Dimensional Complexity of the Resting Brain in Healthy Aging, Using a Normalized MPSE

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.

Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults

“…While this method can address gradual motion over time, it is not effective for correction of short sudden bursts of motion, thus motion spike regression, censoring, or “scrubbing” (i.e., regressing or cutting time points with high FWD rates) were designed to remedy sudden motion effects ( Power et al, 2012 ). While these techniques can remedy motion effects, they come at the cost of altering the temporal cohesiveness and must be deployed carefully and with consideration of the subsequent analysis methods ( Power et al, 2012 ; Pruim et al, 2015a ; Scheel et al, 2018 ). As these procedures are widely used, residuals of subject motion-related signals are often still present after pre-processing and thus impact the results of higher-level analyses ( Scheel et al, 2015 ).…”

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

“…However, most of the current studies were only conducted on young adults. Aging increases the complexity of human brains (Anokhin et al, 1996 ), and this effect is demonstrated in the frontal inferior and sensorimotor areas (Scheel et al, 2018 ). In addition, entropy-based tools are widely used to quantify complexity, and approaches related to time–frequency analysis in separate frequency bands provide a clear physiological interpretation of the changes in EEG signals (Pavlov et al, 2020 ).…”

Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations

“…Such analysis is able to reveal features of brain dynamics in the resting state or during motor/cognitive tasks with various signal processing approaches. In particular, healthy aging increases the complexity of baseline EEGs [ 18 ], and this effect is evident in the frontal inferior and sensorimotor areas [ 19 ]. Therefore, a number of complexity measures can be applied to identify mild impairments arising, e.g., at the latent stages of neurodegenerative disorders [ 20 , 21 ].…”