TY - GEN
T1 - Functional Multi-Connectivity
T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
AU - Iraji, Armin
AU - Lewis, Noah
AU - Faghiri, Ashkan
AU - Fu, Zening
AU - Deramus, Thomas
AU - Abrol, Anees
AU - Qi, Shile
AU - Calhoun, Vince
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - The interactions among brain entities, commonly computed through pair-wise functional connectivity, are assumed to be manifestations of information processing which drive function. However, this focus on large-scale networks and their pair-wise temporal interactions is likely missing important information contained within fMRI data. We propose leveraging multi-connected features at both the voxel- and network-level to capture 'multi-way entanglement' between networks and voxels, providing improved resolution of interconnected brain functional hierarchy. Entanglement refers to each brain network being heavily enmeshed with the activity of other networks. Under our multi-connectivity assumption, elements of a system simultaneously communicate and interact with each other through multiple pathways. As such we move beyond the typical pair-wise temporal partial or full correlation. We propose a framework to estimate functional multi-connectivity (FMC) by computing the relationship between system-wide connections of intrinsic connectivity networks (ICNs). Results show that FMC obtains information which is different from standard pair-wise analyses.
AB - The interactions among brain entities, commonly computed through pair-wise functional connectivity, are assumed to be manifestations of information processing which drive function. However, this focus on large-scale networks and their pair-wise temporal interactions is likely missing important information contained within fMRI data. We propose leveraging multi-connected features at both the voxel- and network-level to capture 'multi-way entanglement' between networks and voxels, providing improved resolution of interconnected brain functional hierarchy. Entanglement refers to each brain network being heavily enmeshed with the activity of other networks. Under our multi-connectivity assumption, elements of a system simultaneously communicate and interact with each other through multiple pathways. As such we move beyond the typical pair-wise temporal partial or full correlation. We propose a framework to estimate functional multi-connectivity (FMC) by computing the relationship between system-wide connections of intrinsic connectivity networks (ICNs). Results show that FMC obtains information which is different from standard pair-wise analyses.
KW - Schizophrenia
KW - functional connectivity
KW - functional entanglement
KW - intrinsic brain network (ICN)
KW - resting state fMRI (rsfMRI)
UR - http://www.scopus.com/inward/record.url?scp=85085863354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085863354&partnerID=8YFLogxK
U2 - 10.1109/ISBI45749.2020.9098679
DO - 10.1109/ISBI45749.2020.9098679
M3 - Conference contribution
AN - SCOPUS:85085863354
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1698
EP - 1701
BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
Y2 - 3 April 2020 through 7 April 2020
ER -