Hong Hsi Lee

Assistant Professor

Connectome 2.0 Lab

hlee84@mgh.harvard.edu

Tagged:Aging, Algorithm Development, Alzheimer's Disease, biophysical modelling, Diffusion MRI, GPU acceleration, High-gradient Imaging, image processing, Machine Learning, MRI, numerical simulation, Preclinical

Hong-Hsi Lee, MD, PhD, is a medical imaging scientist with diverse background and extensive research experience in biophysical modeling, numerical simulations, tissue biology, and in vivo human imaging. His undergraduate and master’s training in medicine and physics in Taiwan and subsequent doctoral and postdoctoral training at New York University (NYU) and Massachusetts General Hospital (MGH) have enabled him to formulate important questions in biomedical research and answer them through a powerful combination of biophysical modeling and experiments in MRI. He currently conducts research on methodology development in neuroimaging to improve the understanding of brain structure and function and aid in the assessment of disease progression and treatment response noninvasively, with a focus on neurodegenerative diseases.

Education

PhD, MPhil, MS, Biomedical Imaging, New York University School of Medicine
MD, MS, National Taiwan University

Select Publications

1. Lee HH, Yaros K, Veraart J, Pathan JL, Liang FX, Kim SG, Novikov DS, Fieremans E. Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI. Brain Structure and Function. 2019 Feb 21;224(4):1469-1488. PMID: 30790073. (Editor’s Choice Award by Cajal Club)

2. Lee HH*, Tian Q*, Sheft M, Coronado-Leija R, Ramos-Llorden G, Abdollahzadeh A, Fieremans E, Novikov DS, Huang SY. The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from 3-dimensional electron microscopy. NMR in Biomedicine. 2024 Jan 2;e5087. PMID: 38168082. (*co-corresponding authors)

3. Lee H*, Lee HH*, Ma Y, Eskandarian L, Gaudet K, Tian Q, Krijnen EA, Russo AW, Salat DH, Klawiter EC, Huang SY. Age-related alterations in human cortical microstructure across the lifespan: Insights form high-gradient diffusion MRI. Aging Cell. 2024 Aug 8:e14267. PMID: 39118344. (*co-first authors)

Highlights

2021 NIH Early Independence Award
2021 ISMRM Junior Fellow
2024 ISMRM AMPC selection

Jessie Fang-Lu Fu

Assistant Professor

flfu@mgh.harvard.edu

Tagged:Aging, Alzheimer's Disease, Brain, Clinical, Machine Learning, PET, PET kinetic modeling, PET-MRI

Dr. Jessie Fang-Lu Fu’s research focuses on the translational application of multimodal neuroimaging, Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), and machine learning in neurodegenerative disorders. Her main research interests include (i) evaluating the capability of novel digital cognitive assessment tools for detecting early pathophysiological deficits measured by PET and MRI in preclinical Alzheimer’s disease using machine learning and deep learning, (ii) evaluating the in-vivo kinetics and improving quantification of novel PET radioligand
using PET/MRI and improving the sensitivity for detecting early disease-related changes, (iii) developing and implementing multivariate analysis for extracting PET spatiotemporal patterns more sensitive for detecting subtle deficits in preclinical or early disease or for tracking disease progression.

Education

Ph.D. in Physics (Medical Physics), M.Sc. in Physics (Medical Physics), B.Sc. in Physics (Biophysics)

Select Publications

1. Fu, J.F., Lois, C., Sanchez, J., Becker, A., Rubinstein, Z., Thibault, E., Salvatore, A.N., Sari, H., Farrell, M., Normandin, M., Guehl, N., El Fakhri, G., Johnson, K., Price, J.C., 2022. Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [18 F]MK-6240 PET uptake. J Cereb Blood Flow Metab. 24, 0271678X2211421.

2. Fu, J.F., Klyuzhin, I., McKenzie, J., Neilson, N., Shahinfard, E., Dinelle, K., McKeown, M.J., Stoessl, A.J., Sossi, V., 2019. Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson’s disease induced presynaptic alterations. NeuroImage Clin. 23, 101856.

3. Fu, J.F., Wegener, T., Klyuzhin, I.S., Mannheim, J.G., McKeown, M.J., Stoessl, A.J., Sossi, V., 2022. Spatiotemporal patterns of putaminal dopamine processing in Parkinson’s disease: A multi-tracer positron emission tomography study. NeuroImage Clin. 36, 103246.

