A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers’ ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data – without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.</p

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers’ ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data – without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work

EFFECT OF THE SPERM MEMBRANE PROTECTOR ON THE FREEZABILITY OF GOAT SEMEN

Antecedentes: Previo a la congelación del semen caprino, es necesario realizar el lavado seminal por centrifugación para eliminar la Fosfolipasa A, con la consecuente pérdida de elementos involucrados en el mantenimiento de las funciones del espermatozoide.  Objetivo: Determinar la concentración de yema de huevo (YH) adecuada, para la congelación del semen caprino en un diluyente liofilizado a base de Tris-glucosa y ácido cítrico, sin realizar el lavado seminal por centrifugación. Metodología: se evaluaron 90 eyaculados con 12 réplicas, colectados dos veces por semana mediante Vagina Artificial. Se midieron volumen, motilidad, concentración, viabilidad y patologías totales. Los eyaculados aptos, fueron unidos y divididos en cinco porciones, cada una recibió el diluyente correspondiente: Tris-glucosa-Ac. Cítrico con YH (2.25%, 3.37%, 4.45% y 5.65 %) y el diluyente control conteniendo lactosa-leche descremada (DC). La concentración espermática final en las muestras fue de 1.5 x 109 mL-1. Se realizó el período de equilibrio a 5°C durante 2 h. Posteriormente se congelo en pastillas de 0.1 mL en vapores de nitrógeno, y se almacenaron por 7 d en nitrógeno líquido, se descongelaron a 37°C y se determinaron los porcentajes de motilidad (30 min, 120 min y 240 min), viabilidad y patologías totales (30 min y 120 min). Los diluyentes fueron comparados mediante un modelo de Regresión Logística Binaria. Resultados: YH (4.45%) y DC presentaron la mayor probabilidad (P&lt;0.05) de motilidad en todos los tiempos. La mayor probabilidad (P&lt;0.05) de viabilidad fue para YH (4.45%), y la menor probabilidad (P&lt;0.05) de patologías totales para 4.45% YH y DC, a 30 min y 120 min. Implicaciones: En la congelación del semen caprino es posible eliminar el proceso de lavado seminal por centrifugación. Conclusión: El semen caprino puede congelarse en un diluyente liofilizado a base de Tris con 4.45% de yema de huevo, sin realizar el lavado seminal por centrifugación.Background: Prior to freezing goat semen, it is necessary to perform seminal washing by centrifugation to eliminate Phospholipase A, with the consequent loss of elements involved in maintaining sperm functions. Objective: Determine the adequate concentration of egg yolk (YH) for freezing goat semen in a lyophilized diluent based on Tris-glucose and citric acid, without performing seminal washing by centrifugation. Methodology: ninety ejaculates were evaluated with 12 replicates, collected twice a week by means of Artificial Vagina. Volume, motility, concentration, viability and total pathologies were measured. The fit ejaculates were united and divided into five portions, each one received the corresponding diluent: Tris-glucose-Ac. Citrus with YH (2.25%, 3.37%, 4.45% and 5.65%) and the control diluent containing lactose-skimmed milk (DC). The final sperm concentration in the samples was 1.5 x 109 mL-1. The equilibrium period was carried out at 5°C for 2 h. Subsequently, it was frozen in 0.1 mL tablets in nitrogen vapors, and stored for 7 d in liquid nitrogen, thawed at 37°C and the percentages of motility (30 min, 120 min and 240 min), viability and total pathologies (30 min and 120 min) were determined. The diluents were compared using a Binary Logistic Regression model. Results: YH (4.45%) and DC had the highest probability (P &lt;0.05) of motility at all times. The highest probability (P &lt;0.05) of viability was for YH (4.45%), and the lowest probability (P &lt;0.05) of total pathologies for 4.45% YH and DC, at 30 min and 120 min. Implications: In the freezing of goat semen, it is possible to eliminate the seminal washing process by centrifugation. Conclusion: Goat semen can be frozen in a Tris-based lyophilized extender with 4.45% egg yolk, without performing seminal washing by centrifugation

Predicting Suicidal Ideation Outcomes in Pharmacogenomic-Guided Antidepressant Treatment – A Machine Learning Approach

Background: Predicting which individual is likely to respond to a particular treatment remains a highly sought-after precision medicine goal, given its potential to guide individual treatment selection and optimize treatment outcomes. Machine learning (ML) analytical methods offer several advantages for predicting who will respond to a treatment, with sufficient accuracy to guide individual treatment decisions. Importantly, ML methods provide the capability to analyze a large number of predictors per individual, model non-linear effects, and complex interactions. The proposed secondary analysis will use deidentified data from a randomized clinical trial (RCT) of pharmacogenomic-guided antidepressant treatment to predict individualized treatment response (based on level of antidepressant drug-gene interaction) on the outcome of new-onset suicidal ideation, using ML analytics. Objective: The proposed analysis will use existing data from a randomized clinical trial of pharmacogenomic-guided antidepressant treatment, for which the original primary outcome was whether provision of personalized drug-gene interaction information affected the proportion of patients who were prescribed medications with predicted drug-gene interactions. The original secondary outcome was whether pharmacogenomic-guided treatment affected remission rates. The current, secondary analysis seeks to predict new-onset moderate-to-severe suicidal ideation, in the follow-up period of 8-24 weeks. The risk of new-onset suicidal ideation will be evaluated separately in three subgroups: participants who received an antidepressant with no drug-gene interaction, moderate drug-gene interaction, or significant drug-gene interaction. Aim 1 will develop a random forest machine learning (ML) model, separately for drug-gene interaction group (none, moderate, significant), using a highly dimensional predictor set of clinical and demographic variables to predict the suicidal ideation outcome. Aim 2 will then evaluate the degree to which adding pharmacogenomic factors to the ML models can improve predictive accuracy. Aim 3 will describe the key factors that drive prediction of new onset suicidal ideation by identifying the top 10 most consistent predictors in the ML models, separately by drug-gene interaction group. Aim 4 will then use these prediction models to develop and evaluate a personalized advantage index (PAI), which is a treatment matching algorithm that predicts new-onset suicidal ideation based on drug-gene interaction of the medication received