Purpose: To develop an age prediction model which is interpretable and robust to demographic and technological variances in brain MRI scans. Materials and Methods: We propose a transformer-based architecture that leverages self-supervised pre-training on large-scale datasets. Our model processes pseudo-3D T1-weighted MRI scans from three anatomical views and incorporates brain volumetric information. By introducing a stem architecture, we reduce the conventional quadratic complexity of transformer models to linear complexity, enabling scalability for high-dimensional MRI data. We trained our model on ADNI2 $\&$ 3 (N=1348) and OASIS3 (N=716) datasets (age range: 42 - 95) from the North America, with an 8:1:1 split for train, validation and test. Then, we validated it on the AIBL dataset (N=768, age range: 60 - 92) from Australia. Results: We achieved an MAE of 3.65 years on ADNI2 $\&$ 3 and OASIS3 test set and a high generalizability of MAE of 3.54 years on AIBL. There was a notable increase in brain age gap (BAG) across cognitive groups, with mean of 0.15 years (95% CI: [-0.22, 0.51]) in CN, 2.55 years ([2.40, 2.70]) in MCI, 6.12 years ([5.82, 6.43]) in AD. Additionally, significant negative correlation between BAG and cognitive scores was observed, with correlation coefficient of -0.185 (p < 0.001) for MoCA and -0.231 (p < 0.001) for MMSE. Gradient-based feature attribution highlighted ventricles and white matter structures as key regions influenced by brain aging. Conclusion: Our model effectively fused information from different views and volumetric information to achieve state-of-the-art brain age prediction accuracy, improved generalizability and interpretability with association to neurodegenerative disorders.

Advances in biomarkers and pathophysiology of vascular contributions to cognitive impairment and dementia (VCID) are expected to bring greater mechanistic insights, more targeted treatments, and potentially disease-modifying therapies. The 2025 Annual Workshop of the Albert Research Institute for White Matter and Cognition, sponsored by the Leo and Anne Albert Charitable Trust since 2015, focused on novel biomarkers for VCID. The meeting highlighted the complexity of dementia, emphasizing that the majority of cases involve multiple brain pathologies, with vascular pathology typically present. Potential novel approaches to diagnosis of disease processes and progression that may result in VCID included measures of microglial senescence and retinal changes, as well as artificial intelligence (AI) integration of multimodal datasets. Proteomic studies identified plasma proteins associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL; a rare genetic disorder affecting brain vessels) and age-related vascular pathology that suggested potential therapeutic targets. Blood-based microglial and brain-derived extracellular vesicles are promising tools for early detection of brain inflammation and other changes that have been associated with cognitive decline. Imaging measures of blood perfusion, oxygen extraction, and cerebrospinal fluid (CSF) flow were discussed as potential VCID biomarkers, in part because of correlations with classic pathological Alzheimer's disease (AD) biomarkers. MRI-visible perivascular spaces, which may be a novel imaging biomarker of sleep-driven glymphatic waste clearance dysfunction, are associated with vascular risk factors, lower cognitive function, and various brain pathologies including Alzheimer's, Parkinson's and cerebral amyloid angiopathy (CAA). People with Down syndrome are at high risk for dementia. Individuals with Down syndrome who develop dementia almost universally experience mixed brain pathologies, with AD pathology and cerebrovascular pathology being the most common. This follows the pattern in the general population where mixed pathologies are also predominant in the brains of people clinically diagnosed with dementia, including AD dementia. Intimate partner violence-related brain injury, hypertension's impact on dementia risk, and the promise of remote ischemic conditioning for treating VCID were additional themes.

