Pragmatics plays a crucial role in effectively conveying messages across various social communication contexts. This aspect is frequently highlighted in the challenges experienced by children diagnosed with autism spectrum disorder (ASD). Notably, there remains a paucity of research investigating how the structural connectome (SC) predicts pragmatic language abilities within this population. Using diffusion tensor imaging (DTI) and deterministic tractography, we constructed the whole-brain white matter structural network (WMSN) in a cohort comprising 92 children with ASD and 52 typically developing (TD) preschoolers, matched for age and gender. We employed network-based statistic (NBS)-Predict, a novel methodology that integrates machine learning (ML) with NBS, to identify dysconnected subnetworks associated with ASD, and then to predict pragmatic language abilities based on the SC derived from the whole-brain WMSN in the ASD group. Initially, NBS-Predict identified a subnetwork characterized by 42 reduced connections across 37 brain regions (p = 0.01), achieving a highest classification accuracy of 79.4% (95% CI: 0.791 ~ 0.796). The dysconnected regions were predominantly localized within the brain's frontotemporal and subcortical areas, with the right superior medial frontal gyrus (SFGmed.R) emerging as the region exhibiting the most extensive disconnection. Moreover, NBS-Predict demonstrated that the optimal correlation coefficient between the predicted pragmatic language scores and the actual measured scores was 0.220 (95% CI: 0.174 ~ 0.265). This analysis revealed a significant association between the pragmatic language abilities of the ASD cohort and the white matter connections linking the SFGmed.R with the bilateral anterior cingulate gyrus (ACG). In summary, our findings suggest that the subnetworks displaying the most significant abnormal connections were concentrated in the frontotemporal and subcortical regions among the ASD group. Furthermore, the observed abnormalities in the white matter connection pathways between the SFGmed.R and ACG may underlie the neurobiological basis for pragmatic language deficits in preschool children with ASD.

Using radio signals from a magnetic field, magnetic resonance imaging (MRI) represents a medical procedure that produces images to provide more information than typical scans. Diagnosing brain tumors from MRI is difficult because of the wide range of tumor shapes, areas, and visual features, thus universal and automated system to handle this task is required. Among the best deep learning methods, the U-Net architecture is the most widely used in diagnostic medical images. Therefore, U-Net-based attention is the most effective automated model in medical image segmentation dealing with various modalities. The self-attention structures that are used in the U-Net design allow for fast global preparation and better feature visualization. This research aims to study the progress of U-Net design and show how it improves the performance of brain tumor segmentation. We have investigated three U-Net designs (standard U-Net, Attention U-Net, and self-attention U-Net) for five epochs to find the last segmentation. An MRI image dataset that includes 3064 images from the Kaggle website is used to give a more comprehensive overview. Also, we offer a comparison with several studies that are based on U-Net structures to illustrate the evolution of this network from an accuracy standpoint. U-Net-based self-attention has demonstrated superior performance compared to other studies because self-attention can enhance segmentation quality, particularly for unclear structures, by concentrating on the most significant parts. Four main metrics are applied with a loss function of 5.03 %, a validation loss function of 4.82 %, a validation accuracy of 98.49 %, and an accuracy of 98.45 %.

This study aimed to develop a novel deep learning model, U₂-Attention-Net (U₂A-Net), for precise segmentation of parotid glands on radiotherapy localization CT images. CT images from 79 patients with head and neck cancer were selected, on which the label maps were delineated by relevant practitioners to construct a dataset. The dataset was divided into the training set (n = 60), validation set (n = 6), and test set (n = 13), with the training set augmented. U₂A-Net, divided into U₂A-Net V₁ (sSE) and U₂A-Net V₂ (cSE) based on different attention mechanisms, was evaluated for parotid gland segmentation based on the DL loss function with U-Net, Attention U-Net, DeepLabV3+, and TransUNet as comparision models. Segmentation was also performed using GDL and GD-BCEL loss functions. Model performance was evaluated using DSC, JSC, PPV, SE, HD, RVD, and VOE metrics. The quantitative results revealed that U₂A-Net based on DL outperformed the comparative models. While U₂A-Net V₁ had the highest PPV, U₂A-Net V₂ demonstrated the best quantitative results in other metrics. Qualitative results showed that U₂A-Net's segmentation closely matched expert delineations, reducing oversegmentation and undersegmentation, with U₂A-Net V₂ being more effective. In comparing loss functions, U₂A-Net V₁ using GD-BCEL and U₂A-Net V₂ using DL performed best. The U₂A-Net model significantly improved parotid gland segmentation on radiotherapy localization CT images. The cSE attention mechanism showed advantages with DL, while sSE performed better with GD-BCEL.

