Neurosurgery Project

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

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

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

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


Select Recent Publications

  1. Chen Z., Tie Y., Olubiyi O., Zhang F., Mehrtash A., Rigolo L., Kahali P., Norton I., Pasternak O., Rathi Y., Golby A.J., O'Donnell L. Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers using Two-Tensor Unscented Kalman Filter Tractography. Int J Comput Assist Radiol Surg. 2016 Aug;11(8):1475-86. PMID: 26762104. PMC4942409.
  2. Santagata S., Eberlin L.S., Norton I., Calligaris D., Feldman D.R., Ide J.L., Liu X., Wiley J.S., Vestal M.L., Ramkissoon S.H., Orringer D.A., Gill K.K., Dunn I.F., Dias-Santagata D., Ligon K.L., Jolesz F.A., Golby A.J., Cooks R.G., Agar N.Y.R. Intraoperative Mass Spectrometry Mapping of an Onco-metabolite to Guide Brain Tumor Surgery. Proc Natl Acad Sci U S A. 2014 Jul;111(30):11121-6. PMID: 24982150. PMC4121790.

Full Publication List

Peer-reviewed publications on our research in Image-Guided Brain Tumor Surgery can be found in the NIH/NLM database of biomedical literature by clicking here.

In addition, abstracts presented at national and international meetings are available online here.


Collaborations are a key component of our research program; We closely interact with researchers within our institution, across the US, and internationally. Active collaborations on funded projects are tabulated below, along with resultant publications.


Multimodal Registration of the Brain's Cortical Surface

Collaborating Investigator:  Michael Miga, PhD
Collaborating Institutions:  Vanderbilt University, Nashville, TN
Grant Number: R01NS049251
Grant Period: 08/01/2004-04/30/2019 
NCIGT Team and Project: Alexandra Golby, Neurosurgery, William Wells, Computation
Joint Publications

Effects of Maintenance Treatment with Olanzapine vs. Placebo on Brain Structure

Collaborating Investigator:  Aristotle Voineskos, PhD
Collaborating Institutions:  Centre for Addiction and Mental Health, Toronto, ON, Canada
Grant Number: R01MH099167
Grant Period: 12/10/2012-11/30/2017
NCIGT Team and Project: Lauren O'Donnell, Neurosurgery
Joint Publications


Evaluating Mass Spectrometry for Intraoperative Tissue Characterization in Breast-Conserving Therapy

Collaborating Investigators:  Nathalie Agar, PhD, Mehra Golshan, MD
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: R01CA201469
Grant Period: 01/06/2015-11/30/2020
NCIGT Projects: Neurosurgery, Guidance
Joint Publications

Novel Diffusion MRI Analysis for Detection of Mild Traumatic Brain Injury

Collaborating Investigator:  Lauren O'Donnell, PhD
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: R03NS088301
Grant Period: 07/01/2015-06/30/2017
NCIGT Project:  Neurosurgery
Joint Publications

Open Source Diffusion MRI Technology for Brain Cancer Research

Collaborating Investigator: Lauren O'Donnell, PhD 
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: U01CA199459
Grant Period: 09/22/2015-07/31/2018
NCIGT Project: Neurosurgery
Joint Publications

Resting-State fMRI for Language Mapping in Brain Tumor Patients

Collaborating Investigators:  Alexandra Golby, MD, Yanmei Tie, PhD
Collaborating Institution: Brigham & Women's Hospital, Boston, MA 
Grant Number: R01CA201469
Grant Period: 07/01/2015-06/30/2017
NCIGT Project: Neurosurgery
Joint Publications

OpenIGTLink: A Network Communication Interface For Closed-Loop Image-Guided Interventions

Collaborating Investigator:  Junichi Tokuda, PhD
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: R01EB020667
Grant Period: 07/01/2015-06/30/2018
NCIGT Project: Guidance, Prostate, Computation, Neurosurgery
Joint Publications

NAC- Neuroimage Analysis Center

Collaborating Investigator:  Ron Kikinis, MD
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: P41EB015902
Grant Period: 08/01/2013-05/31/2018
NCIGT Project: Computation, Neurosurgery
Joint Publications

Software and Documentation

3D Slicer, a comprehensive open source platform for medical image analysis, contains several modules that have been contributed by us for Image-Guided Brain Tumor Surgery. These include:



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