RESUMO
Significance: The identification and manipulation of spatially identified neuronal ensembles with optical methods have been recently used to prove the causal link between neuronal ensemble activity and learned behaviors. However, the standardization of a conceptual framework to identify and manipulate neuronal ensembles from calcium imaging recordings is still lacking. Aim: We propose a conceptual framework for the identification and manipulation of neuronal ensembles using simultaneous calcium imaging and two-photon optogenetics in behaving mice. Approach: We review the computational approaches that have been used to identify and manipulate neuronal ensembles with single cell resolution during behavior in different brain regions using all-optical methods. Results: We proposed three steps as a conceptual framework that could be applied to calcium imaging recordings to identify and manipulate neuronal ensembles in behaving mice: (1) transformation of calcium transients into binary arrays; (2) identification of neuronal ensembles as similar population vectors; and (3) targeting of neuronal ensemble members that significantly impact behavioral performance. Conclusions: The use of simultaneous two-photon calcium imaging and two-photon optogenetics allowed for the experimental demonstration of the causal relation of population activity and learned behaviors. The standardization of analytical tools to identify and manipulate neuronal ensembles could accelerate interventional experiments aiming to reprogram the brain in normal and pathological conditions.
RESUMO
Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model.(AU)
Assuntos
Gráficos por Computador , Análise de Dados/análise , Modelos EstatísticosRESUMO
Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model.
Assuntos
Análise de Dados/análise , Gráficos por Computador , Modelos EstatísticosRESUMO
A estatística descritiva é uma poderosa ferramenta para se analisar conjuntos de dados, entretanto é muito pouco utilizada. Uma análise descritiva bem conduzida pode evitar vários problemas que podem ocorrer em análises mais complexas, além de fornecer um retrato da amostra em estudo. Na estatística descritiva existem os métodos gráficos, que se bem empregados, são bem mais informativos que tabelas. Dentre os tipos de gráficos mais conhecidos existem o boxplot, histograma, gráfico de dispersão, ou de barras. O objetivo desse artigo é descrever um novo tipo de gráfico chamado beanplot que pode ser feito no aplicativo R. Através de exemplos é mostrado como fazer o beanplot no R e como interpretar seus resultados. Nesse gráfico podemos representar várias informações sobre variáveis quantitativas, tais como: média, mediana, distribuição dos dados, etc. Além disso, através desse gráfico podemos comparar distribuições de diversas variáveis ou da mesma variável em diferentes grupos.
Descriptive analysis is a powerful tool to analyze data sets, but is rarely used. It can avoid many problems that can occur in more complex analyses, providing a picture of the sample under study. Some graphical methods are much more informative than tables. There are several types of graphics which are well known: boxplot, histogram, scatter plot, or bar plot. The aim of this paper is to present a new type of graph called beanplot, describing the steps to build the graphs using the statistical software R. Besides, some examples are presented to discuss how to interpret the results. Through beanplot graphs, it is possible to represent a plenty of information regarding quantitative variables, such as mean, median, distribution of data, etc. Moreover, through this graphic we compare distributions of several variables or the same variable in different groups.