Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images. The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation. The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, Dense- Net, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively. The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions. The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.

In functional MRI, a hemodynamic response function (HRF) describes how neural events are translated into a blood oxygenation response detected through imaging. The HRF has the potential to quantify neurovascular mechanisms by which cardiovascular risks modify brain health, but relationships among HRF characteristics, brain health, and cardiovascular modifiers of brain health have not been well studied to date. One hundred and thirty-seven middle-aged participants (mean age: 53.6±4.7, female (62%), 78% White American participants and 22% African American participants) in the exploratory analysis from Bogalusa Heart Study completed clinical evaluations from childhood to midlife and an adaptive Stroop task during fMRI in midlife. The HRFs of each participant within seventeen brain regions of interest (ROIs) previously identified as activated by this task were calculated using a convolutional neural network approach. Faster and more efficient neurovascular functioning was characterized in terms of five HRF characteristics: faster time to peak (TTP), shorter full width at half maximum (FWHM), smaller peak magnitude (PM), smaller trough magnitude (TM), and smaller area under the HRF curve (AUHRF). The composite HRF summary characteristics over all ROIs were calculated for multivariable and simple linear regression analyses. In multivariable models, faster and more efficient HRF characteristic was found in non-smoker compared to smokers (AUHRF, p = 0.029). Faster and more efficient HRF characteristics were associated with lower systolic and diastolic blood pressures (FWHM, TM, and AUHRF, p = 0.030, 0.042, and 0.032) and cerebral amyloid burden (FWHM, p-value = 0.027) in midlife; as well as greater response rate on the Stroop task (FWHM, p = 0.042) in midlife. In simple linear regression models, faster and more efficient HRF characteristics were found in women compared to men (TM, p = 0.019); in White American participants compared to African American participants (AUHRF, p = 0.044); and in non-smokers compared to smokers (TTP and AUHRF, p = 0.019 and 0.010). Faster and more efficient HRF characteristics were associated with lower systolic and diastolic blood pressures (FWHM and TM, p = 0.019 and 0.029), and lower BMI (FWHM and AUHRF, p = 0.025 and 0.017), in childhood and adolescence; and lower BMI (TTP, p = 0.049), cerebral amyloid burden (FWHM, p = 0.002), and white matter hyperintensity burden (FWHM, p = 0.046) in midlife; as well as greater accuracy on the Stroop task (AUHRF, p = 0.037) in midlife. In a diverse middle-aged community sample, HRF-based indicators of faster and more efficient neurovascular functioning were associated with better brain health and cognitive function, as well as better lifespan cardiovascular health.

Deformable image registration is a fundamental task in medical image analysis, which is crucial in enabling early detection and accurate disease diagnosis. Although transformer-based architectures have demonstrated strong potential through attention mechanisms, challenges remain in ineffective feature extraction and spatial alignment, particularly within hierarchical attention frameworks. To address these limitations, we propose a novel registration framework that integrates hierarchical feature encoding in the encoder and an adaptive cascaded refinement strategy in the decoder. The model employs hierarchical cross-attention between fixed and moving images at multiple scales, enabling more precise alignment and improved registration accuracy. The decoder incorporates the Adaptive Cascaded Module (ACM), facilitating progressive deformation field refinement across multiple resolution levels. This approach captures coarse global transformations and acceptable local variations, resulting in smooth and anatomically consistent alignment. However, rather than relying solely on the final decoder output, our framework leverages intermediate representations at each stage of the network, enhancing the robustness and precision of the registration process. Our method achieves superior accuracy and adaptability by integrating deformations across all scales. Comprehensive experiments on two widely used 3D brain MRI datasets, OASIS and LPBA40, demonstrate that the proposed framework consistently outperforms existing state-of-the-art approaches across multiple evaluation metrics regarding accuracy, robustness, and generalizability.

$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainAGE models requires large datasets, often restricted by privacy concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes. $\textbf{Methods:}$ We used FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome. $\textbf{Results:}$ While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery. $\textbf{Conclusion:}$ FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for prognostic modeling in stroke care.

