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.

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.

Iman’s work at LCN involves mostly medical image registration and segmentation, and brain connectivity analysis.

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.

Robert works on MRI sequence development. He focuses on real-time techniques to correct for motion during scans and acquisition strategies for high-resolution diffusion imaging.

Adrian's research focuses on probabilistic models and machine learning techniques that capture relationships between medical images, clinical diagnoses, and other complex medical data. His work spans medical image analysis, computer vision, machine learning and computational biology. Adrian obtained his PhD from CSAIL, MIT, and his BS and MS in Computer Science from the University of Toronto.

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.

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.

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André has been the key developer of sequences that are optimal with respect to brain morphometry, and more recently M. Dylan Tisdall has worked with André 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.

Anastasia is responsible for the development of the diffusion MRI analysis tools in FreeSurfer, including TRACULA (TRActs Constrained by UnderLying Anatomy), a method for automatically reconstructing a set of major white matter pathways from diffusion MRI data using global probabilistic tractography with anatomical priors. She is also interested in ex vivo imaging of human brain circuits with diffusion MRI and optical imaging to both validate and train algorithms for in vivo tractography. 

Website: https://scholar.harvard.edu/a-y

Assistant Professor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital Department of Radiology, MGH

Lilla is currently focusing on perinatal and child brain imaging projects. She is developing a segmentation and surface reconstruction pipeline for infants and is interested in computing automated tractography solutions on this same population. Additionally, she is also invested in learning about perinatal brain development using various ex vivo imaging techniques (MRI, OCT) and is collaborating with clinical experts to characterize brains of infants who passed away from Suddent Infant Death Syndrom (SIDS). She is interested in research initiatives where statistical and information theoretic approaches can be applied as well as deep learning.

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.

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.

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.

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.

Assistant Professor of Radiology
Perelman School of Medicine
University of Pennsylvania

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.

PhD, Cognitive and Brain Sciences, CIMeC Center for Mind/Brain Sciences, Trento University, Italy

Chiara is interested in the application and validation of diffusion-based tractography techniques. Tractography allows the;investigation of the complex white matter architecture. However the strong dependence of this technique on acquisition and algorithm parameters prevents the reliable investigation of some more critical brain connections. My work focuses on combining high resolution validation methods, such like optical imaging and ex-vivo MRI, to enlarge our knowledge of brain connectivity, with the ultimate goal of improving to the in-vivo tractography applications in both clinical and basic research

PhD, Biomedical Engineering, University of Minnesota

Chao works on depth-resolved 3D fiber axis estimation at micrometer resolution as validation for diffusion MRI using advanced polarization-sensitive optical coherence tomography (PS-OCT). Previously he developed advanced optical imaging methods (including OCT and multiphoton microscopy) and applied them to structural and functional brain imaging in healthy and diseased brains. 

Website

PhD, Applied Mathematics, Inria and Grenoble-Alpes University, France

Aina works on the development of methods for diffusion and functional MRI data analysis to study brain connectivity. Previously, she worked on the development of methods for the analysis of BOLD and ASL task fMRI. 

Website: https://ainafp.github.io/

PhD, MRI Physics, University of Cambridge, UK

Malte is interested in MRI sequence development, specifically techniques that correct for subject motion as it happens in the scanner. His work includes the translation of these methods to fetal imaging, where motion is a major challenge. Malte also works on the FreeSurfer longitudinal analysis stream to improve the detection of structural changes in the brain, by which we hope to advance the early diagnosis of disease.

PhD, Medical Physics and Imaging, Paris-Saclay University

Yael works on multimodal image registration and vasculature segmentation with the aim of building high-resolution cellular atlases of the brain. Before that, Yael worked on Bayesian shape modelling, segmentation, registration, and quantitative MRI at University College London.

PhD, Computer Science, Massachusetts Institute of Technology

Danielle's research interests are in medical image analysis, probabilistic modeling, and machine learning, especially for images from patients with severe pathologies. 

Website: http://people.csail.mit.edu/dfpace/

Research Fellow in Radiology at Harvard Medical School
Research Fellow at Massachusetts General Hospital, Brigham and Women's Hospital
Department of Radiology, MGH, BWH

PhD, Biomedical Engineering, Lund University

Hamid's main research focus is on the development of anatomically-informed methods for spatial processing of fMRI data using cortical surface reconstructions and diffusion-weighted MRI data, within a graph signal processing framework. 

Andrew works on various avenues of FreeSurfer development. His focuses include optimization, upkeep, testing, and machine learning.

Rob supports, tests and installs software for the groups that produce Freesurfer and the magnetoencephalography (MEG) software.

