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Machine Learning Detects Pattern of Differences in (fMRI) Data between (CFS) and (GWI), 2020, Baranuik et al

Discussion in 'BioMedical ME/CFS Research' started by Andy, Jul 28, 2020.

  1. Andy

    Andy Committee Member & Outreach

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    Hampshire, UK
    Full title: Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI)
    Open access, https://www.mdpi.com/2076-3425/10/7/456/htm

    This is a follow-on from A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of CFS. Baraniuk et al. 2020
    sebaaa, Kitty, Lidia and 11 others like this.
  2. Hutan

    Hutan Moderator Staff Member

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    I'm a bit sceptical about the conclusion, but then my set point for fMRI studies for now is scepticism.

    As expected, the two samples are quite different in terms of gender:

    Screen Shot 2020-07-29 at 11.52.40 AM.png

    The CFS sample was pretty small and the Fukuda criteria were applied - so PEM wasn't required.

    I wonder how that atlas performs in samples with very different gender percentages. Differences in the education and gender of the two samples might account for the differences identified.
    'Controlled for' - An opportunity for data manipulation that produces a desired result. Not to say that that is what happened.

    So, it sounds as though the results found here were not consistent with what was found in earlier studies comparing each of the conditions with sedentary controls.

    Thanks to the researchers for acknowledging this and not engaging in poorly founded speculation about the deficiencies of people with GWI and CFS.

    If further studies are done, I'd like to see samples matched on gender and educational attainment.
    Last edited: Jul 29, 2020 at 1:40 AM

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