Highlights

K99/R00 Pathway to Independence Award (National Institute on Aging, NIH)

Brain Imaging Council Young Investigator Award (third place), Society of Nuclear Medicine & Molecular Imaging (SNMMI) Annual Meeting, 2022

Young Investigator Award (third place), International Symposium on Functional NeuroReceptor Mapping of the Living Brain, 2021

Xue-Jun (June) Kong

Assistant Professor

Director and PI of Synapse Lab

xkong1@mgh.harvard.edu
617 724 1770

Tagged:Algorithm Development, Alzheimer's Disease, Autism, Brain, Clinical, EEG, fMRI, Gut brain axis, Machine Learning, MRI, PWS, Stroke

Xue-Jun (June) Kong, M.D. Principal Investigator and Director of Synapse Autism program at Martinos Center/Mass General Hospital, Principal Investigator and Attending Physician at Beth Israel Deaconess Medical Center, Assistant Professor of Harvard Medical School. She leads her Autism research team, the research projects involve ASD microbiome and genomic study, biomarkers, autonomic dysfunction, eye-tracking, and brain imaging, non-invasive brain stimulation, drug clinical trials, aiming better understanding the etiology, early detection, clinical subgrouping, evidence based and target treatments of ASD, filed six patents as inventor or co-inventor at MGH. She proposed and modified ASD primary care model and extensive medical evaluation protocol, also established east meets west protocol for ASD as a certified MD acupuncturist.

Education

Select Publications

Kong XJ, Kang J, Liu K. Probiotic and intra-nasal oxytocin combination therapy on autonomic function and gut-brain axis signaling in young children and teens with autism spectrum disorder. J Psychiatr Res. 2023 Aug 12;166:1-9. doi: 10.1016/j.jpsychires.2023.08.006. Epub ahead of print. PMID: 37639877.

Binbin Sun, Bryan Wang, Zhen Wei, Zhe Feng, Zhi-Liu Wu, Walid Yassin, William S. Stone, Xue-Jun Kong* Identification of Diagnostic Markers for ASD: A Restrictive Interest Analysis Based on EEG Combined with Eye Tracking. Front Neuroscience 2023 https://doi.org/10.3389/fnins.2023.1236637

Hannah Tayla Sherman , Kevin Liu , Kenneth Kwong , Suk‐Tak Chan , Alice Chukun Li and Xue‐Jun Kong. Carbon monoxide (CO) correlates with symptom severity, autoimmunity, and responses to probiotics treatment in a cohort of children with autism spectrum disorder (ASD): a post‐hoc analysis of a randomized controlled trial. BMC Psychiatry (2022) 22:536 https://doi.org/10.1186/s12888-022-04151-3.

Kong, X. J., Liu, J., Liu, K., Koh, M., Sherman, H., Liu, S., … & Song, Y. (2021). Probiotic and Oxytocin Combination Therapy in Patients with Autism Spectrum Disorder: A Randomized, Double-Blinded, Placebo-Controlled Pilot Trial. Nutrients, 13(5), 1552.s

Highlights

Inventor: Methods and Composition for the Treatment of Prader-Willi Syndrome Symptoms. U.S. Patent Application No. 63/127,936, filed on Dec.18th 2020. MGH 125141.03558 Licensed to industry 2021-2022 via MGH IP

Inventor: Methods and Composition for the Treatment of Autism Spectrum Disorder. U.S. Provisional Application No. 63/132,081, filed Dec.30th 2020. MGH 125141.03569 In licensing process via MGH IP

Co-Inventor: Methods of Early Identification and Intervention for high-risk neonates who otherwise will develop autistic social impairment later in life. U.S Provisional Application No.63376780, filed June 2nd, 2022. 125141.0410#MGH2022-383 Under exclusive option licensing agreement via MGH IP

Website

Synapse Lab

Lucia Navarro de Lara

Assistant Professor

lnavarrodelara@mgh.harvard.edu
617 726 6584

Tagged:Brain, Depression, EEG, fMRI, Hardware Development, MRI, Multimodal Imaging, OCD, RF Coil Development, Software Development

My research is focused on elucidating the mechanisms of network-level brain responses to non-invasive brain stimulation methods. Based on the development of the “RF-EEG Cap”, the first-of-its-kind wearable RF head coil and the related signal processing tools, I envision a complete solution for concurrent TMS/fMRI/EEG. This triple combination can be seen as a versatile neuroscience tool which will have the potential to perform non-invasive high-resolution causal mapping of human brain. Using this approach, we will build the unique capability to study the brain in ways that were never before possible, and to revolutionize our understanding of human behavior and cognition.

Education

PhD
Medical University of Vienna (Austria), Technical University of Vienna, Universidad Politecnica de Valencia (Spain)

Select Publications

1.Navarro de Lara LI, Stockmann J, Meng Q, Keil B, Mareyam A, Işıl Uluç, Daneshzand M, Wald LL, Nummenmaa A. A novel whole-head RF coil design tailored for concurrent multichannel brain stimulation and imaging at 3T. Brain Stimulation (2023), 1021-1031, 16(4), https://doi.org/10.1016/j.brs.2023.05.025
2. Navarro de Lara LI, Daneshzand M, Mascarenas A, Paulson D, Pratt K, Okada Y, Raij T, Makarov SN and Nummenmaa A. A 3-axis coil design for multichannel TMS arrays. Neuroimage, 2021 (224):117355.
3. Navarro de Lara LI, Windischberger C, Kuehne A, Woletz M, Sieg J, Bestmann S, Weiskopf N, Strasser B, Moser E, Laistler E. A novel coil array for combined TMS/fMRI experiments at 3 T. Magnetic Resonance in Medicine, November, 2015, Vol.74(5), p.1492(10).

