Susie Huang

Associate Professor

Magnetic Resonance Physics & Instrumentation Group
Connectome 2.0

susie.huang@mgh.harvard.edu

Susie Huang, MD, PhD, is a board-certified neuroradiologist and physician-scientist specializing in the development and translation of novel MRI techniques for investigating structure, function and pathology within the brain. Her doctoral training in physical chemistry and subsequent residency in radiology and postdoctoral training at MGH provided her with a strong foundation in biophysical modeling, basic and clinical neuroscience, and the design and evaluation of hardware, software and pulse sequences for pushing the limits of contrast and sensitivity in MRI.

Her current research centers on the development and translation of advanced diffusion MRI methods for probing tissue microstructure in the central nervous system. As part of the NIH Blueprint-funded Human Connectome Project, she contributed to the first publications showcasing the unprecedented sensitivity and resolution of axonal microstructure made possible by the 300 mT/m gradients on the MGH Connectome MRI scanner. She has since developed methods for mapping axon diameter and density across white matter tracts throughout the living human brain, which offer insight into white matter damage in disease processes such as multiple sclerosis, aging and Alzheimer’s disease. Based on the success of these efforts, she now serves as the lead Principal Investigator on a $14-million NIH BRAIN Initiative multi-institutional collaborative grant to develop the next-generation Connectome MRI scanner for multiscale imaging of the human brain.

Education

MD, Harvard Medical School
PhD in Physical Chemistry, University of California, Los Angeles

Select Publications

1. Huang SY, Nummenmaa A, Witzel T, Duval T, Cohen-Adad J, Wald LL, McNab JA. The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter. NeuroImage. 2015 Feb 1;106:464-72.

2. Huang SY, Tian Q, Fan Q, Witzel T, Wichtmann B, McNab JA, Bireley JD, Machado N, Klawiter EC, Mekkaoui C, Wald LL, Nummenmaa A. High-gradient diffusion MRI reveals distinct estimates of axon diameter index within different white matter tracts in the in vivo human brain. Brain Structure and Function. 2020 May;225(4):1277-1291. PMID: 31563995.

3. Huang SY, Witzel T, Keil B, Scholz A, Davids M, Dietz P, Rummert E, Ramb R, Kirsch JE, Yendiki A, Fan Q, Tian Q, Ramos-Llordén G, Lee HH, Nummenmaa A, Bilgic B, Setsompop K, Wang F, Avram AV, Komlosh M, Benjamini D, Magdoom KN, Pathak S, Schneider W, Novikov DS, Fieremans E, Tounekti S, Mekkaoui C, Augustinack J, Berger D, Shapson-Coe A, Lichtman J, Basser PJ, Wald LL, Rosen BR. Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome. Neuroimage. 2021 Nov;243:118530.

Highlights

MGH Claflin Distinguished Scholar Award
I. I. Rabi Young Investigator Award Finalist, ISMRM
Young Investigator Award, European Society for Magnetic Resonance in Medicine and Biology

Website

Magnetic Resonance Physics & Instrumentation Group

Nouchine Hadjikhani

Associate Professor

Neurolimbic Research Laboratory (Director)

nouchine@nmr.mgh.harvard.edu
617-724-5625

Tagged:Autism, Brain, Clinical, Diffusion MRI, fMRI, FreeSurfer, FSL, Functional Magnetic Resonance Imaging, Magnetic Resonance Imaging, MEG, Migraine, MRI, Multimodal Imaging, Pain, PET-MRI

Nouchine Hadjikhani, MD, PhD, is an Associate Professor of Radiology at Harvard Medical School, where she directs the Neurolimbic Research Laboratory. She is also an Assistant in Neurosciences at the Massachusetts General Hospital and a visiting professor at GNC, Gothenburg University, Sweden. She is an author of 105 peer-review articles and 14 books, book chapters and other publications. She has been conducting brain imaging for more than twenty years, using neuroanatomy, histology, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI) and magnetoencephalography (MEG), as well as behavioral methods, including eye-tracking, to study the normal and the diseased brain. Her research stems from her interest in the visual system and includes conditions such as migraine and autism.

Education

MD, University of Lausanne, Switzerland
PhD in Neuroscience, Tilburg University, The Netherlands

Select Publications

1. Hadjikhani N, Sanchez Del Rio M, Wu O, Schwartz D, Bakker D, Fischl B, et al. Mechanisms of migraine aura revealed by functional MRI in human visual cortex. Proc Natl Acad Sci U S A. 2001;98(8):4687-92.

2. Hadjikhani N, Joseph RM, Snyder J, Chabris CF, Clark J, Steele S, et al. Activation of the fusiform gyrus when individuals with autism spectrum disorder view faces. Neuroimage. 2004;22(3):1141-50.

3. Hadjikhani N, Asberg Johnels J, Lassalle A, Zurcher NR, Hippolyte L, Gillberg C, et al. Bumetanide for autism: more eye contact, less amygdala activation. Sci Rep. 2018;8(1):3602.

Highlights

2014: F1000 prime recommendation for “Emotional contagion for pain is intact in Autism Spectrum Disorders.” Translational Psychiatry.