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This review comprehensively explores machine learning (ML) and deep learning (DL) models that enhance the accuracy and efficiency of brain tumor classification using medical imaging data, particularly Magnetic Resonance Imaging (MRI). As a noninvasive imaging technique, MRI plays a central role in detecting, segmenting, and characterizing brain tumors by providing detailed anatomical views that help distinguish various tumor types, including gliomas, meningiomas, and metastatic brain lesions. The review presents a detailed analysis of diverse ML approaches, from classical algorithms such as Support Vector Machines (SVM) and Decision Trees to advanced DL models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid architectures that combine multiple techniques for improved performance. Through comparative analysis of recent studies across various datasets, the review evaluates these methods using metrics such as accuracy, sensitivity, specificity, and AUC-ROC, offering insights into their effectiveness and limitations. Significant challenges in the field are examined, including the scarcity of annotated datasets, computational complexity requirements, model interpretability issues, and barriers to clinical integration. The review proposes future directions to address these challenges, highlighting the potential of multi-modal imaging that combines MRI with other imaging modalities, explainable AI frameworks for enhanced model transparency, and privacy-preserving techniques for securing sensitive patient data. This comprehensive analysis demonstrates the transformative potential of ML and DL in advancing brain tumor diagnosis while emphasizing the necessity for continued research and innovation to overcome current limitations and ensure successful clinical implementation for improved patient care.

Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique providing fast and reliable tissue property measurements. The aim of this study was to develop an MRF-based deep-learning (DL) framework for whole-brain FCD detection. We included patients with pharmacoresistant focal epilepsy and pathologically/radiologically diagnosed FCD, as well as age-matched and sex-matched healthy controls (HCs). All participants underwent 3D whole-brain MRF and clinical MRI scans. T1, T2, gray matter (GM), and white matter (WM) tissue fraction maps were reconstructed from a dictionary-matching algorithm based on the MRF acquisition. A 3D ROI was manually created for each lesion. All MRF maps and lesion labels were registered to the Montreal Neurological Institute space. Mean and SD T1 and T2 maps were calculated voxel-wise across using HC data. T1 and T2 <i>z</i>-score maps for each patient were generated by subtracting the mean HC map and dividing by the SD HC map. MRF-based morphometric maps were produced in the same manner as in the morphometric analysis program (MAP), based on MRF GM and WM maps. A no-new U-Net model was trained using various input combinations, with performance evaluated through leave-one-patient-out cross-validation. We compared model performance using various input combinations from clinical MRI and MRF to assess the impact of different input types on model effectiveness. We included 40 patients with FCD (mean age 28.1 years, 47.5% female; 11 with FCD IIa, 14 with IIb, 12 with mMCD, 3 with MOGHE) and 67 HCs. The DL model with optimal performance used all MRF-based inputs, including MRF-synthesized T1w, T1z, and T2z maps; tissue fraction maps; and morphometric maps. The patient-level sensitivity was 80% with an average of 1.7 false positives (FPs) per patient. Sensitivity was consistent across subtypes, lobar locations, and lesional/nonlesional clinical MRI. Models using clinical images showed lower sensitivity and higher FPs. The MRF-DL model also outperformed the established MAP18 pipeline in sensitivity, FPs, and lesion label overlap. The MRF-DL framework demonstrated efficacy for whole-brain FCD detection. Multiparametric MRF features from a single scan offer promising inputs for developing a deep-learning tool capable of detecting subtle epileptic lesions.

