Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis.

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

Putamen iron quantification in diseases with neurodegeneration: a meta-analysis of the quantitative susceptibility mapping technique.

Quantitative susceptibility mapping (QSM) is an MRI technique that accurately measures iron concentration in brain tissues. This meta-analysis synthesized evidence from 30 studies that used QSM to quantify the iron levels in the putamen. The PRISMA statement was adhered to when conducting the systematic reviews and meta-analyses. We conducted a meta-analysis using a random-effects model, as well as subgroup analyses (disease type, geographic region, field strength, coil, disease type, age, and sex) and sensitivity analysis. A total of 1247 patients and 1035 controls were included in the study. Pooled results showed a standardized mean difference (SMD) of 0.41 (95% CI 0.19 to 0.64), with the strongest effect seen in Alzheimer's disease (AD) at 1.01 (95% CI 0.50 to 1.52). Relapsing-remitting multiple sclerosis (RRMS) also showed increased putaminal iron at 0.37 (95% CI 0.177 to 0.58). No significant differences were observed in Parkinson's disease (PD). No significant differences were found between subgroups based on geographic region, field strength, coil, disease type, age, and sex. The studies revealed significant heterogeneity, with field strength as the primary source, while other factors, such as disease type, location, age, sex, and coil type, may have contributed. The sensitivity analysis showed that these factors did not have a significant influence on the overall results. In summary, this meta-analysis supports abnormalities in putaminal iron content across different diseases with neurodegeneration, especially AD and RRMS, as measured by QSM. This highlights the potential of QSM as an imaging biomarker to better understand disease mechanisms involving disturbances in brain iron homeostasis.

A 65-year-old woman with ALS and bilateral precentral motor band sign.

Advanced MRI techniques, including SWI, MinIP, and QSM, are instrumental in detecting the "motor band sign" in ALS, aiding in the early diagnosis and assessment of upper motor neuron involvement, which is critical for therapeutic interventions.

Thalamic Alterations in Motor Neuron Diseases: A Systematic Review of MRI Findings.

BACKGROUND: Motor neuron diseases (MNDs) are progressive neurodegenerative disorders characterized by motor impairment and non-motor symptoms. The involvement of the thalamus in MNDs, especially in conditions such as amyotrophic lateral sclerosis (ALS), and its interaction with frontotemporal dementia (FTD), has garnered increasing research interest. This systematic review analyzed magnetic resonance imaging (MRI) studies that focused on thalamic alterations in MNDs to understand the significance of these changes and their correlation with clinical outcomes. METHODS: Following PRISMA 2020 guidelines, the PubMed and Scopus databases were searched from inception to June 2023 for studies related to MRI findings in the thalamus of patients with MNDs. Eligible studies included adult patients diagnosed with ALS or other forms of MND who underwent brain MRI, with outcomes related to thalamic alterations. Studies were evaluated for risk of bias using the Newcastle-Ottawa scale. RESULTS: A total of 52 studies (including 3009 MND patients and 2181 healthy controls) used various MRI techniques, including volumetric analysis, diffusion tensor imaging, and functional MRI, to measure thalamic volume, connectivity, and other alterations. This review confirmed significant thalamic changes in MNDs, such as atrophy and microstructural degradation, which are associated with disease severity, progression, and functional disability. Thalamic involvement varies across different MND subtypes and is influenced by the presence of cognitive impairment and mutations in genes including chromosome 9 open reading frame 72 (C9orf72). The synthesis of findings across studies indicates that thalamic pathology is a prevalent early biomarker of MNDs that contributes to motor and cognitive deficits. The thalamus is a promising target for monitoring as its dysfunction underpins a variety of clinical symptoms in MNDs. CONCLUSIONS: Thalamic alterations provide valuable insights into the pathophysiology and progression of MNDs. Multimodal MRI techniques are potent tools for detecting dynamic thalamic changes, indicating structural integrity, connectivity disruption, and metabolic activity.

Breast percent density changes in digital mammography pre- and post-radiotherapy.

