Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach.

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). RESULTS: 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. CONCLUSIONS: To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.

Detection of Acute Brain Injury in Intensive Care Unit Patients on ECMO Support Using Ultra-Low-Field Portable MRI: A Retrospective Analysis Compared to Head CT.

Early detection of acute brain injury (ABI) is critical to intensive care unit (ICU) patient management and intervention to decrease major complications. Head CT (HCT) is the standard of care for the assessment of ABI in ICU patients; however, it has limited sensitivity compared to MRI. We retrospectively compared the ability of ultra-low-field portable MR (ULF-pMR) and head HCT, acquired within 24 h of each other, to detect ABI in ICU patients supported on extracorporeal membrane oxygenation (ECMO). A total of 17 adult patients (median age 55 years; 47% male) were included in the analysis. Of the 17 patients assessed, ABI was not observed on either ULF-pMR or HCT in eight patients (47%). ABI was observed in the remaining nine patients with a total of 10 events (8 ischemic, 2 hemorrhagic). Of the eight ischemic events, ULF-pMR observed all eight, while HCT only observed four events. Regarding hemorrhagic stroke, ULF-pMR observed only one of them, while HCT observed both. ULF-pMR outperformed HCT for the detection of ABI, especially ischemic injury, and may offer diagnostic advantages for ICU patients. The lack of sensitivity to hemorrhage may improve with modification of the imaging acquisition program.

Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation techniques including Bayesian estimation with dropout, bootstrapping, and margins sampling were compared to random query. Strategies to avoid annotating similar images were also considered. We determined the minimum data necessary to achieve performance equivalent to the model trained on the full dataset (α = 0.05). Bayesian approximation with dropout at training and testing showed results equivalent to that of the full data model (target) with around 30% of the training data needed by random query to achieve target performance (p = 0.018). Annotation redundancy restriction techniques can reduce the training data needed by random query to achieve target performance by 20%. We investigated various active learning strategies to minimize the annotation burden for three-dimensional brain tumor segmentation. Dropout uncertainty estimation achieved target performance with the least annotated data.

Clinical Use of Bedside Portable Low-field Brain Magnetic Resonance Imaging in Patients on ECMO: The Results from Multicenter SAFE MRI ECMO Study.

Purpose: Early detection of acute brain injury (ABI) is critical for improving survival for patients with extracorporeal membrane oxygenation (ECMO) support. We aimed to evaluate the safety of ultra-low-field portable MRI (ULF-pMRI) and the frequency and types of ABI observed during ECMO support. Methods: We conducted a multicenter prospective observational study (NCT05469139) at two academic tertiary centers (August 2022-November 2023). Primary outcomes were safety and validation of ULF-pMRI in ECMO, defined as exam completion without adverse events (AEs); secondary outcomes were ABI frequency and type. Results: ULF-pMRI was performed in 50 patients with 34 (68%) on venoarterial (VA)-ECMO (11 central; 23 peripheral) and 16 (32%) with venovenous (VV)-ECMO (9 single lumen; 7 double lumen). All patients were imaged successfully with ULF-pMRI, demonstrating discernible intracranial pathologies with good quality. AEs occurred in 3 (6%) patients (2 minor; 1 serious) without causing significant clinical issues.ABI was observed in ULF-pMRI scans for 22 patients (44%): ischemic stroke (36%), intracranial hemorrhage (6%), and hypoxic-ischemic brain injury (4%). Of 18 patients with both ULF-pMRI and head CT (HCT) within 24 hours, ABI was observed in 9 patients with 10 events: 8 ischemic (8 observed on ULF-oMRI, 4 on HCT) and 2 hemorrhagic (1 observed on ULF-pMRI, 2 on HCT). Conclusions: ULF-pMRI was shown to be safe and valid in ECMO patients across different ECMO cannulation strategies. The incidence of ABI was high, and ULF-pMRI may more sensitive to ischemic ABI than HCT. ULF-pMRI may benefit both clinical care and future studies of ECMO-associated ABI.

Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis.

PURPOSE: In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects. METHODS: We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves. RESULTS: We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however. CONCLUSION: The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.

Staying Virtual: A Survey Study of the Virtual Lecture Experience in Academic Medicine.

