Search Results (665)

This study presents the first dedicated bibliometric analysis of artificial intelligence (AI) and deep learning applications in pediatric radiology and medical imaging, mapping the intellectual structure of a rapidly evolving field. A total of 2688 articles and conference proceedings published between 2005 and 2025 were retrieved from the Web of Science Core Collection and analyzed using Bibliometrix R and VOSviewer. The findings reveal exponential growth in publications, from 7 papers in 2005 to 559 in 2025, with journal articles dominating the corpus (85.9%). The most-cited contributions, led by Kermany et al. (2018) with 2886 citations, are predominantly technical feasibility studies rather than clinical outcome trials, indicating a field that has advanced methodologically but remains in early stages of clinical translation. Thematic mapping identifies convolutional neural networks, pneumonia, and transfer learning as Motor Themes representing methodological maturity in chest imaging, while neuroimaging and image segmentation clusters occupy Niche Themes, reflecting insular development with limited cross-field connectivity. Geographic analysis reveals concentrated co-authorship along US–China and US–Europe corridors, with African, Latin American, and Southeast Asian institutions largely absent from knowledge production networks. Eight of the ten most productive affiliations are North American, highlighting structural inequities that risk producing AI tools optimized for high-resource settings rather than the global pediatric population. This analysis provides an empirical foundation for reorienting the field toward clinical validation, geographic inclusion, and methodological integration across isolated research communities. Full article

With population aging, motor vehicle accidents involving older drivers have increased. Age-related cognitive decline affects driving performance; however, the underlying neural mechanisms remain unclear. This scoping review explored neurophysiological characteristics associated with driving in older adults, including those at risk of dementia. Following PRISMA-ScR guidelines, we searched PubMed, Scopus, and CINAHL for studies examining driving-related neurophysiological measures in older adults aged ≥60 years. Twelve studies were included. Findings converge on load-dependent neural compensation failure: older adults maintain driving performance under low-to-moderate demands, but compensatory mechanisms break down under high cognitive load. Electroencephalography (EEG) studies revealed blunted midfrontal theta upregulation during high-load conditions, associated with reduced steering precision and delayed responses. Event-related potential studies demonstrated that reduced P3b amplitude was associated with missed braking responses and that abnormal visual evoked potentials in Alzheimer’s disease predicted unfit-to-drive classifications. fNIRS studies during driving-related tasks and an fMRI study using a laboratory-based visual task consistently showed prefrontal hyperactivation in older adults. Although some older adults maintained comparable performance to younger adults, the brain–behavior associations observed in younger adults were absent, suggesting that this hyperactivation does not necessarily serve a functional compensatory role. Combined with EEG evidence of impaired oscillatory modulation, these findings suggest that prefrontal hyperactivation does not necessarily compensate for diminished neural synchronization under high-load conditions. Neurophysiological markers hold promise for fitness-to-drive assessments. Future research should employ high-load scenarios and multimodal neuroimaging to verify prefrontal compensatory mechanisms. Full article

Traumatic brain injury (TBI) is a major cause of death and disability, mainly in persons under 45 years of age and it remains clinically challenging due to its heterogeneous pathophysiology and unpredictable course. Except from the initial mechanical damage, secondary injury —largely driven by neuroinflammation—plays a critical role in outcome and extent of recovery. Cytokines are central mediators of this immune response and have therefore been extensively studied as potential biomarkers for TBI diagnosis, need of imaging and prognosis. Among pro-inflammatory cytokines, IL-1β is rapidly upregulated after TBI and contributes to blood–brain barrier disruption and secondary damage. Furthermore, experimental studies suggest that IL-1 inhibition could be neuroprotective. IL-6 is up to date the most extensively studied cytokine and shows strong associations with injury severity, neuroimaging abnormalities, mortality and long-term functional outcomes across multiple adult and pediatric studies. Nevertheless, results vary depending on the biological compartment and timing. Anti-inflammatory IL-10 levels correlate with injury severity and has shown promise in distinguishing CT-positive from CT-negative mild TBI patients, potentially reducing unnecessary imaging, though findings are inconsistent. Other cytokines, including IL-17, TNF-α, IL-8, IL-9, and IL-15, have been correlated to post-traumatic neuroinflammation and may have diagnostic or prognostic value. Overall, IL-6 and IL-10 currently appear to be the most promising cytokine as biomarkers, however future research should focus on standardized cytokines assessment methods and possible use of multimarker panels. Full article

