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Lactate is now recognized as an important intermediate in brain metabolism, but its role is still under investigation. In this work we mapped the distribution of lactate and bicarbonate produced from intravenously injected 13C-pyruvate over the whole brain using a new imaging method, hyperpolarized 13C MRI (N = 14, ages 23 to 77). Segmenting the 13C-lactate images into brain atlas regions revealed a pattern of lactate that was preserved across individuals. Higher lactate signal was observed in cortical grey matter compared to white matter and was highest in the precuneus, cuneus and lingual gyrus. Bicarbonate signal, indicating flux of [1–13C]pyruvate into the TCA cycle, also displayed consistent spatial distribution. One-way ANOVA to test for significant differences in lactate among atlas regions gave F = 87.6 and p < 10−6. This report of a “lactate topography” in the human brain and its consistent pattern is evidence of region-specific lactate biology that is preserved across individuals. [Display omitted] •Hyperpolarized 13C MRI was used to acquire human brain images in control subjects.•13C-lactate images were segmented into brain atlas regions.•The 13C-lactate topography was found to be consistent across individuals.•The preserved pattern provides evidence of region-specific lactate biology.

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI. [Display omitted] •Developed open-source benchmarking platform and metrics for robustness of DNNs.•Quantified sensitivity of DNNs to OOD data on three neuroimaging segmentation tasks.•Modern CNNs are highly susceptible to distribution shift, corruptions and artifacts.•Simple augmentation strategies improve robustness for anatomical segmentation tasks.•Vision transformers exhibit improved robustness over FCNs.

Despite advances in metastatic breast cancer (MBC) management, leptomeningeal disease (LMD) prognosis remains poor. This study evaluates clinicopathological and treatment factors influencing outcomes of MBC patients with LMD treated with radiotherapy (RT). We conducted a retrospective analysis of patients with MBC treated with RT for brain metastases (BrM) between 2005 and 2019. LMD diagnosis was made via magnetic resonance imaging (MRI). Multivariable analysis (MVA) identified variables associated with brain-specific progression-free survival (bsPFS) and overall survival (OS). Among 691 MBC patients treated with RT for BrM, 161 (23%) had LMD, either at initial presentation (50/161) or after BrM treatment. Patients with LMD were younger, more likely to have ER + disease, more likely to have undergone surgery for BrM, and less likely to have received prior whole-brain RT. HER2+ LMD was associated with longer bsPFS (HR 0.47, 95% CI: 0.25–0.86, p  = 0.01) and OS (HR 0.38, 95% CI: 0.2–0.75, p  = 0.002). Median OS for triple-negative breast cancer was 3.7 months, 5.1 months for HR+/HER2 − and 15.4 months for HER2 + MBC. HER2-targeted therapy, either at or after LMD diagnosis, improved long-term survival (> 2 years) (Fisher’s test, p  < 0.05). Low Karnofsky Performance Status (KPS < 60) was linked to shorter bsPFS (HR 2.91, 95% CI: 1.49–5.69, p  < 0.01) and OS (HR 3.37, 95% CI: 1.78–6.41, p  < 0.001). These findings highlight the need for effective CNS-penetrating systemic therapies for HER2-negative breast cancer.

Chemical exchange saturation transfer (CEST) is a relatively novel magnetic resonance imaging (MRI) technique with an image contrast designed for in vivo measurement of certain endogenous molecules with protons that are exchangeable with water protons, such as amide proton transfer commonly used for neuro-oncology applications. Recent technological advances have made it feasible to implement CEST on clinical grade scanners within practical acquisition times, creating new opportunities to integrate CEST in clinical workflow. In addition, the majority of CEST applications used in neuro-oncology are performed without the use gadolinium-based contrast agents which are another appealing feature of this technique. This review is written for clinicians involved in neuro-oncologic care (nonphysicists) as the target audience explaining what they need to know as CEST makes its way into practice. The purpose of this article is to (1) review the basic physics and technical principles of CEST MRI, and (2) review the practical applications of CEST in neuro-oncology.

