Search Results (4,059)

Motor recovery after stroke, spinal cord injury, or traumatic brain injury reflects relearning rather than simple restitution, as surviving circuits retain plastic potential that can be re-engaged through temporally precise stimulation. This review synthesizes convergent findings demonstrating that Hebbian and spike-timing-dependent mechanisms govern reorganization across cortical, striatal, and spinal levels. Leveraging these timing rules to shape excitability during receptive network states enables durable changes in connectivity and behavior. This effect depends on temporal precision, physiological state, and reinforcement—not stimulus intensity alone—within plasticity windows regulated by metaplastic mechanisms that determine whether Hebbian processes are expressed. Together, these principles define a translational framework for neurorehabilitation, emphasizing biomarker-guided, adaptive, and scalable strategies aligned with intrinsic rules of experience-dependent reorganization. Full article

Background/Objectives: Young adulthood is a critical developmental period during which lifestyle behaviors may shape intrinsic brain network dynamics that support cognition. This pilot longitudinal intervention study examined whether variability in physical activity and sedentary behavior during an 8-week exercise and/or cognitive intervention protocol was associated with changes in intrinsic brain dynamics and cognitive and mood outcomes in undergraduate young adults. Methods: Participants (n = 32) completed resting-state functional magnetic resonance imaging (rs-fMRI) at baseline (T1) and post-intervention (T2). Dynamic functional network connectivity (dFNC) was estimated from 53 intrinsic connectivity networks derived using spatially constrained independent component analysis (ICA). Ten recurring dynamic connectivity states were identified and individualized using constrained dynamic double functional independent primitives (c-ddFIPs). State occupancy and dynamic convergence and divergence metrics were computed to characterize network flexibility. Results: Greater moderate-to-vigorous physical activity was modestly but consistently associated with increased occupancy of integrative higher-order states, particularly States 6 and 7, and reduced occupancy of more segregated configurations. More physically active individuals also demonstrated greater divergence between integrative and low-engagement states, whereas greater sedentary time corresponded to increased similarity among segregated configurations. Working memory performance showed parallel associations with more integrative and better-differentiated dynamic patterns. Conclusions: These findings suggest that dynamic functional network reconfiguration may represent a neurobiological mechanism linking lifestyle behaviors and cognitive health in young adulthood. Furthermore, they highlight the translational promise of engagement-driven, low-burden programs for college-aged young adults, showing that even modest variability in habitual physical activity corresponds to greater engagement and differentiation of integrative connectivity states linked to executive and broader cognitive functions. Full article

Background: The glymphatic system, essential for brain waste clearance and neuroimmune regulation, remains underexplored in the context of bipolar disorder (BD) among young populations. Methods: Using diffusion tensor image analysis along the perivascular space (DTI-ALPS), we compared ALPS indices derived from the conventional FSL-based (cFSL) pipeline with those from the individualized ALPS (iALPS) pipeline. A cohort of young adults comprising 77 individuals with BD and 289 healthy controls was analyzed to evaluate methodological consistency and to identify disorder-specific alterations in glymphatic function. Results: The two pipelines showed only moderate agreement (Lin’s concordance correlation coefficient = 0.52–0.60), suggesting that differences in ROI placement strategies significantly affect ALPS estimation. While the cFSL pipeline detected no group differences, the iALPS pipeline identified a trend-level reduction in ALPS index in patients with BD during depressive episodes, particularly in the right hemisphere (p = 0.036, uncorrected, FDR-adjusted p = 0.071). No significant glymphatic alterations were observed in individuals with early-stage BD. Conclusions: These findings suggest that glymphatic dysfunction in psychiatric disorders may be phase-specific on illness. The use of individualized and automated analytical strategies, such as the iALPS pipeline, appears to enhance sensitivity to subtle, state-related brain changes that conventional methods may overlook. This methodological advancement provides a more biologically informed framework for future large-scale and longitudinal studies aimed at elucidating the role of glymphatic function in the pathophysiology of psychiatric disorders. Full article

