Search Results (264)

Investigating brain area functions requires advanced technologies, but meaningful insights depend on correlating neural signals with behavior. Traditional mice behavior annotation methods, including manual and semi-automated approaches, are limited by subjectivity and time constraints. To overcome these limitations, our study employs the YOLO neural network for precise mice tracking and composite RGB frames for behavioral scoring. Our model, trained on over 10,000 frames, accurately classifies sitting, running, and grooming behaviors. Additionally, we provide statistical metrics and data visualization tools. We further combined AI-powered behavior labeling to examine hippocampal neuronal activity using fluorescence microscopy. To analyze neuronal circuit dynamics, we utilized a manifold analysis approach, revealing distinct functional patterns corresponding to transgenic 5xFAD Alzheimer’s model mice. This open-source software enhances the accuracy and efficiency of behavioral and neural data interpretation, advancing neuroscience research. Full article

This study investigates how differing national research governance models impact academic performance by comparing European Research Council (ERC) grant winners in the social sciences and humanities from France and the Netherlands. Situated within the broader context of centralized versus decentralized research systems, the analysis aims to understand how these structures shape publication trends, thematic diversity, and collaboration patterns. Drawing on Scopus and SciVal data covering 9996 publications by 305 ERC winners between 2019 and 2023, we employed a multi-method approach, including latent Dirichlet allocation for topic modeling, compound annual growth rate analysis, and co-authorship network analysis. The results show that neuroscience, climate change, and psychology are dominant domains, with language and linguistics particularly prevalent in France and law and political science in the Netherlands. French ERC winners are more likely to be affiliated with national or sectoral institutions, whereas in the Netherlands, elite universities dominate. Collaboration emerged as a key success factor, with an average of four co-authors per publication and network analyses revealing central figures who bridge topical clusters. International collaborations were consistently linked with higher visibility, while single-authored publications showed limited impact. These findings suggest that institutional context and collaborative practices significantly shape research performance in both countries. Full article

Borderline personality disorder (BPD) is a complex and heterogeneous condition characterized by emotional instability, impulsivity, and impaired regulation of interpersonal relationships. This narrative review integrates findings from recent neuroimaging, neurochemical, and treatment studies to identify core neurobiological mechanisms and highlight translational potential. Evidence from 112 studies published up to 2025 is synthesized, encompassing structural MRI, resting-state and task-based functional MRI, EEG, PET, and emerging machine learning applications. Consistent disruptions are observed across the prefrontal–amygdala circuitry, the default mode network (DMN), and mentalization-related regions. BPD shows a dominant and stable pattern of hyperconnectivity in the precuneus. Transdiagnostic comparisons with PTSD and cocaine use disorder (CUD) suggest partial overlap in DMN dysregulation, though BPD-specific traits emerge in network topology. Machine learning models achieve a classification accuracy of 70–88% and may support the tracking of early treatment responses. Longitudinal fMRI studies indicate that psychodynamic therapy facilitates the progressive normalization of dorsal anterior cingulate cortex (dACC) activity and reductions in alexithymia. We discuss the role of phenotypic heterogeneity (internalizing versus externalizing profiles), the potential of neuromodulation guided by biomarkers, and the need for standardized imaging protocols. Limitations include small sample sizes, a lack of effective connectivity analyses, and minimal multicenter cohort representation. Future research should focus on constructing multimodal biomarker panels that integrate functional connectivity, epigenetics, and computational phenotyping. This review supports the use of a precision psychiatry approach for BPD by aligning neuroscience with scalable clinical tools. Full article

The tip-of-the-tongue (TOT) phenomenon is characterized by a temporary inability to retrieve a word despite a strong sense of familiarity. While extensive research has linked phonological processing to TOT, the exact nature of this relationship remains debated. The “blocking hypothesis” suggests that the retrieval of target words is interfered with by phonological neighbors, whereas the “transmission deficit hypothesis” posits that TOT arises from insufficient phonological activation of the target words. This study revisited this issue by examining the relationship between the microstructural integrity of the phonological processing brain network and TOT, utilizing graph-theoretical analyses of neuroimaging data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), which included diffusion tensor imaging (DTI) data from 576 participants aged 18–87. The results revealed that global efficiency and mean degree centrality of the phonological processing network positively predicted TOT rates. At the nodal level, the nodal efficiency of the bilateral posterior superior temporal gyrus and the clustering coefficient of the left premotor cortex positively predicted TOT rates, while the degree centrality of the left dorsal superior temporal gyrus (dSTG) and the clustering coefficient of the left posterior supramarginal gyrus (pSMG) negatively predicted TOT rates. Overall, these findings suggest that individuals with a more enriched network of phonological representations tend to experience more TOTs, supporting the blocking hypothesis. Additionally, this study highlights the roles of the left dSTG and pSMG in facilitating word retrieval, potentially reducing the occurrence of TOTs. Full article

