Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer’s and Parkinson’s disease

Abstract

Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of ‘scale-free’ behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer’s disease, while a local increase of brain signals complexity characterizes Parkinson’s disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer’s and Parkinson’s diseases.

Electrocorticographic and Astrocytic Signatures of Stearoyl-CoA Desaturase Inhibition in the Triple Transgenic Mouse Model of Alzheimer’s Disease

“…Our findings are suggestive of an overall increased scale-free complexity in mutant mice. Given that reports in AD patients using related metrics are most often indicative of a decreased arrhythmic/fractal complexity of the electroencephalogram (Lau et al, 2022; Averna et al, 2023), it is interesting to note that the deviation from the ‘optimal range’ in the complexity of electroencephalographic activity has been proposed to be a more suitable variable to use for clinical applications in comparison to the directionality of the changes (Lau et al, 2022). It is anticipated that a decrease in dendritic spine density in the targeted cerebral cortex regions, as reported for the hippocampus in this model (Hamilton et al, 2022), or other types of synaptic dysfunctions are contributing to the extensive modifications in ECoG rhythmic and arrhythmic activity in 3xTg-AD animals.…”

“…The present dataset highlights an extensive range of modifications in wake/sleep architecture and ECoG quality in a widely used mouse model of AD. This includes the original finding of higher multifractal complexity during wakefulness and SWS in (relatively young) 3xTg-AD mice, which has a high relevance to the understanding of mechanisms underlying the development of impaired cognitive functioning in neurodegenerative diseases (Averna et al, 2023). These changes in wake/sleep phenotypes were not rescued by the inhibition of SCD.…”

“…More precisely, standard spectral analyses of the ECoG (e.g., dynamics of slow-wave activity [SWA] during SWS) were combined to an analysis of scale-free activity using Wavelet-Leaders to quantify rhythmic and arrhythmic ECoG properties, which are both highly relevant to AD (Averna et al, 2023;Wang and Holtzman, 2020). This was done together with an evaluation of astrocytes using measurements of GFAP and aldehyde dehydrogenase 1 family member L1 (ALDH1L1; also known as 10formyltetrahydrofolate dehydrogenase) in hippocampal regions, in the lateral hypothalamus (LH), and in thalamic nuclei.…”

“…To test this hypothesis, we used an extensive wake/sleep phenotyping comprising advanced ECoG signal analyses to interrogate variables related to the architecture of vigilance states (i.e., time spent in wakefulness, slow wave sleep [SWS], and paradoxical sleep [PS]; alternations between these states), and to the quality of the ECoG signal during these states. More precisely, standard spectral analyses of the ECoG (e.g., dynamics of slow-wave activity [SWA] during SWS) were combined to an analysis of scale-free activity using Wavelet-Leaders to quantify rhythmic and arrhythmic ECoG properties, which are both highly relevant to AD (Averna et al, 2023; Wang and Holtzman, 2020). This was done together with an evaluation of astrocytes using measurements of GFAP and aldehyde dehydrogenase 1 family member L1 (ALDH1L1; also known as 10-formyltetrahydrofolate dehydrogenase) in hippocampal regions, in the lateral hypothalamus (LH), and in thalamic nuclei.…”