2016: LifeWatch Niclas Öberg Foundation award for achievement in autism research

2019: top 100 read neuroscience papers for Scientific Reports 2018, for “Bumetanide for Autism : more eye-contact, less amygdala activation”

Ona Wu

Associate Professor

Clinical Computational Neuroimaging Group (Director)
Director of Clinical Computational Neuroimaging, Neuroradiology, MGH

ona@nmr.mgh.harvard.edu

Tagged:Algorithm Development, Brain, Cardiac Arrest, Clinical, Coma Recovery, Data Science, Diffusion MRI, Machine Learning, MRI, Perfusion MRI, Stroke, TBI, Traumatic Brain Injury

The research goals of Dr. Wu’s group are to improve the diagnosis, prognosis and management of patients with brain injury by quantifying and monitoring injury or recovery on an individual patient basis. The group focuses particularly on stroke, cardiac arrest and traumatic brain injury. Predicting a patient’s response to different treatment strategies prior to therapeutic intervention can aid clinical decision-making and thereby improve patient outcome. Her research concentrates on the development of machine-learning algorithms which combine multiple MRI modalities and clinical data to assess tissue injury and recovery and ultimately patient-centered outcomes. In addition to algorithm development, her research involves the refinement of advanced MRI data acquisition and analysis and development and validation of quantitative imaging biomarkers.

Education

PhD in Electrical Engineering, Massachusetts Institute of Technology (MIT)

Select Publications

1. Wu O, Koroshetz WJ, Ostergaard L, Buonanno FS, Copen WA, Gonzalez RG, Rordorf G, Rosen BR, Schwamm LH, Weisskoff RM, Sorensen AG. Predicting tissue outcome in acute human cerebral ischemia using combined diffusion- and perfusion-weighted MR imaging. Stroke. 2001 Apr;32(4):933-42.

2. Wu O, Østergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG. Tracer arrival timing-insensitive technique for estimating flow in MR perfusion weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med. 2003 Jul;50(1):164-74.

3. Wu O, Sorensen AG, Benner T, Singhal AB, Furie KL, Greer DM. Comatose patients with cardiac arrest: predicting clinical outcome with diffusion-weighted MR imaging. Radiology. 2009 Jul;252(1):173-81.

4. Wu O, Winzeck S, Giese AK, Hancock BL, Etherton MR, Bouts MJRJ, Donahue K, Schirmer MD, Irie RE, Mocking SJT, McIntosh EC, Bezerra R, Kamnitsas K, Frid P, Wasselius J, Cole JW, Xu H, Holmegaard L, Jiménez-Conde J, Lemmens R, Lorentzen E, McArdle PF, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Stanne TM, Thijs V, Vagal A, Woo D, Bevan S, Kittner SJ, Mitchell BD, Rosand J, Worrall BB, Jern C, Lindgren AG, Maguire J, Rost NS. Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data. Stroke. 2019 Jul;50(7):1734-1741.

5. Winzeck S, Mocking SJT, Bezerra R, Bouts MJRJ, McIntosh EC, Diwan I, Garg P, Chutinet A, Kimberly WT, Copen WA, Schaefer PW, Ay H, Singhal AB, Kamnitsas K, Glocker B, Sorensen AG, Wu O. Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI. AJNR Am J Neuroradiol. 2019 Jun;40(6):938-945.

Highlights

Martinos Investigator invited to AHA 2019 Research Leadership Academy

AJNR Paper: Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI

MGH Advances in Motion Article: Deep Learning Automated Algorithms Accurately Segment Stroke Lesions

MGH News Release: Imaging may allow safe tPA treatment of patients with unwitnessed strokes

Website

Clinical Computational Neuroimaging Group

Jonathan Polimeni

Associate Professor

Magnetic Resonance Physics & Instrumentation Group

jonp@nmr.mgh.harvard.edu
617-724-4546

Tagged:Algorithm Development, Brain, Brain Mapping, fMRI, FreeSurfer, High-field Imaging, MRI

Jon Polimeni, PhD, is an Assistant Professor of Radiology at Harvard Medical School. His PhD thesis was on the measurement and modeling of visuotopic maps in macaque and human visual cortex. His postdoctoral training was under the supervision of Professor Lawrence L. Wald at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital and focused on technological development for increasing the spatial resolution and accuracy of the functional measurements. He joined the faculty of the Martinos Center in 2010. His research is currently focused on investigating the functional architecture of the human cerebral cortex using high-resolution functional MRI using seven Tesla field strength scanners, and on characterizing and understanding the biological limits on spatial specificity of the fMRI signals. He is a member of the IEEE.

Education

Ph.D. in Electrical and Computer Engineering, Boston University

Website

Magnetic Resonance Physics & Instrumentation Group

Jayashree Kalpathy-Cramer

Associate Professor

The Quantitative Translational Imaging in Medicine Lab (Co-Director)
Center for Machine Learning (Co-Director)

kalpathy@nmr.mgh.harvard.edu
617-724-4657

Tagged:Algorithm Development, Brain, Cancer, Clinical, Cloud Computing, Crowdsourcing, Deep Learning, Image Acquisition, Image Analysis, Informatics, Machine Learning, Magnetic Resonance Imaging, Mathematical Modeling, MRI, Quantitative Imaging, Retinal Imaging, Software Development, Stroke

Dr. Kalpathy-Cramer is an Associate Professor of Radiology at Harvard Medical School, Co-Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center and Scientific Director at the MGH & BWH Center for Clinical Data Science. Her research areas include machine learning, informatics, image analysis and statistical methods. In addition to developing novel machine learning algorithms, her lab is also actively engaged in the applications of these to clinical problems in radiology, oncology and ophthalmology.

She is funded through NIH to develop quantitative imaging methods in cancer. She is the PI of an NSF-funded project to develop novel algorithms and apply them to build diagnostic tools in ophthalmology. Research from this work has resulted in a deep-learning based algorithm for disease diagnosis and response assessment that is currently being evaluated at several clinics and screening trials in the US and India. Her group has recently applied novel machine learning methods to stroke segmentation, identification, and outcome prediction. She leads an effort to develop open-source tools for deep learning based image analysis and is making trained models for brain tumor, stroke and other diseases publicly available.