INTRODUCTION: Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. METHODS: This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density. CONCLUSIONS: This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis.

Introduction: Iron accumulation in the brain has been linked to diabetes, but its role in subcortical structures involved in motor and cognitive functions remains unclear. Quantitative susceptibility mapping (QSM) allows the non-invasive quantification of iron deposition in the brain. This systematic review and meta-analysis examined magnetic susceptibility measured by QSM in the subcortical nuclei of patients with type 2 diabetes mellitus (T2DM) compared with controls. Methods: PubMed, Scopus, and Web of Science databases were systematically searched [following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines] for studies reporting QSM values in the deep gray matter (DGM) regions of patients with T2DM and controls. Pooled standardized mean differences (SMDs) for susceptibility were calculated using fixed-effects meta-analysis models, and heterogeneity was assessed using I2. Sensitivity analyses were conducted, and publication bias was evaluated using Begg's and Egger's tests. Results: Six studies including 192 patients with T2DM and 245 controls were included. This study found a significant increase in iron deposition in the subcortical nuclei of patients with T2DM compared to the control group. The study found moderate increases in the putamen (SMD = 0.53, 95% CI 0.33 to 0.72, p = 0.00) and dentate nucleus (SMD = 0.56, 95% CI 0.27 to 0.85, p = 0.00) but weak associations between increased iron levels in the caudate nucleus (SMD = 0.32, 95% CI 0.13 to 0.52, p = 0.00) and red nucleus (SMD = 0.22, 95% CI 0.00 0.44, p = 0.05). No statistical significance was found for iron deposition alterations in the globus pallidus (SMD = 0.19; 95% CI -0.01 to 0.38; p = 0.06) and substantia nigra (SMD = 0.12, 95% CI -0.10, 0.34, p = 0.29). Sensitivity analysis showed that the findings remained unaffected by individual studies, and consistent increases were observed in multiple subcortical areas. Discussion: QSM revealed an increase in iron in the DGM/subcortical nuclei in T2DM patients versus controls, particularly in the motor and cognitive nuclei, including the putamen, dentate nucleus, caudate nucleus, and red nucleus. Thus, QSM may serve as a potential biomarker for iron accumulation in T2DM patients. However, further research is needed to validate these findings.

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

BACKGROUND: Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. PURPOSE/HYPOTHESIS: We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. STUDY TYPE: A systematic review and diagnostic meta-analysis. POPULATION/SUBJECTS: Sixty-seven studies with 6,285 patients were included. FIELD STRENGTH/SEQUENCE: Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. ASSESSMENT: The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. STATISTICAL TESTS: Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. RESULTS: sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. DATA CONCLUSION: The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Dysfunctional feedback processing in male methamphetamine abusers: Evidence from neurophysiological and computational approaches.

Methamphetamine use disorder (MUD) as a major public health risk is associated with dysfunctional neural feedback processing. Although dysfunctional feedback processing in people who are substance dependent has been explored in several behavioral, computational, and electrocortical studies, this mechanism in MUDs requires to be well understood. Furthermore, the current understanding of latent components of their behavior such as learning speed and exploration-exploitation dilemma is still limited. In addition, the association between the latent cognitive components and the related neural mechanisms also needs to be explored. Therefore, in this study, the underlying neurocognitive mechanisms of feedback processing of such impairment, and age/gender-matched healthy controls are evaluated within a probabilistic learning task with rewards and punishments. Mathematical modeling results based on the Q-learning paradigm suggested that MUDs show less sensitivity in distinguishing optimal options. Additionally, it may be worth noting that MUDs exhibited a slight decrease in their ability to learn from negative feedback compared to healthy controls. Also through the lens of underlying neural mechanisms, MUDs showed lower theta power at the medial-frontal areas while responding to negative feedback. However, other EEG measures of reinforcement learning including feedback-related negativity, parietal-P300, and activity flow from the medial frontal to lateral prefrontal regions, remained intact in MUDs. On the other hand, the elimination of the linkage between value sensitivity and medial-frontal theta activity in MUDs was observed. The observed dysfunction could be due to the adverse effects of methamphetamine on the cortico-striatal dopamine circuit, which is reflected in the anterior cingulate cortex activity as the most likely region responsible for efficient behavior adjustment. These findings could help us to pave the way toward tailored therapeutic approaches.

