Hao-Ting Wang
@haotingwang.bsky.social
Postdoc @ CRIUGM, Montreal | biomarker-fmri-neuroinformatics-ai
https://wanghaoting.com/
https://wanghaoting.com/
Reposted by Hao-Ting Wang
This release contains a lot of deprecation so make sure to check out the release notes to see if you may be affected by those breaking changes.
nilearn.github.io/stable/chang...
nilearn.github.io/stable/chang...
What’s new
0.13.0: HIGHLIGHTS: Regarding support for surface data, we have added multi maskers for surface image, surface label image and surface maps image: those are meant to be used for data extraction of ...
nilearn.github.io
January 6, 2026 at 1:43 PM
This release contains a lot of deprecation so make sure to check out the release notes to see if you may be affected by those breaking changes.
nilearn.github.io/stable/chang...
nilearn.github.io/stable/chang...
LMAO I listened to both episodes it was a trip
December 26, 2025 at 7:24 PM
LMAO I listened to both episodes it was a trip
Also I found that even the vibe coding output can do the correct thing, it usually doesn't use higher level functions so the cognitive load of reading it is really high 🙃
November 27, 2025 at 3:23 PM
Also I found that even the vibe coding output can do the correct thing, it usually doesn't use higher level functions so the cognitive load of reading it is really high 🙃
Reposted by Hao-Ting Wang
And the next step? Full voxel-level modeling.
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
SIMBA: Scalable Image Modeling using a Bayesian Approach, A Consistent Framework for Including Spatial Dependencies in fMRI Studies
Bayesian spatial modeling provides a flexible framework for whole-brain fMRI analysis by explicitly incorporating spatial dependencies, overcoming the limitations of traditional massive univariate app...
arxiv.org
November 18, 2025 at 10:13 PM
And the next step? Full voxel-level modeling.
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
Thank you Micah. Let's find a time to zoom? I would love to see you again soon.
November 15, 2025 at 12:11 AM
Thank you Micah. Let's find a time to zoom? I would love to see you again soon.
Reposted by Hao-Ting Wang
