Task-based connectivity analysis for functional NIRS data
Borja Blanco, twitter.com/borja_blanco4
Irene Arrieta, twitter.com/irenearrieta3
César Caballero-Gaudes, twitter.com/CaballeroGaudes Mattermost: @CesarCaballeroGaudes
Eneko Uruñuela, twitter.com/eurunuela Mattermost: @eurunuela
Registered Brainhack Global 2020 Event:
Brainhack Donostia 2020, San Sebastián-Donostia
The aim of this project is to learn, comprehend and implement implement two types of task-based connectivity analyses for functional near infrared spectroscopy (fNIRS) data, namely generalized psycho-physiological interactions (gPPI) and Dynamic Causal Modelling (DCM). These approaches will be evaluated in fNIRS data collected in 4-month-old infants while they listened to forward and backward speech sentences during sleep. Coding will mostly be in MATLAB (similar to the main programs for fNIRS data analysis), although implementation in Python can be explored.
Data to use:
Datasets to work with will be available in project’s GitHub repo (see next).
Link to project repository/sources:
Goals for Brainhack Global 2020:
The goals of the project will be split into two different parts and days.
- Implement gPPI algorithms based on the current implementation using Parametric Empirical Bayes estimation available in SPM12, and adapt this formulation to deal with the fNIRS data structure. Moreover, alternative implementation of the deconvolution algorithm based on stability selection will be explored also based on current code available in Matlab. Milestone: Compute gPPI at the channel-level and global-level for several datasets.
Days 4 and 5:
- Learn and understand the implementation of DCM for fNIRS available in SPM12 (see chapter 46 of SPM12 manual, and related articles).
- Using the gPPI results, formulate and implement different models within the DCM framework.
Milestone: Perform DCM data analysis in several datasets and interpret the results. Comparison between gPPI and DCM.
Good first issues:
- Gitelman, D.R., Penny, W.D., Ashburner, J. and Friston, K.J., 2003. Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. Neuroimage, 19(1), pp.200-207. https://doi.org/10.1016/S1053-8119(03)00058-2
- McLaren, D.G., Ries, M.L., Xu, G. and Johnson, S.C., 2012. A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches. Neuroimage, 61(4), pp.1277-1286. https://doi.org/10.1016/j.neuroimage.2012.03.068
- Hassanpour, M.S., Eggebrecht, A.T., Peelle, J.E. and Culver, J.P., 2017. Mapping effective connectivity within cortical networks with diffuse optical tomography. Neurophotonics, 4(4), p.041402. https://doi.org/10.1117/1.NPh.4.4.041402
- Gerchen, M.F., Bernal‐Casas, D. and Kirsch, P., 2014. Analyzing task‐dependent brain network changes by whole‐brain psychophysiological interactions: A comparison to conventional analysis. Human brain mapping, 35(10), pp.5071-5082. https://doi.org/10.1002/hbm.22532
- Tak, S., Kempny, A., Friston, K.J., Leff, A.P. and Penny, W.D., 2015. Dynamic causal modelling for functional near-infrared spectroscopy. Neuroimage, 111, pp.338-349. https://doi.org/10.1016/j.neuroimage.2015.02.035
- Bulgarelli, C., Blasi, A., Arridge, S., Powell, S., de Klerk, C.C., Southgate, V., Brigadoi, S., Penny, W., Tak, S. and Hamilton, A., 2018. Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset. NeuroImage, 175, pp.413-424. https://doi.org/10.1016/j.neuroimage.2018.04.022
- Chapter 46 of SPM12 Manual. https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf
- Knowledge of Matlab
- Familiar with fNIRS data preprocessing and analysis
- Certain knowledge of brain function and neuroscience
- Motivation to learn about functional and effective connectivity analysis
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