Abstract
Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.
Highlights
Resting-state functional magnetic resonance imaging has become one of the main staples for understanding the functioning human brain (Biswal, 2012)
The human brain is one of natures most complex information processing systems, but finding a measure to describe this complexity has been normalized MPSE (nMPSE) of Resting-state functional magnetic resonance imaging (rs-fMRI) in Healthy Aging difficult (Sokunbi, 2016)
In addition to for fMRI we introduced and evaluated the normalized Multivariate Principal Subspace Entropy for restingstate fMRI data
Summary
Resting-state functional magnetic resonance imaging (rs-fMRI) has become one of the main staples for understanding the functioning human brain (Biswal, 2012). When it comes to basic principles of brain function, the brain at rest, meaning in the absence of a dedicated task, seems to be a fruitful resource for brain state interpretations (Zhang and Raichle, 2010). The human brain is one of natures most complex information processing systems, but finding a measure to describe this complexity has been nMPSE of rs-fMRI in Healthy Aging difficult (Sokunbi, 2016). Analyzing age-related changes in brain function is a complex endeavor (Brodoehl et al, 2013)
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