Exploring Ways of Visualizing Functional Connectivity

Functional connectivity is a field within neuroscience where measurements of co-activation between brain regions are used to test various hypotheses or explore how the brain activates depending on a given situation or task. After analysis, the underlying data in the field consists of a n by n adjacenc...

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Bibliographic Details
Main Author: Nylén, Jan
Format: Others
Language:English
Published: Umeå universitet, Institutionen för tillämpad fysik och elektronik 2017
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-141182
Description
Summary:Functional connectivity is a field within neuroscience where measurements of co-activation between brain regions are used to test various hypotheses or explore how the brain activates depending on a given situation or task. After analysis, the underlying data in the field consists of a n by n adjacency matrix where each cell represents a correlation value between two regions in the brain. Depending on the research question the number of regions and matrices incorporated varies and new visualizations are needed in order to portray them. In this thesis the design of an interactive web based visualization tool for functional connectivity was explored through an iterative design process. The design of the tool was based on existing guidelines, interviews and best practices in data visualization as well as an analysis of current visualization solutions used in functional connectivity. The final concept and prototype uses a network plot for functional connectivity called the connectogram as well as a grouped bar graph to provide an intuitive and accessible way of comparing functional connectivity data by interacting with and highlighting networks and specific network data through direct manipulation. Results of qualitative evaluations of a prototype using data from a concurrent scientific project is presented. The prototype was found to be useful, engaging, easily perceivable and offered an easy and quick way of exploring data sets.