Electrophysiological entropy in younger adults, older controls and older cognitively declined adults

Utilizing Entropy of Cadence to Optimize Cycling Rehabilitation in Individuals With Parkinson's Disease

BACKGROUND

Previous studies have established that increased Sample Entropy (SampEn) of cadence, a measure of non-linear variability, during dynamic cycling leads to greater improvements in motor function for individuals with Parkinson's disease (PD). However, there is significant variability in responses among individuals with PD due to symptoms and disease progression.

OBJECTIVES

The aim of this study was to develop and test a paradigm for adapting a cycling exercise intervention using SampEn of cadence and rider effort to improve motor function.

METHODS

Twenty-two participants were randomized into either patient-specific adaptive dynamic cycling (PSADC) or non-adaptive (NA) group. SampEn of cadence was calculated after each of the 12 sessions, and motor function was evaluated using the Kinesia test. Pearson's correlation coefficient was used to analyze the relationship between SampEn of cadence and motor function improvement. Multiple linear regression (MLR) was used to identify the strongest predictors of motor function improvement.

RESULTS

Pearson's correlation coefficient revealed a significant correlation between SampEn of cadence and motor function improvements (R2 = -.545, P = .009), suggesting that higher SampEn of cadence led to greater motor function improvement. MLR demonstrated that SampEn of cadence was the strongest predictor of motor function improvement (β = -8.923, t = -2.632, P = .018) over the BMI, Levodopa equivalent daily dose, and effort.

CONCLUSIONS

The findings show that PSADC paradigm promoted a greater improvement in motor function than NA dynamic cycling. These data will be used to develop a predictive model to optimize motor function improvement after cycling in individuals with PD.

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

Effects of Pharmacological Treatments in Alzheimer's Disease: Permutation Entropy-Based EEG Complexity Study

Alzheimer's disease (AD) is a neurodegenerative brain disease affecting cognitive and physical functioning. The currently available pharmacological treatments for AD mainly contain cholinesterase inhibitors (AChE-I) and N-methyl-D-aspartic acid (NMDA) receptor antagonists (i.e., memantine). Because brain signals have complex nonlinear dynamics, there has been an increase in interest in researching complexity changes in the time series of brain signals in individuals with AD. In this study, we explore the electroencephalographic (EEG) complexity for making better observation of pharmacological therapy-based treatment effects on AD patients using the permutation entropy (PE) method. We examined EEG sub-band (delta, theta, alpha, beta, and gamma) complexity in de-novo, monotherapy (AChE-I), dual therapy (AChE-I and memantine) receiving AD participants compared with healthy elderly controls. We showed that each frequency band depicts its own complexity profile, which is regionally altered between groups. These alterations were also found to be associated with global cognitive scores. Overall, our findings indicate that entropy measures could be useful to show medication effects in AD.

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

Still Wanting to Win: Reward System Stability in Healthy Aging

Healthy aging is accompanied by multi-faceted changes. Especially within the brain, healthy aging exerts substantial impetus on core parts of cognitive and motivational networks. Rewards comprise basic needs, such as food, sleep, and social contact. Thus, a functionally intact reward system remains indispensable for elderly people to cope with everyday life and adapt to their changing environment. Research shows that reward system function is better preserved in the elderly than most cognitive functions. To investigate the compensatory mechanisms providing reward system stability in aging, we employed a well-established reward paradigm (Monetary Incentive Delay Task) in groups of young and old participants while undergoing EEG measurement. As a new approach, we applied EEG connectivity analyses to assess cortical reward-related network connectivity. At the behavioral level, our results confirm that the function of the reward system is preserved in old age. The mechanisms identified for maintaining reward system function in old age do not fit into previously described models of cognitive aging. Overall, older adults exhibit lower reward-related connectivity modulation, higher reliance on posterior and right-lateralized brain areas than younger adults, and connectivity modulation in the opposite direction than younger adults, with usually greater connectivity during non-reward compared to reward conditions. We believe that the reward system has unique compensatory mechanisms distinct from other cognitive functions, probably due to its etymologically very early origin. In summary, this study provides important new insights into cortical reward network connectivity in healthy aging.

Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

Entropy modulation of electroencephalographic signals in physiological aging

Aging is a multifactorial physiological process characterized by the accumulation of degenerative processes impacting on different brain functions, including the cognitive one. A tool largely employed in the investigation of brain networks is the electroencephalogram (EEG). Given the cerebral complexity and dynamism, many non-linear approaches have been applied to explore age-related brain electrical activity modulation detected by the EEG: one of them is the entropy, which measures the disorder of a system. The present study had the aim to investigate aging influence on brain dynamics applying Approximate Entropy (ApEn) parameter to resting state EEG data of 68 healthy adult participants, divided with respect to their age in two groups, focusing on several specialized brain regions. Results showed that elderly participants present higher ApEn values than younger participants in the central, parietal and occipital areas, confirming the hypothesis that aging is characterized by an evolution of brain dynamics. Such findings may reflect a reduced synchronization of the neural networks cyclic activity, due to the reduction of cerebral connections typically found in aging process. Understanding the dynamics of brain networks by applying the entropy parameter could be useful for developing appropriate and personalized rehabilitation programs and for future studies on neurodegenerative diseases.

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review

Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000-2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

Self-initiated learning reveals memory performance and electrophysiological differences between younger, older and older adults with relative memory impairment

Older adults display difficulties in encoding and retrieval of information, resulting in poorer memory. This may be due to an inability of older adults to engage elaborative encoding strategies during learning. This study examined behavioural and electrophysiological effects of explicit cues to self-initiate learning during encoding and subsequent recognition of words in younger adults (YA), older control adults (OA) and older adults with relative memory impairment (OD). The task was a variation of the old/new paradigm, some study items were preceded by a cue to learn the word (L) while others by a do not learn cue (X). Behaviourally, YA outperformed OA and OD on the recognition task, with no significant difference between OA and OD. Event-related potentials at encoding revealed enhanced early visual processing (70-140 ms) for L- versus X-words in young and old. Only YA exhibited a greater late posterior positivity (LPP; 200-500 ms) for all words during encoding perhaps reflecting superior encoding strategy. During recognition, only YA differentiated L- versus X-words with enhanced frontal P200 (150-250 ms) suggesting impaired early word selection for retrieval in older groups; however, OD had enhanced P200 activity compared to OA during L-word retrieval. The LPP (250-500 ms) was reduced in amplitude for L-words compared to both X- and new words. However, YA showed greater LPP amplitude for all words compared to OA. For older groups, we observed reduced left parietal hemispheric asymmetry apparent in YA during encoding and recognition, especially for OD. Findings are interpreted in the light of models of compensation and dedifferentiation associated with age-related changes in memory function.

Pre-treatment EEG signal variability is associated with treatment success in depression

Background

Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response.

Methods

We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed.

Results

Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales.

Conclusion

Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.

•

We measured resting-state EEG prior to antidepressant pharmacotherapy.

•

We examined global vs. local processing in relation to antidepressant response.

•

Greater response was linked with increased global processing.

•

Age-related decreases in global communication were absent in future responders.

•

Baseline brain dynamics in those who are/are not responsive to antidepressants differ.

The effects of cardiorespiratory fitness and acute aerobic exercise on executive functioning and EEG entropy in adolescents

The current study examined the effects of cardiorespiratory fitness, identified with a continuous graded cycle ergometry, and aerobic exercise on cognitive functioning and entropy of the electroencephalogram (EEG) in 30 adolescents between the ages of 13 and 14 years. Higher and lower fit participants performed an executive function task after a bout of acute exercise and after rest while watching a film. EEG entropy, using the sample entropy measure, was repeatedly measured during the 1500 ms post-stimulus interval to evaluate changes in entropy over time. Analysis of the behavioral data for lower and higher fit groups revealed an interaction between fitness levels and acute physical exercise. Notably, lower fit, but not higher fit, participants had higher error rates (ER) for No Go relative to Go trials in the rest condition, whereas in the acute exercise condition there were no differences in ER between groups; higher fit participants also had significantly faster reaction times in the exercise condition in comparison with the rest condition. Analysis of EEG data revealed that higher fit participants demonstrated lower entropy post-stimulus than lower fit participants in the left frontal hemisphere, possibly indicating increased efficiency of early stage stimulus processing and more efficient allocation of cognitive resources to the task demands. The results suggest that EEG entropy is sensitive to stimulus processing demands and varies as a function of physical fitness levels, but not acute exercise. Physical fitness, in turn, may enhance cognition in adolescence by facilitating higher functionality of the attentional system in the context of lower levels of frontal EEG entropy.

