Comparison of ICA based fMRI artifact removal: single subject and group approaches
Yuhui Du1,2, Elena A. Allen1,4, Hao He1,3, Jing Sui1, Vince D. Calhoun1,3
1
The Mind Research Network, Albuquerque, NM, USA
2
School of Information and Communication Engineering, North University of China, Taiyuan, China
3
4
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
K.G. Jebsen Center for Research on Neuropsychiatric Disorders and Department of Biological and Medical Psychology,
University of Bergen, Norway
Introduction
Independent component analysis (ICA) (Calhoun, Adali et al. 2001, Calhoun and Adali 2012) has been widely
applied to identify brain intrinsic networks (INs) from fMRI. However, the best approach to handle artifacts is not yet
clear. In this work, we compare two artifact removal methods with ICA. The first method (Smith, Beckmann et al. 2013),
which we call IRPG for Individual ICA artifact Removal Plus Group ICA, implements ICA on individual data, removes
artifact individual independent components (ICs), and then performs group ICA on the reconstructed data. A second
method, called Group Information Guided ICA (GIG-ICA) (Du and Fan 2013), performs ICA on group data, removes
artifact group ICs, and then estimates individual ICs using the non-artifact group ICs as spatial references.
Methods
We used simulations to evaluate IRPG and GIG-ICA with regard to data quality and quantity, variable number of
sources, and spatially unique artifacts. Traditional group ICA (GICA) without artifact removal was also performed. The
frameworks for IRPG, GIG-ICA and GICA are shown in Fig. 1. Ten fMRI-like datasets with 8 or 7 sources were
generated using SimTB (Allen, Erhardt et al. 2012). Some sources were labeled as artifacts. Datasets with varying
contrast-to-noise ratio (CNRs), number of time points (NTPs), number of true sources, and spatial distributions of
artifacts were used to evaluate those methods.
Resting-state fMRI (Zuo, Kelly et al. 2010) from 25 subjects with 3 scans were used to test the reliability of INs
obtained from IRPG and GIG-ICA. To remove artifacts, seven spatial and temporal parameters for each IC/TC were
computed. To evaluate the reliability of the estimated individual INs, (1), the pair-wise similarity of all individual INs
were computed, (2), all individual INs were projected to a plane using t-Distributed Stochastic Neighbor Embedding (tSNE) method (van der Maaten and Hinton 2008). (3), one-sample t-test was performed for each IN and the maximum
t-value was compared. (4), the intra class coefficient (ICC) between INs from scan 2 and INs from scan 3, and between
INs from scan 1 and mean INs from scan 2 and 3 were calculated.
Results
Fig. 2 shows the accuracy of the different methods in simulations. The accuracy of ICs/TCs improved with the
increasing CNRs and NTPs for all methods but GIG-ICA showed greater accuracy than IRPG and GICA, particularly
at low CNRs and low NTPs. GIG-ICA was more reliable to the model order when subjects had different numbers of
sources, and more accurate when subjects had greatly different artifacts, although IRPG had improved performance
compared to just running GICA without additional artifact removal.
In real data, 12 corresponding INs were found and compared between methods. Fig.3(A,B) shows the spatial
correlations between all subjects INs and the consistency of each IN, and indicates that INs from GIG-ICA were more
spatially consistent. A 2-D projection of all INs are shown in Fig. 3(C), indicating that corresponding INs in GIG-ICA
were in well-separated clusters, while INs from IRPG had mixed pattern. One-sample t-tests (FDR corrected p<0.01) of
all INs are displayed in Fig. 4. The maximum t-value of each IN was larger (Fig. 3(D)) and mean ICC values were
greater (Fig. 3(E) and (F)) in GIG-ICA. All measures suggest that INs from GIG-ICA were more reliable than those
estimated by IRPG.
Conclusions
Experiments using simulations suggest that estimating and removing artifacts at group level, then computing
individual ICs using non-artifact group ICs as references, can achieve more reliable and accurate individual results than
the single-subject artifact removal prior to GICA (i.e. IRPG), even when artifact removal is perfect and subjects have
spatially unique artifacts. GIG-ICA also yields more reliable INs in real data, is more robust to deficiencies in data
quality and quantity, is less sensitive to parameters, and may be more straightforward to implement in large studies.
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