Search Results (57)

Small-cell lung cancer (SCLC) is an aggressive malignancy that exhibits high prevalence for brain metastases. Furthermore, chemotherapy and metastasis-preventive approaches are also linked to neurotoxicity, further aggravating cognitive impairment. Despite evidence supporting structural and functional brain alterations in SCLC, the application of machine learning (ML) to new connectivity biomarkers has remained unexplored. This study is—to the best of our knowledge—the first to apply ML to structural brain connectomics in SCLC, using diffusion tensor imaging (DTI) to identify features discriminating between post-chemotherapy SCLC patients and healthy controls. Specifically, we constructed structural networks via deterministic tractography, applying an adapted feature reduction technique to identify the most informative connections without selection bias. This process isolated 16 connections involving 26 brain regions, predominantly in the frontal, temporal, and parietal lobes, showcasing primarily intra-hemispheric and left-lateralized alterations. Our optimal model leveraged a Gaussian Support Vector Machine (SVM), achieving a weighted accuracy of 0.92, a sensitivity of 0.93, a specificity of 0.91, and an area under the curve of 0.94. The selected feature subset retained high performance when tested with other classifiers, confirming its robustness. Our findings differ from prior studies based on statistically derived features, highlighting the ML-driven connectomics’ potential in uncovering DTI-derived SCLC patterns, offering interpretable insights for neuroimaging-based diagnostics. Full article

Glycosaminoglycans (GAGs), also named ‘mucopolysaccharides’, are nodal constituents of the connective tissue matrix which go through synthesis, demolition, and reconstruction within several cellular structures: an abnormal GAG catabolism is the basis of progressive intra-lysosomal accumulation of non-metabolized GAGs, defining all mucopolysaccharidoses (MPS), protean disorders characterized by physical abnormalities and multi-organ failure depending on the specific site of non-renewable GAGs stored. A severe cognitive decline is typically observed in the Sanfilippo syndrome, which corresponds to MPS type III, a group of four inherited neurodegenerative diseases resulting from the lack of specific enzymes involved in heparan sulfate (HS) metabolism. As a consequence, the storage of partially degraded HS fragments within lysosomes of the central nervous system elicits chain inflammatory reactions involving the NLRP3-inflammasome in microglia and astrocytes, which cease their homeostatic and immune functions and finally compromise neuron survival. This article provides an overview of the neuroinflammatory picture observed in children with MPS type III, postulating a role of HS accumulation to prime innate immunity responses which culminate with pro-inflammatory cytokine release in the brain and highlighting the relevance of interleukin-1 as a main contributor to neuroinflammation. Full article

Background/Objectives: The neonatal and infant periods represent a critical window for brain development, characterized by rapid and heterogeneous processes such as myelination and cortical maturation. Accurate assessment of these changes is essential for understanding normative trajectories and detecting early abnormalities. While conventional MRI provides valuable insights, automated classification remains challenging due to overlapping developmental stages and sex-specific variability. Methods: We propose SEPoolConvNeXt, a novel deep learning framework designed for fine-grained classification of neonatal brain development using T1- and T2-weighted MRI sequences. The dataset comprised 29,516 images organized into four subgroups (T1 Male, T1 Female, T2 Male, T2 Female), each stratified into 14 age-based classes (0–10 days to 12 months). The architecture integrates residual connections, grouped convolutions, and channel attention mechanisms, balancing computational efficiency with discriminative power. Model performance was compared with 19 widely used pre-trained CNNs under identical experimental settings. Results: SEPoolConvNeXt consistently achieved test accuracies above 95%, substantially outperforming pre-trained CNN baselines (average ~70.7%). On the T1 Female dataset, early stages achieved near-perfect recognition, with slight declines at 11–12 months due to intra-class variability. The T1 Male dataset reached >98% overall accuracy, with challenges in intermediate months (2–3 and 8–9). The T2 Female dataset yielded accuracies between 99.47% and 100%, including categories with perfect F1-scores, whereas the T2 Male dataset maintained strong but slightly lower performance (>93%), especially in later infancy. Combined evaluations across T1 + T2 Female and T1 Male + Female datasets confirmed robust generalization, with most subgroups exceeding 98–99% accuracy. The results demonstrate that domain-specific architectural design enables superior sensitivity to subtle developmental transitions compared with generic transfer learning approaches. The lightweight nature of SEPoolConvNeXt (~9.4 M parameters) further supports reproducibility and clinical applicability. Conclusions: SEPoolConvNeXt provides a robust, efficient, and biologically aligned framework for neonatal brain maturation assessment. By integrating sex- and age-specific developmental trajectories, the model establishes a strong foundation for AI-assisted neurodevelopmental evaluation and holds promise for clinical translation, particularly in monitoring high-risk groups such as preterm infants. Full article