Highlights

BRAIN K99/R00 Awardee (2022)
MR Engineering Study Group Award, Outstanding Engineering Achievement, ISMRM 2019
Patent awarded, “Method and system for combined transcranial magnetic simulation (TMS) and functional magnetic resonance imaging (fMRI) studies”. US9924889 (B2

Website

Transcranial Magnetic Stimulation Lab

Hamid Sabet

Assistant Professor

Director of Radiation Physics and Instrumentation Laboratory
hsabet@mgh.harvard.edu
617 – 643 – 1964

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Breast, Cancer, Cardiovascular Disease / Disorders, Cardiovascular System, CT, Data Science, Hardware Development, Intraoperative Imaging, Machine Learning, Molecular Imaging, multi-organ, PET, Software Development, SPECT, Therapy imaging, whole-body

Hamid Sabet, is Assistant Professor of Radiology at Harvard Medical School and a Cyclotron/Medical Physicist at the Division of Nuclear Medicine and Molecular Imaging, Radiology Department, Massachusetts General Hospital. He is the director of Radiation Physics and Instrumentation Lab at the Martinos Center for Biomedical Imaging. While the main research focus of his lab is on radiation imaging and instrumentation for PET, SPECT, Compton Camera, CT, and intraoperative multimodality imaging probes, they work on other research areas including laser micromachining, detector- and system-level monte carlo modeling, image reconstruction, machine learning techniques, and quantum entanglement.

Education

PhD in Quantum Science and Energy Engineering, Tohoku University, Japan

Select Publications

1- Light spread manipulation in scintillators using laser induced optical barriers, L Bläckberg, M Moebius, G El Fakhri, E Mazur, H Sabet, IEEE transactions on nuclear science 65 (8), 2208-2215

2- Novel laser‐processed CsI: Tl detector for SPECT, H Sabet, L Bläckberg, D Uzun‐Ozsahin, G El‐Fakhri, Medical physics 43 (5), 2630-2638

3- A hand-held, intra-operative positron imaging probe for surgical applications, H Sabet, BC Stack, VV Nagarkar
IEEE Transactions on Nuclear Science 62 (5), 1927-1934

Highlights

PI on funded projects such as: R01, High performance SPECT System for Cardiac Imaging;

R21, High-Performance Positron Emission Tomography for brain imaging;

R21, Novel laser-processed scintillation detector for high-resolution PET scanners

Website

Radiation Physics and Instrumentation Laboratory

Albert Kim

Assistant Professor

The Quantitative Translational Imaging in Medicine Laboratory

akim46@mgh.harvard.edu

Tagged:brain metastases, breast cancer, Cancer, cell-free DNA, digital pathology, Machine Learning, MRI, prostate cancer, single-cell genomics, transcriptomics

I am an oncologist with interests in using machine learning and Omics to develop precision-based treatment paradigms and study therapuetic resistance. My laboratory efforts leverage Omics-based techniques, medical imaging (e.g., radiology, pathology), and machine learning to define and target genomic and metabolic vulnerabilities for these tumors. My hope is that these efforts will result in improved biomarkers to re-define precision-based treatments for patients.

Education

MD, Medical College of Georgia

Select Publications

1. Brastianos PK*, Kim AE*, Wang N, Lee EQ, Ligibel J, Cohen JV, et al. Palbociclib demonstrates intracranial activity inprogressive brain metastases harboring cyclin-dependent kinase pathway alterations. Nat Cancer 2, 498-502 (2021). (*denotes equal authorship)

2. Prakadan SM*, Alvarez-Breckenridge CA*, Markson SC*, Kim AE, Klein RH, Nayyar N, et al. Genomic and transcriptomic correlates of immunotherapy response within the tumor microenvironment of leptomeningeal metastases. Nature Comms 12,5955 (2021).

3. Brastianos PK*, Kim AE*, Giobbie-Hurder A, Lee EQ, Lin NU, Overmoyer B, et al. Pembrolizumab in brain metastases of diverse histologies: phase 2 trial results. Nature Medicine 29, 1728-1737 (2023). (*denotes equal authorship)

4. Kim AE, Nieblas-Bedolla E, de Sauvage MA, Brastianos PK. Leveraging translational insights towards precision medicine approaches for brain metastases. Nat Cancer 4, 955-967 (2023).

