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Accurate genotyping and prognosis of glioma patients present significant clinical challenges, often dependent on subjective judgement and insufficient scientific evidence. This study aims to develop a robust, noninvasive preoperative multi‐modal MRI‐based transformer learning model to predict IDH genotyping and glioma prognosis. This multi‐centre study included 563 glioma patients to develop an interpretable classification model utilising various preoperative imaging sequences, including T1‐weighted, T2‐weighted, fluid‐attenuated inversion recovery, contrast‐enhanced T1‐weighted, and diffusion‐weighted imaging. The model employs a multi‐task learning framework to extract and fuse radiomic, deep learning, and clinical features for IDH genotyping and glioma prognosis. Additionally, a multi‐modal transformer strategy is integrated to analyse structural and functional MRI, thereby enhancing model performance. Experimental results indicate that the model demonstrates superior performance, surpassing previous research and other state‐of‐the‐art methods. The model achieves an AUC of 91.40% in the IDH genotyping task and 93.37% in the glioma prognosis task. Group analysis reveals that the model exhibits higher sensitivity to IDH‐mutant cases and more accurately identifies low‐risk groups compared to medium‐ or high‐risk groups. This study aims to achieve accurate IDH genotyping and glioma prognosis through effective classification method, offering valuable diagnostic insights for clinical practice and expediting treatment decisions.

Accurate segmentation of brain tumors from multi-modal MRI scans is critical for diagnosis, treatment planning, and disease monitoring. Tumor heterogeneity and inter-image variability across MRI sequences pose challenging problems to state-of-the-art segmentation models. This paper presents a novel Multi-Modal Multi-Scale Contextual Aggregation with Attention Fusion (MM-MSCA-AF) framework that leverages multi-modal MRI images (T1, T2, FLAIR, and T1-CE) to enhance segmentation performance. The model employs multi-scale contextual aggregation to obtain global and fine-grained spatial features, and gated attention fusion for selectively refining effective feature representations and discarding noise. Evaluated on the BRATS 2020 dataset, MM-MSCA-AF achieves a Dice value of 0.8158 for necrotic tumor regions and 0.8589 in total, outperforming state-of-the-art architectures such as U-Net, nnU-Net, and Attention U-Net. These results demonstrate the effectiveness of MM-MSCA-AF in handling complex tumor shapes and improving segmentation accuracy. The proposed approach has significant clinical value, offering a more accurate and automatic brain tumor segmentation solution in medical imaging.

Over the last two decades, numerous human MRI studies of neuroplasticity have shown compelling evidence for extensive and rapid experience-induced brain plasticity in vivo. To date, most of these studies have consisted of simply detecting a difference in structural or functional images with little concern for their lack of biological specificity. Recent reviews and public debates have stressed the need for advanced imaging techniques to gain a better understanding of the nature of these differences – characterizing their extent in time and space, their underlying biological and network dynamics. The purpose of this article is to give an overview of advanced imaging techniques for an audience of cognitive neuroscientists that can assist them in the design and interpretation of future MRI studies of neuroplasticity. The review encompasses MRI methods that probe the morphology, microstructure, function, and connectivity of the brain with improved specificity. We underline the possible physiological underpinnings of these techniques and their recent applications within the framework of learning- and experience-induced plasticity in healthy adults. Finally, we discuss the advantages of a multi-modal approach to gain a more nuanced and comprehensive description of the process of learning. •Review a collection of MRI techniques for studying neuroplasticity.•Focus on the biological underpinnings of these techniques in this context.•Stress the importance of quantitative MRI in plasticity research.•Propose the use of multi-modal MRI to help tease apart underlying mechanisms.•Encourage a multidisciplinary approach to build longitudinal models of plasticity.

The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only time-consuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3D data sets for four MRI modalities (Flair, T1, T1ce, and T2) for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation. Taking the combined feature as input, a decision tree (DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017 (BraTS 2017) challenge, we achieved F -measure scores of 0.85, 0.81, and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively. Experimental results demonstrate that using SegNet models with 3D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.

Background The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer’s disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task. Results Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification. Conclusion Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.

Multi-modal MRI has become a valuable tool in medical imaging for diagnosing and investigating brain tumors, as it provides complementary information from multiple modalities. However, traditional methods for multi-modal MRI segmentation using UNet architecture typically fuse the modalities at an early or mid-stage of the network, without considering the inter-modal feature fusion or dependencies. To address this, a novel CMMFNet (cross-modal multi-scale fusion network) is proposed in this work, which explores both intra-modality and inter-modality relationships in brain tumor segmentation. The network is built on a transformer-based multi-encoder and single-decoder structure, which performs nested multi-modal fusion for high-level representations of different modalities. Additionally, the proposed CMMFNet uses a focusing mechanism that extracts larger receptive fields more effectively at the low-level scale and connects them to the decoding layer effectively. The multi-modal feature fusion module nests modality-aware feature aggregation, and the multi-modal features are better fused through long-term dependencies within each modality in the self-attention and cross-attention layers. The experiments showed that our CMMFNet outperformed state-of-the-art methods on the BraTS2020 benchmark dataset in brain tumor segmentation.

