Lab Director

Bruce Fischl

Faculty

Iman Aganj David Salat
Jean Augustinack Andre van der Kouwe
Douglas Greve Koen Van Leemput
Martin Reuter Anastasia Yendiki
Mert Sabuncu Lilla Zöllei
Dylan Tisdall Ender Konukoglu

Research Fellows

Juan Eugenio Iglesias Stefano Pedemonte
Dorit Kliemann Gang "Luke" Liu
Tian Ge Christian Wachinger
Caroline Magnain Paul Wighton

Developers

Zeke Kaufman Ruopeng Wang
Nick Schmansky

Lab Manager

Allison Stevens (Player)  

Technical Staff

Ronny Preciado Louis Vinke
Emma Ben-Avi  

Students

Giorgia Grisot Emily Lindemer
Hyun "Monica" Kim  

Collaborators

Yangming Ou Brian Edlow
David Boas James Kolasinski
Randy Buckner Caterina Mainero
Gaelle Desbordes Azma Mareyam
Randy Gollub Rudolph Pienaar
Ellen Grant Jonathan Polimeni
Karl Helmer Zeynep Saygin
Gitte Knudsen Thomas Witzel

Alumni

Thomas Benner Jenni Pacheco
Istvan Csapo Patricia Panneck
Rahul Desikan Gheorghe Postelnicu
Richard Edgar Katyucia Rodrigues
Melanie Ganz Peter Sand
Xiao Han Florent Segonne
Oliver Hinds Priti Srinivasan
Jorge Bernal Thomas Yeo
Oline Olesen Peng Yu

 

 

 

Lab Director

 

Bruce Fischl
Professor in Radiology at Harvard Medical School
Neuroscientist at Massachusetts General Hospital
Director, Computational Core at Martinos Center, MGH
Department of Radiology, MGH
PhD, Cognitive and Neural Systems, Boston University
fischl [at] nmr.mgh.harvard.edu

Bruce's current research involves the development of techniques for the automatic construction and utilization of geometrically accurate and topologically correct models of the human cerebral cortex. These models are based on high-resolution T1-weighted MR images, and have a number of uses. Their primary utility has been as a domain for the visualization of cortical neuroimaging data, as the metric structure of the cortex is significantly more visible when it is viewed as a surface, but they have also been useful as a substrate for combining neuroimaging data from different imaging modalities in order to obtain high spatial and temporal resolution. In addition, Bruce has developed a technique that exploits the correlation between cortical folding patterns and cortical function in order to generate a more accurate mapping across different brains. This high-dimensional nonlinear registration procedure results in a substantial increase in statistical power over more standard methods of inter-subject averaging, and allows the automatic and accurate labeling of many anatomical features of the cortex. While the primary use of the cortical models has been for visualization and more recently high-dimensional inter-subject registration, the models also represent a rich source of information regarding the morphometric properties of the cortex. Another focus of Bruce's research has been increasing the accuracy of the models of both the gray/white surface as well as the pial surface itself. The combination of these two surfaces allows one to measure the thickness of the gray matter of the cortex. The thickness of the cortical ribbon is of great clinical and research significance as many neurodegenerative diseases result in progressive, regionally specific atrophy of the cortical gray matter. This research has shown that measures of thickness using the cortical models are accurate to within ¼ millimeter, or substantially less than the size of a typical MR voxel.

 

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Faculty

 

 

Iman Aganj
Instructor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Electrical Engineering, University of Minnesota
iman [at] nmr.mgh.harvard.edu

Iman's work at LCN mostly involves MRI image registration. Specifically, he focuses on asymmetry in non-rigid image registration, functional MRI alignment, and resampling and interpolation artifacts.

