Brain Magnetic Resonance Imaging Scans Research Articles

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities

Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-β-directed monoclonal antibody therapies.

Brain artery diameters and risk of dementia and stroke.

We tested the association of brain artery diameters with dementia and stroke risk in three distinct population-based studies using conventional T2-weighted brain magnetic resonance imaging (MRI) images. We included 8420 adults> 40 years old from three longitudinal population-based studies with brain MRI scans. We estimated and meta-analyzed the hazard ratios (HRs) of the brain and carotids and basilar diameters associated with dementia and stroke. Overall and carotid artery diameters>95th percentile increased the risk for dementia by 1.74 (95% confidence interval [CI], 1.13-2.68) and 1.48 (95% CI, 1.12-1.96) fold, respectively. For stroke, meta-analyses yielded HRs of 1.59 (95% CI, 1.04-2.42) for overall arteries and 2.11 (95% CI, 1.45-3.08) for basilar artery diameters> 95th percentile. Individuals with dilated brain arteries are at higher risk for dementia and stroke, across distinct populations. Our findings underline the potential value of T2-weighted brain MRI-based brain diameter assessment in estimating the risk of dementia and stroke.

Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes

Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non-brain tissue. BE remains challenging in neonatal brain MRI, and despite the existence of several methods, manual segmentation is still considered the gold standard. Therefore, the purpose of this study was to assess different BE methods in the MRI of preterm neonates and their effects on the estimation of intracranial volumes (ICVs). This study included twenty-two premature neonates (mean gestational age ± standard deviation: 28.4 ± 2.1 weeks) with MRI brain scans acquired at term, without detectable lesions or congenital conditions. Manual segmentation was performed for T2-weighted scans to establish reference brain masks. Four automated BE methods were used: Brain Extraction Tool (BET2); Simple Watershed Scalping (SWS); HD Brain Extraction Tool (HD-BET); and SynthStrip. Regarding segmentation metrics, HD-BET outperformed the other methods with median improvements of +0.031 (BET2), +0.002 (SWS), and +0.011 (SynthStrip) points for the dice coefficient; and −0.786 (BET2), −0.055 (SWS), and −0.124 (SynthStrip) mm for the mean surface distance. Regarding ICVs, SWS and HD-BET provided acceptable levels of agreement with manual segmentation, with mean differences of −1.42% and 2.59%, respectively.

Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.

Previous studies mainly focused on risk factors in patients with mild cognitive impairment (MCI) or dementia. The aim of the study was to provide basis for preventing MCI in cognitive normal populations. The data came from a longitudinal retrospective study involving individuals with brain magnetic resonance imaging scans, clinical visits, and cognitive assessment with interval of more than 3 years. Multiple machine-learning technologies, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, were used to establish a prediction model of a future risk of MCI through a combination of clinical and image variables. Among these machine learning models; eXtreme Gradient Boosting (XGB) was the best classification model. The classification accuracy of clinical variables was 65.90%, of image variables was 79.54%, of a combination of clinical and image variables was 94.32%. The best result of the combination was an accuracy of 94.32%, a precision of 96.21%, and a recall of 93.08%. XGB with a combination of clinical and image variables had a potential prospect for the risk prediction of MCI. From clinical perspective, the degree of white matter hyperintensity (WMH), especially in the frontal lobe, and the control of systolic blood pressure (SBP) were the most important risk factor for the development of MCI. The best MCI classification results came from the XGB model with a combination of both clinical and imaging variables. The degree of WMH in the frontal lobe and SBP control were the most important variables in predicting MCI.

Development of Efficient Brain Age Estimation Method Based on Regional Brain Volume From Structural Magnetic Resonance Imaging.

We aimed to create an efficient and valid predicting model which can estimate individuals' brain age by quantifying their regional brain volumes. A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual's cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.

Lactoferrin intake from maternal milk during the neonatal hospitalization and early brain development among preterm infants.

