Search Results (112)

Alzheimer’s disease is the most prevalent neurodegenerative disorder leading to progressive cognitive decline and functional impairment. Although advanced neuroimaging and cerebrospinal fluid biomarkers have improved early detection, their high costs, invasiveness, and limited accessibility restrict universal screening. Quantitative electroencephalography (qEEG) offers a non-invasive and cost-effective alternative for assessing neurophysiological changes associated with AD. This review critically evaluates current evidence on EEG biomarkers, including spectral, connectivity, and complexity measures, discussing their pathophysiological basis, diagnostic accuracy, and clinical utility in AD. Limitations and future perspectives, especially in developing standardized protocols and integrating machine learning techniques, are also addressed. Full article

Affecting over 50% of stroke patients, dysphagia is still challenging to diagnose and manage due to its complex multifactorial nature and can be the result of disruptions in the coordination of cortical and subcortical neural activity as reflected in electroencephalographic (EEG) signal patterns. Sample Entropy (SampEn), a signal complexity or predictability measure, could serve as a tool to identify any abnormalities associated with dysphagia. The present study aimed to identify quantitative dysphagia biomarkers using SampEn from EEG recordings in post-stroke patients. Sample entropy was calculated in the theta, alpha, and beta bands of EEG recordings in a repetitive swallowing task performed by three groups: 22 stroke patients without dysphagia (controls), 36 stroke patients with dysphagia, and 21 healthy age-matched individuals. Post-stroke patients, both with and without dysphagia, exhibited significant differences in SampEn compared to healthy subjects in the alpha and theta bands, suggesting widespread alterations in brain dynamics. These changes likely reflect impairments in sensorimotor integration and cognitive control mechanisms essential for effective swallowing. A significant cluster was identified in the left parietal region during swallowing in the beta band, where dysphagic patients showed higher entropy compared to healthy individuals and controls. This finding suggests altered neural dynamics in a region crucial for sensorimotor integration, potentially reflecting disrupted cortical coordination associated with dysphagia. The precise quantification of these neurophysiological alterations offers a robust and objective biomarker for diagnosing neurogenic dysphagia and monitoring therapeutic interventions by means of EEG, a non-invasive and cost-efficient technique. Full article

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

This paper introduces a new conceptual framework for interpreting urethral retro-resistance pressure (URP) as a dynamic, intra-procedural tool—ΔURP—for evaluating external urethral sphincter (EUS) engagement during injection therapy. With renewed interest in therapies that directly target the EUS, there is a critical need for real-time functional feedback at the site of action. This conceptual review re-examines URP in the context of emerging EUS-targeted treatments—such as bulking agents, regenerative injections, and neuromodulatory interventions—and proposes a dynamic model (ΔURP) to measure changes in sphincteric resistance as a functional biomarker during intervention. We review the anatomical, neurophysiological, and histological features of the EUS complex; trace the clinical rise and decline of URP; and compare its utility to conventional diagnostic tools. ΔURP, defined as the change in URP from baseline, is explored as an objective measure of EUS function. We outline its potential applications in guiding therapy, evaluating response, and standardizing outcomes across treatments. Conventional urodynamic measures fail to isolate distal sphincter function. In contrast, URP directly challenges the EUS and, when combined with imaging or procedural tools, may provide real-time feedback on sphincter engagement. When reframed as a dynamic, motion-based readout, URP may fill a critical gap in procedural urology—offering a physiologic signal of therapeutic engagement during EUS-targeted interventions. ΔURP has the potential to revive and repurpose a once-abandoned method into a clinically actionable biomarker for next-generation continence care. Full article

Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual’s age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice. Full article

Background: Complex regional pain syndrome (CRPS) is a rare, chronic, painful, neurological, debilitating disorder. Despite the substantial impact on quality of life, diagnosis remains challenging due to its complex pathophysiology and subjective clinical criteria. This integrative review aims to synthesize current research on potential diagnostic biomarkers for CRPS. Methods: A systematic search was conducted using the PubMed and Scopus databases to identify relevant studies published until January 2025. Inclusion criteria focused on adult CRPS patients, with studies examining diagnostic or predictive biomarkers. Results: Key findings highlight the role of inflammatory and immune-related biomarkers, such as elevated levels of cytokines (IL-6, TNF-α), immune cell infiltration, and specific autoantibodies. Neuropeptides, including substance P and calcitonin gene-related peptide, were associated with pain sensitization in acute phases, though their levels normalized in chronic stages. Additionally, genetic and epigenetic markers, brain imaging, and neurophysiological alterations provided insights into CRPS pathogenesis, emphasizing the dynamic nature of these biomarkers across disease stages. Conclusions: This review underscores the need for further research to integrate these biomarkers into diagnostic frameworks, which could enhance early diagnosis and treatment strategies for CRPS. Full article

