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ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.

Feis RA, Smith SM, Filippini N, Douaud G, Dopper EG, Heise V, Trachtenberg AJ, van Swieten JC, van Buchem MA, Rombouts SA, Mackay CE - Front Neurosci (2015)

Bottom Line: Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects.Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX.The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Leiden University Medical Centre Leiden, Netherlands ; FMRIB, Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford Oxford, UK.

ABSTRACT
Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

No MeSH data available.


Related in: MedlinePlus

Combined group differences. Maps show statistically significant (p < 0.05) differences between groups: without the use of FIX (A), after the use of FIX (B) and the interaction between FIX and group differences (C) in all (15) RSNs combined. Color bar represents the number of significantly differing networks.
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Figure 3: Combined group differences. Maps show statistically significant (p < 0.05) differences between groups: without the use of FIX (A), after the use of FIX (B) and the interaction between FIX and group differences (C) in all (15) RSNs combined. Color bar represents the number of significantly differing networks.

Mentions: All RSNs' combined results based on PE-driven spatial maps are shown for family-wise error-corrected group differences before the use of FIX (Figure 3A), group differences after the use FIX (Figure 3B) and for the interaction between applying FIX and group differences (Figure 3C). Dual regression results for each RSN are shown separately in Supplemental Figure 1 (numbers in Supplemental Figure 1 correspond with numbers in Figure 2).


ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.

Feis RA, Smith SM, Filippini N, Douaud G, Dopper EG, Heise V, Trachtenberg AJ, van Swieten JC, van Buchem MA, Rombouts SA, Mackay CE - Front Neurosci (2015)

Combined group differences. Maps show statistically significant (p < 0.05) differences between groups: without the use of FIX (A), after the use of FIX (B) and the interaction between FIX and group differences (C) in all (15) RSNs combined. Color bar represents the number of significantly differing networks.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4621866&req=5

Figure 3: Combined group differences. Maps show statistically significant (p < 0.05) differences between groups: without the use of FIX (A), after the use of FIX (B) and the interaction between FIX and group differences (C) in all (15) RSNs combined. Color bar represents the number of significantly differing networks.
Mentions: All RSNs' combined results based on PE-driven spatial maps are shown for family-wise error-corrected group differences before the use of FIX (Figure 3A), group differences after the use FIX (Figure 3B) and for the interaction between applying FIX and group differences (Figure 3C). Dual regression results for each RSN are shown separately in Supplemental Figure 1 (numbers in Supplemental Figure 1 correspond with numbers in Figure 2).

Bottom Line: Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects.Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX.The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Leiden University Medical Centre Leiden, Netherlands ; FMRIB, Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford Oxford, UK.

ABSTRACT
Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

No MeSH data available.


Related in: MedlinePlus