Update README.md - #1
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| 1. Some regions are so small that when they're mapped to a low-res BOLD/DWI volume they will disappear. Should we allow this? | ||
| In some cases they are from dividing a region into subcomponents. We could recombine them. |
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I wonder what nilearn's behavior is? I assume it just returns NaNs for the missing regions. I'd be fine with NaNs if the region disappears or is entirely outside the brain mask, generally speaking.
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I'm mostly worried about what this means for those interpreting the connectivity matrices without thinking too hard about it. A column of NaNs isn't a big deal software-wise, but if someone is doing network science stuff on these matrices it's a big deal to have a fully disconnected node. We also know that there should almost never be an actually disconnected node in the brains we'll be analyzing. We looked at this problem here and it still haunts me
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| 1. Some regions are so small that when they're mapped to a low-res BOLD/DWI volume they will disappear. Should we allow this? | ||
| In some cases they are from dividing a region into subcomponents. We could recombine them. | ||
| 2. Regions may overlap each other from different atlases. Which should have precedence? |
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If I remember correctly, qsiprep needs everything to be in the same atlas file, right? Because xcpd can just concatenate time series files if necessary, I think. Just saw the next bullet point 😆
If there are overlapping regions, we could use a 4D atlas file, and basically pretend it's a probabilistic segmentation.
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Mrtrix, DSI Studio, etc need a single 3D volume :(
| 3. For DWI the regions need to be defined *all in the same image*. BOLD regions can be in different images and you can calculate | ||
| connectivity from their timeseries later. But this isn't possible for DWI. Therefore, we can't include Tian and dwi/CIT168 atlases | ||
| in the same image |
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Unfortunately not for the DWI usage
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