PMID- 29456485
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211216
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 12
DP  - 2018
TI  - Effect of Spatial Smoothing on Task fMRI ICA and Functional Connectivity.
PG  - 15
LID - 10.3389/fnins.2018.00015 [doi]
LID - 15
AB  - Spatial smoothing is a widely used preprocessing step in functional magnetic 
      resonance imaging (fMRI) data analysis. In this work, we report on the spatial 
      smoothing effect on task-evoked fMRI brain functional mapping and functional 
      connectivity. Initially, we decomposed the task fMRI data into a collection of 
      components or networks by independent component analysis (ICA). The designed task 
      paradigm helps identify task-modulated ICA components (highly correlated with the 
      task stimuli). For the ICA-extracted primary task component, we then measured the 
      task activation volume at the task response foci. We used the task timecourse 
      (designed) as a reference to order the ICA components according to the task 
      correlations of the ICA timecourses. With the re-ordered ICA components, we 
      calculated the inter-component function connectivity (FC) matrix (correlations 
      among the ICA timecourses). By repeating the spatial smoothing of fMRI data with 
      a Gaussian smoothing kernel with a full width at half maximum (FWHM) of 1, 3, 6, 
      9, 12, 15, 20, 25, 30, 35 mm, we measured the spatial smoothing effects. Our 
      results show spatial smoothing reveals the following effects: (1) It decreases 
      the task extraction performance of single-subject ICA more than that of 
      multi-subject ICA; (2) It increases the task volume of multi-subject ICA more 
      than that of single-subject ICA; (3) It strengthens the functional connectivity 
      of single-subject ICA more than that of multi-subject ICA; and (4) It impacts the 
      positive-negative imbalance of single-subject ICA more than that of multi-subject 
      ICA. Our experimental results suggest a 2~3 voxel FWHM spatial smoothing for 
      single-subject ICA in achieving an optimal balance of functional connectivity, 
      and a wide range (2~5 voxels) of FWHM for multi-subject ICA.
FAU - Chen, Zikuan
AU  - Chen Z
AD  - The Mind Research Network and LBERI, Albuquerque, NM, United States.
FAU - Calhoun, Vince
AU  - Calhoun V
AD  - The Mind Research Network and LBERI, Albuquerque, NM, United States.
AD  - Department of Electrical and Computer Engineering, University of New Mexico, 
      Albuquerque, NM, United States.
LA  - eng
GR  - P20 GM103472/GM/NIGMS NIH HHS/United States
GR  - R01 EB006841/EB/NIBIB NIH HHS/United States
GR  - R01 EB020407/EB/NIBIB NIH HHS/United States
PT  - Journal Article
DEP - 20180202
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC5801305
OTO - NOTNLM
OT  - correlation scale invariance
OT  - function connectivity (FC)
OT  - independent component analysis (ICA)
OT  - spatial correlation (scorr)
OT  - spatial smoothing
OT  - task correlation
OT  - task fMRI
OT  - task function mapping
EDAT- 2018/02/20 06:00
MHDA- 2018/02/20 06:01
PMCR- 2018/01/01
CRDT- 2018/02/20 06:00
PHST- 2017/11/03 00:00 [received]
PHST- 2018/01/10 00:00 [accepted]
PHST- 2018/02/20 06:00 [entrez]
PHST- 2018/02/20 06:00 [pubmed]
PHST- 2018/02/20 06:01 [medline]
PHST- 2018/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2018.00015 [doi]
PST - epublish
SO  - Front Neurosci. 2018 Feb 2;12:15. doi: 10.3389/fnins.2018.00015. eCollection 
      2018.