Simulated spiking neural networks have been explored for over a hundred years. Many of these networks are driven by biological considerations and an attempt to simulate brains, but others are used with little biological consideration. This paper gives some history of the development of spiking neural models, their use for modelling biological and cognitive phenomena, and for machine learning. It introduces the current state of the art in computational biological neuron and synapse modelling and plasticity. It introduces and reviews balanced spiking networks and their engineering applications. Spiking networks are also used for machine learning, with the hope that their implementation on neuromorphic hardware will bring energy and time savings. Similarly, neuromorphic hardware can enable massive parallelism, supporting larger spiking networks. The use of spiking nets for machine learning, both with biologically plausible models and without, is discussed, showing that effective models already exist. The paper concludes with some notes about implementing spiking nets and a discussion including open questions and future work. Full article

The growth of brain health initiatives in the United States and worldwide has led to a movement to protect the brain from avoidable injury across the lifespan. With the advancement of our armamentarium of neurologic treatments and preventives during the past several decades, the field of preventive neurology has spawned. Under the umbrella of preventive neurology is perioperative brain health, an under-addressed but important topic in the field of neurology. Perioperative brain health is important because perioperative mortality may be relatively high, and morbidity as quantified by brain injury biomarkers (e.g., MRI brain) and clinical phenotypic manifestations related to stroke can be common. In this perspective, in relation to periprocedural stroke, we review the stroke mechanisms, epidemiology and risk factors, risk stratification measures and long-term outcomes, and potential mitigation and treatment opportunities. As perioperative brain health crosses many medical disciplines, multidisciplinary action is needed to bridge the knowledge gaps and reduce brain injury and attendant neurologic complications. Anesthesiologists and other healthcare professionals working in the surgical and procedural field are well-positioned to make important contributions to this growing discipline of the prevention of brain injury in the perioperative period. Full article

Idiopathic normal pressure hydrocephalus (iNPH) is a potentially reversible cause of gait disturbance and cognitive impairment in older adults, yet its diagnosis remains challenging and controversial. The core difficulty lies in distinguishing true hydrocephalus from ventricular enlargement secondary to cerebral atrophy or neurodegenerative disease, a distinction now recognized as non-binary. In many patients, ventricular enlargement reflects a continuum ranging from predominantly hydrocephalic iNPH to mixed pathological states combining impaired cerebrospinal fluid (CSF) dynamics and neurodegeneration. Conventional neuroradiological markers, including the Evans Index, the callosal angle, and the disproportionately enlarged subarachnoid-space hydrocephalus (DESH) pattern, provide useful qualitative guidance but are limited by their two-dimensional nature, interobserver variability, and poor sensitivity for differential diagnosis and outcome prediction. Over the past decade, advances in artificial intelligence-based brain volumetry (AI-BrV) have introduced a new paradigm for quantitative structural assessment. By enabling automated, anatomically precise, and reproducible three-dimensional quantification of ventricular and extraventricular CSF, cortical and subcortical gray matter, deep gray matter nuclei, and periventricular white matter, AI-BrV addresses many limitations of traditional imaging approaches. Beyond absolute volume measurements, AI-BrV enables the derivation of composite indices and ratios that may capture disease-specific structural phenotypes and better reflect the underlying pathophysiology of ventricular enlargement. Importantly, AI-BrV pipelines can be applied retrospectively to large legacy neuroimaging datasets and compared with extensive publicly available repositories, facilitating normative modeling, cross-disease analyses, and external validation of volumetric biomarkers. When integrated with clinical data and multivariable statistical or machine-learning frameworks, these approaches hold promise for improving patient selection, refining disease categorization, and supporting more rational decision-making regarding CSF diversion. In this context, AI-BrV offers a unifying framework for reconciling divergent clinical perspectives and advancing iNPH toward a more precise, reproducible, and evidence-based diagnostic and therapeutic paradigm. Full article

Alcohol (ethanol, an intoxicating agent in all alcoholic beverages) is the most widely consumed beverage in the United States and is a leading risk-factor for cerebrovascular diseases. Although neurons, microglia, and astrocytes have been moderately studied for their responsiveness to ethanol, the brain vasculature is minimally explored and is emerging as a key player in the interplay between neuroinflammation, cerebrovascular disease, and alcohol use disorder (AUD). The blood–brain barrier (BBB), a critical regulator of brain homeostasis, relies on the coordinated function of various cellular and molecular components to maintain its immune-privileged status. Emerging evidence indicates that chronic ethanol exposure disrupts BBB function, contributes to neurovascular dysfunction, and increases brain permeability to peripheral immune factors. This review introduces the endothelial cells (ECs) that make up the BBB and provides a brief overview of the junction proteins and transporters that assist with EC function and EC interactions with other cells of the neurovascular unit, including pericytes, smooth muscle cells, and perivascular macrophages and glial cells. In addition, this review highlights ethanol’s effects on ECs and the cells that interact with them. Lastly, given the mounting evidence on gender differences in AUD and the supporting sex differences in ethanol consumption in preclinical models, this review discusses the discovered sex differences in EC-specific genes and identifies open questions such as the influence of sex-dependent differences, genetic factors, and their interactions with ethanol on EC function. Taken together, a deeper understanding of how ethanol disrupts EC structure and function will advance therapeutic strategies to mitigate neuroinflammation and related pathologies associated with chronic ethanol exposure. Full article