Sr. Research Technologist & Project Manager
Department of Radiology, MGH

MS, Bioengineering, Cape Town University

lmorgan6 [at] mgh.harvard.edu

Leah manages a number of research projects for the LCN that involve multi-lab collaborations, including the U01 and P41 grants. With a background in bioengineering and MR sequence development, Leah oversees ex vivo scanning operations and leads efforts in process improvement. Additionally, Leah works with Allison to manage the LCN's research assistant team efforts and oversees new RA hiring. Leah leads lab efforts in teaching FreeSurfer users worldwide how to use our software which includes developing both online content and coordinating the in-person FS courses. Leah also takes care of LCN administrative activities such as lab meetings and social events, space management, lab purchases, and machine and data storage oversight. 

Research Technician II
Department of Radiology, MGH

BA, Neuroscience, Wheaton College

knestor1 [at] mgh.harvard.edu

Kim performs histology and anatomical analyses on human brain tissue in order to examine the histology for improved brain mapping of neurotypical brains and those with aging and preclinical Alzheimer's Disease pathology. The histochemistry protocols that Kim uses to microscopically examine brain regions are Nissl, Thioflavin S, Gallyas myelin, Sudan black, Hematoxylin and eosin. She also performs immunohistochemistry for early forms of phosphorylated tau, beta-amyloid, and other antibodies in the medial temporal lobe to better understand the pathology of aging and Alzheimer's Disease. These protocols enable the microscopic cellular study of structures such as neurons, neurofibrillary tangles, myelin, and lipofuscin, as well as extracellular structures, amyloid plaques, and blood vessels. Kim uses Freeview to identify and manually label neuroanatomical structures on ex vivo human brain tissue. The labels are used as ground truth and training data to develop computational algorithms that model the structure, volume and surfaces of the human brain. These models are incorporated in the Freesurfer package.

Research Technician II
Department of Radiology, MGH

BA, Biological Chemistry and Mathematics, Bates College

rjjones [at] mgh.harvard.edu

Robert acquires and analyzes ex vivo brain MRI data for a project on post mortem validation of diffusion MRI. He also assists with in vivo neuroimaging data collection for the Human Connectome Project study. In addition, Robert is responsible for managing IRB human study protocols, and assists with quality assurance of imaging data and testing of image analysis algorithms. 

Research Technician II
Department of Radiology, MGH

BA, Applied Mathematics, Harvard University

mkim68 [at] mgh.harvard.edu

Mingu acquires and analyzes in vivo neuroimaging and clinical data for the Human Connectome Project study of adolescents with depression and anxiety disorders.  He develops statistical methods to investigate whether neuroimaging and clinical data can be used to predict disease progression. In addition, Mingu assists with quality assurance of imaging data and testing of image analysis algorithms.

Research Technician II
Department of Radiology, MGH

MS, Health Psychology, University of the Sciences in Philadelphia

Holly acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). She also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Holly also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Research Technician II
Department of Radiology, MGH

MS, Medical Science, Boston University Graduate School of Medicine

Matt acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). He also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Matt also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Data Analyst I Intern Supervisor 
Department of Radiology, MGH

BA, Psychology, Gordon College

Devani acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). She also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Devani also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Research Technician II
Department of Radiology, MGH

BA, Neuroscience, Hamilton College

Emily performs histology and anatomical analyses on human brain tissue in order to examine the histology for improved brain mapping of neurotypical brains and those with aging and preclinical Alzheimer's Disease pathology. The histochemistry protocols that Emily uses to microscopically examine brain regions are Nissl, Thioflavin S, and Gallyas myelin. She also uses Freeview to identify and manually label neuroanatomical structures on ex vivo human brain tissue. The labels are used as ground truth and training data to develop computational algorithms that model the structure, volume and surfaces of the human brain. These models are incorporated in the Freesurfer package.

Research Technician II
Department of Radiology, MGH

Natalya performs histology and anatomical analyses on human brain tissue in order to examine the histology for improved brain mapping of neurotypical brains and those with aging and preclinical Alzheimer's Disease pathology. The histochemistry protocols that Natalya uses to microscopically examine brain regions are Nissl, Thioflavin S, and Gallyas myelin. She also uses Freeview to identify and manually label neuroanatomical structures on ex vivo human brain tissue. The labels are used as ground truth and training data to develop computational algorithms that model the structure, volume and surfaces of the human brain. These models are incorporated in the Freesurfer package.

