Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity

Abstract

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test–retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level

“…Percent amplitude of fluctuation of normalized mean squared successive difference and variability of the successive difference between the EO-EC sessions [41]. EO and EC difference was associated with spontaneous BOLD oscillations in cortical sensory [42].…”

Magnitude and Temporal Variability of Inter-stimulus EEG Modulate the Linear Relationship Between Laser-Evoked Potentials and Fast-Pain Perception

“…Temporal variability can be calculated in many forms, such as variance (He, 2011 ), standard deviation (SD) (Garrett et al, 2013a ), mean square successive difference (MSSD) (Samanez-Larkin et al, 2010 ; Li et al, 2017 ). Here in this study, the temporal variability of isEEG was quantified by the normalized mean squared successive difference (nMSSD) (Neumann et al, 1941 ).…”

“…Mean squared successive difference (MSSD) is a popularly used metric to characterize the temporal variability of physiological signals such as heart rate and fMRI BOLD signals (Berntson et al, 2005 ; Samanez-Larkin et al, 2010 ). Compared with other measures of temporal variability (such as SD), MSSD is not affected by low frequency drift and thus is more robust and reliable (Neumann et al, 1941 ; Li et al, 2017 ). Furthermore, because of the inherent relationship between signal strength and signal variability (i.e., the temporal variability calculated as MSSD or SD is positively correlated with the magnitude), MSSD should be normalized by RMS to disassociate the influence of signal strength (i.e., the magnitude) from signal variability.…”

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

“…Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states using the functional magnetic resonance imaging (fMRI) technique. From the regional activity aspect of spontaneous brain activity, it was found that regional activity in the EO resting state was significantly higher in the bilateral middle occipital gyrus (MOG), orbital frontal cortex, right cuneus, fronto-parietal cortex and cerebellum regions, but significantly lower in the sensorimotor, visual, auditory, right paracentral lobule (PCL), retrosplenial cortex, insula, thalamus and cingulo-opercular regions compared to that in the EC resting state, by using the amplitude of low frequency fluctuation (ALFF; Yang et al, 2007 ; Yan et al, 2009 ; Liu et al, 2013 ; Zou et al, 2015 ; Qin et al, 2018 ), spectral density of the blood oxygenation level dependent signal (McAvoy et al, 2008 , 2012 ), amplitude of spontaneous activity (Bianciardi et al, 2009 ; Zou et al, 2015 ), fractional ALFF (fALFF; Jao et al, 2013 ; Liang et al, 2014 ; Li Z. et al, 2016 ), and regional homogeneity (ReHo; Liu et al, 2013 ; Song et al, 2015 ) measures. From the regions synchronization aspect of spontaneous brain activity, it was found that functional connectivity in the EO resting state was significantly greater between the posterior cingulate cortex (PCC) and other brain areas, but significantly smaller between the whole thalamus and visual cortex, the PCC and the bilateral perisylvian regions, as compared to the EC resting state (Yan et al, 2009 ; Zou et al, 2009 ; Jao et al, 2013 ).…”