Background: Larger brains are believed to rely more heavily on intra-hemispheric than inter-hemispheric processing, which may lead to a proportionally reduced callosal size. Methods: To test this hypothesis, we used T1-weighted magnetic resonance images from a large population sample (n = 38,034). The sample was drawn from the UK Biobank and included 19,947 females and 18,087 males, aged between 44 and 83 years (mean ± SD: 64 ± 7.72 years). Linear modelling was used to assess the relationship between proportional callosal volume and total intracranial volume, with sex, age, and handedness included as covariates and interaction terms. Results: We observed a significant negative relationship between proportional callosal volume and total brain volume, such that larger brains had proportionally smaller corpora callosa. Conclusion: These findings support the hypothesis that increasing brain size is associated with reduced inter-hemispheric connectivity, potentially due to conduction constraints that promote greater intra-hemispheric processing in larger brains. Full article

Existing methods for diagnosing depression rely heavily on subjective evaluations, whereas electroencephalography (EEG) emerges as a promising approach for objective diagnosis due to its non-invasiveness, low cost, and high temporal resolution. However, current EEG analysis methods are constrained by volume conduction effect and class imbalance, both of which adversely affect classification performance. To address these issues, this paper proposes a multi-stage deep learning model for EEG-based depression classification, integrating a cortical feature extraction strategy (CFE), a feature attention module (FA), a graph convolutional network (GCN), and a focal adversarial domain adaptation module (FADA). Specifically, the CFE strategy reconstructs brain cortical signals using the standardized low-resolution brain electromagnetic tomography (sLORETA) algorithm and extracts both linear and nonlinear features that capture cortical activity variations. The FA module enhances feature representation through a multi-head self-attention mechanism, effectively capturing spatiotemporal relationships across distinct brain regions. Subsequently, the GCN further extracts spatiotemporal EEG features by modeling functional connectivity between brain regions. The FADA module employs Focal Loss and Gradient Reversal Layer (GRL) mechanisms to suppress domain-specific information, alleviate class imbalance, and enhance intra-class sample aggregation. Experimental validation on the publicly available PRED+CT dataset demonstrates that the proposed model achieves a classification accuracy of 85.33%, outperforming current state-of-the-art methods by 2.16%. These results suggest that the proposed model holds strong potential for improving the accuracy and reliability of EEG-based depression classification. Full article

Central nervous system (CNS) disorders present significant therapeutic challenges due to the limited regenerative capacity of neural tissues, resulting in long-term disability for many patients. Consequently, the development of novel therapeutic strategies is urgently warranted. Stem cell therapies show considerable potential for mitigating brain damage and restoring neural connectivity, owing to their multifaceted properties, including anti-apoptotic, anti-inflammatory, neurogenic, and vasculogenic effects. Recent research has also identified exosomes—small vesicles enclosed by a lipid bilayer, secreted by stem cells—as a key mechanism underlying the therapeutic effects of stem cell therapies, and given their enhanced stability and superior blood–brain barrier permeability compared to the stem cells themselves, exosomes have emerged as a promising alternative treatment for CNS disorders. A key challenge in the application of both stem cell and exosome-based therapies for CNS diseases is the method of delivery. Currently, several routes are being investigated, including intracerebral, intrathecal, intravenous, intranasal, and intra-arterial administration. Intracerebral injection can deliver a substantial quantity of stem cells directly to the brain, but it carries the potential risk of inducing additional brain injury. Conversely, intravenous transplantation is minimally invasive but results in limited delivery of cells and exosomes to the brain, which may compromise the therapeutic efficacy. With advancements in catheter technology, intra-arterial administration of stem cells and exosomes has garnered increasing attention as a promising delivery strategy. This approach offers the advantage of delivering a significant number of stem cells and exosomes to the brain while minimizing the risk of additional brain damage. However, the investigation into the therapeutic potential of intra-arterial transplantation for CNS injury is still in its early stages. In this comprehensive review, we aim to summarize both basic and clinical research exploring the intra-arterial administration of stem cells and exosomes for the treatment of CNS diseases. Additionally, we will elucidate the underlying therapeutic mechanisms and provide insights into the future potential of this approach. 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