Advanced magnetic resonance neuroimaging techniques: feasibility and applications in long or post-COVID-19 syndrome - a review.

Long-term or post-COVID-19 syndrome (PCS) is a condition that affects people infected with SARS­CoV­2, the virus that causes COVID-19. PCS is characterized by a wide range of persistent or new symptoms that last months after the initial infection, such as fatigue, shortness of breath, cognitive dysfunction, and pain. Advanced magnetic resonance (MR) neuroimaging techniques can provide valuable information on the structural and functional changes in the brain associated with PCS as well as potential biomarkers for diagnosis and prognosis. In this review, we discuss the feasibility and applications of various advanced MR neuroimaging techniques in PCS, including perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), functional MR imaging (fMRI), diffusion tensor imaging (DTI), and tractography. We summarize the current evidence on neuroimaging findings in PCS, the challenges and limitations of these techniques, and the future directions for research and clinical practice. Although still uncertain, advanced MRI techniques show promise for gaining insight into the pathophysiology and guiding the management of COVID-19 syndrome, pending larger validation studies.

Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.

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.

Iron quantification in basal ganglia: quantitative susceptibility mapping as a potential biomarker for Alzheimer's disease - a systematic review and meta-analysis.

Introduction: Alzheimer's disease (AD), characterized by distinctive pathologies such as amyloid-ß plaques and tau tangles, also involves deregulation of iron homeostasis, which may accelerate neurodegeneration. This meta-analysis evaluated the use of quantitative susceptibility mapping (QSM) to detect iron accumulation in the deep gray matter (DGM) of the basal ganglia in AD, contributing to a better understanding of AD progression, and potentially leading to new diagnostic and therapeutic approaches. Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the PubMed, Scopus, Web of Sciences, and Google Scholar databases up to October 2023 for studies employing QSM in AD research. Eligibility criteria were based on the PECO framework, and we included studies assessing alterations in magnetic susceptibility indicative of iron accumulation in the DGM of patients with AD. After initial screening and quality assessment using the Newcastle-Ottawa Scale, a meta-analysis was conducted to compare iron levels between patients with AD and healthy controls (HCs) using a random-effects model. Results: The meta-analysis included nine studies comprising 267 patients with AD and 272 HCs. There were significantly higher QSM values, indicating greater iron deposition, in the putamen (standardized mean difference (SMD) = 1.23; 95% CI: 0.62 to 1.84; p = 0.00), globus pallidus (SMD = 0.79; 95% CI: 0.07 to 1.52; p = 0.03), and caudate nucleus (SMD = 0.72; 95% CI: 0.39 to 1.06; p = 0.00) of AD patients compared to HCs. However, no significant differences were found in the thalamus (SMD = 1.00; 95% CI: -0.42 to 2.43; p = 0.17). The sensitivity analysis indicated that no single study impacted the overall results. Age was identified as a major contributor to heterogeneity across all basal ganglia nuclei in subgroup analysis. Older age (>69 years) and lower male percentage (≤30%) were associated with greater putamen iron increase in patients with AD. Conclusion: The study suggests that excessive iron deposition is linked to the basal ganglia in AD, especially the putamen. The study underscores the complex nature of AD pathology and the accumulation of iron, influenced by age, sex, and regional differences, necessitating further research for a comprehensive understanding.

MRI biomarkers and neuropsychological assessments of hippocampal and parahippocampal regions affected by ALS: A systematic review.