Highlights

Damon Runyon Cancer Research Foundation Physician Scientist Award
American Association of Cancer Research Breast Cancer Research Fellowship
American Brain Tumor Association Basic Research Fellowship
American Society of Clinical Oncology Young Investigator Award
American Society of Clinical Investigation Emerging-Generation Award

Christopher Bridge

Assistant Professor

Quantitative Translational Imaging in Medicine Laboratory
Laboratory for Computational Neuroscience

cbridge@mgh.harvard.edu

Tagged:Abdomen, Algorithm Development, Brain, Cancer, Data Science, FreeSurfer, Machine Learning, Software Development, Stroke

Dr Bridge is trained as an engineer, with MEng (Distinction) and DPhil degrees from the University of Cambridge and University of Oxford, respectively. He has worked in the area of medical image analysis for nearly a decade. His work on his doctoral thesis involved the development of a system for analyzing fetal cardiac ultrasound videos using machine learning. From 2017 until 2022, he worked as a machine learning scientist at the MGB Data Science Office developing machine learning models across a range of imaging modalities and medical specialties in collaboration with industry partners. As Director of Machine Learning since 2021 he led a team focused on the deployment of AI models into the medical workflow. He has been affiliated with the Martinos Center since 2020 and joined as full time research staff in October 2022 supported by an award from the Rappaport Foundation. His interests include machine learning methodology development for medical image analysis, translation of AI into clinical practice, and open software and standards for AI model deployment.

Education

DPhil, Engineering Science, University of Oxford

Select Publications

“Highdicom: A Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology”. C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann. Journal of Digital Imaging, August 2022

“Development and Clinical Application of a Deep Learning Model to Identify Acute Infarct on Magnetic Resonance Imaging”. C.P. Bridge, B.C. Bizzo, J.M. Hillis, J.K. Chin, D.S. Comeau, R. Gauriau, F. Macruz, J. Pawar, F.T.C. Noro, E. Sharaf, M.S. Takahashi, B. Wright, J.F. Kalafut, K.P. Andriole, S.R. Pomerantz, S. Pedemonte, and R.G. González. Scientific Reports 12, 2154 (2022)

“A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest Computed Tomography”. C.P. Bridge, T.D. Best, M. Wrobel, J. Marquardt, K. Magudia, C. Javidan, J.H. Chung, J. Kalpathy-Cramer, K.P. Andriole, and F.J. Fintelmann. Radiology: Artificial Intelligence, 2022 4:1

Highlights

2022: Rappaport Foundation MGH Research Fellow

Fang Liu

Assistant Professor

fliu12@mgh.harvard.edu
(617) 643-4887
149 13th Street, Suite 2301

Fang Liu is the Director of the Intelligent Imaging Innovation and Translation Lab at Athinoula A. Martinos Center for Biomedical Imaging and Assistant Professor of Radiology at Harvard Medical School. His research focuses on medical image acquisition and reconstruction, image analysis and processing, and physiological modeling of magnetic resonance, molecular, and optical imaging. His interest also includes the development of artificial intelligence methods for improving imaging speed and robustness and automating clinical imaging workflow.

The research at Dr. Liu’s lab centers on three specific areas:

1) Develop artificial intelligence/machine learning techniques to design, optimize and accelerate image acquisition and reconstruction of quantitative, multi-parametric, and dynamic MRI.

2) Develop and optimize novel MRI pulse sequences and acquisition methods for rapid and robust imaging of mesoscale tissue structure, composition, and function.

3) Develop intelligent methods and software solutions for medical signal and image data analysis and processing in translational imaging research.

Before joining Harvard in 2020, Dr. Liu was an Assistant Scientist at Radiology, University of Wisconsin-Madison, from 2015 to 2019, working on translational imaging projects spanning several clinical topics in the brain, body, and musculoskeletal systems. In 2015, he received PhD in Medical Physics from the University of Wisconsin-Madison. His research focused on developing new MR pulse sequences and optimizing imaging biomarkers for improved musculoskeletal and neural tissue assessment. In 2011, he received MSc in Medical Biophysics from Western University, Canada, where his research focused on improving breast MR imaging using machine learning. In 2008, he received BSc in Biomedical Engineering from Sun Yat-sen University, China.

Education

PhD in Medical Physics, University of Wisconsin-Madison

Select Publications

1. Liu F, Zhou Z, Jang H, Zhao G, Samsonov A, Kijowski R: Deep Convolutional Neural Network and 3D Deformable Approach for Tissue Segmentation in Musculoskeletal Magnetic Resonance Imaging. Magn Reson Med. 2017; 79 (4), 2379-2391.

2. Liu F, Zhou Z, Blankenbaker D, Larison W, Kanarek A, Lian K, Kambhampati S, Kijowski R: Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection. Radiology. 2018; 289 (1), 160-169.

3. Liu F, Kijowski R, El Fakhri G, Feng L: Magnetic Resonance Parameter Mapping Using Model-guided Self-supervised Deep Learning. Magn Reson Med. 2021; 85 (6), 3211-3226.

Highlights

2021 – ISMRM Annual Meeting Program Committee

2018 – ISMRM Junior Fellow

2016 – OCSMRM Young Investigator Award

Associated Lab

Intelligent Imaging Innovation and Translation Lab

Anna Blazejewska

Assistant Professor

ablazejewska@mgh.harvard.edu

Tagged:Aging, Algorithm Development, Brain, Brain Mapping, Data Science, fMRI, FreeSurfer, High-field Imaging, Histology, Machine Learning, MRI, Neurodegenerative Disorders, Software Development

Dr. Blazejewska’s research focuses on combining high-resolution anatomical and functional imaging to study the relationships between tissue microstructure and function in the healthy and diseased brain. She is interested in developing the next generation of methodologies allowing for microstructure-informed, cortical depth-resolved fMRI and applying them to study various functions of the human brain.

