Krakencoder -- joint brain connectome translation autoencoder -- Targlatent singleton -- SCsdstream_cocoyeo243_sift2volnorm

Description

Krakencoder (Jamison, Gu, Wang, Tozlu, Sabuncu, Kuceyeski, Nature Methods 2025), ported to JAX / Equinox from the upstream PyTorch release (github.com/kjamison/krakencoder). A linked autoencoder that simultaneously bidirectionally translates between structural and functional brain connectivity across different atlases and processing variants ('flavors') via a common 128-dim L2-normalised latent representation. The Nature Methods 2025 publication's canonical model jointly encodes 15 flavors (3 atlases × {3 functional connectivity types + 2 structural tractography types}) and maps each to / from the shared latent. Architecture (per published recipe): per-flavor 256-dim PCA input transformation -> 256 -> 128 Linear encoder -> 128-dim L2-normalised latent -> 128 -> 256 Linear decoder -> inverse PCA to the destination flavor's full-dim connectivity space. v0 of this port ships the canonical bundle plus its 15-flavor PCA stack (separate krakencoder_pca_stack bundle that the KrakencoderPipeline co-loads).

Intended use

Single-flavor targlatent encoder/decoder for SCsdstream_cocoyeo243_sift2volnorm. Pairs with krakencoder_pca.SCsdstream-cocoyeo243-sift2volnorm-pc256.1; designed to be merged into the canonical 15-flavor bundle via KrakencoderPipeline.with_extra_flavor().

Usage

from ilex.models.krakencoder import Krakencoder
model = Krakencoder.from_pretrained('ilex-hub/krakencoder.SCsdstream-cocoyeo243-sift2volnorm-targlatent.1')

Authors

Keith W. Jamison, Zijin Gu, Qinxin Wang, Ceren Tozlu, Mert R. Sabuncu, Amy Kuceyeski

Citation

Jamison K.W., Gu Z., Wang Q., Tozlu C., Sabuncu M.R., Kuceyeski A. (2025). Krakencoder: a unified brain connectome translation and fusion tool. Nature Methods. DOI: 10.1038/s41592-025-02706-2.

References

  • Jamison K.W., Gu Z., Wang Q., Tozlu C., Sabuncu M.R., Kuceyeski A. (2025). Krakencoder: a unified brain connectome translation and fusion tool. Nature Methods. DOI: 10.1038/s41592-025-02706-2.
  • Preprint: bioRxiv 10.1101/2024.04.12.589274.
  • Upstream code: github.com/kjamison/krakencoder (model.py + fetch.py + per-flavor PCA transforms hosted on OSF: osf.io/dfp92).

License

HF Hub license tag: mit

Effective terms: MIT (copyright (c) 2024 Keith W. Jamison) on both the network code (github.com/kjamison/krakencoder) and the pretrained weights + per-flavor PCA transforms hosted on OSF (osf.io/dfp92). The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0.

Upstream license reference: https://opensource.org/licenses/MIT

Copyright

Network architecture, training code, and pretrained weights -- copyright (c) 2024 Keith W. Jamison; released under the MIT License. JAX / Equinox port code -- copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.

Upstream source

Original weights / reference implementation: https://github.com/kjamison/krakencoder

Provenance

This artefact was produced by ilex's save/load pipeline. The architecture is implemented in ilex.models.krakencoder.Krakencoder and the weights have been converted from their upstream format. See the upstream source above for the canonical reference.

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