Search Results (433)

Background: The gut microbiota and the gut epithelium play a central role in maintaining systemic and brain homeostasis from early life. Stressful experiences during sensitive developmental windows can disrupt this balance, increasing long-term susceptibility to psychiatric disorders. However, the mechanisms through which early-life alterations in the microbiota influence brain development and function remain poorly understood. Here, the sex-specific impact of prenatal stress (PNS) on gut integrity and microbial composition in adult offspring was explored. Methods: Thirty dams were mated and randomly assigned to PNS or control. Offspring microbiota was analysed through 16S rRNA sequencing, intestinal morphology with morphometric analyses, and tight junctions using qPCR and immunofluorescence. Results: Exposure to PNS was associated with reduced intestinal surface area in males and shortened crypts in females. In both sexes, PNS caused a decrease in the expression of ZO-1, suggesting impaired gut barrier integrity. 16S rRNA sequencing revealed, furthermore, that PNS exposure was associated with a decrease in beneficial genera, including Akkermansia in males and Clostridia vadinBB60 in females, along with an increase in the pro-inflammatory genus Anaerotruncus, regardless of sex. Notably, some of these alterations were more pronounced in PNS-exposed animals that showed impaired sociability, highlighting gut microbiota inter-individual variability in the response to early-life adversity. Moreover, selected microbial changes show significant correlations with the behavioural outcomes, as well as with intestinal morphology or brain inflammatory markers. Conclusions: Together, these findings pinpoint the gut as a central player in stress vulnerability and highlight specific microbial signatures as promising biomarkers and therapeutic targets for stress-related disorders. Full article

Acquired Brain injury (ABI) is now widely regarded as a chronic condition but this change in conceptualization has not yet been realized in the way rehabilitation and care are offered and funded in the United States. Similarly, it is widely accepted that an optimized ABI system includes integration across the phases of care and recovery that considers the bio-psycho-socio-ecological (BPSE) dimensions beyond the injury itself. Despite the importance of BPSE factors informing care, typical post-injury care and management remain focused on acute presentation and the biological nature of the injury and there still exists relevant inter-country differences for disorders of consciousness (DoC) neurorehabilitation after severe ABI. This collaboration with Italian colleagues explores and compares the types and locations of rehabilitative services offered in a Post-Coma Unit of neurorehabilitation center in Italy (namely, Santa Lucia Foundation IRCCS in Rome) and in the United States following a “severe” ABI (sABI). This narrative seeks to describe the degree to which both systems utilize a BPSE informed approach to care. Full article

Background: Empagliflozin, a sodium–glucose cotransporter 2 (SGLT2) inhibitor, improves outcomes in heart failure (HF) patients, yet inter-individual variability in response remains unclear. Genetic variants in Brain-Derived Neurotrophic Factor BDNF (rs6265) and ATPase Sarcoplasmic/Endoplasmic Reticulum Ca²⁺ Transporting 2 ATP2A2 (rs1860561) may influence the treatment efficacy. Objective: To assess the association of BDNF and ATP2A2 polymorphisms with the response to low-dose empagliflozin (10 mg) in Pakistani patients with heart failure and a reduced ejection fraction (HFrEF). Methods: In this prospective study, 120 HF patients with an ejection fraction of 25–45% who had been on stable standard heart failure therapy for at least 3 months were initiated on 10 mg of empagliflozin. The brain natriuretic peptide (BNP) and LVEF left ventricular ejection fraction (LVEF) were assessed at 6 and 12 months. Genotyping for rs6265 and rs1860561 was performed via Sanger sequencing. A response was defined as a ≥5% EF increase or ≥20% BNP reduction. Associations were analyzed using chi-square and logistic regression. Results: Among 99 genotyped patients, BDNF T allele carriers (CT/TT) had a significantly lower EF (p = 0.028) and BNP (p < 0.001) response. The CC genotype was associated with improved outcomes (BNP OR: 7.70; EF OR: 5.97). For ATP2A2, the GG genotype showed a strong association with EF improvement (OR: 5.97; p = 0.001), with no BNP association. Variant allele frequencies were higher among Punjabis and Kashmiris than Pathans. Conclusions: BDNF rs6265 and ATP2A2 rs1860561 polymorphisms appear to influence the individual response to empagliflozin in HFrEF patients. These findings underscore the potential of pharmacogenetic profiling to guide personalized therapy and optimize treatment outcomes in heart failure. Full article

Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies—such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets—can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., Akkermansia muciniphila abundance, SCFA production). In parallel, digital health tools—including mobile health applications, wearable devices, and AI-supported platforms—enhance self-monitoring, adherence, and dynamic feedback in real-world settings. Mechanistic pathways such as gut–brain axis regulation, microbial fermentation, gene–diet interactions, and anti-inflammatory responses are explored to explain inter-individual differences in dietary outcomes. However, challenges such as cost, accessibility, and patient motivation remain and should be addressed to ensure the effective implementation of these integrated strategies in real-world settings. Collectively, these insights underscore the pivotal role of precision nutrition as a cornerstone for personalized, scalable, and sustainable obesity interventions. Full article

Background: Brain–computer interfaces (BCIs) based on motor imagery (MI) offer promising solutions for motor rehabilitation and communication. However, electroencephalography (EEG) signals are often characterized by low signal-to-noise ratios, strong non-stationarity, and significant inter-subject variability, which pose significant challenges for accurate decoding. Existing methods often struggle to simultaneously model the spatial interactions between EEG channels, the local fine-grained features within signals, and global semantic patterns. Methods: To address this, we propose the graph attention-based hierarchical temporal network (GAH-TNet), which integrates spatial graph attention modeling with hierarchical temporal feature encoding. Specifically, we design the graph attention temporal encoding block (GATE). The graph attention mechanism is used to model spatial dependencies between EEG channels and encode short-term temporal dynamic features. Subsequently, a hierarchical attention-guided deep temporal feature encoding block (HADTE) is introduced, which extracts local fine-grained and global long-term dependency features through two-stage attention and temporal convolution. Finally, a fully connected classifier is used to obtain the classification results. The proposed model is evaluated on two publicly available MI-EEG datasets. Results: Our method outperforms multiple existing state-of-the-art methods in classification accuracy. On the BCI IV 2a dataset, the average classification accuracy reaches 86.84%, and on BCI IV 2b, it reaches 89.15%. Ablation experiments validate the complementary roles of GATE and HADTE in modeling. Additionally, the model exhibits good generalization ability across subjects. Conclusions: This framework effectively captures the spatio-temporal dynamic characteristics and topological structure of MI-EEG signals. This hierarchical and interpretable framework provides a new approach for improving decoding performance in EEG motor imagery tasks. Full article

This study investigates the relationship between neural and physiological synthonization via comodulation (Synth) in dyadic exchanges centered on negotiation processes. In total, 13 dyads participated in a negotiation task with three phases: Initiation (IP), Negotiation Core (NCP), and Resolution (RP). Electroencephalographic (EEG) frequency bands (i.e., delta, theta, alpha) and autonomic responses (heart rate variability, HRV) were recorded. Synth was analyzed using Euclidean distance (EuDist) for EEG and autonomic indices. Significant Synth in delta, theta, and alpha bands in temporo-central and parieto-occipital regions was observed, indicating social cognitive alignment. HRV Synth was higher during the NCP than IP, suggesting better coordination. Based on this result, a cluster analysis was performed on HRV EuDist to identify distinct groups based on HRV, and eventually personality patterns, that revealed one cluster with higher Synth and reward sensitivity, and another with lower Synth and reward sensitivity. These findings show how neural and autonomic Synth enhances social cognition and emotional regulation. Full article

Background/Objectives: Quantitative analysis of amyloid PET imaging plays a crucial role in diagnosing Alzheimer’s disease (AD), particularly in cases where visual interpretation is equivocal. Multiple commercial software tools are available for this purpose, yet differences in their quantification and diagnostic performance remain understudied, especially for Neurophet SCALE PET. Methods: We retrospectively analyzed ¹⁸F-florbetaben PET/CT scans from 129 patients with cognitive impairment, comprising 39 patients with AD and 90 with non-AD diagnoses, using three software tools: MIMneuro, CortexID Suite, and Neurophet SCALE PET. Standardized uptake value ratios (SUVRs) were obtained for six brain regions known for amyloid accumulation. Diagnostic accuracy was evaluated using ROC curve analysis, while inter-software correlations and reliability were assessed via Pearson correlation coefficients and intraclass correlation coefficients (ICC). Results: All three software programs significantly distinguished AD from non-AD patients in most brain regions. MIMneuro and Neurophet SCALE PET demonstrated the highest diagnostic performance, with MIMneuro achieving an AUC of 1.000 in the anterior cingulate gyrus. While MIMneuro and Neurophet SCALE PET showed moderate-to-strong SUVR correlations (r = 0.715–0.865), CortexID Suite showed limited correlation with the other tools. Inter-software reliability was moderate only in selected regions (ICC ≈ 0.5), indicating potential variability in SUVR measurements across platforms. Conclusions: MIMneuro, CortexID Suite, and Neurophet SCALE PET are effective for the semi-quantitative analysis of amyloid PET and can aid in the diagnosis of AD. However, clinicians should be cautious when interpreting SUVRs across different software tools due to limited inter-software consistency. Standardization efforts or consistent use of a single platform are recommended to avoid diagnostic discrepancies. Full article