Education

PhD in Electrical Engineering, Rensselaer Polytechnic Institute

Select Publications

1. Chang K, Beers AL, Bai HX, Brown JM, Ly KI, Li X, Senders JT, Kavouridis VK, Boaro A, Su C, Bi WL, Rapalino O, Liao W, Shen Q, Zhou H, Xiao B, Wang Y, Zhang PJ, Pinho MC, Wen PY, Batchelor TT, Boxerman JL, Arnaout O, Rosen BR, Gerstner ER, Yang L, Huang RY, Kalpathy-Cramer J. Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement. Neuro Oncol. 2019 Jun 13. pii: noz106.

2. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.

3. Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, Rosen B, Rubin DL, Kalpathy-Cramer J. Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc. 2018 Aug 1;25(8):945-954.

Highlights

Open source DeepNeuro for machine learning applications in radiology

DeepROP tool for diagnosis of retinopathy of prematurity

Website

The Quantitative Translational Imaging in Medicine Lab

Anastasia Yendiki

Associate Professor

Director of Circuit Analysis
Laboratory for Computational Neuroimaging (LCN)

ayendiki@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Data Science, Diffusion MRI, FreeSurfer, Machine Learning, Software Development

Dr. Anastasia Yendiki’s background is in statistical signal and image processing. She received a PhD in Electrical Engineering: Systems from the University of Michigan at Ann Arbor, where she worked on inverse problems in tomographic reconstruction for nuclear imaging under the supervision of Jeff Fessler. As a postdoctoral research fellow at the Athinoula A. Martinos Center for Biomedigal Imaging, Harvard Medical School and Massachusetts General Hospital, she trained in functional and diffusion-weighted MRI. She received an NIH K99/R00 Pathway to Independence award, which led to the development of TRACULA, the diffusion-weighted MRI analysis stream in FreeSurfer, under the supervision of Bruce Fischl. She is now on the faculty at the Martinos Center and a member of the Laboratory for Computational Neuroimaging (LCN), continuing to develop publicly available, open-source algorithms for studying white-matter anatomy in health and disease.

Education

PhD in Electrical Engineering: Systems, University of Michigan at Ann Arbor

Select Publications

1. Haber SN, Lehman J, Maffei C, Yendiki A. The Rostral Zona Incerta: A Subcortical Integrative Hub and Potential Deep Brain Stimulation Target for Obsessive-Compulsive Disorder. Biol Psychiatry. 2023 Jun 1;93(11):1010-1022.

2. Yendiki A, Aggarwal M, Axer M, Howard AFD, van Walsum AVC, Haber SN. Post mortem mapping of connectional anatomy for the validation of diffusion MRI. Neuroimage. 2022 Aug 1;256:119146.

3. Maffei C, Lee C, Planich M, Ramprasad M, Ravi N, Trainor D, Urban Z, Kim M, Jones RJ, Henin A, Hofmann SG, Pizzagalli DA, Auerbach RP, Gabrieli JDE, Whitfield-Gabrieli S, Greve DN, Haber SN, Yendiki A. Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data. Neuroimage. 2021 Dec 15;245:118706.

Highlights

Large-scale Imaging of Neural Circuits (LINC): A BRAIN CONNECTS Center

OHBM2022: Keynote Interview with Anastasia Yendiki: On Track with Anastasia

OHBM Neurosaliance Podcast: Anastasia Yendiki: Diffusion-based tract tracing tool developer and validator

Website

Diffusion MRI at the Martinos Center @ MGH

Caterina Mainero

Associate Professor

Multiple Sclerosis Imaging Laboratory (Director)

CMAINERO@mgh.harvard.edu

Caterina Mainero, MD, PhD, is an Associate Professor of Radiology at Harvard Medical School, Assistant in Neuroscience at Massachusetts General Hospital and Director of Multiple Sclerosis Research at the Athinoula A. Martinos Center for Biomedical Imaging.

Dr. Mainero is a neuroscientist with a background in clinical neurology, specializing in the translation of novel multimodal imaging techniques for investigating structure, function and pathology within the brain and spinal cord of different neurological conditions, with a particular emphasis on multiple sclerosis and migraine.

The Multiple Sclerosis Imaging Laboratory that Dr. Mainero directs at the Martinos Center focuses on using advanced imaging modalities to investigate the heterogenous aspects of multiple sclerosis pathology that include neuroinflammation, demyelination, neurodegeneration, and tissue repair. The goal is to integrate novel imaging methods with clinical and biological markers of the disease to investigate the brain mechanisms underlying disease activity and progression, and to define the most sensitive neuroimaging tools for improving disease diagnosis and monitoring.

Dr. Mainero’s group was the first to image and characterize in vivo, using ultra high field 7 Tesla MRI, the different types of cortical multiple sclerosis lesions described by neuropathology, and to show that the cortical lesion load assessed at 7 Tesla is an independent and main predictor of disease progression. For her pioneering work on imaging cortical lesions in MS, Dr. Mainero has been awarded the “2020 Distinguished Investigator Award” from the Academy for Radiology and Biomedical Imaging Research.

Highlights

MGH Claflin Distinguished Award

2020 Distinguished Investigator Award, Academy for Radiology and Biomedical Imaging Research

Select Publications

See PubMed for a list of Dr. Mainero’s publications.

Website

Multiple Sclerosis Imaging Laboratory

Matthew Rosen

Associate Professor

Low-field MRI and Hyperpolarized Media Laboratory (Director)

msrosen@mgh.harvard.edu
617-643-8636

Dr. Matt Rosen is a physicist, tool-builder and inventor whose research bridges the spectrum from fundamental physics to applied bioimaging work in the field of MRI. He established the Low-Field MRI and Hyperpolarized Media Laboratory at the Athinoula A. Martinos Center for Biomedical Imaging to focus on the continued development of new hyperpolarization methods and MRI-based tools.