This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights. Full article

Alzheimer’s disease (AD) is a leading cause of dementia, characterized by progressive cognitive and language impairments that significantly impact communication and quality of life. Neuromodulation techniques, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS), have emerged as promising interventions. This study employs bibliometric analysis to evaluate global research trends in neuromodulation treatments for AD-related language impairments. A total of 88 publications from the Web of Science Core Collection (2006–2024) were analyzed using bibliometric methods. Key indicators such as publication trends, citation patterns, collaboration networks, and research themes were examined to map the intellectual landscape of this field. The analysis identified 580 authors across 65 journals, with an average of 34.82 citations per article. Nearly half of the publications were produced after 2021, indicating rapid recent growth. The findings highlight a predominant focus on non-invasive neuromodulation methods, particularly rTMS and tDCS, within neurosciences and neurology. While research activity is increasing, significant challenges persist, including ethical concerns, operational constraints, and the translational gap between research and clinical applications. This study provides insights into the current research landscape and future directions for neuromodulation in AD-related language impairments. The results emphasize the need for novel neuromodulation techniques and interdisciplinary collaboration to enhance therapeutic efficacy and clinical integration. Full article

Background: Borderline personality disorder (BPD) is a severe psychiatric condition characterised by emotional instability, impulsivity, interpersonal dysfunction, and self-injurious behaviours. Despite growing clinical interest, the neuropsychological mechanisms underlying these symptoms are still not fully understood. This review aims to summarise findings from neuroimaging, psychophysiological, and neurodevelopmental studies in order to clarify the neurobiological and physiological basis of BPD, with a particular focus on emotional dysregulation and implications for the treatment of adolescents. Methods: A narrative review was conducted, integrating results from longitudinal neurodevelopmental studies, functional and structural neuroimaging research (e.g. FMRI and PET), and psychophysiological assessments (e.g., heart rate variability and cortisol reactivity). Studies were selected based on their contribution to understanding the neural correlates of BPD symptom dimensions, particularly emotion dysregulation, impulsivity, interpersonal dysfunction, and self-harm. Results: Findings suggest that early reductions in amygdala volume, as early as age 13 predict later BPD symptoms. Hyperactivity of the amygdala, combined with hypoactivity in the prefrontal cortex, underlies deficits in emotion regulation. Orbitofrontal abnormalities correlate with impulsivity, while disruptions in the default mode network and oxytocin signaling are related to interpersonal dysfunction. Self-injurious behaviour appears to serve a neuropsychological function in regulating emotional pain and trauma-related arousal. This is linked to disruption of the hypothalamic-pituitary-adrenal (HPA) axis and structural brain alterations. The Unified Protocol for Adolescents (UP-A) was more effective to Mentalization-Based Therapy for Adolescents (MBT-A) at reducing emotional dysregulation compared, though challenges in treating identity disturbance and relational difficulties remain. Discussion: The reviewed evidence suggests that BPD has its in early neurodevelopmental vulnerability and is sustained by maladaptive neurophysiological processes. Emotional dysregulation emerges as a central transdiagnostic mechanism. Self-harm may serve as a strategy for regulating emotions in response to trauma-related neural dysregulation. These findings advocate for the integration of neuroscience into psychotherapeutic practice, including the application of neuromodulation techniques and psychophysiological monitoring. Conclusions: A comprehensive understanding of BPD requires a neuropsychologically informed framework. Personalised treatment approaches combining pharmacotherapy, brain-based interventions, and developmentally adapted psychotherapies—particularly DBT, psychodynamic therapy, and trauma-informed care—are essential. Future research should prioritise interdisciplinary, longitudinal studies to further bridge the gap between neurobiological findings and clinical innovation. Full article

Humans are inherently social beings, and the quality of their interactions is essential for maintaining physical and mental health. Effective social interaction involves understanding not just people’s visible behavior but also the underlying factors like thoughts and emotions. This review investigates the convergence and divergence of meta-analytic brain activation for mentalizing, empathy, and social interaction engagement. To achieve this, we re-analyzed data from our prior meta-analysis on mentalizing and empathy using the same methodology as an existing meta-analysis on social interaction engagement. The comparison of brain activation maps focused on the question of whether the co-activation of cognitive and affective brain systems is an overarching characteristic of intermediate mentalizing/empathy tasks and social interaction engagement. Our findings support the general assumption that social interaction engagement co-recruits cognitive and affective brain systems also implicated in mentalizing and empathy. However, we found little direct overlap of brain activation for intermediate mentalizing/empathy tasks and social interaction engagement. Finally, applying a network neuroscience perspective, we suggest that social interaction engagement, affective/empathy, and intermediate mentalizing/empathy tasks are collectively characterized by co-recruitment of the default mode network and control networks. Full article

