Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals.

Understanding the neural responses to indoor characteristics like temperature and light is crucial for comprehending how the physical environment influences the human brain. Our study introduces an innovative approach using entropy analysis, specifically, approximate entropy (ApEn), applied to electroencephalographic (EEG) signals to investigate neural responses to temperature and light variations in indoor environments. By strategically placing electrodes over specific brain regions linked to temperature and light processing, we show how ApEn can be influenced by indoor factors. We also integrate heart indices from a multi-sensor bracelet to create a machine learning classifier for temperature conditions. Results showed that in anterior frontal and temporoparietal areas, neutral temperature conditions yield higher ApEn values. The anterior frontal area showed a trend of gradually decreasing ApEn values from neutral to warm conditions, with cold being in an intermediate position. There was a significant interaction between light and site factors, only evident in the temporoparietal region. Here, the neutral light condition had higher ApEn values compared to blue and red light conditions. Positive correlations between anterior frontal ApEn and thermal comfort scores suggest a link between entropy and perceived thermal comfort. Our quadratic SVM classifier, incorporating entropy and heart features, demonstrates strong performance (until 90% in terms of AUC, accuracy, sensitivity, and specificity) in classifying temperature sensations. This study offers insights into neural responses to indoor factors and presents a novel approach for temperature classification using EEG entropy and heart features.

Small World derived index to distinguish Alzheimer's type dementia and healthy subjects.

BACKGROUND: This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. METHODS: Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. RESULTS: Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. CONCLUSIONS: These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.

Cognitive resilience/reserve: Myth or reality? A review of definitions and measurement methods.

INTRODUCTION: This review examines the concept of cognitive reserve (CR) in relation to brain aging, particularly in the context of dementia and its early stages. CR refers to an individual's ability to maintain or regain cognitive function despite brain aging, damage, or disease. Various factors, including education, occupation complexity, leisure activities, and genetics are believed to influence CR. METHODS: We revised the literature in the context of CR. A total of 842 articles were identified, then we rigorously assessed the relevance of articles based on titles and abstracts, employing a systematic approach to eliminate studies that did not align with our research objectives. RESULTS: We evaluate-also in a critical way-the methods commonly used to define and measure CR, including sociobehavioral proxies, neuroimaging, and electrophysiological and genetic measures. The challenges and limitations of these measures are discussed, emphasizing the need for more targeted research to improve the understanding, definition, and measurement of CR. CONCLUSIONS: The review underscores the significance of comprehending CR in the context of both normal and pathological brain aging and emphasizes the importance of further research to identify and enhance this protective factor for cognitive preservation in both healthy and neurologically impaired older individuals. HIGHLIGHTS: This review examines the concept of cognitive reserve in brain aging, in the context of dementia and its early stages. We have evaluated the methods commonly used to define and measure cognitive reserve. Sociobehavioral proxies, neuroimaging, and electrophysiological and genetic measures are discussed. The review emphasizes the importance of further research to identify and enhance this protective factor for cognitive preservation.

Prognostic Role of Hemispherical Functional Connectivity in Stroke: A Study via Graph Theory Versus Coherence of Electroencephalography Rhythms.