The Rosen Lab focuses on new methods and tools to enable unconventional approaches to MRI scanner construction. This includes the development of new acquisition strategies for robust ultra-low magnetic field implementations of MRI focused on brain imaging. The laboratory also explores opportunities provided by hyperpolarization including in vivo Overhauser DNP, SABRE and spin-exchange optical pumping. The lab creates new quantitative strategies for the acquisition and the reconstruction of highly undersampled imaging data including neural network deep learning-based approaches such as AUTOMAP that leverage low-cost scalable-compute. Dr. Rosen co-directs the Center for Machine Learning at the Martinos Center.

Education

PhD in Physics, University of Michigan, Ann Arbor

Select Publications

[1] K. N. Sheth, M. H. Mazurek, M. M. Yuen, B. A. Cahn, J. T. Shah, A. Ward, J. A. Kim, E. J. Gilmore, G. J. Falcone, N. Petersen, K. T. Gobeske, F. Kaddouh, D. Y. Hwang, J. Schindler, L. Sansing, C. Matouk, J. Rothberg, G. Sze, J. Siner, M. S. Rosen, S. Spudich, and W. T. Kimberly, “Assessment of Brain Injury Using Portable, Low-Field Magnetic Resonance Imaging at the Bedside of Critically Ill Patients,” JAMA Neurol, Sep. 2020.
https://jamanetwork.com/journals/jamaneurology/fullarticle/2769858

[2] D. E. J. Waddington, T. Boele, R. Maschmeyer, Z. Kuncic, and M. S. Rosen, “High-sensitivity in vivo contrast for ultra-low field magnetic resonance imaging using superparamagnetic iron oxide nanoparticles,” Science Advances, vol. 6, no. 29, p. eabb0998, Jul. 2020.
https://advances.sciencemag.org/content/6/29/eabb0998

[3] B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning ,” Nature, vol. 555, no. 7697, pp. 487–492, Mar. 2018.
https://www.nature.com/articles/nature25988

[4] M. Sarracanie, C. D. LaPierre, N. Salameh, D. E. J. Waddington, T. Witzel, and M. S. Rosen, “Low-Cost High-Performance MRI,” Sci Rep, vol. 5, no. 1, p. 15177, Oct. 2015.
https://www.nature.com/articles/srep15177

Highlights

Co-founder: Hyperfine Research, BlinkAI, Vizma Life Sciences, Intact Data Services

Website

Rosen Lab

David Salat

Associate Professor

Brain Aging and Dementia (BAnD) Laboratory (Director)
The Translational Research Center for TBI and Stress Disorders (TRACTS)

salat@nmr.mgh.harvard.edu

The overarching aim of Dr. Salat’s work is to understand mechanisms of neural disease and to implement novel approaches to reduce the impact of disease on the brain, cognition and clinical status. Clinically, there are two main clinical foci to his research. At the MGH Martinos Center, he directs the Brain Aging and Dementia Laboratory, with a research focus on understanding systemic and neural mechanisms of age-associated cognitive decline and dementia. A major focus of this work is to understand cerebrovascular contributions to brain aging and dementia. He is also the MGH site Principal Investigator for the Human Connectome Project – Mapping the Human Connectome with Typical Aging multisite effort (Central PI: Van Essen).

Education

PhD in Behavioral Neuroscience, Oregon Health & Science University

Select Publications

1. Bookheimer SY, Salat DH, Terpstra M, Ances BM, Barch DM, Buckner RL, et al. The Lifespan Human Connectome Project in Aging: An overview. Neuroimage. 2019;185:335-48.

2. Sitnikova TA, Hughes JW, Ahlfors SP, Woolrich MW, Salat DH. Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer’s disease. Neuroimage Clin. 2018;20:128-52.

3. Robinson ME, Clark DC, Milberg WP, McGlinchey RE, Salat DH. Characterization of Differences in Functional Connectivity Associated with Close-Range Blast Exposure. J Neurotrauma. 2017;34(S1):S53-S61.

Websites

Brain Aging and Dementia (BAnD) Laboratory
The Translational Research Center for TBI and Stress Disorders (TRACTS)

Douglas Greve

Associate Professor

Laboratory for Computational Neuroimaging (LCN)

dgreve@mgh.harvard.edu
617-724-2358

Tagged:Aging, Algorithm Development, Alzheimer's Disease, bbregister, Brain, Data Science, Diffusion MRI, fMRI, FreeSurfer, FSFAST, Functional Magnetic Resonance Imaging, Machine Learning, Meditation, optseq, PET, PETsurfer, Positron Emission Tomography, Software Development

Douglas Greve, PhD, has a passion for delivering cutting-edge tools to the neuroscience community. He joined the FreeSurfer team 20 years ago and has been developing neuroimaging software ever since. His career has offered him an exciting mixture of engineering, physics, software development and neuroscience. He understands the biophysics of many brain imaging modalities (e.g., MRI structural, fMRI, DTI, MRS, as well as PET). He has especially enjoyed collaborating with experts from neurology, psychiatry and psychology — they know what tools they need to study their diseases, and he knows how to realize those tools through technology.

