Complexity and synchronicity of resting state blood oxygenation level-dependent (BOLD) functional MRI in normal aging and cognitive decline

Abstract

Purpose To explore the use of approximate entropy (ApEn) as an index of the complexity and the synchronicity of resting state BOLD fMRI in normal aging and cognitive decline associated with familial Alzheimer’s disease (fAD). Materials and Methods Resting state BOLD fMRI data were acquired at 3T from 2 independent cohorts of subjects consisting of healthy young (age 23±2 years, n=8) and aged volunteers (age 66±3 years, n=8), as well as 22 fAD associated subjects (14 mutation carriers, age 41.2±15.8 years; and 8 non-mutation carrying family members, age 28.8±5.9 years). Mean ApEn values were compared between the two age groups, and correlated with cognitive performance in the fAD group. Cross-ApEn (C-ApEn) was further calculated to assess the asynchrony between precuneus and the rest of the brain. Results Complexity of brain activity measured by mean ApEn in gray and white matter decreased with normal aging. In the fAD group, cognitive impairment was associated with decreased mean ApEn in gray matter as well as decreased regional ApEn in right precuneus, right lateral parietal regions, left precentral gyrus, and right paracentral gyrus. A pattern of asynchrony between BOLD fMRI series emerged from C-ApEn analysis, with significant regional anti-correlation with cross-correlation coefficient of functional connectivity analysis. Conclusion ApEn and C-ApEn may be useful for assessing the complexity and synchronicity of brain activity in normal aging and cognitive decline associated with neurodegenerative diseases

Characterizing Resting-State Brain Function Using Arterial Spin Labeling

“…To date, the majority of resting-state fMRI (rs-fMRI) studies have employed bloodoxygen-level-dependent (BOLD) contrast, focusing primarily on functional connectivity measures (van Dijk et al, 2010;Worsley et al, 1998), power spectral analyses (Duff et al, 2008;Handwerker et al, 2012;Rack-Gomer and Liu, 2012) or nonlinear complexity measures of rsfMRI (Friston et al, 2014;Liu et al, 2013). Despite its success and widespread use, rs-fMRI based on the BOLD contrast has several shortcomings: (1) the BOLD signal is not neuronally specific due to numerous physiological and noise contributions to its contrast mechanism; (2) even in the absence of noise, the BOLD technique offers limited spatial specificity to the site of neuronal activity, due to the contribution of draining veins to the BOLD contrast; (3) the BOLD signal alone does not provide a direct and quantitative measure of brain function during the resting state, and cannot be used in isolation to derive neuronal metabolism metrics.…”

BOLD fMRI complexity predicts changes in brain processes, interactions and patterns, in health and disease

“…The complexity associated with "the process" exhibited by a voxel time series have been predicted in ageing [10,21,24], in familial Alzheimer's disease [10], in attention deficit hyperactivity disorder (ADHD) [23], in cognitive performance [20], in schizophrenia [22], in multiple sclerosis [37] and in health [30]. Bivariate or cross entropy estimates the complexity between the time series of two voxels, which characterises the complexity of "the interaction" between the two voxel time series.…”

“…Bivariate or cross entropy estimates the complexity between the time series of two voxels, which characterises the complexity of "the interaction" between the two voxel time series. This has been shown to be significant in people with familial Alzheimer's disease [10] and in healthy subjects [17]. "Patterns of complexity" are estimated when the interactions among many voxel time series are characterised at different thresholds by using multivariate or multiscale entropy.…”

The Social (Neural) Network: Towards a Unifying Endophenotype Between Genes and Behavior

“…however, both measures have successfully been used to characterize the complexity of neural signals over time [192][193][194] .…”

“…We estimate both ApEn and SampEn using parameter settings recommended in prior work-specifically, we set m = 2 and r = 0.25 ⇥ M AD (where M AD is the median absolute deviation of the regional timeseries) 189,[191][192][193][194] .…”