Neurosurgery Core

Alexandra Golby Lauren O'Donnell Nathalie Agar
Alexandra Golby, MD
Core Lead
Lauren O'Donnell, PhD
Project Lead
Nathalie Agar, PhD
Project Lead

The neurosurgery project is developing new technologies toward the long-term goal of allowing neurosurgeons in diverse settings to implement the advantages of image-guided therapy (IGT) for their patients. We investigate, develop, and validate approaches that address the two key problems in brain tumor surgery: to define the critical brain regions that must not be resected, and to define the extent and nature of the lesion. Put more simply, we create tools that support the neurosurgeon’s crucial decision of what to preserve, and what to remove. Maximizing tumor resection improves patients’ progression-free survival and overall survival; avoiding neurological deficits also improves survival and deeply impacts daily life for patients. Our strategies leverage preoperative and intraoperative imaging data to optimize brain tumor surgery. We are focusing on multimodality imaging data including diffusion MRI (dMRI), functional MRI (fMRI), and on applying mass spectrometry (MS) as a molecular analysis tool for tumor detection. To improve understanding of critical, individual patient brain functional anatomy, we jointly model functional and structural data for semi-automatic and improved localization of eloquent brain structures. To guide surgical decision making by better defining tumor margins, we investigate MS as an intraoperative molecular diagnostic method. Achievement of these goals supports the overall goal of NCIGT that is relevant for brain tumor surgery: to maximize the extent of tumor resection while minimizing the risk of neurologic deficit. Our projects are:

Computer-aided individualized labeling of critical brain structures. fMRI and dMRI provide pre-operative non-invasive maps of patients’ functional activations and white matter connections. fMRI and dMRI have been shown to increase resection and time of survival, but their translation to widespread clinical use faces significant challenges. Interpretation of the data is difficult, requiring extensive experience and time, and requiring software tools that are unwieldy and not clinically oriented. In order to provide more useful pre-operative mapping, we create a system that produces labeled maps of individual brain functional anatomy, even in cases with missing data, distortion, edema, or reorganization. Our overall strategy is to model the anatomical relationship between structural connections and functional activations, and to build models designed to generalize to patients with mass lesions or displacement, with the aid of machine learning algorithms. We are investigating the following novel and complementary tools: labeling of fMRI activations to produce a segmentation of a discrete set of cortical features of importance for neurosurgery, semi-automatic fMRI thresholding, multimodal calculation of language lateralization, and iterative joint labeling of fMRI activations and fiber tracts. We are developing the computational tools in stages so that each tool can be used either alone, or as part of the full system. We especially focus on the challenge of language mapping interpretation that requires identification of both the crucial language-specific functional cortical regions and the crucial language-specific fiber tracts. We are validating results using expert raters and intraoperative electrocortical stimulation data. Overall, we are creating the first image analysis software that can semi-automatically produce a multimodal structure-function map of individual patient anatomy for neurosurgery. (Contact: Lauren O'Donnell)

Optimal resection guided by mass spectrometry. Intraoperative decision making regarding how much tissue to resect during brain tumor surgery is of critical importance, yet as the surgery progresses the surgeon has access to less and less reliable data to guide this decision. To optimize the surgical resection of brain tumors, surgeons need more information to assess the boundaries between tumor and healthy tissue. In order to give surgeons a better understanding of the tissue being resected, we are investigating MS as an intra-operative molecular analysis tool for surgical guidance in the Advanced Multimodality Image Guided Operating Suite (AMIGO). The introduction of MS into routine surgical protocols for real-time characterization of tissue relies on the development and validation of the molecular reference system. The current iteration of the intraoperative platform is based on an ambient ionization methodology that allows for the analysis of tissue with little to no sample preparation. We validate the technology for real-time identification of surgical margins and molecular diagnosis by comparing against standard histopathology. The neurosurgeon stereotactically samples multiple specimens from each brain tumor resection and these are analyzed with a mass spectrometer in the AMIGO suite. We also correlate molecular, imaging and histopathologic findings in the 3D tumor space. Overall, our goal is to provide data equivalent or better to intraoperative MRI with less workflow disruption, less cost, and far less infrastructure needs. (Contact: Nathalie Agar)

Software and Documentation

3D Slicer, a comprehensive open source platform for medical image analysis, contains several modules that have been contributed by us for Image-Guided Brain Tumor Surgery. These include:
  • UKF Tractography Two-tensor modeling with Kalman filtering to track through regions of crossing and edema.