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a transformative tool for probing cortical dynamics with millisecond precision. This review examines the state-dependent nature of TMS-EEG, a critical yet underexplored dimension influencing measurement reliability and clinical applicability. By integrating TMS’s neuromodulatory capacity with EEG’s temporal resolution, this synergy enables real-time analysis of brain network dynamics under varying neural states. We delineate foundational mechanisms of TMS-evoked potentials (TEPs), discuss challenges posed by temporal and inter-individual variability, and evaluate advanced paradigms such as closed-loop and task-embedded TMS-EEG. The former leverages real-time EEG feedback to synchronize stimulation with oscillatory phases, while the latter aligns TMS pulses with task-specific cognitive phases to map transient network activations. Current limitations—including hardware constraints, signal artifacts, and inconsistent preprocessing pipelines—are critically analyzed. Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. By bridging mechanistic insights with personalized neuromodulation strategies, state-dependent TMS-EEG holds promise for advancing both basic neuroscience and precision medicine, particularly in psychiatric and neurological disorders characterized by dynamic neural dysregulation. Full article

Intracranial tumors such as gliomas, meningiomas, and brain metastases induce complex alterations in brain function beyond their focal presence. Modern connectomic and neuroimaging approaches, including resting-state functional MRI (rs-fMRI) and diffusion MRI, have revealed that these tumors disrupt and reorganize large-scale brain networks in heterogeneous ways. In adult patients, diffuse gliomas infiltrate neural circuits, causing both local disconnections and widespread functional changes that often extend into structurally intact regions. Meningiomas and metastases, though typically well-circumscribed, can perturb networks via mass effect, edema, and diaschisis, sometimes provoking global “dysconnectivity” related to cognitive deficits. Therefore, this review synthesizes interdisciplinary evidence from neuroscience, oncology, and neuroimaging on how intracranial tumors disrupt functional brain connectivity pre- and post-surgery. We discuss how functional heterogeneity (i.e., differences in network involvement due to tumor type, location, and histo-molecular profile) manifests in connectomic analyses, from altered default mode and salience network activity to changes in structural–functional coupling. The clinical relevance of these network effects is examined, highlighting implications for pre-surgical planning, prognostication of neurocognitive outcomes, and post-operative recovery. Gliomas demonstrate remarkable functional plasticity, with network remodeling that may correlate with tumor genotype (e.g., IDH mutation), while meningioma-related edema and metastasis location modulate the extent of network disturbance. Finally, we explore future directions, including imaging-guided therapies and “network-aware” neurosurgical strategies that aim to preserve and restore brain connectivity. Understanding functional heterogeneity in brain tumors through a connectomic lens not only provides insights into the neuroscience of cancer but also informs more effective, personalized approaches to neuro-oncologic care. Full article

Despite its significance, hysteresis remains underrepresented in mainstream models of plasticity. In this work, we propose a novel framework that explicitly models hysteresis in simple one- and two-neuron models. Our models capture key feedback-dependent phenomena such as bistability, multistability, periodicity, quasi-periodicity, and chaos, offering a more accurate and general representation of neural adaptation. This opens the door to new insights in computational neuroscience and neuromorphic system design. Synaptic weights change in several contexts or mechanisms including, Bienenstock–Cooper–Munro (BCM) synaptic modification, where synaptic changes depend on the level of post-synaptic activity; homeostatic plasticity, where all of a neuron synapses simultaneously scale up or down to maintain stability; metaplasticity, or plasticity of plasticity; neuromodulation, where neurotransmitters influence synaptic weights; developmental processes, where synaptic connections are actively formed, pruned and refined; disease or injury; for example, neurological conditions can induce maladaptive synaptic changes; spike-time dependent plasticity (STDP), where changes depend on the precise timing of pre- and postsynaptic spikes; and structural plasticity, where changes in dendritic spines and axonal boutons can alter synaptic strength. The ability of synapses and neurons to change in response to activity is fundamental to learning, memory formation, and cognitive adaptation. This paper presents simple continuous and discrete neuro-modules with adapting feedback synapses which in turn are subject to feedback. The dynamics of continuous periodically driven Hopfield neural networks with adapting synapses have been investigated since the 1990s in terms of periodicity and chaotic behaviors. For the first time, one- and two-neuron models are considered as parameters are varied using a feedback mechanism which more accurately represents real-world simulation, as explained earlier. It is shown that these models are history dependent. A simple discrete two-neuron model with adapting feedback synapses is analyzed in terms of stability and bifurcation diagrams are plotted as parameters are increased and decreased. This work has the potential to improve learning algorithms, increase understanding of neural memory formation, and inform neuromorphic engineering research. Full article

