Fractal-Based Analysis of fMRI BOLD Signal During Naturalistic Viewing Conditions

Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions.Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest.Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10−7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal.Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.

FRACTAL: Signal amplification of immunofluorescence via cyclic staining of target molecules

We demonstrate a novel signal amplification technique that can amplify the signal intensity of immunofluorescence staining via simple cyclic staining of secondary antibodies.

Identification of Cardiac Diseases from(ECG) Signals based on Fractal Analysis

This paper investigates the use of fractal geometry for analyzing ECG time series signals. A technique of identifying cardiac diseases is proposed which is based on estimation of Fractal Dimension (FD) of ECG recordings. Using this approach, variations in texture across an ECG signal can be characterized in terms of variations in the FD values. An overview of methods for computing the FD is presented focusing on the Power Spectrum Method (PSM) that makes use of the characteristic of Power Spectral Density Function (PSDF) of a Random Scaling Fractal Signal. A 20 dataset of ECG signals taken from MIT-BIH arrhythmia database has been utilized to estimate the FD, which established ranges of FD for healthy person and persons with various heart diseases. The obtained ranges of FD are presented in tabular fashion with proper analysis. Moreover, the experimental results showing comparison of Normal and Abnormal (arrhythmia) ECG signals and demonstrated that the PSM shows a better distinguish between the ECG signals for healthy and non-healthy persons versus the other methods.

Local Fractional Fourier Series With Applications to Representations of Fractal Signals

In this paper we discuss the local fractional Fourier series representations of fractal signals. Fractal signal processes are described within the local fractional integral operator. Four examples are presented in order to illustrate the developed technique.