BACKGROUND: The objective of the present study is to explore whether acute stroke may result in changes in brain network architecture by electroencephalography functional coupling analysis and graph theory. METHODS: Ninety acute stroke patients and 110 healthy subjects were enrolled in different clinical centers in Rome, Italy, starting from 2013, and for each one electroencephalographies were recorded within <15 days from stroke onset. All patients were clinically evaluated through National Institutes of Health Stroke Scale, Barthel Index, and Action Research Arm Test in the acute stage and during the follow-up. Functional connectivity was assessed using Total Coherence and Small World (SW) by comparing the affected and the unaffected hemisphere between groups (Stroke versus Healthy). Correlations between connectivity and poststroke recovery scores have been carried out. RESULTS: In stroke patients, network hemispheric asymmetry, in terms of Total Coherence, was mainly detected in the affected hemisphere with lower values in Delta, Theta, Alpha1, and Alpha2 (P=0.000001), whereas the unaffected hemisphere showed lower Total Coherence only in Delta and Theta (P=0.000001). SW revealed a significant difference only in the affected hemisphere in all electroencephalography bands (lower SW in Delta (P=0.000003), Theta (P=0.000003), Alpha1 (P=0.000203), and Alpha2 (P=0.028) and higher SW in Beta2 (P=0.000002) and Gamma (P=0.000002)). We also found significant correlations between SW and improvement in National Institutes of Health Stroke Scale (Theta SW: r=-0.2808), Barthel Index (Delta SW: r=0.3692; Theta SW: r=0.3844, Beta2 SW: r=-0.3589; Gamma SW: r=-04948), and Action Research Arm Test (Beta2 SW: r=-0.4274; Gamma SW: r=-0.4370). CONCLUSIONS: These findings demonstrated changes in global functional connectivity and in the balance of network segregation and integration induced by acute stroke. The findings on the correlations between clinical outcome(s) and poststroke network architecture indicate the possibility to identify a predictive index of recovery useful to address and personalize the rehabilitation program.

The Effects of Directional and Non-Directional Stimuli during a Visuomotor Task and Their Correlation with Reaction Time: An ERP Study.

Different visual stimuli can capture and shift attention into different directions. Few studies have explored differences in brain response due to directional (DS) and non-directional visual stimuli (nDS). To explore the latter, event-related potentials (ERP) and contingent negative variation (CNV) during a visuomotor task were evaluated in 19 adults. To examine the relation between task performance and ERPs, the participants were divided into faster (F) and slower (S) groups based on their reaction times (RTs). Moreover, to reveal ERP modulation within the same subject, each recording from the single participants was subdivided into F and S trials based on the specific RT. ERP latencies were analysed between conditions ((DS, nDS); (F, S subjects); (F, S trials)). Correlation was analysed between CNV and RTs. Our results reveal that the ERPs' late components are modulated differently by DS and nDS conditions in terms of amplitude and location. Differences in ERP amplitude, location and latency, were also found according to subjects' performance, i.e., between F and S subjects and trials. In addition, results show that the CNV slope is modulated by the directionality of the stimulus and contributes to motor performance. A better understanding of brain dynamics through ERPs could be useful to explain brain states in healthy subjects and to support diagnoses and personalized rehabilitation in patients with neurological diseases.

Transcranial Direct Current Stimulation Enhances Neuroplasticity and Accelerates Motor Recovery in a Stroke Mouse Model.

BACKGROUND: More effective strategies are needed to promote poststroke functional recovery. Here, we evaluated the impact of bihemispheric transcranial direct current stimulation (tDCS) on forelimb motor function recovery and the underlying mechanisms in mice subjected to focal ischemia of the motor cortex. METHODS: Photothrombotic stroke was induced in the forelimb brain motor area, and tDCS was applied once per day for 3 consecutive days, starting 72 hours after stroke. Grid-walking, single pellet reaching, and grip strength tests were conducted to assess motor function. Local field potentials were recorded to evaluate brain connectivity. Western immunoblotting, ELISA, quantitative real-time polymerase chain reaction, and Golgi-Cox staining were used to uncover tDCS-mediated stroke recovery mechanisms. RESULTS: Among our results, tDCS increased the rate of motor recovery, anticipating it at the early subacute stage. In this window, tDCS enhanced BDNF (brain-derived neurotrophic factor) expression and dendritic spine density in the peri-infarct motor cortex, along with increasing functional connectivity between motor and somatosensory cortices. Treatment with the BDNF TrkB (tropomyosin-related tyrosine kinase B) receptor inhibitor, ANA-12, prevented tDCS effects on motor recovery and connectivity as well as the increase of spine density, pERK (phosphorylated extracellular signal-regulated kinase), pCaMKII (phosphorylated calcium/calmodulin-dependent protein kinase II), pMEF (phosphorylated myocyte-enhancer factor), and PSD (postsynaptic density)-95. The tDCS-promoted rescue was paralleled by enhanced plasma BDNF level, suggesting its potential role as circulating prognostic biomarker. CONCLUSIONS: The rate of motor recovery is accelerated by tDCS applied in the subacute phase of stroke. Anticipation of motor recovery via vicariate pathways or neural reserve recruitment would potentially enhance the efficacy of standard treatments, such as physical therapy, which is often delayed to a later stage when plastic responses are progressively lower.

Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis.

INTRODUCTION: Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence. METHODS: A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods. RESULTS: Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers. DISCUSSION: On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.

tDCS effects on brain network properties during physiological aging.

Brain neural networks undergo relevant changes during physiological aging, which affect cognitive and behavioral functions. Currently, non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), are proposed as tools able to modulate cognitive functions in brain aging, acting on networks properties and connectivity. Segregation and integration measures are used and evaluated by means of local clustering (segregation) and path length (integration). Moreover, to assess the balancing between them, the Small World (SW) parameter is employed, evaluating functional coupling in normal brain aging and in pathological conditions including neurodegeneration. The aim of this study was to systematically investigate the tDCS-induced effects on brain network proprieties in physiological aging. In order to reach this aim, cortical activity was acquired from healthy young and elderly subjects by means of EEG recorded before, during, and after anodal, cathodal, and sham tDCS sessions. Specifically, the aim to exploring tDCS polarity-dependent changes in the age-dependent network dynamics was based on a network graph theory application on two groups divided in young and elderly subjects. Eighteen healthy young (9 females; mean age = 24.7, SD = 3.2) and fifteen elderly subjects (9 females; mean = 70.1, SD = 5.1) were enrolled. Each participant received anodal, cathodal, or sham tDCS over the left prefrontal cortex (PFC) in three separate experimental sessions performed 1 week apart. SW was computed to evaluate brain network organization. The present study demonstrates that tDCS delivered in PFC can change brain network dynamics, and tDCS-EEG coregistration data can be analyzed using graph theory to understand the induced effects of different tDCS polarities in physiological and pathological brain aging.

Brain network modulation in transradial amputee with finger perception restored through biomimetic intraneural stimulation.

INTRODUCTION/AIMS: The hand is a sophisticated tool which allows humans to interact with the external world mainly via the sense of touch. Previous evidences demonstrated that electrical stimulations of the nerve trunks governing the hand are able to restore touch perception in transradial amputees. The aim of this study was to evaluate the neurological correlates of restored perception by the evaluation of network characteristics of the brain connectome via EEG recordings in amputees utilizing a fully sensorized bionic hand. METHODS: A 48-year-old female with a left wrist traumatic disarticulation incurred 23 years prior to the study was enrolled for 6 months, during which experimental sessions were randomized. The control group included 12 healthy subjects performing a similar protocol. RESULTS: Results showed that in both amputee and control group, a trend of SW in delta and beta 1 is recognizable (Delta and Beta 1 opposite spread) selectively in the hemisphere contralateral to the stimulus. DISCUSSION: Delta increases (less orderly network) while Beta 1 decreases (more ordered network). It could be seen as a sign of higher attention and concentration of subjects to understand the perception and this monolateral modulation is similar to the bilateral attention given to the mathematical task difficulty's increment.

Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.

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.

Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E.

OBJECTIVE: Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic-MCI (aMCI) subjects progress to dementia (typically to Alzheimer-Dementia = AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo-E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year. METHODS: 145 aMCI subjects were divided into two sub-groups and, according to the clinical follow-up, were classified as Converted to AD (C-MCI, 71) or Stable (S-MCI, 74). RESULTS: Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first-order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo-E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50% sensitivity, 69% specificity and 59.6% accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%). INTERPRETATION: Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. Ann Neurol 2018 Ann Neurol 2018;84:302-314.

