Jack Reddaway, Peter Eulalio Richardson, Ryan J Bevan, Jessica Stoneman, Marco Palombo
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引用次数: 0
Abstract
Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.
期刊介绍:
Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states.
Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.