Lactoferrin is an immuno-modulatory nutrient in human milk that may be neuroprotective. In 36 infants born <32 weeks' gestation, we sampled human milk at 14 and 28 days of chronologic age and measured lactoferrin by electrochemiluminescence multiplex immunoassay. Using 3T quantitative brain magnetic resonance imaging scans obtained at term equivalent, we estimated total and regional brain volumes. We compared outcomes between infants exposed to low (bottom tertile, range 0.06-0.13 mg/mL) vs. high (top tertile, range 0.22-0.35 mg/mL) lactoferrin using median regression in models adjusted for gestational age, birth weight z-score, sex, and postmenstrual age. Compared to infants exposed to low lactoferrin, infants exposed to high lactoferrin had 43.9 cc (95% CI: 7.6, 80.4) larger total brain volume, 48.3 cc (95% CI: 12.1, 84.6) larger cortical gray matter, and 3.8 cc (95% CI: 0.7, 7.0) larger deep gray matter volume at term equivalent age. Other regional brain volumes were not statistically different between groups. Higher lactoferrin exposure during the neonatal hospitalization was associated with larger total brain and gray matter volumes, suggesting that lactoferrin may have potential as a dietary supplement to enhance brain growth in the neonatal intensive care unit setting. This study suggests that lactoferrin, a whey protein found in human milk, may be beneficial for preterm infant brain development, and therefore has potential as a dietary supplement in the neonatal intensive care unit setting.

Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.

This study aimed to examine the equivalence of computed tomography (CT)-based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology. This prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement. The intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals. This study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan. Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia. • Deep learning-based image synthesis methods generate synthetic T1-weighted imaging from CT scans. • The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated. • Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

To Assess the Factors Effecting Image Quality in Magnetic Resonance Imaging at 1.5T

Background: Good image quality is essential for accurate diagnosis in magnetic resonance imaging (MRI) examinations. This study aimed to investigate how image quality can be improved by implementing improvement strategies on factors that effects the MRI parameters instead of relying solely on conventional techniques. Method: This research employs a Comparative study design to compare the image quality in routine brain Magnetic Resonance Imaging (MRI) scans at 1.5T before and after implementing image quality improvement strategies. The study involved 37 individuals participant’s data (20 females and 17 males) aged 15-70 years who underwent MRI scans. Participants with any systemic disease, acute trauma, pregnancy and participants with absolute contraindications were excluded from the study. Result: The study found that several factors can effects MRI image quality and can be improved by implementing improvement strategies on these factors by manual adjustments. By identifying factors affecting image quality and evaluating improvement strategies, we seek to optimize MRI protocols and enhance the diagnostic utility of routine brain imaging. For instance, increasing the number of Excitation (NEX) values improved image quality. By increasing the echo time (TE) value a 50% images shows improvement in image quality. The volume of shimming showed better image quality than the other two modes (difficult mode and auto mode). Decreasing the flip angle and voxel size also improved image quality Conclusion: In conclusion, image quality can be improved by implementing improvement strategies on factors that effects the MRI parameters in comparison with the conventional techniques.

Newly discovered variants in unexplained neonatal encephalopathy.

The genetic background of neonatal encephalopathy (NE) is complicated and early diagnosis is beneficial to optimizing therapeutic strategy for patients. NE Patients with unclear etiology received regular clinical tests including ammonia test, metabolic screening test, amplitude-integrated electroencephalographic (aEEG) monitoring, brain Magnetic Resonance Imaging (MRI) scanning, and genetic test. The protein structure change was predicted using Dynamut2 and RoseTTAFold. 15 out of a total of 113 NE Patients were detected with newly reported pathogenic variants. In this sub-cohort, (1) seizure was the primary initial symptoms; (2) four patients had abnormal metabolic screening results, and two of them were also diagnosed with excessive blood ammonia concentration; (3) the brain MRI results were irregular in three infants and the brain waves were of moderate-severe abnormality in about a half of the patients. The novel pathogenic variants discovered in this study belonged to 12 genes, and seven of them were predicted to introduce a premature translation termination. In-silicon predictions showed that four variants were destructive to the protein structure of KCNQ2. Our study expands the mutation spectrum of genes associated with NE and introduces new evidence for molecular diagnosis in this newborn illness.

Effect of adding individualized health education for patients with brain metastasis of lung cancer undergoing radiotherapy, as measured by MRI and cognitive testing.