Personal website
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Jean Augustinack
Assistant Professor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Anatomy & Cell Biology, University of Iowa
jean [at] nmr.mgh.harvard.edu

My research focuses on two main concentrations: brain mapping and neuropathological systems in Alzheimer's disease. We study healthy brains to understand neuroanatomical systems, improve cortical area localization, and examine Alzheimer’s samples to investigate neurofibrillary, neuronal and morphological changes in the medial temporal lobe.
Our laboratory utilizes an ex vivo model to study the relationship between MRI and histological tissue in the human brain. This approach gleans information from histological ground truth and relates it to the MRI. In this model, histological sections validate MRI intensities for more accurate neuroanatomical localization of cortical areas and diagnoses in structural MRI. Because this method is based on architecture - cytoarchitecture and myeloarchitecture - of the tissue at high resolution, this validation adds an extra level of information than methods based solely on topography.
We also study the neuroanatomical and neuroimaging correlates of Alzheimer’s disease. Alzheimer's disease pathology severely affects the cerebral cortex. Neurofibrillary tangles, one of the neuropathological markers in Alzheimer’s disease, manifest first in the medial temporal lobe specifically the perirhinal and entorhinal cortices. In previous work, we have demonstrated cortical architecture in the medial temporal lobe with high field and high resolution MRI. We continue to investigate the early morphological changes that occur in Alzheimer’s disease with MRI to understand the effects of aging.

 

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Douglas Greve
Instructor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Cognitive & Neural Systems, Boston University
greve [at] nmr.mgh.harvard.edu

Doug's primary research has been in the analysis and integration of multi-modal MRI. His primary expertise is in the analysis of fMRI at the time series and group levels. Doug has been one of the core FreeSurfer software developers since 1999 during which time he has written the fMRI analysis stream distributed with FreeSurfer (FS-FAST). He has been a member of the Functional Biomedical Informatics Research Network (fBIRN) with the primary responsibility of researching methods to quantify, calibrate, and remove site-specific artifacts from fMRI. He has authored the Boundary-based Registration algorithm which registers an arbitrary MRI volume (eg, fMRI, DTI, ASL) to the same-subject's structural MRI using strong anatomical priors. This allows accurate multi-modal integration. Doug has also written software to analyze DTI and ASL data. In addition to his direct fMRI research, he has led a project using near-infrared spectroscopy (NIRS) optical imaging and physiological monitoring to de-noise fMRI data.

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Martin Reuter
Instructor in Neurology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Neurology, MGH
Department of Radiology, MGH
PhD, Leibniz University Hannover
mreuter [at] nmr.mgh.harvard.edu

Martin has developed tools for unbiased robust image registration that are extremely accurate in the presence of longitudinal change, e.g. atrophy, tumor growth, or jaw, neck, tongue movement. He has taken over primary responsibility for the ongoing development of the FreeSurfer longitudinal analysis stream, and also worked with Peter Sand to develop and validate prototype tools for registering histological and block-face images to high-resolution ex vivo MRI. His interests also include advanced methods for modeling disease progression and treatment response, as well as spectral shape analysis and other topics in computational geometry and topology.

Personal website
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Mert Sabuncu
Assistant Professor of Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Electrical Engineering, Princeton University
msabuncu [at] nmr.mgh.harvard.edu

Mert's work involves cutting edge segmentation using probabilistic label fusion, and more recently has been working on imaging genetics, multivariate pattern analysis, and longitudinal statistics.

Personal website
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David Salat
Assistant Professor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Behavioral Neuroscience, Oregon Health and Science University
salat [at] nmr.mgh.harvard.edu

David's research examines structural and functional changes in the brain with aging and age-associated neurodegenerative disease. A primary focus of this work is to determine how the common decline in vascular health with advancing age contributes to neurodegenerative changes, cognitive attenuation and the development of Alzheimer’s disease and other dementias. Through these studies, we hope to advance procedures for the clinical utilization of imaging technology in the diagnosis, characterization and tracking of neurodegenerative disease as well as towards advancing understanding of the pathological mechanisms that cause dementia.

Lab website
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Andre van der Kouwe
Assistant Professor in Radiology at Harvard Medical School
Assistant in Physics at Massachusetts General Hospital
Director, 7 Tesla MR Imaging at Martinos Center, MGH
Department of Radiology, MGH
PhD, Biomedical Engineering, Ohio State University
andre [at] nmr.mgh.harvard.edu

Andre has been the key developer of sequences that are optimal with respect to brain morphometry, and more recently M. Dylan Tisdall has worked with Andre to develop structural sequences with embedded real-time motion correction that promise to open up structural imaging to an array of clinical populations that were difficult or impossible to image previously.