Education

PhD in Physics, University of Notthingham, UK

Select Publications

1. Blazejewska, A.I., Fischl, B., Wald, L.L., Polimeni, J.R. (2019). Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data. NeuroImage, 189:601-614. PMID: 30690157

2. Blazejewska, A.I., Bhat, H., Wald, L.L. and Polimeni, J.R. (2017). Reduction of run-to-run variability of temporal SNR in accelerated EPI timeseries data through FLEET based robust autocalibration. NeuroImage, 152:348-359. PMID: 28223186

3. Blazejewska, A.I., Shwarz, S.T., Pitiot, A., Stephenson, M.C., Lowe, J., Bajaj, N., Bowtell, R.W., Auer, D.P. and Gowland, P.A. (2013). Visualization of nigrosome 1 and its loss in PD: Pathoanatomical correlation and in vivo 7T MRI. Neurology, 81:534–540. PMID: 24821935

Highlights

BRAIN Initiative K99/R00 Career Development Award
Fund for Medical Discovery Fellowship, Executive Committee on Research at MGH

Bragi Sveinsson

Assistant Professor

bsveinsson@mgh.harvard.edu

Tagged:Aging, Algorithm Development, Clinical, Data Science, Diffusion MRI, High-field Imaging, Low-field Imaging, Machine Learning, MRI, Pain, Software Development, Spectroscopy

Dr. Bragi Sveinsson is an Assistant Professor in Radiology at Harvard Medical School. He earned his Ph.D. in Electrical Engineering from Stanford University, where his focus area was Magnetic Resonance Imaging, or MRI.

In his research, Dr. Sveinsson is mainly interested in imaging methods for the musculoskeletal and peripheral nervous systems, particularly related to conditions associated with aging. More specifically, he has worked in several technical frontiers in the field of MRI: (1) Quantitative imaging in the musculoskeletal system based on modeling of MRI physics. (2) Imaging close to metallic implants. (3) Predicting quantitative microstructural tissue parameters with machine learning. (4) Immersive visualization of MRI data. (5) MRI at low magnetic fields.

Education

Ph.D. in Electrical Engineering, Stanford University

Select Publications

1. Sveinsson B, Chaudhari AS, Gold GE, Hargreaves BA. A Simple Analytic Method for
Estimating T2 in the Knee from DESS. Magn Reson Imaging 2017; 38: 63-70.

2. Sveinsson B, Chaudhari AS, Zhu B, Koonjoo N, Torriani M, Gold GE, Rosen MS. Synthesizing
Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic
Data with a Conditional Generative Adversarial Network. Radiology: Artificial Intelligence
2021;3:5.

3. Sveinsson B, Koonjoo N, Rosen MS. ARmedViewer, an augmented-reality-based fast 3D
reslicer for medical image data on mobile devices: A feasibility study. Computer Methods and
Programs in Biomedicine 2021; 200:105836.

Highlights

K99/R00 Pathway to Independence Award (National Institute on Aging, NIH)

Junior Fellow, International Society for Magnetic Resonance in Medicine

CECI2 Early Career Investigator, The Academy for Radiology and Biomedical Imaging Research

Butler-Williams Scholar (National Institute on Aging, NIH)

Malte Hoffmann

Assistant Professor

Laboratory for Computational Neuroimaging

mhoffmann@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Clinical, Data Science, Fetus, FreeSurfer, Machine Learning, MRI, Perinatal Imaging, Software Development

Malte Hoffmann is a faculty member in Radiology at Harvard Medical School and Affiliated Faculty in the Health Sciences and Technology Division at MIT. He received a Bachelor’s degree in physics from the University of Paris XI and a Master’s degree and PhD from the University of Cambridge, specializing in the correction of subject motion for brain magnetic resonance imaging (MRI).

Dr. Hoffmann’s research focuses on algorithms for medical image processing and analysis using artificial intelligence. His work spans innovations in deep learning, computer vision, and MRI acquisition. He is particularly interested in registration, which captures the spatial relationship between objects from images, and in leveraging this information to advance applications with clinical impact, such as fetal MRI.

Education

PhD in MRI Physics, University of Cambridge

Select Publications

1. Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: learning contrast-invariant registration without acquired images. IEEE Transactions on Medical Imaging (TMI). 2022;41(3):543-58.

2. Hoopes A, Mora JS, Dalca AV, Fischl B*, Hoffmann M* (*equal contribution). SynthStrip: skull-stripping for any brain image. NeuroImage. 2022;260:119474.

3. Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV. Learning the Effect of Registration Hyperparameters with HyperMorph. Journal of Machine Learning for Biomedical Imaging (MELBA). 2022;IPMI 2021 Special Issue:1-30.