Memory load effect in auditory-verbal short-term memory task: EEG fractal and spectral analysis

The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

Brain Dynamics of Aging: Multiscale Variability of EEG Signals at Rest and during an Auditory Oddball Task

Recently, the study of brain signal fluctuations is widely put forward as a promising entry point to characterize brain dynamics in health and disease. Although interesting results have been reported regarding how variability of brain activations can serve as an indicator of performance and adaptability in elderly, many uncertainties and controversies remain with regard to the comparability, reproducibility, and generality of the described findings, as well as the ensuing interpretations.

The present work focused on the study of fluctuations of cortical activity across time scales in young and older healthy adults. The main objective was to offer a comprehensive characterization of the changes of brain (cortical) signal variability during aging, and to make the link with known underlying structural, neurophysiological, and functional modifications, as well as aging theories. We analyzed electroencephalogram (EEG) data of young and elderly adults, which were collected at resting state and during an auditory oddball task. We used a wide battery of metrics that typically are separately applied in the literature, and we compared them with more specific ones that address their limits. Our procedure aimed to overcome some of the methodological limitations of earlier studies and verify whether previous findings can be reproduced and extended to different experimental conditions. In both rest and task conditions, our results mainly revealed that EEG signals presented systematic age-related changes that were time-scale-dependent with regard to the structure of fluctuations (complexity) but not with regard to their magnitude. Namely, compared with young adults, the cortical fluctuations of the elderly were more complex at shorter time scales, but less complex at longer scales, although always showing a lower variance. Additionally, the elderly showed signs of spatial, as well as between, experimental conditions dedifferentiation. By integrating these so far isolated findings across time scales, metrics, and conditions, the present study offers an overview of age-related changes in the fluctuation electrocortical activity while making the link with underlying brain dynamics.

The association of physical activity to neural adaptability during visuo-spatial processing in healthy elderly adults: A multiscale entropy analysis

Physical activity has been shown to benefit brain and cognition in late adulthood. However, this effect is still unexplored in terms of brain signal complexity, which reflects the level of neural adaptability and efficiency during cognitive processing that cannot be acquired via averaged neuroelectric signals. Here we employed multiscale entropy analysis (MSE) of electroencephalography (EEG), a new approach that conveys important information related to the temporal dynamics of brain signal complexity across multiple time scales, to reveal the association of physical activity with neural adaptability and efficiency in elderly adults. A between-subjects design that included 24 participants (aged 66.63±1.31years; female=12) with high physical activity and 24 age- and gender-matched low physical activity participants (aged 67.29±1.20years) was conducted to examine differences related to physical activity in performance and MSE of EEG signals during a visuo-spatial cognition task. We observed that physically active elderly adults had better accuracy on both visuo-spatial attention and working memory conditions relative to their sedentary counterparts. Additionally, these physically active elderly adults displayed greater MSE values at larger time scales at the Fz electrode in both attention and memory conditions. The results suggest that physical activity may be beneficial for adaptability of brain systems in tasks involving visuo-spatial information. MSE thus might be a promising approach to test the effects of the benefits of exercise on cognition.

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.

Cognitive reserve in young and old healthy subjects: differences and similarities in a testing-the-limits paradigm with DSST

Cognitive reserve (CR) is understood as capacity to cope with challenging conditions, e.g. after brain injury or in states of brain dysfunction, or age-related cognitive decline. CR in elderly subjects has attracted much research interest, but differences between healthy older and younger subjects have not been addressed in detail hitherto. Usually, one-time standard individual assessments are used to characterise CR. Here we observe CR as individual improvement in cognitive performance (gain) in a complex testing-the-limits paradigm, the digit symbol substitution test (DSST), with 10 repeated measurements, in 140 younger (20–30 yrs) and 140 older (57–74 yrs) healthy subjects. In addition, we assessed attention, memory and executive function, and mood and personality traits as potential influence factors for CR. We found that both, younger and older subjects showed significant gains, which were significantly correlated with speed of information processing, verbal short-term memory and visual problem solving in the older group only. Gender, personality traits and mood did not significantly influence gains in either group. Surprisingly about half of the older subjects performed at the level of the younger group, suggesting that interindividual differences in CR are possibly age-independent. We propose that these findings may also be understood as indication that one-time standard individual measurements do not allow assessment of CR, and that the use of DSST in a testing-the-limits paradigm is a valuable assessment method for CR in young and elderly subjects.