The human brain is a complex organ made up of billions of neurons that are interconnected
through a vast network of synapses. This network of connections enables the brain to perform a wide
range of cognitive and motor functions. Studying and analyzing these brain networks is important
for understanding how different regions of the brain communicate and work together to carry out
specific tasks and how neurological disorders such as Alzheimer’s disease, Parkinson’s disease, or
schizophrenia impact brain connectivity contributing to the development of these disorders.
Virtual reality technology has proven to be a versatile tool for learning, exploration, and analysis.
It can expand the user’s senses, provide a more detailed and immersive view of the subject
matter, encourage active learning and exploration, and facilitate global analysis of complex data. In
this dissertation, we present VRNConnect, a virtual reality system for interactively exploring brain
connectivity data. VRNConnect enables users to analyze brain networks using either structural
or functional connectivity matrices. By visualizing the 3D brain connectome network as a graph,
users can interact with various regions using hand gestures or controllers to access network analysis
metrics and information about Regions of Interest (ROIs). The system includes features such as
colour coding of nodes and edges, thresholding, and shortest path calculation to enhance usability.
Moreover, VRNConnect has the ability to be tailored to specific needs, allowing for the importation
of connectivity data from various modalities. Our platform was designed with flexibility in mind,
making it easy to incorporate additional features as needed.
In order to evaluate the usability and cognitive workload associated with using our system, we
conducted a study with 16 participants. Our findings suggest that VRNConnect could serve as an
effective academic and analytical tool.