Publications by Year: 2020

2020
Yoon H, Spinelli JB, Zaganjor E, Wong SJ, German NJ, Randall EC, Dean A, Clermont A, Paulo JA, Garcia D, et al. PHD3 Loss Promotes Exercise Capacity and Fat Oxidation in Skeletal Muscle. Cell Metab. 2020;32 (2) :215-228.e7.Abstract
Rapid alterations in cellular metabolism allow tissues to maintain homeostasis during changes in energy availability. The central metabolic regulator acetyl-CoA carboxylase 2 (ACC2) is robustly phosphorylated during cellular energy stress by AMP-activated protein kinase (AMPK) to relieve its suppression of fat oxidation. While ACC2 can also be hydroxylated by prolyl hydroxylase 3 (PHD3), the physiological consequence thereof is poorly understood. We find that ACC2 phosphorylation and hydroxylation occur in an inverse fashion. ACC2 hydroxylation occurs in conditions of high energy and represses fatty acid oxidation. PHD3-null mice demonstrate loss of ACC2 hydroxylation in heart and skeletal muscle and display elevated fatty acid oxidation. Whole body or skeletal muscle-specific PHD3 loss enhances exercise capacity during an endurance exercise challenge. In sum, these data identify an unexpected link between AMPK and PHD3, and a role for PHD3 in acute exercise endurance capacity and skeletal muscle metabolism.
Catalino MP, Yao S, Green DL, Laws ER, Golby AJ, Tie Y. Mapping Cognitive and Emotional Networks in Neurosurgical Patients Using Resting-State Functional Magnetic Resonance Imaging. Neurosurg Focus. 2020;48 (2) :E9.Abstract
Neurosurgery has been at the forefront of a paradigm shift from a localizationist perspective to a network-based approach to brain mapping. Over the last 2 decades, we have seen dramatic improvements in the way we can image the human brain and noninvasively estimate the location of critical functional networks. In certain patients with brain tumors and epilepsy, intraoperative electrical stimulation has revealed direct links between these networks and their function. The focus of these techniques has rightfully been identification and preservation of so-called "eloquent" brain functions (i.e., motor and language), but there is building momentum for more extensive mapping of cognitive and emotional networks. In addition, there is growing interest in mapping these functions in patients with a broad range of neurosurgical diseases. Resting-state functional MRI (rs-fMRI) is a noninvasive imaging modality that is able to measure spontaneous low-frequency blood oxygen level-dependent signal fluctuations at rest to infer neuronal activity. Rs-fMRI may be able to map cognitive and emotional networks for individual patients. In this review, the authors give an overview of the rs-fMRI technique and associated cognitive and emotional resting-state networks, discuss the potential applications of rs-fMRI, and propose future directions for the mapping of cognition and emotion in neurosurgical patients.
