Search Results (122)

Neuromorphic technologies are attracting increasing interest in neuroengineering, as they provide an event-driven, spike-based computational framework that is well suited to temporally structured, sparse, and resource-constrained biological systems. Compared with conventional computing pipelines, neuromorphic approaches enable tighter integration of sensing, encoding, inference, feedback, and actuation under low-power and low-latency conditions. These features make them particularly relevant for wearable, implantable, and other edge-native neuroengineering applications. This review examines neuromorphic neuroengineering from four closely related perspectives: neuromorphic neurostimulation and adaptive actuation; tactile and sensory biointerfaces; spiking neural network (SNN)-based biosignal processing and state decoding; and wearable or implantable neuromorphic platforms. Across these domains, we highlight how neuromorphic systems may facilitate edge-native, closed-loop architectures that operate closer to the body and respond selectively to meaningful state changes. Neurorehabilitation is further discussed as an important translational context, as it involves long-term use, multimodal sensing, adaptive intervention, and substantial real-world deployment constraints. At present, however, the evidence base remains fragmented and is still largely dominated by device demonstrations and proof-of-concept studies rather than robust translational validation. Overall, neuromorphic approaches offer a promising systems-level pathway toward neuroengineering platforms that are not only computationally efficient but also adaptive, deployable, and responsive in real-world settings. Full article

EEG-based classification of mental disorders has increasingly relied on deep learning models, which are computationally intensive and difficult to interpret, limiting reproducibility and clinical deployment in resource-constrained or cross-site settings. We propose a configurable and modular machine learning framework for EEG-based classification that emphasizes interpretability, flexibility, and rigorous evaluation using schizophrenia detection as a representative use case. Our framework integrates standardized preprocessing, multiscale feature extraction, minimum redundancy–maximum relevance feature selection, and configurable ensemble learning. It also supports multiple validation strategies, including random splits, k-fold cross-validation, and leave-one-subject-out (LOSO), enabling systematic assessment of subject-level generalization. We evaluated the framework on two open EEG datasets: Warsaw IPN (Institute of Psychiatry and Neurology, 19 channels, 250 Hz; 28 subjects) and a Moscow adolescent cohort (16 channels, 128 Hz; 84 subjects). Results show that validation strategy strongly affects model performance. While K-fold validation yielded epoch-level accuracies of 98.06% and 91.47%, LOSO results were much lower: 76.12% (epoch-level) and 82.14% (subject-level) for Dataset 1, and 70.71% (epoch-level) and 77.38% (subject-level) for Dataset 2. These findings demonstrate the risk of overestimated performance due to data leakage and underscore the importance of subject-independent evaluation. Our proposed framework provides a low-complexity, interpretable, and extensible benchmark for reproducible EEG-based machine learning, with interpretable feature representations linked to EEG dynamics and potential applicability to broader neuroengineering and clinical decision-support systems. Full article

Exposure to nature has been associated with benefits to human well-being, commonly evaluated using standardized psychological assessments and, more recently, neuroimaging modalities such as Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and functional Near-Infrared Spectroscopy (fNIRS). This systematic review and meta-analysis addresses the following questions. (1) How is the impact of nature on well-being studied using psychological and neuroimaging modalities and what does it reveal? (2) What are the challenges and opportunities for the deployment of wearable neuroimaging modalities to understand the impact of nature on the brain’s health and well-being? A search on PubMed, IEEE Xplore, and ClinicalTrials.gov (March 2024) identified 33 studies combining neuroimaging and psychological assessments during exposure to real, virtual or imagined natural environments. Studies were analyzed by tasks, populations, neuroimaging modality, psychological assessment, and methodological quality. Most studies were conducted in Asia (n = 23 or 70%). Healthy participants were the dominant target population (70%). In total, 61% of the studies were conducted in natural settings, while 39% used visual imagery. EEG was the most common modality (82%). STAI (n = 8) and POMS (n = 8) were the most common psychological assessments. Only seven studies included clinical populations. Two separate meta-analyses of nine studies with explicit experimental and control groups revealed a significant positive effect of nature exposure on psychological outcomes (Hedges’ g = 0.30; p = 0.0021), and a larger effect on neurophysiological outcomes (Hedges’ g = 0.43; p = 0.0004), both with moderate-to-high heterogeneity. Overall, exposure to nature was associated with reductions in negative emotions in clinical populations. In contrast, healthy populations showed a more balanced psychological response, with nature exposure being associated with both increases in positive emotions and reductions in negative emotions. Notably, 88% of the studies presented methodological weaknesses, highlighting key opportunities for future neuroengineering research on the neural and psychological effects of nature exposure. Full article

Background/Objectives: Central vertigo (CV) lacks objective biomarkers for severity assessment. This study examined whether resting-state EEG microstate dynamics across frequency bands can distinguish CV severity. Methods: Resting-state EEG was recorded from 50 patients with stroke-related CV and 31 healthy controls. Patients were classified as moderate (MD, n = 31) or severe (SV, n = 19) based on Dizziness Handicap Inventory scores. Microstate analysis was performed in the delta, theta, alpha, and beta bands to assess microstate topographies, temporal parameters, and transition probabilities. Correlations with clinical measures and receiver operating characteristic analyses were conducted. Results: CV patients showed severity-dependent alterations in EEG microstate dynamics, most pronounced in the delta band. Delta-band microstate transition probabilities correlated significantly with symptom severity and balance confidence. The delta-band transition from microstate C to microstate B accurately differentiated MD from SV patients (AUC = 0.983). Conclusions: Delta-band EEG microstate transition dynamics reflect network dysfunction in CV and may serve as promising candidate biomarkers for CV severity stratification. Full article

Microwave sensing technology is rapidly advancing and increasingly finding its way into biomedical applications, promising significant improvements for medical care. Concurrently, the rise of artificial intelligence (AI) is enabling significant enhancements in the biomedical domain. Close scrutiny of the recent literature reveals intense activity in both fields, with particularly impactful outcomes deriving from the combined use of advanced microwave techniques and AI for biomedical monitoring. In this review, an up-to-date compilation, from the perspective of the authors, of the most significant works published on these topics in recent years is given, focusing on their integration and current challenges. With the objective of analyzing the current landscape, we survey and compare state-of-the-art biosensors and imaging systems at all healthcare levels, from outpatient contexts to specialized medical equipment and laboratory analysis tools. We also delve into the relevant applications of AI in medicine for processing microwave-derived data. As our core focus, we analyze the synergistic integration of AI in the design of microwave devices and the processing of the acquired data, which have shown notable performances, opening new avenues for compact, affordable, and multi-functional medical devices. We conclude by synthesizing the prevailing technical, algorithmic, and translational challenges that must be addressed to realize this potential. Full article

Bio-cybernetic organisms combine biological locomotion with electronic control but face significant challenges regarding individual variability and stimulus habituation. This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) designed to dynamically calibrate to individual Gromphadorhina portentosa specimens. Using a miniaturized neural controller, we compared ANFIS’s performance against natural behavior and non-adaptive control methods. Results demonstrate ANFIS’s superiority: obstacle navigation efficiency reached 81% (compared to 42% for non-adaptive methods), and effective behavioral modulation was sustained for 47 min (versus 26 min). Furthermore, the system achieved 73% target acquisition in complex terrain and maintained stimulus responsiveness 3.5-fold longer through sophisticated habituation compensation. Biocompatibility assessments confirmed interface functionality over 14-day periods. This research establishes foundational benchmarks for arthropod bio-cybernetics, demonstrating that adaptive neuro-fuzzy architectures significantly outperform conventional methods, enabling robust bio-hybrid platforms suitable for confined-space search-and-rescue operations. Full article

Background/Objectives: Suicidal behaviors are a major clinical concern in adolescents, particularly among those with disorders marked by emotion dysregulation and impulsivity. Although attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) each heighten suicide risk, little is known about whether their occurrence confers additive or interactive effects in youth. This study examined whether ADHD and BPD diagnoses show additive or interactive associations with the suicide risk in adolescents. Methods: In this cross-sectional observational clinical study, the sample included 108 Polish adolescents (66.7% female; aged 13–17 years) recruited from inpatient and outpatient psychiatric settings (Independent Public Healthcare Facility, Children and Youth Treatment Center in Zabór, the Youth Sociotherapy Center No. 2 in Wrocław, and the District Educational Center in Jerzmanice-Zdrój (Poland)). The data collection for our study was conducted between May 2024 and July 2025. Diagnoses and suicide risk were assessed using the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID 7.02). Associations of ADHD and BPD with suicide risk were tested using linear and logistic regression models while accounting for age, sex, the current depressive episode, and the use of psychiatric medications. Results: Unadjusted analyses revealed significant main, but not interactive, associations of BPD and ADHD with suicide risk. When covariates were included in the model, BPD remained strongly associated with suicidality severity and with the presence of any suicide risk (adjusted OR = 7.00, 95% CI [1.55–31.57]), whereas the association between ADHD and suicidality was attenuated and did not reach conventional levels of statistical significance (adjusted OR = 3.48, 95% CI [0.93–13.08]). No statistically detectable ADHD × BPD interaction was observed. Estimates for ADHD were directionally consistent across models but characterized by wide confidence intervals. Conclusions: Adolescents with BPD appear to be at particularly high risk of suicide and should receive focused assessment, safety planning, and early intervention as part of routine care. In contrast, suicidality among adolescents with ADHD appears to be influenced by co-occurring clinical conditions, and its independent association with suicide risk remains statistically uncertain after adjustment. Clinicians should therefore remain alert to suicidality in youth with ADHD, while paying particular attention to accompanying symptoms and comorbid diagnoses that may further increase risk. Full article

Cognitive deterioration and the transition to neurodegenerative disease does not develop through simple, linear regression; it develops as rapid and global transitions from one state to another within the neural network. Developing understanding and control over these events is among the largest tasks facing contemporary neuroscience. This paper will discuss a conceptual reframing of cognitive decline as a transitional phase of the functional state of complex neural networks resulting from the intertwining of molecular degradation, vascular dysfunction and systemic disarray. The paper will integrate the latest findings that have demonstrated how the disruptive changes in glymphatic clearance mechanisms, aquaporin-4 polarity, venous output, and neuroimmune signaling increasingly correlate with the neurophysiologic homeostasis landscape, ultimately leading to the destabilization of the network attraction sites of memory, consciousness, and cognitive resilience. Furthermore, the destabilizing processes are exacerbated by epigenetic silencing; neurovascular decoupling; remodeling of the extracellular matrix; and metabolic collapse that result in accelerating the trajectory of neural circuits towards the pathological tipping point of various neurodegenerative diseases including Alzheimer’s disease; Parkinson’s disease; traumatic brain injury; and intracranial hypertension. New paradigms in systems neuroscience (connectomics; network neuroscience; and critical transition theory) provide an intellectual toolkit to describe and predict these state changes at the systems level. With artificial intelligence and machine learning combined with single cell multi-omics; radiogenomic profiling; and digital twin modeling, the predictive biomarkers and early warnings of impending collapse of the system are beginning to emerge. In terms of therapeutic intervention, the possibility of reprogramming the circuitry of the brain into stable attractor states using precision neurointervention (CRISPR-based neural circuit reprogramming; RNA guided modulation of transcription; lineage switching of glia to neurons; and adaptive neuromodulation) represents an opportunity to prevent further progression of neurodegenerative disease. The paper will address the ethical and regulatory implications of this revolutionary technology, e.g., algorithmic transparency; genomic and other structural safety; and equity of access to advanced neurointervention. We do not intend to present a list of the many vertices through which the mechanisms listed above instigate, exacerbate, or maintain the neurodegenerative disease state. Instead, we aim to present a unified model where the phenomena of molecular pathology; circuit behavior; and computational intelligence converge in describing cognitive decline as a translatable change of state, rather than an irreversible succumbing to degeneration. Thus, we provide a framework for precision neurointervention, regenerative brain medicine, and adaptive intervention, to modulate the trajectory of neurodegeneration. Full article

Development of new therapeutic approaches and strategies for common neuropsychiatric disorders, including Major Depressive Disorder, anxiety disorders, and Post-Traumatic Stress Disorder, represent a significant global health challenge. Recent research indicates that emotional dysregulation and persistent inflammation are closely linked and serve as key pathophysiological features of these conditions. Emotional dysregulation is mechanistically coupled to locus coeruleus and norepinephrine (LC-NE) or noradrenergic system activity. Stemming from chronic stress, persistently elevated activity of the LC-NE system leads to hypervigilance, anxious states, and depressed mood. Concurrently, these symptoms are marked by systemic inflammation as indicated by elevated pro-inflammatory cytokines, and central neuroinflammation indicated by microglial activation in brain regions and networks involved in mood regulation and emotional control. In turn, chronic inflammation increases sympathetic tone and LC-NE activity resulting in a vortex of psychoneuroimmunological dysfunction that worsens mental health. Transcutaneous auricular vagus nerve stimulation (taVNS) in a non-invasive neuromodulation method uniquely positioned to address both noradrenergic dysfunction and chronic inflammation in neuropsychiatric applications. Evidence spanning the past decade demonstrates taVNS works via two complementary mechanisms. An ascending pathway engages vagal afferents projecting to the LC-NE system in the brain stem, which has been shown to modulate cortical arousal, cognitive function, mood, and stress responses. Through descending circuits, taVNS also modulates the cholinergic anti-inflammatory pathway to suppress the production of pro-inflammatory cytokines like TNF-α and IL-6 mitigating poor health outcomes caused by inflammation. By enhancing both central brain function and peripheral immune responses, taVNS has shown significant potential for recalibrating perturbed affective-cognitive processing. The present article describes and discusses recent evidence suggesting that taVNS offers a promising network-based paradigm for restoring psychoneuroimmunological homeostasis in common neuropsychiatric conditions. Full article

Background: Gene therapy has experienced significant development since its origin decades ago, resulting in therapies for a wide range of diseases. In this context, optogenetics has emerged as a promising therapy for treating diseases in a precise spatiotemporal way using light. Transporting optogenetic genes to target cells is achieved using viral vectors, specifically AAV vectors. These vectors present limited cargo capacity, and a large percentage of the population carries AAV neutralizing antibodies. In this regard, lipid nanoparticles can overcome some of the previously mentioned problems of AAV vectors, making them prime candidates for optogenetic delivery. Methods: In this study, we evaluated their suitability for the delivery of the ChrimsonR plasmid in neurons in vitro. Results: In rat cortical neurons, in most of the concentrations tested, there was no reduction in several neuron morphological parameters that we measured when compared to another non-viral nanoparticle called lipofectamine. Transfection efficiency was significantly higher compared to lipofectamine in almost all treatments. Further in vitro analysis showed that electrophysiological parameters were altered, with reduced signal amplitudes; however, cell viability assays showed no decline in cell viability. Conclusions: These results demonstrate that lipid nanoparticles represent a promising non-viral platform for optogenetic delivery, though formulation optimization is required to achieve full functional efficacy. Full article

Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, therapy, and brain-machine interfacing. This review presents the latest breakthroughs in implantable neural sensor technologies, emphasizing bio-integration, signal fidelity, and functional adaptability. We highlight innovations such as CMOS-integrated flexible probes, internal ion-gated organic electrochemical transistors (IGTs), multimodal neurotransmitter-electrophysiology sensors, and wireless energy systems. Finally, we discuss the clinical potential, translational challenges, and future directions for brain science and neuroengineering. We further benchmark transduction and analytical performance in physiological media and outline in vivo calibration, antifouling/packaging, and on-node data-efficient processing for long-term stability. Full article

Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain–computer interfaces, artificial intelligence, and organoid technologies, making them pivotal instruments for diagnosis, monitoring, disease modeling, treatment development, and rehabilitation of various diseases. Nonetheless, the majority of neural interface platforms focus on unidirectional control paradigms, neglecting the need for co-adaptive systems where both the human user and the interface continually learn and adapt. This selected review consolidates information from neuroscience, artificial intelligence, and organoid engineering to identify the conceptual underpinnings of co-adaptive and symbiotic human–machine interaction. We emphasize significant shortcomings in the advancement of long-term AI-facilitated co-adaptation, which permits individualized diagnostics and progression tracking in Alzheimer’s disease and Parkinson’s disease. We concentrate on incorporating deep learning for adaptive decoding, reinforcement learning for bidirectional feedback, and hybrid organoid–brain–computer interface platforms to mimic disease dynamics and expedite therapy discoveries. This study outlines the trends and limitations of the topics at hand, proposing a research framework for next-generation AI-enhanced neural interfaces targeting neurodegenerative diseases and neurological disorders that are both technologically sophisticated and clinically viable, while adhering to ethical standards. Full article

Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming with MI at different speeds (30, 45, and 60 rpm) to improve EEG-based classification. Ten healthy participants performed PP followed by MI tasks while EEG data were recorded. An increase in spectral relative power around Cz associated with both PP and MI was observed, varying with speed and suggesting that PP may enhance cortical engagement during MI. Furthermore, our classification strategy, based on Convolutional Neural Networks (CNNs), achieved an accuracy of 0.87–0.89 across four classes (three speeds and rest). This performance was also compared with the standard Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA), which achieved an accuracy of 0.67–0.76. These results demonstrate the feasibility of multiclass decoding of imagined pedaling velocities and lay the groundwork for speed-adaptive BCIs, supporting future personalized and user-centered neurorehabilitation interventions. Full article

The concept of Brain Health is transforming the neuroscientific landscape, promoting an integrative and preventive approach to care under a unifying vision. This position paper, developed by Swiss junior societies in neurology and psychiatry, presents a trainee perspective on how Brain Health should be addressed from the earliest stages of postgraduate training. It explores current gaps in postgraduate training, including the continued separation of neurology, psychiatry and other specialties involved in brain disorder care, limited interdisciplinary and interprofessional exposure, and gaps in leadership, public health, and advocacy skills. We highlight promising models such as Switzerland’s integrated training components and the proposed “brain medicine” framework, inspired by internal medicine. Additionally, we examine innovative initiatives from trainee associations that promote collaborative learning, advocacy, and Brain Health awareness through academic and creative channels. The paper also stresses the importance of equitable global access to training, the integration of research into clinical education, and the urgent need to address burnout and working conditions among early-career professionals. By reframing trainees not as passive learners but as active agents of change, we call for systemic reforms that support their role in advancing Brain Health. Ultimately, we advocate for the development of international core competencies, adaptable curricula, and structured interdisciplinary pathways that embed Brain Health into every level of medical training. Only through this comprehensive approach can we equip the next generation of clinicians to promote lifelong Brain Health across specialties, systems, and populations. Full article

Neuromarketing studies the brain function as a response to marketing stimuli. A large amount of neuromarketing research uses data from electroencephalography (EEG) recordings as a response of individuals’ brains to marketing stimuli, aiming to identify the factors that influence consumer behaviour that they cannot articulate or are reluctant to reveal. Evidence suggests that individuals’ processing styles affect their reaction to marketing stimuli. In this study, we propose and evaluate a predictive model that classifies consumers as verbalizers or visualizers based on EEG signals recorded during exposure to verbal, visual, and mixed advertisements. Participants (N = 22) were categorized into verbalizers and visualizers using the Style of Processing (SOP) scale and underwent EEG recording while viewing ads. The EEG signals were preprocessed and the five EEG frequency bands were extracted. We employed three classification models for every set of ads: SVM, Decision Tree, and kNN. While all three classifiers performed around the same, with accuracy between 86 and 93%, during cross-validation SVM proved to be the more effective model, with kNN and Decision Tree showing sensitivity to data imbalances. Additionally, we conducted independent t-tests to look for statistically significant differences between the two classes. The t-tests implicated the Theta frequency band. Therefore, these findings highlight the potential of leveraging EEG-based technology to effectively predict a consumer’s processing style for advertisements and offers practical applications in fields such as interactive content designs and user-experience personalization. Full article