Creutzfeldt–Jakob disease (CJD) is a rare and still fatal neurodegenerative disorder caused by prion protein misfolding in the central nervous system. Accumulation of the pathogenic isoform leads to neuronal damage, spongiform degeneration, and rapidly progressive dementia. The disease is divided into sporadic, familial, iatrogenic, and variant forms, with sporadic cases accounting for the majority of cases. Diagnosis remains challenging and relies on a combination of clinical assessment, neuroimaging, and laboratory biomarkers. Key diagnostic methods include electroencephalography, Magnetic Resonance Imaging, and cerebrospinal fluid analysis for proteins as well as advanced amplification tests that improve diagnostic accuracy. Despite these advances, early detection remains challenging and misdiagnosis can occur. Currently, there is no effective disease-modifying therapy, and treatment is primarily supportive, focusing on symptom control and palliative care. Ongoing research aims to better understand the molecular mechanisms underlying prion propagation and develop targeted therapeutic strategies. This review summarizes current diagnostic methods and therapeutic approaches, focusing on molecular applications and their potential clinical implications. Full article

Alzheimer’s disease (AD) is the most common form of dementia marked by cognitive decline and memory loss. Early detection is essential for timely intervention; however, traditional biomarkers, including cerebrospinal fluid (CSF) assays, neuroimaging, and cognitive assessments, are limited by cost, invasiveness, and accessibility. Digital biomarkers, obtained from wearable sensors, smartphone applications, speech analysis, and other passive monitoring technologies, represent a promising alternative for scalable, non-invasive, and continuous assessment of early cognitive decline. This paper provides a comprehensive review of the current landscape of digital biomarkers for AD diagnosis, emphasizing their potential application in the preclinical and prodromal stages of the disease. In addition, a bibliometric analysis demonstrates the rapid expansion of digital biomarker research, highlighting key trends in publication volume, influential authors, institutions, and interdisciplinary collaborations. Despite the significant promise of digital biomarkers, challenges remain regarding validation, regulatory approval, data privacy, and integration into clinical practice. The results indicate that future research should prioritize standardization, multimodal biomarker integration, and large-scale longitudinal studies to fully realize the potential of digital technologies in AD detection. Full article

Recently, photoacoustic (PA) imaging has made a significant impact on biomedical imaging, providing detailed information on tissue structure and function by integrating optical and acoustic techniques. PA imaging can provide functional information at the cellular (e.g., oxygen saturation, hemoglobin concentration, metabolic rate) and molecular levels, owing to its substantial advantages over conventional imaging techniques. PA imaging is particularly useful for neuroimaging, cancer detection, and cardiovascular studies. Over the last decade, there has been a tremendous amount of research and development dedicated to nanomaterials that are ideal for PA imaging. Examples of nanomaterials include carbon-based and gold nanorods, both of which demonstrate greatly enhanced light absorption capabilities in the near-infrared range. Therefore, the properties of these materials make them perfect for achieving deep penetration into tissues. In addition, they exhibit biocompatibility, tunable optical properties, and enhance the acoustic signal for PA imaging, resulting in greater accuracy and precision in PA results. Researchers working in this area have focused on developing nanomaterials with fabrication capabilities, enabling real-time visualization of therapeutic events and enhancing light absorption. This review critically examines recent advances in nanomaterials for PA imaging, emphasizing strategies for signal enhancement and evaluating their impact on imaging performance, including imaging depth, photostability, and signal intensity, as well as their suitability for biomedical applications. Furthermore, complementary approaches for PA signal enhancement are discussed to provide a broader perspective and guide the selection and design of effective contrast agents for clinical and preclinical use. Full article

Background/Objectives: Fibromyalgia is increasingly viewed as a disorder of central sensitization, involving altered nociceptive processing and dysregulated stress and affective neural systems. Evidence supports shared neurobiological mechanisms linking chronic pain, emotional distress, and affect regulation, including corticolimbic and hypothalamic–pituitary–adrenal axis alterations. However, predictive models evaluating psychological distress as markers of these brain-based processes remain scarce. This study aimed to internally validate a preliminary model of fibromyalgia diagnosis using self-reported distress indicators as proxies of central dysregulation. Methods: A case-control design study with 180 participants was performed. Medically diagnosed fibromyalgia cases were recruited via a pain facility or referrals, alongside geographically matched controls from the general population. Psychological variables were conceptualized as neurobehavioral indicators reflecting central sensitization and stress-system dysregulation. Predictors were selected using LASSO penalized regression with 10-fold cross-validation. Retained variables were re-estimated using logistic regression. Model performance was evaluated through Nagelkerke’s pseudo-$R^2$, a likelihood ratio test, and area under the curve (AUC). Internal validation was conducted via 1000-bootstrap resampling with calibration-slope-based shrinkage. Results: The final model included global psychological distress, positive affect, sex, and age ($R^2=0.359$, with good discrimination [AUC = 0.81; optimism-corrected AUC ≈ 0.79]). Higher distress and age were associated with increased odds of fibromyalgia. Conclusions: Self-reported psychological distress, particularly global distress and reduced positive affect, combined with sex and age, showed internal validity in predicting fibromyalgia diagnosis. These findings support the hypothesis that behavioral markers of emotional dysregulation may reflect underlying central sensitization and stress-system alterations implicated in chronic pain. Future research integrating psychological measures with neuroimaging and neuroendocrine markers may further clarify the neural mechanisms linking affective dysregulation and chronic pain vulnerability. Full article

