Neurosurgery Research Publications

2019
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) :6800-6807.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.
Machado I, Toews M, George E, Unadkat P, Essayed W, Luo J, Teodoro P, Carvalho H, Martins J, Golland P, et al. Deformable MRI-Ultrasound Registration using Correlation-based Attribute Matching for Brain Shift Correction: Accuracy and Generality in Multi-site Data. Neuroimage. 2019;202 :116094.Abstract
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.
Basu SS, Regan MS, Randall EC, Abdelmoula WM, Clark AR, Gimenez-Cassina Lopez B, Cornett DS, Haase A, Santagata S, Agar NYR. Rapid MALDI Mass Spectrometry Imaging for Surgical Pathology. NPJ Precis Oncol. 2019;3 :17.Abstract
Matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) is an emerging analytical technique, which generates spatially resolved proteomic and metabolomic images from tissue specimens. Conventional MALDI MSI processing and data acquisition can take over 30 min, limiting its clinical utility for intraoperative diagnostics. We present a rapid MALDI MSI method, completed under 5 min, including sample preparation and analysis, providing a workflow compatible with the clinical frozen section procedure.
Frisken S, Luo M, Machado I, Unadkat P, Juvekar P, Bunevicius A, Toews M, Wells WM, Miga MI, Golby AJ. Preliminary Results Comparing Thin Plate Splines with Finite Element Methods for Modeling Brain Deformation during Neurosurgery using Intraoperative Ultrasound. Proc SPIE Int Soc Opt Eng. 2019;10951.Abstract
Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.
Lampinen B, Szczepankiewicz F, Novén M, van Westen D, Hansson O, Englund E, Mårtensson J, Westin C-F, Nilsson M. Searching for the Neurite Density with Diffusion MRI: Challenges for Biophysical Modeling. Hum Brain Mapp. 2019;40 (8) :2529-45.Abstract
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies.
Zhang F, Ning L, O'Donnell LJ, Pasternak O. MK-curve - Characterizing the Relation between Mean Kurtosis and Alterations in the Diffusion MRI Signal. Neuroimage. 2019;196 :68-80.Abstract
Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.
Nitsch J, Klein J, Dammann P, Wrede K, Gembruch O, Moltz JH, Meine H, Sure U, Kikinis R, Miller D. Automatic and Efficient MRI-US Segmentations for Improving Intraoperative Image Fusion in Image-guided Neurosurgery. Neuroimage Clin. 2019;22 :101766.Abstract
Knowledge of the exact tumor location and structures at risk in its vicinity are crucial for neurosurgical interventions. Neuronavigation systems support navigation within the patient's brain, based on preoperative MRI (preMRI). However, increasing tissue deformation during the course of tumor resection reduces navigation accuracy based on preMRI. Intraoperative ultrasound (iUS) is therefore used as real-time intraoperative imaging. Registration of preMRI and iUS remains a challenge due to different or varying contrasts in iUS and preMRI. Here, we present an automatic and efficient segmentation of B-mode US images to support the registration process. The falx cerebri and the tentorium cerebelli were identified as examples for central cerebral structures and their segmentations can serve as guiding frame for multi-modal image registration. Segmentations of the falx and tentorium were performed with an average Dice coefficient of 0.74 and an average Hausdorff distance of 12.2 mm. The subsequent registration incorporates these segmentations and increases accuracy, robustness and speed of the overall registration process compared to purely intensity-based registration. For validation an expert manually located corresponding landmarks. Our approach reduces the initial mean Target Registration Error from 16.9 mm to 3.8 mm using our intensity-based registration and to 2.2 mm with our combined segmentation and registration approach. The intensity-based registration reduced the maximum initial TRE from 19.4 mm to 5.6 mm, with the approach incorporating segmentations this is reduced to 3.0 mm. Mean volumetric intensity-based registration of preMRI and iUS took 40.5 s, including segmentations 12.0 s.
Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O'Donnell LJ. Test-retest Reproducibility of White Matter Parcellation using Diffusion MRI Tractography Fiber Clustering. Hum Brain Mapp. 2019;40 (10) :3041-57.Abstract
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
Randall EC, Zadra G, Chetta P, Lopez BGC, Syamala S, Basu SS, Agar JN, Loda M, Tempany CM, Fennessy FM, et al. Molecular Characterization of Prostate Cancer with Associated Gleason Score using Mass Spectrometry Imaging. Mol Cancer Res. 2019;17 (5) :1155-65.Abstract
Diagnosis of prostate cancer is based on histological evaluation of tumor architecture using a system known as the 'Gleason score'. This diagnostic paradigm, while the standard of care, is time-consuming, shows intra-observer variability and provides no information about the altered metabolic pathways, which result in altered tissue architecture. Characterization of the molecular composition of prostate cancer and how it changes with respect to the Gleason score (GS) could enable a more objective and faster diagnosis. It may also aid in our understanding of disease onset and progression. In this work, we present mass spectrometry imaging for identification and mapping of lipids and metabolites in prostate tissue from patients with known prostate cancer with GS from 6 to 9. A gradient of changes in the intensity of various lipids was observed, which correlated with increasing GS. Interestingly, these changes were identified in both regions of high tumor cell density, and in regions of tissue that appeared histologically benign, possibly suggestive of pre-cancerous metabolomic changes. A total of 31 lipids, including several phosphatidylcholines, phosphatidic acids, phosphatidylserines, phosphatidylinositols and cardiolipins were detected with higher intensity in GS (4+3) compared with GS (3+4), suggesting they may be markers of prostate cancer aggression. Results obtained through mass spectrometry imaging studies were subsequently correlated with a fast, ambient mass spectrometry method for potential use as a clinical tool to support image-guided prostate biopsy. Implications: In this study we suggest that metabolomic differences between prostate cancers with different Gleason scores can be detected by mass spectrometry imaging.
Stojanovski S, Felsky D, Viviano JD, Shahab S, Bangali R, Burton CL, Devenyi GA, O'Donnell LJ, Szatmari P, Chakravarty MM, et al. Polygenic Risk and Neural Substrates of Attention-Deficit/Hyperactivity Disorder Symptoms in Youths With a History of Mild Traumatic Brain Injury. Biol Psychiatry. 2019;85 (5) :408-16.Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a major sequela of traumatic brain injury (TBI) in youths. The objective of this study was to examine whether ADHD symptoms are differentially associated with genetic risk and brain structure in youths with and without a history of TBI. METHODS: Medical history, ADHD symptoms, genetic data, and neuroimaging data were obtained from a community sample of youths. ADHD symptom severity was compared between those with and without TBI (TBI n = 418, no TBI n = 3193). The relationship of TBI history, genetic vulnerability, brain structure, and ADHD symptoms was examined by assessing 1) ADHD polygenic score (discovery sample ADHD n = 19,099, control sample n = 34,194), 2) basal ganglia volumes, and 3) fractional anisotropy in the corpus callosum and corona radiata. RESULTS: Youths with TBI reported greater ADHD symptom severity compared with those without TBI. Polygenic score was positively associated with ADHD symptoms in youths without TBI but not in youths with TBI. The negative association between the caudate volume and ADHD symptoms was not moderated by a history of TBI. However, the relationship between ADHD symptoms and structure of the genu of the corpus callosum was negative in youths with TBI and positive in youths without TBI. CONCLUSIONS: The identification of distinct ADHD etiology in youths with TBI provides neurobiological insight into the clinical heterogeneity in the disorder. Results indicate that genetic predisposition to ADHD does not increase the risk for ADHD symptoms associated with TBI. ADHD symptoms associated with TBI may be a result of a mechanical insult rather than neurodevelopmental factors.
Unadkat P, Fumagalli L, Rigolo L, Vangel MG, Young GS, Huang R, Mukundan S, Golby A, Tie Y. Functional MRI Task Comparison for Language Mapping in Neurosurgical Patients. J Neuroimaging. 2019;29 (3) :348-56.Abstract
BACKGROUND AND PURPOSE: Language task-based functional MRI (fMRI) is increasingly used for presurgical planning in patients with brain lesions. Different paradigms elicit activations of different components of the language network. The aim of this study is to optimize fMRI clinical usage by comparing the effectiveness of three language tasks for language lateralization and localization in a large group of patients with brain lesions. METHODS: We analyzed fMRI data from a sequential retrospective cohort of 51 patients with brain lesions who underwent presurgical fMRI language mapping. We compared the effectiveness of three language tasks (Antonym Generation, Sentence Completion (SC), and Auditory Naming) for lateralizing language function and for activating cortex within patient-specific regions-of-interest representing eloquent language areas, and assessed the degree of spatial overlap of the areas of activation elicited by each task. RESULTS: The tasks were similarly effective for lateralizing language within the anterior language areas. The SC task produced higher laterality indices within the posterior language areas and had a significantly higher agreement with the clinical report. Dice coefficients between the task pairs were in the range of .351-.458, confirming substantial variation in the components of the language network activated by each task. CONCLUSIONS: SC task consistently produced large activations within the dominant hemisphere and was more effective for lateralizing language within the posterior language areas. The low degree of spatial overlap among the tasks strongly supports the practice of using a battery of tasks to help the surgeon to avoid eloquent language areas.
2018
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.
Wu Y, Zhang F, Makris N, Ning Y, Norton I, She S, Peng H, Rathi Y, Feng Y, Wu H, et al. Investigation into Local White Matter Abnormality in Emotional Processing and Sensorimotor Areas using an Automatically Annotated Fiber Clustering in Major Depressive Disorder. Neuroimage. 2018;181 :16-29.Abstract
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
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.
Basu SS, Randall EC, Regan MS, Lopez BGC, Clark AR, Schmitt ND, Agar JN, Dillon DA, Agar NYR. In Vitro Liquid Extraction Surface Analysis Mass Spectrometry (ivLESA-MS) for Direct Metabolic Analysis of Adherent Cells in Culture. Anal Chem. 2018;90 (8) :4987-91.Abstract
Conventional metabolomic methods include extensive sample preparation steps and long analytical run times, increasing the likelihood of processing artifacts and limiting high throughput applications. We present here in vitro liquid extraction surface analysis mass spectrometry (ivLESA-MS), a variation on LESA-MS, performed directly on adherent cells grown in 96-well cell culture plates. To accomplish this, culture medium was aspirated immediately prior to analysis, and metabolites were extracted using LESA from the cell monolayer surface, followed by nano-electrospray ionization and MS analysis in negative ion mode. We applied this platform to characterize and compare lipidomic profiles of multiple breast cancer cell lines growing in culture (MCF-7, ZR-75-1, MDA-MB-453, and MDA-MB-231) and revealed distinct and reproducible lipidomic signatures between the cell lines. Additionally, we demonstrated time-dependent processing artifacts, underscoring the importance of immediate analysis. ivLESA-MS represents a rapid in vitro metabolomic method, which precludes the need for quenching, cell harvesting, sample preparation, and chromatography, significantly shortening preparation and analysis time while minimizing processing artifacts. This method could be further adapted to test drugs in vitro in a high throughput manner.
Luo J, Frisken S, Machado I, Zhang M, Pieper S, Golland P, Toews M, Unadkat P, Sedghi A, Zhou H, et al. Using the Variogram for Vector Outlier Screening: Application to Feature-based Image Registration. Int J Comput Assist Radiol Surg. 2018;13 (12) :1871-80.Abstract
PURPOSE: Matching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-based registration in image-guided neurosurgery, even a few mismatches, in the form of invalid displacement vectors, could cause serious consequences. As a result, having an effective tool by which operators can manually screen all matches for outliers could substantially benefit the outcome of those applications. METHODS: We introduce a novel variogram-based outlier screening method for vectors. The variogram is a powerful geostatistical tool for characterizing the spatial dependence of stochastic processes. Since the spatial correlation of invalid displacement vectors, which are considered as vector outliers, tends to behave differently than normal displacement vectors, they can be efficiently identified on the variogram. RESULTS: We validate the proposed method on 9 sets of clinically acquired ultrasound data. In the experiment, potential outliers are flagged on the variogram by one operator and further evaluated by 8 experienced medical imaging researchers. The matching quality of those potential outliers is approximately 1.5 lower, on a scale from 1 (bad) to 5 (good), than valid displacement vectors. CONCLUSION: The variogram is a simple yet informative tool. While being used extensively in geostatistical analysis, it has not received enough attention in the medical imaging field. We believe there is a good deal of potential for clinically applying the proposed outlier screening method. By way of this paper, we also expect researchers to find variogram useful in other medical applications that involve motion vectors analyses.
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.

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