BACKGROUND: Increasing clinical demands can adversely impact academic advancement, including the ability to deliver lectures and disseminate scholarly work. The virtual lecture platform became mainstream during the height of the coronavirus-19 pandemic. Lessons learned from this period may offer insight into supporting academic productivity among physicians who must balance multiple demands, including high clinical workloads and family care responsibilities. We evaluated perceptions on delivering virtual lectures to determine whether virtual venues merit continuation beyond the pandemic's initial phase and whether these perceptions differ by gender and rank. METHODS: In a survey study, faculty who spoke in 1 of 3 virtual lecture programs in the Departments of Anesthesiology and Critical Care Medicine, Otolaryngology, and Radiology at a university hospital in 2020 to 2022 were queried about their experience. Speakers' motivations to lecture virtually and the perceived advantages and disadvantages of virtual and in-person lectures were analyzed using descriptive statistics and qualitative analyses. RESULTS: Seventy-two of 95 (76%) faculty members responded (40% women, 38% men, and 22% gender undisclosed). Virtual lectures supported the speakers "a lot" to "extremely" with the following goals: enhancing one's reputation and credibility (76%), networking (70%), receiving feedback (63%), and advancing prospects for promotion (59%). Virtual programs also increased the speakers' sense of accomplishment (70%) and professional optimism (61%) by at least "a lot," including instructors and assistant professors who previously had difficulty obtaining invitations to speak outside their institution. Many respondents had declined prior invitations to speak in-person due to clinical workload (66%) and family care responsibilities (58%). Previous opportunities to lecture in-person were also refused due to finances (39%), teaching (26%), and research (19%) requirements, personal medical conditions or disabilities (9%), and religious obligations (5%). Promotion was a stronger motivating factor to lecture virtually for instructors and assistant professors than for associate and full professors. By contrast, disseminating work and ideas was a stronger motivator for associate and full professors. Associate and full professors also reported greater improvement in work-related well-being than earlier career faculty from the virtual lecture experience. Very few differences were found by gender. CONCLUSIONS: Virtual lecture programs support faculty who might not otherwise have the opportunity to lecture in-person due to multiple constraints. To increase the dissemination of scholarly work and expand opportunities to all faculty, virtual lectures should continue even as in-person venues are reestablished.

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.

PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. EXPERIMENTAL DESIGN: We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS: For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS: A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.

Trial Readiness of Cavernous Malformations With Symptomatic Hemorrhage, Part I: Event Rates and Clinical Outcome.

BACKGROUND: Cerebral cavernous malformation with symptomatic hemorrhage (SH) are targets for novel therapies. A multisite trial-readiness project (https://www.clinicaltrials.gov; Unique identifier: NCT03652181) aimed to identify clinical, imaging, and functional changes in these patients. METHODS: We enrolled adult cerebral cavernous malformation patients from 5 high-volume centers with SH within the prior year and no planned surgery. In addition to clinical and imaging review, we assessed baseline, 1- and 2-year National Institutes of Health Stroke Scale, modified Rankin Scale, European Quality of Life 5D-3 L, and patient-reported outcome-measurement information system, Version 2.0. SH and asymptomatic change rates were adjudicated. Changes in functional scores were assessed as a marker for hemorrhage. RESULTS: One hundred twenty-three, 102, and 69 patients completed baseline, 1- and 2-year clinical assessments, respectively. There were 21 SH during 178.3 patient years of follow-up (11.8% per patient year). At baseline, 62.6% and 95.1% of patients had a modified Rankin Scale score of 1 and National Institutes of Health Stroke Scale score of 0 to 4, respectively, which improved to 75.4% (P=0.03) and 100% (P=0.06) at 2 years. At baseline, 74.8% had at least one abnormal patient-reported outcome-measurement information system, Version 2.0 domain compared with 61.2% at 2 years (P=0.004). The most common abnormal European Quality of Life 5D-3 L domains were pain (48.7%), anxiety (41.5%), and participation in usual activities (41.4%). Patients with prospective SH were more likely than those without SH to display functional decline in sleep, fatigue, and social function patient-reported outcome-measurement information system, Version 2.0 domains at 2 years. Other score changes did not differ significantly between groups at 2 years. The sensitivity of scores as an SH marker remained poor at the time interval assessed. CONCLUSIONS: We report SH rate, functional, and patient-reported outcomes in trial-eligible cerebral cavernous malformation with SH patients. Functional outcomes and patient-reported outcomes generally improved over 2 years. No score change was highly sensitive or specific for SH and could not be used as a primary end point in a trial.

