PMID- 31310824
OWN - NLM
STAT- MEDLINE
DCOM- 20201019
LR  - 20201019
IS  - 1872-678X (Electronic)
IS  - 0165-0270 (Linking)
VI  - 326
DP  - 2019 Oct 1
TI  - An ICA based approach for steady-state and transient analysis of task fMRI data: 
      Application to study of thermal pain response.
PG  - 108356
LID - S0165-0270(19)30213-4 [pii]
LID - 10.1016/j.jneumeth.2019.108356 [doi]
AB  - BACKGROUND: Data driven analysis methods such as independent component analysis 
      (ICA) offer the advantage of estimating subject contributions when used in a 
      second-level analysis. With the traditionally used regression-based methods this 
      is achieved with a design matrix that has to be specified a priori. NEW METHOD: 
      We show that the ability of ICA to estimate subject contributions can be 
      effectively used to perform steady-state as well as transient analysis of task 
      functional magnetic resonance imaging (fMRI) data, which can help reveal 
      important group differences. RESULTS: We apply the method to steady-state and 
      transient analysis of block designed thermal pain stimulated fMRI data, and 
      identify distinct sex differences, in parts of the pain matrix: brain stem, 
      thalamus, amygdala, frontal pole (FP), temporal pole (TP), operculum (second 
      somatosensory cortex, SII), anterior insular (AI), dorsal anterior cingulate 
      cortex (dACC), and default mode network (DMN). We also show that the identified 
      regions have significant correlation with weekly exercise and anxiety. Using 
      transient analysis, we identify regions (SII, AI, dACC, DMN) specific to female 
      group showing difference mainly in the initial stages of the experiments. 
      COMPARISON WITH EXISTING METHOD: With exact same spatial components input in the 
      second level, permutation analysis of linear models cannot identify any 
      significant group difference. In addition, the proposed transient analysis cannot 
      be realized if user is required to input a design matrix as is the case with 
      regression-based analyses. CONCLUSIONS: The proposed two-level ICA is an 
      effective multi-variate analysis method for both steady-state and transient 
      analysis of task data.
CI  - Copyright (c) 2019 Elsevier B.V. All rights reserved.
FAU - Song, Xiaowei
AU  - Song X
AD  - Department of Computer Science and Electrical Engineering, University of 
      Maryland, Baltimore County, MD 21250, United States.
FAU - Bhinge, Suchita
AU  - Bhinge S
AD  - Department of Computer Science and Electrical Engineering, University of 
      Maryland, Baltimore County, MD 21250, United States.
FAU - Quiton, Raimi L
AU  - Quiton RL
AD  - Department of Psychology, University of Maryland, Baltimore County, MD 21250, 
      United States.
FAU - Adali, Tulay
AU  - Adali T
AD  - Department of Computer Science and Electrical Engineering, University of 
      Maryland, Baltimore County, MD 21250, United States.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20190713
PL  - Netherlands
TA  - J Neurosci Methods
JT  - Journal of neuroscience methods
JID - 7905558
SB  - IM
MH  - Adult
MH  - Brain Mapping/*methods
MH  - Cerebral Cortex/diagnostic imaging/*physiopathology
MH  - Female
MH  - Humans
MH  - Image Processing, Computer-Assisted/*methods
MH  - Magnetic Resonance Imaging/*methods
MH  - Male
MH  - Models, Theoretical
MH  - Nerve Net/diagnostic imaging/*physiopathology
MH  - Pain/diagnostic imaging/*physiopathology
MH  - Principal Component Analysis
MH  - Sex Factors
OTO - NOTNLM
OT  - FMRI
OT  - ICA
OT  - Thermal pain
OT  - Transient analysis
EDAT- 2019/07/17 06:00
MHDA- 2020/10/21 06:00
CRDT- 2019/07/17 06:00
PHST- 2018/12/13 00:00 [received]
PHST- 2019/04/22 00:00 [revised]
PHST- 2019/07/10 00:00 [accepted]
PHST- 2019/07/17 06:00 [pubmed]
PHST- 2020/10/21 06:00 [medline]
PHST- 2019/07/17 06:00 [entrez]
AID - S0165-0270(19)30213-4 [pii]
AID - 10.1016/j.jneumeth.2019.108356 [doi]
PST - ppublish
SO  - J Neurosci Methods. 2019 Oct 1;326:108356. doi: 10.1016/j.jneumeth.2019.108356. 
      Epub 2019 Jul 13.