Stopa BM, Senders JT, Broekman MLD, Vangel M, Golby AJ. Preoperative Functional MRI Use in Neurooncology Patients: A Clinician Survey. Neurosurg Focus. 2020;48 (2) :E11.Abstract
OBJECTIVE: Functional MRI (fMRI) is increasingly being investigated for use in neurosurgical patient care. In the current study, the authors characterize the clinical use of fMRI by surveying neurosurgeons' use of and attitudes toward fMRI as a surgical planning tool in neurooncology patients. METHODS: A survey was developed to inquire about clinicians' use of and experiences with preoperative fMRI in the neurooncology patient population, including example case images. The survey was distributed to all neurosurgical departments with a residency program in the US. RESULTS: After excluding incomplete surveys and responders that do not use fMRI (n = 11), 50 complete responses were included in the final analysis. Responders were predominantly from academic programs (88%), with 20 years or more in practice (40%), with a main area of practice in neurooncology (48%) and treating an adult population (90%). All 50 responders currently use fMRI in neurooncology patients, mostly for low- (94%) and high-grade glioma (82%). The leading decision factors for ordering fMRI were location of mass in dominant hemisphere, location in a functional area, motor symptoms, and aphasia. Across 10 cases, language fMRI yielded the highest interrater reliability agreement (Fleiss' kappa 0.437). The most common reasons for ordering fMRI were to identify language laterality, plan extent of resection, and discuss neurological risks with patients. Clinicians reported that fMRI results were not obtained when ordered a median 10% of the time and were suboptimal a median 27% of the time. Of responders, 70% reported that they had ever resected an fMRI-positive functional site, of whom 77% did so because the site was "cleared" by cortical stimulation. Responders reported disagreement between fMRI and awake surgery 30% of the time. Overall, 98% of responders reported that if results of fMRI and intraoperative mapping disagreed, they would rely on intraoperative mapping. CONCLUSIONS: Although fMRI is increasingly being adopted as a practical preoperative planning tool for brain tumor resection, there remains a substantial degree of discrepancy with regard to its current use and presumed utility. There is a need for further research to evaluate the use of preoperative fMRI in neurooncology patients. As fMRI continues to gain prominence, it will be important for clinicians to collectively share best practices and develop guidelines for the use of fMRI in the preoperative planning phase of brain tumor patients.
Canalini L, Klein J, Miller D, Kikinis R. Enhanced Registration of Ultrasound Volumes by Segmentation of Resection Cavity in Neurosurgical Procedures. Int J Comput Assist Radiol Surg. 2020;15 (12) :1963-74.Abstract
PURPOSE: Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. METHODS: The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. RESULTS: Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. CONCLUSIONS: We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.
Lee TC, Guenette JP, Moses ZB, Lee JW, Annino DJ, Chi JH. MRI and CT Guided Cryoablation for Intracranial Extension of Malignancies along the Trigeminal Nerve. J Neurol Surg B Skull Base. 2020;81 (5) :511-4.Abstract
 To describe the technical aspects and early clinical outcomes of patients undergoing percutaneous magnetic resonance imaging (MRI)-guided tumor cryoablation along the intracranial trigeminal nerve. This study is a retrospective case review. Large academic tertiary care hospital. Patients who underwent MRI-guided cryoablation of perineural tumor along the intracranial trigeminal nerve. Technical success, pain relief, local control. Percutaneous MRI-guided cryoablation of tumor spread along the intracranial portion of the trigeminal nerve was performed in two patients without complication, with subsequent pain relief, and with local control in the patient with follow-up imaging. Percutaneous MRI-guided cryoablation is a feasible treatment option for malignancies tracking intracranially along the trigeminal nerve.
Narasimhan S, Weis JA, Luo M, Simpson AL, Thompson RC, Miga MI. Accounting for Intraoperative Brain Shift Ascribable to Cavity Collapse During Intracranial Tumor Resection. J Med Imaging (Bellingham). 2020;7 (3) :031506.Abstract
For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.
Rushmore RJ, Wilson-Braun P, Papadimitriou G, Ng I, Rathi Y, Zhang F, O'Donnell LJ, Kubicki M, Bouix S, Yeterian E, et al. 3D Exploration of the Brainstem in 50-Micron Resolution MRI. Front Neuroanat. 2020;14 :40.Abstract
The brainstem, a structure of vital importance in mammals, is currently becoming a principal focus in cognitive, affective, and clinical neuroscience. Midbrain, pontine and medullary structures serve as the conduit for signals between the forebrain and spinal cord, are the epicenter of cranial nerve-circuits and systems, and subserve such integrative functions as consciousness, emotional processing, pain, and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high-resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Given the unique capability of MRI-based morphometric analysis in generating and preserving the morphology of 3D objects from individual 2D sections, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community for applications in basic neuroscience as well as in neurology, psychiatry, and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.
Mehrtash A, Wells WM, Tempany CM, Abolmaesumi P, Kapur T. Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation. IEEE Trans Med Imaging. 2020;39 (12) :3868-78.Abstract
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2)We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration.
Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, et al. Creation of a Novel Trigeminal Tractography Atlas for Automated Trigeminal Nerve Identification. Neuroimage. 2020;220 :117063.Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
Kikinis R, Wells WM. Detection of Brain Metastases with Deep Learning Single-Shot Detector Algorithms. Radiology. 2020;295 (2) :416-7.
Epprecht L, Qureshi A, Kozin ED, Vachicouras N, Huber AM, Kikinis R, Makris N, Brown CM, Reinshagen KL, Lee DJ. Human Cochlear Nucleus on 7 Tesla Diffusion Tensor Imaging: Insights Into Micro-anatomy and Function for Auditory Brainstem Implant Surgery. Otol Neurotol. 2020;41 (4) :e484-e493.Abstract
OBJECTIVE: The cochlear nucleus (CN) is the target of the auditory brainstem implant (ABI). Most ABI candidates have Neurofibromatosis Type 2 (NF2) and distorted brainstem anatomy from bilateral vestibular schwannomas. The CN is difficult to characterize as routine structural MRI does not resolve detailed anatomy. We hypothesize that diffusion tensor imaging (DTI) enables both in vivo localization and quantitative measurements of CN morphology. STUDY DESIGN: We analyzed 7 Tesla (T) DTI images of 100 subjects (200 CN) and relevant anatomic structures using an MRI brainstem atlas with submillimetric (50 μm) resolution. SETTING: Tertiary referral center. PATIENTS: Young healthy normal hearing adults. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Diffusion scalar measures such as fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (Mode), principal eigenvectors of the CN, and the adjacent inferior cerebellar peduncle (ICP). RESULTS: The CN had a lamellar structure and ventral-dorsal fiber orientation and could be localized lateral to the inferior cerebellar peduncle (ICP). This fiber orientation was orthogonal to tracts of the adjacent ICP where the fibers run mainly caudal-rostrally. The CN had lower FA compared to the medial aspect of the ICP (0.44 ± 0.09 vs. 0.64 ± 0.08, p < 0.001). CONCLUSIONS: 7T DTI enables characterization of human CN morphology and neuronal substructure. An ABI array insertion vector directed more caudally would better correspond to the main fiber axis of CN. State-of-the-art DTI has implications for ABI preoperative planning and future image guidance-assisted placement of the electrode array.
Steiner A, Alban G, Cheng T, Kapur T, Bay C, McLaughlin P-Y, King M, Tempany C, Lee LJ. Vaginal Recurrence of Endometrial Cancer: MRI Characteristics and Correlation With Patient Outcome After Salvage Radiation Therapy. Abdom Radiol (NY). 2020;45 (4) :1122-31.Abstract
PURPOSE: To evaluate MRI characteristics in vaginal recurrence of endometrial cancer (EC) including tumor volume shrinkage during salvage radiotherapy, and to identify imaging features associated with survival. METHODS: Patients with vaginal recurrence of EC treated with external beam radiotherapy (EBRT) followed by brachytherapy (BT), and with available pelvic MRI at two time points: baseline and/or before BT were retrospectively identified from 2004 to 2017. MRI features including recurrence location and tissue characteristics on T2- and T1-weighted images were evaluated at baseline only. Tumor volumes were measured both at baseline and pre-BT. Survival rates and associations were evaluated by Cox regression and Fisher's exact test, respectively. RESULTS: Sixty-two patients with 36 baseline and 50 pre-BT pelvic MRIs were included (24/62 with both MRIs). Vaginal recurrence of EC was most commonly located in the vaginal apex (27/36, 75%). Tumors with a post-contrast enhancing peripheral rim or low T2 signal rim at baseline showed longer recurrence-free survival (RFS) (HR 0.2, 95% CI 0.1-0.9, P < 0.05 adjusted for histology; HR 0.2, 95% CI 0.1-0.8, P < 0.05, respectively). The median tumor shrinkage at pre-BT was 69% (range 1-99%). Neither absolute tumor volumes nor volume regression at pre-BT were associated with RFS. Lymphovascular space invasion (LVSI) at hysterectomy and adjuvant RT were associated with recurrence involving the distal vagina (both P < 0.05). CONCLUSION: Vaginal recurrences with rim enhancement at baseline MRI predicted improved RFS, while tumor volume shrinkage at pre-BT did not. Distal vaginal recurrence was more common in patients with LVSI and adjuvant RT at EC diagnosis.