BACKGROUND AND OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra-motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and neuropsychological assessments of the hippocampal and parahippocampal regions in patients with ALS. METHODS: A systematic review was conducted in the Scopus and PubMed databases for studies published between January 2000 and July 2023. The inclusion criteria were (1) MRI studies to assess hippocampal and parahippocampal regions in ALS patients, and (2) studies reporting neuropsychological data in patients with ALS. RESULTS: A total of 46 studies were included. Structural MRI revealed hippocampal atrophy, especially in ALS-FTD, involving specific subregions (CA1, dentate gyrus). Disease progression and genetic factors impacted atrophy patterns. Diffusion tensor imaging (DTI) showed increased mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in the hippocampal tracts and adjacent regions, indicating loss of neuronal and white matter integrity. Functional MRI (fMRI) revealed reduced functional connectivity (FC) between the hippocampus, parahippocampus, and other regions, suggesting disrupted networks. Perfusion MRI showed hypoperfusion in parahippocampal gyri. Magnetic resonance spectroscopy (MRS) found changes in the hippocampus, indicating neuronal loss. Neuropsychological tests showed associations between poorer memory and hippocampal atrophy or connectivity changes. CA1-2, dentate gyrus, and fimbria atrophy were correlated with worse memory. CONCLUSIONS: The hippocampus and the connected regions are involved in ALS. Hippocampal atrophy disrupted connectivity and metabolite changes correlate with cognitive and functional decline. Specific subregions can be particularly affected. The hippocampus is a potential biomarker for disease monitoring and prognosis.

Post-COVID-19 conditions: a systematic review on advanced magnetic resonance neuroimaging findings.

Post-COVID conditions (PCCs) cover a wide spectrum of lingering symptoms experienced by survivors of coronavirus disease 2019 (COVID-19). Neurological and neuropsychiatric sequelae are common in PCCs. Advanced magnetic resonance imaging (MRI) techniques can reveal subtle alterations in brain structure, function, and perfusion that underlie these sequelae. This systematic review aimed to synthesize findings from studies that used advanced MRI to characterize brain changes in individuals with PCCs. A detailed literature search was conducted in the PubMed and Scopus databases to identify relevant studies that used advanced MRI modalities, such as structural MRI (sMRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and perfusion-weighted imaging (PWI), to evaluate brain changes in PCCs. Twenty-five studies met the inclusion criteria, comprising 1219 participants with PCCs. The most consistent findings from sMRI were reduced gray matter volume (GMV) and cortical thickness (CTh) in cortical and subcortical regions. DTI frequently reveals increased mean diffusivity (MD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in white matter tracts (WMTs) such as the corpus callosum, corona radiata, and superior longitudinal fasciculus. fMRI demonstrated altered functional connectivity (FC) within the default mode, salience, frontoparietal, somatomotor, subcortical, and cerebellar networks. PWI showed decreased cerebral blood flow (CBF) in the frontotemporal area, thalamus, and basal ganglia. Advanced MRI shows changes in the brain networks and regions of the PCCs, which may cause neurological and neuropsychiatric problems. Multimodal neuroimaging may help understand brain-behavior relationships. Longitudinal studies are necessary to better understand the progression of these brain anomalies.

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.

INTRODUCTION: Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. METHODS: This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. RESULTS: Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. CONCLUSION: Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.

Brain metastases from breast cancer using magnetic resonance imaging: A systematic review.

Despite improvements in imaging and treatment approaches, brain metastases (BMs) continue to be the primary cause of mortality and morbidity in about 20% of adult cancer patients. This research aimed to review the magnetic resonance imaging (MRI) and clinical characteristics of BMs resulting from breast cancer (BC). A systematic review of original research articles published from January 2000 to June 2023. We selected studies that reported MRI findings of BMs in BC patients. We excluded reviews, case reports, books/book chapters, animal studies and irrelevant records. We identified 24 studies that included 1580 BC patients with BMs. T1-weighted (T1-w) (pre- and postcontrast), T2-weighted (T2-w), fluid-attenuated inversion recovery (FLAIR) and T2*-weighted (T2*-w) was used to measure the lesion size, shape and area. In other studies, advanced structural techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and susceptibility-weighted imaging (SWI) were used to more precisely and sensitively evaluate the pathological area. Furthermore, functional and metabolic techniques like functional MRI (fMRI), magnetic resonance spectroscopy (MRS) and perfusion-weighted imaging (PWI) have also been utilised. The MRI findings of BMs varied depending on the MRI technique, the BC subtype, the lesion size and shape, the presence of haemorrhage or necrosis and the comparison with other brain tumours. Some MRI findings were associated with prognosis, recurrence or cognitive impairment in BC patients with BMs. MRI detects, characterises and monitors BMs from BC. Findings vary by MRI technique, BC subtype, lesion characteristics and comparison with other brain tumours. More research should validate emerging MRI techniques, determine the clinical implications of findings and explore the underlying mechanisms and biology of BMs from BC. MRI is a valuable tool for diagnosis, targeted therapy and studying BC metastasis.