  • White Matter Analysis Software for modeling and segmentation of white matter tracts. The output is visualized in 3D Slicer.

  • Diffusion MRI in 3D Slicer Diffusion magnetic resonance imaging in 3D Slicer open-source software.



These presentations have been selected as tutorials for readers interested in learning about the clinical science and technology of the Neurosurgery Core.


Full Publication List

In NIH/NLM database and in our Abstracts Database.

Select Recent Publications

Richard Jarrett Rushmore, Peter Wilson-Braun, George Papadimitriou, Isaac Ng, Yogesh Rathi, Fan Zhang, Lauren Jean O'Donnell, Marek Kubicki, Sylvain Bouix, Edward Yeterian, Jean-Jacques Lemaire, Evan Calabrese, Allan G Johnson, Ron Kikinis, and Nikos Makris. 9/2020. “3D Exploration of the Brainstem in 50-Micron Resolution MRI.” Front Neuroanat, 14, Pp. 40.Abstract
The brainstem, a structure of vital importance in mammals, is currently becoming a principal focus in cognitive, affective, and clinical neuroscience. Midbrain, pontine and medullary structures serve as the conduit for signals between the forebrain and spinal cord, are the epicenter of cranial nerve-circuits and systems, and subserve such integrative functions as consciousness, emotional processing, pain, and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high-resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Given the unique capability of MRI-based morphometric analysis in generating and preserving the morphology of 3D objects from individual 2D sections, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community for applications in basic neuroscience as well as in neurology, psychiatry, and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.
Lorenz Epprecht, Ahad Qureshi, Elliott D Kozin, Nicolas Vachicouras, Alexander M Huber, Ron Kikinis, Nikos Makris, Christian M Brown, Katherine L Reinshagen, and Daniel J Lee. 4/2020. “Human Cochlear Nucleus on 7 Tesla Diffusion Tensor Imaging: Insights Into Micro-anatomy and Function for Auditory Brainstem Implant Surgery.” Otol Neurotol, 41, 4, Pp. e484-e493.Abstract
OBJECTIVE: The cochlear nucleus (CN) is the target of the auditory brainstem implant (ABI). Most ABI candidates have Neurofibromatosis Type 2 (NF2) and distorted brainstem anatomy from bilateral vestibular schwannomas. The CN is difficult to characterize as routine structural MRI does not resolve detailed anatomy. We hypothesize that diffusion tensor imaging (DTI) enables both in vivo localization and quantitative measurements of CN morphology. STUDY DESIGN: We analyzed 7 Tesla (T) DTI images of 100 subjects (200 CN) and relevant anatomic structures using an MRI brainstem atlas with submillimetric (50 μm) resolution. SETTING: Tertiary referral center. PATIENTS: Young healthy normal hearing adults. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Diffusion scalar measures such as fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (Mode), principal eigenvectors of the CN, and the adjacent inferior cerebellar peduncle (ICP). RESULTS: The CN had a lamellar structure and ventral-dorsal fiber orientation and could be localized lateral to the inferior cerebellar peduncle (ICP). This fiber orientation was orthogonal to tracts of the adjacent ICP where the fibers run mainly caudal-rostrally. The CN had lower FA compared to the medial aspect of the ICP (0.44 ± 0.09 vs. 0.64 ± 0.08, p < 0.001). CONCLUSIONS: 7T DTI enables characterization of human CN morphology and neuronal substructure. An ABI array insertion vector directed more caudally would better correspond to the main fiber axis of CN. State-of-the-art DTI has implications for ABI preoperative planning and future image guidance-assisted placement of the electrode array.
Fan Zhang, Guoqiang Xie, Laura Leung, Michael A Mooney, Lorenz Epprecht, Isaiah Norton, Yogesh Rathi, Ron Kikinis, Ossama Al-Mefty, Nikos Makris, Alexandra J Golby, and Lauren J O'Donnell. 6/2020. “Creation of a Novel Trigeminal Tractography Atlas for Automated Trigeminal Nerve Identification.” Neuroimage, 220, Pp. 117063.Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
Nityanand Miskin, Prashin Unadkat, Michael E Carlton, Alexandra J Golby, Geoffrey S Young, and Raymond Y Huang. 8/2020. “Frequency and Evolution of New Postoperative Enhancement on 3 Tesla Intraoperative and Early Postoperative Magnetic Resonance Imaging.” Neurosurgery, 87, 2, Pp. 238-46.Abstract
