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A translational rodent assay of affective biases in depression and antidepressant therapy.

ABSTRACT: The subjective measures used to study mood disorders in humans cannot be replicated in animals; however, the increasing application of objective neuropsychological methods provides opportunities to develop translational animal tasks. Here we describe a novel behavioral approach, which has enabled us to investigate similar affective biases in rodents. In our affective bias test (ABT), rats encounter two independent positive experiences--the association between food reward and specific digging substrate--during discrimination learning sessions. These are performed on separate days under either neutral conditions or during a pharmacological or affective state manipulation. Affective bias is then quantified using a preference test where both previously rewarded substrates are presented together and the rat's choices recorded. The absolute value of the experience is kept consistent and all other factors are counterbalanced so that any bias at recall can be attributed to treatment. Replicating previous findings from studies in healthy volunteers, we observe significant positive affective biases following acute treatment with typical (fluoxetine, citalopram, reboxetine, venlafaxine, clomipramine) and atypical antidepressants (agomelatine, mirtazapine), and significant negative affective biases following treatment with drugs associated with inducing negative affective states in humans (FG7142, rimonabant, 13-cis retinoic acid). We also observed that acute psychosocial stress and environmental enrichment induce significant negative and positive affective biases, respectively, and provide evidence that these affective biases involve memory consolidation. The positive and negative affective biases induced in our test also mirror the antidepressant and pro-depressant effects of these drugs in patients suggesting our test has both translational and predictive validity. Our results suggest that cognitive affective biases could contribute to drug- or stress-induced mood changes in people and support the hypothesis that a cognitive neuropsychological mechanism contributes to antidepressant drug efficacy.

SUBMITTER: Stuart SA

PROVIDER: S-EPMC3717539 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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A translational rodent assay of affective biases in depression and antidepressant therapy.

Stuart Sarah A SA Butler Paul P Munafò Marcus R MR Nutt David J DJ Robinson Emma Sj ES

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20130315 9

The subjective measures used to study mood disorders in humans cannot be replicated in animals; however, the increasing application of objective neuropsychological methods provides opportunities to develop translational animal tasks. Here we describe a novel behavioral approach, which has enabled us to investigate similar affective biases in rodents. In our affective bias test (ABT), rats encounter two independent positive experiences--the association between food reward and specific digging sub ...[more]

PMID: 23503126

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Project description:BackgroundWith the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.MethodsIn this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases.ResultsIn this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results.ConclusionsOur studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a generic solution for the label skewness bias overcome due to the powerful feature extraction capability from derivative component analysis. Our work identifies and solves an important but less addressed problem in translational research. It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

Dataset Information

A translational rodent assay of affective biases in depression and antidepressant therapy.

Publications

A translational rodent assay of affective biases in depression and antidepressant therapy.

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