Background/Objectives: Emerging evidence suggests that individuals with autism spectrum disorder (ASD) exhibit altered neural connectivity and disrupted brain network dynamics, which can be captured through EEG microstate analysis. Most research to date has focused on older children, adolescents, or adults with ASD, while studies focusing on preschool-aged children with ASD remain limited. Given that early brain development is critical for understanding the onset and progression of ASD, more research targeting this age group is essential. Methods: In this study, resting EEG data were collected from 59 preschool-aged children with ASD and 59 typically developing (TD) participants. Results: The results revealed a reduction in global explained variance and coverage of microstate in children with ASD, indicating poorer social performance that was independent of alpha power after the removal of the 1/f-like aperiodic signal. These findings reflect the social symptoms commonly observed in ASD. Additionally, alpha power was found to modulate the occurrence and duration of microstates in both groups. Conclusions: Our findings highlight that atypical microstates can serve as reliable biomarkers for ASD, offering valuable insights into the neurophysiological mechanisms underlying the disorder and paving the way for future research directions. Full article

Background/Objectives: Transcranial Direct Current Stimulation (tDCS) has proven to be a promising intervention for major depressive disorder (MDD). Even so, the specific neurophysiological mechanisms underlying its therapeutic effects, particularly regarding frontal EEG markers, remain insufficiently understood. This pilot study investigated both the clinical efficacy and neurophysiological impact of frontal tDCS in individuals with mild to severe depression, with particular focus on mood changes and alterations in Frontal Alpha Asymmetry (FAA), Beta Symmetry, and Theta/Alpha Ratios at the F3 and F4 electrode sites. Methods: A total of thirty–one participants were enrolled and completed a standardized Flow Neuroscience tDCS protocol targeting the dorsolateral prefrontal cortex using a bilateral F3/F4 montage. The intervention included an active phase of five stimulations per week for three weeks, followed by a Strengthening Phase with two stimulations per week. Clinical outcomes were assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS), while neurophysiological changes were evaluated via standardized quantitative EEG (QEEG) recordings obtained before and after the treatment course. Among the participants, fourteen individuals had a baseline MADRS score of ≥20, indicating moderate to severe depressive symptoms. Results: Following tDCS treatment, significant reductions in MADRS scores were observed across the cohort, with clinical response rates notably higher in the moderate/severe group (71.4%) compared to the mild depression group (20.0%). Neurophysiological effects were modest: no significant changes were detected in FAA or Beta Symmetry measures. However, a substantial reduction in the Theta/Alpha Ratio at F4 was found in participants with moderate to severe depression (p = 0.018, Cohen’s d = −0.72), suggesting enhanced frontal cortical activation associated with clinical improvement. Conclusions: These findings indicate that frontal tDCS is effective in reducing depressive symptoms, particularly in cases of moderate to severe depression. While improvements in FAA and Beta Symmetry were not significant, changes in the Theta/Alpha Ratio at F4 point toward dynamic neurophysiological reorganization potentially linked to therapeutic outcomes. The Theta/Alpha Ratio may serve as a promising biomarker for tracking tDCS response, whereas other EEG metrics might represent more stable trait characteristics. Future research should prioritize individualized stimulation protocols and incorporate more sensitive neurophysiological assessments, including functional connectivity analyses and task-evoked EEG paradigms, to understand the mechanisms underlying clinical improvements. Full article

Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and auditory sources of inattention. Unlike previous work, we evaluated multiple neurophysiological metrics—including oxygenated, deoxygenated, and combined haemoglobin—to identify the most reliable biomarker for distraction detection. Neurophysiological data were collected, and three multi-class classifiers (SVM, KNN, decision tree) were applied across different fNIRS metrics. Our results show that oxygenated haemoglobin outperforms other signals in distinguishing distracted from non-distracted states, while the combined signal performs best in differentiating distraction from baseline. The proposed SVM model achieved ≈ 77.9% accuracy in detecting distracted and relaxed driving states based on brain oxygen levels. Our findings also show that increased distraction correlates with elevated activity in the dorsolateral prefrontal cortex and premotor cortex, whereas driving without distraction exhibits lower neurovascular engagement. This study contributes to affective computing and intelligent transportation systems and could support the development of future driver distraction monitoring systems for safer and more adaptive vehicle control. Full article

Brain stimulation therapies may be used to correct motor, social, emotional, and cognitive consequences of traumatic brain injury (TBI). Neuromodulation applied with anatomical specificity can ameliorate desired symptoms while leaving functional circuits intact. Before applying precision medicine approaches, preclinical animal studies are needed to explore potential neurophysiological signatures that could be modulated with neurostimulation. This review discusses potential neural signatures of cognition, particularly reward processing, which is chronically impaired after brain injury. Electrophysiology, compared to other types of biomarkers, can detect deficits missed by structural measures, holds translational potential between humans and animals, and directly informs neuromodulatory treatments. Disturbances in oscillatory activity underscore structural, molecular, and behavioral impairments seen following TBI. For instance, cortico-striatal beta frequency activity (15–30 Hz) during reward processing represents subjective value and is chronically disturbed after frontal TBI in rodents. We use the example of evoked beta oscillations in the cortico-striatal network as a putative marker of reward processing that could be targeted with electrical stimulation to improve decision making after TBI. This review highlights the necessity of collecting electrophysiological data in preclinical models to understand the underlying mechanisms of cognitive behavioral deficits after TBI and to develop targeted stimulation treatments in humans. Full article

Problematic internet use (PIU) is linked to psychological distress and cognitive alterations, yet its early pre-clinical effects remain unclear. This study explored the psychological, behavioral, and neurophysiological correlates of PIU in a healthy, non-clinical population, focusing on response inhibition and execution within internet use patterns. A total of 133 participants (74 females, aged 18–35) were assessed using PIUQ-9 and DPIU questionnaires, along with measures of anxiety, depression, and obsessive–compulsive symptoms. An auditory equiprobable Go/NoGo task was used and event-related potentials (ERPs; N1/N2/P2/P3) were analyzed in relation to PIU severity and different online activities engagement. Additionally, behavioral, psychological, and neurophysiological profiles of individuals with high and low PIU levels were compared. PIU severity correlated with anxiety, depression, and obsessive–compulsive symptoms, while Go/NoGo task accuracy was unaffected. N1 amplitudes negatively correlated with PIU severity and gaming engagement, suggesting altered early sensory processing. NoGo-P3 latency positively correlated with information search engagement, indicating delayed inhibitory processing in frequent online searchers. High and low PIU groups differed in psychological measures but not in ERP or behavioral measures. Our findings confirm psychological distress in PIU alongside subtle neurophysiological alterations, suggesting that ERP measures in the equiprobable Go/NoGo task may not be highly sensitive PIU risk biomarkers in non-clinical populations. Full article

Background/Objectives: Lower-limb amputation (LLA) leads to disability, impaired mobility, and reduced quality of life, affecting 1.6 million people in the USA. Post-amputation, motor cortex reorganization occurs, contributing to phantom limb pain (PLP). Transcranial magnetic stimulation (TMS) assesses changes in cortical excitability, helping to identify compensatory mechanisms. This study investigated the association between TMS metrics and clinical and neurophysiological outcomes in LLA patients. Methods: A cross-sectional analysis of the DEFINE cohort, with 59 participants, was carried out. TMS metrics included resting motor threshold (rMT), motor-evoked potential (MEP) amplitude, short intracortical inhibition (SICI), and intracortical facilitation (ICF). Results: Multivariate analysis revealed increased ICF and rMT in the affected hemisphere of PLP patients, while SICI was reduced with the presence of PLP. A positive correlation between SICI and EEG theta oscillations in the frontal, central, and parietal regions suggested compensatory mechanisms in the unaffected hemisphere. Increased MEP was associated with reduced functional independence. Conclusions: SICI appears to be a key factor linked to the presence of PLP, but not its intensity. Reduced SICI may indicate impaired cortical compensation, contributing to PLP. Other neural mechanisms, including central sensitization and altered thalamocortical connectivity, may influence PLP’s severity. Our findings align with those of prior studies, reinforcing low SICI as a marker of maladaptive neuroplasticity in amputation-related pain. Additionally, longer amputation duration was associated with disrupted SICI, suggesting an impact of long-term plasticity changes. Full article

