Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following pathological changes in the cerebrovascular system. The primary clinical manifestations include behavioral abnormalities and diminished learning and memory cognitive functions. If the location and extent of brain injury are not identified early and therapeutic interventions are not promptly administered, it may lead to irreversible cognitive impairment. Therefore, the early diagnosis of VCI is crucial for its prevention and treatment. Prior to the onset of cognitive impairment in VCI, magnetic resonance imaging (MRI) radiomics can be utilized for early assessment and diagnosis, thereby guiding clinicians in providing precise treatment for patients, which holds significant potential for development. This article reviews the classification of VCI, the concept of radiomics, the application of MRI radiomics in VCI, and the limitations of radiomics in the context of advancements in its application within the central nervous system. CRITICAL RELEVANCE STATEMENT: This article explores how MRI radiomics can be used to detect VCI early, enhancing clinical radiology practice by offering a reliable method for prediction, diagnosis, and identification, which also promotes standardization in research and integration of disciplines. KEY POINTS: MRI radiomics can predict VCI early. MRI radiomics can diagnose VCI. MRI radiomics distinguishes VCI from Alzheimer's disease.

Cerebrovascular morphology plays a critical role in brain health, influencing cerebral blood flow (CBF) and contributing to the pathogenesis of various neurological diseases. This review examines the anatomical structure of the cerebrovascular network and its variations in healthy and diseased populations and highlights age-related changes and their implications in various neurological conditions. Normal variations, including the completeness and anatomical anomalies of the Circle of Willis and collateral circulation, are discussed in relation to their impact on CBF and susceptibility to ischemic events. Age-related changes in the cerebrovascular system, such as alterations in vessel geometry and density, are explored for their contributions to age-related neurological disorders, including Alzheimer's disease and vascular dementia. Advances in medical imaging and computational methods have enabled automatic quantitative assessment of cerebrovascular structures, facilitating the identification of pathological changes in both acute and chronic cerebrovascular disorders. Emerging technologies, including machine learning and computational fluid dynamics, offer new tools for predicting disease risk and patient outcomes based on vascular morphology. This review underscores the importance of understanding cerebrovascular remodeling for early diagnosis and the development of novel therapeutic approaches in brain diseases.

In this study, the authors aimed to establish a supervised machine learning (ML) model based on multiple tree-based algorithms to predict long-term biochemical outcomes and intervention-free survival (IFS) after endonasal transsphenoidal surgery (ETS) in patients with Cushing's disease (CD). The medical records of patients who underwent ETS for CD between 2013 and 2023 were reviewed. Data were collected on the patient's baseline characteristics, intervention details, histopathology, surgical outcomes, and postoperative endocrine functions. The study's primary outcome was IFS, and the therapeutic outcomes were labeled as "under control" or "treatment failure," depending on whether additional therapeutic interventions after primary ETS were required. The decision tree and random forest classifiers were trained and tested to predict long-term IFS based on unseen data, using an 80/20 cohort split. Data from 150 patients, with a median follow-up period of 56 months, were extracted. In the cohort, 42 (28%) patients required additional intervention for persistent or recurrent CD. Consequently, the IFS rates following ETS alone were 83% at 3 years and 78% at 5 years. Multivariable Cox proportional hazards analysis demonstrated that a smaller tumor diameter that could be detected by MRI (hazard ratio 0.95, 95% CI 0.90-0.99; p = 0.047) was significantly associated with greater IFS. However, the lack of tumor detection on MRI was a poor predictor. The ML-based model using a decision tree model displayed 91% accuracy (95% CI 0.70-0.94, sensitivity 87.0%, specificity 89.0%) in predicting IFS in the unseen test dataset. Random forest analysis revealed that tumor size (mean minimal depth 1.67), Knosp grade (1.75), patient age (1.80), and BMI (1.99) were the four most significant predictors of long-term IFS. The ML algorithm could predict long-term postoperative endocrinological remission in CD with high accuracy, indicating that prognosis may vary not only with previously reported factors such as tumor size, Knosp grade, gross-total resection, and patient age but also with BMI. The decision tree flowchart could potentially stratify patients with CD before ETS, allowing for the selection of personalized treatment options and thereby assisting in determining treatment plans for these patients. This ML model may lead to a deeper understanding of the complex mechanisms of CD by uncovering patterns embedded within the data.