Background: Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder characterized by progressive neurodegeneration and connectivity deterioration. While resting-state functional magnetic resonance imaging (fMRI) provides critical insights into brain network abnormalities, traditional mutual information-based methods exhibit inherent limitations in characterizing the dynamic synergistic mechanisms between cerebral regions. Method: This study pioneered the application of an Integrated Information Decomposition (ΦID) framework in AD brain network analysis, constructing single-sample network models based on ΦID-derived synergy metrics to systematically compare their differences with mutual information-based methods in pathological sensitivity, computational robustness, and network representation capability, while detecting brain regions with declining dynamic synergy during AD progression through intergroup t-tests. Result: The key finding are as follows: (1) synergy metrics exhibited lower intra-group coefficient of variation than mutual information metrics, indicating higher computational stability; (2) single-sample reconstruction significantly enhanced the statistical power in intergroup difference detection; (3) synergy metrics captured brain network features that are undetectable by traditional mutual information methods, with more pronounced differences between networks; (4) key node analysis demonstrated spatiotemporal degradation patterns progressing from initial dysfunction in orbitofrontal–striatal–temporoparietal pathways accompanied by multi-regional impairments during prodromal stages, through moderate-phase decline located in the right middle frontal and postcentral gyri, to advanced-stage degeneration of the right supramarginal gyrus and left inferior parietal lobule. ΦID-driven dynamic synergy network analysis provides novel information integration theory-based biomarkers for AD progression diagnosis and potentially lays the foundation for pathological understanding and subsequent targeted therapy development. Full article

This study examined spontaneous activity in children aged 3–11 years with specific language impairment (SLI) using an electroencephalogram (EEG). We compared SLI-diagnosed children with a normo-development group (ND). The signal complexity, multiscale entropy (MSE) and parameterized power spectral density (FOOOF) were analyzed, decomposing the PSD into its aperiodic (AP, proportional to 1/fx) and periodic (P) components. The results showed increases in complexity across scales in both groups. Although the topographic distributions were similar, children with SLI exhibited an increased AP component over a broad frequency range (13–45 Hz) in the medial regions. The P component showed differences in brain activity according to the frequency and region. At 9–12 Hz, ND presented greater central–anterior activity, whereas, in SLI, this was seen for posterior–central. At 33–36 Hz, anterior activity was greater in SLI than in ND. At 37–45 Hz, SLI showed greater activity than ND, with a specific increase in the left, medial and right regions at 41–45 Hz. These findings suggest alterations in the excitatory–inhibitory balance and impaired intra- and interhemispheric connectivity, indicating difficulties in neuronal modulation possibly associated with the cognitive and linguistic characteristics of SLI. Full article

Background/Objectives: Schizophrenia is a severe psychiatric disorder characterized by deficits in perception and advanced cognitive functions. Prior studies have reported abnormal lateralization in cortical morphology and functional connectivity in schizophrenia. However, it remains unclear whether schizophrenia affects hemispheric asymmetry in the hierarchical organization of functional connectome. Methods: Here, we apply a gradient mapping framework to the hemispheric functional connectome to estimate the first three gradients, which characterize unimodal-to-transmodal, visual-to-somatomotor, and somatomotor/default mode-to-multiple demand hierarchy axes. We then assess between-group differences in intra- and inter-hemispheric asymmetries of these three functional gradients. Results: We find that, compared to healthy controls, patients with schizophrenia exhibit significantly altered hemispheric asymmetry in functional gradient across multiple networks, including the dorsal attention, ventral attention, visual, and control networks. Region-level analyses further reveal that patients with schizophrenia show significantly abnormal hemispheric gradient asymmetries in several cortical regions in the dorsal prefrontal gyrus, medial superior frontal gyrus, and somatomotor areas. Lastly, we find that hemispheric asymmetries in functional gradients can differentiate between patients and healthy controls and predict the severity of positive symptoms in schizophrenia. Conclusions: Collectively, these findings suggest that schizophrenia is associated with altered hemispheric asymmetry in functional hierarchy, providing novel perspectives for understanding the atypical brain lateralization in schizophrenia. Full article

Cerebral lateralisation is a core organising principle of the brain that is characterised by a complex pattern of hemispheric specialisations and interhemispheric interactions. In various mental disorders, functional and/or structural hemispheric asymmetries are changed compared to healthy controls, and these alterations may contribute to the primary symptoms and cognitive impairments of a specific disorder. Since multiple genetic and epigenetic factors influence both the pathogenesis of mental illness and the development of brain asymmetries, it is likely that the neural developmental pathways overlap or are even causally intertwined, although the timing, magnitude, and direction of interactions may vary depending on the specific disorder. However, the underlying developmental steps and neuronal mechanisms are still unclear. In this review article, we briefly summarise what we know about structural, functional, and developmental relationships and outline hypothetical connections, which could be investigated in appropriate animal models. Altered cerebral asymmetries may causally contribute to the development of the structural and/or functional features of a disorder, as neural mechanisms that trigger neuropathogenesis are embedded in the asymmetrical organisation of the developing brain. Therefore, the occurrence and severity of impairments in neural processing and cognition probably cannot be understood independently of the development of the lateralised organisation of intra- and interhemispheric neuronal networks. Conversely, impaired cellular processes can also hinder favourable asymmetry development and lead to cognitive deficits in particular. Full article

