Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.

Manabu I, Tanigaki K, Ogawa N, Nitta N, Shiino A

DOI: 10.3389/fnins.2026.1688282
Published: 2026
Container: Frontiers in neuroscience
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fnins.2026.1688282,
  title = {Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.},
  author = {Manabu I and Tanigaki K and Ogawa N and Nitta N and Shiino A},
  year = {2026},
  journal = {Frontiers in neuroscience},
  doi = {10.3389/fnins.2026.1688282},
  url = {https://doi.org/10.3389/fnins.2026.1688282}
}

RIS

TY  - JOUR
TI  - Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.
AU  - Manabu I
AU  - Tanigaki K
AU  - Ogawa N
AU  - Nitta N
AU  - Shiino A
PY  - 2026
JO  - Frontiers in neuroscience
DO  - 10.3389/fnins.2026.1688282
UR  - https://doi.org/10.3389/fnins.2026.1688282
ER  -

APA

I, M., K, T., N, O., N, N., & A, S. (2026). Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.. Frontiers in neuroscience. https://doi.org/10.3389/fnins.2026.1688282

Source records

pubmed · retrieved 2026-09-25T14:24:05.745Z