IntroductionPost-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) is a growing concern, with headache being a particularly debilitating symptom with high prevalence. The long-term effects of COVID-19 and post-COVID headache on brain function remain poorly understood, particularly among non-hospitalized individuals. This study focused on the power-law scaling behavior of functional brain dynamics, indexed by the Hurst exponent (H). This measure is suppressed during physiological and psychological distress and was thus hypothesized to be reduced in individuals with post-COVID syndrome, with greatest reductions among those with persistent headache.MethodsResting-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging data were collected for 57 individuals who had COVID-19 (32 with no headache, 14 with ongoing headache, 11 recovered) and 17 controls who had cold and flu-like symptoms but tested negative for COVID-19. Individuals were assessed an average of 4–5 months after COVID testing, in a cross-sectional, observational study design.ResultsNo significant differences in H values were found between non-headache COVID-19 and control groups., while those with ongoing headache had significantly reduced H values, and those who had recovered from headache had elevated H values, relative to non-headache groups. Effects were greatest in temporal, sensorimotor, and insular brain regions. Reduced H in these regions was also associated with decreased BOLD activity and local functional connectivity.ConclusionsThese findings provide new insights into the neurophysiological mechanisms that underlie persistent post-COVID headache, with reduced BOLD scaling as a potential biomarker that is specific to this debilitating condition.

Background The goal of the study is to use CT imaging in patients with aSAH to evaluate the anatomic distribution of hemorrhage and compartmentalization of subarachnoid space to investigate potential in vivo visualization of recently discovered layer named subarachnoid lymphatic-like membrane (SLYM). Methods We conducted a retrospective cohort study of cases with aneurysmal SAH (aSAH) at our institution between January 2015 and June 2022. Subarachnoid hemorrhage distribution into superficial and deep subarachnoid spaces was classified based on proximity to the dural or pial surfaces, respectively, as seen on multiplanar CT head. Results A total of 97 patients with aSAH were included. Patients with lower modified Fisher score (MFS) of 1-2 were more likely to have SAH compartmentalizing in the “deep” pial-adjacent subarachnoid space. Patients with higher MFS of 3-4 were more likely to have SAH in both “superficial” and “deep” compartments along the brainstem. There is a significant association between the severity of aSAH - quantified by the MFS - and the distribution of the blood. Patients with higher MFS scores were roughly 7.6 times ( p -value = 0.049) more likely to have hemorrhage at the “Superficial” juxta-dural subarachnoid compartment when compared to those with lower MFS scores. Conclusion This study suggests an imaging correlate to the recently discovered “SLYM”, potentially influencing aSAH compartmentalization, particularly in low-grade bleeds. While compartmentalization is limited in high grade cases, these findings warrant further investigation with advanced imaging techniques to validate this membrane’s role and potential impact on CSF flow and aSAH pathophysiology.

Abstract BACKGROUND Cavity stereotactic radiotherapy has emerged as a standard option following resection of brain metastases. However, the optimal approach with either single-fraction or hypofractionated stereotactic radiotherapy (HSRT) remains a significant question. OBJECTIVE To report outcomes for 5-fraction HSRT to the surgical cavity, based on contouring according to a recently reported international consensus guideline. METHODS Patients treated with cavity HSRT were identified from a prospective institutional database. Local brain control (LC), distant brain failure (DBF), leptomeningeal disease (LMD), and overall survival rates were determined. Univariate and multivariable analyses were performed on potential predictive factors. RESULTS One hundred thirty-seven cavities in 122 patients were treated at a median total dose of 30 Gy (range, 25-35 Gy). The median follow-up was 16 mo (range, 1-60 mo). Nonsmall cell lung cancer was the most common histology (44%), followed by breast cancer (21%). In 57% of surgical cavities, the preoperative tumor diameter was >3 cm. One-year LC, DBF, LMD, and overall survival rates were 84%, 45%, 22%, and 62%, respectively. Multivariable analyses identified colorectal (hazard ratio [HR] 4.1, P = .0066) and melanoma (HR 2.4, P = .012) metastases as predictors of local recurrence; preoperative tumor diameter >2 cm (HR 8.9, P = .012) and absence of targeted therapy (HR 4.4, P = .03) as predictors of DBF; and breast cancer histology (HR 2.1, P = .05) and subtotal resection (HR 2.6, P = .009) as predictors of LMD. Symptomatic radiation necrosis was observed in 7 patients (6%). CONCLUSION High rates of LC were observed following this 5-fraction HSRT regimen. Superiority as compared to single-fraction SRS requires a randomized trial.