Physical exercise can influence cognitive performance and neurobiological processes, but evidence spans diverse modalities, intensities, and adult populations. Acute exercise represents a state of transient skeletal muscle activation that induces systemic signaling through metabolic, endocrine, and myokine-mediated pathways, which may contribute to neurocognitive modulation. To map the breadth of acute exercise–cognition research, characterize cognitive and biological outcomes, and identify consistent patterns and gaps. Studies of adults (≥18 years) involving a single exercise session or short microcycle (≤7 days) with pre–post assessment of cognition and/or neurobiological markers across any exercise modality (aerobic, resistance, high-intensity interval training/HIIT, combined, vibration, mind–body) were included. PubMed and CENTRAL were systematically searched, yielding 101 studies. Data were extracted using a structured framework capturing exercise modality, dose, cognitive domains, biomarkers, neuroimaging outcomes, population characteristics, and study design features. Most studies examined young adults (53%) or older adults (32%). Aerobic exercise predominated (62%), followed by resistance (18%) and combined modalities (12%). Moderate-to-vigorous aerobic exercise consistently improved executive function, processing speed, and working memory. Resistance exercise also enhanced executive function in several trials (31 studies). Neurobiological correlates included increases in Brain-Derived Neurotrophic Factor (BDNF), lactate, catecholamines, and prefrontal activation, though variability in sampling limited mechanistic conclusions. Acute exercise is consistently associated with improvements in executive function and processing speed across modalities. Standardized exercise protocols, biomarker timing, and cognitive assessments are needed to strengthen mechanistic synthesis. Full article

Ultra-high-field (UHF) magnetic resonance imaging, defined as imaging at field strengths of 7 Tesla (7T) and above, represents a frontier technology in neuroimaging with emerging applications in prenatal brain research. This narrative review examines the current evidence on the potential benefits of UHF-MRI for investigating embryonic and fetal brain development. Through analysis of 97 studies identified across multiple databases, we find that UHF-MRI offers substantial advantages in spatial resolution, tissue contrast, and anatomical detail compared to conventional clinical field strengths (1.5T and 3T). The primary applications to date have been in ex vivo imaging of post-mortem fetal specimens and preclinical animal models, where UHF-MRI has enabled unprecedented visualization of laminar cortical organization, early sulcation patterns, microstructural development, and subtle anatomical features critical for understanding normal and abnormal neurodevelopment. Key benefits include enhanced delineation of transient developmental zones, improved characterization of cortical folding, superior detection of subtle malformations, and the ability to create high-resolution three-dimensional atlases of fetal brain development. However, significant technical and safety challenges currently limit in utero human applications, including concerns about specific absorption rate, acoustic noise, and fetal motion artifacts. This review identifies critical knowledge gaps and future directions for translating UHF-MRI technology to clinical prenatal diagnostics. Full article

Background/Objectives: The global incidence of diabetes in childhood is increasing, raising concern about its long-term effects on the developing brain. Although paediatric diabetes research has traditionally focused on microvascular and macrovascular complications, accumulating evidence indicates that the brain is also a vulnerable target. Methods: This narrative review synthesizes current knowledge on the impact of diabetes on brain health in children and adolescents, with emphasis on epidemiology, neuroimaging and cognitive outcomes, underlying mechanisms, risk and protective factors, and clinical implications. Results: In type 1 diabetes (T1D), studies consistently demonstrate subtle but measurable alterations in brain structure, including reduced growth of total, grey, and white matter volumes, alongside functional and microstructural changes. These neurobiological differences are associated with mild deficits in cognition, particularly in attention, executive function, memory, and processing speed. While clinically significant impairment affects a minority, subclinical alterations are common and may accumulate over time. Key risk factors include chronic hyperglycaemia, glycaemic variability, severe hypoglycaemia, diabetic ketoacidosis, and younger age at onset, whereas good glycaemic stability, diabetes technologies, supportive psychosocial environments, and adequate sleep appear protective. Proposed mechanisms involve oxidative stress, neuroinflammation, disrupted insulin signalling, altered cerebral metabolism, and vulnerability of the immature brain during critical developmental windows. Type 2 diabetes (T2D), increasingly diagnosed in youth, is also associated with adverse brain outcomes. Emerging data link early-onset T2D to alterations in brain structure and connectivity, poorer cognitive performance, and increased mental health burden, mediated by hyperglycaemia, insulin resistance, inflammation, and psychosocial stressors. Conclusions: Overall, childhood diabetes—both T1D and T2D—is associated with meaningful effects on brain development and function. Longitudinal and interventional studies are needed to establish causality and determine whether optimizing glycaemic control and psychosocial support can mitigate neurocognitive risk. Recognizing brain health as a potential complication of paediatric diabetes has important implications for monitoring, prevention, and clinical care. 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

