Ischemic stroke elicits a strong neuroinflammatory response characterized by activation of microglia and infiltration of monocytes-derived macrophages (iMM), which have been speculated to have differential functions in stroke. However, because the gene expression profiles of microglia and iMM overlap in the injured brain, distinguishing these two cell populations has posed a challenge for the field. Using a recently characterized microglia-specific TMEM119CreER-Ai14 tdTomato reporter mouse model and single cell RNA sequencing (scRNA-seq) analysis, we prelabeled microglia in the brain prior to stroke, enabling detailed characterization of the transcriptomic and spatial distributions of microglia vs. iMM across different stages of post-stroke. ScRNA-seq findings were validated through immunohistochemistry, RNAscope, and animal models. Here, we report that microglia and iMM are enriched at distinct locations and exhibit differential temporal dynamics in the stroke brain. Our genetic tracing data reveals that iMM does not survive in the chronic stroke brain past 30 days in our model. Additionally, scRNA-seq further revealed distinct transcriptomic states between microglia and iMM at D7 and D14 in the stroke brain. Genetic tracing also allowed us to identify novel markers for activated microglia vs. iMM in the acute stroke brain such as Gm21188. Finally, our data also showed that Igf1 is up-regulated in both microglia and iMM after stroke. Cx3cr1ERT2 Igf1fl/fl mediated myeloid-specific Igf1 gene deletion at 5-9 Days after stroke lead to decreased CD8+ T-cell infiltration in the stroke brain. Functionally, myeloid specific Igf1 iKO mice show expedited sensorimotor function recovery but worsened anxiety-like behavior. In summary, Tmem119CreER-Ai14 tdTomato reporter mouse line is a useful tool for the field to clearly delineate the cellular dynamics and transcriptomic profiles of microglia vs. iMM in a variety of central nervous system pathological conditions, which could have broad implications beyond stroke studies. Moreover, we have characterized novel subtypes and markers for activated microglia and iMM, as well as a possible novel role for myeloid cell-derived IGF-1 in driving T-cell infiltration and/or survival in the post-stroke brain. This study provides valuable insights for future investigations aimed at modulating microglia vs. iMM to promote stroke resolution and functional recovery in vivo.
Previous brain-wide association studies (BWAS) have linked specific environmental and behavioral variables to brain variability. In this work, we mapped 649 variables to children's brains and compared the resultant BWAS maps with each other and with neurobiological reference patterns. Socioeconomic status (SES) showed the strongest brain-wide associations. The SES associations were strongest in motor and sensory but not cognitive regions, a pattern shared across many BWAS maps, including intelligence quotient (IQ). A single, common BWAS brain pattern existed across variables that was most reflective of a child's socioeconomics. Adjusting for SES weakened brain-IQ associations, eliminating the BWAS motor and sensory pattern. Brain-with-IQ associations also did not generalize when trained on higher-SES subsamples. Thus, children's brains vary the most with SES, potentially through SES-dependent sleep deprivation and stress.
The multi-factorial Parkinson's disease (PD) remains an incurable disease to date. Significant efforts have gone into identifying PD targets through high-throughput screening technologies such as microarrays and RNA sequencing. However, many studies often have low sample counts due to difficulty in obtaining human tissues, especially in neurodegenerative diseases, resulting in insufficient sample sizes for good statistical analysis. In this meta-analysis, we analysed 47 human blood and brain PD-associated datasets to identify clinically significant targets in PD. Our study excluded datasets with animal/in vitro models to eliminate species effects and in vitro artifacts. To analyse all eligible human blood and brain PD-associated datasets available on Gene Expression Omnibus (GEO), including both microarrays and RNA-sequencing datasets, to provide a more comprehensive understanding of the clinically significant targets in PD. Differentially expressed gene (DEG) and gene ontology analyses of 1922 human tissues samples identified 19 potential targets (13 in blood, 6 in brain) that were not previously PD-associated. DEGs involved in immune regulation in the blood, and apoptosis and cell growth/proliferation in the brain were consistently identified across multiple PD datasets. Separate cell type/tissue region analyses for both blood (leukocytes and PBMCs) and brain datasets (7 different regions) revealed more functionally-specific potential targets of PD. Via Cohen's d effect size, RNA-sequencing datasets display lower variability as compared to microarray datasets (p < 0.01). Separate region analysis also reduced data variability (p < 0.01). This is the largest human tissue meta-analysis conducted for PD to date. Importantly, we identified six significant DEGs dysregulated in the same direction in both the blood and brain of PD patients. Examining the roles of these targets in PD could advance our understanding of the disease in hopes of developing a neuroprotective treatment and/or early diagnostic biomarkers.
