To investigate whether pulse pressure or mean arterial pressure mediates the relationship between age and white matter hyperintensity load and to examine the mediating effect of white matter hyperintensities on cognition. Demographic information, blood pressure, current medication lists, and Montreal Cognitive Assessment scores for 231 stroke- and dementia-free adults were retrospectively obtained from the Aging Brain Cohort study. Total WMH load was determined from T2-FLAIR magnetic resonance scans using the TrUE-Net deep learning tool for white matter segmentation. In separate models, we used mediation analysis to assess whether pulse pressure or MAP mediates the relationship between age and total white matter hyperintensity load, controlling for cardiovascular confounds. We also assessed whether white matter hyperintensity load mediated the relationship between age and cognitive scores. Pulse pressure, but not mean arterial pressure, significantly mediated the relationship between age and white matter hyperintensity load. White matter hyperintensity load partially mediated the relationship between age and Montreal Cognitive Assessment score. Our results indicate that pulse pressure, but not mean arterial pressure, is mechanistically associated with age-related accumulation of white matter hyperintensities, independent of other cardiovascular risk factors. White matter hyperintensity load was a mediator of cognitive scores across the adult lifespan. Effective management of pulse pressure may be especially important for maintenance of brain health and cognition.

'Brain age' is a biological clock typically used to describe brain health with one number, but its relationship with established gradients of cortical organization remains unclear. We address this gap by leveraging a data-driven, region-specific brain age approach in 335 neurologically intact adults, using a convolutional neural network (volBrain) to estimate regional brain ages directly from structural MRI without a predefined set of morphometric properties. Six distinct gradients of brain aging are replicated in two independent cohorts. Spatial patterns of accelerated brain aging in older adults quantitatively align with the archetypal sensorimotor-to-association axis of cortical organization. Other brain aging gradients reflect neurobiological hierarchies such as gene expression and externopyramidization. Participant-level correspondences to brain age gradients are associated with cognitive and sensorimotor performance and explained behavioral variance more effectively than global brain age. These results suggest that regional brain age patterns reflect fundamental principles of cortical organization and behavior.

The cerebrospinal fluid (CSF) is known to serve as a unique environment for neurodevelopment, with specific proteins secreted by epithelial cells of the choroid plexus (CP) playing crucial roles in cortical development and cell differentiation. Sex-related differences in the brain in early life have been widely identified, but few studies have investigated the neonatal CSF system and associated transcriptional signatures. This study included 75 full-term neonates [44 males and 31 females; gestational age (GA) = 37-42 weeks] without significant MRI abnormalities from the dHCP (developing Human Connectome Project) database. Deep-learning automated segmentation was used to measure various metrics of the brain ventricular system and CSF. Sex-related differences and relationships with postnatal age were analyzed by linear regression. Correlations between the CP and CSF space metrics were also examined. LASSO regression was further applied to identify the key genes contributing to the sex-related CSF system differences by using regional gene expression data from the Allen Human Brain Atlas. Right lateral ventricles [2.42 ± 0.98 vs. 2.04 ± 0.45 cm3 (mean ± standard deviation), p = 0.036] and right CP (0.16 ± 0.07 vs. 0.13 ± 0.04 cm3, p = 0.024) were larger in males, with a stronger volume correlation (male/female correlation coefficients r: 0.798 vs. 0.649, p < 1 × 10^- 4). No difference was found in total CSF volume, while peripheral CSF (male/female β: 1.218 vs. 1.064) and CP (male/female β: 0.008 vs. 0.005) exhibited relatively faster growth in males. Additionally, the volumes of the lateral ventricular system, third ventricle, peripheral CSF, and total CSF were significantly correlated with their corresponding CP volume (r: 0.362 to 0.799, p < 0.05). DERL2 (Degradation in Endoplasmic Reticulum Protein 2) (r = 0.1319) and MRPL48 (Mitochondrial Large Ribosomal Subunit Protein) (r=-0.0370) were identified as potential key genes associated with sex-related differences in CSF system. Male neonates present larger volumes and faster growth of the right lateral ventricle, likely linked to corresponding CP volume and growth pattern. The downregulation of DERL2 and upregulation of MRPL48 may contribute to these sex-related variations in the CSF system, suggesting a molecular basis for sex-specific brain development.

Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, explainable, and replicable method of segmentation of the tumour area and related tissues is therefore an open research challenge. In this paper, we first propose a novel symbolic approach to brain segmentation and delineation of brain lesions based on spatial model checking. This method has its foundations in the theory of closure spaces, a generalisation of topological spaces, and spatial logics. At its core is a high-level declarative logic language for image analysis, ImgQL, and an efficient spatial model checker, VoxLogicA, exploiting state-of-the-art image analysis libraries in its model checking algorithm. We then illustrate how this technique can be combined with Machine Learning techniques leading to a hybrid AI approach that provides accurate and explainable segmentation results. We show the results of the application of the symbolic approach on several public datasets with 3D magnetic resonance (MR) images. Three datasets are provided by the 2017, 2019 and 2020 international MICCAI BraTS Challenges with 210, 259 and 293 MR images, respectively, and the fourth is the BrainWeb dataset with 20 (synthetic) 3D patient images of the normal brain. We then apply the hybrid AI method to the BraTS 2020 training set. Our segmentation results are shown to be in line with the state-of-the-art with respect to other recent approaches, both from the accuracy point of view as well as from the view of computational efficiency, but with the advantage of them being explainable.

High-resolution magnetic resonance imaging (MRI) is essential for diagnosing and treating brain diseases. Transformer-based approaches demonstrate strong potential in MRI super-resolution by capturing long-range dependencies effectively. However, existing Transformer-based super-resolution methods face several challenges: (1) they primarily focus on low-frequency information, neglecting the utilization of high-frequency information; (2) they lack effective mechanisms to integrate both low-frequency and high-frequency information; (3) they struggle to effectively eliminate redundant information during the reconstruction process. To address these issues, we propose the Cross-fusion Adaptive Feature Enhancement Transformer (CAFET). Our model maximizes the potential of both CNNs and Transformers. It consists of four key blocks: a high-frequency enhancement block for extracting high-frequency information; a hybrid attention block for capturing global information and local fitting, which includes channel attention and shifted rectangular window attention; a large-window fusion attention block for integrating local high-frequency features and global low-frequency features; and an adaptive sparse overlapping attention block for dynamically retaining key information and enhancing the aggregation of cross-window features. Extensive experiments validate the effectiveness of the proposed method. On the BraTS and IXI datasets, with an upsampling factor of ×2, the proposed method achieves a maximum PSNR improvement of 2.4 dB and 1.3 dB compared to state-of-the-art methods, along with an SSIM improvement of up to 0.16% and 1.42%. Similarly, at an upsampling factor of ×4, the proposed method achieves a maximum PSNR improvement of 1.04 dB and 0.3 dB over the current leading methods, along with an SSIM improvement of up to 0.25% and 1.66%. Our method is capable of reconstructing high-quality super-resolution brain MRI images, demonstrating significant clinical potential.

To introduce MATI (Microstructural Analysis Toolbox for Imaging), a versatile MATLAB-based toolbox that combines both simulation and data fitting capabilities for microstructural dMRI research. MATI provides a user-friendly, graphical user interface that enables researchers, including those without much programming experience, to perform advanced simulations and data analyses for microstructural MRI research. For simulation, MATI supports arbitrary microstructural tissues and pulse sequences. For data fitting, MATI supports a range of fitting methods, including traditional non-linear least squares, Bayesian approaches, machine learning, and dictionary matching methods, allowing users to tailor analyses based on specific research needs. Optimized with vectorized matrix operations and high-performance numerical libraries, MATI achieves high computational efficiency, enabling rapid simulations and data fitting on CPU and GPU hardware. While designed for microstructural dMRI, MATI's generalized framework can be extended to other imaging methods, making it a flexible and scalable tool for quantitative MRI research. MATI offers a significant step toward translating advanced microstructural MRI techniques into clinical applications.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the Relational Bi-level Aggregation Graph Convolutional Network with Dynamic Graph Learning and Puzzle Optimization for Alzheimer's Classification (RBAGCN-DGL-PO-AC), using denoised T1-weighted Magnetic Resonance Images (MRIs) collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. Addressing the impact of noise in medical imaging, the method employs advanced denoising techniques includes: the Modified Spline-Kernelled Chirplet Transform (MSKCT), Jump Gain Integral Recurrent Neural Network (JGIRNN), and Newton Time Extracting Wavelet Transform (NTEWT), to enhance the image quality. Key brain regions, crucial for classification such as hippocampal, lateral ventricle and posterior cingulate cortex are segmented using Attention Guided Generalized Intuitionistic Fuzzy C-Means Clustering (AG-GIFCMC). Feature extraction and classification using segmented outputs are performed with RBAGCN-DGL and puzzle optimization, categorize input images into Healthy Controls (HC), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). To assess the effectiveness of the proposed method, we systematically examined the structural modifications to the RBAGCN-DGL-PO-AC model through extensive ablation studies. Experimental findings highlight that RBAGCN-DGL-PO-AC state-of-the art performance, with 99.25 % accuracy, outperforming existing methods including MSFFGCN_ADC, CNN_CAD_DBMRI, and FCNN_ADC, while reducing training time by 28.5 % and increasing inference speed by 32.7 %. Hence, the RBAGCN-DGL-PO-AC method enhances AD classification by integrating denoising, segmentation, and dynamic graph-based feature extraction, achieving superior accuracy and making it a valuable tool for clinical applications, ultimately improving patient outcomes and disease management.