Objective: Our study aimed to evaluate and validate PanSegNet, a deep learning (DL) algorithm for pediatric pancreas segmentation on MRI in children with acute pancreatitis (AP), chronic pancreatitis (CP), and healthy controls. Methods: With IRB approval, we retrospectively collected 84 MRI scans (1.5T/3T Siemens Aera/Verio) from children aged 2-19 years at Gazi University (2015-2024). The dataset includes healthy children as well as patients diagnosed with AP or CP based on clinical criteria. Pediatric and general radiologists manually segmented the pancreas, then confirmed by a senior pediatric radiologist. PanSegNet-generated segmentations were assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff distance (HD95). Cohen's kappa measured observer agreement. Results: Pancreas MRI T2W scans were obtained from 42 children with AP/CP (mean age: 11.73 +/- 3.9 years) and 42 healthy children (mean age: 11.19 +/- 4.88 years). PanSegNet achieved DSC scores of 88% (controls), 81% (AP), and 80% (CP), with HD95 values of 3.98 mm (controls), 9.85 mm (AP), and 15.67 mm (CP). Inter-observer kappa was 0.86 (controls), 0.82 (pancreatitis), and intra-observer agreement reached 0.88 and 0.81. Strong agreement was observed between automated and manual volumes (R^2 = 0.85 in controls, 0.77 in diseased), demonstrating clinical reliability. Conclusion: PanSegNet represents the first validated deep learning solution for pancreatic MRI segmentation, achieving expert-level performance across healthy and diseased states. This tool, algorithm, along with our annotated dataset, are freely available on GitHub and OSF, advancing accessible, radiation-free pediatric pancreatic imaging and fostering collaborative research in this underserved domain.

Diffusion magnetic resonance imaging (dMRI) enables non-invasive investigation of tissue microstructure. The Standard Model (SM) of white matter aims to disentangle dMRI signal contributions from intra- and extra-axonal water compartments. However, due to the model its high-dimensional nature, extensive acquisition protocols with multiple b-values and diffusion tensor shapes are typically required to mitigate parameter degeneracies. Even then, accurate estimation remains challenging due to noise. This work introduces a novel estimation framework based on implicit neural representations (INRs), which incorporate spatial regularization through the sinusoidal encoding of the input coordinates. The INR method is evaluated on both synthetic and in vivo datasets and compared to parameter estimates using cubic polynomials, supervised neural networks, and nonlinear least squares. Results demonstrate superior accuracy of the INR method in estimating SM parameters, particularly in low signal-to-noise conditions. Additionally, spatial upsampling of the INR can represent the underlying dataset anatomically plausibly in a continuous way, which is unattainable with linear or cubic interpolation. The INR is fully unsupervised, eliminating the need for labeled training data. It achieves fast inference ($\sim$6 minutes), is robust to both Gaussian and Rician noise, supports joint estimation of SM kernel parameters and the fiber orientation distribution function with spherical harmonics orders up to at least 8 and non-negativity constraints, and accommodates spatially varying acquisition protocols caused by magnetic gradient non-uniformities. The combination of these properties along with the possibility to easily adapt the framework to other dMRI models, positions INRs as a potentially important tool for analyzing and interpreting diffusion MRI data.

Multi contrast MRI synthesis is inherently challenging due to the complex and nonlinear relationships among different contrasts. Each MRI contrast highlights unique tissue properties, but their complementary information is difficult to exploit due to variations in intensity distributions and contrast specific textures. Existing methods for multi contrast MRI synthesis primarily utilize spatial domain features, which capture localized anatomical structures but struggle to model global intensity variations and distributed patterns. Conversely, frequency domain features provide structured inter contrast correlations but lack spatial precision, limiting their ability to retain finer details. To address this, we propose a dual domain learning framework that integrates spatial and frequency domain information across multiple MRI contrasts for enhanced synthesis. Our method employs two mutually trained denoising networks, one conditioned on spatial domain and the other on frequency domain contrast features through a shared critic network. Additionally, an uncertainty driven mask loss directs the models focus toward more critical regions, further improving synthesis accuracy. Extensive experiments show that our method outperforms SOTA baselines, and the downstream segmentation performance highlights the diagnostic value of the synthetic results.

