INTRODUCTION:The aging brain's vulnerability to decline emphasizes the importance of strategies preserving cognitive function and independence. As age-related brain changes do not always match clinical symptoms, reserve, encompassing brain reserve (BR) and cognitive reserve (CR), has emerged as a key mechanism supporting resilience and mitigating cognitive decline during aging. This study adopts an exploratory approach to examine whether age- and CR-related differences are reflected in large-scale brain network organization, as assessed through electroencephalographic (EEG) connectivity metrics. METHODS:Resilience to age-related cognitive changes was investigated by relating CR, measured using the Cognitive Reserve Index (CRI), to brain network topology quantified by the Small-World (SW) index derived from resting-state EEG recordings. SW differences were first examined between age groups and then within each age group according to CRI levels, across EEG frequency bands (Delta, Theta, Alpha 1, Alpha 2, Beta 1, Beta 2, Gamma). RESULTS:The analysis of SW values showed distinct patterns between groups. Adults exhibited significant higher SW values in Delta and Theta bands, but lower values in Alpha 2 compared to Elderly. Within Elderly, higher CRI correlated with greater SW values in Delta, Beta 2, and Gamma bands, and marginally significant lower Alpha 2 values. DISCUSSION:Similar Mini-Mental State Examination (MMSE) scores across groups suggest these differences may reflect compensatory mechanisms, highlighting CR's potential role in preserving cognitive efficiency and resilience during aging. In line with the cognitive reserve framework, these findings provide supportive-though indirect-evidence that individual differences in CR are associated with distinct patterns of brain network organization during healthy aging. Overall, this study highlights the potential relevance of large-scale neural network dynamics as markers of adaptive brain functioning in later life, while underscoring the noncausal and exploratory nature of these associations.

Electroencephalography (EEG) preprocessing is a critical yet time-consuming step that often relies on expert-driven, semi-automatic pipelines, limiting scalability and reproducibility across large datasets. In this work, we present sEEGnal, a fully automated and modular pipeline for EEG preprocessing designed to produce outputs comparable to expert-driven preprocessing while ensuring consistency and computational efficiency. The pipeline integrates three main modules: data standardisation following the EEG extension of the Brain Imaging Data Structure (BIDS), bad channel detection, and artefact identification, combining physiologically grounded criteria with independent component analysis and ICLabel-based classification. Performance was evaluated against manual preprocessing performed by EEG experts at two complementary levels: preprocessing metadata (bad channels, artefact duration, and rejected components) and EEG-derived measures. In addition, test-retest analyses were conducted to assess the stability of the pipeline across repeated recordings. Results show that sEEGnal achieves performance comparable to expert-driven preprocessing while preserving key neurophysiological features. Furthermore, the pipeline demonstrates reduced variability and increased consistency compared to human experts. These findings support sEEGnal as a robust and scalable solution for automated EEG preprocessing in both research and large-scale applications.

In an era marked by a rapidly aging global population, delving into the intricate neurophysiological changes that accompany the aging process assumes paramount importance. This narrative review offers a comprehensive exploration of the intricate relationship between electromagnetic neuromodulation and electroencephalography (EEG) within the context of aging. Moreover, it showed the promising landscape of non-invasive neuromodulation techniques, encompassing established methodologies like transcranial magnetic stimulation (TMS) and transcranial direct and alternating current stimulation (tDCS/tACS). These modalities are analyzed for their potential to shape EEG marks in the aging population. These associations not only could broaden our understanding of the aging brain but could also suggest exciting scenarios for therapeutic interventions and cognitive enhancement among the elderly. Consequently, the comprehension of these mechanisms emerges as a critical key player for the development of precisely tailored interventions, aimed at mitigating age-associated cognitive decline and supporting robust brain health in the elderly.

Centenarians are an increasing population, in particular in high income countries. Studying cognitively intact Centenarians' brain becomes fundamental to understand physiological ageing and how it diverges from pathological one. Resting state EEG were recorded using 27 channels in more than 130 subjects (referred to Young, Adults, Elderly, Centenarians and Alzheimer Disease patients), and the power spectral density (PSD) was computed. The paper demonstrates that Centenarians electrical brain activity is more similar to Elderly's than expected, despite approximately 30 years of age gap, provided they are cognitively intact. Centenarians EEG signal was expected to progressively approach AD one, but surprisingly they seem to slow-down their ageing and maintain non-pathological and resilient EEG patterns, particularly in Alpha bands: occipital region Centenarians PSD has lower values than Young and Adults but not than Elderly, and higher values than AD. These interesting results suggests that Centenarians brain needs to be investigated to extrapolate its characteristics and try to replicate its mechanisms for a widespread healthy ageing.