Background: Major Depressive Disorder (MDD) is a significant challenge in modern medicine due to its unclear underlying causes. Brain network dysfunction is believed to play a key role in its pathophysiology. Resting-state functional MRI (rs-fMRI), a neuroimaging technique, enables the in vivo assessment of functional connectivity (FC) between brain regions, offering insights into these network dysfunctions. The aim of this study was to evaluate abnormalities in FC within major brain networks in patients with drug-resistant MDD. Methods: The study group consisted of 26 patients with drug-resistant MDD and an age-matched control group (CG) of 26 healthy subjects. The rs-fMRI studies were performed on a 3T MR scanner (Philips, Ingenia) using a 32-channel head and neck coil. Imaging data were statistically analyzed, focusing on the intra- and inter-network FC of the following networks: default mode (DMN), sensorimotor (SMN), visual (VN), salience (SN), cerebellar (CN), dorsal attention (DAN), language (LN), and frontoparietal (FPN). Results: In patients with MDD, the intra-network analysis showed significantly decreased FC between nodes within VN compared to CG. In contrast, the inter-network analysis showed significantly increased FC between nodes from VN and SN or VN and DAN compared to CG. Decreased FC was found between SN and CN or SN and FPN as well as VN and DAN nodes compared to CG. Conclusions: Patients with MDD showed significant abnormalities in resting-state cortical activity, mainly regarding inter-network functional connectivity. These results contribute to the knowledge on the pathomechanism of MDD and may also be useful for developing new treatments. Full article

The insula is often considered the fifth lobe of the brain and is increasingly recognized as one of the most connected regions in the brain, with widespread connections to cortical and subcortical structures. As a follow-up to our previous tractography work, we investigated the resting-state functional connectivity (rsFC) profiles of insular subregions and assessed their concordance with structural connectivity. We used the CONN toolbox to analyze the rsFC of the same 19 insular regions of interest (ROIs) we used in our prior tractography work and regrouped them into six subregions based on their connectivity pattern similarity. Our analysis of 50 healthy participants confirms the known broad connectivity of the insula and shows novel and specific whole-brain and intra-connectivity patterns of insular subregions. By examining such subregions, our findings provide a more detailed pattern of connectivity than prior studies that may prove useful for comparison between patients. Full article

The malfunctioning of the brain synucleins is associated with pathogenesis of Parkinson’s disease. Synucleins’ ability to modulate various pre-synaptic processes suggests their modifying effects on the electroencephalogram (EEG) recorded from different brain structures. Disturbances in interrelations between them are critical for the onset and evolution of neurodegenerative diseases. Recently, we have shown that, in mice lacking several synucleins, differences between the frequency spectra of EEG from different brain structures are correlated with specificity of synucleins’ combinations. Given that EEG spectra are indirect characteristics of inter-structural relations, in this study, we analyzed a coherence of instantaneous values for EEGs recorded from different structures as a direct measure of “functional connectivity” between them. Methods: EEG data from seven groups of knock-out (KO) mice with combined deletions of alpha, beta, and gamma synucleins versus a group of wild-type (WT) mice were compared. EEG coherence was estimated between the cortex (MC), putamen (Pt), ventral tegmental area (VTA), and substantia nigra (SN) in all combinations. Results: EEG coherence suppression, predominantly in the beta frequency band, was observed in KO mice versus WT littermates. The suppression was minimal in MC-Pt and VTA-SN interrelations in all KO groups and in all inter-structural relations in mice lacking either all synucleins or only beta synuclein. In other combinations of deleted synucleins, significant EEG coherence suppression in KO mice was dominant in relations with VTA and SN. Conclusion: Deletions of the synucleins produced significant attenuation of intra-cerebral EEG coherence depending on the imbalance of different types of synucleins. Full article

This study aimed to investigate the effects of intranasal air-puffing on cognitive impairments and brain cortical activity following one night of partial sleep deprivation (PSD) in adults. A total of 26 healthy adults underwent the numerical Stroop test (NST) and electroencephalography (EEG) before and after one night of PSD. Following PSD, subjects in the treatment group (n = 13) received nasal air-puffs (5 Hz, 3 min) before beginning the NST and EEG recording. Administration of nasal air-puffs in the treatment group restored the PSD-induced increase in error rate and decrease in reaction time and missing rate in the NST. Intranasal air-puffs recovered the PSD-induced augmentation of delta and theta power and the reduction of beta and gamma power in the EEG, particularly in the frontal lobes. Intranasal air-puffing also almost reversed the PSD-induced decrease in EEG signal complexity. Furthermore, it had a restorative effect on PSD-induced alteration in intra-default mode network functional connectivity in the beta and gamma frequency bands. Rhythmic nasal air-puffing can mitigate acute PSD-induced impairments in cognitive functions. It exerts part of its ameliorating effect by restoring neuronal activity in cortical brain areas involved in cognitive processing. Full article