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. Our novel parametrization preserves an important aspect of tree structure, namely its branch order. The model is amenable to standard methods of analysis, like generalized linear/additive models. RESULTS: The model fit the distribution of the observed data quite well when applied to a collection of 98 brain artery systems. The estimated probability of branching decreases log linearly with branch order. Likewise, the average diameter of arteries decreases, while average length increases with branch order. Frontal arterial branches are on average longer and thinner than those in the back at equivalent branch orders. A mechanistic arterial branching model based on Poiseuille's blood flow law, which uses vessel length and diameter information, fit the observed branching structure significantly better. This model is further improved by including branch order, suggesting viscoelastic flow impacts branching in narrower vessels. CONCLUSION: After adjustment for branch order, brain arterial branching probabilities decreased significantly with age and length, but increased with diameter. Arteries become thicker and branch less frequently with increasing age, but the age effect decreases with branch order.

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. The correlation with age continues to be significant even after controlling for correlations from earlier significant summaries.

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. RESULTS: Glyoxylase 1 (GLO1) and guanine nucleotide-binding protein 1 (GNB1) mostly account for baseline anxiety-like and depressive-like behavior, indicating a common biological link between depression and anxiety. Fluoxetine-induced biochemical alterations discriminated positive responders, while baseline neurobiochemical differences differentiated negative responders (p < 0.006). Results show that glial fibrillary acidic protein, S100 beta protein, GLO1, and histone deacetylase 5 contributed most to fluoxetine response. These proteins are linked within a cellular growth/proliferation pathway, suggesting the involvement of cellular genesis in fluoxetine response. Furthermore, a candidate genetic locus that associates with baseline depressive-like behavior contains a gene that encodes for cellular proliferation/adhesion molecule (Cadm1), supporting a genetic basis for the role of neuro/gliogenesis in depression. CONCLUSION: We provided a comprehensive analysis of behavioral, neurobiochemical, and transcriptome data across 30 mouse inbred strains that has not been accomplished before. We identified biomarkers that influence fluoxetine response, which, altogether, implicate the importance of cellular genesis in fluoxetine treatment. More broadly, this approach can be used to assess a wide range of drug response phenotypes that are challenging to address in human samples.