Psychology as an empirical science has targeted human cognition for more than a century. Typically, the focus of these investigations was on isolated mental processes, which were studied in individual participants in confined laboratory settings. The present commentary aims to show how a relatively recent paradigm shift, the (renewed) conception of humans as fundamentally social, can shape our understanding of the mind and our scientific approach to studying spirituality. In the first sections, I will shortly review advances of psychological research in core processes and capacities of social understanding (empathy, compassion, perspective taking) and social interaction (communication, cooperation) that are also considered relevant in spiritual practices and traditions. Subsequently, a large-scale intervention study, the Resource Project, is presented to exemplify how the investigation of meditation-based mental trainings can decidedly include social practices (so-called contemplative dyads) and how these practices benefit interpersonal capacities. Arguing that cognition, spirituality, and scientific endeavors are not confined to individual minds and brains but arise in the dynamic in-between of interacting agents, I will outline possible avenues for future inter-disciplinary research at the interface of religious sciences/theology and psychology. Full article

Background: Despite its central role in pediatric pre-surgical evaluation of drug-resistant focal epilepsy, conventional analog ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT (aPET) systems often yield modest epileptogenic zone (EZ) detection rates (~50–60%). Silicon photomultiplier–based digital PET/CT (dPET) promises enhanced image quality, but its performance in pediatric epilepsy remains untested. Methods: We retrospectively analyzed 22 children (mean age 11.5 ± 2.6 years) who underwent interictal brain ¹⁸F-FDG PET/CT: 11 on an analog system (Discovery ST, 2018–2019) and 11 on a digital system (Biograph Vision 450, 2020–2021). Three blinded nuclear medicine physicians independently scored EZ localization and image quality (4-point scale); post-surgical histology and ≥1-year clinical follow-up served as reference. Results: The EZ was correctly identified in 8/11 analog scans (72.7%) versus 10/11 digital scans (90.9%). Average image quality was significantly higher with dPET (3.0 ± 0.9 vs. 2.1 ± 0.9; p < 0.05), and inter-reader agreement improved from good (ICC = 0.63) to excellent (ICC = 0.91). Conclusions: Our preliminary findings suggest that dPET enhances image clarity and reader consistency, potentially improving localization accuracy in pediatric epilepsy presurgical workups. Full article

Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions. Methods: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability. Results: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods. Conclusions: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments. Full article

The hypothalamus belongs to the central brain structure designed for the neuroendocrine regulation of many organismal functions, including the stress response, cardiovascular system, and blood pressure, and it is well known that the serotonergic hypothalamic system plays a significant role in these processes. Unfortunately, the genetic determination of serotonergic hypothalamic mechanisms has been little studied. The aim of this article is to describe the expression profile of the genes in the hypothalamic serotonergic synapses in hypertensive ISIAH rats in comparison with normotensive WAG rats in control conditions and under the influence of a single short-term restraint stress. It was found that 14 differentially expressed genes (DEGs) may provide the inter-strain differences in the serotonergic synaptic function in the hypothalamus between the hyper- and normotensive rats studied. In hypertensive rats, downregulation of Slc18a1 gene in the presynaptic serotoninergic ends and decreased expression of Cacna1s and Htr3a genes determining the postsynaptic membrane conductance may be considered as a main factors causing differences in the function of hypothalamic serotoninergic synapses in hypertensive ISIAH and normotensive WAG rats at the basal conditions. Under basal conditions, glial cell genes were not involved in the formation of inter-strain differences in serotonergic synaptic function. The analysis of transcriptional responses to restraint stress revealed key genes whose expression is involved in the regulation of serotonergic signaling, and a cascade of interrelated changes in biological processes and metabolic pathways. Stress-dependent changes in the expression of some DEGs are similar in the hypothalamus of hypertensive and normotensive rats, but the expression of a number of genes changes in a strain-specific manner. The results suggest that in hypothalamic glial cells of both strains, restraint stress induces changes in the expression of DEGs associated with the synthesis of Ip3 and its receptors. Many of the identified serotonergic DEGs participate in the regulation of not only serotonergic synapses but may also be involved in the regulation of cholinergic, GABAergic, glutamatergic, and dopaminergic synapses. The results of the study provide new information on the genetic mechanisms of inter-strain differences in the functioning of the hypothalamic serotonergic system in hypertensive ISIAH and normotensive WAG rats at rest and under the influence of a single short-term restraint (emotional) stress. Full article