Education

PhD in Cognitive and Neural Systems, Boston University

Publications

1. Greve DN, Fischl B. False positive rates in surface-based anatomical analysis. Neuroimage. 2018 May 1;171:6-14.

2. Greve DN, Salat DH, Bowen SL, Izquierdo-Garcia D, Schultz AP, Catana C, Becker JA, Svarer C, Knudsen GM, Sperling RA, Johnson KA. Different partial volume correction methods lead to different conclusions: An (18)F-FDG-PET study of aging. Neuroimage. 2016 May 15;132:334-343.

3. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 2009 Oct 15;48(1):63-72.

Highlights

FreeSurfer software development
Group statistics for neuroimaging
Multi-modal neuroimaging integration
PET partial volume correction
Surface-based kinetic modeling

Website

Laboratory for Computational Neuroimaging

Jerome Ackerman

Associate Professor

15T and Extremity Scanner Labs (Director)

jerry@nmr.mgh.harvard.edu
617-726-3083

Tagged:Algorithm Development, High-field Imaging, Interventional MR, Magnetic Resonance Imaging, Metabolic Bone Disease, MRI, Point of Care MR, Preclinical, Scanner and Interventional Device Development, Skeleton, Solid State MR Imaging and Spectroscopy, Spectroscopy

Jerome Ackerman, PhD, has conducted research in magnetic resonance for over 45 years, and has led the solid-state MR program at MGH for over 30 years. As of May, 2019, his work (over 100 peer-reviewed journal articles, reviews, chapters and patents; over 200 abstracts) has been cited 5048 times (886 times since 2014). He has trained or supervised approximately 53 postdoctoral fellows and PhD, MS and undergraduate students and two staff engineers (prime faculty advisor for eight PhD and three MS dissertations, mentor for one K99/R00 awardee) and hosted two visiting scientists.

He has been developing NMR spectrometers and MRI scanners and their associated components and software since the 1970s. In the Martinos Center, he has been the Principal Investigator of four Shared Instrumentation and High End Instrumentation grants. Also, he pioneered the first use of high-resolution magic angle spinning (HRMAS) spectroscopy for tissue specimens, the use of true solid-state MRI for MR-PET attenuation correction, and the use of RF microcoils for position tracking.

Education

PhD in Physical Chemistry (Solid State NMR Spectroscopy), Massachusetts Institute of Technology (MIT)

Select Publications

1. Wu Y, Chesler DA, Glimcher MJ, Garrido L, Wang J, Jiang HJ, et al. Multinuclear solid-state three-dimensional MRI of bone and synthetic calcium phosphates. Proc Natl Acad Sci U S A. 1999;96(4):1574-8.

2. Cho G, Wu Y, Ackerman JL. Detection of hydroxyl ions in bone mineral by solid-state NMR spectroscopy. Science. 2003;300(5622):1123-7.

3. Cohen O, Zhao M, Nevo E, Ackerman JL. MR Coagulation: A Novel Minimally Invasive Approach to Aneurysm Repair. J Vasc Interv Radiol. 2017;28(11):1592-8.

Highlights

Dr. Ackerman’s current interests include:

Development of a cryogen-free compact point-of-care superconducting extremity MRI scanner for conventional and solid state MR characterization of bone (collaboration with Superconducting Systems, Inc.)
A magnesium diboride/solid nitrogen table-top superconducting MRI scanner (collaboration with MIT)
Ultrahigh field (15T) MR
MR-mediated RF ablation and coagulation technologies in which the scanner sources and controls the energy for these interventional procedures (collaboration with Robin Medical, Inc.)

Website

15T MR Laboratory

Steven Stufflebeam

Associate Professor

Stufflebeam Laboratory (PI), TMS Lab, The David Cohen MEG Laboratory

sms@nmr.mgh.harvard.edu

Steven Stufflebeam, MD, translates basic science and advanced imaging technology into everyday clinical practice. His laboratory aims to improve the health care for patients with epilepsy, schizophrenia, brain tumors and hearing impairments. His training is in biomedical engineering, mathematics and neuroradiology. He has experience with the development of clinically relevant MRI pulse sequences and 3T and 7T. His research focuses on combining advanced imaging techniques: magnetoencephalography (MEG), electroencephalography (EEG), near-infrared spectroscopy (optical imaging), and magnetic resonance imaging (MRI). During the past few years, he has personally scanned over 2000 subjects with various neurological disorders, including epilepsy and brain tumors, with MEG and positron emission tomography (PET) as well as 3T and 7T MRI. Additionally, he is a board-certified radiologist with fellowship training in neuroradiology and the Medical Director of the Martinos Center for Biomedical Imaging.

Education

MD, Harvard Medical School

Publications

1. Stufflebeam SM, Liu H, Sepulcre J, Tanaka N, Buckner RL, Madsen JR. Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging. J Neurosurg. 2011 Jun;114(6):1693-7.

2. Douw L, Wakeman DG, Tanaka N, Liu H, Stufflebeam SM. State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience. 2016 Dec 17;339:12-21.

3. DeSalvo MN, Tanaka N, Douw L, Leveroni CL, Buchbinder BR, Greve DN, Stufflebeam SM. Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy. Radiology. 2016 Oct;281(1):264-9.

Highlights

Medical Director, Martinos Center for Biomedical Imaging

Co-Founder, Functional Imaging Neural Datasets, Llc.

Founder, American Clinical MEG Society (ACMEGS)

Websites

Stufflebeam Laboratory
The David Cohen MEG Laboratory

Daphne Holt

Associate Professor

Emotion and Social Neuroscience Laboratory (Director)

dholt@mgh.harvard.edu

Tagged:Brain, Clinical, fMRI, FreeSurfer, High-field Imaging, MRI, Schizophrenia

Dr. Holt has studied the neural basis of psychosis throughout her career, initially in post-mortem samples and subsequently (since 2002) using neuroimaging. Using functional neuroimaging in combination with physiology, behavioral tasks and clinical assessments, she has investigated the neurocognitive basis of the core symptoms of psychotic illness, including delusions, negative affect and social impairment, reporting some of the first evidence for abnormalities in basic mechanisms that underlie emotional perception (e.g. fear extinction memory, fear generalization, response to perceptual threat) in psychotic illness.

