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A new statistical method to analyze Morris Water Maze data using Dirichlet distribution.

ABSTRACT: The Morris Water Maze (MWM) is a behavioral test widely used in the field of neuroscience to evaluate spatial learning memory of rodents. However, the interpretation of results is often impaired by the common use of statistical tests based on independence and normal distributions that do not reflect basic properties of the test data, such as the constant-sum constraint. In this work, we propose to analyze MWM data with the Dirichlet distribution, which describes constant-sum data with minimal hypotheses, and we introduce a statistical test based on uniformity (equal amount of time spent in each quadrant of the maze) that evaluates memory impairments. We demonstrate that this test better represents MWM data and show its efficiency on simulated as well as in vivo data. Based on Dirichlet distribution, we also propose a new way to plot MWM data, showing mean values and inter-individual variability at the same time, on an easily interpretable chart. Finally, we conclude with a perspective on using Bayesian analysis for MWM data.

SUBMITTER: Maugard M

PROVIDER: S-EPMC6833991 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A new statistical method to analyze Morris Water Maze data using Dirichlet distribution.

Maugard Marianne M Doux Cyrille C Bonvento Gilles G

F1000Research 20190906

The Morris Water Maze (MWM) is a behavioral test widely used in the field of neuroscience to evaluate spatial learning memory of rodents. However, the interpretation of results is often impaired by the common use of statistical tests based on independence and normal distributions that do not reflect basic properties of the test data, such as the constant-sum constraint. In this work, we propose to analyze MWM data with the Dirichlet distribution, which describes constant-sum data with minimal hy ...[more]

PMID: 31723422

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Project description:Learning and memory are among higher-order cognitive functions that are based on numerous molecular processes including changes in the expression of genes. To identify genes associated with learning and memory formation, here, we used the RNA-seq (high-throughput mRNA sequencing) technology to compare hippocampal transcriptomes between mice with high and low Morris water maze (MWM) cognitive performance. We identified 88 differentially expressed genes (DEGs) and 24 differentially alternatively spliced transcripts between the high- and low-MWM-performance mice. Although the sets of DEGs and differentially alternatively spliced transcripts did not overlap, both were found to be enriched with genes related to the same type of biological processes: trans-synaptic signaling, cognition, and glutamatergic transmission. These findings were supported by the results of weighted-gene co-expression network analysis (WGCNA) revealing the enrichment of MWM-cognitive-performance-correlating gene modules with very similar Gene Ontology terms. High-MWM-performance mice manifested mostly higher expression of the genes associated with glutamatergic transmission and long-term potentiation implementation, which are processes necessary for memory acquisition and consolidation. In this set, there were genes participating in the regulation of trans-synaptic signaling, primarily AMPA receptor signaling (Nrn1, Nptx1, Homer3, Prkce, Napa, Camk2b, Syt7, and Nrgn) and calcium turnover (Hpca, Caln1, Orai2, Cpne4, and Cpne9). In high-MWM-performance mice, we also demonstrated significant upregulation of the "flip" splice variant of Gria1 and Gria2 transcripts encoding subunits of AMPA receptor. Altogether, our data helped to identify specific genes in the hippocampus that are associated with learning and long-term memory. We hypothesized that the differences in MWM cognitive performance between the mouse groups are linked with increased long-term potentiation, which is mainly mediated by increased glutamatergic transmission, primarily AMPA receptor signaling.

Project description:Gene transcription and translation in the hippocampus is of critical importance in hippocampus-dependent memory formation, including during Morris water maze (MWM) learning. Previous work using gene deletion models has shown that the immediate-early genes (IEGs) c-Fos, Egr-1, and Arc are crucial for such learning. Recently, we reported that induction of IEGs in sparse dentate gyrus neurons requires ERK MAPK signaling and downstream formation of a distinct epigenetic histone mark (i.e., phospho-acetylated histone H3). Until now, this signaling, epigenetic and gene transcriptional pathway has not been comprehensively studied in the MWM model. Therefore, we conducted a detailed study of the phosphorylation of ERK1/2 and serine10 in histone H3 (H3S10p) and induction of IEGs in the hippocampus of MWM trained rats and matched controls. MWM training evoked consecutive waves of ERK1/2 phosphorylation and H3S10 phosphorylation, as well as c-Fos, Egr-1, and Arc induction in sparse hippocampal neurons. The observed effects were most pronounced in the dentate gyrus. A positive correlation was found between the average latency to find the platform and the number of H3S10p-positive dentate gyrus neurons. Furthermore, chromatin immuno-precipitation (ChIP) revealed a significantly increased association of phospho-acetylated histone H3 (H3K9ac-S10p) with the gene promoters of c-Fos and Egr-1, but not Arc, after MWM exposure compared with controls. Surprisingly, however, we found very little difference between IEG responses (regarding both protein and mRNA) in MWM-trained rats compared with matched swim controls. We conclude that exposure to the water maze evokes ERK MAPK activation, distinct epigenetic changes and IEG induction predominantly in sparse dentate gyrus neurons. It appears, however, that a specific role for IEGs in the learning aspect of MWM training may become apparent in downstream AP-1- and Egr-1-regulated (second wave) genes and Arc-dependent effector mechanisms.

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A new statistical method to analyze Morris Water Maze data using Dirichlet distribution.

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A new statistical method to analyze Morris Water Maze data using Dirichlet distribution.

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