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that requires early diagnosis for better intervention. However, current clinical behavioural examinations are time-consuming and prone to human error. Objective and effective biomarkers are essential for the diagnosis and prognosis of the disorder. Electroencephalography (EEG) is a non-invasive and inexpensive brain-imaging technique that is widely applied in the diagnosis of ASD. Feature-based methods have shown better performance in EEG-based applications. Here, we present a prediction framework based on massive feature extraction using the highly comparative time-series analysis (HCTSA) method and a hybrid feature selection method for the classification of ASD from resting-state EEG. Machine-learning models are trained and tested on a different number of selected features. Our models demonstrated 100% accuracy with ≥50 features on a balanced dataset of 56 participants. The most discriminating EEG channels and features were used in the prediction process, as well as those using Shapley values to provide explainability of our framework. Whilst these results are promising, we acknowledge the limitations of a single small-scale dataset and emphasise the need for validation on larger independent cohorts before clinical translation. Full article

Frontal alpha asymmetry (FAA), a pattern of brain activity that reflects the difference in alpha wave power between the left and right frontal areas of the brain, is considered a stable marker for an individual’s tendency to experience either more approach-related or withdrawal-related emotions. On the other hand, electrodermal activity (EDA) measures arousal by tracking changes in skin sweat, which are controlled by the sympathetic nervous system. This study explores the interrelation between EDA features, obtained from time and frequency domains, with FAA by means of the mutual information. Multiple cognitive tasks such as EAT, ship search, PVT and N-Back were analyzed in 10 participants in intervals of two hours over 24 h (12 trials), in which they had to face sleep deprivation conditions. The most informative EDA features about FAA, were used to identify the two main clusters associated to high and low FAA values through the hierarchical agglomerative clustering approach. Once data is labeled, a supervised classifier based on support vector machines (SVMs) is used to identify positive and negative emotional states by using a rigorous one-trial out cross-validation scheme. Results show consistent performance within tasks and trials, achieving accuracy values over 80% on average, giving an important insight about the use of EDA signal as an alternative to the more complex FAA measurement for tracking positive or negative emotional states. Full article

Background: Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy-drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the research examining neurobiological correlates of mindfulness has used static functional connectivity, suggesting that brain activity goes unchanged for the entire length of an MRI scan. Methods: In the current study, we used a state-based dynamic functional connectivity model to examine brain states during either a 10 min mindfulness session or resting control that followed an individually tailored stress imagery task. Using a hidden semi-Markov model (HSMM), six brain states and the associated dynamics of state traversal were estimated for a population of moderate-to-heavy drinkers (N = 32). We modeled the 36 Schaefer atlas regions spanning the salience and default mode networks, and the HSMM characterized each state by its distinct multivariate pattern of activity and covariance structure. Group differences in dwell times, transition behavior, and overall state dynamics were evaluated using permutation tests and mixed-effects models. Results: Participants that experienced the mindfulness session had more transitions and longer time spent in states in which the salience network was more active. Participants assigned to the control group had more transitions and increased time spent in states in which nodes of the default mode network were more active. Moreover, for control participants, increased occupancy time to SN-dominant states was associated with lower perceived stress. Conclusions: Using HSMM provided a unique insight into network connectivity during mindful states; we believe it offers a novel approach to testing and optimizing mindful-based therapies. Full article

This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach brings AI-based emotional state recognition closer to human perception, provides a better explainability and adaptability than existing AI systems. It also results in a higher or competitive accuracy, even though this was not the main goal here. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 88.9% when multimodal audio–visual data are used, and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio–visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; cognitive robotics; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability. This research shows a step toward building brain-inspired AI systems. Full article

