Evidence map›Paper›PMID 35025127›Full record

ArticleHippocampus2022

Novel measures of Morris water maze performance that use vector field maps to assess accuracy, uncertainty, and intention of navigational searches.

Prabod Kumar Meenakshi, Dhruv Mehrotra, N Nruthyathi, Daniel Almeida-Filho, Yong-Seok Lee, Alcino Silva, J Balaji

Abstract read
In one paragraph

Article in Hippocampus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Prabod Kumar MeenakshiCentre for Neuroscience, Indian Institute of Science, Bengaluru, India.ORCID 0000-0002-0711-5762
Dhruv MehrotraCentre for Neuroscience, Indian Institute of Science, Bengaluru, India.ORCID 0000-0001-5813-3218
N NruthyathiCentre for Neuroscience, Indian Institute of Science, Bengaluru, India.
Daniel Almeida-FilhoDepartments of Neurobiology, Psychology, Psychiatry, Integrative Center for Learning and Memory and Brain Research Institute, University of California Los Angeles, Los Angeles, California, USA.
Yong-Seok LeeDepartments of Physiology, Biomedical Sciences, Neuroscience Research Institute, Seoul National University College of Medicine, Seoul, South Korea.
Alcino SilvaDepartments of Neurobiology, Psychology, Psychiatry, Integrative Center for Learning and Memory and Brain Research Institute, University of California Los Angeles, Los Angeles, California, USA.
J BalajiCentre for Neuroscience, Indian Institute of Science, Bengaluru, India.ORCID 0000-0002-4442-6981

Funding

Mechanisms for Neurological Dysfunction in NF1 and Noonan SyndromeR01MH084315 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SILVA, ALCINO J. · 2008 to 2020
$5.0M
Molecular, Cellular and Circuit Mechanisms for Age-Related Deficits in Memory-LinkingRF1AG013622 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SILVA, ALCINO J. · 2017 to 2022
$4.9M
CCR5 Activation and Cognitive Dysfunction Caused by HIV-ProteinsR01NS106969 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SILVA, ALCINO J. · 2017 to 2021
$3.7M
Molecular, Cellular and Circuit Mechnisms for Memory-linking deficits in Psychiatric DisordersR01MH113071 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SILVA, ALCINO J. · 2017 to 2021
$2.3M
NIA NIH HHS RF1 AG013622NIMH NIH HHS R01 MH084315NIMH NIH HHS R01 MH113071NINDS NIH HHS R01 NS106969
6 · The paper itself

Abstract

Most commonly used behavioral measures for testing learning and memory in the Morris water maze (MWM) involve comparisons of an animal's residence time in different quadrants of the pool. Such measures are limited in their ability to test different aspects of the animal's performance. Here, we describe novel measures of performance in the MWM that use vector fields to capture the motion of mice as well as their search pattern in the maze. Using these vector fields, we develop quantitative measures of performance that are intuitive and more sensitive than classical measures. First, we describe search patterns in terms of vector field properties and use these properties to define three metrics of spatial memory namely Spatial Accuracy, Uncertainty and, Intensity of Search. We demonstrate the usefulness of these measures using four different data sets including comparisons between different strains of mice, an analysis of two mouse models of Noonan syndrome (NS; Ptpn11 D61G and Ptpn11 N308D/+), and a study of goal reversal training. Importantly, besides highlighting novel aspects of performance in this widely used spatial task, our measures were able to uncover previously undetected differences, including in an animal model of NS, which we rescued with the mitogen activated protein kinase kinase (MEK) inhibitor SL327. Thus, our results show that our approach breaks down performance in the MWM into sensitive measurable independent components that highlight differences in spatial learning and memory in the MWM that were undetected by conventional measures.

Indexed as

IntentionMorris Water Maze TestAnimalsDisease Models, AnimalMaze LearningMiceSpatial LearningUncertaintyconvergence hotspotsintensity of searchMorris water mazesearch center accuracysearch center spreadvector field properties

Identifiers

PMID35025127
PMCPMC8921876

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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.