Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

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.

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.

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.

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 S-Factor, a New Measure of Disease Severity in Spinocerebellar Ataxia: Findings and Implications

Spinocerebellar ataxias (SCAs) are progressive neurodegenerative disorders, but there is no metric that predicts disease severity over time. We hypothesized that by developing a new metric, the Severity Factor (S-Factor) using immutable disease parameters, it would be possible to capture disease severity independent of clinical rating scales. Extracting data from the CRC-SCA and READISCA natural history studies, we calculated the S-Factor for 438 participants with symptomatic SCA1, SCA2, SCA3, or SCA6, as follows: ((length of CAG repeat expansion - maximum normal repeat length) /maximum normal repeat length) × (current age - age at disease onset) × 10). Within each SCA type, the S-Factor at the first Scale for the Assessment and Rating of Ataxia (SARA) visit (baseline) was correlated against scores on SARA and other motor and cognitive assessments. In 281 participants with longitudinal data, the slope of the S-Factor over time was correlated against slopes of scores on SARA and other motor rating scales. At baseline, the S-Factor showed moderate-to-strong correlations with SARA and other motor rating scales at the group level, but not with cognitive performance. Longitudinally the S-Factor slope showed no consistent association with the slope of performance on motor scales. Approximately 30% of SARA slopes reflected a trend of non-progression in motor symptoms. The S-Factor is an observer-independent metric of disease burden in SCAs. It may be useful at the group level to compare cohorts at baseline in clinical studies. Derivation and examination of the S-factor highlighted challenges in the use of clinical rating scales in this population.

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.

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.

Post-COVID-19 Brain [18F] FDG-PET Findings: A Retrospective Single-Center Study in the United States

BACKGROUND AND PURPOSE

The pathophysiology of neurologic manifestations of postacute sequelae of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection is not clearly understood. Our aim was to investigate brain metabolic activity on [¹⁸F] FDG-PET/CT scans in patients with a history of coronavirus disease 2019 (COVID-19) infection before imaging.

MATERIALS AND METHODS

This retrospective study included 45 patients who underwent [¹⁸F] FDG-PET/CT imaging for any reason and had, at least once, tested positive for COVID-19 at any time before imaging. Fifteen patients had available [¹⁸F] FDG-PET scans obtained under identical conditions before the infection. A group of 52 patients with melanoma or multiple myeloma who underwent [¹⁸F] FDG-PET/CT were used as controls. Whole-brain 2-sample t test analysis was performed using SPM software to identify clusters of hypo- and hypermetabolism and compare brain metabolic activity between patients with COVID-19 and controls. Paired sample t test comparison was also performed for 15 patients, and correlations between metabolic values of clusters and clinical data were measured.

RESULTS

Compared with the control group, patients with a history of COVID-19 infection exhibited focal areas of hypometabolism in the bilateral frontal, parietal, occipital, and posterior temporal lobes and cerebellum (P = .05 uncorrected at the voxel level, family-wise error-corrected at the cluster level) that peaked during the first 2 months, improved to near-complete recovery around 6 months, and disappeared at 12 months. Hypermetabolism involving the brainstem, cerebellum, limbic structures, frontal cortex, and periventricular white matter was observed only at 2-6 months after infection. Older age, neurologic symptoms, and worse disease severity scores positively correlated with the metabolic changes.

CONCLUSIONS

This study demonstrates a profile of time-dependent brain PET hypo- and hypermetabolism in patients with confirmed SARS-CoV-2 infection.

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 ten patients with IMSC subependymomas. The ribbon sign was demonstrated on nine (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 tumor. 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- vs. sub-total 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 100 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.

Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?

BACKGROUND AND PURPOSE

Resting-state fMRI helps identify neural networks in presurgical patients who may be limited in their ability to undergo task-fMRI. The purpose of this study was to determine the accuracy of identifying the language network from resting-state-fMRI independent component analysis (ICA) maps.

MATERIALS AND METHODS

Through retrospective analysis, patients who underwent both resting-state-fMRI and task-fMRI were compared by identifying the language network from the resting-state-fMRI data by 3 reviewers. Blinded to task-fMRI maps, these investigators independently reviewed resting-state-fMRI ICA maps to potentially identify the language network. Reviewers ranked up to 3 top choices for the candidate resting-state-fMRI language map. We evaluated associations between the probability of correct identification of the language network and some potential factors.

RESULTS

Patients included 29 men and 14 women with a mean age of 41 years. Reviewer 1 (with 17 years' experience) demonstrated the highest overall accuracy with 72%; reviewers 2 and 3 (with 2 and 7 years' experience, respectively) had a similar percentage of correct responses (50% and 55%). The highest accuracy used ICA50 and the top 3 choices (81%, 65%, and 60% for reviewers 1, 2, and 3, respectively). The lowest accuracy used ICA50, limiting each reviewer to the top choice (58%, 35%, and 42%).

CONCLUSIONS

We demonstrate variability in the accuracy of blinded identification of resting-state-fMRI language networks across reviewers with different years of experience.

