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.

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 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.

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.

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.

Your mileage may vary: impact of data input method for a deep learning bone age app's predictions.

OBJECTIVE: The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods. METHODS: The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic. RESULTS: Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively. DISCUSSION: Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.

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.

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.

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.

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.

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.

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.

The association of neuropsychiatric symptoms with regional brain volumes from patients in a tertiary multi-disciplinary memory clinic.

BACKGROUND: To examine the interaction between structural brain volume measures derived from a clinical magnetic resonance imaging (MRI) and occurrence of neuropsychiatric symptoms (NPS) in outpatient memory clinic patients. METHODS: Clinical and neuroimaging data were collected from the medical records of outpatient memory clinic patients who were seen by neurologists, geriatric neuropsychiatrists, and geriatricians. MRI scan acquisition was carried out on a 3 T Siemens Verio scanner at Johns Hopkins Bayview Medical Center. Image analyses used an automated multi-label atlas fusion method with a geriatric atlas inventory to generate 193 anatomical regions from which volumes were measured. Regions of interest were generated a priori based on previous literature review of NPS in dementia. Regional volumes for agitation, apathy, and delusions were carried forward in a linear regression analysis. RESULTS: Seventy-two patients had clinical and usable neuroimaging data that were analyzed and grouped by Mini-Mental State Exam (MMSE). Neuropsychiatric Inventory Questionnaire (NPI-Q) agitation was inversely associated with rostral anterior cingulate cortex (ACC) bilaterally and left subcallosal ACC volumes in the moderate severity group. Delusions were positively associated with left ACC volumes in both severe and mild groups but inversely associated with the right dorsolateral prefrontal cortex (DLPFC) in the moderate subgroup. CONCLUSIONS: Agitation, apathy, and delusions are associated with volumes of a priori selected brain regions using clinical data and clinically acquired MRI scans. The ACC is an anatomic region common to these symptoms, particularly agitation and delusions, which closely mirror the findings of research-quality studies and suggest its importance as a behavioral hub.

Prognostic Value of a Structural Brain MRI Score in the Acute Phase of Aneurysmal Subarachnoid Hemorrhage: A Pilot Study.

BACKGROUND/OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high morbidity and mortality despite advances in management. We evaluated the prognostic significance of a qualitative score using brain magnetic resonance imaging (MRI) features obtained early after aSAH. METHODS: Patients with aSAH were enrolled in a prospective observational cohort and underwent brain MRI during their acute hospitalization. MRIs were rated using a scoring system that considers the anatomical location of signal intensity changes on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences. The relationship between MRI scores and functional outcome defined by modified Rankin scale (mRS) at 6 months was evaluated in uni- and multivariable models. RESULTS: The cohort included 45 aSAH patients (median World Federation of Neurologic Surgeons (IQR) 2 (1-4)) who underwent brain MRI a mean (SD) of 9.0 ± 8.0 days after aSAH. At 6 months after aSAH, 26 patients had achieved a favorable outcome (mRS ≤ 2) while 15 had an unfavorable outcome (mRS > 2). Deep gray nuclei (DGN) score (p = 0.016), cortex + DGN score (p = 0.015), FLAIR score (p = 0.016), DWI score (p = 0.0045), and overall score (p = 0.0081) were significantly lower in patients with favorable outcome compared to those with unfavorable outcome. However, MRI scores were not independent predictors of outcome in multivariable models adjusting for admission Hunt and Hess, Glasgow Coma Scale, or World Federation of Neurologic Surgeons scales. CONCLUSIONS: In this pilot study, a qualitative scoring system using anatomically defined MRI FLAIR and DWI signal abnormalities identified in the acute phase of aSAH was linked to 6-month functional outcome. However, these scores did not add prognostic value to established indices of neurological severity.

Radiology "forensics": determination of age and sex from chest radiographs using deep learning.

PURPOSE: To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR). METHODS: We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11-18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance. RESULTS: DCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11-18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001). CONCLUSION: DCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.

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.

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.

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.