BACKGROUND: Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. OBJECTIVE: To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. METHODS: We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. RESULTS: A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. CONCLUSION: New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.
Fan Zhang, Nico Hoffmann, Suheyla Cetin Karayumak, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 10/2019. “Deep White Matter Analysis: Fast, Consistent Tractography Segmentation Across Populations and dMRI Acquisitions.” Med Image Comput Comput Assist Interv, 11766, Pp. 599-608.Abstract
We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classification accuracy of 90.99%. The method is evaluated on a test dataset of 374 dMRI scans from three independently acquired populations across health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art white matter tract segmentation methods. Experimental results show that our method obtains a highly consistent segmentation result, where over 99% of the fiber tracts are successfully detected across all subjects under study, most importantly, including patients with space occupying brain tumors. The proposed method leverages deep learning techniques and provides a much faster and more efficient tool for large data analysis than methods using traditional machine learning techniques.
Fan Zhang, Suheyla Cetin Karayumak, Nico Hoffmann, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 6/2020. “Deep White Matter Analysis (DeepWMA): Fast and Consistent Tractography Segmentation.” Med Image Anal, 65, Pp. 101761.Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
Elizabeth C Randall, Begoña GC Lopez, Sen Peng, Michael S Regan, Walid M Abdelmoula, Sankha S Basu, Sandro Santagata, Haejin Yoon, Marcia C Haigis, Jeffrey N Agar, Nhan L Tran, William F Elmquist, Forest M White, Jann N Sarkaria, and Nathalie YR Agar. 3/2020. “Localized Metabolomic Gradients in Patient-Derived Xenograft Models of Glioblastoma.” Cancer Res, 80, 6, Pp. 1258-67.Abstract
Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood-brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. SIGNIFICANCE: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment..
Shun Yao, Pan Lin, Matthew Vera, Farhana Akter, Ru-Yuan Zhang, Ailiang Zeng, Alexandra J Golby, Guozheng Xu, Yanmei Tie, and Jian Song. 4/2020. “Hormone Levels are Related to Functional Compensation in Prolactinomas: A Resting-state fMRI Study.” J Neurol Sci, 411, Pp. 116720.Abstract
Prolactinomas are tumors of the pituitary gland, which overproduces prolactin leading to dramatic fluctuations of endogenous hormone levels throughout the body. While it is not fully understood how endogenous hormone disorders affect a patient's brain, it is well known that fluctuating hormone levels can have negative neuropsychological effects. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated whole-brain functional connectivity (FC) and its relationship with hormone levels in prolactinomas. By performing seed-based FC analyses, we compared FC metrics between 33 prolactinoma patients and 31 healthy controls matched for age, sex, and hand dominance. We then carried out a partial correlation analysis to examine the relationship between FC metrics and hormone levels. Compared to healthy controls, prolactinoma patients showed significantly increased thalamocortical and cerebellar-cerebral FC. Endogenous hormone levels were also positively correlated with increased FC metrics, and these hormone-FC relationships exhibited sex differences in prolactinoma patients. Our study is the first to reveal altered FC patterns in prolactinomas and to quantify the hormone-FC relationships. These results indicate the importance of endogenous hormones on functional compensation of the brain in patients with prolactinomas.