Recently Dr. Holt’s group has also focused on identifying changes in brain function and behavior linked with risk for serious mental illness and has been developing novel interventions aimed at increasing resilience and potentially preventing serious mental illnesses in at-risk youth. Dr. Holt also serves as Co-Director of the MGH Schizophrenia Clinical and Research Program, which includes a First-Episode and Early Psychosis Program (FEPP) focused on comprehensive treatment of psychosis in the earliest phases of illness.

Education

PhD in Neurobiology, University of Chicago
MD, University of Chicago

Select Publications

1. Holt DJ, Kunkel L, Weiss AP, Goff DC, Wright CI, Shin LM, Rauch SL, Hootnick J, Heckers S. Increased medial temporal lobe activation during the passive viewing of emotional and neutral facial expressions in schizophrenia. Schizophr Res. 2006 Feb 28;82(2-3):153-62.

2. Holt DJ, Coombs G, Zeidan MA, Goff DC, Milad MR. Failure of neural responses to safety cues in schizophrenia. Arch Gen Psychiatry. 2012 Sep;69(9):893-903.

3. Ho NF, Iglesias JE, Sum MY, Kuswanto CN, Sitoh YY, De Souza J, Hong Z, Fischl B, Roffman JL, Zhou J, Sim K, Holt DJ. Progression from selective to general involvement of hippocampal subfields in schizophrenia. Mol Psychiatry. 2017 Jan;22(1):142-152.

Highlights

2018: MGH Scholar Award recipient, MGH Executive Committee on Research (ECOR)

Website

Emotion and Social Neuroscience Laboratory

Zdravka Medarova

Associate Professor

Image-Guided Cancer Therapy (Director)

zmedarova@mgh.harvard.edu
617-643-4889

Tagged:Cell Biology, Genetics, Metastasis, Molecular Biology, Molecular Imaging, MRI, Multimodal Imaging, Optical Imaging, Optics

Zdravka Medarova, PhD, is an Associate Professor of Radiology at Harvard Medical School. She is a geneticist/cancer biologist by training and has an extensive background in molecular biology, genetics, and tumor biology and therapy. The focus of her research has been the development and testing of multi-functional imaging/delivery vehicles for combined cancer imaging and therapy. Dr. Medarova’s earliest work described, for the first time, the design and application of ultrasmall iron oxide nanoparticles as imaging-capable carriers of siRNA to tumors. This work generated substantial interest in the research community, since it illustrated the value of these nanoparticles for the delivery of small RNA therapy to challenging organ targets and also described an approach for the noninvasive monitoring of small RNA delivery. More recently, her lab developed magnetic nanoparticles as delivery vehicles of miRNA-targeted therapy to metastases. This work resulted in multiple publications in high-impact journals such as Cancer Research, Nature Medicine, Oncogene, and Scientific Reports, as well as grants from private foundations and the NIH.

Dr. Medarova obtained a BA degree in pre-medicine from the University of Southern Maine (1998) and a PhD in Genetics from the University of New Hampshire (2002).

Education

PhD in Genetics, University of New Hampshire

Select Publications

1. Yigit MV, Ghosh SK, Kumar M, Petkova V, Kavishwar A, Moore A, Medarova Z. Context-dependent differences in miR-10b breast oncogenesis can be targeted for the prevention and arrest of lymph node metastasis. Oncogene. 2013 Mar 21;32(12):1530-8.

2. Yoo B, Kavishwar A, Ross A, Wang P, Tabassum DP, Polyak K, Barteneva N, Petkova V, Pantazopoulos P, Tena A, Moore A, Medarova Z. Combining miR-10b-Targeted Nanotherapy with Low-Dose Doxorubicin Elicits Durable Regressions of Metastatic Breast Cancer. Cancer Res. 2015 Oct 15;75(20):4407-15.

3. Yoo B, Kavishwar A, Wang P, Ross A, Pantazopoulos P, Dudley M, Moore A, Medarova Z. Therapy targeted to the metastatic niche is effective in a model of stage IV breast cancer. Sci Rep. 2017 Mar 21;7:45060.

Highlights

Anna V. Moore, Zdravka Medarova. miRNA profiling compositions and methods of use. USPTO: 10086093. October 2, 2018.

Zdravka Medarova, Mehmet V. Yigit, Anna Moore. Therapeutic nanoparticles and methods of use thereof. USPTO: 9763891 and 9629812. September 19, 2017 and April 25, 2017.

Stephen J. Lippard, Xiao-an Zhang, Zdravka Medarova, Anna Moore. Methods for mobile zinc measurement. USPTO: 8574914. November 5, 2013.

Stefan Carp

Associate Professor

Optics @ Martinos

stefan.carp@mgh.harvard.edu
617-643-2230

Tagged:Algorithm Development, Brain, Breast, Cancer, Cardiovascular Disease / Disorders, Cardiovascular System, Clinical, Hardware Development, Machine Learning, Magnetic Resonance Imaging, Molecular Imaging, MRI, Near Infrared Spectroscopy, Optical Imaging, Optics, Probe Development, Software Development, Spectroscopy

Dr. Carp’s research group focuses on the development and clinical translation of light-based non-invasive sensing and imaging methods for disease detection and management. Major thrusts include the use of near-infrared spectroscopy and tomography as well as diffuse correlation spectroscopy to advance brain health monitoring and breast cancer management. Through collaborations with clinicians at MGH and beyond, he and his group are able to test their devices and data processing algorithms in the intended use setting to guide our technology development towards meaningful clinical integration.