•We proposed a multi-modal-MRI based similarity measure for neonatal cortical parcellation.•The parcellation is stable to the choice of MRI features and repeatable to the choice of neonatal groups.•Multi-resolution parcellations were created to facilitate the future studies. The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.

Fluid transport in the neurovascular unit is essential for maintaining brain health through nutrient delivery and waste clearance. However, these systems are complex and the inter-dependencies between elements of these systems and how they may change through aging is not well understood. MRI outcomes provide insight into the underlying biological mechanisms of these systems in vivo, including water exchange rate through the neurovascular unit (BBB k w ), enlarged perivascular spaces (ePVS), cerebral blood flow (CBF), free water (FW), and white matter hyperintensities (WMH). To explore the relationships between functional elements of the neurovascular unit, this study investigated relationships between these MRI measures using Bayesian mixed models, and their variation with chronological age or atrophy-related brain age (brainageR) using linear regression. In 132 non-clinical older adults (mean age = 67 years; 68% female), BBB k w positively associated with CBF (β^ = 0.08, 95% credible interval (CI) = [0.02, 0.15]). FW positively associated with both ePVS (β^ = 0.44, CI = [0.30, 0.63]) and WMH (β^ = 0.13, CI = [0.04, 0.21]). BBB k w , CBF and ePVS decreased with age, while FW and WMH increased (all p  < 0.05). There were no associations with atrophy-related brain age (all p  > 0.05). Relationships between FW, ePVS and WMH likely reflect interconnectivity of fluid regulation within different compartments, while the relationship between BBB k w and CBF indicates a link between neurovascular fluid flow and vessel function. While individual metrics of neurovascular integrity are associated with age, their inter-relationships appear stable, providing a baseline for future research in fluid transport and vascular health in neurodegenerative disease. Graphical Abstract

Background Gliomas are among the most common malignant brain tumors and exhibit substantial heterogeneity, complicating accurate detection and segmentation. Although multi-modal MRI is the clinical standard for glioma imaging, variability across modalities and high computational demands hamper effective automated segmentation. Methods We propose UKAN-EP, a novel 3D extension of the original 2D U-KAN model for multi-modal MRI brain tumor segmentation. While U-KAN integrates Kolmogorov-Arnold Network (KAN) layers into a U-Net backbone, UKAN-EP further incorporates Efficient Channel Attention (ECA) and Pyramid Feature Aggregation (PFA) modules to enhance inter-modality feature fusion and multi-scale feature representation. We also introduce a dynamic loss weighting strategy that adaptively balances cross-entropy and Dice losses during training. Results On the 2024 BraTS-GLI dataset, UKAN-EP achieves superior segmentation performance (e.g., Dice = 0.9001 0.0127 and IoU = 0.8257 0.0186 for the whole tumor) while requiring substantially fewer computational resources (223.57 GFLOPs and 11.30M parameters) compared to strong baselines including U-Net, Attention U-Net, Swin UNETR, VT-Unet, TransBTS, and 3D U-KAN. An extensive ablation study further confirms the effectiveness of ECA and PFA and shows the limited utility of self-attention and spatial attention alternatives. Conclusion UKAN-EP demonstrates that combining the expressive power of KAN layers with lightweight channel-wise attention and multi-scale feature aggregation improves the accuracy and efficiency of brain tumor segmentation.

Cancer is a lethal illness that requires an initial stage prognosis to enhance patient survival rate. Accurate brain tumor and their sub-structure segmentation through Magnetic Resonance Images (MRIs) is a tough endeavor. Owing to the heterogeneous tumor areas, automatically segmenting brain tumors has proved to be a critical task even for neural network-based algorithms, some tumor regions remain unidentified due to their small size and the variation in area occupancy among tumor sub-classes. Current progress in the area of neural networks has been employed to enhance the segmentation performance. This study designed an intelligent 3D U-Net encoder-decoder-based system for automatic detection and brain tumor sub-structure segmentation. Our proposed 3D model constitutes neural units (the basic building blocks) followed by transition layer blocks and skip connections. BraTS 2018 and private local datasets are used to evaluate the proposed model which segments the Whole Tumor (WT), Tumor Core (TC), and the Enhancing Tumor (ET). The training accuracy, validation accuracy, dice score, sensitivities, and specificities of WT, CT, and ET zones are computed. The experimental results demonstrate that dice scores are 0.913, 0.874, and 0.801 for the BraTS 2018 dataset. The developed models performance was further evaluated by utilizing the dataset from a local hospital containing 71 subjects. The dice scores of 0.891, 0.834, and 0.776 are achieved by the proposed model on the private dataset. The practicability of the proposed model was assessed by the comparative studies of our model with existing literature.