s and parameters (MyInputs.m or MyInputsTest.m for synthetic data).
The user should enter the path to the data, atlas and the results and then enter the type of analysis he/she wants to perform. TA has various temporal and spatial regularization options:
3. ResultsThe results are saved in "path_results" folder defined in "MyInputs.m".
"TCN_.nii" = NORMALIZED-DETRENDED DATA
"TC_OUT_.nii" = RECOVERED ACTIVITY-RELATED SIGNALS
"TC_D_OUT_.nii" = RECOVERED ACTIVITY-INDUCING SIGNALS
"TC_D2_OUT_.nii" = RECOVERED INNOVATION SIGNALSThe results are 4D datasets and can be visualized with an appropriate data visualization tool (e.g., AFNI).
Here, we show TA analysis results for one subject performing 10 visual stimuli (Neuroimage paper).