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J Neurosci Methods. 2017 Nov 1;291:1-12. doi: 10.1016/j.jneumeth.2017.08.003. Epub 2017 Aug 5.

Functional overestimation due to spatial smoothing of fMRI data.

Author information

1
Life Science Research Center, School of Life Science and Technology, Xidian University, Xi'an 710071, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710071, China. Electronic address: liupengphd@gmail.com.
2
The Mind Research Network and LBERI, Albuquerque, NM 87106, USA; University of New Mexico, Department of Electrical and Computer Engineering, Albuquerque, NM 87131, USA.
3
The Mind Research Network and LBERI, Albuquerque, NM 87106, USA.

Abstract

BACKGROUND:

Pearson correlation (simply correlation) is a basic technique for neuroimage function analysis. It has been observed that the spatial smoothing may cause functional overestimation, which however remains a lack of complete understanding. Herein, we present a theoretical explanation from the perspective of correlation scale invariance.

NEW METHODS:

For a task-evoked spatiotemporal functional dataset, we can extract the functional spatial map by calculating the temporal correlations (tcorr) of voxel timecourses against the task timecourse. From the relationship between image noise level (changed through spatial smoothing) and the tcorr map calculation, we show that the spatial smoothing causes a noise reduction, which in turn smooths the tcorr map and leads to a spatial expansion on neuroactivity blob estimation.

RESULTS:

Through numerical simulations and subject experiments, we show that the spatial smoothing of fMRI data may overestimate activation spots in the correlation functional map. Our results suggest a small spatial smoothing (with a smoothing kernel with a full width at half maximum (FWHM) of no more than two voxels) on fMRI data processing for correlation-based functional mapping COMPARISON WITH EXISTING METHODS: In extreme noiselessness, the correlation of scale-invariance property defines a meaningless binary tcorr map. In reality, a functional activity blob in a tcorr map is shaped due to the spoilage of image noise on correlative responses. We may reduce data noise level by smoothing processing, which poses a smoothing effect on correlation. This logic allows us to understand the noise dependence and the smoothing effect of correlation-based fMRI data analysis.

KEYWORDS:

Correlation scale invariance; Functional magnetic resonance imaging (fMRI); Functional overestimation; Neuroimage correlation analysis; Spatial smoothing; Temporal correlation (tcorr)

PMID:
28789993
DOI:
10.1016/j.jneumeth.2017.08.003
[Indexed for MEDLINE]

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