An uncommon presentation of early brainstem high-grade glioma in a 33-year-old male: A case study and review of literature.

INTRODUCTION AND IMPORTANCE: Unlike children, high-grade brainstem glioma (HG-BSG) in adults is a rare and diverse group of tumors. They can be classified based on their location and physical characteristics, which distinguishes them from pediatric brainstem gliomas. They are rare in adults, constituting only 1 % to 2 % of intracranial gliomas. They are often aggressive and have a poor prognosis, with a median survival time of 24 months. The diagnosis of brainstem gliomas typically involves a combination of clinical evaluation and imaging studies, mainly magnetic resonance imaging (MRI), which provides detailed images and can help identify the characteristics of the tumor. CASE PRESENTATION AND METHODS: We present a case study of an uncommon presentation of an early stage of HG-BSG in a 33-year-old male, who had a contrast-enhancing lesion in the ventrolateral medulla that extended to the lower aspect of the fourth ventricle and caused ventricular compression. CLINICAL DISCUSSION: The findings were consistent with the literature on the current state of HG-BSG MRI findings, which typically show contrast-enhancing, hyperintense, and infiltrative lesions that involve the pons, midbrain, or medulla oblongata. The diagnosis of HG-BSG was based on clinical and radiological criteria, as the patient refused to undergo a surgical biopsy. We also performed a literature review on the current state of brainstem HG-BSG MRI findings, summarizing the main features and patterns of these tumors. CONCLUSION: MRI can offer useful information regarding the tumor's location, size, and features, as well as its impact on surrounding tissues and cerebrospinal fluid circulation.

Iron quantification in basal ganglia using quantitative susceptibility mapping in a patient with ALS: a case report and literature review.

Background: Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can measure the magnetic susceptibility of tissues, which can reflect their iron content. QSM has been used to detect iron accumulation in cortical and subcortical brain regions. However, its application in subcortical regions such as the basal ganglia, particularly the putamen, is rare in patients with amyotrophic lateral sclerosis (ALS). Case presentation and literature review: We present the case of a 40-year-old male patient with ALS who underwent an MRI for QSM. We compared his QSM images with those of a control subject and performed a quantitative analysis of the magnetic susceptibility values in the putamen regions. We also reviewed the literature on previous QSM studies in ALS and summarized their methods and findings. Our QSM analysis revealed increased magnetic susceptibility values in the bilateral putamen of the ALS patient compared to controls, indicating iron overload. This finding is consistent with previous studies reporting iron dysregulation in subcortical nuclei in ALS. We also discussed the QSM processing techniques used in our study and in the literature, highlighting their advantages and limitations. Conclusion: This case report demonstrates the potential of QSM as a sensitive MRI biomarker for evaluating iron levels in subcortical regions of ALS patients. QSM can provide quantitative information on iron deposition patterns in both motor and extra-motor areas of ALS patients, which may help understand the pathophysiology of ALS and monitor disease progression. Further studies with larger samples are needed to validate these results and explore the clinical implications of QSM in ALS.

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments.

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.

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm.

INTRODUCTION AND IMPORTANCE: 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. CASE PRESENTATION AND METHODS: 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. CLINICAL DISCUSSION: 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. CONCLUSION: 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.

Post-COVID-19 Vaccination CNS Magnetic Resonance Imaging Findings: A Systematic Review.

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.