Research Technician I
Department of Radiology, MGH

BA, Neuroscience, Wheaton College, MA

Jocelyn acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). She also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Jocelyn also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Research Technician II
Department of Radiology, MGH

BS, Biomedical Enginering, Union College

egarciapallares@mgh.harvard.edu

Erendira acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). She also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Erendira also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Research Technician I
Department of Radiology, MGH

BSc, Biology, University of Massachusetts Lowell

wammon@mgh.harvard.edu

Bill acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). He also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Bill also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Research Technician II
Department of Radiology, MGH

BS, Physics, Suffolk University 

jnolan5@mgh.harvard.edu

Jackson acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). He also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Jackson also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Research Technician I
Department of Radiology, MGH

BS, Cognitive and Brain Sciences, Tufts University 

jllamasrodriguez@mgh.harvard.edu

Josue performs histology and anatomical analyses on human brain tissue in order to examine the histology for improved brain mapping of neurotypical brains and those with aging and preclinical Alzheimer's Disease pathology. The histochemistry protocols that Josue uses to microscopically examine brain regions are Nissl, Immhunohistochemistry, Thioflavin S, and Gallyas myelin. He also uses Freeview to identify and manually label neuroanatomical structures on ex vivo human brain tissue. The labels are used as ground truth and training data to develop computational algorithms that model the structure, volume and surfaces of the human brain. These models are incorporated in the FreeSurfer package. 

Research Technician II
Department of Radiology, MGH

BS, Psychology, University of Massachusetts Boston
Minor in Cognitive Science

Matt acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). He also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Mattialso assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Sr. Clinical Research Coordinator
Department of Radiology, MGH

BS, Brain and Cognitive Sciences, University of Rochester
Minor in Clinical Psychology 

brdiamond [at] mgh.harvard.edu

Bram acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). He also analyzes in vivo data for studies focused on clinical populations, including traumatic brain injury (TBI) and epilepsy. In addition to brain imaging acquisition and analysis, Bram manages the lab’s IRB regulatory documents. He also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

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PhD, Department of Electronic Engineering, City University of Hong Kong, Hong Kong

Jieyu works on surface and volumetric registration of brain MRI images. She is currently focused on the development of longitudinal processing pipeline for young children and the quantification of reconstructed brain surfaces. She is also interested in image segmentation and volumetric registration using deep learning. 

Research Technician II
Department of Radiology, MGH

BS, Biopsychology, Tufts University

eboyd2 [at] nmr.mgh.harvard.edu

Emma acquires and analyzes ex vivo brain MRI data, both structural and diffusion, at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). She also analyzes in vivo data for studies focused on clinical populations. In addition to brain imaging acquisition and analysis, Emma also assists with the testing and development of FreeSurfer, as well as teaching members of the neuroimaging research field how to use the software at local and international courses.

Amod works on image synthesis approaches to reduce the bias and variance in analysis results introduced due to MR scanner differences. He has previously worked on biological motion analysis using MRI, image segmentation, and image super-resolution. His general interests include computer vision and machine learning. 

Website: https://asjog.github.io/

Isik focuses on developing new methods for fMRI and EEG data analysis. She is currently investigating motion-related artifacts, and functional and structural connectivity measures, especially in minimally verbal children with autism, using fMRI and DWI.

My work mostly focuses on validation of diffusion MRI tractography. Specifically, I use a multimodal approach that links chemical tracing in non-human primates to diffusion MRI in humans to validate, map and characterize critical connections of the human brain.

Emily works on automatic segmentation procedures for white matter lesions to be included in FreeSurfer releases. In addition to the segmentation process, she develops new methods of analyzing the qualitative changes of white matter within lesions using multimodal MRI. She is interested in relating these changes to cerebrovascular integrity, particularly in the context of aging and cognitive decline as it leads to Alzheimer's disease. 

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.

Ani is mainly involved ex vivo brain imaging, but also helps out with in vivo studies as well. She acquires and analyzes both structural and diffusion MRI data at a variety of resolutions (100um-1mm) and field strengths (7T, 3T, 1.5T). She also acquires optical coherence tomography (OCT) data and performs histology to help fully characterize the brain tissue.  In addition to her scanning and wet-lab responsibilities, Ani also assists with the testing and development of the FreeSurfer software.

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.

Christian currently works on BrainPrint, a discriminative characterization of brain morphology. BrainPrint permits the computation of similarities between brains. Applications include the identification of subjects by their brain and the diagnosis of Alzheimer’s disease. Christian also works on image segmentation and robust multi-modal registration. 

Stefano's work focuses on tomographic acquisition and image formation. He develops acquisition and reconstruction techniques for dynamic, motion-aware Positron Emission Tomography, Magnetic Resonance and Single Photon Emission Computed Tomography.  He is the ideator of the software occiput.io. 

Tong's research interest focuses on brain image analysis and machine learning techniques. His current work is to develop algorithms for laminar modelling using ultra-high resolution ex vivo MRI and optical coherence tomography (OCT) images.

Dorit is a postdoctoral research fellow interested in how humans process social information in the brain, using a multi-methodal approach, including behavioral and eye-tracking measures, structural and functional neuroimaging, as well as diffusion weighted imaging to investigate connectivity patterns. Her research at LCN mainly investigates amygdalar nuclei specific function (e.g., resting state pattern), shape and structure (e.g., high-res ex-vivo data) in neurotypically developed individuals as well as individuals with ASD

Tian works primarily on developing novel mathematical models and statistical approaches for neuroimging data analysis and computational imaging genetics.