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• W2942789550 abstract "Abstract Background Computation and visualization of connectivity among the brain regions is vital for tasks such as disease identification and drug discovery. An effective visualization can aid clinicians and biologists to perform these tasks addressing a genuine research and industrial need. In this paper, we present SMT-Neurophysiology, a web-based tool in a form of an approximation to the Steiner Minimal Tree (SMT) algorithm to search neurophysiology partonomy and connectivity graph in order to find biomedically-meaningful paths that could explain, to neurologists and neuroscientists, the mechanistic relationship, for example, among specific neurophysiological examinations. We also present SMT-Genetic, a web-based tool in a form of a Genetic Algorithm (GA) to find better paths than SMT-Neurophysiology. Results We introduce an approximation to the SMT algorithm to identify the most parsimonious connectivity among the brain regions of interest. We have implemented our algorithm as a highly interactive web application called SMT-Neurophysiology that enables such computation and visualization. It operates on brain region connectivity dataset curated from the Neuroscience Information Framework (NIF) for four species – human, monkey, rat and bird. We present two case studies on finding the most biomedically-meaningful solutions that identifies connections among a set of brain regions over a specific route. The case studies demonstrate that SMT-Neurophysiology is able to find connection among brain regions of interest. Furthermore, SMT-Neurophysiology is modular and generic in nature allowing the underlying connectivity graph to model any data on which the tool can operate. In order to find better connections among a set of brain regions than SMT-Neurophysiology, we have implemented a web-based tool in a form of a GA called SMT-Genetic. We present further three case studies where SMT-Genetic finds better connections among a set of brain regions than SMT-Neurophysiology. SMT-Genetic gives better connections because SMT-Genetic finds global optimum whereas SMT-Neurophysiology finds local optimum although execution time of SMT-Genetic is higher than SMT-Neurophysiology. Conclusion Our analysis would provide key insights to clinical investigators about potential mechanisms underlying a particular neurological disease. The web-based tools and the underlying data are useful to clinicians and scientists to understand neurological disease mechanisms; discover pharmacological or surgical targets; and design diagnostic or therapeutic clinical trials. The source codes and links to the live tools are available at https://github.com/dewancse/connected-brain-regions and https://github.com/dewancse/SMT-Genetic ." @default.
• W2942789550 created "2019-05-09" @default.
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• W2942789550 date "2019-05-03" @default.
• W2942789550 modified "2023-09-26" @default.
• W2942789550 title "Identifying Brain Region Connectivity using Steiner Minimal Tree Approximation and a Genetic Algorithm" @default.
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• W2942789550 doi "https://doi.org/10.1101/626598" @default.
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