14.2.2. Chen et al. (2023). BOLD response is more than just magnitude: improving detection …

Introduction

Here we present commands used in the following paper:

  • Chen G, Taylor PA, Reynolds RC, Leibenluft E, Pine DS, Brotmas MA, Pagliaccio D, Haller SP (2023). BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles. Neuroimage 277:120224.
    https://pubmed.ncbi.nlm.nih.gov/37327955/
Abstract: Typical FMRI analyses assume a canonical hemodynamic response function (HRF) with a focus on the overshoot peak height, while other morphological aspects are largely ignored. Thus, in most reported analyses, the overall effect is reduced from a curve to a single scalar. Here, we adopt a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a profile at the individual level. Then, we estimate the BOLD response in its entirety with a smoothness constraint at the population level to improve predictive accuracy and inferential efficiency. Instead of using just the scalar that represents the effect magnitude, we assess the whole HRF shape, which reveals additional information that may prove relevant for many aspects of a study, as well as for cross-study reproducibility. Through a fast event-related FMRI dataset, we demonstrate the extent of under-fitting and information loss that occurs when adopting the canonical approach. We also address the following questions:
1. How much does the HRF shape vary across regions, conditions, and clinical groups?
2. Does an agnostic approach improve sensitivity to detect an effect compared to an assumed HRF?
3. Can examining HRF shape help validate the presence of an effect complementing statistical evidence?
4. Could the HRF shape provide evidence for whole-brain BOLD response during a simple task?

Study keywords: FMRI, AFNI, task-based analysis, hemodynamic response function (HRF), processing, regularization

Main programs: afni_proc.py, @SSwarper, recon-all (FS), @chauffeur_afni, 3dMVM, 3dMSS

Github page:
See these authors’ github page for full descriptions and downloads of codes and supplementary text files:
https://github.com/afni/apaper_hrf_profiles

Download scripts

To download, either:

View scripts

Because there are so many scripts for this project, just recommend downloading the full set from the github pages, above. There are helpful README* files there, as well, to describe the contents in details.

Note that these scripts were run on the NIH’s Biowulf HPC, so some scriptiness deals with those specific features (batch/swarm submission, etc.).

We just point to a couple specific examples of the afni_proc.py processing scripts here:

do_24_ap_task_NL.tcsh

Full processing (through regression modeling) of a resting state FMRI session for a single subject (with blurring, for voxelwise analysis).

https://github.com/afni/apaper_hrf_profiles/blob/main/scripts_biowulf/do_24_ap_task_NL.tcsh