Using causal models to distinguish between neurogenesis-dependent and -independent effects on behaviour

There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past fifteen years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenesis mediates the effect of an intervention on behaviour. Previous studies may have incorrectly attributed changes in behavioural performance to neurogenesis because the role of known (or unknown) neurogenesis-independent mechanisms were not formally taken into consideration during the analysis. Causal models can tease apart complex causal relationships and were used to demonstrate that the effect of exercise on pattern separation is via neurogenesis-independent mechanisms. Many studies in the neurogenesis literature would benefit from the use of statistical methods that can separate neurogenesis-dependent from neurogenesis-independent effects on behaviour

Improving basic and translational science by accounting for litter-to-litter variation in animal models

Background: Animals from the same litter are often more alike compared with animals from different litters. This litter-to-litter variation, or "litter effects", can influence the results in addition to the experimental factors of interest. Furthermore, an experimental treatment can be applied to whole litters rather than to individual offspring. For example, in the valproic acid (VPA) model of autism, VPA is administered to pregnant females thereby inducing the disease phenotype in the offspring. With this type of experiment the sample size is the number of litters and not the total number of offspring. If such experiments are not appropriately designed and analysed, the results can be severely biased as well as extremely underpowered. Results: A review of the VPA literature showed that only 9% (3/34) of studies correctly determined that the experimental unit (n) was the litter and therefore made valid statistical inferences. In addition, litter effects accounted for up to 61% (p <0.001) of the variation in behavioural outcomes, which was larger than the treatment effects. In addition, few studies reported using randomisation (12%) or blinding (18%), and none indicated that a sample size calculation or power analysis had been conducted. Conclusions: Litter effects are common, large, and ignoring them can make replication of findings difficult and can contribute to the low rate of translating preclinical in vivo studies into successful therapies. Only a minority of studies reported using rigorous experimental methods, which is consistent with much of the preclinical in vivo literature.Comment: http://www.biomedcentral.com/1471-2202/14/37/abstrac

Why we should use simpler models if the data allow this: relevance for ANOVA designs in experimental biology.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Analysis of variance (ANOVA) is a common statistical technique in physiological research, and often one or more of the independent/predictor variables such as dose, time, or age, can be treated as a continuous, rather than a categorical variable during analysis - even if subjects were randomly assigned to treatment groups. While this is not common, there are a number of advantages of such an approach, including greater statistical power due to increased precision, a simpler and more informative interpretation of the results, greater parsimony, and transformation of the predictor variable is possible. RESULTS: An example is given from an experiment where rats were randomly assigned to receive either 0, 60, 180, or 240 mg/L of fluoxetine in their drinking water, with performance on the forced swim test as the outcome measure. Dose was treated as either a categorical or continuous variable during analysis, with the latter analysis leading to a more powerful test (p = 0.021 vs. p = 0.159). This will be true in general, and the reasons for this are discussed. CONCLUSION: There are many advantages to treating variables as continuous numeric variables if the data allow this, and this should be employed more often in experimental biology. Failure to use the optimal analysis runs the risk of missing significant effects or relationships

Quantifying the behavioural relevance of hippocampal neurogenesis

Few studies that examine the neurogenesis--behaviour relationship formally establish covariation between neurogenesis and behaviour or rule out competing explanations. The behavioural relevance of neurogenesis might therefore be overestimated if other mechanisms account for some, or even all, of the experimental effects. A systematic review of the literature was conducted and the data reanalysed using causal mediation analysis, which can estimate the behavioural contribution of new hippocampal neurons separately from other mechanisms that might be operating. Results from eleven eligible individual studies were then combined in a meta-analysis to increase precision (representing data from 215 animals) and showed that neurogenesis made a negligible contribution to behaviour (standarised effect = 0.15; 95% CI = -0.04 to 0.34; p = 0.128); other mechanisms accounted for the majority of experimental effects (standardised effect = 1.06; 95% CI = 0.74 to 1.38; p = 1.7 $\times 10^{-11}$).Comment: To be published in PLoS ON

Visualising disease progression on multiple variables with vector plots and path plots.

BACKGROUND: It is often desirable to observe how a disease progresses over time in individual patients, rather than graphing group averages; and since multiple outcomes are typically recorded on each patient, it would be advantageous to visualise disease progression on multiple variables simultaneously. METHODS: A variety of vector plots and a path plot have been developed for this purpose, and data from a longitudinal Huntington's disease study are used to illustrate the utility of these graphical methods for exploratory data analysis. RESULTS: Initial and final values for three outcome variables can be easily visualised per patient, along with the change in these variables over time. In addition to the disease trajectory, the path individual patients take from initial to final observation can be traced. Categorical variables can be coded with different types of vectors or paths (e.g. different colours, line types, line thickness) and separate panels can be used to include further categorical or continuous variables, allowing clear visualisation of further information for each individual. In addition, summary statistics such as mean vectors, bivariate interquartile ranges and convex polygons can be included to assist in interpreting trajectories, comparing groups, and detecting multivariate outliers. CONCLUSION: Vector and path plots are useful graphical methods for exploratory data analysis when individual-level information on multiple variables over time is desired, and they have several advantages over plotting each variable separately.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

What exactly is ‘N’ in cell culture and animal experiments?

