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Record W4409392944 · doi:10.1016/j.tibtech.2025.02.009

Brain organoids: building higher-order complexity and neural circuitry models

2025· review· en· W4409392944 on OpenAlexaff
Gulimiheranmu Maisumu, Stephanie M. Willerth, Michael W. Nestor, Ben Waldau, Stefan Schülke, Francesco Nardi, You Zhou, Madel Durens, Bo Liang, Abraam M. Yakoub

Bibliographic record

VenueTrends in biotechnology · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Victoria
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institutes of Health
KeywordsOrganoidOrder (exchange)Computer scienceNeuroscienceBiologyComputational biologyBusiness

Abstract

fetched live from OpenAlex

Brain organoids are 3D tissue models of the human brain that are derived from pluripotent stem cells (PSCs). They have enabled studies that were previously stymied by the inaccessibility of human brain tissue or the limitations of mouse models of some brain diseases. Despite their enormous potential, brain organoids have had significant limitations that prevented them from recapitulating the full complexity of the human brain and reduced their utility in disease studies. We describe recent progress in addressing these limitations, especially building complex organoids that recapitulate the interactions between multiple brain regions, and reconstructing in vitro the neural circuitry present in vivo. These major advances in the human brain organoid technology will remarkably facilitate brain disease modeling and neuroscience research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.140
GPT teacher head0.366
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2025
Admission routes1
Has abstractyes

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