README

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Notes

• PDF and BIB files: file:./aux-refs/
• Parcellations/annotations file:./aux-annots/fsaverage/label/
• All parcellations transformed to fsaverage:
  • ?h.aparc.annot
    • Desikan-Killiany Atlas parencite:&r2006PaperDesikanEtAlN2
    • Available as a standard option in FreeSurfer/MNE distribution
  • ?h.aparc_sub.annot
    • A more detailed version of the Desikan-Killiany Atlas parencite:&r2006PaperDesikanEtAlN2
    • Includes sub-parcellations based on textcite:&r2018PaperKhanEtAlN2
      • QUOTE: Cortical parcellation (labels) FreeSurfer was used to automatically divide the cortex into 72 regions parencite:&r1999PaperDaleEtAlN2. /After discarding “medial wall” and “corpus callosum”, these regions were further divided in to a total of N = 448 cortical “labels (Fig. S4)”, so that each label covers a similar area again using FreeSurfer. _We employed this high-resolution parcellation scheme because cortical surface is very convoluted and averaging across a large label, which crosses multiple sulci and gyri, can result in signal cancellation across the parcel_. Lastly, _a high-resolution parcellation also reduces the dependence of the results on the specific selection of the parcels_./
    • NB: Recently used in textcite:&r2025PaperRuuskanenAvendano-DiazN1
      • Sub-parcellations were methodologically justified in the context of addressing the same problem we face
      • QUOTE: Cortical parcellation /To decrease computational complexity and increase interpretability, we employed a cortical parcellation to reduce the dimensionality of the source-space data. To that end, the data were organized into 448 ROIs as defined in the aparc sub parcellation scheme developed by Khan and colleagues parencite:&r2018PaperKhanEtAlN2 based on the Desikan–Killiany atlas/.
      • QUOTE: /The time courses within each ROI were then averaged to form a representative time series. To prevent signal cancellation at the opposite sides of sulci, a sign-flip operation was applied to the signals of the sources whose orientation differed from the dominant direction within the ROI by more than 90◦. The use of a relatively dense parcellation further reduces signal cancellation and flattening during the averaging procedure/.
      • 
      • BTW: ↓ Not a bad template for localization visualization (if one adds a histogram to such circle).
      • 
    • NB: Interesingly, both textcite:&r2018PaperKhanEtAlN2 and textcite:&r2025PaperRuuskanenAvendano-DiazN1 relied on ?h.aparc_sub.annot to solve a problems very similar to the ones we discossed today.
    • BTW: ?h.aparc_sub_FIXED.annot contains manually corrected label strings.
  • ?h.HCPMMP1.annot
    • From textcite:&r2016PaperGlasserEtAlN1
    • A strong alternative candidate for consideration.
    • ?h.HCPMMP1_ORIG.annot is OK
  • ?h.power.annot
    • From textcite:&r2011PaperPowerEtAlN2
    • Not a preferred option for my taste (although, just in case, please find it in the fsaverage space)
    • This parcellation is highly non-uniform in parcel size; some areas are very small, while others cover large cortical surface regions (likely due to idiosyncratic data used, which may reflect their specific research constrains but is suboptimal for exploring new data).

References

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