To explore the clinical efficacy of individualized health education (IHE) and care mode based on magnetic resonance imaging (MRI) combined with Mini-Mental State Examination (MMSE) for lung cancer patients with brain metastases undergoing radiotherapy. This retrospective study involved 50 lung cancer patients with brain metastases. Patients were divided into a control group (n=25, conventional care) and an intervention group (n=25, individualized health education (IHE) care) according to their nursing model. Both groups underwent enhanced brain MRI scans. The patients were assessed using the Mini Mental State Scale (MMSE) before and at 1 month after radiotherapy. At the same time, Montreal Cognitive Assessment (MoCA) was used to assess the degree of cognitive impairment in both groups before and after the intervention. Finally, the European Organization for Research and Treatment of Cancer (EORTC QLQ-C30) questionnaire was used to evaluate the overall health status and quality of life (QOL) (including physical function, emotional function, and social function) of the two groups of patients after radiotherapy. The patients' self-care ability in daily life was assessed using Alzheimer's Disease Collaborative Study Activities of Daily Living (ADCS-ADL). Following intervention, there was no significant difference in MMSE total scores between the control and intervention groups (P > 0.05), or in physical function scores (P > 0.05). However, the intervention group had significantly higher overall QOL scores compared to the control group (P < 0.05), particularly in emotional and social function (P < 0.05). There was no significant difference in total MoCA scores between the two groups (P > 0.05), but the intervention group showed superior scores in visual-spatial, executive function, naming, and attention compared to the control group (all P < 0.05). Following intervention, the intervention group demonstrated better ADCS-ADL scores than the control group (P < 0.05). The IHE mode effectively improved emotional and social functions and enhanced QOL in lung cancer patients with brain metastases undergoing radiotherapy.

Brain Tumor Detection Using Machine Learning with CNN Algorithm

Abstract: Brain tumors are a major global health issue, and successful treatment frequently depends on an early and precise diagnosis. Traditional methods of brain tumor detection, such as manual interpretation of medical images, can be timeconsuming and prone to human error. Machine learning techniques have emerged as a promising approach to assist medical professionals in the early detection and classification of brain tumors. This study presents a novel method for brain tumor detection utilizing machine learning algorithms .The dataset used in this research comprises a collection of brain MRI (Magnetic Resonance Imaging)scans from diverse sources, including both tumor and non-tumor cases. We preprocess the data by enhancing image quality and for classification, different machine learning techniques are used, such as random forests, support vector machines (SVMs), and convolutional neural networks (CNNs).

Unconjugated bilirubin is correlated with the severeness and neurodevelopmental outcomes in neonatal hypoxic-ischemic encephalopathy

Unconjugated bilirubin (UB) levels during the first week after birth are related to outcomes in neonatal hypoxic-ischemic encephalopathy (HIE). Clinical Sarnat staging of HIE, brain magnetic resonance imaging (MRI), hearing outcomes, and neurodevelopmental outcomes ≥ 1 year were used to correlate UB in 82 HIE patients. The initial UB level was significantly correlated with lactic acid levels. The peak UB was higher (p < 0.001) in stage I (10.13 ± 4.03 mg/dL, n = 34) than in stages II and III (6.11 ± 2.88 mg/dL, n = 48). Among the 48 patients receiving hypothermia treatment, a higher peak UB was significantly (p < 0.001) correlated with unremarkable brain MRI scans and unremarkable neurodevelopmental outcomes at age ≥ 1 year. The peak UB were higher (P = 0.015) in patients free of seizures until 1 year of age (6.63 ± 2.91 mg/dL) than in patients with seizures (4.17 ± 1.77 mg/dL). Regarding hearing outcomes, there were no significant differences between patients with and without hearing loss. The UB level in the first week after birth is an important biomarker for clinical staging, MRI findings, seizures after discharge before 1 year of age, and neurodevelopmental outcomes at ≥ 1 year of age.

Effects of healthy aging and mnemonic strategies on verbal memory performance across the adult lifespan: Mediating role of posterior hippocampus.

In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1-3 [CA1-3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age-related verbal memory decline. Healthy participants were administered the California Verbal Learning Test-II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age-related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1-3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1-3 volumes within the hippocampal body subregion suggested an indirect effect of age-related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age-related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease.

