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 mostly involves medical image registration 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.

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 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.

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

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.

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.

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

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.

Adrian is a postdoctoral fellow at MGH, Harvard medical School, working with Mert Sabuncu. Until recently, he was a graduate student at CSAIL, MIT, advised by Polina Golland. He is interested in mathematical models and machine learning for medical image analysis, with a focus on characterizing genetic and clinical and genetic effects on imaging phenotypes. He is also interested and active in healthcare entrepreneurship and translation of algorithms to the clinic.

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.

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/

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

Aina works on the development of method 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.

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

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.

Research Technician II
Department of Radiology, MGH

BS, Brain and Cognitive Sciences, University of Rochester

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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Research Technician I
Department of Radiology, MGH

BS, Biomedical Engineering, Illinois Institute of Technology

mfogarty1 [at] mgh.harvard.edu

Morgan 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, Morgan 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 Technologist
Department of Radiology, MGH

MS, Engineering, Cape Town University

lmorgan [at] mgh.harvard.edu

Leah 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, Leah 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

BA, Neuroscience, Wheaton College

knestor1 [at] mgh.harvard.edu

Kimberly performs histology and anatomical analysis on human brain tissue in order to examine the histology for improved brain mapping and the pathology of the preclinical phases of Alzheimer's Disease. The histochemistry protocols that Kimberly uses to microscopically examine brain regions are Nissl, thioflavine S, Gallyas myelin, sudan black, hematoxylin and eosin. She also performs immunohistochemistry for early forms phosphorylated tau, beta-amyloid, and other antibodies in the medial temporal to better understand the pathoogy of aging and Alzheimer's Disease. These protocols will enable Kimberly to microscopically study cellular structures such as neurons, neurofibrillary tangles, myelin, and lipofuscin, as well as extracellular structures, amyloid plaques, and blood vessels. Kimberly uses Freeview to identify and manually lable neuroanatomical structures for FreeSurfer. 

Research Technician I
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, I
Department of Radiology, MGH

BA, Cognitive Science, UC Berkeley

jwang90 [at] mgh.harvard.edu

Jonathan currently works on acquiring and analyzing MRI brain data for a human connectome project study. He assists with data quality assurance, analysis, and transfer of processed data between collaborating clinical sites. He is also responsible for coordinating study visits to the Martinos Center, where he helps perform MRI scanning and behavioral testing on study participants. 

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