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SCOS stroop task

- Citation Author(s):
- Submitted by:
- Alexander Howard
- Last updated:
- Mon, 03/17/2025 - 14:14
- DOI:
- 10.21227/a0rm-0445
- Data Format:
- Research Article Link:
- License:
- Categories:
- Keywords:
Abstract
Speckle contrast optical spectroscopy (SCOS) is an optical technique capable of measuring human cerebral blood flow and brain function non-invasively. Its tomographic extension, speckle contrast optical tomography (SCOT), can provide blood flow variation maps with measurements using overlapping source-detector channel pairs. Linearity is often assumed in most image reconstruction methods, but non-linearity could exist in the relations between measured signals and blood flow variations. We have constructed a fast-computing forward model for SCOT using the Rytov approximation to solve the correlation diffusion equation and compared it with the first Born approximation as well as the more accurate, microscopic Monte Carlo simulations. We have shown that the results obtained using the Rytov approximation are in good agreement with the Monte Carlo simulations, while the first Born approximation deviates from the other two methods for large blood flow variations. For instance, the Born approximation breaks down at 30% cerebral blood flow (CBF) changes within a volume of size 60 × 50 × 40 mm³, therefore we recommend using the Rytov approximation above this threshold. We have shown that our defined blood flow index (BFi) measured in SCOT is linearly related to local CBF variations, thus the forward and inverse problems can be solved linearly. We have then demonstrated image reconstruction experimentally showing human brain activations using our recently developed high-density SCOS system. Our method guides experimental system designs and data analysis for SCOT.
The data provided is in a .snirf file format. It can be loaded with many publicly availible packages including Cedalion. For further information on how to load .snirf files using Cedalion please refer to the github (https://github.com/ibs-lab/cedalion).
Dataset Files
- .snirf file containing the subject data. data1subjrytov.zip (806.72 kB)
- Code to do image reconstruction using the .snirf file and sensitivity matrix. do_IR_from_rytov_hrf.py (40.22 kB)
- Code to generate the sensitivity matrix used for image reconstruction. run_fwm_rytov_DCS.py (28.10 kB)