There has been growing attention on the effect of COVID‐19 on white‐matter microstructure, especially among those that self‐isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single‐shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single‐shell‐compatible diffusion MRI modeling methods are compared for detecting the effect of COVID‐19, including diffusion‐tensor imaging, diffusion‐tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self‐isolated patients at the study initiation and 3‐month follow‐up, along with age‐ and sex‐matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single‐shell methods to demonstrate COVID‐19‐related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID‐19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID‐19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID‐19 related white‐matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b‐values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3‐month follow‐up, likely due to the limited size of the follow‐up cohort. To summarize, correlated diffusion imaging is shown to be a viable single‐shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID‐19 patients, suggesting the two regions react differently to viral infection. We used simulations and experimental data to demonstrate the feasibility of the novel correlated diffusion imaging for detecting microstructural changes in human white matter. We demonstrate in the case of mild COVID‐19, correlated diffusion imaging is superior to diffusion tensor imaging when only single‐shell data are available. Moreover, correlated diffusion imaging may exhibit sensitivities to different pathologies at different b‐values.

Purpose Recurrent high-grade glioma (rHGG) is a heterogeneous population, and the ideal patient selection for re-irradiation (re-RT) has yet to be established. This study aims to identify prognostic factors for rHGG patients treated with re-RT. Methods We retrospectively reviewed consecutive adults with rHGG who underwent re-RT from 2009 to 2020 from our institutional database. The primary objective was overall survival (OS). Secondary endpoints included prognostic factors for early death (< 6 months after re-RT) and predictors of radiation necrosis (RN). Results For the 79 patients identified, the median OS after re-RT was 9.9 months (95% CI 8.3–11.6). On multivariate analyses, re-resection at progression (HR 0.56, p = 0.027), interval from primary treatment to first progression ≥ 16.3 months (HR 0.61, p = 0.034), interval from primary treatment to re-RT ≥ 23.9 months (HR 0.35, p < 0.001), and re-RT PTV volume < 112 cc (HR 0.27, p < 0.001) were prognostic for improved OS. Patients who had unmethylated-MGMT tumours (OR 12.4, p = 0.034), ≥ 3 prior systemic treatment lines (OR 29.1, p = 0.022), interval to re-RT < 23.9 months (OR 9.0, p = 0.039), and re-RT PTV volume ≥ 112 cc (OR 17.8, p = 0.003) were more likely to die within 6 months of re-RT. The cumulative incidence of RN was 11.4% (95% CI 4.3–18.5) at 12 months. Concurrent bevacizumab use (HR < 0.001, p < 0.001) and cumulative equivalent dose in 2 Gy fractions (EQD2, α/β = 2) < 99 Gy 2 (HR < 0.001, p < 0.001) were independent protective factors against RN. Re-RT allowed for less corticosteroid dependency. Sixty-six percent of failures after re-RT were in-field. Conclusion We observe favorable OS rates following re-RT and identified prognostic factors, including methylation status, that can assist in patient selection and clinical trial design. Concurrent use of bevacizumab mitigated the risk of RN.

White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI‐based segmentation methods are often sensitive to acquisition protocols, scanners, noise‐level, and image contrast, failing to generalize to other populations and out‐of‐distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U‐Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control, and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. Four hundred thirty‐two subjects were recruited to train the CNNs from four multisite imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multisite study. We compared our model to two established state‐of‐the‐art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U‐Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on “clinical adversarial cases” simulating data with low signal‐to‐noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io. We present a robust and efficient WMH segmentation model, which also generates an uncertainty map for quality control. In addition, we present a second model to classify dWMH and pvWMH using the initial total WMH segmentation. Our segmentation models achieved high accuracy compared to SOTA algorithms on a wide spectrum of WMH burdens, especially mild WMH. Additionally, we used an augmentation scheme to make our model robust to simulated images with SNR, low resolution, and different contrasts.