Transcranial direct current stimulation generates a transient increase of small-world in brain connectivity: an EEG graph theoretical analysis.

Transcranial direct current stimulation (tDCS) is a non-invasive technique able to modulate cortical excitability in a polarity-dependent way. At present, only few studies investigated the effects of tDCS on the modulation of functional connectivity between remote cortical areas. The aim of this study was to investigate-through graph theory analysis-how bipolar tDCS modulate cortical networks high-density EEG recordings were acquired before and after bipolar cathodal, anodal and sham tDCS involving the primary motor and pre-motor cortices of the dominant hemispherein 14 healthy subjects. Results showed that, after bipolar anodal tDCS stimulation, brain networks presented a less evident "small world" organization with a global tendency to be more random in its functional connections with respect to prestimulus condition in both hemispheres. Results suggest that tDCS globally modulates the cortical connectivity of the brain, modifying the underlying functional organization of the stimulated networks, which might be related to changes in synaptic efficiency of the motor network and related brain areas. This study demonstrated that graph analysis approach to EEG recordings is able to intercept changes in cortical functions mediated by bipolar anodal tDCS mainly involving the dominant M1 and related motor areas. Concluding, tDCS could be an useful technique to help understanding brain rhythms and their topographic functional organization and specificity.

Cortical connectivity modulation during sleep onset: A study via graph theory on EEG data.

Sleep onset is characterized by a specific and orchestrated pattern of frequency and topographical EEG changes. Conventional power analyses of electroencephalographic (EEG) and computational assessments of network dynamics have described an earlier synchronization of the centrofrontal areas rhythms and a spread of synchronizing signals from associative prefrontal to posterior areas. Here, we assess how "small world" characteristics of the brain networks, as reflected in the EEG rhythms, are modified in the wakefulness-sleep transition comparing the pre- and post-sleep onset epochs. The results show that sleep onset is characterized by a less ordered brain network (as reflected by the higher value of small world) in the sigma band for the frontal lobes indicating stronger connectivity, and a more ordered brain network in the low frequency delta and theta bands indicating disconnection on the remaining brain areas. Our results depict the timing and topography of the specific mechanisms for the maintenance of functional connectivity of frontal brain regions at the sleep onset, also providing a possible explanation for the prevalence of the frontal-to-posterior information flow directionality previously observed after sleep onset. Hum Brain Mapp 38:5456-5464, 2017. © 2017 Wiley Periodicals, Inc.

Effects of transcranial direct current stimulation on the functional coupling of the sensorimotor cortical network.

Transcranial direct current stimulation (tDCS) is well established-among the non-invasive brain stimulation techniques-as a method to modulate brain excitability. Polarity-dependent modulations of membrane potentials are detected after the application of anodal and cathodal stimulation, leading to changes in the electrical activity of the neurons. The main aim of the present study was to test the hypothesis that tDCS can affect-in a polarity-specific manner-the functional coupling of the sensorimotor areas during the eyes-open resting condition as revealed by total EEG coherence (i.e., coherence across the average of all combinations of the electrode pairs placed around the stimulation electrode). The changes in the total EEG coherence were evaluated pre-, during, and post-anodal and cathodal tDCS. While no differences were observed in the connectivity characteristics of the two pre-stimulation periods, a connectivity increase was observed in the alpha 2 band in the post-anodal tDCS with respect to pre-anodal and post-cathodal tDCS. The present study suggests that a specific approach based on the analyses of the functional coupling of EEG rhythms might enhance understanding of tDCS-induced effects on cortical connectivity. Moreover, this result suggests that anodal tDCS could possibly modify cortical connectivity more effectively with respect to cathodal tDCS.

Sensorimotor cortex excitability and connectivity in Alzheimer's disease: A TMS-EEG Co-registration study.

