This work is a part of Google Summer of Code 2020 (INCF - Project 8: Improvoing Personalized Models of fMRI Recordings Including Individual Region-Specific HRF in The Virtual Brain, under the mentorship of Prof. Daniele Marinazzo)

Here, we demonstrate various tests/tutorials for the tasks tasks that have been accomplished. To understand the following familiarity with The Virtual Brain(TVB) is necessary. For more information on TVB, visit: https://www.thevirtualbrain.org/tvb/zwei.

When using The Virtual Brain for scientific publications, please cite it as follows:

Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
    The Virtual Brain: a simulator of primate brain network dynamics.
Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)

fMRI is an indirect measure of neural activity. The resulting BOLD signal attributes to the underlying neural activity as well as the Hemodynamic Response Function (HRF). Hence, variability in the HRF can be confused with variability in the neural activity. Several studies have established that HRF varies across subjects as well as across brain regions for a particular subject. This makes it necessary to individually estimate the resting state HRF (rsHRF) across different regions of a brain. An effective methodology for the same has been suggested by Wu et.al.
TVB makes use of a constant HRF (across subjects, and across brain-regions within a subject) which is convolved with the neural response to obtain BOLD simulation. Here, we have integrated the rsHRF-toolbox with TVB to account for region-specific HRF for BOLD simulations.

exploring_the_rsHRF_BOLD_Monitor.ipynb
rsHRF_Mediated_Brain_Dynamics.ipynb
parameter_space_exploration.py

Input
|   FC.txt
|   ROIts.txt
|   areas.txt
|   average_orientations.txt
|   centres.txt
|   connectivity.h5
|   cortical.txt
|   dummy_rsHRF.txt
|   hemisphere.txt
|   region_labels.txt
|   tract_lengths.txt
└─> weights.txt

C_Input
|   param_set.txt
|   ROIts_retrievedHRF.txt
|   tract_lengths.txt
└─> weights.txt

Data
│   YYYXX (YYY = CON(Control)/PAT(Patients) + XX=number)
|   |   FC.mat
│   │   tract_lengths.txt
│   │   weights.txt
|   |   Output
|   |   |   J_i.txt
|   └─> └─> PCorr.txt
│   
└───YYYXX 
    │   ...

1. FC.txt: Empirical Functional Connectivity Matrix
2. FC.mat: Contains the Empirical Functional Connectivity Matrix, Region-wise fMRI Time Series and Corresponding BOLD Repetition Time for each Subject
3. ROIts.txt: Empirical fMRI time-series (region-wise)
4. connectivity.h5: Contains relevant data from the other-files for TVB input.
5. J_i.txt: Feedback Inhibhition Control parameter values.
6. PCorr.txt: Pearson's R values for correlation between empirical functional connectivity matrix and simulated functional connectivity matrix.
7. dummy_rsHRF.txt: Contains Resting-State HRF (dummy) values for each region.
8. All the other files contain relevant information for defining the input in TVB.

• exploring_the_rsHRF_BOLD_Monitor.ipynb is a tutorial for using the RestingStateHRF kernel which has been proposed as an addition to the existing TVB BOLD monitor.
• rsHRF_Mediated_Brain_Dynamics.ipynb is a tutorial for the complete workflow as described below:
  1. Using TVB to build a subject's virtual brain model.
  2. Obtaining the BOLD simulations from the model
     • Using the default TVB approach
     • Using the region-wise resting-state HRF approach.
  3. Parameter space exploration based on the obtained simulations.
     This is carried out along similar lines to the study at https://github.com/the-virtual-brain/tvb-educase-braintumor.

WIP

• parameter_space_exploration.py uses the main.c, which is theoretically similar, but computationally efficient implementation of the TVB simulations (for more information, visit: https://github.com/BrainModes/fast_tvb). To perform the similar experimation as the second-point in Tutorial, over a number of subjects (in the directory Subjects). It performs a parameter space exploration over the global coupling parameter and the feedback inhibhition parameter. The results are stored in the Output directory for each subject.

The dataset has been obtained from https://openneuro.org/datasets/ds001226/versions/00001, and preprocessed according to TVB requirements.

1. The version used for python TVB in the above tutorials correspond to https://github.com/AmoghJohri/tvb-root/tree/amogh. These changes have not yet been reflected in the TVB software and hence, this code does not represent the final form. The jupyter notebooks require to be in the same directory as tvb-root (or appropriate changes can be made to the import paths).
2. The main.c file corresponds to https://github.com/AmoghJohri/fast_tvb/tree/rsHRF_convolution. These changes have not yet been reflected in fast_tvb and hence, the code does not represent the final form.