Stroke is one of the leading causes of disability worldwide. There are many different rehabilitation approaches aimed at improving clinical outcomes for stroke survivors. One of the latest therapeutic techniques is the non-invasive brain stimulation. Among non-invasive brain stimulation, transcranial direct current stimulation has shown promising results in enhancing motor and cognitive recovery both in animal models of stroke and stroke survivors. In this framework, one of the most innovative methods is the bihemispheric transcranial direct current stimulation that simultaneously increases excitability in one hemisphere and decreases excitability in the contralateral one. As bihemispheric transcranial direct current stimulation can create a more balanced modulation of brain activity, this approach may be particularly useful in counteracting imbalanced brain activity, such as in stroke. Given these premises, the aim of the current study has been to explore the recovery after stroke in mice that underwent a bihemispheric transcranial direct current stimulation treatment, by recording their electric brain activity with local field potential and by measuring behavioural outcomes of Grip Strength test. An innovative parameter that explores the complexity of signals, namely the Entropy, recently adopted to describe brain activity in physiopathological states, was evaluated to analyse local field potential data. Results showed that stroke mice had higher values of Entropy compared to healthy mice, indicating an increase in brain complexity and signal disorder due to the stroke. Additionally, the bihemispheric transcranial direct current stimulation reduced Entropy in both healthy and stroke mice compared to sham stimulated mice, with a greater effect in stroke mice. Moreover, correlation analysis showed a negative correlation between Entropy and Grip Strength values, indicating that higher Entropy values resulted in lower Grip Strength engagement. Concluding, the current evidence suggests that the Entropy index of brain complexity characterizes stroke pathology and recovery. Together with this, bihemispheric transcranial direct current stimulation can modulate brain rhythms in animal models of stroke, providing potentially new avenues for rehabilitation in humans.

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75

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.

Virtual reality (VR) allows to create controlled scenarios in which the quantity of stimuli can be modulated, as happen in real-life, where humans are subjected to various multisensory—often overlapping—stimuli. The present research aimed to study changes in attentional processes within an auditory oddball paradigm during a virtual exploration, while varying the amount of distractors. Twenty healthy volunteers underwent electroencephalography (EEG) during three different experimental conditions: an auditory oddball without VR (No-VR condition), an auditory oddball during VR exploration without distractors (VR-Empty condition), and an auditory oddball during VR exploration with a high level of distractors (VR-Full condition). Event-related potentials (ERPs) were computed averaging epochs of EEGs and analyzing peaks at 100 ms (N100) and 300 ms (P300) latencies. Results showed modulation of N100 amplitude in Fz and of P300 amplitude in Pz. Statistically significant differences in latency were observed only for P300 where the latency results delayed from the No-VR to VR-Full. The scalp topography revealed for P100 no significant differences between frequent and rare stimuli in either the No-VR and VR-Empty conditions. However, significant results were found in N100 in VR-Full condition. For P300, results showed differences between frequent and rare stimuli, in every condition. However, this difference is gradually less widespread from No-VR condition to the VR-Full. The emerging integration of VR with EEG may have important implications for studying brain attentional processing.

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.

INTRODUCTION:Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seems to provide a better understanding of the pathological mechanisms underlying the onset of dementia. The purpose of this review was to discuss the current ML application in the field of neuropsychology and electrophysiology, exploring its results in both prediction and diagnosis for different forms of dementia, such as Alzheimer's disease (AD), Vascular Dementia (VaD), Dementia with Lewy bodies (DLB), and Frontotemporal Dementia (FTD). METHODS:Main ML-based papers focusing on neuropsychological assessments and electroencephalogram (EEG) studies were analyzed for each type of dementia. RESULTS:An accuracy ranging between 70 % and 90 % or even more was observed in all neurophysiological and electrophysiological results trained by ML. Among all forms of dementia, the most significant findings were observed for AD. Relevant results were mostly related to diagnosis rather than prediction, because of the lack of longitudinal studies with appropriate follow-up duration. However, it remains unclear which ML algorithm performs better in diagnosing or predicting dementia. CONCLUSIONS:Neuropsychological and electrophysiological measurements, together with ML analysis, may be considered as reliable instruments for early detection of dementia.