The rapid expansion of complex metal oxide particles (CMOPs) in energy technologies raises emerging health concerns, yet their neuropsychiatric impacts remain unclear. Using intranasal exposure to lithium iron phosphate (LFP) and nickel-cobalt-manganese oxide (NCM) at dose levels selected with reference to reported ambient and occupational monitoring scenarios (0.8 and 8 mg/kg/day, n = 8 per group), we show that short-term CMOP exposure induces distinct neurobehavioral alterations in mice, characterized by changes in cognitive performance, risk assessment, and stress-related coping behavior. These changes co-occurred with dose-dependent brain accumulation of Li, Ni, Mn, and Co. Unexpectedly, low-dose exposure yielded 5-17-fold higher brain bioaccumulation factors than high-dose exposure, indicating disproportionate brain retention at lower exposure levels. Neurotransmitter profiling showed alterations consistent with perturbation of catecholamine metabolism, the tryptophan-kynurenine pathway, and the glutamate-glutamine cycle. At the molecular level, brain metal burden was associated with changes in barrier-related, neuroimmune, and synaptic signaling markers prioritized in relation to behavioral outcomes. Collectively, the findings indicate that short-term CMOP exposure can co-occur with brain metal bioaccumulation and neurobehavioral dysfunction, supporting the need for future inhalation-based and chronic studies to better define toxicokinetics, exposure relevance, and long-term health implications.
This data article describes an original synthetic/simulated dataset designed to support materials-informatics and comparative formulation analysis of PLGA nanoparticles and liposomes for brain-cancer-relevant drug delivery. Open row-level datasets that jointly cover formulation descriptors, release kinetics, blood-brain-barrier transport proxies, and paired tumor/non-tumor cell-assay outcomes for PLGA and liposome carriers were not identified by us in a single harmonized open resource during preparation of this package, motivating a transparent synthetic benchmark for methodological and machine-learning reuse. The dataset was inspired by the scientific themes synthesized in the related review article by Makalew and Abrori, but it does not reproduce bibliometric records, published tables, or experimental rows. The package contains 6000 unique virtual formulations in a formulation master table and three linked long-format data tables describing time-resolved release profiles (360,000 rows), blood-brain-barrier-related transport proxies (54,000 rows), and paired tumor/non-tumor cell-assay proxies (432,000 rows), totaling approximately 846,000 assay-like rows. Variables include composition descriptors, preparation routes, physicochemical properties, targeting features, encapsulation efficiency, drug loading, stability, biodegradation proxy, serum stability proxy, integrated blood-brain-barrier transport score, cellular uptake score, biocompatibility score, tumor-directed cytotoxicity proxy, off-target toxicity proxy, and derived multi-criteria performance scores. The synthetic data were generated with a transparent, reproducible workflow that combines domain-informed priors, hierarchical conditional rules, latent heterogeneity, batch effects, replicate variation, bounded noise, sparse scientifically motivated missingness, and post-generation quality filters. The dataset is distributed in open tabular formats together with generation code, a codebook, validation documentation, and reproducible figure-generation scripts.
Consciousness spans a range of phenomenological experiences, from effortless immersion to disengaged monotony, yet how such phenomenology emerges from brain activity is not well understood. Flow, a phenomenological experience frequently elicited by interactive media, has drawn attention for its links to performance and wellbeing, but existing neural accounts rely on single-region or small-network analyses that overlook the brain's distributed and dynamic nature. Complexity science offers tools that capture brain-wide dynamics, but this approach has rarely been applied to flow or to its natural comparisons: boredom and frustration. Consequently, it remains unclear whether tools drawn from complexity science can objectively discriminate between these phenomenological experiences while also clarifying their neural basis. To address this uncertainty, we induced each phenomenological experience with a difficulty-titrated video game during functional magnetic resonance imaging and collected concurrent behavioral and self-report data. Our complex systems analyses revealed that flow, in this experimental setup, shows an inverse relationship to global entropy with moderate explanatory power, and is not explained by either synchronization or metastability, whereas boredom and frustration exhibit different configurations of brain-dynamics metrics. Notably, these findings integrate previously separate prefrontal and network-synchrony observations within a single dynamical systems framework and identify complexity-based markers with the potential to map the neural underpinnings of media-related benefits.
