TY - GEN
T1 - Large scale fusion of brain imaging modalities and features using Markov-style dynamics in a feature meta-space
AU - Miller, Robyn L.
AU - Vergara, Victor M.
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Brain imaging technology provides a way to sample various aspects of the brain albeit incompletely, providing a rich set of features crossing rest and task conditions, and an ever-growing number of imaging modalities. The conditions being studied with brain imaging data are often extremely complex and it is becoming more common for researchers to employ more than one imaging or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features, modalities or tasks. We propose an intuitive framework based on Markov-style flows for understanding information exchange between features in what we are calling a feature meta-space: that is, a space consisting of an arbitrary number of individual feature spaces, where the features can have any dimension and can be drawn from any data source or modality. We present preliminary work demonstrating the ability of this new framework to identify relationships between disparate features of varying dimensionality.
AB - Brain imaging technology provides a way to sample various aspects of the brain albeit incompletely, providing a rich set of features crossing rest and task conditions, and an ever-growing number of imaging modalities. The conditions being studied with brain imaging data are often extremely complex and it is becoming more common for researchers to employ more than one imaging or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features, modalities or tasks. We propose an intuitive framework based on Markov-style flows for understanding information exchange between features in what we are calling a feature meta-space: that is, a space consisting of an arbitrary number of individual feature spaces, where the features can have any dimension and can be drawn from any data source or modality. We present preliminary work demonstrating the ability of this new framework to identify relationships between disparate features of varying dimensionality.
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U2 - 10.1109/EMBC.2015.7320180
DO - 10.1109/EMBC.2015.7320180
M3 - Conference contribution
C2 - 26738080
AN - SCOPUS:84953339572
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7716
EP - 7719
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -