EEG multifractal analysis correlates with cognitive testing scores and clinical staging in mild cognitive impairment

Abstract

Alzheimer's disease and mild cognitive impairment are increasingly prevalent global health concerns in aging industrialized societies. There are only limited non-invasive biomarkers for the cognitive and functional impairment associated with dementia. Multifractal analysis of EEG has recently been proposed as having the potential to be an improved method of quantitative EEG analysis compared to existing techniques (e.g., spectral analysis). We utilized an existing database of a study of healthy elderly patients (N = 20) who were assessed with cognitive testing (Folstein Mini Mental Status Exam; MMSE) and resting state EEG (4 leads). Each subject's EEG was separated into two 30 s tracings for training and testing a statistical model against the MMSE scores. We compared multifractal detrended fluctuation analysis (MF-DFA) against Fourier Transform (FT) in the ability to produce an accurate classification and regression trees estimator for the testing EEG segments. The MF-DFA-based statistical model MMSE estimation strongly correlated with the actual MMSE when applied to the test EEG parameter dataset, whereas the corresponding FT-based model did not. Using a standardized cutoff value for MMSE-based clinical staging, the MF-DFA-based statistical model was both sensitive and specific for clinical staging of both mild Alzheimer's disease and mild cognitive impairment. MF-DFA shows promise as a method of quantitative EEG analysis to accurately estimate cognition in Alzheimer's disease.

Postural constraints recruit shorter-timescale processes into the non-Gaussian cascade processes

“…This network cooperates directly and uninterruptedly with the nervous system and exhibits similar cascade-like structure as found in "neuronal avalanches" [52]. It can be no small coincidence that tensegrity and avalanche models all converge on the same multifractal geometry [53,54] that inspired the development of multiscale PDF quantification of non-Gaussianity [22,24,55]. Hence, this bodywide aspect suggests multiple points of entry through which stimulation can elicit cascade processes to spread from longer to shorter timescales-from the surface underfoot [30] and now from the loads at hand.…”

Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis

“…In addition to EEG's temporal dependency in AD, MSE analysis also showed lower complexity on a small temporal scale in the frontal region in AD; in comparison, higher complexity was observed across this brain region in AD on a larger temporal scale (Mizuno et al, 2010;Ni et al, 2016). Zorick et al reported that a statistical model based on MF analysis could detect clinical stages of severity and degree of progress from cognitive impairment to AD (Zorick et al, 2020). As described above, MSE and MF analyses have a high ability to detect the complexity in EEG signals of AD.…”

“…Zorick et al . reported that a statistical model based on MF analysis could detect clinical stages of severity and degree of progress from cognitive impairment to AD (Zorick et al, 2020 ). As described above, MSE and MF analyses have a high ability to detect the complexity in EEG signals of AD.…”

Visual effort moderates postural cascade dynamics

“…This network cooperates directly and uninterruptedly with the nervous system and exhibits similar cascade-like dynamics as in "neuronal avalanches" [46,47]. It is no small coincidence that tensegrity and avalanche models all converge on multiplicativity [48,49] that inspired multiscale quantification of non-Gaussianity [27][28][29]. Hence, MFT approaches suggest multiple points of entry through which stimulation elicits cascades to spread across scalesfrom the surface underfoot [8] to the loads at hand [9] and now from the incoming visual information.…”