Different types of sports training can induce distinct changes in brain activity and function; however, it remains unclear if there are commonalities across various sports disciplines. Moreover, the relationship between these brain activity alterations and the duration of sports training requires further investigation. This study employed resting-state functional magnetic resonance imaging (rs-fMRI) techniques to analyze spontaneous brain activity using the amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) in 86 highly trained athletes compared to 74 age- and gender-matched non-athletes. Our findings revealed significantly higher ALFF values in the Insula_R (Right Insula), OFCpost_R (Right Posterior orbital gyrus), and OFClat_R (Right Lateral orbital gyrus) in athletes compared to controls, whereas fALFF in the Postcentral_R (Right Postcentral) was notably higher in controls. Additionally, we identified a significant negative correlation between fALFF values in the Postcentral_R of athletes and their years of professional training. Utilizing machine learning algorithms, we achieved accurate classification of brain activity patterns distinguishing athletes from non-athletes with over 96.97% accuracy. These results suggest that the functional reorganization observed in athletes' brains may signify an adaptation to prolonged training, potentially reflecting enhanced processing efficiency. This study emphasizes the importance of examining the impact of long-term sports training on brain function, which could influence cognitive and sensory systems crucial for optimal athletic performance. Furthermore, machine learning methods could be used in the future to select athletes based on differences in brain activity.

Quantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF) conventionally obtained from arterial blood samples. The AIF can also be non-invasively estimated from blood pools in PET images, often identified using co-registered MRI images. Deploying methods without blood sampling or the use of MRI generally requires total body PET systems with a long axial field-of-view (LAFOV) that includes a large cardiovascular blood pool. However, the number of such systems in clinical use is currently much smaller than that of short axial field-of-view (SAFOV) scanners. We propose a data-driven approach for AIF estimation for SAFOV PET scanners, which is non-invasive and does not require MRI or blood sampling using brain PET scans. The proposed method was validated using dynamic ¹⁸F-fluorodeoxyglucose [¹⁸F]FDG total body PET data from 10 subjects. A variational inference-based machine learning approach was employed to correct for peak activity. The prior was estimated using a probabilistic vascular MRI atlas, registered to each subject's PET image to identify cerebral arteries in the brain. The estimated AIF using brain PET images (IDIF-Brain) was compared to that obtained using data from the descending aorta of the heart (IDIF-DA). Kinetic rate constants (K₁, k₂, k₃) and net radiotracer influx (K_i) for both cases were computed and compared. Qualitatively, the shape of IDIF-Brain matched that of IDIF-DA, capturing information on both the peak and tail of the AIF. The area under the curve (AUC) of IDIF-Brain and IDIF-DA were similar, with an average relative error of 9%. The mean Pearson correlations between kinetic parameters (K₁, k₂, k₃) estimated with IDIF-DA and IDIF-Brain for each voxel were between 0.92 and 0.99 in all subjects, and for K_i, it was above 0.97. This study introduces a new approach for AIF estimation in dynamic PET using brain PET images, a probabilistic vascular atlas, and machine learning techniques. The findings demonstrate the feasibility of non-invasive and subject-specific AIF estimation for SAFOV scanners.

Background and purposeMRI is critical for the detection of subtle cortical pathology in epilepsy surgery assessment. This can be aided by improved MRI quality and resolution using ultra-high field (7T). But poor access and long scan durations limit widespread use, particularly in a paediatric setting. AI-based learning approaches may provide similar information by enhancing data obtained with conventional MRI (3T). We used a convolutional neural network trained on matched 3T and 7T images to enhance quantitative R1-maps (longitudinal relaxation rate) obtained at 3T in paediatric epilepsy patients and to determine their potential clinical value for lesion identification. Materials and MethodsA 3D U-Net was trained using paired patches from 3T and 7T R1-maps from n=10 healthy volunteers. The trained network was applied to enhance paediatric focal epilepsy 3T R1 images from a different scanner/site (n=17 MRI lesion positive / n=14 MR-negative). Radiological review assessed image quality, as well as lesion identification and visualization of enhanced maps in comparison to the 3T R1-maps without clinical information. Lesion appearance was then compared to 3D-FLAIR. ResultsAI enhanced R1 maps were superior in terms of image quality in comparison to the original 3T R1 maps, while preserving and enhancing the visibility of lesions. After exclusion of 5/31 patients (due to movement artefact or incomplete data), lesions were detected in AI Enhanced R1 maps for 14/15 (93%) MR-positive and 4/11 (36%) MR-negative patients. ConclusionAI enhanced R1 maps improved the visibility of lesions in MR positive patients, as well as providing higher sensitivity in the MR-negative group compared to either the original 3T R1-maps or 3D-FLAIR. This provides promising initial evidence that 3T quantitative maps can outperform conventional 3T imaging via enhancement by an AI model trained on 7T MRI data, without the need for pathology-specific information.