Explore the vast range of titles available.

Deep brain stimulation (DBS) is believed to exert its therapeutic effects through modulation of brain circuitry, yet conventional preoperative planning does not allow direct targeting or visualization of white matter pathways. Diffusion MRI tractography (DT) is virtually the only non-invasive method of visualizing structural connectivity in the brain, leading many to suggest its use to guide DBS targeting. DT-guided DBS not only has the potential to allow direct white matter targeting for established applications [e.g., Parkinson's disease (PD), essential tremor (ET), dystonia], but may also aid in the discovery of new therapeutic targets for a variety of other neurologic and psychiatric diseases. Despite these exciting opportunities, DT lacks standardization and rigorous anatomic validation, raising significant concern for the use of such data in stereotactic brain surgery. This review covers the technical details, proposed methods, and initial clinical data for the use of DT in DBS surgery. Rather than focusing on specific disease applications, this review focuses on methods that can be applied to virtually any DBS target.

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care—resection and chemoradiation—is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).

Three-dimensional digital brain atlases represent an important new generation of neuroinformatics tools for understanding complex brain anatomy, assigning location to experimental data, and planning of experiments. We have acquired a microscopic resolution isotropic MRI and DTI atlasing template for the Sprague Dawley rat brain with 39μm isotropic voxels for the MRI volume and 78μm isotropic voxels for the DTI. Building on this template, we have delineated 76 major anatomical structures in the brain. Delineation criteria are provided for each structure. We have applied a spatial reference system based on internal brain landmarks according to the Waxholm Space standard, previously developed for the mouse brain, and furthermore connected this spatial reference system to the widely used stereotaxic coordinate system by identifying cranial sutures and related stereotaxic landmarks in the template using contrast given by the active staining technique applied to the tissue. With the release of the present atlasing template and anatomical delineations, we provide a new tool for spatial orientationanalysis of neuroanatomical location, and planning and guidance of experimental procedures in the rat brain. The use of Waxholm Space and related infrastructures will connect the atlas to interoperable resources and services for multi-level data integration and analysis across reference spaces. [Display omitted] •High-resolution MRI and DTI template for the Sprague Dawley rat brain•Atlas of major anatomical structures with detailed delineation criteria•Internal landmarks for implementing ‘Waxholm Space’ spatial reference system•Cranial landmarks for translation to stereotaxic coordinate system•The template and atlas form a new tool for spatial orientation in the rat brain.

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.

•One complete, postmortem human brainstem was scanned with MRI for 208 h.•Anatomic and diffusion images achieved isotropic resolutions of 50 and 200 μm.•Ninety gray and white matter structures were manually segmented in the brainstem.•Diffusion tractography was used to reconstruct 11 unique brainstem tracts.•Data are rendered into a high-resolution, interactive 3D atlas available online. Conventional atlases of the human brainstem are limited by the inflexible, sparsely-sampled, two-dimensional nature of histology, or the low spatial resolution of conventional magnetic resonance imaging (MRI). Postmortem high-resolution MRI circumvents the challenges associated with both modalities. A single human brainstem specimen extending from the rostral diencephalon through the caudal medulla was prepared for imaging after the brain was removed from a 65-year-old male within 24 h of death. The specimen was formalin-fixed for two weeks, then rehydrated and placed in a custom-made MRI compatible tube and immersed in liquid fluorocarbon. MRI was performed in a 7-Tesla scanner with 120 unique diffusion directions. Acquisition time for anatomic and diffusion images were 14 h and 208 h, respectively. Segmentation was performed manually. Deterministic fiber tractography was done using strategically chosen regions of interest and avoidance, with manual editing using expert knowledge of human neuroanatomy. Anatomic and diffusion images were rendered with isotropic resolutions of 50 μm and 200 μm, respectively. Ninety different structures were segmented and labeled, and 11 different fiber bundles were rendered with tractography. The complete atlas is available online for interactive use at https://www.civmvoxport.vm.duke.edu/voxbase/login.php?return_url=%2Fvoxbase%2F. This atlas presents multiple contrasting datasets and selected tract reconstruction with unprecedented resolution for MR imaging of the human brainstem. There are immediate applications in neuroanatomical education, with the potential to serve future applications for neuroanatomical research and enhanced neurosurgical planning through “safe” zones of entry into the human brainstem.

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.

Many cancers evade immunosurveillance by downregulating surface major histocompatibility class (MHC)-I. Proprotein convertase subtilisin/kexin type 9 (PCSK9) promotes MHC-I degradation and is elevated in glioma. Evolocumab is a clinically approved PCSK9 inhibitor which restores MHC-I expression in pre-clinical cancer models. However, monoclonal antibodies have limited blood brain/tumor barrier penetrance (BBB/BTB). We conducted a window-of-opportunity trial, evaluating evolocumab’s BBB/BTB penetrance and biological effect (PesKE; NCT04937413). Patients with newly diagnosed or recurrent glioma undergoing a clinically indicated biopsy or resection were enrolled (n = 32, M: 16, F: 16; control average age: 51.85, evolocumab: 53). Intervention participants (n = 6) received a single subcutaneous evolocumab dose pre-procedure, of which 4 provided research tissue. No significant adverse events were observed. Evolocumab was detected in all analyzed intervention tissue, with an average tumor: blood ratio of 0.0222 (SD ± 0.0190), akin to other monoclonals. Evolocumab quantitation was 4.44× greater in contrast-enhancing (mean 0.0068 fmol/mcg (SD ± 0.001)) vs non-contrast enhancing cases (mean 0.0015 fmol/mcg (SD ± 0.0004)). Proteomic analysis found positive trends between evolocumab and MHC-I subtypes (HLA-A-C, E-G), with a significant positive correlation with HLA-H (R 2  = 0.9584, p  = 0.021*). Tumor tissue with higher evolocumab titers demonstrated increased surface MHC-I and CD8 + T cell infiltration. Increased CD8 + TNF , FASLG and GZMA transcription was observed in high titer tissue compared to low titer tissue and untreated controls. Pre-resection evolocumab is well tolerated but exhibits BBB/BTB penetrance akin to other monoclonal antibodies. Increased tumoral evolocumab/PCSK9i may enhance tumoral MHC-I/effector CD8 + infiltration. Future work will explore combining evolocumab with BBB/BTB opening therapies like low-intensity focused ultrasound.