Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and shown significant promise in medical imaging by enabling robust performance with limited labeled data. Although numerous surveys have reviewed the application of FM in healthcare care, brain imaging remains underrepresented, despite its critical role in the diagnosis and treatment of neurological diseases using modalities such as MRI, CT, and PET. Existing reviews either marginalize brain imaging or lack depth on the unique challenges and requirements of FM in this domain, such as multimodal data integration, support for diverse clinical tasks, and handling of heterogeneous, fragmented datasets. To address this gap, we present the first comprehensive and curated review of FMs for brain imaging. We systematically analyze 161 brain imaging datasets and 86 FM architectures, providing information on key design choices, training paradigms, and optimizations driving recent advances. Our review highlights the leading models for various brain imaging tasks, summarizes their innovations, and critically examines current limitations and blind spots in the literature. We conclude by outlining future research directions to advance FM applications in brain imaging, with the aim of fostering progress in both clinical and research settings.

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy (r = .78) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure.

Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation, but developing robust AI models requires comprehensive training data representative of real-world imaging studies. Thus, there is an urgent necessity for a large dataset with heterogeneity in imaging protocols and disease presentation. To address this, we present an open-access dataset of 11,884 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases, paired with clinical and image metadata. The provided dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis.

This paper proposes a novel clustering method for noisy image segmentation using a kernelized weighted local information approach under the Picture Fuzzy Set (PFS) framework. Existing kernel-based fuzzy clustering methods struggle with noisy environments and non-linear structures, while intuitionistic fuzzy clustering methods face limitations in handling uncertainty in real-world medical images. To address these challenges, we introduce a local picture fuzzy information measure, developed for the first time using Multivariate Coefficient of Variation (MCV) theory, enhancing robustness in segmentation. Additionally, we integrate non-Euclidean distance measures, including kernel distance for local information computation and modified total Bregman divergence (MTBD) measure for improving clustering accuracy. This combination enhances both local spatial consistency and global membership estimation, leading to precise segmentation. The proposed method is extensively evaluated on synthetic images with Gaussian, Salt and Pepper, and mixed noise, along with Brainweb, IBSR, and MRBrainS18 MRI datasets under varying Rician noise levels, and a CT image template. Furthermore, we benchmark our proposed method against two deep learning-based segmentation models, ResNet34-LinkNet and patch-based U-Net. Experimental results demonstrate significant improvements in segmentation accuracy, as validated by metrics such as Dice Score, Fuzzy Performance Index, Modified Partition Entropy, Average Volume Difference (AVD), and the XB index. Additionally, Friedman's statistical test confirms the superior performance of our approach compared to state-of-the-art clustering methods for noisy image segmentation. To facilitate reproducibility, the implementation of our proposed method is made publicly available at: Google Drive Repository.

The human brain exhibits intricate spatiotemporal dynamics, which can be described and understood through the framework of complex dynamic systems theory. In this study, we leverage functional magnetic resonance imaging (fMRI) data to investigate reaction-diffusion processes in the brain. A reaction-diffusion process refers to the interaction between two or more substances that spread through space and react with each other over time, often resulting in the formation of patterns or waves of activity. Building on this empirical foundation, we apply a reaction-diffusion framework inspired by theoretical physics to simulate the emergence of brain spacetime vortices within the brain. By exploring this framework, we investigate how reaction-diffusion processes can serve as a compelling model to govern the formation and propagation of brain spacetime vortices, which are dynamic, swirling patterns of brain activity that emerge and evolve across both time and space within the brain. Our approach integrates computational modeling with fMRI data to investigate the spatiotemporal properties of these vortices, offering new insights into the fundamental principles of brain organization. This work highlights the potential of reaction-diffusion models as an alternative framework for understanding brain spacetime dynamics.

