Resting state HRF estimation from BOLD-fMRI signal
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README.md

Code for resting state HRF estimation and deconvolution - Matlab (standalone and SPM plugin)

Please refer to https://github.com/BIDS-Apps/rsHRF for Python standalone and BIDS app through Dockers

The basic idea

This toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal. It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using either the canonical shape with two derivatives, or a (smoothed) Finite Impulse Response.

BOLD HRF

Once that the HRF has been retrieved for each voxel, it can be deconvolved from the time series (for example to improve lag-based connectivity estimates), or one can map the shape parameters everywhere in the brain (including white matter), and use the shape as a pathophysiological indicator.

HRF map

How to use the toolbox - Matlab standalone

The input is voxelwise BOLD signal, already preprocessed according to your favorite recipe. Important things are:

  • bandpass filter in the 0.01-0.08 Hz interval (or something like that)
  • z-score the voxel BOLD time series

To be on the safe side, these steps are performed again in the code.

The input can be images (3D or 4D), or directly matrices of [observation x voxels].

It is possible to use a temporal mask to exclude some time points (for example after scrubbing).

The demos allow you to run the analyses on several formats of input data.

How to use the toolbox - SPM plugin

The script spm_rsHRF.m is the main one, and it calls rsHRF.m. These two files are specific to the SPM plugin.

Collaborators

  • Guorong Wu

  • Nigel Colenbier

  • Sofie Van Den Bossche

  • Daniele Marinazzo

  • Madhur Tandon (Python - BIDS)

  • Asier Erramuzpe (Python - BIDS)

References

  1. Guo-Rong Wu, Wei Liao, Sebastiano Stramaglia, Ju-Rong Ding, Huafu Chen, Daniele Marinazzo*. "A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data." Medical Image Analysis, 2013, 17:365-374. PDF

  2. Guo-Rong Wu, Daniele Marinazzo. "Sensitivity of the resting state hemodynamic response function estimation to autonomic nervous system fluctuations." Philosophical Transactions of the Royal Society A, 2016, 374: 20150190. PDF

  3. Guo-Rong Wu, Daniele Marinazzo. "Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications." PeerJ PrePrints, 2015. PDF