BackgroundCognitive dysfunction often co-occurs with psychopathology. Advances in neuroimaging and machine learning have led to neural indicators that predict individual differences in cognition with reasonable performance. We examined whether these neural indicators explain the relationship between cognition and mental health in the UK Biobank cohort (n > 14000). MethodsUsing machine learning, we quantified the covariation between general cognition and 133 mental health indices and derived neural indicators of cognition from 72 neuroimaging phenotypes across diffusion-weighted MRI (dwMRI), resting-state functional MRI (rsMRI), and structural MRI (sMRI). With commonality analyses, we investigated how much of the cognition-mental health covariation is captured by each neural indicator and neural indicators combined within and across MRI modalities. ResultsThe predictive association between mental health and cognition was at out-of-sample r = 0.3. Neuroimaging phenotypes captured 2.1% to 25.8% of the cognition-mental health covariation. The highest proportion of variance explained by dwMRI was attributed to the number of streamlines connecting cortical regions (19.3%), by rsMRI through functional connectivity between 55 large-scale networks (25.8%), and by sMRI via the volumetric characteristics of subcortical structures (21.8%). Combining neuroimaging phenotypes within modalities improved the explanation to 25.5% for dwMRI, 29.8% for rsMRI, and 31.6% for sMRI, and combining them across all MRI modalities enhanced the explanation to 48%. ConclusionsWe present an integrated approach to derive multimodal MRI markers of cognition that can be transdiagnostically linked to psychopathology. This demonstrates that the predictive ability of neural indicators extends beyond the prediction of cognition itself, enabling us to capture the cognition-mental health covariation.

This study compares the classification accuracy of novel transformer-based deep learning models (ViT and BEiT) on brain MRIs of gliomas and meningiomas through a feature-driven approach. Meta's Segment Anything Model was used for semi-automatic segmentation, therefore proposing a total neural network-based workflow for this classification task. ViT and BEiT models were finetuned to a publicly available brain MRI dataset. Gliomas/meningiomas cases (625/507) were used for training and 520 cases (260/260; gliomas/meningiomas) for testing. The extracted deep radiomic features from ViT and BEiT underwent normalization, dimensionality reduction based on the Pearson correlation coefficient (PCC), and feature selection using analysis of variance (ANOVA). A multi-layer perceptron (MLP) with 1 hidden layer, 100 units, rectified linear unit activation, and Adam optimizer was utilized. Hyperparameter tuning was performed via 5-fold cross-validation. The ViT model achieved the highest AUC on the validation dataset using 7 features, yielding an AUC of 0.985 and accuracy of 0.952. On the independent testing dataset, the model exhibited an AUC of 0.962 and an accuracy of 0.904. The BEiT model yielded an AUC of 0.939 and an accuracy of 0.871 on the testing dataset. This study demonstrates the effectiveness of transformer-based models, especially ViT, for glioma and meningioma classification, achieving high AUC scores and accuracy. However, the study is limited by the use of a single dataset, which may affect generalizability. Future work should focus on expanding datasets and further optimizing models to improve performance and applicability across different institutions. This study introduces a feature-driven methodology for glioma and meningioma classification, showcasing advancements in the accuracy and model robustness of transformer-based models.

New imaging techniques appearing over the last few decades have replaced procedures that were uncomfortable, of low specificity, and prone to adverse events. While computed tomography remains useful for imaging patients with seizures in acute settings, structural magnetic resonance imaging (MRI) has become the most important imaging modality for epilepsy evaluation, with adjunctive functional imaging also increasingly well established in presurgical evaluation, including positron emission tomography (PET), single photon ictal-interictal subtraction computed tomography co-registered to MRI and functional MRI for preoperative cognitive mapping. Neuroimaging in inherited metabolic epilepsies is integral to diagnosis, monitoring, and assessment of treatment response. Neurotransmitter receptor PET and magnetic resonance spectroscopy can help delineate the pathophysiology of these disorders. Machine learning and artificial intelligence analyses based on large MRI datasets composed of healthy volunteers and people with epilepsy have been initiated to detect lesions that are not found visually, particularly focal cortical dysplasia. These methods, not yet approved for patient care, depend on careful clinical correlation and training sets that fully sample broad populations.