The four-repeat (4R) tauopathies are a group of neurodegenerative diseases, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and globular glial tauopathy (GGT). This study aimed to characterize spatiotemporal atrophy progression using structural magnetic resonance imaging (MRI) and to examine its relationship with clinical course and neuropathology in a cohort of autopsy-confirmed 4R tauopathies. The study included 85 autopsied patients (54 with PSP, 28 with CBD, and 3 with GGT) who underwent multiple 3T MRI scans, as well as neuropsychological, neurological, and speech/language examinations, and standardized postmortem neuropathological evaluations. An unsupervised machine-learning algorithm, Subtype and Stage Inference (SuStaIn), was applied to the cross-sectional brain volumes to estimate spatiotemporal atrophy patterns and data-driven subtypes and stages in each patient. The relationships among estimated subtypes, pathological diagnoses, and longitudinal changes in clinical testing were examined. The SuStaIn algorithm identified 2 distinct subtypes: (1) the subcortical subtype, in which atrophy progresses from the midbrain to the cortex, and (2) the cortical subtype, in which atrophy progresses from the frontal cortex to the subcortical regions. The subcortical subtype was more associated with typical PSP, whereas the cortical subtype was more associated with atypical PSP with a cortical distribution of pathology and CBD (p < 0.001). The cortical subtype had a faster rate of change on the PSP Rating Scale than the subcortical subtype (p < 0.05). SuStaIn analysis revealed 2 MRI-driven subtypes with distinct spatiotemporal atrophy patterns, clinical courses, and neuropathology. Our findings contribute to a comprehensive and improved understanding of disease progression and its relationship to tau pathology in 4R tauopathies. ANN NEUROL 2025.

Shortened Abstract Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT suffers from increased noise, limited soft-tissue contrast, and artifacts, resulting in unreliable Hounsfield unit values and hindering direct dose calculation. Synthetic CT (sCT) generation from CBCT addresses these issues, especially using deep learning (DL) methods. Existing approaches are limited by institutional heterogeneity, scanner-dependent variations, and data privacy regulations that prevent multi-center data sharing. To overcome these challenges, we propose a cross-silo horizontal federated learning (FL) approach for CBCT-to-sCT synthesis in the head and neck region, extending our FedSynthCT framework. A conditional generative adversarial network was collaboratively trained on data from three European medical centers in the public SynthRAD2025 challenge dataset. The federated model demonstrated effective generalization across centers, with mean absolute error (MAE) ranging from $64.38\pm13.63$ to $85.90\pm7.10$ HU, structural similarity index (SSIM) from $0.882\pm0.022$ to $0.922\pm0.039$, and peak signal-to-noise ratio (PSNR) from $32.86\pm0.94$ to $34.91\pm1.04$ dB. Notably, on an external validation dataset of 60 patients, comparable performance was achieved (MAE: $75.22\pm11.81$ HU, SSIM: $0.904\pm0.034$, PSNR: $33.52\pm2.06$ dB) without additional training, confirming robust generalization despite protocol, scanner differences and registration errors. These findings demonstrate the technical feasibility of FL for CBCT-to-sCT synthesis while preserving data privacy and offer a collaborative solution for developing generalizable models across institutions without centralized data sharing or site-specific fine-tuning.

Early and accurate detection of brain abnormalities, such as tumors and strokes, is essential for timely intervention and improved patient outcomes. In this study, we present a deep learning-based system capable of identifying both brain tumors and strokes from MRI images, along with their respective stages. We have executed two groundbreaking strategies involving convolutional neural networks, MobileNet V2 and ResNet-50-optimized through transfer learning to classify MRI scans into five diagnostic categories. Our dataset, aggregated and augmented from various publicly available MRI sources, was carefully curated to ensure class balance and image diversity. To enhance model generalization and prevent overfitting, we applied dropout layers and extensive data augmentation. The models achieved strong performance, with training accuracy reaching 93\% and validation accuracy up to 88\%. While ResNet-50 demonstrated slightly better results, Mobile Net V2 remains a promising option for real-time diagnosis in low resource settings due to its lightweight architecture. This research offers a practical AI-driven solution for early brain abnormality detection, with potential for clinical deployment and future enhancement through larger datasets and multi modal inputs.