Clinical application of brain perfusion imaging in detecting stroke mimics: A review

Stroke mimics constitute a significant proportion of patients with suspected acute ischemic stroke. These conditions may resemble acute ischemic stroke and demonstrate abnormalities on perfusion imaging sequences. The most common stroke mimics include seizure/epilepsy, migraine with aura, brain tumors, functional disorders, infectious encephalopathies, Wernicke's encephalopathy, and metabolic abnormalities. Brain perfusion imaging techniques, particularly computed tomography perfusion and magnetic resonance perfusion, are being widely used in routine clinical practice for treatment selection in patients presenting with large vessel occlusion. At the same time, the utilization of these imaging modalities enables the opportunity to better diagnose patients with stroke mimics in a time-sensitive setting, leading to appropriate management, decision-making, and resource allocation. In this review, we describe patterns of perfusion abnormalities that could discriminate patients with stroke mimics from those with acute ischemic stroke and provide specific case examples to illustrate these perfusion abnormalities. In addition, we discuss the challenges associated with interpretation of perfusion images in stroke-related pathologies. In general, perfusion imaging can provide additional information in some cases-when used in combination with conventional magnetic resonance imaging and computed tomography-and might help in detecting stroke mimics among patients who present with acute onset focal neurological symptoms.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

External ventricular drain use is associated with functional outcome in aneurysmal subarachnoid hemorrhage

Purpose

External ventricular drains (EVD) are commonly used in aneurysmal subarachnoid hemorrhage (aSAH) patients and can be life-saving by diverting cerebrospinal fluid. However, the overall relationship between EVD use and outcome is poorly understood.

Methods

In an exploratory analysis of an aSAH patient cohort, we examined EVD use in relation to modified Rankin Scale (mRS) at hospital discharge and at 6 months (unfavorable outcome = mRS > 2) using univariable and multivariable analyses.

Results

EVDs were placed in 31 of 56 (55.4%) patients and more often in women than men (66.7% vs 35.0%, p = 0.022) despite similar rates of hydrocephalus. Women had greater ICU [18 (13.5–25) vs 11.5 (6.5–18.5) days, p = 0.014] and hospital lengths of stay (LOS) [20.5 (16.5–34) vs 13.5 (10.5–27) days, p = 0.015] than men and greater mRS at discharge [4 (3–5) vs 3 (2–3.5), p = 0.011] although mRS at 6 months was similar. Patients with EVDs had longer ICU and hospital LOS and greater mRS at discharge [5 (3–6) vs 2 (2–3), p < 0.001] and at 6 months [4 (2–6) vs 1 (0–2), p = 0.001] than those without an EVD. In multivariable models, EVD use was associated with unfavorable 6-month outcome accounting for age, sex, and admission modified Fisher scale, but not in models adjusting for Hunt and Hess scale and World Federation of Neurological Surgeons scale.

Conclusion

In an aSAH cohort, the use of EVDs was associated with female sex and longer LOS, and may be linked to functional outcomes at discharge and at 6 months, although these associations warrant further investigation.

Safety of Bedside Portable Low-Field Brain MRI in ECMO Patients Supported on Intra-Aortic Balloon Pump

(1) Background: Fifty percent of patients supported on veno-arterial extracorporeal membrane oxygenation (VA-ECMO) are concurrently supported with an intra-aortic balloon pump (IABP). Acute brain injury (ABI) is a devastating complication related to ECMO and IABP use. The standard of care for ABI diagnosis requires transport to a head CT (HCT) scanner. Recent data suggest that point-of-care (POC) magnetic resonance imaging (MRI) is safe and may be effective in diagnosing ABI in ECMO patients; however, no data exist in patients supported on ECMO with an IABP. We report pre-clinical safety data and a case series to evaluate the safety and feasibility of POC brain MRI in ECMO patients supported with IABP. (2) Methods: Prior to patient use, ex vivo testing with an IABP catheter within the Swoop^® Portable MRI (0.064 T) System™ was conducted. After IRB approval, clinical testing was performed for the safety and feasibility of early ABI detection. (3) Results: No deflection force was measured with a 7.5 French Maquet Linear IABP within the 0.064 T field. Three adult ECMO patients (average age: 40 years; 67% female) supported with IABP completed four POC brain MRI exams (median exam time: 30 min). Multiple signal abnormalities were detected on the POC brain MRI, corresponding to HCT results. (4) Conclusions: Our preliminary results suggest that adult VA-ECMO patients with IABP support can be safely imaged with low-field POC brain MRI in the intensive care unit, allowing for the early and bedside imaging of patients.

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 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 PRISMA compliant literature search was conducted in PubMed (MEDLINE), PsychINFO, 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, comprised 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.

The role of preoperative MRI imaging in assessing neurovascular compression prior to microvascular decompression in trigeminal neuralgia

INTRODUCTION

Preoperative MRI is a standard component of the preoperative clinical workup for patients prior to microvascular decompression (MVD). However, its ability to accurately exclude neurovascular compression of the trigeminal nerve is not well understood.

METHODS

We retrospectively reviewed 1020 patients with available preoperative MRI data prior to microvascular decompression. General patient demographics and clinical characteristics were collected for each case. We recorded both evidence of neurovascular conflict on preoperative MRI radiology notes and intraoperative compression from operative notes. Sensitivity, specificity, positive predictive value, and negative predictive value was determined for general MRI, high-resolution MRI, and non-high resolution.

RESULTS

Overall, preoperative MRI prior to MVD demonstrated a sensitivity of 75.8%, specificity of 65.8%, positive predictive value of 92.4%, and negative predictive value of 33.3% in predicting neurovascular compression of the trigeminal nerve. In particular, MRI was unable to identify 21.0% cases of sole arterial compression, 42.5% cases of sole venous compression, and combined arterial and venous compression in 18.5% of cases. 958 (93.9%) patients underwent high-resolution preoperative MRI with skull base sequences. This exhibited a sensitivity of 75.6%, specificity of 66.9%, positive predictive value of 92.5% and a negative predictive value of 33.4% in predicting trigeminal nerve neurovascular compression. Finally, non-high resolution MRI showed a sensitivity of 78.8%, specificity of 50.0%, positive predictive value of 89.1%, and negative predictive value of 31.3%. Importantly, the negative predictive values of general, high resolution, and non-high resolution MRIs were all below 50%.

CONCLUSIONS

Preoperative MRI may offer a high predictive value for neurovascular conflict and should be part of the standard preoperative care workup for trigeminal neuralgia patients. However, lack of neurovascular conflict on preoperative imaging is not sufficient to exclude patients from undergoing MVD.

A systematic review and meta-analysis of structural and functional brain alterations in individuals with genetic and clinical high-risk for psychosis and bipolar disorder

Neuroimaging findings in people at either genetic risk or at clinical high-risk for psychosis (CHR-P) or bipolar disorder (CHR-B) remain unclear. A meta-analytic review of whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies in individuals with genetic risk or CHR-P or CHR-B and controls identified 94 datasets (N = 7942). Notwithstanding no significant findings were observed following adjustment for multiple comparisons, several findings were noted at a more liberal threshold. Subjects at genetic risk for schizophrenia or bipolar disorder or at CHR-P exhibited lower gray matter (GM) volumes in the gyrus rectus (Hedges' g = -0.19). Genetic risk for psychosis was associated with GM reductions in the right cerebellum and left amygdala. CHR-P was associated with decreased GM volumes in the frontal superior gyrus and hypoactivation in the right precuneus, the superior frontal gyrus and the right inferior frontal gyrus. Genetic and CHR-P were associated with small structural and functional alterations involving regions implicated in psychosis. Further neuroimaging studies in individuals with genetic or CHR-B are warranted.

Assessing the SAfety and FEasibility of bedside portable low-field brain Magnetic Resonance Imaging in patients on ECMO (SAFE-MRI ECMO study): study protocol and first case series experience

Background

To assess the safety and feasibility of imaging of the brain with a point-of-care (POC) magnetic resonance imaging (MRI) system in patients on extracorporeal membrane oxygenation (ECMO). Early detection of acute brain injury (ABI) is critical in improving survival for patients with ECMO support.

Methods

Patients from a single tertiary academic ECMO center who underwent head CT (HCT), followed by POC brain MRI examinations within 24 h following HCT while on ECMO. Primary outcomes were safety and feasibility, defined as completion of MRI examination without serious adverse events (SAEs). Secondary outcome was the quality of MR images in assessing ABIs.

Results

We report 3 consecutive adult patients (median age 47 years; 67% male) with veno-arterial (n = 1) and veno-venous ECMO (n = 2) (VA- and VV-ECMO) support. All patients were imaged successfully without SAEs. Times to complete POC brain MRI examinations were 34, 40, and 43 min. Two patients had ECMO suction events, resolved with fluid and repositioning. Two patients were found to have an unsuspected acute stroke, well visualized with MRI.

Conclusions

Adult patients with VA- or VV-ECMO support can be safely imaged with low-field POC brain MRI in the intensive care unit, allowing for the assessment of presence and timing of ABI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-03990-6.

Apparent diffusion coefficient of piriform cortex and seizure outcome in mesial temporal lobe epilepsy after MR-guided laser interstitial thermal therapy: a single-institution experience

OBJECTIVE

Piriform cortex (PC) is one of the critical structures in the epileptogenesis of mesial temporal lobe epilepsy (mTLE), but its role is poorly understood. The authors examined the utility of apparent diffusion coefficient (ADC; an MR-based marker of tissue pathology) of the PC as a predictor of seizure outcome in patients with mTLE undergoing MR-guided laser interstitial thermal therapy (MRgLITT).

METHODS

A total of 33 patients diagnosed with mTLE who underwent MRgLITT at the authors' institution were included in the study. The 6-month postoperative seizure outcomes were classified using the International League Against Epilepsy (ILAE) system as good (complete seizure freedom, ILAE class I) and poor (seizure present, ILAE classes II-VI). The PC and ablation volumes were manually segmented from both the preoperative and intraoperative MRI sequences, respectively. The mean ADC intensities of 1) preablation PC; 2) total ablation volume; 3) ablated portion of PC; and 4) postablation residual PC were calculated and compared between good and poor outcome groups. Additionally, the preoperative PC volumes and proportion of PC volume ablated were examined and compared between the subjects in the two outcome groups.

RESULTS

The mean age at surgery was 36.5 ± 3.0 years, and the mean follow-up duration was 1.9 ± 0.2 years. Thirteen patients (39.4%) had a good outcome. The proportion of PC ablated was significantly associated with seizure outcome (10.16 vs 3.30, p < 0.05). After accounting for the variability in diffusion tensor imaging acquisition parameters, patients with good outcome had a significantly higher mean ADC of the preablation PC (0.3770 vs -0.0108, p < 0.05) and the postoperative residual PC (0.4197 vs 0.0309, p < 0.05) regions compared to those with poor outcomes. No significant differences in ADC of the ablated portion of PC were observed (0.2758 vs -0.4628, p = 0.12) after performing multivariate analysis.

CONCLUSIONS

A higher proportion of PC ablated was associated with complete seizure freedom. Preoperative and postoperative residual ADC measures of PC were significantly higher in the good seizure outcome group in patients with mTLE who underwent MRgLITT, suggesting that ADC analysis can assist with postablation outcome prediction and patient stratification.

Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm

OBJECTIVE

Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans.

METHODS

All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity.

RESULTS

A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively.

CONCLUSIONS

In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.