This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and SHapley Additive exPlanations (SHAP). This IRB-approved study included 101 patients with glioma brain tumor acquired MR images with the T2W-FLAIR sequence. We extracted DL bottleneck features using ResNet50 from glioma MR images. Principal component analysis (PCA) was deployed for dimensionality reduction. XAI was used to identify potential features. The XGBosst classified the histologic grades of the glioma and the level of Ki-67. We integrated potential DL features with patient demographics (age and sex) and Ki-67 biomarkers, utilizing SHAP to determine the model's essential features and interactions. Glioma grade classification and Ki-67 level predictions achieved overall accuracies of 0.94 and 0.91, respectively. It achieved precision scores of 0.92, 0.94, and 0.96 for glioma grades 2, 3, and 4, and 0.88, 0.94, and 0.97 for Ki-67 levels (low: 5%≤Ki-67<10%, moderate: 10%≤Ki-67≤20, and high: Ki-67>20%). Corresponding F1-scores were 0.95, 0.88, and 0.96 for glioma grades and 0.92, 0.93, and 0.87 for Ki-67 levels. SHAP analysis further highlighted a strong association between bottleneck DL features and Ki-67 biomarkers, demonstrating their potential to differentiate glioma grades and Ki-67 levels while offering valuable insights into glioma aggressiveness. This study demonstrates the precise classification of glioma grades and the prediction of Ki-67 levels to underscore the potential of AI-driven MRI analysis to enhance clinical decision-making in glioma management.

The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insights into the pathological process of aging and diseases. Here we evaluated the performance of machine learning models for biological age prediction by using the integrated features from multi-biological data of 140 healthy controls and 43 patients with schizophrenia, including brain MRI, gut microbiome, and blood data. Our results revealed that the models using multi-biological data achieved higher predictive accuracy than those using only brain MRI. Feature interpretability analysis of the optimal model elucidated that the substantial contributions of the frontal lobe, the temporal lobe and the fornix were effective for biological age prediction. Notably, patients with schizophrenia exhibited a pronounced increase in the predicted biological age gap (BAG) when compared to healthy controls. Moreover, the BAG in the SZ group was negatively and positively correlated with the MCCB and PANSS scores, respectively. These findings underscore the potential of BAG as a valuable biomarker for assessing cognitive decline and symptom severity of neuropsychiatric disorders.

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy. A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated. ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD. This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.

Generative models based on deep learning have shown significant potential in medical imaging, particularly for modality transformation and multimodal fusion in MRI-based brain imaging. This study introduces GM-LDM, a novel framework that leverages the latent diffusion model (LDM) to enhance the efficiency and precision of MRI generation tasks. GM-LDM integrates a 3D autoencoder, pre-trained on the large-scale ABCD MRI dataset, achieving statistical consistency through KL divergence loss. We employ a Vision Transformer (ViT)-based encoder-decoder as the denoising network to optimize generation quality. The framework flexibly incorporates conditional data, such as functional network connectivity (FNC) data, enabling personalized brain imaging, biomarker identification, and functional-to-structural information translation for brain diseases like schizophrenia.

Brain stroke is a leading cause of disability and mortality worldwide, necessitating the development of accurate and efficient diagnostic models. In this study, we explore the integration of Genetic Algorithm (GA)-based feature selection with three state-of-the-art deep learning architectures InceptionV3, VGG19, and MobileNetV2 to enhance stroke detection from neuroimaging data. GA is employed to optimize feature selection, reducing redundancy and improving model performance. The selected features are subsequently fed into the respective deep-learning models for classification. The dataset used in this study comprises neuroimages categorized into "Normal" and "Stroke" classes. Experimental results demonstrate that incorporating GA improves classification accuracy while reducing computational complexity. A comparative analysis of the three architectures reveals their effectiveness in stroke detection, with MobileNetV2 achieving the highest accuracy of 97.21%. Notably, the integration of Genetic Algorithms with MobileNetV2 for feature selection represents a novel contribution, setting this study apart from prior approaches that rely solely on traditional CNN pipelines. Owing to its lightweight design and low computational demands, MobileNetV2 also offers significant advantages for real-time clinical deployment, making it highly applicable for use in emergency care settings where rapid diagnosis is critical. Additionally, performance metrics such as precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curves are evaluated to provide comprehensive insights into model efficacy. This research underscores the potential of genetic algorithm-driven optimization in enhancing deep learning-based medical image classification, paving the way for more efficient and reliable stroke diagnosis.