Abstract Background In recent years, significant efforts have been directed towards the research and development of disease-modifying therapies for dementia. These drugs focus on prodromal (mild cognitive impairment, MCI) and/or early stages of Alzheimer’s disease (AD). Literature evidence indicates that a considerable proportion of individuals with MCI do not progress to dementia. Identifying individuals at higher risk of developing dementia is essential for appropriate management, including the prescription of new disease-modifying therapies expected to become available in clinical practice in the near future. Methods The ongoing INTERCEPTOR study is a multicenter, longitudinal, interventional, non-therapeutic cohort study designed to enroll 500 individuals with MCI aged 50–85 years. The primary aim is to identify a biomarker or a set of biomarkers able to accurately predict the conversion from MCI to AD dementia within 3 years of follow-up. The biomarkers investigated in this study are neuropsychological tests (mini-mental state examination (MMSE) and delayed free recall), brain glucose metabolism ([18F]FDG-PET), MRI volumetry of the hippocampus, EEG brain connectivity, cerebrospinal fluid (CSF) markers (p-tau, t-tau, Aβ1-42, Aβ1-42/1–40 ratio, Aβ1-42/p-Tau ratio) and APOE genotype. The baseline visit includes a full cognitive and neuropsychological evaluation, as well as the collection of clinical and socio-demographic information. Prognostic models will be developed using Cox regression, incorporating individual characteristics and biomarkers through stepwise selection. Model performance will be evaluated in terms of discrimination and calibration and subjected to internal validation using the bootstrapping procedure. The final model will be visually represented as a nomogram. Discussion This paper contains a detailed description of the statistical analysis plan to ensure the reproducibility and transparency of the analysis. The prognostic model developed in this study aims to identify the population with MCI at higher risk of developing AD dementia, potentially eligible for drug prescriptions. The nomogram could provide a valuable tool for clinicians for risk stratification and early treatment decisions. Trial registration ClinicalTrials.gov NCT03834402. Registered on February 8, 2019

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Convolutional networks (ConvNets) are computationally expensive but well known for their performance on image data. One way to reduce their complexity is to explore inherited data sparsity. However, since the gradients involved in ConvNets require dynamic updates, applying data sparsity in the training step is not straightforward. Dictionary-based learning methods can be useful since they encode the original data in a sparse form. This paper proposes a new dictionary-based training paradigm for ConvNets by exploiting redundancy in the training data while keeping the distinctive features intact. The ConvNet is then trained on the reduced, sparse dataset. The new approach significantly reduces the training time without compromising accuracy.To the best of our knowledge, this is the first implementation of ConvNet on dictionary-based sparse training data. The proposed method is validated on three publicly available datasets –MNIST, USPS, and MNIST FASHION. The experimental results show a significant reduction of 4.5 times in the overall computational burden of vanilla ConvNet for all the datasets. Whereas the accuracy is intact at 97.21% for MNIST, 96.81% for USPS, and 88.4% for FASHION datasets. These results are comparable to state-of-the-art algorithms, such as ResNet-{18,34,50}, trained on the full training dataset.

Disentangled representations of shape and pose are essential for animating human body meshes in computer animation, computer games, and virtual reality applications. While recent deep neural networks have achieved impressive effectiveness, their performance in terms of interpretability, reconstruction precision, and fine-grained control is not satisfactory. To address these issues, we propose the Part-aware Shape and Pose Disentanglement neural network (PSPDNet), a framework for disentangling the shape and pose of 3D human meshes with the same connectivity. PSPDNet utilizes part mesh autoencoders to learn representations of different human body parts, enhancing the interpretability of the latent codes by corresponding them with local motions. While mesh autoencoders alone can decouple shape and pose information from animated meshes, they fail to control local motions. In addition, PSPDNet employs an additional rotation-translation module to remove global rigid motion, i.e., rotation and translation, from the sequence. Finally, we propose a novel loss function which includes disentanglement loss and alignment loss to train PSPDNet in an unsupervised manner. Our experiments show that PSPDNet greatly improves disentangled representation with strong interpretability, insensitivity to global rigid transformation, and locality of editing and controlling.

