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.

Data

Presentations

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

Links

Full Publication List

In NIH/NLM database and in our Abstracts Database.

Select Recent Publications

Randall EC, Lopez BGC, Peng S, Regan MS, Abdelmoula WM, Basu SS, Santagata S, Yoon H, Haigis MC, Agar JN, et al. Localized Metabolomic Gradients in Patient-Derived Xenograft Models of Glioblastoma. Cancer Res. 2020;80 (6) :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..
Yao S, Lin P, Vera M, Akter F, Zhang R-Y, Zeng A, Golby AJ, Xu G, Tie Y, Song J. Hormone Levels are Related to Functional Compensation in Prolactinomas: A Resting-state fMRI Study. J Neurol Sci. 2020;411 :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.
Xie G, Zhang F, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, et al. Anatomical Assessment of Trigeminal Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition B-Values and Single- and Multi-Fiber Tracking Strategies. Neuroimage Clin. 2020;25 :102160.Abstract
BACKGROUND: The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. OBJECTIVE: We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN. METHODS: We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm, b = 2000 s/mm and b = 3000 s/mm, using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result. RESULTS: The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result. CONCLUSIONS: Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.
Lee TC, Guenette JP, Moses ZB, Chi JH. MRI-Guided Cryoablation of Epidural Malignancies in the Spinal Canal Resulting in Neural Decompression and Regrowth of Bone. AJR Am J Roentgenol. 2019;212 (1) :205-8.Abstract
OBJECTIVE: The purpose of this article is to describe the use of MRI to safely monitor cryoablation for the treatment of spinal epidural malignancies. CONCLUSION: Use of MRI guidance to monitor percutaneous cryoablation allows ablation margins more distinct than those allowed by heat-based ablation modalities. MRI-guided cryoablation is a feasible option for treating epidural tumors involving the spinal canal, resulting in successful decompression of the tumor away from the spinal cord with regrowth of previously eroded bone around the spinal canal.
Bunevicius A, Schregel K, Sinkus R, Golby A, Patz S. REVIEW: MR Elastography of Brain Tumors. Neuroimage Clin. 2020;25 :102109.Abstract
MR elastography allows non-invasive quantification of the shear modulus of tissue, i.e. tissue stiffness and viscosity, information that offers the potential to guide presurgical planning for brain tumor resection. Here, we review brain tumor MRE studies with particular attention to clinical applications. Studies that investigated MRE in patients with intracranial tumors, both malignant and benign as well as primary and metastatic, were queried from the Pubmed/Medline database in August 2018. Reported tumor and normal appearing white matter stiffness values were extracted and compared as a function of tumor histopathological diagnosis and MRE vibration frequencies. Because different studies used different elastography hardware, pulse sequences, reconstruction inversion algorithms, and different symmetry assumptions about the mechanical properties of tissue, effort was directed to ensure that similar quantities were used when making inter-study comparisons. In addition, because different methodologies and processing pipelines will necessarily bias the results, when pooling data from different studies, whenever possible, tumor values were compared with the same subject's contralateral normal appearing white matter to minimize any study-dependent bias. The literature search yielded 10 studies with a total of 184 primary and metastatic brain tumor patients. The group mean tumor stiffness, as measured with MRE, correlated with intra-operatively assessed stiffness of meningiomas and pituitary adenomas. Pooled data analysis showed significant overlap between shear modulus values across brain tumor types. When adjusting for the same patient normal appearing white matter shear modulus values, meningiomas were the stiffest tumor-type. MRE is increasingly being examined for potential in brain tumor imaging and might have value for surgical planning. However, significant overlap of shear modulus values between a number of different tumor types limits applicability of MRE for diagnostic purposes. Thus, further rigorous studies are needed to determine specific clinical applications of MRE for surgical planning, disease monitoring and molecular stratification of brain tumors.
Rigolo L, Essayed WI, Tie Y, Norton I, Mukundan S, Golby A. Intraoperative Use of Functional MRI for Surgical Decision Making after Limited or Infeasible Electrocortical Stimulation Mapping. J Neuroimaging. 2020;30 (2) :184-91.Abstract
