HRfunc

Getting Started



The HRfunc tool can be used to estimate latent hemodynamic response functions and neural activity in funcitonal near infrared spectroscopy (fNIRS). To accomplish this, one must...

  1. Estimate subject level estimates for each subject
  2. Generate a subject pool HRF distribution
  3. Estimate channel-wise neural activity for each subject


To first estimate subject level HRFs, initialize a hrfunc.montage() object and then iterate through each of your raw scans passing them into the montage.estimate_hrf() function. The HRfunc tool will automatically preprocess them for deconvolution, which is distinctly different than standard hemoglobin preprocessing. The _lambda variable can be tuned to increase and decrease noise suppresion within the signal.


import hrfunc as hrf

# Load in NIRX scans
raw_scan = mne.io.read_raw_nirx("path/to/your/nirx/scan/")

# Load a hrfunc montage
montage = hrf.montage(raw_scan)
    
# Iterate through each of your MNE NIRX scans and estimate an HRF for it
for scan in raw_scans:
    montage.estimate_hrf(duration = 30.0, _lambda = 1.0)


After passing each scan into your montage to have HRFs estimated, you can now average across subject estimates to generate a subject-pool wide HRF estimate for each channel. 


# Generate subject pool wide distribution
montage.generate_distribution() 
montage.save('study_HRFs.json')


After generating a subject-pool wide distribution, an HRF estimate will be attached to each of your hrf montage channel nodes for use in estimating neural activity.Save the object as a json object to use with other study analysis, share with collaborators, or share with the wider neuroimaging community. Now using our estimated HRFs we can estimate neural activity!

To accomplish this you must iterate through each of your scans and pass them into your montage.estimate_activity(raw). This will replace channel hemoglobin data in place using using MNEs raw.apply_function() to each channel using you're estimated channel HRF.


# Load previously estimated HRF montage
montage = hrf.load_montage('study_HRFs.json')

# Estimate neural activity in place and save the nirx object
for scan in raw_scans:
    montage.estimate_activity(scan)
    scan.save('sub-123_deconvolved.fif')