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

Pseudoreplication invalidates the results of many neuroscientific studies

Background: Pseudoreplication occurs when observations are not statistically independent, but treated as if they are. This can occur when there are multiple observations on the same subjects, when samples are nested or hierarchically organised, or when measurements are correlated in time or space. Analysis of such data without taking these dependencies into account can lead to meaningless results, and examples can easily be found in the neuroscience literature.\ud \ud Results: A single issue of Nature Neuroscience provided a number of examples and is used as a case study to highlight how pseudoreplication arises in neuroscientific studies, why the analyses in these papers are incorrect, and appropriate analytical methods are provided. 12% of papers had pseudoreplication and a further 36% were suspected of having pseudoreplication, but it was not possible to determine for certain because insufficient information about the analysis was provided.\ud \ud Conclusions: Pseudoreplication undermines the conclusions from statistical analysis of data, and it would be easier to detect if the sample size, degrees of freedom, the test statistic, and precise p-values are reported. This information should be a requirement for all publications

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

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

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

Surface acoustic wave modulation of single photon emission from GaN/InGaN nanowire quantum dots

On-chip quantum information processing requires controllable quantum light sources that can be operated on-demand at high-speeds and with the possibility of in-situ control of the photon emission wavelength and its optical polarization properties. Here, we report on the dynamic control of the optical emission from core-shell GaN/InGaN nanowire (NW) heterostructures using radio frequency surface acoustic waves (SAWs). The SAWs are excited on the surface of a piezoelectric lithium niobate crystal equipped with a SAW delay line onto which the NWs were mechanically transferred. Luminescent quantum dot (QD)-like exciton localization centers induced by compositional fluctuations within the InGaN nanoshell were identified using stroboscopic micro-photoluminescence (micro-PL) spectroscopy. They exhibit narrow and almost fully linearly polarized emission lines in the micro-PL spectra and a pronounced anti-bunching signature of single photon emission in the photon correlation experiments. When the nanowire is perturbed by the propagating SAW, the embedded QD is periodically strained and its excitonic transitions are modulated by the acousto-mechanical coupling, giving rise to a spectral fine-tuning within a ~1.5 meV bandwidth at the acoustic frequency of ~330 MHz. This outcome can be further combined with spectral detection filtering for temporal control of the emitted photons. The effect of the SAW piezoelectric field on the QD charge population and on the optical polarization degree is also observed. The advantage of the acousto-optoelectric over other control schemes is that it allows in-situ manipulation of the optical emission properties over a wide frequency range (up to GHz frequencies).Comment: arXiv admin note: text overlap with arXiv:1902.0791

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