Hypnosis has traditionally been conceptualized as a clinical technique for reducing physiological symptoms (e.g., pain, nausea) and psychological symptoms (e.g., anxiety, intrusive thoughts), yet emerging neuroscientific evidence suggests it operates through the fundamental mechanisms of emotional regulation and self-integration. This integrative review synthesizes research on clinical hypnosis from cognitive neuroscience, affective science, and clinical practice to examine how hypnotic phenomena modulate large-scale brain networks—particularly the default mode network (DMN), executive control network (ECN), and salience network (SaN)—to reorganize emotional experience and self-referential processing. We propose a formal mechanistic model in which hypnotic induction produces heightened experiential plasticity through coordinated network reconfiguration, enabling adaptive emotion regulation and reduced dissociative fragmentation. Central to this framework is the construct of Fundamental Peace (FP), operationalized as a dynamic neuro-experiential state characterized by: (1) flexible attentional control without effortful suppression; (2) emotional coherence across self-states; (3) reduced self-referential rigidity; (4) compassionate self-awareness. Unlike equanimity (affective neutrality) or well-being (positive evaluation), Fundamental Peace represents integrated regulatory capacity under changing conditions. Key findings from neuroimaging studies demonstrate that hypnotic states consistently reduce DMN activity, enhance ECN-SaN coupling, and modulate connectivity patterns associated with self-referential processing. Meta-analytic evidence from 85 controlled experimental trials shows robust pain reduction effects, while clinical studies document improvements in trauma-related dissociation and emotional dysregulation. We critically evaluate this framework against alternative theories (dissociated control, cold control, predictive processing, social-cognitive models), specify testable predictions, and assess evidence quality across neuroimaging and clinical domains. Implications for trauma treatment, clinical implementation, and future research integrating causal inference methods are discussed, alongside ethical and cultural considerations. Full article

Exercise as a non-pharmacological measure is important to increase the brain plasticity hence improving cognitive performance as well as mental health. This narrative review describes in depth the hierarchical multiscale processes of neuroplasticity to exercise, including the presence of neurotrophic factor regulation, cellular metabolic adaptations and neurotransmitter remodeling, up to the structure and functional reorganization of brain networks as seen through neuroimaging, and concluding with adaptive cognitive and behavioral outcomes. We further investigate the role of personal variations in genetic time and social environments in moderating the neuroplasticity of exercise. Furthermore, the review identifies the importance of combining multimodal visualization methods with computational models in generating accurate workout prescriptions and their potential of translation into clinical and educational practice. Lastly, the research problems and “grand challenges” are addressed, with a focus on the importance of exercise as a pleiotropic behavior-intervention and its general implications to the area of promoting brain health. Full article

Epilepsy remains one of the most prevalent neurological disorders, characterised by complex aetiology encompassing genetic, structural, metabolic, and inflammatory factors. Despite advances in neuroimaging and neurophysiological diagnostics, there is a persistent lack of sensitive and specific biomarkers to enable early diagnosis, risk stratification, and monitoring of therapeutic efficacy. Key epilepsy biomarkers include neurotransmitters, energy–related compounds, tryptophan pathway metabolites, and choline derivatives. Their determination employs liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS), high–performance liquid chromatography (HPLC) with electrochemical or fluorescence detection, gas chromatography with tandem mass spectrometry (GC–MS/MS), high–resolution mass spectrometry (HRMS), and proton nuclear magnetic resonance (¹H–NMR) spectroscopy, revealing metabolic disturbances in neurotransmission, energy metabolism, and oxidative stress associated with epileptogenesis. Among these techniques, LC–MS/MS currently provides the highest analytical sensitivity and specificity for quantifying low–abundance epilepsy–related metabolites, while HPLC with conventional detection remains a simpler and more cost–effective alternative for routine clinical laboratories. This review presents the current state of knowledge regarding chromatographic techniques applied to the analysis of mentioned metabolites, as well as therapeutic drug monitoring of antiepileptic drugs. Key sample preparation stages are also discussed. Various biological matrices–plasma, serum, urine, cerebrospinal fluid (CSF), dried blood spots (DBSs), and brain tissue—are evaluated. Novel approaches are also presented, including hair samples, microsampling techniques, and headspace analysis of volatile metabolites. Chromatographic techniques constitute the foundation of contemporary metabolomic research in epileptology, enabling biomarker identification and supporting personalised medicine. Further standardisation and translational validation remain necessary, as current evidence is insufficient for routine clinical implementation. Full article

Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain–computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity (“black-box” issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes. Full article