A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively. The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Severe CNS involvement in a subset of long-term treated children with infantile-onset Pompe disease

IntroductionThe standard of care for patients with infantile-onset Pompe disease (IOPD) is enzyme replacement therapy (ERT), which does not cross the blood brain barrier. While neuromuscular manifestations of IOPD are well-described, central nervous system (CNS) manifestations of this disorder are far less characterized. Here we describe severe CNS-related neurological manifestations including seizures and encephalopathy in six individuals with IOPD. MethodWe identified six children with IOPD who developed CNS manifestations such as seizures and/or encephalopathy. We studied their brain magnetic resonance imaging scans (MRIs) and graded the severity of white matter hyperintensities (WMHI) using the Fazekas scale scoring system as previously published. Longitudinal cognitive measures were available from 4/6 children. ResultsAll six IOPD patients (4 males/2 females) had been treated with ERT for 12–15 years. Seizures and/or encephalopathy were noted at a median age at onset of 11.9 years (range 9–15 years). All were noted to have extensive WMHI in the brain MRIs and very high Fazekas scores which preceded the onset of neurological symptoms. Longitudinal IQ scores from four of these children suggested developmental plateauing. DiscussionAmong a subset of IOPD patients on long-term ERT, CNS manifestations including hyperreflexia, encephalopathy and seizures may become prominent, and there is likely an association between these symptoms and significant WMHI on MRI. Further study is needed to identify risk factors for CNS deterioration among children with IOPD and develop interventions to prevent neurological decline.

Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging

Abstract Given that scientific practices contribute to the climate crisis, scientists should reflect on the planetary impact of their work. Research computing can have a substantial carbon footprint in cases where researchers employ computationally expensive processes with large amounts of data. Analysis of human neuroimaging data, such as Magnetic Resonance Imaging brain scans, is one such case. Here, we consider ten ways in which those who conduct human neuroimaging research can reduce the carbon footprint of their research computing, by making adjustments to the ways in which studies are planned, executed, and analysed; as well as where and how data are stored.

An effective Alzheimer’s disease segmentation and classification using Deep ResUnet and Efficientnet

Alzheimer’s disease (AD) is a degenerative neurologic condition that results in the deterioration of several brain processes (e.g. memory loss). The most notable physical alteration in AD is the impairment of brain cells. An accurate examination of brain pictures may help to find the disease earlier because early diagnosis is crucial to enhancing patient care and treatment outcomes. Therefore, an easy and error-free system for AD diagnosis has recently received much research attention. Conventional image processing techniques sometimes cannot observe the significant features. As a result, the objective of this research is to develop an accurate and efficient method for identifying AD using magnetic resonance imaging (MRI). To begin with, the brain regions in the MRI images are segmented using a powerful Deep ResUnet-based approach. Then, the global and local features from the segmented images are recovered using a Multi-Scale Attention Siamese Network (MASNet)-based network. After extracting the features, the Slime Mould Algorithm-based feature selection process is conducted. Finally, the stages of AD are categorized using the EfficientNetB7 model. The efficacy of the presented method has been tested using brain MRI scans from the Kaggle dataset and the AD Neuroimaging Initiative (ADNI) dataset, and it achieves 99.31% and 99.38% accuracy, respectively. Finally, the study results show that the suggested method is helpful for accurate AD categorization. Communicated by Ramaswamy H. Sarma

Solitary atrial Rhabdomyoma in an infant with tuberous sclerosis: a case report and review of the literature

