TY - JOUR
T1 - Connectivity in fMRI
T2 - Blind Spots and Breakthroughs
AU - Solo, Victor
AU - Poline, Jean Baptiste
AU - Lindquist, Martin A.
AU - Simpson, Sean L.
AU - Bowman, F. Dubois
AU - Chung, Moo K.
AU - Cassidy, Ben
N1 - Funding Information:
Manuscript received February 8, 2018; revised April 17, 2018; accepted April 17, 2018. Date of publication April 30, 2018; date of current version June 30, 2018. This work was supported in part by NIH-NIMH (CANDIShare) under Grant R01 MH083320 and in part by NIH (NIF) under Grant 5U24 DA039832. The work of V. Solo was supported by NIH under Grant P41EB015896. The work of J.-B. Poline was supported by NIH-NIBIB (ReproNim) under Grant P41 EB019936. The work of M. A. Lindquist was supported by NIH under Grant R01-EB016061. The work of S. L. Simpson was supported in part by NIH under Grant K25-EB012236 and in part the Wake Forest Clinical and Translational Science Institute under Grant UL1TR001420. The work of F. D. Bowman and B. Cassidy was supported by NIH/NINDS under Grant U18 NS082143. The work of M. K. Chung was supported by NIH under Grant R01-EB022856. (Corresponding author: Victor Solo.) V. Solo is with the School of Electrical Engineering, University of New South Wales, Sydney, NSW 2052, Australia, and also with the MGH/MIT-Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA 02129 USA (e-mail: v.solo@unsw.edu.au).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2018/7
Y1 - 2018/7
N2 - In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.
AB - In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.
KW - ERGM
KW - causality
KW - fMRI
KW - graph
KW - small world network
KW - system identification
KW - time-varying
KW - topological data analysis
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U2 - 10.1109/TMI.2018.2831261
DO - 10.1109/TMI.2018.2831261
M3 - Article
C2 - 29969406
AN - SCOPUS:85046343796
VL - 37
SP - 1537
EP - 1550
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 7
ER -