Task-based connectivity analysis for functional NIRS data

Project info

Title: Task-based connectivity analysis for functional NIRS data


Project lead:

Borja Blanco, twitter.com/borja_blanco4 Irene Arrieta, twitter.com/irenearrieta3

Project collaborators:

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

Project Description: 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: https://github.com/borjablanco/BHDonostia_2020_fNIRS

Goals for Brainhack Global 2020:

The goals of the project will be split into two different parts and days. Days 1-3:

Days 4 and 5:

Good first issues:

Recommended Readings: gPPI:

  1. 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
  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
  3. 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
  4. 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


  1. 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
  2. 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
  3. Chapter 46 of SPM12 Manual. https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf


Communication channels:

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Jan 1, 0001 12:00 AM