Preoperative workup of trigeminal neuralgia (TN) consists of identification of neurovascular features on MRI. In this study, the authors apply and evaluate the performance of deep learning models for segmentation of the trigeminal nerve and surrounding vasculature to quantify anatomical features of the nerve and vessels. Six U-Net-based neural networks, each with a different encoder backbone, were trained to label constructive interference in steady-state MRI voxels as nerve, vasculature, or background. A retrospective dataset of 50 TN patients at the authors' institution who underwent preoperative high-resolution MRI in 2022 was utilized to train and test the models. Performance was measured by the Dice coefficient and intersection over union (IoU) metrics. Anatomical characteristics, such as surface area of neurovascular contact and distance to the contact point, were computed and compared between the predicted and ground truth segmentations. Of the evaluated models, the best performing was U-Net with an SE-ResNet50 backbone (Dice score = 0.775 ± 0.015, IoU score = 0.681 ± 0.015). When the SE-ResNet50 backbone was used, the average surface area of neurovascular contact in the testing dataset was 6.90 mm2, which was not significantly different from the surface area calculated from manual segmentation (p = 0.83). The average calculated distance from the brainstem to the contact point was 4.34 mm, which was also not significantly different from manual segmentation (p = 0.29). U-Net-based neural networks perform well for segmenting trigeminal nerve and vessels from preoperative MRI volumes. This technology enables the development of quantitative and objective metrics for radiographic evaluation of TN.

Giant pituitary adenomas (GPAs) are challenging skull base tumors due to their size and proximity to critical neurovascular structures. Achieving gross-total resection (GTR) can be difficult, and residual tumor burden is commonly reported. This study evaluated the ability of convolutional neural networks (CNNs) to predict the extent of resection (EOR) from preoperative MRI with the goals of enhancing surgical planning, improving preoperative patient counseling, and enhancing multidisciplinary postoperative coordination of care. A retrospective study of 100 consecutive patients with GPAs was conducted. Patients underwent surgery via the endoscopic endonasal transsphenoidal approach. CNN models were trained on DICOM images from preoperative MR images to predict EOR, using a split of 80 patients for training and 20 for validation. The models included different architectural modules to refine image selection and predict EOR based on tumor-contained images in various anatomical planes. The model design, training, and validation were conducted in a local environment in Python using the TensorFlow machine learning system. The median preoperative tumor volume was 19.4 cm3. The median EOR was 94.5%, with GTR achieved in 49% of cases. The CNN model showed high predictive accuracy, especially when analyzing images from the coronal plane, with a root mean square error of 2.9916 and a mean absolute error of 2.6225. The coefficient of determination (R2) was 0.9823, indicating excellent model performance. CNN-based models may effectively predict the EOR for GPAs from preoperative MRI scans, offering a promising tool for presurgical assessment and patient counseling. Confirmatory studies with large patient samples are needed to definitively validate these findings.

Major Depressive Disorder (MDD) diagnosis mainly relies on subjective self-reporting and clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) and its analysis of Effective Connectivity (EC) offer a quantitative approach to understand the directional interactions between brain regions, presenting a potential objective method for MDD classification. Granger causality analysis was used to extract EC features from a large, multi-site rs-fMRI dataset of MDD patients. The ComBat algorithm was applied to adjust for site differences, while multivariate linear regression was employed to control for age and sex differences. Discriminative EC features for MDD were identified using two-sample t-tests and model-based feature selection, with the LightGBM algorithm being used for classification. The performance and generalizability of the model was evaluated using nested five-fold cross-validation and tested for generalizability on an independent dataset. Ninety-seven EC features belonging to the cerebellum and front-temporal regions were identified as highly discriminative for MDD. The classification model using these features achieved an accuracy of 94.35 %, with a sensitivity of 93.52 % and specificity of 95.25 % in cross-validation. Generalization of the model to an independent dataset resulted in an accuracy of 94.74 %, sensitivity of 90.59 %, and specificity of 96.75 %. The study demonstrates that EC features from rs-fMRI can effectively discriminate MDD from healthy controls, suggesting that EC analysis could be a valuable tool in assisting the clinical diagnosis of MDD. This method shows promise in enhancing the objectivity of MDD diagnosis through the use of neuroimaging biomarkers.

