Neurosurgery Research Publications

2018
Randall EC, Emdal KB, Laramy JK, Kim M, Roos A, Calligaris D, Regan MS, Gupta SK, Mladek AC, Carlson BL, et al. Integrated Mapping of Pharmacokinetics and Pharmacodynamics in a Patient-derived Xenograft Model of Glioblastoma. Nat Commun. 2018;9 (1) :4904.Abstract
Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.
Zhang F, Wu Y, Norton I, Rigolo L, Rathi Y, Makris N, O'Donnell LJ. An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan. Neuroimage. 2018;179 :429-47.Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.
Hong Y, O'Donnell LJ, Savadjiev P, Zhang F, Wassermann D, Pasternak O, Johnson H, Paulsen J, Vonsattel J-P, Makris N, et al. Genetic Load Determines Atrophy in Hand Cortico-striatal Pathways in Presymptomatic Huntington's Disease. Hum Brain Mapp. 2018;39 (10) :3871-83.Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.
Machado I, Toews M, Luo J, Unadkat P, Essayed W, George E, Teodoro P, Carvalho H, Martins J, Golland P, et al. Non-rigid Registration of 3D Ultrasound for Neurosurgery using Automatic Feature Detection and Matching. Int J Comput Assist Radiol Surg. 2018;13 (10) :1525-38.Abstract
PURPOSE: The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images. METHODS: A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform. RESULTS: Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm. CONCLUSIONS: This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.
Gong S, Zhang F, Norton I, Essayed WI, Unadkat P, Rigolo L, Pasternak O, Rathi Y, Hou L, Golby AJ, et al. Free Water Modeling of Peritumoral Edema using Multi-fiber Tractography: Application to Tracking the Arcuate Fasciculus for Neurosurgical Planning. PLoS One. 2018;13 (5) :e0197056.Abstract
PURPOSE: Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. MATERIALS AND METHODS: We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. RESULTS: The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route. CONCLUSION: Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.
Essayed WI, Unadkat P, Hosny A, Frisken S, Rassi MS, Mukundan S, Weaver JC, Al-Mefty O, Golby AJ, Dunn IF. 3D Printing and Intraoperative Neuronavigation Tailoring for Skull Base Reconstruction after Extended Endoscopic Endonasal Surgery: Proof of Concept. J Neurosurg. 2018 :1-8.Abstract
OBJECTIVE Endoscopic endonasal approaches are increasingly performed for the surgical treatment of multiple skull base pathologies. Preventing postoperative CSF leaks remains a major challenge, particularly in extended approaches. In this study, the authors assessed the potential use of modern multimaterial 3D printing and neuronavigation to help model these extended defects and develop specifically tailored prostheses for reconstructive purposes. METHODS Extended endoscopic endonasal skull base approaches were performed on 3 human cadaveric heads. Preprocedure and intraprocedure CT scans were completed and were used to segment and design extended and tailored skull base models. Multimaterial models with different core/edge interfaces were 3D printed for implantation trials. A novel application of the intraoperative landmark acquisition method was used to transfer the navigation, helping to tailor the extended models. RESULTS Prostheses were created based on preoperative and intraoperative CT scans. The navigation transfer offered sufficiently accurate data to tailor the preprinted extended skull base defect prostheses. Successful implantation of the skull base prostheses was achieved in all specimens. The progressive flexibility gradient of the models' edges offered the best compromise for easy intranasal maneuverability, anchoring, and structural stability. Prostheses printed based on intraprocedure CT scans were accurate in shape but slightly undersized. CONCLUSIONS Preoperative 3D printing of patient-specific skull base models is achievable for extended endoscopic endonasal surgery. The careful spatial modeling and the use of a flexibility gradient in the design helped achieve the most stable reconstruction. Neuronavigation can help tailor preprinted prostheses.
Albi A, Meola A, Zhang F, Kahali P, Rigolo L, Tax CMW, Ciris PA, Essayed WI, Unadkat P, Norton I, et al. Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects. J Neuroimaging. 2018;28 (2) :173-82.Abstract
BACKGROUND AND PURPOSE: Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients' white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. METHODS: Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. RESULTS: Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. CONCLUSIONS: Quantitative results of mean tract distortions on the order of 1-2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings.
Zhang F, Wu W, Ning L, McAnulty G, Waber D, Gagoski B, Sarill K, Hamoda HM, Song Y, Cai W, et al. Suprathreshold Fiber Cluster Statistics: Leveraging White Matter Geometry to Enhance Tractography Statistical Analysis. Neuroimage. 2018;171 :341-54.Abstract
This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.
Zhang F, Savadjiev P, Cai W, Song Y, Rathi Y, Tunç B, Parker D, Kapur T, Schultz RT, Makris N, et al. Whole Brain White Matter Connectivity Analysis using Machine Learning: An Application to Autism. Neuroimage. 2018;172 :826-37.Abstract
In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.
2017
Silva MA, See AP, Essayed WI, Golby AJ, Tie Y. Challenges and Techniques for Presurgical Brain Mapping with Functional MRI. Neuroimage Clin. 2017;17 :794-803.Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.
Norton I, Essayed WI, Zhang F, Pujol S, Yarmarkovich A, Golby AJ, Kindlmann G, Wasserman D, Estepar RSJ, Rathi Y, et al. SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research. Cancer Res. 2017;77 (21) :e101-e103.Abstract
Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org Cancer Res; 77(21); e101-3. ©2017 AACR.
Luo M, Frisken SF, Weis JA, Clements LW, Unadkat P, Thompson RC, Golby AJ, Miga MI. Retrospective Study Comparing Model-Based Deformation Correction to Intraoperative Magnetic Resonance Imaging for Image-Guided Neurosurgery. J Med Imaging (Bellingham). 2017;4 (3) :035003.Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
Liao R, Ning L, Chen Z, Rigolo L, Gong S, Pasternak O, Golby AJ, Rathi Y, O'Donnell LJ, ckovic JV. Performance of Unscented Kalman Filter Tractography in Edema: Analysis of the Two-tensor Model. Neuroimage Clin. 2017;15 :819-31.Abstract
Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema. Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors.
Essayed WI, Zhang F, Unadkat P, Cosgrove RG, Golby AJ, O'Donnell LJ. White Matter Tractography for Neurosurgical Planning: A Topography-based Review of the Current State of the Art. Neuroimage Clin. 2017;15 :659-72.Abstract
We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.
O'Donnell LJ, Suter Y, Rigolo L, Kahali P, Zhang F, Norton I, Albi A, Olubiyi O, Meola A, Essayed WI, et al. Automated White Matter Fiber Tract Identification in Patients with Brain Tumors. Neuroimage Clin. 2017;13 :138-53.Abstract

