PMID- 28132931
OWN - NLM
STAT- MEDLINE
DCOM- 20180227
LR  - 20181113
IS  - 1095-9572 (Electronic)
IS  - 1053-8119 (Print)
IS  - 1053-8119 (Linking)
VI  - 149
DP  - 2017 Apr 1
TI  - Assessing uncertainty in dynamic functional connectivity.
PG  - 165-177
LID - S1053-8119(17)30065-4 [pii]
LID - 10.1016/j.neuroimage.2017.01.056 [doi]
AB  - Functional connectivity (FC) - the study of the statistical association between 
      time series from anatomically distinct regions (Friston, 1994, 2011) - has become 
      one of the primary areas of research in the field surrounding resting state 
      functional magnetic resonance imaging (rs-fMRI). Although for many years 
      researchers have implicitly assumed that FC was stationary across time in 
      rs-fMRI, it has recently become increasingly clear that this is not the case and 
      the ability to assess dynamic changes in FC is critical for better understanding 
      of the inner workings of the human brain (Hutchison et al., 2013; Chang and 
      Glover, 2010). Currently, the most common strategy for estimating these dynamic 
      changes is to use the sliding-window technique. However, its greatest shortcoming 
      is the inherent variation present in the estimate, even for null data, which is 
      easily confused with true time-varying changes in connectivity (Lindquist et al., 
      2014). This can have serious consequences as even spurious fluctuations caused by 
      noise can easily be confused with an important signal. For these reasons, 
      assessment of uncertainty in the sliding-window correlation estimates is of 
      critical importance. Here we propose a new approach that combines the 
      multivariate linear process bootstrap (MLPB) method and a sliding-window 
      technique to assess the uncertainty in a dynamic FC estimate by providing its 
      confidence bands. Both numerical results and an application to rs-fMRI study are 
      presented, showing the efficacy of the proposed method.
CI  - Copyright (c) 2017 Elsevier Inc. All rights reserved.
FAU - Kudela, Maria
AU  - Kudela M
AD  - Indiana University RM Fairbanks School of Public Health, Department of 
      Biostatistics, Indianapolis, IN 46202, United States.
FAU - Harezlak, Jaroslaw
AU  - Harezlak J
AD  - Indiana University School of Public Health, Department of Epidemiology and 
      Biostatistics, Bloomington, IN 47405, United States. Electronic address: 
      harezlak@iu.edu.
FAU - Lindquist, Martin A
AU  - Lindquist MA
AD  - Johns Hopkins University, Department of Biostatistics, Baltimore, MD 21205, 
      United States.
LA  - eng
GR  - R01 EB016061/EB/NIBIB NIH HHS/United States
GR  - R01 MH108467/MH/NIMH NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20170127
PL  - United States
TA  - Neuroimage
JT  - NeuroImage
JID - 9215515
SB  - IM
MH  - Brain/*physiology
MH  - Brain Mapping/*methods
MH  - Humans
MH  - Image Processing, Computer-Assisted/methods
MH  - Magnetic Resonance Imaging
MH  - *Models, Neurological
MH  - Neural Pathways/*physiology
PMC - PMC5384341
MID - NIHMS851019
OTO - NOTNLM
OT  - Dynamic confidence bands
OT  - Dynamic functional connectivity
OT  - Multivariate time series bootstrap
OT  - Time-varying correlation
EDAT- 2017/01/31 06:00
MHDA- 2018/02/28 06:00
PMCR- 2018/04/01
CRDT- 2017/01/31 06:00
PHST- 2016/10/25 00:00 [received]
PHST- 2017/01/12 00:00 [revised]
PHST- 2017/01/22 00:00 [accepted]
PHST- 2017/01/31 06:00 [pubmed]
PHST- 2018/02/28 06:00 [medline]
PHST- 2017/01/31 06:00 [entrez]
PHST- 2018/04/01 00:00 [pmc-release]
AID - S1053-8119(17)30065-4 [pii]
AID - 10.1016/j.neuroimage.2017.01.056 [doi]
PST - ppublish
SO  - Neuroimage. 2017 Apr 1;149:165-177. doi: 10.1016/j.neuroimage.2017.01.056. Epub 
      2017 Jan 27.