Background: Multicultural education and second-language acquisition engaged neural networks, supporting executive function, memory, and social cognition in adulthood, represent powerful forms of brain-inspired multisensory learning. The neuroeducational framework integrates neuroscience with pedagogical practice to understand how linguistically and culturally rich environments drive neuroplasticity and cognitive adaptation in adult learners. Objective: This systematic review synthesizes findings from 80 studies examining neuroplasticity and cognitive outcomes in adults undergoing multicultural and second-language acquisition, focusing on underlying neural mechanisms and educational effectiveness. Methods: The analysis included randomized controlled trials and longitudinal studies employing diverse neuroimaging techniques (fMRI, MEG, DTI) to assess structural and functional brain network changes. Interventions varied in terms of immersion intensity (ranging from limited classroom contact to complete environmental immersion), multimodal approaches (integrating visual, auditory, and kinesthetic elements), feedback mechanisms (immediate vs. delayed, social vs. automated), and learning contexts (formal instruction, naturalistic acquisition, and technology-enhanced environments). Outcomes encompassed cognitive domains (executive function, working memory, attention) and socio-emotional processes (empathy, cultural adaptation). Results: Strong evidence demonstrates that multicultural and second-language acquisition induce specific neuroplastic adaptations, including enhanced connectivity between language and executive networks, increased cortical thickness in frontal–temporal regions, and white matter reorganization supporting processing efficiency. These neural changes are correlated with significant improvements in working memory, attentional control, and cognitive flexibility. Immersion intensity, multimodal design features, learning context, and individual differences, including age and sociocultural background, moderate the effectiveness of interventions across adult populations. Conclusions: Adult multicultural and second-language acquisition represents a biologically aligned educational approach that leverages natural neuroplastic mechanisms to enhance cognitive resilience. Findings support the design of interventions that engage integrated neural networks through rich, culturally relevant environments, with significant implications for cognitive health across the adult lifespan and for evidence-based educational practice. Full article

The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain–computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields. Full article

Autism spectrum disorder is a complex and diverse neurobiological condition. Understanding the mechanisms and causes of the disorder requires an in-depth study and modeling of the immune, mitochondrial, and neurological systems. Computational neuroscience enhances psychiatric science by employing machine learning techniques on neural networks, combining data on brain activity with the pathophysiological and biological characteristics of psychiatric–neurobiological disorders. The research explores the integration of neurotransmitter activity into computational models and their potential roles in diagnosing and treating autism using computational methods. This research employs a narrative review that focuses on four neurotransmitter systems directly related to the manifestation of autism, specifically the following neurotransmitters: serotonin, dopamine, glutamate, and gamma-aminobutyric acid (GABA). This study reveals that computational neuroscience advances autism diagnosis and treatment by identifying genetic factors and improving the efficiency of diagnosis. Neurotransmitters play a crucial role in the function of brain cells, enhancing synaptic conduction and signal transmission. However, the interaction of chemical compounds with genetic factors and network alterations influences the pathophysiology of autism. This study integrates the investigation of computational approaches in four neurotransmitter systems associated with ASD. It improves our understanding of the disorder and provides insights that could stimulate further research, thereby contributing to the development of effective treatments. Full article

Single-cell RNA sequencing (scRNA-seq) has revolutionized neuroscience by enabling the analysis of cellular heterogeneity and dynamic molecular processes at the single-cell resolution. In spinal cord research, scRNA-seq provides critical insights into cell type diversity, developmental trajectories, and pathological mechanisms. This review summarizes recent progress in the application of scRNA-seq to spinal cord development, injury, and neurodegenerative diseases and discusses the current challenges and future directions. Relevant studies focusing on the key applications of scRNA-seq, including advances in spatial transcriptomics and multi-omics integration, were retrieved from PubMed and the Web of Science. scRNA-seq has enabled the identification of distinct spinal cord cell populations and revealed the gene regulatory networks driving development. Injury models have revealed the temporal dynamics of immune and glial responses, alongside potential regenerative processes. In neurodegenerative conditions, scRNA-seq highlights cell-specific vulnerabilities and molecular changes. The integration of spatial transcriptomics and computational tools, such as machine learning, has further improved the resolution of spinal cord biology. However, challenges remain in terms of data complexity, sample acquisition, and clinical translation. Single-cell transcriptomics is a powerful approach for understanding spinal cord biology. Its integration with emerging technologies will advance both basic research and clinical applications, supporting personalized and regenerative therapy. Addressing these technical and analytical barriers is essential to fully realize the potential of scRNA-seq in spinal cord science. Full article