Several studies have shown that, in spite of the fact that motor symptoms manifest late in the course of Alzheimer's disease (AD), neuropathological progression in the motor cortex parallels that in other brain areas generally considered more specific targets of the neurodegenerative process. It has been suggested that motor cortex excitability is enhanced in AD from the early stages, and that this is related to disease's severity and progression. To investigate the neurophysiological hallmarks of motor cortex functionality in early AD we combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We demonstrated that in mild AD the sensorimotor system is hyperexcitable, despite the lack of clinically evident motor manifestations. This phenomenon causes a stronger response to stimulation in a specific time window, possibly due to locally acting reinforcing circuits, while network activity and connectivity is reduced. These changes could be interpreted as a compensatory mechanism allowing for the preservation of sensorimotor programming and execution over a long period of time, regardless of the disease's progression. Hum Brain Mapp 37:2083-2096, 2016. © 2016 Wiley Periodicals, Inc.

Prestimulus interhemispheric coupling of brain rhythms predicts cognitive-motor performance in healthy humans.

Physiological and neuroimaging studies suggest that human actions are characterized by time-varying engagement of functional distributed networks within the brain. In this study, we investigated whether specific prestimulus interhemispheric connectivity, as a measure of synchronized network between the two hemispheres, could lead to a better performance (as revealed by RT) in a simple visuomotor task. Eighteen healthy adults underwent EEG recording during a visual go/no-go task. In the go/no-go task, a central fixation stimulus was followed by a green (50% of probability) or red visual stimulus. Participants had to press the mouse button after the green stimuli (go trials). Interhemispheric coupling was evaluated by the spectral coherence among all the electrodes covering one hemisphere and matched with those on the other. The frequency bands of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). The task-related results showed that interhemispheric connectivity decreased in delta and increased in alpha band. Furthermore, we observed positive delta and negative alpha correlations with the RT; namely, the faster the RT, the lower delta and the higher alpha connection between the two hemispheres. These results suggested that the best performance is anticipated by the better functional coupling of cortical circuits involved during the processing of the sensorimotor information, occurring between the two hemispheres pending cognitive go/no-go task.

Time-varying coupling of EEG oscillations predicts excitability fluctuations in the primary motor cortex as reflected by motor evoked potentials amplitude: an EEG-TMS study.

PURPOSE: Motor evoked potentials (MEPs) elicited by a train of consecutive, individual transcranial magnetic stimuli demonstrate fluctuations in amplitude with respect to time when recorded from a relaxed muscle. The influence of time-varying, instantaneous modifications of the electroencephalography (EEG) properties immediately preceding the transcranial magnetic stimulation (TMS) has rarely been explored. The aim of this study was to investigate the influence of the pre-TMS motor cortex and related areas EEG profile on time variants of the MEPs amplitude. METHOD: MRI-navigated TMS and multichannel TMS-compatible EEG devices were used. For each experimental subject, post-hoc analysis of the MEPs amplitude that was based on the 50th percentile of the MEPs amplitude distribution provided two subgroups corresponding to "high" (large amplitude) and "low" (small amplitude). The pre-stimulus EEG characteristics (coherence and spectral profile) from the motor cortex and related areas were analyzed separately for the "high" and "low" MEPs and were then compared. RESULTS: On the stimulated hemisphere, EEG coupling was observed more often in the high compared to the low MEP trials. Moreover, a paradigmatic pattern in which TMS was able to lead to significantly larger MEPs was found when the EEG of the stimulated motor cortex was coupled in the beta 2 band with the ipsilateral prefrontal cortex and in the delta band with the bilateral centro-parietal-occipital cortices. CONCLUSION: This data provide evidence for a statistically significant influence of time-varying and spatially patterned synchronization of EEG rhythms in determining cortical excitability, namely motor cortex excitability in response to TMS.

EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.

In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients.

Exploring the complexity of EEG patterns in Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.