Nonhuman primates, particularly rhesus macaques, have served as crucial animal models for investigating complex brain functions. While previous studies have explored neural activity features in macaques, the gyro-sulcal functional dissociation characteristics are largely unknown. In this study, we employ a deep learning model named one-dimensional convolutional neural network to differentiate resting state functional magnetic resonance imaging signals between gyri and sulci in macaque brains, and further investigate the frequency-specific dissociations between gyri and sulci inferred from the power spectral density of resting state functional magnetic resonance imaging. Experimental results based on a large cohort of 440 macaques from two independent sites demonstrate substantial frequency-specific dissociation between gyral and sulcal signals at both whole-brain and regional levels. The magnitude of gyral power spectral density is significantly larger than that of sulcal power spectral density within the range of 0.01 to 0.1 Hz, suggesting that gyri and sulci may play distinct roles as the global hubs and local processing units for functional activity transmission and interaction in macaque brains. In conclusion, our study has established one of the first power spectrum maps of gyro-sulcal functional activity dissociation in macaque brains, providing a novel perspective for systematically exploring the neural mechanism of functional dissociation in mammalian brains.

As technological innovation in computers has advanced, radiologists may now diagnose brain tumors (BT) with the use of artificial intelligence (AI). In the medical field, early disease identification enables further therapies, where the use of AI systems is essential for time and money savings. The difficulties presented by various forms of Magnetic Resonance (MR) imaging for BT detection are frequently not addressed by conventional techniques. To get around frequent problems with traditional tumor detection approaches, deep learning techniques have been expanded. Thus, for BT segmentation utilizing MR images, a U-Net-based architecture combined with Attention Mechanisms has been developed in this work. Moreover, by fine-tuning essential variables, Hyperparameter Optimization (HPO) is used using the Bayesian Optimization Algorithm to strengthen the segmentation model's performance. Tumor regions are pinpointed for segmentation using Region-Adaptive Thresholding technique, and the segmentation results are validated against ground truth annotated images to assess the performance of the suggested model. Experiments are conducted using the LGG, Healthcare, and BraTS 2021 MRI brain tumor datasets. Lastly, the importance of the suggested model has been demonstrated through comparing several metrics, such as IoU, accuracy, and DICE Score, with current state-of-the-art methods. The U-Net-based method gained a higher DICE score of 0.89687 in the segmentation of MRI-BT.

This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomography (CCT) images. A total of 521 CCT scans from sub-adult Malaysians aged 0 to 20 were analysed using Mimics software (Materialise Mimics Ver. 21). Plane-to-plane (PTP) protocol was used for measuring 14 chosen craniometric parameters. A trio of machine learning algorithms RF, SVM, and LDA with GridSearchCV was used to produce classification models for sex estimation. In addition, performance was measured in the form of accuracy, precision, recall, and F1-score, among others. RF produced testing accuracy of 73%, with the best hyperparameters of max_depth = 6, max_samples = 40, and n_estimators = 45. SVM obtained an accuracy of 67% with the best hyperparameters: learning rate (C) = 10, gamma = 0.01, and kernel = radial basis function (RBF). LDA obtained the lowest accuracy of 65% with shrinkage of 0.02. Among the tested ML methods, RF showed the highest testing accuracy in comparison to SVM and LDA. This is the first AI-based classification model that can be used for estimating sex in sub-adults using CCT scans.

Precise delineation of brain tissues, including lesions, in MR images is crucial for data analysis and objectively assessing conditions like neurological disorders and brain tumors. Existing methods for tissue segmentation often fall short in addressing patients with lesions, particularly those with brain tumors. This study aimed to develop and evaluate a robust pipeline utilizing convolutional neural networks for rapid and automatic segmentation of whole brain tissues, including tumor lesions. The proposed pipeline was developed using BraTS'21 data (1251 patients) and tested on local hospital data (100 patients). Ground truth masks for lesions as well as brain tissues were generated. Two convolutional neural networks based on deep residual U-Net framework were trained for segmenting brain tissues and tumor lesions. The performance of the pipeline was evaluated on independent test data using dice similarity coefficient (DSC) and volume similarity (VS). The proposed pipeline achieved a mean DSC of 0.84 and a mean VS of 0.93 on the BraTS'21 test data set. On the local hospital test data set, it attained a mean DSC of 0.78 and a mean VS of 0.91. The proposed pipeline also generated satisfactory masks in cases where the SPM12 software performed inadequately. The proposed pipeline offers a reliable and automatic solution for segmenting brain tissues and tumor lesions in MR images. Its adaptability makes it a valuable tool for both research and clinical applications, potentially streamlining workflows and enhancing the precision of analyses in neurological and oncological studies.