Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space.
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
The paper is loaded when this pane is shown.
The authors' code
Python · 33 lines · 1.2 KB · no license
- from scipy import linalg
- def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
- mu1 = np.atleast_1d(mu1)
- mu2 = np.atleast_1d(mu2)
- sigma1 = np.atleast_2d(sigma1)
- sigma2 = np.atleast_2d(sigma2)
- assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
- assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"
- diff = mu1 - mu2
- # product might be almost singular
- covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
- if not np.isfinite(covmean).all():
- msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
- warnings.warn(msg)
- offset = np.eye(sigma1.shape[0]) * eps
- covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
- # numerical error might give slight imaginary component
- if np.iscomplexobj(covmean):
- if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
- m = np.max(np.abs(covmean.imag))
- raise ValueError("Imaginary component {}".format(m))
- covmean = covmean.real
- tr_covmean = np.trace(covmean)
- return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
fid.py at commit fb2c30b, no license · at the source
Overview
- Department of Computational Brain Imaging, Advanced Telecommunication Research Institute International (ATR),Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN,Tokyo, Japan
- Department of Neural Computation for Decision Making, Advanced Telecommunication Research Institute International (ATR),Kyoto, Japan
- Department of Biomedical Data Science, School of Medicine, Fujita Health University,Aichi, Japan
- International Center for Brain Science, Fujita Health University,Aichi, Japan
Abstract
The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.
Repository
Its files are read in the Code ↔ Paper reader above.
LeonBai/rsFC_embedding
fb2c30b6e6daa3e2b67383ccc29757faa1cc9f69, 7 June 2024Availability: 1 check, the latest on 29 September 2026: the link answers
- 29 September 2026: the link answers
3 files
- fid.py, Python, 33 lines
- relation-embedding.py, Python, 278 lines
- README.md, Text, 27 lines
Code availability statement
The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:
- it points to the authors' code: LeonBai/
rsFC_embedding
Read it in the paper: doi.org/10.1038/s41540-026-00699-y.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 2 scripts, each with its path and the digest of its content;
- no match between paragraphs and code yet;
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Data availability statement
The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:
- no repository, dataset or request procedure was recognized in it
Read it in the paper: doi.org/10.1038/s41540-026-00699-y.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 29 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 11 MeSH terms, 2 funders, 47 references.
Cite
This paper
Bai, W., Yamashita, O., Sakai, Y., & Yoshimoto, J. (2026). Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space. NPJ systems biology and applications, 12(1), 82. https://
BibTeX
@article{bai2026inferrin
author = {Bai, Wenjun and Yamashita, Okito and Sakai, Yuki and Yoshimoto, Junichiro},
title = {{Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space}},
journal = {NPJ systems biology and applications},
year = {2026},
month = apr,
volume = {12},
number = {1},
pages = {82},
publisher = {Nature Publishing Group},
issn = {2056-7189},
doi = {10.1038/
url = {https://
pmid = {41942500},
pmcid = {PMC13249975}
}
RIS
TY - JOUR
AU - Bai, Wenjun
AU - Yamashita, Okito
AU - Sakai, Yuki
AU - Yoshimoto, Junichiro
TI - Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space
T2 - NPJ systems biology and applications
J2 - NPJ Syst Biol Appl
PY - 2026
DA - 2026/
VL - 12
IS - 1
SP - 82
SN - 2056-7189
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1038/
"type": "article-journal",
"title": "Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space",
"container-title": "NPJ systems biology and applications",
"author": [
{
"family": "Bai",
"given": "Wenjun"
},
{
"family": "Yamashita",
"given": "Okito"
},
{
"family": "Sakai",
"given": "Yuki"
},
{
"family": "Yoshimoto",
"given": "Junichiro"
}
],
"container-title-short":
"volume": "12",
"issue": "1",
"page": "82",
"DOI": "10.1038/
"PMID": "41942500",
"PMCID": "PMC13249975",
"ISSN": "2056-7189",
"publisher": "Nature Publishing Group",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
6
]
]
}
}
The tracing map gets a citation of its own once an author has validated it and it has a DOI.
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.1038/s41398-026-04206-z
- Disrupted hierarchical functional brain organization in affective and psychotic disorders: insights from functional brain gradients.Journal: Translational psychiatryIn common: schizophrenia / psychosis, depression, 5 references
- [2] doi:10.1155/da/8792696 [code]
- Connectome Gradient-Based Subtyping of Major Depressive Disorder Reveals Distinct Neurobiological and Transcriptomic Signatures.Journal: Depression and anxietyIn common: SciPy, depression, fMRI, genetics / omics, 4 references
- [3] doi:10.64898/2026.08.13.26359403 [code]
- NeuroMorph: A Unified Morphological Reference Space for Cross-Disease Brain ProfilingJournal: medRxiv (preprint)In common: 5 references
- [4] doi:10.1038/s41380-026-03497-4 [code]
- Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders.Journal: Molecular psychiatryIn common: SciPy, schizophrenia / psychosis, autism, depression, 1 other category, 2 references
- [5] doi:10.1007/s44192-026-00521-5 [code]
- Associations between anterior hypothalamic subunits and ADHD and autistic traits revealed by deep learning MRI segmentation.Journal: Discover mental healthIn common: Keras, TensorFlow, SciPy, autism, 1 reference
- [6] doi:10.1111/ene.70678 [code]
- Who Falls After a Stroke? Evidence From a Prospective Stroke Cohort.Journal: European journal of neurologyIn common: Keras, TensorFlow, SciPy, 2 references
- [7] doi:10.7554/elife.104053 [code]
- Brain cognition gaps reveal associations with dopamine and factors related to brain health through artificial intelligence prediction of functional connectome.Journal: eLifeIn common: Keras, TensorFlow, SciPy, 1 reference
- [8] doi:10.1038/s41597-025-05174-7 [code]
- A large-scale MEG and EEG dataset for object recognition in naturalistic scenesJournal: n/aIn common: Keras, TensorFlow, SciPy, fMRI, methods / tools
- [9] doi:10.3389/fnsys.2026.1822122 [code]
- Convergence-divergence circuits for multimodal integration of innate and learned opponent valences.Journal: Frontiers in systems neuroscienceIn common: Keras, TensorFlow, SciPy, 1 reference
- [10] doi:10.1038/s41592-026-03057-2 [code]
- CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.Journal: Nature methodsIn common: Keras, TensorFlow, SciPy, methods / tools, genetics / omics
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Validate its tracing map
You validate the map as this page shows it: 1 repository of the authors' code, each at its verified commit and with its license, 2 scripts, and 0 matches between paragraphs and code (see the Code and Map sections). It then receives a DOI on Zenodo, with you (your ORCID iD) and OSCR as its creators; the code itself is not deposited.
The map's fingerprint: sha256:ada0a13dc6b0ffe6…
Add the badge to its README
The badge links the code to this page. Copy one of these into the README of the paper's code: only you decide where it goes, and nothing is changed for you.
Markdown
[, paste the snippet at the top, then “Commit changes…” and, to review it first, “Create a new branch and start a pull request”. You open the pull request; OSCR asks for no permission.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.