Education

PhD in Biomedical Optics, University of California, Irvine

Select Publications

1. Carp SA, Farzam P, Redes N, Hueber DM, Franceschini MA. Combined multi-distance frequency domain and diffuse correlation spectroscopy system with simultaneous data acquisition and real-time analysis. Biomed Opt Express. 2017 Aug 7;8(9):3993-4006.

2. Zimmermann BB, Deng B, Singh B, Martino M, Selb J, Fang Q, Sajjadi AY, Cormier J, Moore RH, Kopans DB, Boas DA, Saksena MA, Carp SA. Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis. J Biomed Opt. 2017 Apr 1;22(4):46008.

3. Boas DA, Sakadžić S, Selb J, Farzam P, Franceschini MA, Carp SA. Establishing the diffuse correlation spectroscopy signal relationship with blood flow. Neurophotonics. 2016 Jul;3(3):031412.

Highlights

Inventions:

Cancer detection by optical measurement of compression-induced transients, US10010277B2
Systems and methods for characterizing biological material using near-infrared spectroscopy, WO2018052933A1
Optical fiber probe arrangement for use with X-ray mammography, US9265460B2

Website

Optics @ Martinos

Marco Loggia

Associate Professor

Pain and Neuroinflammation Imaging Lab (Director)
Center for integrative Pain NeuroImaging, CiPNI (co-Director)

marco.loggia@mgh.harvard.edu
617-643-7267

Tagged:Brain, Clinical, Data Science, fMRI, FreeSurfer, Molecular Imaging, MRI, Multimodal Imaging, Neuroinflammation, Pain, PET, PET-MRI, Spectroscopy, Spine

In 2008, Marco Loggia was awarded a PhD In Neurological Sciences by McGill University in Montreal, QC (Canada). During his graduate studies, he had the opportunity to work at the Alan Edwards Centre for Research on Pain (formerly McGill Centre for Research on Pain) under the mentorship of its first director, Prof. M. Catherine Bushnell, a pioneer in the field of human pain imaging. Between 2008 and 2013, Dr. Loggia held the position of Research Fellow at Harvard Medical School, Brigham and Women’s Hospital, and Massachusetts General Hospital. As of 2013 Dr. Loggia became faculty at Harvard Medical School and Massachusetts General Hospital.

Over the past few years, Dr. Loggia’s work has been focused on the study of the role of neuroinflammation in human pain and other conditions

Education

PhD in Neurological Sciences, McGill University

Select Publications

1. Loggia ML, Kim J, Gollub RL, Vangel MG, Kirsch I, Kong J, Wasan AD, Napadow V. Default mode network connectivity encodes clinical pain: an arterial spin labeling study. Pain. 2013 Jan;154(1):24-33.

2. Loggia ML, Chonde DB, Akeju O, Arabasz G, Catana C, Edwards RR, Hill E, Hsu S, Izquierdo-Garcia D, Ji RR, Riley M, Wasan AD, Zürcher NR, Albrecht DS, Vangel MG, Rosen BR, Napadow V, Hooker JM. Evidence for brain glial activation in chronic pain patients. Brain. 2015 Mar;138(Pt 3):604-15.

3. Albrecht DS, Forsberg A, Sandström A, Bergan C, Kadetoff D, Protsenko E, Lampa J, Lee YC, Höglund CO, Catana C, Cervenka S, Akeju O, Lekander M, Cohen G, Halldin C, Taylor N, Kim M, Hooker JM, Edwards RR, Napadow V, Kosek E, Loggia ML. Brain glial activation in fibromyalgia – A multi-site positron emission tomography investigation. Brain Behav Immun. 2019 Jan;75:72-83.

Highlights

2013: Early Career Award from the International Association for the Study of Pain (IASP)

2016: IASP Ulf Lindblom Young Investigator Award for Clinical Science

Section Editor for PAIN
Chief Specialty Editor for Frontiers in Pain Research
Editorial Board member for the Journal of Pain and Pain Medicine

Websites

Pain and Neuroinflammation Imaging Lab

Chongzhao Ran

Associate Professor

Chongzhao Ran Lab

cran@mgh.harvard.edu
617-643-4886

Tagged:Aging, Alzheimer's Disease, Brain, Cancer, Chemistry, Magnetic Resonance Imaging, Molecular Imaging, MRI, Multimodal Imaging, Optical Imaging, Optics, PET, PET-MRI, Positron Emission Tomography, Preclinical, Probe Development, Spectroscopy

Chongzhao Ran, PhD, is an Associate Professor in Radiology at Massachusetts General Hospital and Harvard Medical School. He received a Master’s degree in medicinal chemistry from China Pharmaceutical University and a PhD in medicinal chemistry from Shanghai Institute of Pharmaceutical Industry, China. He did his postdoctoral training at the University of Chicago and Harvard Medical School. Dr. Ran’s research focuses on the development of molecular imaging probe and imaging technologies. He has published nearly 60 papers in the fields of chemistry and molecular imaging, some in high-ranking journals such as PNAS and J. Amer. Chem. Soc. Since 2010, his research has been continuously supported by multiple NIH grants from NIA, NIDDK and other foundations. In recent years, his research group has successfully developed numerous near-infrared fluorescence (NIRF) imaging probes, which revolve around their own brand CRANAD-X, for in vivo detection of amyloid beta in mouse models of Alzheimer’s disease (AD). Particularly, his research has been focusing on seeking “smart” NIR probes for soluble amyloid beta species, which are widely believed to be the most neurotoxic species at the early stage of AD. Recently, his research group has successfully designed and synthesized secnd-generation PET tracers for amyloid beta species. In addition, his group has discovered several NIRF probes and PET tracers for imaging brown adipose tissue. These probes have remarkable potential for future diagnosis and monitoring the efficacy of drugs for Alzheimer’s disease, diabetes and obesity in preclinical studies and clinical trials.