BACKGROUND AND PURPOSE: Functional magnetic resonance imaging (fMRI) is becoming widely recognized as a key component of preoperative neurosurgical planning, although intraoperative electrocortical stimulation (ECS) is considered the gold standard surgical brain mapping method. However, acquiring and interpreting ECS results can sometimes be challenging. This retrospective study assesses whether intraoperative availability of fMRI impacted surgical decision-making when ECS was problematic or unobtainable. METHODS: Records were reviewed for 191 patients who underwent presurgical fMRI with fMRI loaded into the neuronavigation system. Four patients were excluded as a bur-hole biopsy was performed. Imaging was acquired at 3 Tesla and analyzed using the general linear model with significantly activated pixels determined via individually determined thresholds. fMRI maps were displayed intraoperatively via commercial neuronavigation systems. RESULTS: Seventy-one cases were planned ECS; however, 18 (25.35%) of these procedures were either not attempted or aborted/limited due to: seizure (10), patient difficulty cooperating with the ECS mapping (4), scarring/limited dural opening (3), or dural bleeding (1). In all aborted/limited ECS cases, the surgeon continued surgery using fMRI to guide surgical decision-making. There was no significant difference in the incidence of postoperative deficits between cases with completed ECS and those with limited/aborted ECS. CONCLUSIONS: Preoperative fMRI allowed for continuation of surgery in over one-fourth of patients in which planned ECS was incomplete or impossible, without a significantly different incidence of postoperative deficits compared to the patients with completed ECS. This demonstrates additional value of fMRI beyond presurgical planning, as fMRI data served as a backup method to ECS.
Basu SS, McMinn MH, Giménez-Cassina Lopéz B, Regan MS, Randall EC, Clark AR, Cox CR, Agar NYR. Metal Oxide Laser Ionization Mass Spectrometry Imaging (MOLI MSI) Using Cerium(IV) Oxide. Anal Chem. 2019;91 (10) :6800-7.Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful technique for spatially resolved metabolomics. A variation on MALDI, termed metal oxide laser ionization (MOLI), capitalizes on the unique property of cerium(IV) oxide (CeO) to induce laser-catalyzed fatty acyl cleavage from lipids and has been utilized for bacterial identification. In this study, we present the development and utilization of CeO as an MSI catalyst. The method was developed using a MALDI TOF instrument in negative ion mode, equipped with a high frequency laser. Instrument parameters for MOLI MS fatty acid catalysis with CeO were optimized with phospholipid standards and fatty acid catalysis was confirmed using lipid extracts from reference bacterial strains, and sample preparation was optimized using mouse brain tissue. MOLI MSI was applied to the imaging of normal mouse brain revealing differentiable fatty acyl pools in myelinated and nonmyelinated regions. Similarly, MOLI MSI showed distinct fatty acyl composition in tumor regions of a patient derived xenograft mouse model of glioblastoma. To assess the potential of MOLI MSI to detect pathogens directly from tissue, a pseudoinfection model was prepared by spotting Escherichia coli lipid extracts on mouse brain tissue sections and imaged by MOLI MSI. The spotted regions were molecularly resolved from the supporting mouse brain tissue by the diagnostic odd-chained fatty acids and reflected control bacterial MOLI MS signatures. We describe MOLI MSI for the first time and highlight its potential for spatially resolved fatty acyl analysis, characterization of fatty acyl composition in tumors, and its potential for pathogen detection directly from tissue.
Sjölund J, Eklund A, Özarslan E, Herberthson M, Bånkestad M, Knutsson H. Bayesian Uncertainty Quantification in Linear Models for Diffusion MRI. Neuroimage. 2018;175 :272-85.Abstract
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.
Kurreck A, Vandergrift LA, Fuss TL, Habbel P, Agar NYR, Cheng LL. Prostate Cancer Diagnosis and Characterization with Mass Spectrometry Imaging. Prostate Cancer Prostatic Dis. 2018;21 (3) :297-305.Abstract
BACKGROUND: Prostate cancer (PCa), the most common cancer and second leading cause of cancer death in American men, presents the clinical challenge of distinguishing between indolent and aggressive tumors for proper treatment. PCa presents significant alterations in metabolic pathways that can potentially be measured using techniques like mass spectrometry (MS) or MS imaging (MSI) and used to characterize PCa aggressiveness. MS quantifies metabolomic, proteomic, and lipidomic profiles of biological systems that can be further visualized for their spatial distributions through MSI. METHODS: PubMed was queried for all publications relating to MS and MSI in human PCa from April 2007 to April 2017. With the goal of reviewing the utility of MSI in diagnosis and prognostication of human PCa, MSI articles that reported investigations of PCa-specific metabolites or metabolites indicating PCa aggressiveness were selected for inclusion. Articles were included that covered MS and MSI principles, limitations, and applications in PCa. RESULTS: We identified nine key studies on MSI in intact human prostate tissue specimens that determined metabolites which could either differentiate between benign and malignant prostate tissue or indicate PCa aggressiveness. These MSI-detected biomarkers show promise in reliably identifying PCa and determining disease aggressiveness. CONCLUSIONS: MSI represents an innovative technique with the ability to interrogate cancer biomarkers in relation to tissue pathologies and investigate tumor aggressiveness. We propose MSI as a powerful adjuvant histopathology imaging tool for prostate tissue evaluations, where clinical translation of this ex vivo technique could make possible the use of MSI for personalized medicine in diagnosis and prognosis of PCa. Moreover, the knowledge provided from this technique can majorly contribute to the understanding of molecular pathogenesis of PCa and other malignant diseases.
Sydnor VJ, Rivas-Grajales AM, Lyall AE, Zhang F, Bouix S, Karmacharya S, Shenton ME, Westin C-F, Makris N, Wassermann D, et al. A Comparison of Three Fiber Tract Delineation Methods and their Impact on White Matter Analysis. Neuroimage. 2018;178 :318-31.Abstract
Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.