BackgroundDespite its rare incidence of 1/40,000, fetal cardiac rhabdomyoma (CR) represents the prevailing type of benign cardiac fetal tumors, which commonly affects the ventricles. Fetal CRs rarely occur in the right atrium. Thus, the presentation of atrial fibrillation and premature atrial contractions (PAC) due to a solitary cardiac rhabdomyoma is an extremely rare scenario. Our literature review found that only 2% (1 out of 61) of rhabdomyoma cases were found in the right atrium. The majority of fetal cardiac rhabdomyomas are associated with tuberous sclerosis complex (TSC).Case presentationA 7-day-old male neonate presented with arrhythmias and an atrial mass for further evaluation. Echocardiography revealed a hyperechoic, round, uniform right atrial mass (25 mm). An abdominal and testicular ultrasound showed multiple thin-walled cortical cysts in both kidneys and a scrotal hydrocele, respectively. His laboratory workup was insignificant except for hypomagnesemia. Electrocardiography revealed junctional rhythm and PACs with wave distortions. A brain magnetic resonance imaging scan revealed multiple subependymal lesions on the frontal and occipital horns of the lateral ventricles. These findings (Fig. 1), along with a family history of TSC, confirmed the diagnosis of TSC with associated CR. The patient was treated symptomatically with an anti-convulsant and monitored with regular follow-ups. Surgical resection was not required.ConclusionDespite CR’s predominance in the ventricles, a diagnosis of rhabdomyoma should be kept in mind in the presence of a solitary atrial mass and PACs. Physicians should evaluate systemic findings related to TSC and provide appropriate follow-up and family screening. Surgical resection is not always required, and symptom management can be achieved through medical treatment alone.

Sarcopenia and Hypoxia in Patients with Obstructive Sleep Apnea

Objectives. Obstructive sleep apnea (OSA) is associated with a variety of health issues. Both OSA and sarcopenia are associated with metabolic disorders; however, there is limited literature assessing the correlation between them. Therefore, we aimed to investigate sarcopenia using temporal muscle thickness (TMT) in patients with obstructive sleep apnea (OSA) and the association between sarcopenia and hypoxia. Methods. We enrolled patients with OSA and healthy controls. Both groups underwent brain magnetic resonance imaging (MRI) scans, including three-dimensional T1-weighted imaging. TMT, a marker for sarcopenia, was obtained based on the T1-weighted imaging and compared between the groups. Additionally, we analyzed the correlation between TMT and clinical factors in patients with OSA. Results. In total, 40 patients with OSA and 52 healthy controls were evaluated. There was no difference in TMT between patients with OSA and healthy controls ( 10.425 ± 2.13 vs. 10.400 ± 1.94 mm, p = 0.953 ). In the correlation analysis, age was negatively correlated with TMT ( r = − 0.356 , p = 0.023 ), and lowest oxygen saturation ( r = − 0.558 , p &lt; 0.001 ) in patients with OSA. Conclusion. Our results demonstrated negative correlations between TMT and lowest oxygen saturation in the patients with OSA. These findings suggest potential relationships between sarcopenia and hypoxia in patients with OSA. Thus, these results underline the importance of maintaining oxygen saturation in patients with OSA by implementing active treatment. This study also demonstrates the feasibility of sarcopenia assessment by measuring TMT through conventional head MRI in patients with various neurological disorders.

SPATIAL AND STRUCTURAL COMPLEXITY OF CEREBRAL HEMISPHERES IN MALE AND FEMALE BRAIN: FRACTAL AND QUANTITATIVE ANALYSES OF MRI BRAIN SCANS

Objectives: The aim of the present study was to compare the features of the structural complexity of the cerebral hemispheres in men and women using fractal analysis of outlined and skeletonized images, as well as quantitative analysis of digital skeletons of the cerebral hemispheres. Material and Methods: Magnetic resonance imaging brain scans of 100 individuals aged 18-86 years (44 males and 56 females) were investigated. Five tomographic sections of each brain were selected for morphometric study (4 coronal and 1 axial sections). The sections were preprocessed, and outlined and skeletonized images were obtained. Fractal analysis was conducted using the two-dimensional box counting method, and fractal dimensions of outlined and skeletonized images were determined. Additionally, quantitative analysis of skeletonized images was performed, determining the following parameters: branches, junctions, end-point voxels, junction voxels, slab voxels, triple points, quadruple points, average branch length, and maximum branch length. Results: We observed that both variants of fractal dimension in males and females did not show significant differences, although most quantitative parameters in males were larger than those in females. Conclusions: The spatial and structural complexity of the cerebral hemispheres, as characterized by fractal dimensions, is almost indistinguishable between males and females. However, in some individual tomographic sections, the male brain may exhibit a slightly higher number of end-point voxels, corresponding to the gyri of the cerebral hemispheres. The obtained data can be used in clinical practice for diagnostic purposes (e.g., for detecting malformations) and for theoretical studies in neuroanatomy.