Evidence map›Paper›PMID 38298784›Full record

ArticleBiological psychiatry global open science2024

Enhancing Associative Learning in Rats With a Computationally Designed Training Protocol.

Xu O Zhang, Yili Zhang, Claire E Cho, Douglas S Engelke, Paul Smolen, John H Byrne, Fabricio H Do-Monte

Open access · goldAbstract read
In one paragraph

Article in Biological psychiatry global open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
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

7 authors at 1 institution in 1 country.

Xu O ZhangDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
Yili ZhangDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
Claire E ChoDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
Douglas S EngelkeDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
Paul SmolenDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
John H ByrneDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
Fabricio H Do-MonteDepartment of Neurobiology and Anatomy, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.
The University of Texas Health Science Center at Houston · US

Funding

Modeling the Molecular Networks that Underlie the Formation and Consolidation of MemoryR01NS102490 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI John H Byrne · 2018 to 2026
$3.6M
Neural Circuits Balancing Reward-Approaching with Threat-AvoidanceR01MH120136 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI DO MONTE, FABRICIO HOFFMANN · 2020 to 2024
$2.8M
Corticothalamic circuits mediating behavioral adaptations to unexpected reward omissionR01MH131570 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BEIERLEIN, MICHAEL, DO MONTE, FABRICIO HOFFMANN · 2023 to 2025
$1.8M
Thalamic circuits in the integration of fear and rewardR00MH105549 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI DO MONTE, FABRICIO HOFFMANN · 2016 to 2018
$747k
Thalamic circuits in the integration of fear and rewardK99MH105549 · NIMH · UNIVERSITY OF PUERTO RICO MED SCIENCES · PI DO MONTE, FABRICIO HOFFMANN · 2015 to 2016
$137k
NIMH NIH HHS K99 MH105549NIMH NIH HHS R00 MH105549NIMH NIH HHS R01 MH120136NIMH NIH HHS R01 MH131570NINDS NIH HHS R01 NS102490
6 · The paper itself

Abstract

Background: Learning requires the activation of protein kinases with distinct temporal dynamics. In Methods: We simulated 1000 training protocols with varying ITIs to predict an optimal protocol based on empirical data for PKA and ERK dynamics in rat hippocampus. Adult male rats received the optimal protocol or control protocols in auditory fear conditioning and fear extinction experiments. Immunohistochemistry was performed to evaluate pCREB (phosphorylated cAMP response element binding)\protein levels in brain regions that have been implicated in fear acquisition. Results: Rats exposed to the optimal conditioning protocol with irregular ITIs exhibited impaired extinction memory acquisition within the session using a standard footshock intensity, and stronger fear memory retrieval and spontaneous recovery with a weaker footshock intensity, compared with rats that received massed or spaced conditioning protocols with fixed ITIs. Rats exposed to the optimal extinction protocol displayed improved extinction of contextual fear memory and reduced spontaneous recovery compared with rats that received standard extinction protocols. Moreover, the optimal conditioning protocol increased pCREB levels in the dentate gyrus of the dorsal hippocampus, suggesting enhanced induction of long-term potentiation. Conclusions: These findings demonstrate that a computational model-driven behavioral intervention can enhance associative learning in mammals and may provide insight into strategies to improve cognition in humans.

Indexed as

Associative memoryComputational modelFear conditioningFear extinctionLong-term potentiationSpaced learning

Identifiers

PMID38298784
PMCPMC10829654
OpenAlexW4385463202

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.