Paper Info
Title
Visual mining of neuro-metaspaces
Abstract
Large scale neuroimaging data archival protocols are gradually becoming ubiquitous in both research as well as clinical settings. Current user-database interfaces are limited to textual searches and often require data-specific knowledge for performing queries. This is proving to be an obstacle for researchers who wish to obtain a holistic view of the data before designing pilot neuroscientific studies or even formulating statistical hypotheses. Instead of providing a restricted, unidimensional view of the data, we seek to place a multi-dimensional view of the entire neurodatabase at the user's disposal. With the aim of visual navigation of complete neuro-repositories, we introduce the concept of brain meta-spaces. The meta-space models the implicit nonlinear manifold where the neurological data resides, and encodes pair-wise dissimilarities between all individuals in a population. Additionally, the novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis)similarity in morphometry.
Year
DOI
Venue
2010
10.1145/1836845.1837008
SIGGRAPH Posters
Keywords
Field
DocType
visual mining,encodes pair-wise dissimilarity,unidimensional view,meta-space model,neurological data resides,data archival protocol,multi-dimensional view,brain meta-spaces,holistic view,vast meta-space,user ability,biomolecule,force feedback
Population,Computer vision,Obstacle,Visual mining,Computer graphics (images),Computer science,Visual navigation,Artificial intelligence,Neuroimaging,Nonlinear manifold,Novelty,Haptic technology
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Shantanu H. Joshi184550.12
Ian Bowman2123.18
Robin Jennings320.76
David Hasson400.34
Zhizhong Liu5643.15
Arthur W. Toga63128261.46
John D Van Horn731628.50