Brain tumors present a significant health challenge, making an accurate diagnosis crucial for effective treatment and improved patient outcomes. This study aims to develop SCSFE-DiagBT, an advanced deep learning framework that integrates classification and segmentation. It is designed for enhanced brain tumor diagnosis using MRI scans. We propose a Stacked Classification and Segmentation Model with Feature Extraction (SCSFE-DiagBT). This model unifies the processes of classification and segmentation into a single diagnostic pipeline. It leverages the complementarity of both tasks by employing a deep learning approach to efficiently analyze MRI scans. Experimental results indicate substantial improvements in performance metrics. In classification, metrics such as accuracy, precision, and recall improved by up to 31% compared to baseline models, with high accuracy. For segmentation, the model achieved a 12% improvement in the Dice coefficient, reaching an accuracy of 99.58% and a Dice score of 86.30%. This demonstrates robust generalization and minimal overfitting. The findings underscore the effectiveness of integrating classification and segmentation in the diagnosis of brain tumors. The enhanced interpretability through feature extraction further supports the model's utility in clinical settings, potentially reducing diagnostic variability associated with manual interpretations. SCSFE-DiagBT emerges as a highly accurate and viable tool for brain tumor diagnosis. It offers significant advancements over traditional methods and promising implications for improving patient care and outcomes in neuro-oncology.
The relationship between sleep and brain metabolism remains poorly understood. This study aimed to determine whether overnight sleep features, estimated using a home sleep apnea system based on peripheral arterial tonometry, are associated with regional FDG-PET uptake in older adults. We analyzed cognitively unimpaired participants (≥60 years) from the Mayo Clinic Study of Aging. Each participant underwent overnight objective sleep assessment with Watch PAT, followed by FDG-PET imaging the next morning. PET data were co-registered to 3T MRI and segmented into 47 regions of interest (ROIs) after estimating voxel number-weighted median FDG uptake across hemispheres. Associations between sleep macrostructure and sleep apnea/hypoxemia burden with regional FDG levels were examined using partial Spearman's rank correlations adjusted for age or grey matter volume (GMV). Thirty-seven participants were studied (70.3% female, median age was 72 [IQR 68-78]). We found widespread negative associations between wake after sleep onset (WASO) and FDG level in cortical areas, primarily involving ventromedial/ventrolateral prefrontal cortex, lateral temporal and occipital cortices (ρ = -0.47 to -0.34, P < 0.05). Total sleep time was associated with FDG uptake in medial structures (ρ = 0.39 to 0.42, P < 0.05), including posterior cingulate, retrosplenial cortex, hippocampus, parahippocampus, thalamus and caudate. Hypoxic burden was associated with medial occipital FDG level (ρ = 0.35 to 0.39, P < 0.05). Regional GMV did not mediate these associations. WASO was associated with higher insomnia severity index and less restful sleep. Sleep macrostructure and hypoxic burden exhibited significant associations with brain metabolism, potentially contributing to brain health, cognitive function, and daytime symptoms.
Structural neuroplasticity supports learning, development, and shapes vulnerability to brain disorders, making it a central priority in neuroscience research. However, progress in humans has remained limited by the inability to probe cellular processes in vivo, leaving mechanistic insight largely dependent on animal models. To address this gap, here we combined the sub-voxel sensitivity of ultra-high-gradient diffusion MRI with the cell-compartment specificity of the Soma and Neurite Density Imaging (SANDI) model to probe structural plasticity directly in the living human brain. By tracking how learning modulates the temporal dynamics of cell bodies and cell processes, we aimed to distinguish plastic from nonplastic biological processes driving changes in microstructure. We found that learning a motor skill triggered two distinct temporal responses: a transient expansion of cell bodies across all brain regions engaged by the task, consistent with a short-lived homeostatic mechanism, and a sustained increase in cell-process density restricted to key motor regions, consistent with structural plasticity. Our approach provides a mechanistic window into human neuroplasticity and marks a significant step toward bridging the gap between animal and human neuroscience.
