A Monte Carlo Simulation of Rat Choice Behavior with Interdependent Outcomes

Preclinical behavioral neuroscience often uses choice paradigms to capture psychiatric symptoms. In particular, the subfield of operant research produces nested datasets with many discrete choices in a session. The standard analytic practice is to aggregate choice into a continuous variable and analyze using ANOVA or linear regression. However, choice data often have multiple interdependent outcomes of interest, violating an assumption of general linear models. The aim of the current study was to quantify the accuracy of linear mixed-effects regression (LMER) for analyzing data from a 4-choice operant task called the Rodent Gambling Task (RGT), which measures decision-making in the context of various manipulations (e.g., brain injury). Prior analysis of RGT data from intact rats (Sham; n = 58) and brain-injured rats (TBI; n = 51) revealed five distinct decision-making phenotypes for this task. To generate datasets for parametric analysis, trial-level data was simulated using a Monte Carlo approach recapitulating those phenotypes. Population parameters were defined from existing data, and repeated sampling was conducted to generate 1000 datasets for four sample sizes (n = 6, 10, 14, 20) and four effect sizes (f = 0.0, 0.3, 0.4 and 0.5). Two LMER models were performed to compare TBI versus Sham across datasets: a full LMER where choice of all four outcomes was analyzed simultaneously, and a control LMER where choice of a single outcome was analyzed. The full LMER exceeded 75% false positives across all sample sizes, and the control LMER was underpowered to detect expected effects. These results suggest analyzing trial-level data in a mixed effects logistic regression will be necessary to accurately analyze RGT data. More broadly, these types of errors must be remedied to improve translation to clinical research

Infants' neural processing of facial attractiveness

textThe relationship between infants’ neural processing of and visual preferences for attractive and unattractive faces was investigated through the integration of event-related potential and preferential looking methods. Six-month-olds viewed color images of female faces previously rated by adults for attractiveness. The faces were presented in contrasting pairs of attractiveness (attractive/unattractive) for 1.5-second durations. The results showed that compared to attractive faces, unattractive faces elicited larger N290 amplitudes at left hemisphere electrode sites (PO9) and smaller P400 amplitudes at electrode sites across both hemispheres (PO9 and PO10). There were no significant differences between infants’ overall looking times based on attractiveness, however, a significant relationship was found between amplitude and trial looking time; larger N290 amplitudes were associated with longer trial looking times. The results suggest that compared to attractive faces, unattractive faces require greater cognitive resources and longer initial attention for visual processing.Psycholog

Database Analysis to Support Nutrient Criteria Development (Phase II)

The intent of this publication of the Arkansas Water Resources Center is to provide a location whereby a final report on water research to a funding agency can be archived. The Texas Commission on Environmental Quality (TCEQ) contracted with University of Arkansas researchers for a multiple year project titled “Database Analysis to Support Nutrient Criteria Development”. This publication covers the second of three phases of that project and has maintained the original format of the report as submitted to TCEQ. This report can be cited either as an AWRC publication (see below) or directly as the final report to TCEQ

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks