Search Results (109)

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

Axon polarization is a fundamental process in neuronal development, providing the structural basis for directional signaling in neural circuits. Precise control of axon specification is, thus, essential for the bottom-up construction of neuronal networks with defined architecture and connectivity. Although neurite length and elongation timing have both been implicated as determinants of axonal fate, their relative contributions have remained unresolved due to technical limitations in manipulating these factors independently in conventional culture systems. Here, we developed a constructive neuroengineering platform based on modifiable agarose gel microstructures that enables dynamic, in situ control of neurite outgrowth length and timing during neuronal cultivation. This approach allowed us to directly address whether axon polarization depends primarily on neurite length or the order of neurite extension. Using a single-neurite elongation paradigm, we quantitatively defined two length thresholds for axon specification: a critical length of 43.3 μm, corresponding to a 50% probability of axonal differentiation, and a definitive length of 95.4 μm, beyond which axonal fate was reliably established. In experiments involving simultaneous or sequential elongation of two neurites, we observed that neurite length—not elongation order—consistently predicted axonal identity, even when a second neurite was introduced after the first had already begun to grow. The presence of a competing neurite modestly elevated the effective critical length, suggesting inhibitory interactions that modulate length thresholds. These findings provide the first direct experimental confirmation that neurite length is the primary determinant of axon polarization and demonstrate the utility of constructive microfabrication approaches for dissecting fundamental principles of neuronal polarity. Our platform establishes a powerful experimental foundation for future efforts to achieve complete control over axon and dendrite orientation during the engineered construction of functional neuronal circuits. Full article

Neurodegeneration is increasingly recognized not as a linear trajectory of protein accumulation, but as a multidimensional collapse of biological organization—spanning intracellular signaling, transcriptional identity, proteostatic integrity, organelle communication, and network-level computation. This review intends to synthesize emerging frameworks that reposition neurodegenerative diseases (ND) as progressive breakdowns of interpretive cellular logic, rather than mere terminal consequences of protein aggregation or synaptic attrition. The discussion aims to provide a detailed mapping of how critical signaling pathways—including PI3K–AKT–mTOR, MAPK, Wnt/β-catenin, and integrated stress response cascades—undergo spatial and temporal disintegration. Special attention is directed toward the roles of RNA-binding proteins (e.g., TDP-43, FUS, ELAVL2), m6A epitranscriptomic modifiers (METTL3, YTHDF1, IGF2BP1), and non-canonical post-translational modifications (SUMOylation, crotonylation) in disrupting translation fidelity, proteostasis, and subcellular targeting. At the organelle level, the review seeks to highlight how the failure of ribosome-associated quality control (RQC), autophagosome–lysosome fusion machinery (STX17, SNAP29), and mitochondrial import/export systems (TIM/TOM complexes) generates cumulative stress and impairs neuronal triage. These dysfunctions are compounded by mitochondrial protease overload (LONP1, CLPP), UPR maladaptation, and phase-transitioned stress granules that sequester nucleocytoplasmic transport proteins and ribosomal subunits, especially in ALS and FTD contexts. Synaptic disassembly is treated not only as a downstream event, but as an early tipping point, driven by impaired PSD scaffolding, aberrant endosomal recycling (Rab5, Rab11), complement-mediated pruning (C1q/C3–CR3 axis), and excitatory–inhibitory imbalance linked to parvalbumin interneuron decay. Using insights from single-cell and spatial transcriptomics, the review illustrates how regional vulnerability to proteostatic and metabolic stress converges with signaling noise to produce entropic attractor collapse within core networks such as the DMN, SN, and FPCN. By framing neurodegeneration as an active loss of cellular and network “meaning-making”—a collapse of coordinated signal interpretation, triage prioritization, and adaptive response—the review aims to support a more integrative conceptual model. In this context, therapeutic direction may shift from damage containment toward restoring high-dimensional neuronal agency, via strategies that include the following elements: reprogrammable proteome-targeting agents (e.g., PROTACs), engineered autophagy adaptors, CRISPR-based BDNF enhancers, mitochondrial gatekeeping stabilizers, and glial-exosome neuroengineering. This synthesis intends to offer a translational scaffold for viewing neurodegeneration as not only a disorder of accumulation but as a systems-level failure of cellular reasoning—a perspective that may inform future efforts in resilience-based intervention and precision neurorestoration. Full article

Approximately 185,000 people in the United states experience limb loss each year. There is a need for an intuitive neural interface that can offer high-fidelity control signals to optimize the advanced functionality of prosthetic devices. Regenerative peripheral nerve interface (RPNI) is a pioneering advancement in neuroengineering that combines surgical techniques with biocompatible materials to create an interface for individuals with limb loss. RPNIs are surgically constructed from autologous muscle grafts that are neurotized by the residual peripheral nerves of an individual with limb loss. RPNIs amplify neural signals and demonstrate long term stability. In this narrative review, the terms “Regenerative Peripheral Nerve Interface (RPNI)” and “RPNI surgery” are used interchangeably to refer to the same surgical and biological construct. This narrative review specifically focuses on RPNIs as a targeted approach to enhance prosthetic control through surgically created nerve–muscle interfaces. This area of research offers a promising solution to overcome the limitations of existing prosthetic control systems and could help improve the quality of life for people suffering from limb loss. It allows for multi-channel control and bidirectional communication, while enhancing the functionality of prosthetics through improved sensory feedback. RPNI surgery holds significant promise for improving the quality of life for individuals with limb loss by providing a more intuitive and responsive prosthetic experience. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive dysfunction and working memory impairment, with early hippocampal damage being a prominent feature. Transcranial magneto-acoustic stimulation (TMAS) has been shown to target specific brain regions for neuroregulation. Methods: This study investigated the effects of TMAS on cognitive function, working memory, and hippocampal CA3 neural rhythms in AD rats by specifically stimulating the hippocampal region. Results: The novel object recognition test and T-maze test were employed to assess behavioral performance, while time-frequency analyses were conducted to evaluate memory-related activity, neural synchronization, and cross-frequency phase-amplitude coupling. TMAS significantly improved cognitive and working memory deficits in AD rats, enhancing long-term memory performance. Additionally, the abnormal energy levels observed in the θ and γ rhythm power spectra of the CA3 region were markedly restored, suggesting the recovery of normal neural function. This improvement was accompanied by a partial resurgence of neural activity, indicating enhanced inter-neuronal communication. Furthermore, the previously damaged coupling between the θ-fast γ and θ-slow γ rhythms was successfully improved, resulting in a notable enhancement of synchronized activity. Conclusions: These findings suggest that TMAS effectively alleviates cognitive and working memory impairments in AD rats and may provide experimental support for developing new treatments for AD. Full article

Deficits in social interaction and communication characterize Autism Spectrum Disorder (ASD). Although widely recognized by its symptoms, diagnosing ASD remains challenging due to its wide range of clinical presentations. Methods: In this study, we propose a method to assist in the early diagnosis of autism, which is currently primarily based on clinical assessments. Our approach aims to develop an early differential diagnosis based on electroencephalogram (EEG) signals, seeking to identify patterns associated with ASD. In this study, we used EEG data from 56 participants obtained from the Sheffield dataset, including 28 individuals diagnosed with Autism Spectrum Conditions (ASC) and 28 neurotypical controls, applying numerical techniques to handle missing data. Subsequently, after a detailed analysis of the signals, we applied three different starting approaches: one with the original database and the other two with selection of the most significant attributes using the PSO and evolutionary search methods. In each of these approaches, we applied a series of machine learning models, where relatively high performances for classification were observed. Results: We achieved accuracies of 99.13% ± 0.44 for the dataset with original signals, 99.23% ± 0.38 for the dataset after applying PSO, and 93.91% ± 1.10 for the dataset after the evolutionary search methodology. These results were obtained using classical classifiers, with SVM being the most effective among the first two approaches, while Random Forest with 500 trees proved more efficient in the third approach. Conclusions: Even with all the limitations of the base, the results of the experiments demonstrated promising findings in identifying patterns associated with Autism Spectrum Disorder through the analysis of EEG signals. Finally, we emphasize that this work is the starting point for a larger project with the objective of supporting and democratizing the diagnosis of ASD both in children early and later in adults. Full article

Constructing stable and flexible neuronal networks with multi-neurite wiring is essential for the in vitro modeling of brain function, connectivity, and neuroplasticity. However, most existing neuroengineering platforms rely on static microfabrication techniques, which limit the ability to dynamically control circuit architecture during cultivation. In this study, we developed a modifiable agarose gel-based platform that enables real-time microstructure fabrication using an infrared (IR) laser system under live-cell conditions. This approach allows for the stepwise construction of directional neurite paths, including sequential microchannel formation, cell chamber fabrication, and controlled neurite–neurite crossings. To support long-term neuronal health and network integrity in agarose microstructures, we incorporated direct glial co-culture into the system. A comparative analysis showed that co-culture significantly enhanced neuronal adhesion, neurite outgrowth, and survival over several weeks. The feeder layer configuration provided localized trophic support while maintaining a clear separation between glial and neuronal populations. Dynamic wiring experiments further confirmed the platform’s precision and compatibility. Neurites extended through newly fabricated channels and crossed pre-existing neurites without morphological damage, even when laser fabrication occurred after initial outgrowth. Time-lapse imaging showed a temporary growth cone stalling at crossing points, followed by successful elongation in all tested samples. Furthermore, the direct laser irradiation of extending neurites during microstructure modification did not visibly impair neurite elongation, suggesting minimal morphological damage under the applied conditions. However, potential effects on molecular signaling and electrophysiological function remain to be evaluated in future studies. Together, these findings establish a powerful, flexible system for constructive neuroengineering. The platform supports long-term culture, real-time modification, and multidirectional wiring, offering new opportunities for studying neural development, synaptic integration, and regeneration in vitro. Full article

Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2–3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke. Full article

Lower limb prosthesis abandonment is a significant challenge, leading to reliance on walking aids, such as wheelchairs, which frequently do not match the patient’s needs and lead to increased morbidity. Prosthesis abandonment is driven by a lack of embodiment, the latter defined as the integration of a prosthetic device into one’s body schema. This review evaluates interventions enhancing embodiment through three dimensions: ownership, agency, and co-location. The aim of this narrative review is to ask what interventions are available to improve embodiment, and what dimensions of embodiment should be included in the standard of care for lower-limb amputation surgery and componentry development. This narrative is constructed through a thorough literature search on how the aforementioned dimensions of embodiment can be optimized. In the studies reviewed, standardization of embodiment metrics and longitudinal data are lacking, hindering clinical translation. Future work must prioritize patient-centered design, integrate multidimensional assessments, and address practical issues to expand eligibility for advanced interventions. Full article

Background/Objectives: Spinal cord injury (SCI) is a devastating condition that leads to a cascade of cellular and molecular events, resulting in both primary and secondary damage. Among the many cells involved in the post-SCI environment, glial cells in the spinal cord and brain are pivotal in determining the trajectory of injury and repair. Methods: While recent SCI studies have shown changes in the genotype of glial cells following injury, exactly how these alterations occur after damage remains unknown. In this sense, the systemic inflammatory molecules could be involved in the connection between the spinal cord and brain, inducing glial activation by different signaling pathways. Preclinical studies have shown that nuclear factor-κB (NF-κB), Janus kinase/signal transducer and activator of transcription (JAK/STAT), and phosphoinositide 3-kinase/Akt (PI3K/Akt) signaling pathways are involved in the change in glial type. Results: These cells, which include astrocytes and microglia, exhibit dynamic responses following spinal injury, contributing to both neuroprotection and neurodegeneration. These different effects indicate that the molecular environment causes changes in the type of astrocytes and microglia, leading to different actions. Conclusions: Understanding the mechanisms of glial cell activation, it is possible to clarify the roles of these glial cells in pathophysiology and their potential repair mechanisms post-injury. Full article

Background: In rare cases, isolated hypoglossal palsy may arise from dissection and/or pseudoaneurysm of either the internal carotid artery (ICA) or the vertebral artery (VA). However, the mechanism of this pathology has not been elucidated, and no high-quality randomized data exist to guide its management. Case Description: A 43-year-old man without a significant medical history presented with signs of isolated right hypoglossal palsy following a vigorous coughing episode. Imaging demonstrated dissection and pseudoaneurysm of the left ICA in addition to dissection of the right VA. After 2 weeks on 325 mg aspirin daily, the patient presented with left (rather than right) tongue symptoms and worsening ICA and VA stenosis. While on 325 mg aspirin plus 75 mg clopidogrel daily without additional endovascular intervention, the patient improved with no residual symptoms at 6 weeks from symptom onset. Conclusions: Acute hypoglossal nerve palsy may present with ipsilateral swelling, which could be mistaken for contralateral atrophy. We suggest ordering a CT angiogram initially to delineate a potential ICA versus VA dissection, as well as to rule out other etiologies. In our case, dissection and pseudoaneurysm from the ICA likely led to hypoglossal palsy through a mass effect on the nerve. Our comprehensive literature review favors initial management with dual-antiplatelet agents, and to then escalate to procedural interventions if symptoms worsen. Full article

Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Methods: Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Results: Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. Conclusions: This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis. Full article

Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification. Full article

Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI’s performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of “low risk of bias” and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability. Full article