This study was undertaken to develop and validate an automatic, artificial intelligence-enhanced software tool for hippocampal sclerosis (HS) detection, using a variety of standard magnetic resonance imaging (MRI) protocols from different MRI scanners for routine clinical practice. First, MRI scans of 36 epilepsy patients with unilateral HS and 36 control patients with epilepsy of other etiologies were analyzed. MRI features, including hippocampal subfield volumes from three-dimensional (3D) magnetization-prepared rapid acquisition gradient echo (MPRAGE) scans and fluid-attenuated inversion recovery (FLAIR) intensities, were calculated. Hippocampal subfield volumes were corrected for total brain volume and z-scored using a dataset of 256 healthy controls. Hippocampal subfield FLAIR intensities were z-scored in relation to each subject's mean cortical FLAIR signal. Additionally, left-right ratios of FLAIR intensities and volume features were obtained. Support vector classifiers were trained on the above features to predict HS presence and laterality. In a second step, the algorithm was validated using two independent, external cohorts, including 118 patients and 116 controls in sum, scanned with different MRI scanners and acquisition protocols. Classifiers demonstrated high accuracy in HS detection and lateralization, with slight variations depending on the input image availability. The best cross-validation accuracy was achieved using both 3D MPRAGE and 3D FLAIR scans (mean accuracy = 1.0, confidence interval [CI] = .939-1.0). External validation of trained classifiers in two independent cohorts yielded accuracies of .951 (CI = .902-.980) and .889 (CI = .805-.945), respectively. In both validation cohorts, the additional use of FLAIR scans led to significantly better classification performance than the use of MPRAGE data alone (p = .016 and p = .031, respectively). A further model was trained on both validation cohorts and tested on the former training cohort, providing additional evidence for good validation performance. Comparison to a previously published algorithm showed no significant difference in performance (p = 1). The method presented achieves accurate automated HS detection using standard clinical MRI protocols. It is robust and flexible and requires no image processing expertise.

Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains high sensitivity and precision in tumor classification, even when trained predominantly on synthetic data. When only a small portion of GAN data was added, such as 900 real images and 100 GAN images, the model achieved excellent performance, with test accuracy reaching 95.2%, and precision, recall, and F1-score all exceeding 95%. However, as the proportion of GAN images increased further, performance gradually declined. This study suggests that while GANs are useful for augmenting limited datasets especially when real data is scarce, too much synthetic data can introduce artifacts that affect the model's ability to generalize to real world cases.

Deep learning has demonstrated remarkable success in medical image segmentation and computer-aided diagnosis. In particular, numerous advanced methods have achieved state-of-the-art performance in brain tumor segmentation from MRI scans. While recent studies in other medical imaging domains have revealed that integrating textual reports with visual data can enhance segmentation accuracy, the field of brain tumor analysis lacks a comprehensive dataset that combines radiological images with corresponding textual annotations. This limitation has hindered the exploration of multimodal approaches that leverage both imaging and textual data. To bridge this critical gap, we introduce the TextBraTS dataset, the first publicly available volume-level multimodal dataset that contains paired MRI volumes and rich textual annotations, derived from the widely adopted BraTS2020 benchmark. Building upon this novel dataset, we propose a novel baseline framework and sequential cross-attention method for text-guided volumetric medical image segmentation. Through extensive experiments with various text-image fusion strategies and templated text formulations, our approach demonstrates significant improvements in brain tumor segmentation accuracy, offering valuable insights into effective multimodal integration techniques. Our dataset, implementation code, and pre-trained models are publicly available at https://github.com/Jupitern52/TextBraTS.

Breast cancer is the most prevalent malignancy in women and a leading cause of mortality. Accurate assessment of axillary lymph node metastasis (LNM) is critical for breast cancer management. Exploring non-invasive methods such as radiomics for the detection of LNM is highly important. We systematically searched Pubmed, Embase, Scopus, Web of Science and google scholar until 11 March 2024. To assess the risk of bias and quality of studies, we utilized the quality assessment of diagnostic accuracy studies (QUADAS) tool as well as the radiomics quality score (RQS). Area under the curve (AUC), sensitivity, specificity and accuracy were determined for each study to evaluate the diagnostic accuracy of radiomics in magnetic resonance imaging (MRI) for detecting LNM in patients with breast cancer. This meta-analysis of 20 studies (5072 patients) demonstrated an overall AUC of 0.83 (95% confidence interval (CI): 0.80-0.86). Subgroup analysis revealed a trend towards higher specificity when radiomics was combined with clinical factors (0.83) compared to radiomics alone (0.79). Sensitivity analysis confirmed the robustness of the findings and publication bias was not evident. The radiomics models increased the likelihood of a positive LNM outcome from 37% to 73.2% when initial probability was positive and decreased the likelihood to 8% when initial probability was negative, highlighting their potential clinical utility. Radiomics as a non-invasive method demonstrates strong potential for detecting LNM in breast cancer, offering clinical promise. However, further standardization and validation are needed in future studies.

