Abstract
BackgroundThe Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes—to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics—wide-scale, systematic study of phenotypes—to neuropsychiatry research.ResultsThis paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles—patterns of values across phenotypes—that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.ConclusionsThe ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports ‘natural selection’ on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics.
Highlights
The Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes—to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia
Dissatisfaction with the current situation has been evident throughout the development of DSM-V, and its inclusion of dimensional classification [3] as a step beyond DSM-IV ‘chinese menu’ diagnosis and categories that do not always fit, toward quantitative assessments of severity and treatment response that are grounded in data
ViVA directly links visualization with variance structure models, permitting interactive visual exploration of natural models in a large database. It includes VISOVA, for example, a combination of visualization and analysis of variance with the exploratory flavor that is associated with ANOVA in biomedical hypothesis generation
Summary
The Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes—to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation It represented an application of phenomics—wide-scale, systematic study of phenotypes—to neuropsychiatry research. Motivation: better neuropsychiatric diagnostics Diagnosis in neuropsychiatry rests on an elaborate taxonomy of syndromes and explicit decision trees for classification These decision trees have been codified in DSM-IV [1] and its very recent revision DSM-V [2]. Many research efforts are seeking better models for existing categories such as ADHD, Bipolar Disorder, and Schizophrenia. These rubrics are often said to be inadequate for classification because they rest on inaccurate descriptions. A related criticism is that different diagnoses overlap significantly, in some cases using different terminology for the same concept
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