Glioma molecular characterization is essential for risk stratification and treatment planning. Noninvasive imaging biomarkers such as apparent diffusion coefficient (ADC) values have shown potential for predicting glioma genotypes. However, manual segmentation of gliomas is time-consuming and operator-dependent. To address this limitation, we aimed to establish a single-sequence-derived automatic ADC extraction pipeline using T2-weighted imaging to support glioma isocitrate dehydrogenase (IDH) genotyping. Glioma volumes from a hospital data set (University College London Hospitals; n=247) were manually segmented on T2-weighted MRI scans using ITK-Snap Toolbox and co-registered to ADC maps sequences using the FMRIB Linear Image Registration Tool in FSL, followed by ADC histogram extraction (Python). Separately, a nnUNet deep learning algorithm was trained to segment glioma volumes using T2w only from BraTS 2021 data (n=500, 80% training, 5% validation and 15% test split). nnUnet was then applied to the University College London Hospitals (UCLH) data for segmentation and ADC read-outs. Univariable logistic regression was used to test the performance manual and nnUNet derived ADC metrics for IDH status prediction. Statistical equivalence was tested (paired two-sided t-test). nnUnet segmentation achieved a median Dice of 0.85 on BraTS data, and 0.83 on UCLH data. For the best performing metric (rADCmean) the area under the receiver operating characteristic curve (AUC) for differentiating IDH-mutant from IDHwildtype gliomas was 0.82 (95% CI: 0.78-0.88), compared to the manual segmentation AUC 0.84 (95% CI: 0.77-0.89). For all ADC metrics, manually and nnUNet extracted ADC were statistically equivalent (p<0.01). nnUNet identified one area of glioma infiltration missed by human observers. In 0.8% gliomas, nnUnet missed glioma components. In 6% of cases, over-segmentation of brain remote from the tumor occurred (e.g. temporal poles). The T2w trained nnUnet algorithm achieved ADC readouts for IDH genotyping with a performance statistically equivalent to human observers. This approach could support rapid ADC based identification of glioblastoma at an early disease stage, even with limited input data. AUC = Area under the receiver operating characteristic curve, BraTS = The brain tumor segmentation challenge held by MICCAI, Dice = Dice Similarity Coefficient, IDH = Isocitrate dehydrogenase, mGBM = Molecular glioblastoma, ADCmin = Fifth ADC histogram percentile, ADCmean = Mean ADC value, ADCNAWM = ADC in the contralateral centrum semiovale normal white matter, rADCmin = Normalized ADCmin, VOI rADCmean = Normalized ADCmean.

Differentiating major depressive disorder (MDD) from bipolar disorder (BD) remains a significant clinical challenge, as both disorders exhibit overlapping symptoms but require distinct treatment approaches. Advances in voxel-based morphometry and surface-based morphometry have facilitated the identification of structural brain abnormalities that may serve as diagnostic biomarkers. This study aimed to explore the relationships between brain morphological features, such as grey matter volume (GMV) and cortical thickness (CT), and demographic and clinical variables in patients with MDD and BD and healthy controls (HC) using multivariate analysis methods. A total of 263 participants, including 120 HC, 95 patients with MDD and 48 patients with BD, underwent T1-weighted MRI. GMV and CT were computed for standardised brain regions, followed by multivariate partial least squares (PLS) regression to assess associations with demographic and diagnostic variables. Reductions in frontotemporal CT were observed in MDD and BD compared with HC, but distinct trends between BD and MDD were also detected for the CT of selective temporal, frontal and parietal regions. Differential patterns in cerebellar GMV were also identified, with lobule CI larger in MDD and lobule CII larger in BD. Additionally, BD showed the same trend as ageing concerning reductions in CT and posterior cerebellar and striatal GMV. Depression severity showed a transdiagnostic link with reduced frontotemporal CT. This study highlights shared and distinct structural brain alterations in MDD and BD, emphasising the potential of neuroimaging biomarkers to enhance diagnostic accuracy. Accelerated cortical thinning and differential cerebellar changes in BD may serve as targets for future research and clinical interventions. Our findings underscore the value of objective neuroimaging markers in increasing the precision of mood disorder diagnoses, improving treatment outcomes.