Highlights

2020, 2021 ISMRM Summa Cum Laude Merit Awards for top 5% abstract

2020 NIH K99/R00 Pathway to Independence Award

2019 ISMRM Detection & Correction of Motion in MRI & MRS Study Group Award

Website

Malte Hoffmann

Joseph Mandeville

Assistant Professor

jmandeville@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Diffusion MRI, fMRI, image, MRI, Multimodal Imaging, PET, PET-MRI, Preclinical, Software Development

Dr. Mandeville focuses on understanding relationships between imaging signals and physiology and using this information to improve information content derived from noninvasive neuroimaging. Research leverages all aspects of multimodal imaging to understand functional imaging methods and the brain; work includes characterization of imaging biomarkers, improvement of functional imaging capabilities (e.g., contrast to noise ratios), and development of optimal analysis strategies. Prior work included development of contrast-enhanced fMRI and quantitative models of BOLD signal, and more recent efforts have focused on combined PET/fMRI to understand occupancy-function relationships and to quantitatively model PET and fMRI signals in the context of pharmacological stimuli.

Education

PhD in Physics, University of Illinois Urbana-Champaign

Select Publications

1. Mandeville JB, Marota JJ, Ayata C, Zaharchuk G, Moskowitz MA, Rosen BR,
Weisskoff RM. Evidence of a cerebrovascular postarteriole windkessel with
delayed compliance. J Cereb Blood Flow Metab. 1999; 19(6):679-689.

2. Mandeville JB. IRON fMRI measurements of CBV and implications for BOLD
signal. NeuroImage 2012; 62(2): 1000-1008.8-2385.

3. Sander CY, Hooker JM, Catana C, Normandin MD, Alpert NM, Knudsen GM,
Vanduffel W, Rosen BR, Mandeville JB. Neurovascular coupling to D2/D3
dopamine receptor occupancy using simultaneous PET/functional MRI. PNAS 2013;
110(27): 11169-11174.7.

Highlights

Adler Award for Outstanding graduate nuclear physics research at University
of Illinois at UC

Peter Demos Award for Outstanding Ph.D. Thesis from Bates Laboratory,
Massachusetts Institute of Technology

Patent: Method for diagnosing neurological, neurodegenerative, and
psychiatric diseases by MRI using contrast agents with high magnetic
susceptibility and extended plasma half life. (Co-inventors Bruce Jenkins,
and Freidrich Cavagna)

Website

The Mandeville Lab

Fuyixue Wang

Assistant Professor

fwang18@mgh.harvard.edu
Magnetic Resonance Physics & Instrumentation Group

Tagged:Aging, Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, Data Science, Diffusion MRI, fMRI, High-field Imaging, MRI, Multimodal Imaging, Software Development

Dr. Wang is an Assistant Professor of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS). She obtained her PhD degree from MIT in the Harvard-MIT Health Sciences and Technology department, receiving interdisciplinary training in electrical engineering, medical physics, neuroscience, and clinical science (completed the pre-clinical curriculum at HMS). Following the completion of her PhD, Dr. Wang joined the faculty of MGH and Harvard Medical School, and has been working in the A. A. Martinos Center for Biomedical Imaging.

Dr. Wang’s research centers on developing novel MRI acquisition and reconstruction techniques to provide higher imaging sensitivity, specificity, and efficiency. Dr. Wang has led the development of a number of imaging technologies that effectively address long-standing challenges of MRI, and successfully demonstrated their value in a wide range of applications, such as functional, diffusion, and quantitative MRI. These techniques are being adopted worldwide to map brain functions and structures with unprecedented acquisition speed, image resolution, data quality, and information content.

For example, the EPTI technique she developed introduces a novel MRI readout that addresses EPI’s major limitations while providing rich multi-contrast information. EPTI-based fMRI techniques have improved the sensitivity and specificity of functional mapping, and are supported by multiple NIH and BRAIN Initiative grants as the next-gen fMRI acquisition to advance brain function studies. Dr. Wang’s research also focuses on diffusion MRI. Her dMRI techniques have resulted in substantial improvements in SNR efficiency, motion robustness, and spatial resolution. The submillimeter in-vivo diffusion Connectome dataset she published has been used by researchers globally to study previously inaccessible but vital brain circuitries important in Alzheimer’s disease, Parkinson’s disease, etc. Her recent dMRI developments further push the boundaries of achievable resolution in in-vivo dMRI to a mesoscopic scale (~0.1 mm3) at both 3T and 7T. Another area of her research is quantitative MRI, where she has developed ultra-fast multi-parametric imaging with high isotropic resolution (T1, T2, T2*, PD, and B1 maps at 1-mm iso within 3 mins) that has been employed in clinical studies with patients.

Full list of publication:
https://scholar.google.com/citations?user=vu0JZ2YAAAAJ&hl=en

Education

PhD, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology (MIT)

Select Publications

1. Wang F, Dong Z, Reese TG, Bilgic B, Katherine Manhard M, Chen J, Polimeni JR, Wald LL, Setsompop K. Echo planar time-resolved imaging (EPTI). Magn Reson Med. 2019 Jun;81(6):3599-3615. doi: 10.1002/mrm.27673. Epub 2019 Feb 3. PMID: 30714198; PMCID: PMC6435385.

