Fractal-based Correlation Analysis for Resting State Functional Connectivity of the Rat Brain in Functional MRI

TL;DR

This paper introduces a fractal-based wavelet correlation method to analyze resting state BOLD signals in rat brains, effectively addressing noise issues and revealing unique low-frequency neural connectivity patterns.

Contribution

It proposes a novel fractal and wavelet correlation approach to study resting state functional connectivity in rat brains, accounting for noise and low-frequency neural activity.

Findings

Identified unique correlation patterns in very low-frequency bands.

Mapped the distribution of Hurst exponents across rat brain regions.

Demonstrated scale-invariance of wavelet correlation spectra.

Abstract

The most studies on functional connectivity have been done by analyzing the brain's hemodynamic response to a stimulation. On the other hand, the low-frequency spontaneous fluctuations in the blood oxygen level dependent (BOLD) signals of functional MRI have been observed in the resting state. However, the BOLD signals in resting state are significantly corrupted by huge noises arising from cardiac pulsation, respiration, subject motion, scanner, and so forth. Especially, the noise compounds are stronger in the rat brain than in the human brain. To overcome such an artifact, we assumed that fractal behavior in BOLD signals reflects low frequency neural activity, and applied the theorem such that the wavelet correlation spectrum between long memory processes is scale-invariant over low frequency scales. Here, we report an experiment that shows special correlation patterns not only in correlation of scaling coefficients in very low-frequency band (less than 0.0078Hz) but also in asymptotic wavelet correlation. In addition, we show the distribution of the Hurst exponents in the rat brain.