Miskin N, Unadkat P, Carlton ME, Golby AJ, Young GS, Huang RY. Frequency and Evolution of New Postoperative Enhancement on 3 Tesla Intraoperative and Early Postoperative Magnetic Resonance Imaging. Neurosurgery. 2020;87 (2) :238-46.Abstract
BACKGROUND: Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. OBJECTIVE: To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. METHODS: We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. RESULTS: A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. CONCLUSION: New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.
Zhang F, Cetin Karayumak S, Hoffmann N, Rathi Y, Golby AJ, O'Donnell LJ. Deep White Matter Analysis (DeepWMA): Fast and Consistent Tractography Segmentation. Med Image Anal. 2020;65 :101761.Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
Fennessy FM, Fedorov A, Vangel MG, Mulkern RV, Tretiakova M, Lis RT, Tempany C, Taplin M-E. Multiparametric MRI as a Biomarker of Response to Neoadjuvant Therapy for Localized Prostate Cancer-A Pilot Study. Acad Radiol. 2020;27 (10) :1432-9.Abstract
RATIONALE AND OBJECTIVES: To explore a role for multiparametric MRI (mpMRI) as a biomarker of response to neoadjuvant androgen deprivation therapy (ADT) for prostate cancer (PCa). MATERIALS AND METHODS: This prospective study was approved by the institutional review board and was HIPAA compliant. Eight patients with localized PCa had a baseline mpMRI, repeated after 6-months of ADT, followed by prostatectomy. mpMRI indices were extracted from tumor and normal regions of interest (TROI/NROI). Residual cancer burden (RCB) was measured on mpMRI and on the prostatectomy specimen. Paired t-tests compared TROI/NROI mpMRI indices and pre/post-treatment TROI mpMRI indices. Spearman's rank tested for correlations between MRI/pathology-based RCB, and between pathological RCB and mpMRI indices. RESULTS: At baseline, TROI apparent diffusion coefficient (ADC) was lower and dynamic contrast enhanced (DCE) metrics were higher, compared to NROI (ADC: 806 ± 137 × 10 vs. 1277 ± 213 × 10 mm/sec, p = 0.0005; K: 0.346 ± 0.16 vs. 0.144 ± 0.06 min, p = 0.002; AUC: 0.213 ± 0.08 vs. 0.11 ± 0.03, p = 0.002). Post-treatment, there was no change in TROI ADC, but a decrease in TROI K (0.346 ± 0.16 to 0.188 ± 0.08 min; p = 0.02) and AUC (0.213 ± 0.08 to 0.13 ± 0.06; p = 0.02). Tumor volume decreased with ADT. There was no difference between mpMRI-based and pathology-based RCB, which positively correlated (⍴ = 0.74-0.81, p < 0.05). Pathology-based RCB positively correlated with post-treatment DCE metrics (⍴ = 0.76-0.70, p < 0.05) and negatively with ADC (⍴ = -0.79, p = 0.03). CONCLUSION: Given the heterogeneity of PCa, an individualized approach to ADT may maximize potential benefit. This pilot study suggests that mpMRI may serve as a biomarker of ADT response and as a surrogate for RCB at prostatectomy.