The mechanisms underlying racial/ethnic differences in dementia incidence and pathology are multifactorial, and hypertension represents an actionable target for reducing these differences. We aimed to estimate the extent to which controlling for hypertension mediates racial/ethnic inequities in neuroimaging markers of brain aging. The Health and Aging Brain Study-Health Disparities cohort is a highly phenotyped, racially and ethnically diverse cohort of cognitive aging. We used marginal structural models with inverse probability weights to estimate total and controlled direct effects of race/ethnicity, hypertension, and systolic blood pressure (SBP) at baseline, with neuroimaging markers measured on average 2 years later. Neuroimaging markers of brain aging were measured at the 2-year follow-up. Among Black and Hispanic participants with any neuroimaging data at the second visit (overall N = 1,347), 68% and 71% were women, 75% and 67% had hypertension, and the mean age was 61 and 63 years, respectively. Black and Hispanic participants had greater white matter hyperintensity volume (WMHV) compared with non-Hispanic White (NHW) participants (n = 1,333, β [95% CI]: Black 2.08 [1.68-2.59], Hispanic 0.99 [0.91-1.08]). After analytically setting hypertension status to absent, Black-NHW inequities in WMHV were attenuated (β [95% CI]: 1.3 [1.01-1.65]). Black participants had lower amyloid deposition compared with NHW participants (n = 679, β [95% CI]: -0.29 [-0.46 to -0.12]), but analytically controlling for hypertension did not appreciably change estimates. Compared with NHW participants, Hispanic participants had lower Alzheimer disease meta-region of interest cortical thickness (n = 1,005, β [95% CI]: -0.20 [-0.34 to -0.07]), but neither hypertension nor SBP significantly mediated this difference. Medial temporal lobe tau-PET standardized uptake value ratio did not significantly differ in Black or Hispanic participants compared with NHW participants (n = 408). Black-NHW inequities in subclinical cerebral small vessel disease may be mitigated by population-level efforts to reduce hypertension prevalence. Future studies should extend this work to examine clinical outcomes.
Linking genetic variation to human brain structure is a key step toward understanding the biological basis of cognition and disease. Progress in this area, however, has been limited by a major challenge: imaging features are often predefined, restricting the discovery of novel associations. Here, we present a framework that applies a Vision Transformer (ViT)-based autoencoder to derive 128-dimensional representations from T1-weighted brain MRI scans of 6,130 UK Biobank participants, which we call unsupervised learning derived image phenotypes from ViT (ViT-UDIP). These ViT-UDIP phenotypes are used in genome-wide association studies (GWAS) of 22,867 UK Biobank participants to identify significant genetic variants, which were further aggregated into genetic loci. The ViT-based approach uncovers a total of 63 loci and out of which 24 were not detected by the CNN-based method. Importantly, feature interpretation reveals that the model captured local as well as non-local anatomical patterns such as left-right hemisphere symmetry within brain MRI data by leveraging its attention mechanism and positional embeddings. This ability of capturing non-local patterns distinguishes the ViT from the previous CNN model. Together, these results demonstrate the value of transformer-based architectures in discovering novel and robust imaging phenotypes for genetic discovery.
Ambient air pollution increases Alzheimer's disease (AD) risk, yet exposure associations with cortical thickness (CTh) in AD-vulnerable brain regions is unclear. Here we examined the associations between PM2.5 and NO2 with CTh in an AD meta-region of interest (ROI), and across the cortex, in participants from the Vietnam Era Twin Study of Aging (VETSA) and Women's Health Initiative Memory Study (WHIMS). We conducted a cross-sectional study using data from 387 VETSA men (Mage=61.9±2.6) and 1097 WHIMS women (Mage=77.9±3.7) without dementia or stroke prior to MRI. Long-term residential exposures to PM2.5 and NO2 were quantified as the 3-year average of monthly estimates prior to MRI that were derived from spatiotemporal models with regionalized universal kriging. Brain MRI scans were processed using FreeSurfer-v.5.3.0 to estimate CTh in 34 bilateral regions parcellated with the Desikan-Killiany atlas. An AD meta-ROI was calculated as the surface-area weighted average of CTh in four bilateral regions (entorhinal, fusiform, inferior temporal, and middle temporal cortices) that are vulnerable to AD. Linear mixed models were conducted separately in each cohort with appropriate covariates. In WHIMS, exposures were negatively associated with CTh in the AD meta-ROI (pPM2.5<0.001; pNO2=0.018) and diffusely across the cortex. In VETSA, exposures were positively associated with CTh in the AD meta-ROI (pPM2.5=0.017; pNO2=0.021) and temporal pole, but effects were age-dependent, becoming negative (though nonsignificant) after age 64. Positive associations in younger VETSA men, coupled with negative associations in older WHIMS women, may suggest nonmonotonic AD-related neurodegeneration.