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

Sastry R, Bi WL, Pieper S, Frisken S, Kapur T, Wells W, Golby AJ. Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging. 2017;27 (1) :5-15.Abstract

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

2016
Lu FK, Calligaris D, Olubiyi OI, Norton I, Yang W, Santagata S, Xie SX, Golby AJ, Agar NYR. Label-Free Neurosurgical Pathology with Stimulated Raman Imaging. Cancer Res. 2016;76 (12) :3451-62.Abstract

The goal of brain tumor surgery is to maximize tumor removal without injuring critical brain structures. Achieving this goal is challenging as it can be difficult to distinguish tumor from nontumor tissue. While standard histopathology provides information that could assist tumor delineation, it cannot be performed iteratively during surgery as freezing, sectioning, and staining of the tissue require too much time. Stimulated Raman scattering (SRS) microscopy is a powerful label-free chemical imaging technology that enables rapid mapping of lipids and proteins within a fresh specimen. This information can be rendered into pathology-like images. Although this approach has been used to assess the density of glioma cells in murine orthotopic xenografts models and human brain tumors, tissue heterogeneity in clinical brain tumors has not yet been fully evaluated with SRS imaging. Here we profile 41 specimens resected from 12 patients with a range of brain tumors. By evaluating large-scale stimulated Raman imaging data and correlating this data with current clinical gold standard of histopathology for 4,422 fields of view, we capture many essential diagnostic hallmarks for glioma classification. Notably, in fresh tumor samples, we observe additional features, not seen by conventional methods, including extensive lipid droplets within glioma cells, collagen deposition in gliosarcoma, and irregularity and disruption of myelinated fibers in areas infiltrated by oligodendroglioma cells. The data are freely available in a public resource to foster diagnostic training and to permit additional interrogation. Our work establishes the methodology and provides a significant collection of reference images for label-free neurosurgical pathology. Cancer Res; 76(12); 3451-62. ©2016 AACR.

