Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan.

Jianxin Dong,Xiangyu Ma,Xiao Mo,Han Liu,Bin Jing,Haiyun Li

doi:10.3389/fnins.2018.00034

Abstract

Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI) signal in the human brain across the adult lifespan using Hurst exponent (HE). We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females) from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected). However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected). Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process.

Highlights

As the elderly population increases, age-related cognitive changes across healthy lifespan emerges as a major concern which can interfere with daily routines and has an impact on quality of life (Hedden and Gabrieli, 2004; St John and Montgomery, 2010; Abrahamson et al, 2012)
Rs-Functional magnetic resonance imaging (fMRI) is based on low frequency fluctuations in the BOLD signal, and these fluctuations arise primarily from endogenous oscillations of brain metabolism and neurophysiological activity (Fox and Raichle, 2007; Yan et al, 2009)
The complexity of resting-state BOLD signals could provide some evidence of dynamics of intrinsic brain activity (Yang et al, 2013)

Summary

Introduction

As the elderly population increases, age-related cognitive changes across healthy lifespan emerges as a major concern which can interfere with daily routines and has an impact on quality of life (Hedden and Gabrieli, 2004; St John and Montgomery, 2010; Abrahamson et al, 2012). There is a need of more profound comprehension of the law of brain functional changes associated with healthy aging. Functional magnetic resonance imaging (fMRI) provides non-invasive techniques to explore aging human brain in vivo (Bandettini, 2007; Grady, 2008; Dosenbach et al, 2010; Uddin et al, 2010). FMRI study is generally based on task or resting state. Resting state studies of spontaneous fluctuations in fMRI signals have demonstrated huge potential in mapping the brain’s intrinsic functional features (Kruger and Glover, 2001; Yan et al, 2009). Ciuciu et al found that spontaneous brain activities had scale-free dynamics (Ciuciu et al, 2012).

Methods

Results

Conclusion