Dataset Information

Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data.

ABSTRACT: Traumatic brain injury (TBI) induces cognitive deficits clinically and in animal models. Learning and memory testing is critical when evaluating potential therapeutic strategies and treatments to manage the effects of TBI. We evaluated three data analysis methods for the Morris water maze (MWM), a learning and memory assessment widely used in the neurotrauma field, to determine which statistical tool is optimal for MWM data. Hidden platform spatial MWM data aggregated from three separate experiments from the same laboratory were analyzed using 1) a logistic regression model, 2) an analysis of variance (ANOVA) model, and 3) an accelerated failure time (AFT) time-to-event model. The logistic regression model showed no significant evidence of differences between treatments among any swims over all days of the study, p?>?0.11. Although the ANOVA model found significant evidence of differences between sham and TBI groups on three out of four swims on the third day, results are potentially biased due to the failure of this model to account for censoring. The time-to-event AFT model showed significant differences between sham and TBI over all swims on the third day, p?

SUBMITTER: Andersen CR

PROVIDER: S-EPMC7875609 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data.

Andersen Clark R CR Wolf Jordan J Jennings Kristofer K Prough Donald S DS Hawkins Bridget E BE

Journal of neurotrauma 20201019 4

Traumatic brain injury (TBI) induces cognitive deficits clinically and in animal models. Learning and memory testing is critical when evaluating potential therapeutic strategies and treatments to manage the effects of TBI. We evaluated three data analysis methods for the Morris water maze (MWM), a learning and memory assessment widely used in the neurotrauma field, to determine which statistical tool is optimal for MWM data. Hidden platform spatial MWM data aggregated from three separate experim ...[more]

PMID: 32829672

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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:BackgroundSeparation or monotone likelihood may exist in fitting process of the accelerated failure time (AFT) model using maximum likelihood approach when sample size is small and/or rate of censoring is high (rare event) or there is at least one strong covariate in the model, resulting in infinite estimates of at least one regression coefficient.MethodsThis paper investigated the properties of the maximum likelihood estimator (MLE) of the regression parameters of the AFT models for small sample and/or rare-event situation and addressed the problems by introducing a penalized likelihood approach. The penalized likelihood function and the corresponding score equation is derived by adding a penalty term to the existing likelihood function, which was originally proposed by Firth (Biometrika, 1993) for the exponential family models. Further, a post-hoc adjustment of intercept and scale parameters is discussed keeping them out of penalization to ensure accurate prediction of survival probability. The penalized method was illustrated for the widely used log-location-scale family models such as Weibull, Log-normal and Log-logistic distributions and compared the models and methods uisng an extensive simulation study.ResultsThe simulation study, performed separately for each of the log-location-scale models, showed that Firth's penalized likelihood succeeded to solve the problem of separation and achieve convergence, providing finite estimates of the regression coefficients, which are not often possible by the MLE. Furthermore, the proposed penalized method showed substantial improvement over MLE by providing smaller amount of bias, mean squared error (MSE), narrower confidence interval and reasonably accurate prediction of survival probabilities. The methods are illustrated using prostate cancer data with existence of separation, and results supported the simulation findings.ConclusionWhen sample size is small (≤ 50) or event is rare (i.e., censoring proportion is high) and/or there is any evidence of separation in the data, we recommend to use Firth's penalized likelihood method for fitting AFT model.

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.

Dataset Information

Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data.

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Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data.

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