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Koen Van Leemput
Assistant Professor in Radiology at Harvard Medical School
Assistant Neuroscientist at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Electrical Engineering, K.U.Leuven, Belgium
koen [at] nmr.mgh.harvard.edu

Koen is an expert in model-based segmentation and registration of brain imaging data. He has developed the FreeSurfer techniques to perform a fully-automated segmentation of the subfields of the hippocampus.

Personal website
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Anastasia Yendiki
Assistant Professor in Radiology at Harvard Medical School
Assistant in Physics at Massachusetts General Hospital Department of Radiology, MGH
PhD, Electrical Engineering: Systems, The University of Michigan, Ann Arbor, MI
ayendiki [at] nmr.mgh.harvard.edu

Anastasia works on image analysis and reconstruction algorithms for diffusion MRI. She is responsible for the development and support of TRACULA (TRActs Constrained by UnderLying Anatomy), which is a FreeSurfer tool for automatically reconstructing a set of major white matter pathways in the human brain from diffusion weighted images using probabilistic tractography. This method eliminates the need for manual intervention for tract solutions and thus facilitates the application of tractography to large datasets.

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Lilla Zöllei
Instructor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital Department of Radiology, MGH
PhD, Electricial Eng & Computer Sci, MIT
lzollei [at] nmr.mgh.harvard.edu

Lilla works on various registration projects, including surface and volumetric registration of brain MRI images and diffusion tensor image (DTI) alignment. She is interested in research initiatives where statistical and information theoretic approaches can be applied. She is currently focused on finding mathematical correspondence between various types of MRI acquisitions of the developing brain, designing automatic segmentation tools, and analyzing diffusion weighted images.

Personal website
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Research Fellows

 

Juan Eugenio Iglesias
Research Fellow in Radiology at Harvard Medical School
Research Fellow at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Biomedical Engineering, UCLA
iglesias [at] nmr.mgh.harvard.edu

Eugenio is a postdoctoral researcher under the supervision of Koen Van Leemput. Eugenio is using ultra-high resolution MRI data from ex-vivo hippocampi to build statistical models of the hippocampal subfields. The output of his research will be a set of tools that can be used to automatically analyze the subfields in large-scale imaging studies.

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Ender Konukoglu
Research Fellow in Radiology at Harvard Medical School
Research Fellow at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Computer Science, Asclepios Project - INRIA Sophia Antipolis / University of Nice
enderk [at] nmr.mgh.harvard.edu

 

 
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Caroline Magnain
Research Fellow in Radiology at Harvard Medical School
Research Fellow at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Optics, INSP UPMC, Paris, France
cmagnain [at] nmr.mgh.harvard.edu

Caroline is working on 3D high resolution human brain imaging using Optical Coherence Tomography (OCT). Part of her study is to identify anatomical brain regions (Brodmann areas) by examining optical property changes in the cortical layers. The tissue samples are then registered to high resolution MRI imaging data to look for potential MR markers of the boundaries identified in OCT. The boundaries observed using OCT are validated with histology/immunochemistry. Additionally, Caroline is investigating using OCT to perform high resolution tractography, which she compares to ex vivo MRI diffusion data.

Personal website
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Dylan Tisdall
Research Fellow in Radiology at Harvard Medical School
Research Fellow at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Computing Science, Simon Fraser University, Canada
tisdall [at] nmr.mgh.harvard.edu

Dylan focuses on developing novel MRI acquisition and data analysis techniques. His work includes prospectively motion-corrected MRI sequences for clinical and research anatomical studies, diffusion sequences and eddy-current measurement for high-amplitude gradient systems, and the application of statistical signal processing methods to the estimation of tissue parameters.

Personal website
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Christian Wachinger
Research Fellow in Neurology at Harvard Medical School
Research Fellow at the Computer Science and Artificial Intelligence Lab, MIT
PhD, Technische Universität München, Germany
wachinge [at] nmr.mgh.harvard.edu

Christian works on the segmentation of brain tumor images, as well as the accurate registration of brain images from different modalities.

