The 3-dimensional cranial nerve imaging (CRANI) sequence may assist visualization of anatomical details of extraforaminal cranial nerves and aid in clinical diagnosis and preoperative planning. In this study, we investigated the feasibility of using a combined CRANI and magnetization-prepared rapid-acquisition gradient-echo (MPRAGE) imaging protocol to comprehensively identify trigeminal nerve projections.
We evaluated the detection of distal regions of three branches of the ophthalmic nerve (V1), three branches of the maxillary nerve (V2), and five branches of the mandibular nerve (V3) in seven healthy adult subjects, with and without contrast injection. Nerve branches were rated on a 5-point scale by three observers. Interobserver reliability was studied using weighted kappa statistics and percentage agreement.
Among V1 and V2 branches, the frontal nerve and infraorbital nerve were most successfully identified (average rating of 3.9, agreement >80%) in precontrast MPRAGE images. In V3 branches, lingual and inferior alveolar nerves were most successfully identified (average rating of 3.9, agreement >80%) in precontrast CRANI images, with an excellent average rating. In all cases except one, interobserver reliability was rated good to excellent. The buccal nerve was the only branch with a low average interobserver rating. Gadolinium contrast did not improve nerve segment visualization in our study. This may relate to the specific anatomic regions assessed, gadolinium dose, postcontrast image timing, and lack of pathology.
A combined CRANI and MPRAGE protocol can be combined to visualize distal branches of V1, V2, and V3 and has potential for clinical use.
Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics.
We adhered to the Joanna Briggs Institute methodology for scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. The databases Medline (Ovid), Embase (Ovid), CINAHL (Ebsco), and Web of Science (University of British Columbia institutional access) were searched on August 24, 2023 using a combination of medical subject headings and keyword terms for the topic of serum biomarkers in MS.
A total of 9880 articles were retrieved in total of which 6271 unique titles and abstracts were screened. Twelve of the 259 resultant papers contained sGFAP data and proceeded to extraction. It was found that lesion MRI metrics generally had a positive relationship with sGFAP, while gray matter and white matter metrics, including normal-appearing white matter, were related negatively or not at all.
These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression.
This study sought to explore dynamic degree centrality (DC) variability in particular regions of the brain in patients with poststroke Broca aphasia (BA) using a resting-state functional magnetic resonance imaging (rs-fMRI) approach, comparing differences between Uyghur and Chinese BA patients.
This study investigated two factors, language and BA status, and divided patients into four groups: Uyghur aphasia patients (UA), Uyghur normal control subjects (UN), Chinese aphasia patients (CA), and Chinese normal subjects (CN) who underwent rs-fMRI analysis. Two-way analysis of variance (ANOVA) was used to calculate the comprehensive differences in dynamic DC among these four groups. Correlations between DC and language behavior were assessed with partial correlation analyses.
Two-way ANOVA revealed comparable results for the results of pairwise comparisons of dynamic DC variability among the four groups in the right middle frontal gyrus/orbital part (ORBmid.R), right superior frontal gyrus/dorsolateral, and right precuneus (PCUN.R), with results as follows: UA < UN, CA > CN, UA < CA, and UN > CN (p < .05, with the exception of the p-values for UA and UN in superior frontal gyrus/dorsolateral). In contrast, the opposite results were observed for the right calcarine fissure and surrounding cortex (CAL.R, p < .05).
The observed enhancement of dynamic DC variability in ORBmid.R and PCUN.R among Chinese BA patients and in CAL.R in Uyghur BA patients may be attributable to language network restructuring. Overall, these results suggest that BA patients who use different language families may exhibit differences in the network mechanisms that characterize observed impairments of language function.
The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.
Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.