Education

PhD in medicinal chemistry, Shanghai Institute of Pharmaceutical Industry, China

Select Publications

1. Ran C, Xu X, Raymond SB, Ferrara BJ, Neal K, Bacskai BJ, Medarova Z, Moore A. Design, synthesis, and testing of difluoroboron-derivatized curcumins as near-infrared probes for in vivo detection of amyloid-beta deposits. J Am Chem Soc. 2009 Oct 28;131(42):15257-61.

2. Zhang X, Tian Y, Li Z, Tian X, Sun H, Liu H, Moore A, Ran C. Design and synthesis of curcumin analogues for in vivo fluorescence imaging and inhibiting copper-induced cross-linking of amyloid beta species in Alzheimer’s disease. J Am Chem Soc. 2013 Nov 6;135(44):16397-409.

3. Zhang X, Tian Y, Zhang C, Tian X, Ross AW, Moir RD, Sun H, Tanzi RE, Moore A, Ran C. Near-infrared fluorescence molecular imaging of amyloid beta species and monitoring therapy in animal models of Alzheimer’s disease. Proc Natl Acad Sci U S A. 2015 Aug 4;112(31):9734-9.

4. Yang J, Zhang X, Yuan P, Yang J, Xu Y, Grutzendler J, Shao Y, Moore A, Ran C. Oxalate-curcumin-based probe for micro- and macroimaging of reactive oxygen species in Alzheimer’s disease. Proc Natl Acad Sci U S A. 2017 Nov 21;114(47):12384-12389.

Highlights

2010 K25 NIH/NIA Career Development Award

Anand Kumar

Associate Professor

Optical Molecular Imaging Laboratory (Director)

ankumar@nmr.mgh.harvard.edu
617-726-6542

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Breast, Cancer, Cardiovascular Disease / Disorders, Cardiovascular System, Clinical, Hardware Development, Liver, Molecular Imaging, Optical Imaging, Optics, Preclinical, Software Development, Stroke

Dr. Kumar’s research is focused on development and translation of novel biomedical optical techniques for preclinical and clinical applications. He has more than 15 years of experience in theory, modeling and experimental aspects of biological optical imaging. Over the past decade, his group has advanced several novel concepts for optical molecular imaging using time-domain fluorescence tomography, specifically exploiting fluorescence lifetime as a contrast mechanism. They have validated this technology for the longitudinal tracking of cancer metastasis with greater sensitivity than currently possible in whole living mice. In addition to preclinical research, they are applying time-domain technology for image guidance during liver and head and neck cancer surgery. They are also actively working towards commercialization of recently patented innovations in their laboratory.

Dr. Kumar is actively involved in collaborations with biochemists, biologists and clinicians for optimizing and applying this technology to address challenging questions related to cancer, cardiac disease and neuropathology. In 2009, he demonstrated the first use of fluorescence lifetime contrast to enhance the sensitivity of whole-body imaging using fluorescent proteins in the visible region from intact living mice (Kumar et al., Opt. Lett, 2009). In 2012, he led a collaborative project that demonstrated the first application of fluorescence lifetime contrast in mouse models of heart disease (Goergen et al., J. Biomed. Opt, 2012). In 2015, he and his group demonstrated the first tomographic optical imaging of disseminated metastasis in live mouse lungs using fluorescence lifetime detection of near-infrared fluorescent proteins (Rice, W. L. et al., Cancer Res. 2015). More recently, they demonstrated the high accuracy for tumor detection in ICG-labelled tumors in mice using fluorescence lifetime contrast (Kumar, ATN, et. al., J. Biomed. Opt. 2017). His laboratory is currently involved in pilot clinical trials for lifetime-based margin assessment during surgical resection of head and neck and liver cancers.

Education

PhD in Physics, Northeastern University

Select Publications

1. Kumar ATN, Hou SS. Tomographic phosphorescence lifetime multiplexing. Opt Lett. 2018 Jul 1;43(13):3104-3107.

2. Kumar ATN, Hou SS, Rice WL. Tomographic fluorescence lifetime multiplexing in the spatial frequency domain. Optica. 2018 May;5(5):624-627.

3. Funane T, Hou SS, Zoltowska KM, van Veluw SJ, Berezovska O, Kumar ATN, Bacskai BJ. Selective plane illumination microscopy (SPIM) with time domain fluorescence lifetime imaging microscopy (FLIM) for volumetric measurement of cleared mouse brain samples. Rev Sci Instrum. 2018 May;89(5):053705.

Highlights

2018: Anand T. N. Kumar, Steven S. Hou and William L. Rice, “Tomographic fluorescence lifetime multiplexing in the spatial frequency domain,” Optica 5(5), 624-627.

2015: William L. Rice, Daria Shcherbakova, Vladislav Verkhusha and Anand T. N. Kumar, “In vivo tomographic imaging of deep seated cancer using fluorescence lifetime contrast,” Cancer Research, Vol. 75 (7), pp 1236-43

2016: Anand T.N. Kumar, Scott B. Raymond, Gregory Boverman, David A. Boas and Brian J. Bacskai,”Time resolved fluorescence tomography based on lifetime contrast,” Opt. Express 14 12255.

Website

Optical Molecular Imaging Laboratory