Evidence map›Paper›PMID 42213668›Full record

ArticlePLoS biology2026

Flexible goal learning involves coordinated population activity in dCA1 and medial orbitofrontal cortex.

Jiasong Li, Lingwei Tang, Xinhang Wei, Yumin Chen, Haibing Xu

Abstract read
In one paragraph

Article in PLoS biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jiasong LiKey Laboratory of Mental Health of the Ministry of Education, Guangdong‑Hong Kong‑Macao Greater Bay Area Center for Brain Science and Brain‑Inspired Intelligence, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Lingwei TangKey Laboratory of Mental Health of the Ministry of Education, Guangdong‑Hong Kong‑Macao Greater Bay Area Center for Brain Science and Brain‑Inspired Intelligence, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Xinhang WeiKey Laboratory of Mental Health of the Ministry of Education, Guangdong‑Hong Kong‑Macao Greater Bay Area Center for Brain Science and Brain‑Inspired Intelligence, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Yumin ChenKey Laboratory of Mental Health of the Ministry of Education, Guangdong‑Hong Kong‑Macao Greater Bay Area Center for Brain Science and Brain‑Inspired Intelligence, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Haibing XuKey Laboratory of Mental Health of the Ministry of Education, Guangdong‑Hong Kong‑Macao Greater Bay Area Center for Brain Science and Brain‑Inspired Intelligence, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.ORCID https://orcid.org/0009-0005-4788-9294

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flexible goal‑directed navigation requires integrating changing goal information with a stable spatial map, yet how cortico-hippocampal circuits accomplish this remains unclear. We simultaneously recorded medial orbitofrontal cortex (mOFC) and dorsal CA1 (dCA1) while rats learned daily changing goal locations on a cheeseboard maze. Rats rapidly learned new goal locations and retained memory for them in the post‑probe session. Both regions contained goal‑related neuronal representations, but their profiles differed: dCA1 showed stronger spatial specificity, whereas mOFC showed more prominent learning‑related updating of goal‑related activity. Combining dCA1 and mOFC activity improved decoding of behavioral stage and learning block relative to either region alone, consistent with complementary contributions to ongoing behavior and learning state. Across learning, these population‑level differences were accompanied by stronger theta‑range synchronization and theta-gamma coupling during navigation than during goal periods. A recurrent network model with dynamic synaptic efficacy captured qualitative features of efficient acquisition and flexible goal updating, providing a candidate computational framework for how learning‑related temporal coordination could contribute to adaptive navigation.

Indexed as

CA1 Region, HippocampalLearningPrefrontal CortexAnimalsGoalsMaleMaze LearningMemoryNeuronsRatsRats, Long-Evans

Identifiers

PMID42213668
PMCPMC13221071

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.