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Overview_Usage.md

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Overview and Usage

Overview

The basic idea. According to the point process theory discrete BOLD events (i.e. pseudo-events in the absence of an external stimulus) govern the brain dynamics at rest (e.g. Tagliazucchi et al. 2012). The rsHRF toolbox is aimed to retrieve the neuronal onsets of these pseudo-events with no explicit stimulus and timing together with the hemodynamic response (rsHRF) it set off (Wu et al., 2013; Wu & Marinazzo, 2015; Wu & Marinazzo, 2016). To this end, the rsHRF toolbox first identifies the pseudo-events, i.e. when the standardized resting-state BOLD signal crosses a given threshold (1 SD). Thereafter, a model is fitted to retrieve:

  1. the optimal lag between the pseudo-events and the neuronal (rsHRF) onset;
  2. the shape of the estimated rsHRF which will depend on the by-the-toolbox predefined HRF basis functions. Users of the rsHRF toolbox can choose one of eight options:

    Code HRF basis functions
    rsHRF_estimation_temporal_basis.m canontd: a canonical HRF with its time derivative
    canontdd: a canonical HRF with its time and dispersion derivatives
    Gamma Functions with a variable number of basis functions (k), e.g. 3-5
    Fourier Set with a default number of basis functions (k) equal to 3
    Fourier Set (Hanning) with a default number of basis functions (k) equal to 3
    rsHRF_estimation_FIR.m FIR: Finite Impulse Response
    sFIR: smoothed Finite Impulse Response
    (rsHRF_estimation_impulseest.m) non-parametric impulse response estimation: not included in the rsHRF GUI

Usage

Once that the rsHRF has been retrieved for each voxel/vertex in the brain, you can:

use the rsHRF as a pathophysiological indicator (by mapping the rsHRF shape onto the brain surface and looking at the inter-subject variability);

The shape of the rsHRF can be characterized by three parameters, namely response height (RH), time to peak (TTP), and Full Width at Half Maximum (FWHM). Each of these parameters can be mapped onto the brain surface (see Figure for an example: full brain map of the response height estimated using the Finite Impulse Response basis functions). Note that the full brain map covers the full brain surface, including white matter and CSF. To consult some example data, head over to NeuroVault. The number of pseudo-events per voxel/vertex can also be mapped onto the brain surface. After mapping all parameters (i.e. RH, TTP, FWHM, number of pseudo-events) onto the brain surface for each voxel/vertex and subject, the subject-specific brain maps can be used to examine whether/how the rsHRF is modulated by psycho-physiological factors (i.e. inter-subject hemodynamic variability; e.g. post-traumatic stress disorder, autism spectrum disorder, chronic pain, consciousness). With the 3dMVM function embedded in AFNI, one can even run a multivariate analysis in which the three rsHRF parameters are modeled as multiple, simultaneous response variables (Chen, Adleman, Saad, Leibenluft, & Cox, 2014).

deconvolve the rsHRF from the resting-state fMRI BOLD signal (for example to improve lag-based connectivity estimates).

References