Despite the growing interest in brain structural plasticity and the substantial body of knowledge that has accumulated over recent decades, some issues remain poorly defined, leading to confusion in the interpretation of results. In addition to stem cell-driven neurogenesis in adult neurogenic niches (adult neurogenesis), neuronal precursors in a state of arrested maturation have also been described, representing a form of neurogenesis without division based on so-called “immature” or late-maturing neurons. These processes occur in different brain regions yet share certain molecular markers and temporal windows. Recent advances in comparative neuroplasticity have further complicated our understanding. Studies reveal a reduction in adult neurogenesis in the olfactory bulb and hippocampus of large-brained, gyrencephalic mammals compared with small-brained species such as rodents. Conversely, a higher prevalence of immature neurons has been reported in the neocortex and amygdala of larger-brained mammals. It is becoming evident that evolutionary trade-offs took place in distinct plastic processes, resulting in the predominance of certain forms in particular species, while others coexist and share overlapping markers. Regardless of the approach employed (neuroanatomical, immunocytochemical, phylogenetic, or transcriptional), current evidence indicates substantial heterogeneity in cell types with different origins and fates across diverse mammalian species. These patterns appear to be sculpted by evolutionary pressures yet unified by shared transient maturational states. Full article

Glioblastoma (GBM) is a highly aggressive brain tumor with a complex tumor microenvironment (TME) that includes immune cell infiltration, notably macrophages. The role of macrophages in GBM progression is influenced by their polarization state, which can be either pro-inflammatory (M1) or immunosuppressive (M2). This study investigates the macrophage polarization in GBM, identifying key macrophage-related genes and their impact on tumor progression. Analysis of TCGA-GBM data revealed that macrophage infiltration correlates with poor prognosis, with 41 risk-associated genes identified. DSP dataset analysis highlighted 378 differentially expressed genes between CD68⁺ macrophages and GFAP⁺ controls, including immune-related genes like SPP1, CD74, and C3. Cross-validation with single-cell RNA-seq confirmed the expression of 9 key genes, with 7 genes being macrophage-specific. In vitro experiments using conditioned media from GBM cell lines demonstrated that GBM cells promote macrophage polarization towards an M2-like phenotype. Overexpression of CD74, CLEC7A, and IFI30 in macrophages further enhanced M2 polarization, which was associated with increased tumor-promoting functions, including enhanced invasion and reduced apoptosis in GBM cells. Together, these findings highlight the role of M2 macrophage polarization in promoting GBM progression and suggest that targeting macrophage polarization pathways may offer therapeutic potential. Full article

This paper presents a unified framework for reliable motion control of permanent magnet linear motors (PMLMs) by integrating fault detection (FD) and fault-tolerant control (FTC). The framework combines a brain emotional learning-based intelligent controller (BELBIC) with a linear extended state observer (LESO) to enable rapid detection and mitigation of abrupt and incipient faults, as well as disturbances and sensor noise that degrade tracking accuracy and system reliability. The LESO is employed to estimate unknown dynamics and lumped disturbances and to generate residuals for reliable fault detection, while BELBIC provides adaptive and robust control actions without requiring prior knowledge of system parameters or explicit fault models. Extensive simulation studies under actuator faults, system dynamics faults, external disturbances, and measurement noise are conducted. Comparative evaluations with benchmark approaches demonstrate improved fault detection speed, tracking accuracy, and robustness of the proposed framework, highlighting its potential for enhancing reliability and operational continuity in high-precision industrial applications. Full article

This study investigates the ongoing electrical activity of local neural networks—referred to as neurodynamics—across 37 anatomically defined brain regions. We analyzed stereotactic intracranial EEG (sEEG) recordings from 106 subjects during wakeful rest, focusing on scale-free (power-law) properties to determine whether distinct brain regions exhibit unique neurodynamic signatures. Results revealed a power-law regime in two frequency ranges (approximately 0.5–4 Hz and 33–80 Hz). Notably, the power-law exponent (slope) in the high-frequency band differed significantly between cortical and subcortical areas (p < 0.01). These findings suggest that local neurodynamics, as reflected in scale-free characteristics, may serve as a functional “fingerprint” for brain region classification. This approach may contribute to functional brain parcellation efforts and offer new insights into the intrinsic organization of neuronal networks as revealed by resting-state activity analysis. Full article