BSX

This repository contains model definitions, scripts and data used in the paper Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy.

Models

The models directory contains model definitions in BCMD format. The main model from the paper is in the bsx.modeldef file. The file bsx_pulse.modeldef defines a variant that generates its own internal stimulus pulse instead of expecting inputs; this is for use with the optimisation scripts below.

The definition files in the top-level models directory are fully self-contained. Because there is a great deal of overlap between both of these and many earlier models, actual model development was performed using definitions decomposed into multiple files, with the shared details referenced from various master files. For completeness, all these files included in the decomposed subdirectory. These files are not required to build the models and their structure is somewhat opaque, so for most purposes it is probably simpler to just use the self-contained versions.

Data

The data directory contains simulated and experimental data used in the paper, arranged into several subdirectories. Within each data set, the input directory includes the model input files used to drive the simulations, while the out directory contains the simulation results. Unless otherwise noted, inputs are for use with the main bsx model. The individual subdirectories are listed below in the order their contents are used in the text.

sim/bsx_compare

Steady state simulations for variations in single parameters, for the BSX model, and also for the original BrainSignals and the 2015 variant B1M2 (implementations of both models can be found in the neighbouring BrainSignals Revisited repository). Results are presented in Figure 2.

sim/basic

Simulated single and joint haemodynamic responses, as shown in Figures 3 & 4.

sim/false_opt

Optimised false positive and false negative examples, as shown in Figure 5. These input files are for use with the bsx_pulse model rather than the main bsx model. They set parameters defining step changes in the driving signals rather than supplying those signals directly.

sim/false

Quasi-steady state simulations with varying levels of both arterial pressure and CO2, as used in Figure 6. In pc.input, systemic variables are changed with no change in demand, potentially giving rise to false positive results. In pcu.input, systemic variable changes occur alongside demand changes, potentially giving rise to false negative results.

fs_2013

Grouped data from Scholkmann et al 2013, as used in section 3.3, Figure 7. Original data as supplied are in the excel directory. Results from the four different challenges are collated in CSV form in the csv subdirectory, which also contains the same data upsampled to 1 Hz by linear interpolation. Simulation input and output files are included for the three different cases considered: file names ending in _u include a synthetic demand input (Figure 7B), those ending in _c include measured CO2 as an input (Figure 7C) and those ending in _uc include both (Figure 7D).

ck_2012

Individual data from Kolyva et al 2012, as used in section 3.4, Figures 8-10. Original data as supplied (already resampled) is in the csv subdirectory. For each subject (S1-S8) there are separate A and B files for the left and right hemispheres. Channel A was used for all simulations. Global data are present in both files, but of course are identical for both channels.

Optim

The optim directory contains optimisation jobs and target data for the false positive and false negative results presented in Figure 5 of the paper. These can be executed via the optim.py script in the BCMD distribution (which in turn depends on the OpenOpt library). They require that the bsx_pulse model has been built and is available to optim.py (typically in the build directory).