Background/Objectives: Accurate segmentation of brain stroke lesions from MRI images is a critical task in medical image analysis that is essential for timely diagnosis and treatment planning. Methods: This paper presents a novel approach for segmenting brain stroke lesions using a deep learning model based on the U-Net neural network architecture. We enhanced the traditional U-Net by integrating a transformer-based backbone, specifically the Mix Vision Transformer (MiT), and compared its performance against other commonly used backbones such as ResNet and EfficientNet. Additionally, we implemented a 2.5D method, which leverages 2D networks to process three-dimensional data slices, effectively balancing the rich spatial context of 3D methods and the simplicity of 2D methods. The 2.5D approach captures inter-slice dependencies, leading to improved lesion delineation without the computational complexity of full 3D models. Utilizing the 2015 ISLES dataset, which includes MRI images and corresponding lesion masks for 20 patients, we conducted our experiments with 4-fold cross-validation to ensure robustness and reliability. To evaluate the effectiveness of our method, we conducted comparative experiments with several state-of-the-art (SOTA) segmentation models, including CNN-based UNet, nnU-Net, TransUNet, and SwinUNet. Results: Our proposed model outperformed all competing methods in terms of Dice Coefficient and Intersection over Union (IoU), demonstrating its robustness and superiority. Our extensive experiments demonstrate that the proposed U-Net with the MiT Backbone, combined with 2.5D data preparation, achieves superior performance metrics, specifically achieving DICE and IoU scores of 0.8153 ± 0.0101 and 0.7835 ± 0.0079, respectively, outperforming other backbone configurations. Conclusions: These results indicate that the integration of transformer-based backbones and 2.5D techniques offers a significant advancement in the accurate segmentation of brain stroke lesions, paving the way for more reliable and efficient diagnostic tools in clinical settings. 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

Quantitative FDG-PET brain imaging across multiple centers is challenged by inter-scanner variability, impacting the comparability of neuroimaging data. This study proposes a data-driven image-based harmonization protocol to address these discrepancies without relying on traditional phantom scans. The protocol uses spatially normalized FDG-PET brain images to estimate scanner-specific Gaussian smoothing filters, optimizing parameters via the structural similarity index (SSIM). Validation was performed using images from cognitively normal individuals and Alzheimer’s disease patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results demonstrated robust harmonization at moderate target resolutions (8 and 10 mm FWHM), with filter estimates consistently within 1.2 mm of phantom-derived ground truths. However, at higher resolutions (6 mm FWHM), discrepancies reached up to 3 mm, reflecting reduced accuracy. These deviations were particularly evident for high-resolution scanners like HRRT, likely due to elevated noise levels and smaller sample sizes. The presented harmonization method effectively reduces inter-scanner variability in retrospective FDG-PET studies, especially valuable when phantom scans are unavailable. Nonetheless, the current limitations at finer resolutions underline the necessity for methodological refinements to meet the demands of evolving high-resolution PET imaging technologies. Full article

Recent advancements in decoding electroencephalogram (EEG) signals for motor imagery tasks have shown significant potential. However, the intricate time–frequency dynamics and inter-channel redundancy of EEG signals remain key challenges, often limiting the effectiveness of single-scale feature extraction methods. To address this issue, we propose the Dual-Branch Blocked-Integration Self-Attention Network (DB-BISAN), a novel deep learning framework for EEG motor imagery classification. The proposed method includes a Dual-Branch Feature Extraction Module designed to capture both temporal features and spatial patterns across different scales. Additionally, a novel Blocked-Integration Self-Attention Mechanism is employed to selectively highlight important features while minimizing the impact of redundant information. The experimental results show that DB-BISAN achieves state-of-the-art performance. Also, ablation studies confirm that the Dual-Branch Feature Extraction and Blocked-Integration Self-Attention Mechanism are critical to the model’s performance. Our approach offers an effective solution for motor imagery decoding, with significant potential for the development of efficient and accurate brain–computer interfaces. Full article