Brain development during adolescence and early adulthood coincides with shifts in emotion regulation and sleep. Despite this, few existing datasets simultaneously characterize affective dynamics, sleep variation, and multimodal measures of brain development. Here, we describe the study protocol and initial release (n = 10) of an open data resource of neuroimaging paired with densely sampled behavioral measures in adolescents and young adults. All participants complete multi-echo functional MRI, compressed-sensing diffusion MRI, and advanced arterial spin-labeled MRI. Behavioral measures include ecological momentary assessment, actigraphy, extensive cognitive assessments, and detailed clinical phenotyping focused on emotion regulation. Raw and processed data are openly available without a data use agreement and will be regularly updated as accrual continues. Together, this resource will accelerate research on the links between mood, sleep, and brain development.
Background/Objectives: Immediate patient access to radiology reports has increased the need for communication that patients can understand, yet it remains unclear whether simplifying report language improves comprehension without worsening psychological distress. This study aimed to determine whether AI-based simplification of a brain MRI report improves patient understanding, to assess whether anxiety differs between standard and AI-simplified reports, and to examine the relationships among anxiety, report understanding, and health literacy. Methods: We conducted a minimal-risk, survey-based randomized experimental study using Qualtrics and Amazon Mechanical Turk. A total of 803 participants were randomized 1:1 to view either an original radiology report (control, n = 402) or an AI-simplified version of the same report (intervention, n = 401). The stimulus was a single de-identified brain MRI/MRV report. Primary outcomes were report understanding and post-exposure anxiety, and secondary measures included radiology literacy and general health literacy assessed with the Short Test of Functional Health Literacy in Adults (S-TOFHLA). Between-group differences were analyzed using Mann-Whitney U tests, and associations between variables were examined using correlation analyses. Results: Participants who received the AI-simplified report achieved significantly higher understanding scores than those who viewed the original report (mean 5.78 ± 1.31 vs. 5.61 ± 1.49; p = 0.007). Anxiety scores were similar between groups (mean 3.24 ± 0.84 vs. 3.23 ± 0.85; p = 0.103). A positive correlation was observed between anxiety and general health literacy (r = 0.283, p < 0.001), and report understanding was also positively correlated with anxiety (r = 0.182, p < 0.001). Age was negatively associated with anxiety, whereas income showed a weak positive association. Conclusions: AI-based simplification improved patient understanding of radiology reports but did not reduce anxiety. Greater understanding was associated with higher anxiety, suggesting that clearer language alone may be insufficient to address the emotional burden of reading radiology results without clinical context or reassurance.
This paper synthesizes ten years of experience from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study's Data Analysis, Informatics, & Resource Center (DAIRC), focusing on strategies for the collection, management, and release of data for a large, multi-site, multi-modal longitudinal study. We highlight lessons learned in two major domains: First, we describe the development and ongoing refinement of internal processes, including the design of cognitively ergonomic electronic data capture instruments, improvements to data ingestion and quality control procedures, and the development of recruitment, retention, and protocol completeness metrics as well as related tools to support results-based accountability efforts. We further describe how structure and adaptability have enhanced the DAIRC's efficiency and resilience in the face of challenges such as the COVID-19 pandemic, participant mobility, and staff turnover at data collection sites. Second, we detail the development of standards for data curation and organization, as well as a sharing infrastructure that supports open science, transparency, and reproducibility, with the goal to lower barriers to scientific use of the ABCD data resource, facilitate collaboration, and promote best practices. We conclude by considering how these strategies can inform future multi-site longitudinal studies, providing a framework for scalable and sustainable data center operations.