Background: Early evaluation of treatment efficacy in adolescents and young adults with major depressive episodes (MDEs) remains a clinical challenge, often delaying timely therapeutic adjustments. Electroencephalography (EEG) alpha traveling waves, particularly those elicited by intermittent photic stimulation (IPS), may serve as biomarkers reflecting neural dynamics. This study aimed to investigate whether IPS-induced alpha traveling waves could predict early treatment outcomes in transitional-aged youth with MDEs. Methods: We recorded EEG signals from 119 patients aged 16–24 years at admission, prior to a standardized two-week treatment regimen. IPS was applied using multiple stimulus frequencies, and alpha traveling waves were analyzed in terms of directionality (forward vs. backward) and hemispheric lateralization. Results: Alpha traveling wave amplitudes varied across individuals, depending on stimulus frequency and hemisphere. Notably, a higher amplitude of backward alpha traveling waves at 10 Hz IPS in the left hemisphere significantly predicted positive early treatment response. In contrast, forward waves and right hemisphere responses did not show predictive value. Conclusions: IPS-induced backward alpha traveling waves in the left hemisphere may represent promising EEG biomarkers for early prediction of treatment efficacy in youth with MDEs. These findings offer a potential neurophysiological tool to support personalized treatment strategies and inform future clinical applications in adolescent and young adult depression. Full article

Background/Objectives: Disorder of consciousness (DoC) poses diagnostic challenges due to behavioral assessment limitations. This study evaluates the spectral exponent (SE)—a neurophysiological biomarker quantifying the decay slope of electroencephalography (EEG) aperiodic activity—as an objective tool for consciousness stratification and clinical behavior scores correlation. Methods: The study involved 15 DoC patients, nine conscious brain-injured controls (BI), and 23 healthy controls (HC). Resting-state 32-channel EEG data were analyzed to compute SE across broadband (1–40 Hz) and narrowband (1–20 Hz, 20–40 Hz). Statistical frameworks included Bonferroni-corrected Kruskal–Wallis H tests, Bayesian ANOVA, and correlation analyses with CRS-R behavioral scores. Results: Narrowband SE (1–20 Hz) showed superior diagnostic sensitivity, differentiating DoC from controls (HC vs. DoC: p < 0.0001; BI vs. DoC: p = 0.0006) and MCS from VS/UWS (p = 0.0014). SE correlated positively with CRS-R index (1–20 Hz: r = 0.590, p = 0.021) and visual subscale (1–20 Hz: r = 0.684, p = 0.005). High-frequency (20–40 Hz) SE exhibited inconsistent results. Longitudinal tracking in an individual revealed a reduction in SE negativity, a flattening of the 1/f slope, and behavioral recovery occurring in parallel. Conclusions: Narrowband SE (1–20 Hz) is a robust biomarker for consciousness quantification, overcoming behavioral assessment subjectivity. Its correlation with visual function highlights potential clinical utility. Future studies should validate SE in larger cohorts and integrate multimodal neuroimaging. Full article

Background: Autism spectrum disorders (ASDs) comprise a neurodevelopmental disease marked by impaired social communication and repetitive activities. An imbalance between excitatory and inhibitory neurotransmitters, such as glutamate and GABA, may play a significant function in ASDs. The neurophysiological process behind epilepsy is abnormal neuronal excitatory firing in particular brain regions brought on by a lack of GABAergic inhibition. The study of GABAergic dysfunction could explain the substantial comorbidity with epilepsy or increased susceptibility to seizures observed in people with autism. Objective: This study analyzes molecular indicators directly and indirectly related to GABAergic inhibitory signaling in individuals with autism and healthy controls, with the purpose of uncovering probable diagnoses. Methods: The study included 46 male autistic participants and 26 age- and gender-matched healthy controls. Plasma levels of two chloride co-transporters (KCC2, NKCC1), and vitamin D3 were evaluated using ELISA. Results: Autistic individuals showed a significant drop in all three examined variables when compared to healthy controls. Statistical methods such as correlation, combined receiver operating characteristic (ROC) curve analysis, and multiple regression modeling were used to assess the diagnostic value and interrelationships of these biomarkers. A significant increase in the area under the curve was seen using the combined ROC curve analysis. The combined variables also exhibited significantly higher sensitivity and specificity as an index of high predictiveness values. Measurement of plasma levels of vitamin D status and chloride co-transporters (KCC2, NKCC1) in children with ASD may help to better understand how sleep disturbances and epilepsy as comorbidities of ASD linked to vitamin D deficiency and peculiar inhibitory/excitatory effects of GABA. Full article