Rachel A Freedman, Rebecca S Gelman, Nathalie YR Agar, Sandro Santagata, Elizabeth C Randall, Begoña Gimenez-Cassina Lopez, Roisin M Connolly, Ian F Dunn, Catherine H Van Poznak, Carey K Anders, Michelle E Melisko, Kelly Silvestri, Christine M Cotter, Kathryn P Componeschi, Juan M Marte, Beverly Moy, Kimberly L Blackwell, Shannon L Puhalla, Nuhad Ibrahim, Timothy J Moynihan, Julie Nangia, Nadine Tung, Robyn Burns, Mothaffar F Rimawi, Ian E Krop, Antonio C Wolff, Eric P Winer, Nancy U Lin, and Nancy U Lin. 4/2020. “Pre- and Postoperative Neratinib for HER2-Positive Breast Cancer Brain Metastases: Translational Breast Cancer Research Consortium 022.” Clin Breast Cancer, 20, 2, Pp. 145-51.Abstract
PURPOSE: This pilot study was performed to test our ability to administer neratinib monotherapy before clinically recommended craniotomy in patients with HER2-positive metastatic breast cancer to the central nervous system, to examine neratinib's central nervous system penetration at craniotomy, and to examine postoperative neratinib maintenance. PATIENTS AND METHODS: Patients with HER2-positive brain metastases undergoing clinically indicated cranial resection of a parenchymal tumor received neratinib 240 mg orally once a day for 7 to 21 days preoperatively, and resumed therapy postoperatively in 28-day cycles. Exploratory evaluations of time to disease progression, survival, and correlative tissue, cerebrospinal fluid (CSF), and blood-based analyses examining neratinib concentrations were planned. The study was registered at under number NCT01494662. RESULTS: We enrolled 5 patients between May 22, 2013, and October 18, 2016. As of March 1, 2019, patients had remained on the study protocol for 1 to 75+ postoperative cycles pf therapy. Two patients had grade 3 diarrhea. Evaluation of the CSF showed low concentrations of neratinib; nonetheless, 2 patients continued to receive therapy without disease progression for at least 13 cycles, with one on-study treatment lasting for nearly 6 years. Neratinib distribution in surgical tissue was variable for 1 patient, while specimens from 2 others did not produce conclusive results as a result of limited available samples. CONCLUSION: Neratinib resulted in expected rates of diarrhea in this small cohort, with 2 of 5 patients receiving the study treatment for durable periods. Although logistically challenging, we were able to test a limited number of CSF- and parenchymal-based neratinib concentrations. Our findings from resected tumor tissue in one patient revealed heterogeneity in drug distribution and tumor histopathology.
Adomas Bunevicius, Nathan Judson McDannold, and Alexandra J Golby. 7/2020. “Focused Ultrasound Strategies for Brain Tumor Therapy.” Oper Neurosurg (Hagerstown), 19, 1, Pp. 9-18.Abstract
BACKGROUND: A key challenge in the medical treatment of brain tumors is the limited penetration of most chemotherapeutic agents across the blood-brain barrier (BBB) into the tumor and the infiltrative margin around the tumor. Magnetic resonance-guided focused ultrasound (MRgFUS) is a promising tool to enhance the delivery of chemotherapeutic agents into brain tumors. OBJECTIVE: To review the mechanism of FUS, preclinical evidence, and clinical studies that used low-frequency FUS for a BBB opening in gliomas. METHODS: Literature review. RESULTS: The potential of externally delivered low-intensity ultrasound for a temporally and spatially precise and predictable disruption of the BBB has been investigated for over a decade, yielding extensive preclinical literature demonstrating that FUS can disrupt the BBB in a spatially targeted and temporally reversible manner. Studies in animal models documented that FUS enhanced the delivery of numerous chemotherapeutic and investigational agents across the BBB and into brain tumors, including temozolomide, bevacizumab, 1,3-bis (2-chloroethyl)-1-nitrosourea, doxorubicin, viral vectors, and cells. Chemotherapeutic interventions combined with FUS slowed tumor progression and improved animal survival. Recent advances of MRgFUS systems allow precise, temporally and spatially controllable, and safe transcranial delivery of ultrasound energy. Initial clinical evidence in glioma patients has shown the efficacy of MRgFUS in disrupting the BBB, as demonstrated by an enhanced gadolinium penetration. CONCLUSION: Thus far, a temporary disruption of the BBB followed by the administration of chemotherapy has been both feasible and safe. Further studies are needed to determine the actual drug delivery, including the drug distribution at a tissue-level scale, as well as effects on tumor growth and patient prognosis.