Precise intraoperative localisation of subcortical brain structures remains a critical challenge in deep brain stimulation, yet openly available microelectrode recording datasets are scarce. We present a dataset of 6,646 processed MER recordings from 132 patients with neurological disorders, including Parkinson's disease, dystonia, Huntington's disease, epilepsy and others, acquired during DBS procedures. Signals were band-pass filtered and cleaned using an automated machine learning-based artifact rejection pipeline; annotation quality was confirmed by independent review. In addition an experienced electrophysiologist annotated representative examples of three basal ganglia structures encountered along the electrode trajectories: striatum/putamen, external globus pallidus (GPe), and internal globus pallidus (GPi). The dataset, released together with the full processing pipeline and metadata, is intended to support semi-supervised subcortical structure classification, pathological neuronal activity analysis, and the development of novel DBS targeting methods.
Brain tumour detection and analysis using medical imaging requires the extraction of both local spatial features and global contextual representations. Although convolutional neural networks (CNNs) excel at capturing local spatial patterns and Transformer-based architectures model long-range dependencies effectively, the optimal architectural paradigm for clinical deployment remains unresolved. This systematic review and meta-analysis evaluates hybrid CNN-Transformer architectures for brain tumour detection, focusing on the integration of local and global feature learning, diagnostic accuracy and computational efficiency. The roles of generative adversarial networks (GANs) for addressing data scarcity and multimodal imaging fusion for diagnostic completeness are also critically examined. A systematic search was conducted across IEEE Xplore, PubMed, Scopus and Google Scholar for studies published between January 2021 and May 2025. From 1876 initially identified articles, 94 met the prespecified inclusion criteria following quality assessment using the QUADAS-2 and ROBINS-I frameworks. A random-effects meta-analysis of diagnostic accuracy was performed using the DerSimonian-Laird estimator, with statistical heterogeneity quantified using I2 and publication bias assessed using funnel plot asymmetry and Egger's test. Computational efficiency was standardised to GigaFLOPs using a reference input of 240 × 240 × 155 voxels (BraTS benchmark), with FLOP estimates derived from primary publications where available and bounded by theoretical complexity formulas otherwise, with estimated values explicitly distinguished throughout. Across all 94 included studies, the pooled diagnostic accuracy was 93.5% (95% CI: 92.7%-94.4%); however, confirmed publication bias (Egger's p = 0.043) indicates this represents an upper-bound approximation rather than an unbiased population estimate. Because subgroup study counts were insufficient for formal random-effects pooling (CNN-only: n = 3; Transformer-only: n = 2; CNN-Transformer hybrid: n = 4; minimum recommended n = 10 per subgroup), no subgroup meta-analysis was performed. Instead, descriptive mean accuracies are reported as hypothesis-generating observations only: CNN-only models 91.7%, Transformer-only models 93.6% and CNN-Transformer hybrid models 94.6%. These figures must not be interpreted as pooled meta-analytic estimates; they reflect mean observed accuracy across a small number of included studies and are reported solely to illustrate directional trends consistent with the mechanistic rationale for hybridisation. Substantial heterogeneity was observed (I2 = 78.3%; p < 0.001). Three integration paradigms were identified: sequential (45% of models; 93.8% accuracy; 1.8 GFLOPs), parallel (32%; 94.3%; 2.8 GFLOPs) and hierarchical (23%; 94.9%; 3.5 GFLOPs). Parallel architectures demonstrated optimal clinical viability, balancing accuracy with a mean inference time of 2.1 s. GAN-based augmentation improved rare tumour class detection by 7%-10%, with conditional GANs outperforming vanilla architectures. Multimodal MRI + PET fusion achieved 94.2% accuracy at 2.8 GFLOPs, whereas triple-modality integration yielded marginal additional gains (95.1%) at substantially elevated computational cost (9.1 GFLOPs). Notably, 65% of included studies used the BraTS benchmark exclusively, and hybrid model accuracy declined from 94.6% on high-grade gliomas to 88.3% on low-grade gliomas, with hybrid architectures exhibiting 2.3× greater susceptibility to Gaussian noise than CNN-only equivalents, limitations that constrain generalisation to real-world clinical settings. Descriptive comparison of mean observed accuracies based on study counts is insufficient for confirmatory meta-analysis, suggesting hybrid CNN-Transformer architectures may offer diagnostic accuracy advantages over CNN- and Transformer-only approaches; this observation is hypothesis-generating only and requires validation in a larger, more balanced evidence base. Among integration strategies, parallel architectures demonstrated the most favourable accuracy efficiency balance in the reviewed evidence. GANs and multimodal imaging function as essential architectural enablers, addressing data scarcity and diagnostic incompleteness, respectively. Significant challenges remain in computational efficiency, noise robustness and generalisation to rare tumour subtypes, representing priority directions for future research.
