Jonathan Power
  • Papers
    • 2020 Respiratory Patterns
    • 2020 fMRI denoising events
    • 2019 Respiratory Measures
    • 2019 PNAS reply to Spreng
    • 2019 Respiratory Motion
    • 2018 Glasser comment
    • 2018 Head molds
    • 2018 PNAS multiecho
    • 2017 TiCS response
    • 2017 PLOS ONE Despiking
    • 2017 NI Global signals
    • 2017 NI The Plot
    • 2015 NI Motion Review
    • 2014 Neuron RSFC Primer
    • 2014 PNAS lesion
    • 2014 HBM task censoring
    • 2014 NI motion #2
    • 2013 Neuron hubs
    • 2013 NI comment
    • 2013 CONEUR
    • 2012 NI motion #1
    • 2011 Neuron bigbrain
    • 2010 Neuron devo review
  • Contact
  • Resources
  • Positions


Temporal ICA has not properly separated global fMRI signals: a comment on Glasser et al., 2018
Jonathan Power
Neuroimage 2019 Aug 15; 197: 650-651
Pubmed link
Figure (.pptx)

​

Below are files associated with the article "Temporal ICA has not properly separated global fMRI signals: a comment on Glasser et al., 2018". All movies are 1080p and will look best at full-screen resolutions. Movies stream from YouTube and are organized as playlists. Click on links next to the captions to download movies. Files to download are hosted on Dropbox Pro; if the links don't work our traffic has exceeded its 200GB/day limit and the links will be re-enabled the following day.

Movie 1 (.mov - 1.3 GB)
​
This movie shows the unprocessed physiology records for all subjects of the "900 subject" Human Connectome Project data release with 4 complete resting state fMRI scans and 4 accompanying complete sets of physiology traces. Pulse oximeter records are in orange and abdominal belt records are in blue. The total scan time is 14.4 minutes in all cases. The physiology data are acquired at 400 Hz. Units are arbitrary for both traces.

​The questions an investigator must ask when viewing these records are:
1) Can I, using my eyes, identify peaks with good confidence?
2) Can I train a peak-finding algorithm to find the peaks I see?
-- and will it be worth the effort to correct the algorithm's mistakes (i.e., how mistake-laden will the results be)?
​

In my view, of the 760 subjects in the above movie (the subjects with 4 runs of fMRI data and 4 runs of physio data), 446 have physiology traces on which I would try to run and correct a peak-finding algorithm to obtain cardiac and respiratory parameters. My decisions on a subject by subject basis are here (.txt).

Software to help visualize and correct physiological records is here.

​