2. Wang F, Dong Z, Wald LL, Polimeni JR, Setsompop K. Simultaneous pure T2 and varying T2′-weighted BOLD fMRI using Echo Planar Time-resolved Imaging for mapping cortical-depth dependent responses. Neuroimage. 2021 Oct 13;245:118641. doi: 10.1016/j.neuroimage.2021.118641. Epub ahead of print. PMID: 34655771.

3. Wang F, Dong Z, Tian Q, Liao C, Fan Q, Hoge WS, Keil B, Polimeni JR, Wald LL, Huang SY, Setsompop K. In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution. Sci Data. 2021 Apr 29;8(1):122. doi: 10.1038/s41597-021-00904-z. PMID: 33927203; PMCID: PMC8084962.

Highlights

ISMRM I.I. Rabi Young Investigator Award Finalist (2019 for EPTI)

United States Patent #11,022,665: Method for echo planar time-resolved magnetic resonance imaging (US20190369185A1)

United States Patent #10,871,534: Accelerated magnetic resonance imaging using a tilted reconstruction kernel in phase encoded and point spread function encoded K-space (US20190369186A1)

Tommi Raij

Assistant Professor

TMS Laboratory
MEG Core

raij@nmr.mgh.harvard.edu

Dr. Raij is a researcher currently serving as the Director of Transcranial Magnetic Stimulation (TMS) Clinical Research at the Martinos Center. He aims to understand the human brain mechanisms and improve the treatment of psychiatric and neurological disease, such as depression, schizophrenia, cocaine addiction, chronic pain, traumatic brain injury and stroke.

In psychiatry, his recent work focuses on optimizing the efficacy of brain stimulation treatments in depression and cocaine addiction. He is also examining basic mechanisms and potential TMS treatments of PTSD and anxiety, including manipulation of memories by using non-invasive brain stimulation. In addition, he is mapping neuronal activation trace lifetimes in schizophrenia and depression, and studying how their abnormalities may be linked to symptoms. In cognitive neuroscience and neurology, he studies how large-scale integration over multiple human brain systems occurs (multisensory integration), and how cortico-cortical paired associative stimulation with multi-channel TMS could be used to re-wire connectivity between brain areas in health and in disease. To reach these goals, he uses a wide array of non-invasive human brain imaging and stimulation techniques (TMS, MEG/EEG, MRI/DTI/fMRI) and their combinations.

Education

MD, University of Helsinki, Finland
PhD in Neurosciences, University of Helsinki, Finland

Select Publications

1. Raij T, Nummenmaa A, Marin MF, Porter D, Furtak S, Setsompop K, Milad MR. Prefrontal Cortex Stimulation Enhances Fear Extinction Memory in Humans. Biol Psychiatry. 2018 Jul 15;84(2):129-137. doi: 10.1016/j.biopsych.2017.10.022. Epub 2017 Nov 6. PMID: 29246436; PMCID: PMC5936658.

2. Herrold AA, Siddiqi SH, Livengood SL, Bender Pape TL, Higgins JP, Adamson MM, Leung A, Raij T. Customizing TMS Applications in Traumatic Brain Injury Using Neuroimaging. J Head Trauma Rehabil. 2020 Nov-Dec;35(6):401-411. doi: 10.1097/HTR.0000000000000627. PMID: 33165153.

3. Diana M, Raij T, Melis M, Nummenmaa A, Leggio L, Bonci A. Rehabilitating the addicted brain with transcranial magnetic stimulation. Nat Rev Neurosci. 2017 Nov;18(11):685-693. doi: 10.1038/nrn.2017.113. Epub 2017 Sep 29. PMID: 28951609.

Highlights

Brain Stimulation Atlases

Adrian Dalca

Assistant Professor

Laboratory for Computational Neuroimaging

adalca@mit.edu
617-981-9164

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, Data Science, FreeSurfer, Machine Learning, MRI, Stroke

Adrian V. Dalca is an Assistant Professor at A.A. Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, and Research Scientist at CSAIL, MIT. He obtained his PhD from CSAIL, MIT. His research focuses on developing new machine learning techniques and probabilistic models to analyze medical images, clinical diagnoses, and other complex medical data. His work spans medical image analysis, machine learning, computer vision and photography, and computational biology.

Education

PhD in Electrical Engineering and Computer Science, MIT

Select Publications

1. Billot B, Greve D, Van Leemput K, Fischl B, Iglesias JE, Dalca AV. A Learning Strategy for Contrast-agnostic MRI Segmentation. MIDL: Medical Imaging with Deep Learning. PMLR 121. 2020; 75-93.

2. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. VoxelMorph: A Learning Framework for Deformable Medical Image Registration. IEEE TMI: Transactions on Medical Imaging. 2019; 38(8): 1788-1800.

3. Dalca AV, Rakic M, Guttag J, Sabuncu MR.Learning Conditional Deformable Templates with Convolutional Networks. NeurIPS: Advances in Neural Information Processing Systems. 2019; 804-816.