Acute aerobic exercise may alter neural recruitment supporting executive function; however, age-related differences and task repetition effects remain unclear. To investigate the effects of acute aerobic exercise and task repetition on brain activity during task switching in young and older adults. Prospective randomized crossover study. Seventeen young and nineteen older healthy adults (18 females). 3.0 T, BOLD fMRI. fMRI was conducted during a task-switching paradigm before and at 15 and 45 min following 30 min of moderate-intensity walking or sitting on separate days. Neural and behavioral switch costs were computed as differences in BOLD activation, reaction time, and error rates between switch and nonswitch trials. Linear mixed-effects model examined BOLD signals and behavioral performance. Repeated measures correlation examined within-subject associations. p < 0.05 was considered statistically significant. Older adults exhibited significantly greater task-related BOLD activation in the bilateral superior frontal and middle temporal gyri, right angular gyrus, and left precuneus than young adults, despite slightly lower performance (p ≤ 0.002). Task repetition significantly reduced activation in the left middle occipital and middle frontal gyri and right cerebellar cortex in both groups, whereas reaction time significantly improved only in older adults. Following walking, activation in the right dorsolateral prefrontal cortex remained stable in older adults but significantly decreased in young adults, without corresponding behavioral changes (p ≥ 0.365). Regardless of age, activation in the right postcentral gyrus and bilateral superior parietal lobules remained stable after walking but significantly decreased after sitting. Reduced activation with task repetition was significantly associated with faster reaction time, particularly in older adults. Acute aerobic exercise may preserve regional brain activation without measurable behavioral benefits, whereas task repetition reduces brain activation across age groups, suggesting modest neural effects of acute aerobic exercise on executive function. 2. This study examined how a single bout of 30‐min moderate‐intensity walking affects brain activity during a cognitive task in young and older adults. Participants completed brain MRI scans while performing a task that required switching between rules, before and after walking or sitting. The results showed that walking helped maintain brain activity in regions involved in task switching, although task performance did not improve. Repeating the task reduced brain activity and improved performance, especially in older adults. These findings suggest that acute aerobic exercise may support brain function, highlighting the importance of considering both age and repetition effects in studies.
Socioeconomic factors top the list of influences on children's brain structure and function.
Brain metastases (BM) from renal cell carcinoma (RCC) are associated with poor prognosis and limited survival. Prognostic tools specific to patients with RCC undergoing surgical resection of BM are lacking, and current models do not incorporate advanced machine learning (ML) approaches. This study aimed to develop and validate an ML-based model to predict overall survival (OS) after BM resection in RCC. We retrospectively analyzed 253 patients with histologically confirmed RCC and radiographically or pathologically confirmed BM who underwent neurosurgical resection at a tertiary referral center (1993-2021). Clinical and radiologic features were used to train and internally validate multiple ML models for OS prediction. Model performance was assessed using the concordance index (C-index) and time-dependent Area Under the Curve (AUC) at 1, 2, and 5 years. Feature importance and interpretability were evaluated using SHapley Additive exPlanations (SHAP). The XGBoostCox + plsRcox model outperformed other algorithms, achieving a test C-Index of 0.59. AUCs at 1, 2, and 5 years were 0.61, 0.64, and 0.69 in the test cohort. SHAP analysis revealed extracranial disease status, number of BM, preoperative symptoms, and age at surgical resection as the most influential predictors. Kaplan-Meier analysis using an optimal cutoff based on the training cohort demonstrated significant survival differences between high- and low-risk groups in the test cohort (HR: 2.06 [1.26-3.35], P = .004). An explainable XGBoostCox + plsRcox model accurately predicts OS after BM resection in RCC and enables personalized risk assessment via an online calculator (https://hasanovlab-rcc-bm-resect.share.connect.posit.cloud/).