Biologists determine experimental effects by perturbing biological entities or units. When done appropriately, independent replication of the entity-intervention pair contributes to the sample size (N) and forms the basis of statistical inference. If the wrong entity-intervention pair is chosen, an experiment cannot address the question of interest. We surveyed a random sample of published animal experiments from 2011 to 2016 where interventions were applied to parents and effects examined in the offspring, as regulatory authorities provide clear guidelines on replication with such designs. We found that only 22% of studies (95% CI = 17%-29%) replicated the correct entity-intervention pair and thus made valid statistical inferences. Nearly half of the studies (46%, 95% CI = 38%-53%) had pseudoreplication while 32% (95% CI = 26%-39%) provided insufficient information to make a judgement. Pseudoreplication artificially inflates the sample size, and thus the evidence for a scientific claim, resulting in false positives. We argue that distinguishing between biological units, experimental units, and observational units clarifies where replication should occur, describe the criteria for genuine replication, and provide concrete examples of in vitro, ex vivo, and in vivo experimental designs

Optimal experimental design for efficient toxicity testing in microphysiological systems: A bone marrow application

Introduction: Microphysiological systems (MPS; organ-on-a-chip) aim to recapitulate the 3D organ microenvironment and improve clinical predictivity relative to previous approaches. Though MPS studies provide great promise to explore treatment options in a multifactorial manner, they are often very complex. It is therefore important to assess and manage technical confounding factors, to maximise power, efficiency and scalability.Methods: As an illustration of how MPS studies can benefit from a systematic evaluation of confounders, we developed an experimental design approach for a bone marrow (BM) MPS and tested it for a specified context of use, the assessment of lineage-specific toxicity.Results: We demonstrated the accuracy of our multicolour flow cytometry set-up to determine cell type and maturity, and the viability of a “repeated measures” design where we sample from chips repeatedly for increased scalability and robustness. Importantly, we demonstrated an optimal way to arrange technical confounders. Accounting for these confounders in a mixed-model analysis pipeline increased power, which meant that the expected lineage-specific toxicities following treatment with olaparib or carboplatin were detected earlier and at lower doses. Furthermore, we performed a sample size analysis to estimate the appropriate number of replicates required for different effect sizes. This experimental design-based approach will generalise to other MPS set-ups.Discussion: This design of experiments approach has established a groundwork for a reliable and reproducible in vitro analysis of BM toxicity in a MPS, and the lineage-specific toxicity data demonstrate the utility of this model for BM toxicity assessment. Toxicity data demonstrate the utility of this model for BM toxicity assessment

An open-label study to assess the feasibility and tolerability of rilmenidine for the treatment of Huntington's disease.

UNLABELLED: Preclinical data have shown that rilmenidine can regulate autophagy in models of Huntington's disease (HD), providing a potential route to alter the disease course in patients. Consequently, a 2-year open-label study examining the tolerability and feasibility of rilmenidine in mild-moderate HD was undertaken. 18 non-demented patients with mild to moderate HD took daily doses of 1 mg Rilmenidine for 6 months and 2 mg for a further 18 months followed by a 3-month washout period. The primary outcome was the number of withdrawals and serious adverse events. Secondary outcomes included safety parameters and changes in disease-specific variables, such as motor, cognitive and functional performance, structural MRI and serum metabolomic analysis. 12 patients completed the study; reasons for withdrawal included problems tolerating study procedures (MRI, and venepuncture), depression requiring hospital admission and logistical reasons. Three serious adverse events were recorded, including hospitalisation for depression, but none were thought to be drug-related. Changes in secondary outcomes were analysed as the annual rate of change in the study group. The overall change was comparable to changes seen in recent large observational studies in HD patients, though direct statistical comparisons to these studies were not made. Chronic oral administration of rilmenidine is feasible and well-tolerated and future, larger, placebo-controlled, studies in HD are warranted. TRIAL REGISTRATION: EudraCT number 2009-018119-14.This work was supported by an NIHR grant of a Biomedical Research Centre to the University of Cambridge/ Addenbrookes Hospital, Wellcome Trust (Principal Research Fellowship to DCR (095317/Z/11/Z) and JBR (Senior Research Fellowship, 103838) and DCR is grateful for funding from the UK Dementia Research Institute (funded by the MRC, Alzheimer’s Research UK and the Alzheimer’s Society). In addition the imaging part of the study was supported by a grant from the Rosetrees Trus