Trial Readiness of Cavernous Malformations With Symptomatic Hemorrhage, Part II: Biomarkers and Trial Modeling.

BACKGROUND: Quantitative susceptibility mapping (QSM) and dynamic contrast-enhanced quantitative perfusion (DCEQP) magnetic resonance imaging sequences assessing iron deposition and vascular permeability were previously correlated with new hemorrhage in cerebral cavernous malformations. We assessed their prospective changes in a multisite trial-readiness project. METHODS: Patients with cavernous malformation and symptomatic hemorrhage (SH) in the prior year, without prior or planned lesion resection or irradiation were enrolled. Mean QSM and DCEQP of the SH lesion were acquired at baseline and at 1- and 2-year follow-ups. Sensitivity and specificity of biomarker changes were analyzed in relation to predefined criteria for recurrent SH or asymptomatic change. Sample size calculations for hypothesized therapeutic effects were conducted. RESULTS: We logged 143 QSM and 130 DCEQP paired annual assessments. Annual QSM change was greater in cases with SH than in cases without SH (P=0.019). Annual QSM increase by ≥6% occurred in 7 of 7 cases (100%) with recurrent SH and in 7 of 10 cases (70%) with asymptomatic change during the same epoch and 3.82× more frequently than clinical events. DCEQP change had lower sensitivity for SH and asymptomatic change than QSM change and greater variance. A trial with the smallest sample size would detect a 30% difference in QSM annual change during 2 years of follow-up in 34 or 42 subjects (1 and 2 tailed, respectively); power, 0.8, α=0.05. CONCLUSIONS: Assessment of QSM change is feasible and sensitive to recurrent bleeding in cavernous malformations. Evaluation of an intervention on QSM percent change may be used as a time-averaged difference between 2 arms using a repeated measures analysis. DCEQP change is associated with lesser sensitivity and higher variability than QSM. These results are the basis of an application for certification by the US Food and Drug Administration of QSM as a biomarker of drug effect on bleeding in cavernous malformations. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03652181.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas.

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.

The association between post-traumatic stress disorder (PTSD) and cognitive impairment: A systematic review of neuroimaging findings.

BACKGROUND: Accumulating evidence suggests that post-traumatic stress disorder (PTSD) may increase the risk of various types of dementia. Despite the large number of studies linking these critical conditions, the underlying mechanisms remain unclear. The past decade has witnessed an exponential increase in interest on brain imaging research to assess the neuroanatomical underpinnings of PTSD. This systematic review provides a critical assessment of available evidence of neuroimaging correlates linking PTSD to a higher risk of dementia. METHODS: The EMBASE, PubMed/MEDLINE, and SCOPUS electronic databases were systematically searched from 1980 to May 22, 2021 for original references on neuroimaging correlates of PTSD and risk of dementia. Literature search, screening of references, methodological quality appraisal of included articles as well as data extractions were independently conducted by at least two investigators. Eligibility criteria included: 1) a clear PTSD definition; 2) a subset of included participants must have developed dementia or cognitive impairment at any time point after the diagnosis of PTSD through any diagnostic criteria; and 3) brain imaging protocols [structural, molecular or functional], including whole-brain morphologic and functional MRI, and PET imaging studies linking PTSD to a higher risk of cognitive impairment/dementia. RESULTS: Overall, seven articles met eligibility criteria, comprising findings from 366 participants with PTSD. Spatially convergent structural abnormalities in individuals with PTSD and co-occurring cognitive dysfunction involved primarily the bilateral frontal (e.g., prefrontal, orbitofrontal, cingulate cortices), temporal (particularly in those with damage to the hippocampi), and parietal (e.g., superior and precuneus) regions. LIMITATIONS: A meta-analysis could not be performed due to heterogeneity and paucity of measurable data in the eligible studies. CONCLUSIONS: Our systematic review provides putative neuroimaging correlates associated with PTSD and co-occurring dementia/cognitive impairment particularly involving the hippocampi. Further research examining neuroimaging features linking PTSD to dementia are clearly an unmet need of the field. Future imaging studies should provide a better control for relevant confounders, such as the selection of more homogeneous samples (e.g., age, race, education), a proper control for co-occurring disorders (e.g., co-occurring major depressive and anxiety disorders) as well as the putative effects of psychotropic medication use. Furthermore, prospective studies examining imaging biomarkers associated with a higher rate of conversion from PTSD to dementia could aid in the stratification of people with PTSD at higher risk for developing dementia for whom putative preventative interventions could be especially beneficial.

