Branch order regression for modeling brain vasculature.

Purpose: Many biological objects, including neuronal dendrites, blood vasculature, airways, phylogenetic trees, produce tree structured data. Current methods of analysis either ignore the complex structure of trees or use distance-based methods which limit the scope of multivariate modeling.

Methods: We propose a branching process model which enables analysis of both the branching structure and associated properties.

Persistent Homology Analysis of Brain Artery Trees.

New representations of tree-structured data objects, using ideas from topological data analysis, enable improved statistical analyses of a population of brain artery trees. A number of representations of each data tree arise from persistence diagrams that quantify branching and looping of vessels at multiple scales. Novel approaches to the statistical analysis, through various summaries of the persistence diagrams, lead to heightened correlations with covariates such as age and sex, relative to earlier analyses of this data set.

Evaluating genetic markers and neurobiochemical analytes for fluoxetine response using a panel of mouse inbred strains.

Rationale: Identification of biomarkers that establish diagnosis or treatment response is critical to the advancement of research and management of patients with depression.

Objective: Our goal was to identify biomarkers that can potentially assess fluoxetine response and risk to poor treatment outcome.

Methods: We measured behavior, gene expression, and the levels of 36 neurobiochemical analytes across a panel of genetically diverse mouse inbred lines after chronic treatment with water or fluoxetine.