 
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Paul Wighton
Research Fellow in Radiology at Harvard Medical School
Research Fellow at Massachusetts General Hospital
Department of Radiology, MGH
PhD, Computing Science, Simon Fraser University, Canada
pwighton [at] nmr.mgh.harvard.edu

Paul works on methods to display fMRI neurofeedback data to subjects in real-time while they are in the MRI scanner. He is also involved in other MRI sequence development projects and is exploring methods to acquire the BOLD signal using functional spectroscopy.

 
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Developers

 

 

Zeke Kaufman
Department of Radiology, MGH
MS, Computer Science, Boston University
zkaufman [at] nmr.mgh.harvard.edu

Zeke works primarily on the testing, development, and distribution of FreeSurfer. He is responsible for building and maintaining up-to-date Linux and Mac compatible versions of FreeSurfer. He tests new software releases, and also provides minor updates and bug-fixes when necessary.

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Nick Schmansky
Department of Radiology, MGH
MA, Cognitive and Neural Systems, Boston University
MSc, Artificial Intelligence, University of Edinburgh
nicks [at] nmr.mgh.harvard.edu

Nick has been the lead software engineer for many years, and is responsible for taking FreeSurfer from a loosely organized set of binaries and turning it into a well-documented, extensively tested, easy to install and useful suite of tools.

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Ruopeng Wang
Department of Radiology, MGH
MS, Nuclear Engineering, MIT
rpwang [at] nmr.mgh.harvard.edu
 
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Technical Staff

 

Emma Ben-Avi
Department of Radiology, MGH
BA, Anthropology/Neuroscience, Smith College
emma [at] nmr.mgh.harvard.edu

Emma works primarily on manually segmenting high resolution in vivo images of infant and neonate brains which will be used to build an atlas. Emma also helps edit FreeSurfer recons from the Ellison dataset.

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Maritza Ebling
Department of Radiology, MGH
BA, Mathematics and Neuroscience, Wesleyan University
maritza [at] nmr.mgh.harvard.edu

Maritza is involved in the acquisition of ex vivo and in vivo MRI, histology and ex vivo imaging data processing. She scans ex vivo brains at 3T to acquire ex vivo diffusion and 7T to acquire high-resolution images of brain structures. Additionally, she is responsible for scheduling and scanning in vivo subjects for the VETSA Study. Maritza also conducts histological experiments, which involve sectioning, mounting, and staining or performing immunochemistry on ex vivo samples.

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Sita Kakunoori
Department of Radiology, MGH
MS, Biomedical Engineering, University of Memphis
sita [at] nmr.mgh.harvard.edu

Sita is a Research Technologist whose work involves high resolution ex vivo MR data acquisition at 7T and 4.7T scanners, QA of Ellison dataset and fBIRN dataset, maintaining all the lab IRBs, editing of labels and post processing of Zilles dataset. Sita also helps with FreeSurfer testing and development.

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Allison Stevens Player
Department of Radiology, MGH
MA, Psychology, Boston University
astevens [at] nmr.mgh.harvard.edu

Allison serves as the lab manager for LCN. She is also responsible for organizing local and international FreeSurfer courses and helping with FreeSurfer testing and development. Aside from that, she is primarily involved with ex vivo brain imaging and processing on 3T and 7T scanners.

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Louis Vinke
Department of Radiology, MGH
MA, Experimental Psychology, Bowling Green State University
vinke [at] nmr.mgh.harvard.edu

Louis is involved in high resolution (4.7T & 7T) and low resolution (1.5T) ex vivo brain imaging, as well as in vivo imaging at 3T. He also assists with the testing and development of FreeSurfer, and is working on developing tools to help QA large datasets processed with FreeSurfer. Lastly, he maintains the lab website and helps out in the biochemistry lab.

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- Laboratory for Computational Neuroimaging • 149 Thirteenth Street, Rm 2301, Charlestown, MA 02129 -