Predictive models avoid excessive reductionism in cognitive neuroimaging

Addressing Confounding in Predictive Models with an Application to Neuroimaging.

Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In part...

SGPP: spatial Gaussian predictive process models for neuroimaging data

The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve a better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model spat...

Neural Sufficiency, Reductionism, and Cognitive Neuropsychiatry

Discovering Predictive Variables When Evolving Cognitive Models

A non-dominated sorting genetic algorithm is used to evolve models of learning from different theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population’s current range. Experimental result...

Meiotic Nuclear Oscillations Are Necessary to Avoid Excessive Chromosome Associations.

Pairing of homologous chromosomes is a crucial step in meiosis, which in fission yeast depends on nuclear oscillations. However, how nuclear oscillations help pairing is unknown. Here, we show that homologous loci typically pair when the spindle pole body is at the cell pole and the nucleus is elongated, whereas they unpair when the spindle pole body is in the cell center and the nucleus is rou...