Background and objective: Obstructive sleep apnea (OSA) is a common sleep disorder that causes intermittent hypoxia and sleep fragmentation, leading to attention impairment and other cognitive deficits. Magnetic resonance imaging (MRI) is a powerful modality that can reveal the structural and functional brain alterations associated with attention impairment in OSA patients. The objective of this systematic review is to identify and synthesize the evidence on MRI biomarkers and neuropsychological assessments of attention deficits in OSA patients.Methods: We searched the Scopus and PubMed databases for studies that used MRI to measure biomarkers related to attention alteration in OSA patients and reported qualitative and quantitative data on the association between MRI biomarkers and attention outcomes. We also included studies that found an association between neuropsychological assessments and MRI findings in OSA patients with attention deficits.Results: We included 19 studies that met our inclusion criteria and extracted the relevant data from each study. We categorized the studies into three groups based on the MRI modality and the cognitive domain they used: structural and diffusion tensor imaging MRI findings, functional, perfusion, and metabolic MRI findings, and neuropsychological assessment findings.Conclusions: We found that OSA is associated with structural, functional, and metabolic brain alterations in multiple regions and networks that are involved in attention processing. Treatment with continuous positive airway pressure can partially reverse some of the brain changes and improve cognitive function in some domains and in some studies. This review suggests that MRI techniques and neuropsychological assessments can be useful tools for monitoring the progression and response to treatment of OSA patients.

Objective. This systematic review aims to synthesize and analyze the available literature on central nervous system (CNS) magnetic resonance imaging (MRI) findings in individuals who have received COVID‐19 vaccinations. Our objective is to enhance understanding of potential neurological side effects, inform clinical practice, and guide future research on the neurological implications of COVID‐19 vaccination. Methods. In this systematic review, we conducted a comprehensive search in PubMed, Scopus, and Web of Science from January 2020 to April 2023, using terms related to COVID‐19 vaccination and CNS MRI findings. We evaluated the quality of the study, extracted relevant data, and included 89 eligible studies that covered various vaccines, demographics of patients, symptoms, and MRI findings to provide a thorough understanding of SARS‐CoV‐2 vaccination‐related CNS problems. Results. We investigated CNS MRI findings following COVID‐19 vaccination across various vaccine types. Common diseases associated with post‐vaccination CNS MRI findings included cerebral venous sinus thrombosis (CVST), vaccine‐induced immune thrombotic thrombocytopenia (VITT), acute disseminated encephalomyelitis (ADEM), acute myelitis, autoimmune encephalitis (AE), and others. Patients presented with diverse onset symptoms and neurological manifestations. Abnormalities identified in CNS MRI findings included white matter (WM) hyperintensity. Our analysis offers a comprehensive overview of the current literature on post‐vaccination CNS MRI findings. Discussion. We highlight a range of post‐COVID‐19 vaccination CNS MRI findings, including CVST, with a higher incidence in individuals receiving the ChAdOx1 (AstraZeneca) vaccine. Other notable observations include cases of ADEM, myelitis or transverse myelitis (TM), Guillain–Barré syndrome (GBS), and acute encephalopathy following COVID‐19 vaccination. The incidence of these neurological complications is extremely rare, and the benefits of vaccination outweigh the risks. The reviewed studies were primarily case reports or case series, and thus large‐scale epidemiological studies and controlled clinical trials are needed to better understand the underlying mechanisms and risk factors associated with these neurological complications following COVID‐19 vaccination.

Accurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. CE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. The approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. The MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Introduction and importance: Automated segmentation of glioblastoma multiforme (GBM) from MRI images is crucial for accurate diagnosis and treatment planning. This paper presents a new and innovative approach for automating the segmentation of GBM from MRI images using the marker-controlled watershed segmentation (MCWS) algorithm. Case presentation and methods: The technique involves several image processing techniques, including adaptive thresholding, morphological filtering, gradient magnitude calculation, and regional maxima identification. The MCWS algorithm efficiently segments images based on local intensity structures using the watershed transform, and fuzzy c-means (FCM) clustering improves segmentation accuracy. The presented approach achieved improved segmentation accuracy in detecting and segmenting GBM tumours from axial T2-weighted (T2-w) MRI images, as demonstrated by the mean characteristics performance metrics for GBM segmentation (sensitivity: 0.9905, specificity: 0.9483, accuracy: 0.9508, precision: 0.5481, F_measure: 0.7052, and jaccard: 0.9340). Clinical discussion: The results of this study underline the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumours. Conclusion: The MCWS technique provides an effective and efficient approach for the segmentation of challenging medical images.

Background: Obstructive sleep apnea (OSA) is characterized by disruptive breathing, resulting in a decline in cognitive performance. This study investigates the role of oxygen extraction fraction (OEF) and quantitative susceptibility mapping (QSM) in OSA‐related cognitive impairment.Methods: The study recruited 15 patients with confirmed OSA and 16 healthy controls, who underwent overnight polysomnography and brain MRI using a 3 Tesla machine and 64‐channel head coil. A two‐step MRI analysis was employed to measure OEF. QSM was first created by processing separate phase and magnitude images. OEF maps were then generated by identifying veins based on their susceptibility. Volumetric analysis was performed using the FreeSurfer. Neuropsychological tests were administered to evaluate cognition.Results: The analysis of OEF revealed significantly lower values in various cerebral cortical regions of OSA patients than in controls. Notably, OEF in the cerebral cortex and frontal, temporal, and occipital regions showed negative correlations with the duration of stage N2 sleep (highest correlation between N2 and right temporal OEF: p = 0.005, r = −0.681). Furthermore, poorer performance on neuropsychological tests, such as the backward digit span test, was significantly correlated with reduced OEF in the left hemisphere (p = 0.016), left cerebral cortex (p = 0.019), right frontal (p = 0.034), left frontal (p = 0.014), left parietal (p = 0.008), left temporal (p = 0.048), and left occipital lobes (p = 0.015). No significant differences in QSM or brain volume were observed.Conclusions: Decreased OEF emerges as a potential biomarker for cognitive deficits in OSA, suggesting disturbances in cerebral oxygen metabolism may underlie cognitive impairments. These findings underscore the importance of investigating physiological markers in understanding OSA‐related cognitive dysfunction.