Epilepsy, affecting 0.5%-1% of the global population, presents a significant challenge with 30% of patients resistant to medical treatment. Temporal lobe epilepsy, a common cause of medically refractory epilepsy, is often caused by hippocampal sclerosis (HS). HS can be divided further by subtype, as defined by the International League Against Epilepsy (ILAE). Type 1 HS, the most prevalent form (60%-80% of all cases), is characterized by cell loss and gliosis predominantly in the subfields cornu ammonis (CA1) and CA4. Type 2 HS features cell loss and gliosis primarily in the CA1 sector, and type 3 HS features cell loss and gliosis predominantly in the CA4 subfield. This literature review evaluates studies on hippocampal subfields, exploring whether observable atrophy patterns from in vivo and ex vivo magnetic resonance imaging (MRI) scans correlate with histopathological examinations with manual or automated segmentation techniques. Our findings suggest only ex vivo 1.5 Tesla (T) or 3T MRI with manual segmentation or in vivo 7T MRI with manual or automated segmentations can consistently correlate subfield patterns with histopathologically derived ILAE-HS subtypes. In conclusion, manual and automated segmentation methods offer advantages and limitations in diagnosing ILAE-HS subtypes, with ongoing research crucial for refining hippocampal subfield segmentation techniques and enhancing clinical applicability.
High-resolution magnetic resonance imaging (HR-MRI) can provide valuable insights into the histopathological characteristics of moyamoya disease (MMD). However, the patterns of vessel wall contrast enhancement have not been well established. We aimed to identify the contrast enhancement patterns of the vessel walls associated with acute cerebral infarction using HR-MRI in MMD.
In this retrospective study, we conducted genetic tests for Ring Finger Protein 213 (RNF 213) and performed HR-MRI on patients suspected of having MMD. We analyzed wall enhancement patterns including concentric, eccentric, or mixed enhancement types, and the occurrence of acute cerebral infarction in patients who simultaneously tested positive for RNF 213 and exhibited definite features of MMD on HR-MRI.
Among 306 patients who underwent RNF 213 tests for the evaluation of MMD, 56 showed positive RNF 213, and HR-MRI was performed on 32 of them. Among the patients with acute cerebral infarction, the incidence rate was significantly higher in the group with concentric wall enhancement compared to patients without acute cerebral infarction (73.3% vs. 17.0%, p < .002). Furthermore, the incidence was notably elevated, even in patients with pure concentric wall enhancement (40.0% vs. 5.9%, p = .033). The area under the curve (AUC) for the group with any concentric wall enhancement showed a significant result of .78 (95% confidence interval [CI]: .61-.95, p = .007), whereas the predictive ability for pure concentric wall enhancement did not reach significance (AUC = .67, 95% CI: .48-.86, p = .100).
Concentric wall enhancement was a significant predictor of acute cerebral infarction in patients with MMD.
Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT.
A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method.
We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively.
AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.
The optic nerve sheath diameter (ONSD) is a commonly used estimate of intracranial pressure (ICP). The rationale behind this is that pressure changes in the cerebrospinal fluid affect the optic nerve subarachnoid space (ONSAS) thickness. Still, possible effects on other compartments of the optic nerve sheath (ONS) have not been studied. This is the first study ever to analyze all measurable compartments of the ONS for associations with elevated ICP.
We measured changes in ICP and changes in ONS compartments in 75 patients treated with invasive ICP monitoring at the Karolinska University Hospital. Associations between changes in ICP and changes in ONS compartments were estimated with generalized estimating equations. The potential to identify elevated ICP was assessed with the area under the receiver operating characteristic curve (AUROC) for ONS compartments associated with ICP changes.
Both ONSAS and perioptic dura mater thickness were significantly associated with changes in ICP in multivariable modeling. ONSAS was the only compartment that independently predicted changes in ICP, with an AUROC of 0.69 for predicting ICP increase. Still, both the perioptic dura mater thickness and the optic nerve diameter added value in predicting ICP changes in multivariable modeling.
The results from this study challenge the current understanding of the mechanism behind the association between ICP and ONSD. Contrary to the common opinion that ONSAS is the only affected compartment, this study shows a more complex picture. It suggests that all ONS compartments may add value in predicting changes in ICP.
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