Valdés PA, Roberts DW, Lu F-K, Golby A. Optical Technologies for Intraoperative Neurosurgical Guidance. Neurosurg Focus. 2016;40 (3) :E8.Abstract

Biomedical optics is a broadly interdisciplinary field at the interface of optical engineering, biophysics, computer science, medicine, biology, and chemistry, helping us understand light-tissue interactions to create applications with diagnostic and therapeutic value in medicine. Implementation of biomedical optics tools and principles has had a notable scientific and clinical resurgence in recent years in the neurosurgical community. This is in great part due to work in fluorescence-guided surgery of brain tumors leading to reports of significant improvement in maximizing the rates of gross-total resection. Multiple additional optical technologies have been implemented clinically, including diffuse reflectance spectroscopy and imaging, optical coherence tomography, Raman spectroscopy and imaging, and advanced quantitative methods, including quantitative fluorescence and lifetime imaging. Here we present a clinically relevant and technologically informed overview and discussion of some of the major clinical implementations of optical technologies as intraoperative guidance tools in neurosurgery.

Fischer DB, Perez DL, Prasad S, Rigolo L, O'Donnell L, Acar D, Meadows M-E, Baslet G, Boes AD, Golby AJ, et al. Right Inferior Longitudinal Fasciculus Lesions Disrupt Visual-emotional Integration. Soc Cogn Affect Neurosci. 2016;11 (6) :945-51.Abstract

The mechanism by which the brain integrates visual and emotional information remains incompletely understood, and can be studied through focal lesions that selectively disrupt this process. To date, three reported cases of visual hypoemotionality, a vision-specific form of derealization, have resulted from lesions of the temporo-occipital junction. We present a fourth case of this rare phenomenon, and investigate the role of the inferior longitudinal fasciculus (ILF) in the underlying pathophysiology. A 50-year-old right-handed male was found to have a right medial temporal lobe tumor following new-onset seizures. Interstitial laser ablation of the lesion was complicated by a right temporo-parieto-occipital intraparenchymal hemorrhage. The patient subsequently experienced emotional estrangement from visual stimuli. A lesion overlap analysis was conducted to assess involvement of the ILF by this patient's lesion and those of the three previously described cases, and diffusion tensor imaging was acquired in our case to further investigate ILF disruption. All four lesions specifically overlapped with the expected trajectory of the right ILF, and diminished structural integrity of the right ILF was observed in our case. These findings implicate the ILF in visual hypoemotionality, suggesting that the ILF is critical for integrating visual information with its emotional content.

Incekara F, Olubiyi O, Ozdemir A, Lee T, Rigolo L, Golby A. The Value of Pre- and Intraoperative Adjuncts on the Extent of Resection of Hemispheric Low-Grade Gliomas: A Retrospective Analysis. J Neurol Surg A Cent Eur Neurosurg. 2016;77 (2) :79-87.Abstract

Background To achieve maximal resection with minimal risk of postoperative neurologic morbidity, different neurosurgical adjuncts are being used during low-grade glioma (LGG) surgery. Objectives To investigate the effect of pre- and intraoperative adjuncts on the extent of resection (EOR) of hemispheric LGGs. Methods Medical records were reviewed to identify patients of any sex, ≥ 18 years of age, who underwent LGG surgery at X Hospital between January 2005 and July 2013. Patients were divided into eight subgroups based on the use of various combinations of a neuronavigation system alone (NN), functional MRI-diffusion tensor imaging (fMRI-DTI) guided neuronavigation (FD), intraoperative MRI (MR), and direct electrical stimulation (DES). Initial and residual tumors were measured, and mean EOR was compared between groups. Results Of all 128 patients, gross total resection was achieved in 23.4%. Overall mean EOR was 81.3% ± 20.5%. Using DES in combination with fMRI-DTI (mean EOR: 86.7% ± 12.4%) on eloquent tumors improved mean EOR significantly after adjustment for potential confounders when compared with NN alone (mean EOR: 76.4% ± 25.5%; p = 0.001). Conclusions Using DES in combination with fMRI and DTI significantly improves EOR when LGGs are located in eloquent areas compared with craniotomies in which only NN was used.

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