Physiological Contribution in Spontaneous Oscillations: An Approximate Quality-Assurance Index for Resting-State fMRI Signals

Ai-Ling Hsu, Kun-Hsien Chou, Yi-Ping Chao, Hsin-Ya Fan, Changwei W Wu and Jyh-Horng Chen

PLOS ONE, 2016, vol. 11, issue 2, 1-18

Abstract: Resting-state fMRI (rs-fMRI) is receiving substantial attention for its sensitivity to functional abnormality in the brain networks of people with psychiatric and neurological disorders. However, because of the variety of rs-fMRI processing methods, the necessity of rs-fMRI quality assurance is increasing. Conventionally, the temporal signal-to-noise ratio (tSNR) is generally adopted for quality examination, but the tSNR does not guarantee reliable functional connectivity (FC) outcomes. Theoretically, intrinsic FC is supposed to reflect the spontaneous synchronization of neuronal basis, rather than that from thermal noise or non-neuronal physiological noise. Therefore, we proposed a new quality-assurance index for rs-fMRI to estimate the physiological contributions in spontaneous oscillations (PICSO). The PICSO index was designed as a voxel-wise measure for facilitating practical applications to all existing rs-fMRI data sets on the basis of two assumptions: Gaussian distributions in temporal fluctuations and ultra-slow changes of neural-based physiological fluctuations. To thoroughly validate the sensitivity of the proposed PICSO index to FC, we calibrated the preprocessing steps according to phantom data and verified the relationship between the PICSO and factors that are considered to affect FC in healthy participants (n = 12). Our results demonstrated that FC showed a significantly positive correlation with the PICSO. Moreover, for generating robust FC outcomes, directly acquiring data at a relatively large voxel size was more effective than performing smoothness on high-resolution data sets. In conclusion, compared with tSNR, the PICSO index is more sensitive to the resulting FC, providing a practical quality-assurance indicator for all existing rs-fMRI data sets.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0148393

DOI: 10.1371/journal.pone.0148393

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