Abstract
Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain functions. We investigated the two functional maps: variance and first order autocorrelation coefficient (r 1). These two maps had distinct spatial distributions and the values were significantly different among the subdivisions of the precuneus and posterior cingulate cortex that were identified in functional connectivity (FC) studies. The results reinforce the functional segregation of these subdivisions and indicate that the intrinsic properties of the slow brain activity have physiological relevance. Further, we propose a sample size (degree of freedom) correction when assessing the statistical significance of FC strength with r 1 values, which enables a better understanding of the network changes related to various brain diseases.
Highlights
Spontaneous fluctuations of blood oxygen level-dependent (BOLD) signals, as measured by functional magnetic resonance imaging, are not caused by random noise but represent brain functions
Garrett et al showed the relationship between standard deviation of BOLD signals and chronological age [8] and Baria et al showed distinct spatial distributions of BOLD signals that reflect regional functional complexity [9]
We created two human brain maps using the distribution of the variance (v) and r1, both of which were calculated from the autocorrelation function for each voxel
Summary
Spontaneous fluctuations of blood oxygen level-dependent (BOLD) signals, as measured by functional magnetic resonance imaging (fMRI), are not caused by random noise but represent brain functions. The investigation of intrinsic properties of spontaneous BOLD fluctuations revealed the other aspects of the brain function. Garrett et al showed the relationship between standard deviation of BOLD signals and chronological age [8] and Baria et al showed distinct spatial distributions of BOLD signals that reflect regional functional complexity [9]. These studies suggest that the intrinsic properties of BOLD signals provide novel information about regional differentiation of the brain. We measured the intrinsic properties of spontaneous BOLD fluctuations using the two parameters, variance and autocorrelation coefficient, both of which were extracted by the autocorrelation function. The autocorrelation function has been used to extract periodicity in unitary neuronal activity [10–
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