Neuroimaging correlates of depression after traumatic brain injury: A systematic review

Depression is the most frequent neuropsychiatric complication after traumatic brain injury (TBI) and is associated with poorer outcomes. Neuroimaging has the potential to improve our understanding of the neural correlates of depression after TBI and may improve our capacity to accurately predict and effectively treat this condition. We conducted a systematic review of structural and functional neuroimaging studies that examined the association between depression after TBI, and neuroimaging measures. Electronic searches were conducted in four databases and were complemented by manual searches. In total, 2,035 citations were identified and, ultimately, 38 articles were included totaling 1,793 individuals (median [25%-75%] sample size of 38.5 (21.8-54.3) individuals). The most frequently used modality was structural magnetic resonance imaging (MRI) (n=17, 45%), followed by diffusion tensor imaging (n=11, 29%), resting-state functional MRI (n=10, 26%), task-based functional MRI (n=4, 8%), and positron emission tomography (n=2, 4%). Most studies (n=27, 71%) were cross-sectional. Overall, depression after TBI was associated with lower grey matter measures (volume, thickness, and/or density) and greater white matter damage. However, identification of specific brain areas was somewhat inconsistent. Findings that were replicated in more than one study included reduced grey matter in the rostral anterior cingulate cortex, prefrontal cortex and hippocampus, and damage in five white matter tracts (cingulum, internal capsule, superior longitudinal fasciculi, anterior, and posterior corona radiata). This systematic review found that the available data did not converge on a clear neuroimaging biomarker for depression after TBI. However, there are promising targets that warrant further study.

Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs

OBJECTIVE

To evaluate the behavior of a publicly available deep convolutional neural network (DCNN) bone age algorithm when presented with inappropriate data inputs in both radiological and non-radiological domains.

METHODS

We evaluated a publicly available DCNN-based bone age application. The DCNN was trained on 12,612 pediatric hand radiographs and won the 2017 RSNA Pediatric Bone Age Challenge (concordance of 0.991 with radiologist ground-truth). We used the application to analyze 50 left-hand radiographs (appropriate data inputs) and seven classes of inappropriate data inputs in radiological (i.e., chest radiographs) and non-radiological (i.e., image of street numbers) domains. For each image, we noted if (1) the application distinguished between appropriate and inappropriate data inputs and (2) inference time per image. Mean inference times were compared using ANOVA.

RESULTS

The 16Bit Bone Age application calculated bone age for all pediatric hand radiographs with mean inference time of 1.1 s. The application did not distinguish between pediatric hand radiographs and inappropriate image types, including radiological and non-radiological domains. The application inappropriately calculated bone age for all inappropriate image types, with mean inference time of 1.1 s for all categories (p = 1).

CONCLUSION

A publicly available DCNN-based bone age application failed to distinguish between appropriate and inappropriate data inputs and calculated bone age for inappropriate images. The awareness of inappropriate outputs based on inappropriate DCNN input is important if tasks such as bone age determination are automated, emphasizing the need for appropriate oversight at the data input and verification stage to avoid unrecognized erroneous results.

Neuroimaging correlates of syndromal depression following traumatic brain injury: A systematic review of the literature

Objective

To complete a systematic review of the literature examining neuroimaging findings unique to co-occurring syndromal depression in the setting of TBI.

Methods

A PRISMA compliant literature search was conducted in PubMed (MEDLINE), PsychINFO, EMBASE, and Scopus databases for articles published prior to April of 2022. The database query yielded 4447 unique articles. These articles were narrowed based on specific inclusion criteria (e.g., clear TBI definition, clear depression construct commenting on the syndrome of major depressive disorder, conducted empirical analyses comparing neuroimaging correlates in TBI subjects with depression versus TBI subjects without depression, controlled for the time interval between TBI occurrence and acquisition of neuroimaging).

Results

A final cohort of 10 articles resulted, comprising the findings from 423 civilians with brain injury, 129 of which developed post-TBI depression. Four articles studied mild TBI, three mild/moderate, one moderate/severe, and two all-comers, with nine articles focusing on single TBI and one including both single and recurrent injuries. Spatially convergent structural abnormalities in individuals with TBI and co-occurring syndromal depression were identified primarily in bilateral frontal regions, particularly in those with damage to the left frontal lobe and prefrontal cortices, as well as temporal regions including bilateral temporal lobes, the left superior temporal gyrus, and bilateral hippocampi. Various parietal regions and the nucleus accumbens were also implicated. EEG studies showed supporting evidence of functional changes in frontal regions.

Conclusion

Additional inquiry with attention to TBI without depression control groups, consistent TBI definitions, previous TBI, clinically diagnosed syndromal depression, imaging timing post-injury, acute prospective design, functional neuroimaging, and well-defined neuroanatomical regions of interest is crucial to extrapolating finer discrepancies between primary and TBI-related depression.

Neuroimaging Correlates of Syndromal Anxiety Following Traumatic Brain Injury: A Systematic Review of the Literature

BACKGROUND

Traumatic brain injury (TBI) can precipitate new-onset psychiatric symptoms or worsen existing psychiatric conditions. To elucidate specific mechanisms for this interaction, neuroimaging is often used to study both psychiatric conditions and TBI. This systematic review aims to synthesize the existing literature of neuroimaging findings among patients with anxiety after TBI.

METHODS

We conducted a Preferred Reporting Items for Systematic Review and Meta-Analyses-compliant literature search via PubMed (MEDLINE), PsychINFO, EMBASE, and Scopus databases before May, 2019. We included studies that clearly defined TBI, measured syndromic anxiety as a primary outcome, and statistically analyzed the relationship between neuroimaging findings and anxiety symptoms.

RESULTS

A total of 5982 articles were retrieved from the systematic search, of which 65 studied anxiety and 13 met eligibility criteria. These studies were published between 2004 and 2017, collectively analyzing 764 participants comprised of 470 patients with TBI and 294 non-TBI controls. Imaging modalities used included magnetic resonance imaging, functional magnetic resonance imaging, diffusion tensor imaging, electroencephalogram, magnetic resonance spectrometry, and magnetoencephalography. Eight of 13 studies presented at least one significant finding and together reflect a complex set of changes that lead to anxiety in the setting of TBI. The left cingulate gyrus in particular was found to be significant in 2 studies using different imaging modalities. Two studies also revealed perturbances in functional connectivity within the default mode network.

CONCLUSIONS

This is the first systemic review of neuroimaging changes associated with anxiety after TBI, which implicated multiple brain structures and circuits, such as the default mode network. Future research with consistent, rigorous measurements of TBI and syndromic anxiety, as well as attention to control groups, previous TBIs, and time interval between TBI and neuroimaging, are warranted. By understanding neuroimaging correlates of psychiatric symptoms, this work could inform future post-TBI screening and surveillance, preventative efforts, and early interventions to improve neuropsychiatric outcomes.

Identification of the Somatomotor Network from Language Task-based fMRI Compared with Resting-State fMRI in Patients with Brain Lesions

Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using t tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Field and Birn in this issue.

A novel radiographic marker of sarcopenia with prognostic value in glioblastoma

OBJECTIVE

Sarcopenia is an important prognostic consideration in surgical oncology that has received relatively little attention in brain tumor patients. Temporal muscle thickness (TMT) has recently been proposed as a novel radiographic marker of sarcopenia that can be efficiently obtained within existing workflows. We investigated the prognostic value of TMT in primary and progressive glioblastoma.

METHODS

TMT measurements were performed on magnetic resonance images of 384 patients undergoing 541 surgeries for glioblastoma. Relationships between TMT and clinical characteristics were examined on bivariate analysis. Optimal TMT cutpoints were established using maximally selected rank statistics. Predictive value of TMT upon postoperative survival (PS) was assessed using Cox proportional hazards regression adjusted for age, sex, Karnofsky performance status (KPS), Stupp protocol completion, extent of resection, and tumor molecular markers.

RESULTS

Average TMT for the primary and progressive glioblastoma cohorts was 9.55 mm and 9.40 mm, respectively. TMT was associated with age (r = -0.14, p = 0.0008), BMI (r = 0.29, p < 0.0001), albumin (r = 0.11, p = 0.0239), and KPS (r = 0.11, p = 0.0101). Optimal TMT cutpoints for the primary and progressive cohorts were ≤ 7.15 mm and ≤ 7.10 mm, respectively. High TMT was associated with increased Stupp protocol completion (p = 0.001). On Cox proportional hazards regression, high TMT predicted increased PS in progressive [HR 0.47 (95% confidence interval (CI)) 0.25-0.90), p = 0.023] but not primary [HR 0.99 (95% CI 0.64-1.51), p = 0.949] glioblastoma.

CONCLUSIONS

TMT correlates with important prognostic variables in glioblastoma and predicts PS in patients with progressive, but not primary, disease. TMT may represent a pragmatic neurosurgical biomarker in glioblastoma that could inform treatment planning and perioperative optimization.

Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning

PURPOSE

To assess the ability of deep convolutional neural networks (DCNNs) to predict coronary artery calcium (CAC) and cardiovascular risk on chest radiographs.

MATERIALS AND METHODS

In this retrospective study, 1689 radiographs in patients who underwent cardiac CT and chest radiography within the same year, between 2013 and 2018, were included (mean age, 56 years ± 11 [standard deviation]; 969 radiographs in women). Agatston scores were used as ground truth labels for DCNN training on radiographs. DCNNs were trained for binary classification of (a) nonzero or zero total calcium scores, (b) presence or absence of calcium in each coronary artery, and (c) total calcium scores above or below varying thresholds. Results from classification of test images were compared with established 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores in each cohort. Classifier performance was measured using area under the receiver operating characteristic curve (AUC) with attention maps to highlight areas of decision-making.

RESULTS

Binary classification between zero and nonzero total calcium scores reached an AUC of 0.73 on frontal radiographs, with similar performance on laterals (AUC, 0.70; P = .56). Performance was similar for binary classification of absolute total calcium score above or below 100 (AUC, 0.74). Frontal radiographs that tested positive for a predicted nonzero CAC score correlated with a higher 10-year ASCVD risk of 17.2% ± 10.9 compared with 11.9% ± 10.2 for a negative test, indicating predicted CAC score of zero (P < .001). Multivariate logistic regression demonstrated the algorithm could predict a nonzero calcium score independent of traditional cardiovascular risk factors. Performance was reduced for individual coronary arteries. Heat maps primarily localized to the cardiac silhouette and occasionally other cardiovascular findings.

CONCLUSION

DCNNs trained on chest radiographs had modest accuracy for predicting the presence of CAC correlating with cardiovascular risk.Keywords: Coronary Arteries, Cardiac, Calcifications/Calculi, Neural NetworksSee also the commentary by Gupta and Blankstein in this issue.©RSNA, 2021.

A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize The Eloquent Cortex in Brain Tumor Patients

We present a deep neural network architecture that combines multi-scale spatial attention with temporal attention to simultaneously localize the language and motor areas of the eloquent cortex from dynamic functional connectivity data. Our multi-scale spatial attention operates on graph-based features extracted from the connectivity matrices, thus honing in on the inter-regional interactions that collectively define the eloquent cortex. At the same time, our temporal attention model selects the intervals during which these interactions are most pronounced. The final stage of our model employs multi-task learning to differentiate between the eloquent subsystems. Our training strategy enables us to handle missing eloquent class labels by freezing the weights in those branches while updating the rest of the network weights. We evaluate our method on resting-state fMRI data from one synthetic dataset and one in-house brain tumor dataset while using task fMRI activations as ground-truth labels for the eloquent cortex. Our model achieves higher localization accuracies than conventional deep learning approaches. It also produces interpretable spatial and temporal attention features which can provide further insights for presurgical planning. Thus, our model shows translational promise for improving the safety of brain tumor resections.

Can Preoperative Mapping with Functional MRI Reduce Morbidity in Brain Tumor Resection? A Systematic Review and Meta-Analysis of 68 Observational Studies

Background Preoperative functional MRI (fMRI) is one of several techniques developed to localize critical brain structures and brain tumors. However, the usefulness of fMRI for preoperative surgical planning and its potential effect on neurologic outcomes remain unclear. Purpose To assess the overall postoperative morbidity among patients with brain tumors by using preoperative fMRI versus surgery without this tool or with use of standard (nonfunctional) neuronavigation. Materials and Methods A systematic review and meta-analysis of studies across major databases from 1946 to June 20, 2020, were conducted. Inclusion criteria were original studies that (a) included patients with brain tumors, (b) performed preoperative neuroimaging workup with fMRI, (c) investigated the usefulness of a preoperative or intraoperative functional neuroimaging technique and used that technique to resect cerebral tumors, and (d) reported postoperative clinical measures. Pooled estimates for adverse event rate (ER) effect size (log ER, log odds ratio, or Hedges g) with 95% CIs were computed by using a random-effects model. Results Sixty-eight studies met eligibility criteria (3280 participants; 58.9% men [1555 of 2641]; mean age, 46 years ± 8 [standard deviation]). Functional deterioration after surgical procedure was less likely to occur when fMRI mapping was performed before the operation (odds ratio, 0.25; 95% CI: 0.12, 0.53; P < .001]), and postsurgical Karnofsky performance status scores were higher in patients who underwent fMRI mapping (Hedges g, 0.66; 95% CI: 0.21, 1.11; P = .004]). Craniotomies for tumor resection performed with preoperative fMRI were associated with a pooled adverse ER of 11% (95% CI: 8.4, 13.1), compared with a 21.0% ER (95% CI: 12.2, 33.5) in patients who did not undergo fMRI mapping. Conclusion From the currently available data, the benefit of preoperative functional MRI planning for the resection of brain tumors appears to reduce postsurgical morbidity, especially when used with other advanced imaging techniques, such as diffusion-tensor imaging, intraoperative MRI, or cortical stimulation. © RSNA, 2021 Online supplemental material is available for this article.

Leveraging artificial intelligence in ischemic stroke imaging

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.

Role of Functional Magnetic Resonance Imaging in the Presurgical Mapping of Brain Tumors

When planning for brain tumor resection, a balance between maximizing resection and minimizing injury to eloquent brain parenchyma is paramount. The advent of blood oxygenation level-dependent functional magnetic resonance (fMR) imaging has allowed researchers and clinicians to reliably measure physiologic fluctuations in brain oxygenation related to neuronal activity with good spatial resolution. fMR imaging can offer a unique insight into preoperative planning for brain tumors by identifying eloquent areas of the brain affected or spared by the neoplasm. This article discusses the fMR imaging techniques and their applications in neurosurgical planning.

DECT in Detection of Vertebral Fracture-associated Bone Marrow Edema: A Systematic Review and Meta-Analysis with Emphasis on Technical and Imaging Interpretation Parameters

Background Dual-energy CT (DECT) shows promising performance in detecting bone marrow edema (BME) associated with vertebral body fractures. However, the optimal technical and image interpretation parameters are not well described. Purpose To conduct a systematic review and meta-analysis to determine the diagnostic performance of DECT in detecting BME associated with vertebral fractures (VFs), using different technical and image interpretation parameters, compared with MRI as the reference standard. Materials and Methods A systematic literature search was performed on July 9, 2020, to identify studies evaluating DECT performance for in vivo detection of vertebral BME. A random-effects model was used to derive estimates of the diagnostic accuracy parameters of DECT. The impact of relevant covariates in technical, image interpretation, and study design parameters on the diagnostic performance of DECT was investigated using subgroup analyses. Results Seventeen studies (with 742 of 2468 vertebrae with BME at MRI) met inclusion criteria. Pooled estimates of sensitivity, specificity, and area under the curve of DECT for vertebral body BME were 89% (95% CI: 84%, 92%), 96% (95% CI: 92%, 98%), and 96% (95% CI: 94%, 97%), respectively. Single-source consecutive scanning showed poor specificity (78%) compared with the dual-source technique (98%, P < .001). Specificity was higher using bone and soft-tissue kernels (98%) compared with using only soft-tissue kernels (90%, P = .001). Qualitative assessment had a better specificity (97%) versus quantitative assessment (90%) of DECT images (P = .01). Experienced readers showed considerably higher specificity (96%) compared with trainees (79%, P = .01). DECT sensitivity improved using a higher difference between low- and high-energy spectra (90% vs 83%, P = .04). Conclusion Given its high specificity, the detection of vertebral bone marrow edema with dual-energy CT (DECT) associated with vertebral fracture may obviate confirmatory MRI in an emergency setting. Technical parameters, such as the dual-source technique, both bone and soft-tissue kernels, and qualitative assessment by experienced readers, can ensure the high specificity of DECT. © RSNA, 2021 Online supplemental material is available for this article.

Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs

Ensuring correct radiograph view labeling is important for machine learning algorithm development and quality control of studies obtained from multiple facilities. The purpose of this study was to develop and test the performance of a deep convolutional neural network (DCNN) for the automated classification of frontal chest radiographs (CXRs) into anteroposterior (AP) or posteroanterior (PA) views. We obtained 112,120 CXRs from the NIH ChestX-ray14 database, a publicly available CXR database performed in adult (106,179 (95%)) and pediatric (5941 (5%)) patients consisting of 44,810 (40%) AP and 67,310 (60%) PA views. CXRs were used to train, validate, and test the ResNet-18 DCNN for classification of radiographs into anteroposterior and posteroanterior views. A second DCNN was developed in the same manner using only the pediatric CXRs (2885 (49%) AP and 3056 (51%) PA). Receiver operating characteristic (ROC) curves with area under the curve (AUC) and standard diagnostic measures were used to evaluate the DCNN's performance on the test dataset. The DCNNs trained on the entire CXR dataset and pediatric CXR dataset had AUCs of 1.0 and 0.997, respectively, and accuracy of 99.6% and 98%, respectively, for distinguishing between AP and PA CXR. Sensitivity and specificity were 99.6% and 99.5%, respectively, for the DCNN trained on the entire dataset and 98% for both sensitivity and specificity for the DCNN trained on the pediatric dataset. The observed difference in performance between the two algorithms was not statistically significant (p = 0.17). Our DCNNs have high accuracy for classifying AP/PA orientation of frontal CXRs, with only slight reduction in performance when the training dataset was reduced by 95%. Rapid classification of CXRs by the DCNN can facilitate annotation of large image datasets for machine learning and quality assurance purposes.

Loss of Consciousness and Altered Mental State as Predictors of Functional Recovery Within 6 Months Following Mild Traumatic Brain Injury

OBJECTIVE

The authors tested the hypothesis that a combination of loss of consciousness (LOC) and altered mental state (AMS) predicts the highest risk of incomplete functional recovery within 6 months after mild traumatic brain injury (mTBI), compared with either condition alone, and that LOC alone is more strongly associated with incomplete recovery, compared with AMS alone.

METHODS

Data were analyzed from 407 patients with mTBI from Head injury Serum Markers for Assessing Response to Trauma (HeadSMART), a prospective cohort study of TBI patients presenting to two urban emergency departments. Four patient subgroups were constructed based on information documented at the time of injury: neither LOC nor AMS, LOC only, AMS only, and both. Logistic regression models assessed LOC and AMS as predictors of functional recovery at 1, 3, and 6 months.

RESULTS

A gradient of risk of incomplete functional recovery at 1, 3, and 6 months postinjury was noted, moving from neither LOC nor AMS, to LOC or AMS alone, to both. LOC was associated with incomplete functional recovery at 1 and 3 months (odds ratio=2.17, SE=0.46, p<0.001; and odds ratio=1.80, SE=0.40, p=0.008, respectively). AMS was associated with incomplete functional recovery at 1 month only (odds ratio=1.77, SE=0.37 p=0.007). No association was found between AMS and functional recovery in patients with no LOC. Neither LOC nor AMS was predictive of functional recovery at later times.

CONCLUSIONS

These findings highlight the need to include symptom-focused clinical variables that pertain to the injury itself when assessing who might be at highest risk of incomplete functional recovery post-mTBI.

Dynamic Brain Connectivity in Resting State Functional MR Imaging

Dynamic functional connectivity adds another dimension to resting-state functional MR imaging analysis. In recent years, dynamic functional connectivity has been increasingly used in resting-state functional MR imaging, and several studies have demonstrated that dynamic functional connectivity patterns correlate with different physiologic and pathologic brain states. In fact, evidence suggests that dynamic functional connectivity is a more sensitive marker than static functional connectivity; therefore, it might be a promising tool to add to clinical functional neuroimaging. This article provides a broad overview of dynamic functional connectivity and reviews its general principles, techniques, and potential clinical applications.

The Problem of Neurovascular Uncoupling

Neurovascular uncoupling (NVU) is one of the most important confounds of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMR imaging) in the setting of focal brain lesions such as brain tumors. This article reviews the assessment of NVU related to focal brain lesions with emphasis on the use of cerebrovascular reactivity mapping measurement methods and resting state BOLD fMR imaging metrics in the detection of NVU, as well as the use of amplitude of low-frequency fluctuation metrics to mitigate the effects of NVU on clinical fMR imaging activation.

Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks

Machine learning has several potential uses in medical imaging for semantic labeling of images to improve radiologist workflow and to triage studies for review. The purpose of this study was to (1) develop deep convolutional neural networks (DCNNs) for automated classification of 2D mammography views, determination of breast laterality, and assessment and of breast tissue density; and (2) compare the performance of DCNNs on these tasks of varying complexity to each other. We obtained 3034 2D-mammographic images from the Digital Database for Screening Mammography, annotated with mammographic view, image laterality, and breast tissue density. These images were used to train a DCNN to classify images for these three tasks. The DCNN trained to classify mammographic view achieved receiver-operating-characteristic (ROC) area under the curve (AUC) of 1. The DCNN trained to classify breast image laterality initially misclassified right and left breasts (AUC 0.75); however, after discontinuing horizontal flips during data augmentation, AUC improved to 0.93 (p < 0.0001). Breast density classification proved more difficult, with the DCNN achieving 68% accuracy. Automated semantic labeling of 2D mammography is feasible using DCNNs and can be performed with small datasets. However, automated classification of differences in breast density is more difficult, likely requiring larger datasets.

Automated detection and classification of shoulder arthroplasty models using deep learning

OBJECTIVE

To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models.

MATERIALS AND METHODS

We included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN-based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions.

RESULTS

The DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features.

CONCLUSION

DCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification.

A direct visuosensory cortical connectivity of the human limbic system. Dissecting the trajectory of the parieto-occipito-hypothalamic tract in the human brain using diffusion weighted tractography

The human hypothalamus is at the center of the human limbic system anatomically and physiologically. The hypothalamus plays pivotal roles in controlling autonomic responses and instinctive behaviors such as regulating fear, aggression, learning, feeding behavior, circadian rhythm, and reproductive activities. The detailed anatomy of the pathways responsible for mediating these responses, however, is yet to be determined. The inhibitory effect of the cerebral cortex on the hypothalamus in many autonomic responses, suggests the presence of direct connection between the cortex and hypothalamic nuclei. While, there is ample information to support the cortico-hypothalamic association between the prefrontal cortex and hypothalamic nuclei, the information regarding a direct posterior cortico-hypothalamic alliance is scant. The visuosensory information may be crucial for the limbic system to regulate some of the important limbic functions. Multiple dissection animal studies revealed direct posterior cortical connectivity with the hypothalamic nuclei. However, a direct cortico-hypothalamic connectivity from the parieto-occipital cortices has not been revealed in the human brain yet. Diffusion weighted imaging (DWI) may be helpful in better visualizing the anatomy of this direct posterior cortico-limbic connectivity noninvasively in the human brain. We studied 30 healthy human subjects. Using a high-spatial and high angular resolution diffusion weighted tractography technique, for the first time, we were able to delineate and reconstruct the trajectory of the parieto-occipito-hypothalamic tract.

Automated detection & classification of knee arthroplasty using deep learning

BACKGROUND

Preoperative identification of knee arthroplasty is important for planning revision surgery. However, up to 10% of implants are not identified prior to surgery. The purposes of this study were to develop and test the performance of a deep learning system (DLS) for the automated radiographic 1) identification of the presence or absence of a total knee arthroplasty (TKA); 2) classification of TKA vs. unicompartmental knee arthroplasty (UKA); and 3) differentiation between two different primary TKA models.

METHOD

We collected 237 anteroposterior (AP) knee radiographs with equal proportions of native knees, TKA, and UKA and 274 AP knee radiographs with equal proportions of two TKA models. Data augmentation was used to increase the number of images for deep convolutional neural network (DCNN) training. A DLS based on DCNNs was trained on these images. Receiver operating characteristic (ROC) curves with area under the curve (AUC) were generated. Heatmaps were created using class activation mapping (CAM) to identify image features most important for DCNN decision-making.

RESULTS

DCNNs trained to detect TKA and distinguish between TKA and UKA both achieved AUC of 1. Heatmaps demonstrated appropriate emphasis of arthroplasty components in decision-making. The DCNN trained to distinguish between the two TKA models achieved AUC of 1. Heatmaps showed emphasis of specific unique features of the TKA model designs, such as the femoral component anterior flange shape.

CONCLUSIONS

DCNNs can accurately identify presence of TKA and distinguish between specific arthroplasty designs. This proof-of-concept could be applied towards identifying other prosthesis models and prosthesis-related complications.

Age differences in outcome after mild traumatic brain injury: results from the HeadSMART study

This study longitudinally examined age differences across multiple outcome domains in individuals diagnosed with acute mild traumatic brain injury (mTBI). A sample of 447 adults meeting VA/DoD criteria for mTBI was dichotomized by age into older (≥65 years; n = 88) and younger (<65 years; n = 359) sub-groups. All participants presented to the emergency department within 24 hours of sustaining a head injury, and outcomes were assessed at 1-, 3-, and 6-month intervals. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), post-concussive symptoms (PCS) were ascertained with the Rivermead Post-Concussion Questionnaire (RPQ), and functional recovery from the Extended Glasgow Outcome Scale (GOSE). Mixed effects logistic regression models showed that the rate of change over time in odds of functional improvement and symptom alleviation did not significantly differ between age groups (p = 0.200-0.088). Contrary to expectation, older adults showed equivalent outcome trajectories to younger persons across time. This is a compelling finding when viewed in light of the majority opinion that older adults are at risk for significantly worse outcomes. Future work is needed to identify the protective factors inherent to sub-groups of older individuals such as this.

Characterizing the Link Between Glial Activation and Changed Functional Connectivity in National Football League Players Using Multimodal Neuroimaging

OBJECTIVE

The primary objective of this preliminary study was to examine the impact of NFL play on interregional functional connectivity between two brain regions, the supramarginal gyrus (SMG) and the thalamus, identified as having higher binding of [¹¹C]DPA-713 in NFL players. The authors' secondary objective was to examine the effect of years since play on the interregional connectivity.

METHODS

Resting-state functional MRI was used to examine functional brain changes between regions with evidence of past injury in active or recently retired NFL players (defined as ≤12 years since NFL play) and distantly retired players (defined as >12 years since NFL play). Age-comparable individuals without a history of concussion or participation in collegiate or professional collision sports were included as a control group.

RESULTS

Compared with healthy control subjects, NFL players showed a loss of anticorrelation between the left SMG and bilateral thalami (mean z score=-2.434, p=0.015). No difference was observed when examining right SMG connectivity. The pattern of connectivity in active and recently retired players mimicked the pattern observed in distantly retired players and older control subjects.

CONCLUSIONS

Further study of the clinical significance of this altered pattern of interregional connectivity in active and recently retired NFL players is needed.