PMID- 34891906
OWN - NLM
STAT- MEDLINE
DCOM- 20211230
LR  - 20211230
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2021
DP  - 2021 Nov
TI  - An ICA Investigation into the Effect of Physiological Noise Correction on Dynamic 
      Functional Network Connectivity and Meta-state Metrics.
PG  - 3137-3140
LID - 10.1109/EMBC46164.2021.9630968 [doi]
AB  - Physiological fluctuations such as cardiac pulsations (heart rate) and 
      respiratory rhythm (breathing) have been studied in the resting state functional 
      magnetic resonance imaging (rs-fMRI) studies as the potential sources of 
      confounds in functional connectivity. Independent component analysis (ICA) 
      provides a data driven approach to investigate functional connectivity at the 
      network level. However, the effect of physiological noise correction on the 
      dynamic of ICA-derived networks has not yet been studied. The goal of this study 
      was to investigate the effect of retrospective correction of cardiorespiratory 
      artifacts on the time-varying aspects of functional network connectivity. Blood 
      oxygenation-level dependent (BOLD) rs-fMRI data were collected from healthy 
      subjects using a 3.0T MRI scanner. Whole-brain dynamic functional network 
      connectivity (dFNC) was computed using sliding time window correlation, and 
      k-means clustering of windowed correlation matrices. Results showed significant 
      effects of physiological denoising on dFNC between several network pairs in 
      particular the subcortical, and cognitive/attention networks (false discovery 
      rate [FDR]-corrected p < 0.01). Meta-state dynamics further revealed significant 
      changes in the number of unique windows for each subject, number of times each 
      subject changes from one meta-state to other, and sum of L1 distances between 
      successive meta-states. In conclusion, removal of artifacts is important for 
      achieving reliable fMRI results, however a more cautious approach should be 
      adapted in regressing such "noise" in ICA functional connectivity approach. More 
      experiments are needed to investigate impact of denoising on dFNC especially 
      across different datasets.
FAU - Jarrahi, Behnaz
AU  - Jarrahi B
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - *Benchmarking
MH  - Brain/diagnostic imaging
MH  - *Brain Mapping
MH  - Humans
MH  - Magnetic Resonance Imaging
MH  - Retrospective Studies
EDAT- 2021/12/12 06:00
MHDA- 2021/12/31 06:00
CRDT- 2021/12/11 01:03
PHST- 2021/12/11 01:03 [entrez]
PHST- 2021/12/12 06:00 [pubmed]
PHST- 2021/12/31 06:00 [medline]
AID - 10.1109/EMBC46164.2021.9630968 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3137-3140. doi: 
      10.1109/EMBC46164.2021.9630968.