In reality, interactive friend is added may one after another, instead of interacting with all at first. This work has proposed a progressive-interaction behavior driven by consistent aspiration, in which individuals interact with replacement neighbor one after another. Furthermore, payoff and interaction-efficiency are two factors considered in a modified Fermi rule, which particularly emphasizes that individuals achieving identical aspirations with less interaction frequency per unit of time will have higher interaction-efficiency and are more likely to be imitated. Thus, this work introduces the interaction-efficiency-driven strategy updating rules under progressive-interaction behavior in a spatial prisoner's dilemma game. As shown by numerical simulations, the progressive-interaction significantly enhances cooperation more than traditional versions, which may be caused by a heterogeneous number of interacting neighbors. When the interaction-efficiency-driven strategy update is taken into account, the cooperation is elevated to a higher level. Additionally, a specific point is employed to simulate the relationship between the aspiration and the b. As indicated by results, different b values have appropriate aspiration levels to promote the system to achieve the optimal cooperative level. More specifically, an obvious phase transition phenomenon can be found for the promotion results of individual aspiration level on cooperation. Besides, the phase transition phenomenon is existent when we simulate the relationship between the aspiration and the parameter delta for a given b value. In short, this work puts forward a new perspective on understanding cooperative evolution.

The automotive augmented reality head-up-display (AR-HUD) system relies on multiple free-form surfaces to project the virtual image into human eyes. A wide range of views and the extensive display area result in extraordinarily complex distortions. Providing a way to correct such distortions is a major leap forward. Methods widely used for calibration, such as lookup tables and interpolation, require considerable memory capacity, complicated calibration procedures, etc. Additionally, when attempting to meet the high-accuracy requirements for such systems, computational memory grow rapidly. In this context, we propose a fully connected neural network (HUDNet) for an automotive AR-HUD to correct dynamic distortion. Here, we propose a parallel prediction framework for the center and the edge of the image, and separate the distortion information from the disparity information in order to balance the unstable central field of view error. Ultimately, we introduce the transfer learning method to improve accuracy and its effect on accuracy is assessed. The present study provides a reference for us to apply deep learning methods to predict dynamic image distortion of various complex multi-freeform surface reflection systems in the future.

In Morocco, as in other olive oil producing countries, the rate of released olive mill wastewater (OMW) is growing exponentially, this hazardous liquid effluent is considered to be the most polluting waste of food industry given its high concentrations of COD and phenolic compounds.The aim of this study is to uncover an easy and rapid method for recovering and extracting a new product out of these liquid wastes. After separation of the remaining oil phase in the OMW, we used a method based on the inclusion with urea to extract oleic and linoleic acids with high purity 97.25% and 95.67% respectively. This was achieved by four successive crystallizations at different temperatures and led to the extraction of polyphenols, biodegradable soap and production of glycerol with high quality. In order to ensure the economic viability of the OMWW treatment project, a case study was performed based on the Marrakech region data. The technico-economic feasibility was also examined. The design and evaluation of the proposed unit yielded positive results, even without any process optimization.

Recent high resolution multiphoton photoemission studies of low index Ag surfaces have revealed spectral features whose energetics was controlled by multiple quanta of plasmon energy rather than the photon energies appearing in the standard Einstein’s one-electron energy scaling in photoeffect. To elucidate these peculiar features we introduce and elaborate the mechanism of bulk- and surface plasmon-induced electron emission from metal surfaces, conveniently termed plasmoemission. Our point of departure is the cloud of hot plasmons generated in the primary interactions of external electromagnetic (EM) field(s) with the system. Such hot plasmon distributions acquire the form of a coherent state plasmonic bath which may serve as a source of energy and momentum required for electron emission from the system. These plasmoemission channels are complementary to the standard photoemission channels driven directly by the primary EM fields. Adopting this paradigm we analyze the plasmonically induced electron yield by using perturbative and nonperturbative approaches in the length and velocity gauge representations of the electron–plasmon interaction. Pursuing the perturbative approach to one- and two bulk plasmon-induced electron emission from Ag(110) surface we have investigated the effects of underlying band structure on the electron yield and proposed as how to discern them in the measured spectra. This also enables putting the perturbative descriptions of plasmoemission into the general context of pump–probe spectroscopy. The more demanding nonperturbative approach has been implemented by invoking the Volkov ansatz type of electron wavefunction in the velocity gauge and applied to surface plasmon-induced electron emission from quasi-two-dimensional surface bands on Ag(111). In this formulation the electrons emanate from the surface Floquet bands generated from the parent surface state band by the action of prepumped plasmonic coherent state field. A quantitative assessment of the multiplasmoemission yield is presented in terms of the plasmonic coherent state parameters controlled by the external pumping fields. The opposite limit of plasmonically induced electron tunneling regime is recovered in the quasistatic strong field limit. The pump–probe concept can be established also in the nonperturbative picture albeit in a more complex form.

Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer’s disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration.