Apparent Diffusion Coefficient (ADC) Differentiates Retinoblastoma from Coats Disease on MRI.

PURPOSE: Coats' disease can be difficult to differentiate from retinoblastoma. While MR imaging of retinoblastoma and Coats' disease have been examined for differentiating features such as eye size, vitreous seeding, and shape of retinal detachment, there is a lack of data on apparent diffusion coefficient (ADC). ADC is a measure of the diffusion (of water molecules) within tissue, and is commonly clinically calculated using MRI. DESIGN: Retrospective cross-sectional study. METHODS: Patient or study population: Children < 18 diagnosed with Coats disease or Retinoblastoma between January 1, 2018 and January 8, 2022 who had MRI imaging that was reviewable. MAIN OUTCOME MEASURE: Apparent diffusion coefficient (ADC) of the intraocular lesion. Retrospective brain MRIs were obtained from records of 5 eyes of 5 Coats' patients and 29 eyes of 23 patients with retinoblastoma. All MRIs were obtained prior to treatment. The eyedropper tool in Epic's default viewer (Ambra DICOM) was used to measure the ADC of five to eight randomly sampled points within the eye lesions seen on MRI. Average ADC was calculated for each affected eye. Internal reliability was confirmed by re-measuring mean ADC for a random sample of patients masked to their diagnosis and prior measurements. T-test was used to determine if ADC values differ between groups. RESULTS: The mean ADC for retinoblastoma patients (442 +/- 210 mm2/s) differed significantly from the mean for Coats' patients (1364 +/- 309 mm2/s), (P < .001). T-test between baseline and repeat measurements was not significantly different. Since ADC values can differ between different scanners and DW MRI pulse sequences, an ADC threshold may be difficult to generalize across institutes, in our data set a threshold of 900 mm2/s was useful in separating the two diagnoses with a high degree of accuracy. CONCLUSIONS: Clinical features of retinoblastoma and Coats' disease often resemble each other and can lead to misdiagnosis. Since ADCs are derived from diffusion-weighted MRI as an objective parameter, it has the potential to aid in establishing or confirming the diagnosis when retinoblastoma and/or Coats' disease are suspected.

Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care. METHODS: Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70). RESULTS: The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor. CONCLUSION: The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

Optimal approaches to analyzing functional MRI data in glioma patients.

BACKGROUND: Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results. NEW METHOD: We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163). RESULTS: Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.

Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy.

Purpose: Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry. Participants and Methods: Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems. Results: Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05). Conclusion: Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.

Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.

PURPOSE: Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care. METHODS: GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models. RESULTS: The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks. CONCLUSION: We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.

Cognitive deficits and altered functional brain network organization in pediatric brain tumor patients.

Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Resting state functional magnetic resonance imaging allows for the study of the spontaneous fluctuations in bulk neural activity, providing insight into brain organization and function. Here, we evaluated cognitive performance and functional network architecture in pediatric brain tumor patients. Forty-nine patients (7-18 years old) with a primary brain tumor diagnosis underwent resting state imaging during regularly scheduled clinical visits. All patients were tested with a battery of cognitive assessments. Extant data from 139 typically developing children were used as controls. We found that obtaining high-quality imaging data during routine clinical scanning was feasible. Functional network organization was significantly altered in patients, with the largest disruptions observed in patients who received propofol sedation. Awake patients demonstrated significant decreases in association network segregation compared to controls. Interestingly, there was no difference in the segregation of sensorimotor networks. With a median follow-up of 3.1 years, patients demonstrated cognitive deficits in multiple domains of executive function. Finally, there was a weak correlation between decreased default mode network segregation and poor picture vocabulary score. Future work with longer follow-up, longitudinal analyses, and a larger cohort will provide further insight into this potential predictor.

Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival.

Glioblastoma, a highly aggressive form of brain tumor, is a brain-wide disease. We evaluated the impact of tumor burden on whole brain resting-state functional magnetic resonance imaging (rs-fMRI) activity. Specifically, we analyzed rs-fMRI signals in the temporal frequency domain in terms of the power-law exponent and fractional amplitude of low-frequency fluctuations (fALFF). We contrasted 189 patients with newly-diagnosed glioblastoma versus 189 age-matched healthy reference participants from an external dataset. The patient and reference datasets were matched for age and head motion. The principal finding was markedly flatter spectra and reduced grey matter fALFF in the patients as compared to the reference dataset. We posit that the whole-brain spectral change is attributable to global dysregulation of excitatory and inhibitory balance and metabolic demand in the tumor-bearing brain. Additionally, we observed that clinical comorbidities, in particular, seizures, and MGMT promoter methylation, were associated with flatter spectra. Notably, the degree of change in spectra was predictive of overall survival. Our findings suggest that frequency domain analysis of rs-fMRI activity provides prognostic information in glioblastoma patients and offers a means of noninvasively studying the effects of glioblastoma on the whole brain.

Neuroinflammation and white matter alterations in occupational manganese exposure assessed by diffusion basis spectrum imaging.

OBJECTIVE: To evaluate in-vivo neuroinflammation and white matter (WM) microstructural integrity in occupational manganese (Mn) exposure. METHODS: We assessed brain inflammation using Diffusion Basis Spectrum Imaging (DBSI) in 26 Mn-exposed welders, 17 Mn-exposed workers, and 26 non-exposed participants. Cumulative Mn exposure was estimated from work histories and the Unified Parkinson's Disease Rating Scale motor subsection 3 (UPDRS3) scores were completed by a movement specialist. Tract-based Spatial Statistics allowed for whole-brain voxel-wise WM analyses to compare WM DBSI-derived measures between the Mn-exposed and non-exposed groups. Exploratory grey matter region of interest (ROI) analyses examined the presence of similar alterations in the basal ganglia. We used voxelwise general linear modeling and linear regression to evaluate the association between cumulative Mn exposure, WM or basal ganglia DBSI metrics, and UPDRS3 scores, while adjusting for age. RESULTS: Mn-exposed welders had higher DBSI-derived restricted fraction (DBSI-RF), higher DBSI-derived nonrestricted fraction (DBSI-NRF), and lower DBSI-derived fiber fraction (DBSI-FF) in multiple WM tracts (all p < 0.05) in comparison to less-exposed workers and non-exposed participants. Basal ganglia ROI analyses revealed higher average caudate DBSI-NRF and DBSI-derived radial diffusion (DBSI-RD) values in Mn-exposed welders relative to non-exposed participants (p < 0.05). Caudate DBSI-NRF was also associated with greater cumulative Mn exposure and higher UPRDS3 scores. CONCLUSIONS: Mn-exposed welders demonstrate greater DBSI-derived indicators of neuroinflammation-related cellularity (DBSI-RF), greater extracellular edema (DBSI-NRF), and lower apparent axonal density (DBSI-FF) in multiple WM tracts suggesting a neuroinflammatory component in the pathophysiology of Mn neurotoxicity. Caudate DBSI-NRF was positively associated with both cumulative Mn exposure and clinical parkinsonism, indicating a possible dose-dependent effect on extracellular edema with associated motor effects.

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.

Background: Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients. Methods: We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival. Results: Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%. Conclusions: These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.

Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.

OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. METHODS: Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. RESULTS: The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. CONCLUSIONS: Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Preoperative functional connectivity by magnetic resonance imaging for refractory neocortical epilepsy.

Objective: Patients with refractory epilepsy experience extensive and invasive clinical testing for seizure onset zones treatable by surgical resection. However, surgical resection can fail to provide therapeutic benefit, and patients with neocortical epilepsy have the poorest therapeutic outcomes. This case series studied patients with neocortical epilepsy who were referred for surgical treatment. Prior to surgery, patients volunteered for resting-state functional magnetic resonance imaging (rs-fMRI) in addition to imaging for the clinical standard of care. This work examined the variability of functional connectivity in patients, estimated from rs-fMRI, for associations with surgical outcomes. Methods: This work examined pre-operative structural imaging, pre-operative rs-fMRI, and post-operative structural imaging from seven epilepsy patients. Review of the clinical record provided Engel classifications for surgical outcomes. A novel method assessed pre-operative rs-fMRI from patients using comparative rs-fMRI from a large cohort of healthy control subjects and estimated Gibbs distributions for functional connectivity in patients compared to healthy controls. Results: Three patients had Engel classification Ia, one patient had Engel classification IIa, and three patients had Engel classification IV. Metrics for variability of functional connectivity, including absolute differences of the functional connectivity of each patient from healthy control averages and probabilistic scores for absolute differences, were higher for patients classified as Engel IV, for whom epilepsy surgery provided no meaningful improvement. Significance: This work continues on-going efforts to use rs-fMRI to characterize abnormal functional connectivity in the brain. Preliminary evidence indicates that the topography of variant functional connectivity in epilepsy patients may be clinically relevant for identifying patients unlikely to have favorable outcomes after epilepsy surgery. Widespread topographic variations of functional connectivity also support the hypothesis that epilepsy is a disease of brain resting-state networks.

Normalization of cerebral hemodynamics after hematopoietic stem cell transplant in children with sickle cell disease.

Children with sickle cell disease (SCD) demonstrate cerebral hemodynamic stress and are at high risk of strokes. We hypothesized that curative hematopoietic stem cell transplant (HSCT) normalizes cerebral hemodynamics in children with SCD compared with pre-transplant baseline. Whole-brain cerebral blood flow (CBF) and oxygen extraction fraction (OEF) were measured by magnetic resonance imaging 1 to 3 months before and 12 to 24 months after HSCT in 10 children with SCD. Three children had prior overt strokes, 5 children had prior silent strokes, and 1 child had abnormal transcranial Doppler ultrasound velocities. CBF and OEF of HSCT recipients were compared with non-SCD control participants and with SCD participants receiving chronic red blood cell transfusion therapy (CRTT) before and after a scheduled transfusion. Seven participants received matched sibling donor HSCT, and 3 participants received 8 out of 8 matched unrelated donor HSCT. All received reduced-intensity preparation and maintained engraftment, free of hemolytic anemia and SCD symptoms. Pre-transplant, CBF (93.5 mL/100 g/min) and OEF (36.8%) were elevated compared with non-SCD control participants, declining significantly 1 to 2 years after HSCT (CBF, 72.7 mL/100 g per minute; P = .004; OEF, 27.0%; P = .002), with post-HSCT CBF and OEF similar to non-SCD control participants. Furthermore, HSCT recipients demonstrated greater reduction in CBF (-19.4 mL/100 g/min) and OEF (-8.1%) after HSCT than children with SCD receiving CRTT after a scheduled transfusion (CBF, -0.9 mL/100 g/min; P = .024; OEF, -3.3%; P = .001). Curative HSCT normalizes whole-brain hemodynamics in children with SCD. This restoration of cerebral oxygen reserve may explain stroke protection after HSCT in this high-risk patient population.

Neonatal motor functional connectivity and motor outcomes at age two years in very preterm children with and without high-grade brain injury.

Preterm-born children have high rates of motor impairments, but mechanisms for early identification remain limited. We hypothesized that neonatal motor system functional connectivity (FC) would relate to motor outcomes at age two years; currently, this relationship is not yet well-described in very preterm (VPT; born <32 weeks' gestation) infants with and without brain injury. We recruited 107 VPT infants - including 55 with brain injury (grade III-IV intraventricular hemorrhage, cystic periventricular leukomalacia, post-hemorrhagic hydrocephalus) - and collected FC data at/near term-equivalent age (35-45 weeks postmenstrual age). Correlation coefficients were used to calculate the FC between bilateral motor and visual cortices and thalami. At two years corrected-age, motor outcomes were assessed with the Bayley Scales of Infant and Toddler Development, 3rd edition. Multiple imputation was used to estimate missing data, and regression models related FC measures to motor outcomes. Within the brain-injured group only, interhemispheric motor cortex FC was positively related to gross motor outcomes. Thalamocortical and visual FC were not related to motor scores. This suggests neonatal alterations in motor system FC may provide prognostic information about impairments in children with brain injury.

Corrigendum to: A phase II study of laser interstitial thermal therapy combined with doxorubicin in patients with recurrent glioblastoma.

[This corrects the article DOI: 10.1093/noajnl/vdab164.].

Resting state network mapping in individuals using deep learning.

Introduction: Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings. Methods: In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in n = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings. Results: Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD. Discussion: The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.

Task-based and Resting State Functional MRI in Children.

Functional MR imaging (MRI) is a valuable tool for presurgical planning and is well established in adult patients. The use of task-based fMRI is increasing in pediatric populations because it provides similar benefits for pre-surgical planning in children. This article reviews special adaptations that are required for successful applications of task-based fMRI in children, especially in the motor and language systems. The more recently introduced method of resting state fMRI is reviewed and its relative advantages and disadvantages discussed. Common pitfalls and other systems and networks that may be of interest in special circumstances also are reviewed.

Alterations in resting-state functional connectivity in pediatric patients with tuberous sclerosis complex.

OBJECTIVE: To investigate resting-state functional connectivity (FC) in pediatric patients with tuberous sclerosis complex and intractable epilepsy requiring surgery. METHODS: Resting-state functional MRI was utilized to investigate functional connectivity in 13 pediatric patients with tuberous sclerosis complex (TSC) and intractable epilepsy requiring surgery. RESULTS: The majority of patients demonstrated a resting-state network architecture similar to those reported in healthy individuals. However, preoperative differences were evident between patients with high versus low tuber burden, as well as those with good versus poor neurodevelopmental outcomes, most notably in the cingulo-opercular and visual resting-state networks. One patient with high tuber burden and poor preoperative development and seizure control had nearly normal development and seizure resolution after surgery. This was accompanied by significant improvement in resting-state network architecture just one day postoperatively. SIGNIFICANCE: Although many patients with tuberous sclerosis complex and medically refractory epilepsy demonstrate functional connectivity patterns similar to healthy children, relationships within and between RSNs demonstrate clear differences in patients with higher tuber burden and worse outcomes. Improvements in resting-state network organization postoperatively may be related to epilepsy surgery outcomes, providing candidate biomarkers for clinical management in this high-risk population.

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients.

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62-0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

Dural augmentation approaches and complication rates after posterior fossa decompression for Chiari I malformation and syringomyelia: a Park-Reeves Syringomyelia Research Consortium study.

OBJECTIVE: Posterior fossa decompression with duraplasty (PFDD) is commonly performed for Chiari I malformation (CM-I) with syringomyelia (SM). However, complication rates associated with various dural graft types are not well established. The objective of this study was to elucidate complication rates within 6 months of surgery among autograft and commonly used nonautologous grafts for pediatric patients who underwent PFDD for CM-I/SM. METHODS: The Park-Reeves Syringomyelia Research Consortium database was queried for pediatric patients who had undergone PFDD for CM-I with SM. All patients had tonsillar ectopia ≥ 5 mm, syrinx diameter ≥ 3 mm, and ≥ 6 months of postoperative follow-up after PFDD. Complications (e.g., pseudomeningocele, CSF leak, meningitis, and hydrocephalus) and postoperative changes in syrinx size, headaches, and neck pain were compared for autograft versus nonautologous graft. RESULTS: A total of 781 PFDD cases were analyzed (359 autograft, 422 nonautologous graft). Nonautologous grafts included bovine pericardium (n = 63), bovine collagen (n = 225), synthetic (n = 99), and human cadaveric allograft (n = 35). Autograft (103/359, 28.7%) had a similar overall complication rate compared to nonautologous graft (143/422, 33.9%) (p = 0.12). However, nonautologous graft was associated with significantly higher rates of pseudomeningocele (p = 0.04) and meningitis (p < 0.001). The higher rate of meningitis was influenced particularly by the higher rate of chemical meningitis (p = 0.002) versus infectious meningitis (p = 0.132). Among 4 types of nonautologous grafts, there were differences in complication rates (p = 0.02), including chemical meningitis (p = 0.01) and postoperative nausea/vomiting (p = 0.03). Allograft demonstrated the lowest complication rates overall (14.3%) and yielded significantly fewer complications compared to bovine collagen (p = 0.02) and synthetic (p = 0.003) grafts. Synthetic graft yielded higher complication rates than autograft (p = 0.01). Autograft and nonautologous graft resulted in equal improvements in syrinx size (p < 0.0001). No differences were found for postoperative changes in headaches or neck pain. CONCLUSIONS: In the largest multicenter cohort to date, complication rates for dural autograft and nonautologous graft are similar after PFDD for CM-I/SM, although nonautologous graft results in higher rates of pseudomeningocele and meningitis. Rates of meningitis differ among nonautologous graft types. Autograft and nonautologous graft are equivalent for reducing syrinx size, headaches, and neck pain.