Pulmonary valve regurgitation after repaired Tetralogy of Fallot (TOF) or double-outlet right ventricle (DORV) causes hypertrophy and papillary muscle enlargement. Cardiac magnetic resonance imaging (CMR) can evaluate the right ventricular (RV) dilatation, but the effect of trabecular and papillary muscle (TPM) exclusion on RV volume for TOF or DORV reoperation decision is unclear. Twenty-three patients with repaired TOF or DORV, and 19 healthy controls aged ≥15, underwent CMR from 2012 to 2022. TPM volume is measured by artificial intelligence. Reoperation was considered when RV end-diastolic volume index (RVEDVI) >150 mL/m² or RV end-systolic volume index (RVESVI) >80 mL/m². RV volumes were higher in the disease group than controls (P α 0.001). RV mass and TPM volumes were higher in the disease group (P α 0.001). The reduction rate of RV volumes due to the exclusion of TPM volume was 6.3% (2.1-10.5), 11.7% (6.9-13.8), and 13.9% (9.5-19.4) in the control, volume load, and volume α pressure load groups, respectively. TPM/RV volumes were higher in the volume α pressure load group (control: 0.07 g/mL, volume: 0.14 g/mL, volume α pressure: 0.17 g/mL), and correlated with QRS duration (R α 0.77). In 3 patients in the volume α pressure, RV volume included TPM was indicated for reoperation, but when RV volume was reduced by TPM removal, reoperation was no indicated. RV volume measurements, including TPM in volume α pressure load, may help determine appropriate volume recommendations for reoperation.

Predicting human behavior from neuroimaging data remains a complex challenge in neuroscience. To address this, we propose a systematic and multi-faceted framework that incorporates a model-based workflow using dynamical brain models. This approach utilizes multi-modal MRI data for brain modeling and applies the optimized modeling outcome to machine learning. We demonstrate the performance of such an approach through several examples such as sex classification and prediction of cognition or personality traits. We in particular show that incorporating the simulated data into machine learning can significantly improve the prediction performance compared to using empirical features alone. These results suggest considering the output of the dynamical brain models as an additional neuroimaging data modality that complements empirical data by capturing brain features that are difficult to measure directly. The discussed model-based workflow can offer a promising avenue for investigating and understanding inter-individual variability in brain-behavior relationships and enhancing prediction performance in neuroimaging research.

Spinal cord injury (SCI) can severely impair motor and autonomic function, with long-term consequences for quality of life. Epidural stimulation has emerged as a promising intervention, offering partial recovery by activating neural circuits below the injury. To make this therapy effective in practice, precise placement of stimulation electrodes is essential -- and that requires accurate segmentation of spinal cord structures in MRI data. We present a protocol for manual segmentation tailored to SCI anatomy, and evaluated a deep learning approach using a U-Net architecture to automate this segmentation process. Our approach yields accurate, efficient segmentation that identify potential electrode placement sites with high fidelity. Preliminary results suggest that this framework can accelerate SCI MRI analysis and improve planning for epidural stimulation, helping bridge the gap between advanced neurotechnologies and real-world clinical application with faster surgeries and more accurate electrode placement.

Brain tumors, known for their life-threatening implications, underscore the urgency of precise and interpretable early detection. Expertise remains essential for accurate identification through MRI scans due to the intricacies involved. However, the growing recognition of automated detection systems holds the potential to enhance accuracy and improve interpretability. By consistently providing easily comprehensible results, these automated solutions could boost the overall efficiency and effectiveness of brain tumor diagnosis, promising a transformative era in healthcare. This paper introduces a new hybrid model, BioTransX, which uses a bi-former encoder mechanism, a dynamic sparse attention-based transformer, in conjunction with ensemble convolutional networks. Recognizing the importance of better contrast and data quality, we applied Contrast-Limited Adaptive Histogram Equalization (CLAHE) during the initial data processing stage. Additionally, to address the crucial aspect of model interpretability, we integrated Grad-CAM and Gradient Attention Rollout, which elucidate decisions by highlighting influential regions within medical images. Our hybrid deep learning model was primarily evaluated on the Kaggle MRI dataset for multi-class brain tumor classification, achieving a mean accuracy and F1-score of 99.29%. To validate its generalizability and robustness, BioTransX was further tested on two additional benchmark datasets, BraTS and Figshare, where it consistently maintained high performance across key evaluation metrics. The transformer-based hybrid model demonstrated promising performance in explainable identification and offered notable advantages in computational efficiency and memory usage. These strengths differentiate BioTransX from existing models in the literature and make it ideal for real-world deployment in resource-constrained clinical infrastructures.