Zong S, Shen G, Mei C-S, Madore B. Improved PRF-Based MR Thermometry Using k-Space Energy Spectrum Analysis. Magn Reson Med. 2020;84 (6) :3325-32.Abstract
PURPOSE: Proton resonance frequency (PRF) thermometry encodes information in the phase of MRI signals. A multiplicative factor converts phase changes into temperature changes, and this factor includes the TE. However, phase variations caused by B and/or B inhomogeneities can effectively change TE in ways that vary from pixel to pixel. This work presents how spatial phase variations affect temperature maps and how to correct for corresponding errors. METHODS: A method called "k-space energy spectrum analysis" was used to map regions in the object domain to regions in the k-space domain. Focused ultrasound heating experiments were performed in tissue-mimicking gel phantoms under two scenarios: with and without proper shimming. The second scenario, with deliberately de-adjusted shimming, was meant to emulate B inhomogeneities in a controlled manner. The TE errors were mapped and compensated for using k-space energy spectrum analysis, and corrected results were compared with reference results. Furthermore, a volunteer was recruited to help evaluate the magnitude of the errors being corrected. RESULTS: The in vivo abdominal results showed that the TE and heating errors being corrected can readily exceed 10%. In phantom results, a linear regression between reference and corrected temperatures results provided a slope of 0.971 and R of 0.9964. Analysis based on the Bland-Altman method provided a bias of -0.0977°C and 95% limits of agreement that were 0.75°C apart. CONCLUSION: Spatially varying TE errors, such as caused by B and/or B inhomogeneities, can be detected and corrected using the k-space energy spectrum analysis method, for increased accuracy in proton resonance frequency thermometry.
Levitt JJ, Nestor PG, Kubicki M, Lyall AE, Zhang F, Riklin-Raviv T, O Donnell LJ, McCarley RW, Shenton ME, Rathi Y. Miswiring of Frontostriatal Projections in Schizophrenia. Schizophr Bull. 2020;8 (46) :990-8.Abstract
We investigated brain wiring in chronic schizophrenia and healthy controls in frontostriatal circuits using diffusion magnetic resonance imaging tractography in a novel way. We extracted diffusion streamlines in 27 chronic schizophrenia and 26 healthy controls connecting 4 frontal subregions to the striatum. We labeled the projection zone striatal surface voxels into 2 subtypes: dominant-input from a single cortical subregion, and, functionally integrative, with mixed-input from diverse cortical subregions. We showed: 1) a group difference for total striatal surface voxel number (P = .045) driven by fewer mixed-input voxels in the left (P  = .007), but not right, hemisphere; 2) a group by hemisphere interaction for the ratio quotient between voxel subtypes (P  = .04) with a left (P  = .006), but not right, hemisphere increase in schizophrenia, also reflecting fewer mixed-input voxels; and 3) fewer mixed-input voxel counts in schizophrenia (P  = .045) driven by differences in left hemisphere limbic (P  = .007) and associative (P  = .01), but not sensorimotor, striatum. These results demonstrate a less integrative pattern of frontostriatal structural connectivity in chronic schizophrenia. A diminished integrative pattern yields a less complex input pattern to the striatum from the cortex with less circuit integration at the level of the striatum. Further, as brain wiring occurs during early development, aberrant brain wiring could serve as a developmental biomarker for schizophrenia.
Zhang F, Noh T, Juvekar P, Frisken SF, Rigolo L, Norton I, Kapur T, Pujol S, Wells W, Yarmarkovich A, et al. SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization. JCO Clin Cancer Inform. 2020;4 :299-309.Abstract
PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.
Senders JT, Staples P, Mehrtash A, Cote DJ, Taphoorn MJB, Reardon DA, Gormley WB, Smith TR, Broekman ML, Arnaout O. An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning. Neurosurgery. 2020;86 (2) :E184-E192.Abstract
BACKGROUND: Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata. OBJECTIVE: To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator. METHODS: Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves. RESULTS: In total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/). CONCLUSION: The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.
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.

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