Cavernous Angioma Symptomatic Hemorrhage (CASH) Trial Readiness II: Imaging Biomarkers and Trial Modeling.

Background: Quantitative susceptibility mapping (QSM) and dynamic contrast enhanced quantitative perfusion (DCEQP) MRI sequences assessing iron deposition and vascular permeability were previously correlated with new hemorrhage in cavernous angiomas. We assessed their prospective changes in cavernous angiomas with symptomatic hemorrhage (CASH) in a multisite trial readiness project ( clinicaltrials.gov NCT03652181 ). Methods: Patients with CASH in the prior year, without prior or planned lesion resection or irradiation were enrolled. Mean QSM and DCEQP of CASH lesion were acquired at baseline, and at 1- and 2-year follow-ups. Sensitivity and specificity of biomarker changes were analyzed in relation to predefined lesional symptomatic hemorrhage (SH) or asymptomatic change (AC). Sample size calculations for hypothesized therapeutic effects were conducted. Results: We logged 143 QSM and 130 DCEQP paired annual assessments. Annual QSM change was greater in cases with SH than in cases without SH (p= 0.019). Annual QSM increase by ≥ 6% occurred in 7 of 7 cases (100%) with recurrent SH and in 7 of 10 cases (70%) with AC during the same epoch, and 3.82 times more frequently than clinical events. DCEQP change had lower sensitivity for SH and AC than QSM change, and greater variance. A trial with smallest sample size would detect a 30% difference in QSM annual change in 34 or 42 subjects (one and two-tailed, respectively), power 0.8, alpha 0.05. Conclusions: Assessment of QSM change is feasible and sensitive to recurrent bleeding in CASH. Evaluation of an intervention on QSM percent change may be used as a time-averaged difference between 2 arms using a repeated measures analysis. DCEQP change is associated with lesser sensitivity and higher variability than QSM. These results are the basis of an application for certification by the U.S. F.D.A. of QSM as a biomarker of drug effect in CASH.

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences.

The Cortico-Limbo-Thalamo-Cortical Circuits: An Update to the Original Papez Circuit of the Human Limbic System.

The Papez circuit, first proposed by James Papez in 1937, is a circuit believed to control memory and emotions, composed of the cingulate cortex, entorhinal cortex, parahippocampal gyrus, hippocampus, hypothalamus, and thalamus. Pursuant to James Papez, Paul Yakovlev and Paul MacLean incorporated the prefrontal/orbitofrontal cortex, septum, amygdalae, and anterior temporal lobes into the limbic system. Over the past few years, diffusion-weighted tractography techniques revealed additional limbic fiber connectivity, which incorporates multiple circuits to the already known complex limbic network. In the current review, we aimed to comprehensively summarize the anatomy of the limbic system and elaborate on the anatomical connectivity of the limbic circuits based on the published literature as an update to the original Papez circuit.

The Ribbon Sign as a Radiological Indicator of Intramedullary Spinal Cord Subependymomas.

OBJECTIVE: Intramedullary spinal cord (IMSC) subependymomas are rare World Health Organization grade 1 ependymal tumors. The potential presence of functional neural tissue within the tumor and poorly demarcated planes presents a risk to resection. Anticipating a subependymoma on preoperative imaging can inform surgical decision-making and improve patient counseling. Here, we present our experience recognizing IMSC subependymomas on preoperative magnetic resonance imaging (MRI) based on a distinctive characteristic termed the "ribbon sign." METHODS: We retrospectively reviewed preoperative MRIs of patients presenting with IMSC tumors at a large tertiary academic institution between April 2005 and January 2022. The diagnosis was confirmed histologically. The "ribbon sign" was defined as a ribbon-like structure of T2 isointense spinal cord tissue interwoven between regions of T2 hyperintense tumor. The ribbon sign was confirmed by an expert neuroradiologist. RESULTS: MRIs from 151 patients were reviewed, including 10 patients with IMSC subependymomas. The ribbon sign was demonstrated on 9 (90%) patients with histologically proven subependymomas. Other tumor types did not display the ribbon sign. CONCLUSION: The ribbon sign is a potentially distinctive imaging feature of IMSC subependymomas and indicates the presence of spinal cord tissue between eccentrically located tumors. Recognition of the ribbon sign should prompt clinicians to consider a diagnosis of subependymoma, aiding the neurosurgeon in planning the surgical approach and adjusting the surgical outcome expectation. Consequently, the risks and benefits of gross-versus subtotal resection for palliative debulking should be carefully considered and discussed with patients.

Interrater Reliability of Cervical Neural Foraminal Stenosis Using Traditional and Splayed Reconstructions: Analysis of One Hundred Scans.

OBJECTIVE: The oblique sagittal orientation of the cervical neural foramina hinders the evaluation of cervical neural foraminal stenosis (CNFS) on traditional axial and sagittal slices. Traditional image reconstruction techniques to generate oblique slices provide only a view of the foramina unilaterally. We present a simple technique for generating splayed slices that show the bilateral neuroforamina simultaneously and assess its reliability compared with traditional axial windows. METHODS: Cervical computed tomography (CT) scans from 100 patients were retrospectively collected and de-identified. The axial slices were reformatted into a curved reformat with the plane of the reformat extending across the bilateral neuroforamina. The foramina along the C2-T1 vertebral levels were assessed by 4 neuroradiologists using the axial and splayed slices. The intrarater agreement across the axial and splayed slices for a given foramen and the interrater agreement for the axial and splayed slices individually were calculated using the Cohen κ statistic. RESULTS: Interrater agreement was overall higher for the splayed slices (κ = 0.25) compared with the axial slices (κ = 0.20). The splayed slices were more likely to have fair agreement across raters compared with the axial slices. Intrarater agreement between the axial and splayed slices was poorer for residents compared with fellows. CONCLUSIONS: Splayed reconstructions showing the bilateral neuroforamina en face can be readily generated from axial CT imaging. These splayed reconstructions can improve the consistency of CNFS evaluation compared with traditional CT slices and should be considered in the workup of CNFS, particularly for less experienced readers.

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. PURPOSE: This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. STUDY TYPE: Retrospective and prospective. POPULATION: For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males). FIELD STRENGTH/SEQUENCE: 5T and 3T, T2-weighted turbo spin echo imaging. ASSESSMENT: Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. STATISTICAL TESTS: Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant. RESULTS: In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%. DATA CONCLUSION: In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. EVIDENCE LEVEL: 2 Technical Efficacy: Stage 2.

Neuroimaging Correlates of Post-Traumatic Stress Disorder in Traumatic Brain Injury: A Systematic Review of the Literature.

Neuroimaging is widely utilized in studying traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD). The risk for PTSD is greater after TBI than after non-TBI trauma, and PTSD is associated with worse outcomes after TBI. Studying the neuroimaging correlates of TBI-related PTSD may provide insights into the etiology of both conditions and help identify those TBI patients most at risk of developing persistent symptoms. The objectives of this systematic review were to examine the current literature on neuroimaging in TBI-related PTSD, summarize key findings, and highlight strengths and limitations to guide future research. A Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) compliant literature search was conducted in PubMed (MEDLINE®), PsycINFO, Embase, and Scopus databases prior to January 2022. The database query yielded 4486 articles, which were narrowed based on specified inclusion criteria to a final cohort of 16 studies, composed of 854 participants with TBI. There was no consensus regarding neuroimaging correlates of TBI-related PTSD among the included articles. A small number of studies suggest that TBI-related PTSD is associated with white matter tract changes, particularly in frontotemporal regions, as well as changes in whole-brain networks of resting-state connectivity. Future studies hoping to identify reliable neuroimaging correlates of TBI-related PTSD would benefit from ensuring consistent case definition, preferably with clinician-diagnosed TBI and PTSD, selection of comparable control groups, and attention to imaging timing post-injury. Prospective studies are needed and should aim to further differentiate predisposing factors from sequelae of TBI-related PTSD.