4. Zhao A, Balakrishnan G, Durand F, Guttag J, Dalca AV. Data Augmentation with Spatial and Appearance Transforms for One-shot Medical Image Segmentation. CVPR: Computer Vision and Pattern Recognition. 2019; 8543-8553.

Websites

Adrian Vasile Dalca
Laboratory for Computational Neuroimaging

Clarissa Cooley

Assistant Professor

czcooley@mgh.harvard.edu

Tagged:Hardware Development, Hypoxic Ischemic Encephalopathy (HIE), Low-field Imaging, Magnetic Particle Imaging, MPI, MRI, Point-of-Care Anatomical Imaging, Portable MRI

Dr. Cooley’s research interests lie in the development of high-impact imaging systems based on new approaches to hardware, image encoding, and signal processing. Her primary scientific contribution has been the development of a portable MRI brain scanner that uses a new image encoding technique. Low-cost, portable MRI systems have the potential to make MR imaging possible at sites where it is currently unavailable (due to cost and siting requirements). Conventional MRI scanners rely on a large homogeneous superconducting magnet and linear fast-slewing gradient magnetic fields for image encoding. It is infeasible to scale down a scanner with these design constraints to a truly portable human brain scanner. Instead, we developed a new approach which uses a lightweight, low-cost permanent magnet array to produce an inhomogeneous magnet field. Instead of considering this field variation as a nuisance, Dr. Cooley and colleagues use it to their advantage for image encoding in lieu of a linear read-out gradient system. Careful measurements and modeling of the system is conducted to enable a generalized encoding-matrix based generalized image reconstruction.

Dr. Cooley is also involved in the Magnetic Particle Imaging (MPI) hardware development effort at MGH. MPI shows great potential as a clinical, tracer-based imaging method with much higher sensitivity than MRI. Although the potential has been demonstrated in rodents for applications like angiography and cell-tracking, functional brain imaging has not been demonstrated. She and colleagues have developed a single-sided MPI detector to produce a low-distortion AC drive field and record the non-linear response of injected superparamagnetic iron-oxide nanoparticles (SPIONs). The measured signal tracks the local blood volume of a rat under the detector. They have recently obtained experimental results clearly demonstrating a dynamic MPI signal corresponding to CBV changes with hypercapnia. This work is has been expanded to the development of a functional MPI (fMPI) scanner for rats. Their work is now ongoing to develop the first human-scale MPI scanner for fMPI.

Education

PhD in Electrical Engineering

Select Publications

1. Cooley CZ, McDaniel PC, Stockmann JP, Srinivas SA, Cauley SF, Śliwiak M, Sappo CR, Vaughn CF, Guerin B, Rosen MS, Lev MH, Wald LL. A portable scanner for magnetic resonance imaging of the brain. Nat Biomed Eng. 2020 Nov 23. doi: 10.1038/s41551-020-00641-5. Epub ahead of print. PMID: 33230306.

2. Cooley CZ, Haskell MW, Cauley SF, Sappo C, Lapierre CD, Ha CG, Stockmann JP, Wald LL. Design of sparse Halbach magnet arrays for portable MRI using a genetic algorithm. IEEE Trans Magn. 2018 Jan;54(1):5100112. doi: 10.1109/TMAG.2017.2751001. Epub 2017 Oct 23. PMID: 29749974; PMCID: PMC5937527.

3. Cooley CZ, Mandeville JB, Mason EE, Mandeville ET, Wald LL. Rodent Cerebral Blood Volume (CBV) changes during hypercapnia observed using Magnetic Particle Imaging (MPI) detection. Neuroimage. 2018 Sep;178:713-720. doi: 10.1016/j.neuroimage.2018.05.004. Epub 2018 May 5. PMID: 29738908; PMCID: PMC6344028.

Highlights

2020: MGH Department of Radiology Shore Award

2014: ISMRM Rabi Young Investigator Award

Yi-Fen Yen

Assistant Professor

Hyperpolarized Imaging Lab (Director)

yyen1@mgh.harvard.edu

Over the past two decades, Dr. Yen has devoted herself to the development of advanced MRI techniques, for hyperpolarized metabolic imaging, cancer imaging and functional imaging in clinical and pre-clinical research. She is an MR physicist with >100 publications in peer-reviewed scientific journals, book chapters, reviews and patents. Her pioneering work in hyperpolarized 13C MR spectroscopic imaging of metabolism has resulted in 30+ publications, including the first in vivo tracking of altered metabolic pathways in prostate cancer, hepatocellular carcinoma and glioma in animal studies.

Hyperpolarized imaging will revolutionize how we study and manage diseases. Dr. Yen’s group explores new hyperpolarized imaging contrast agents, investigates potential applications in cancer and aging research, and develops multinuclear MR techniques to advance hyperpolarized imaging for translational research.

In addition, her group is developing advanced dynamic MRI technologies to study brain perfusion, permeability of blood brain barrier, and brain function. They are currently investigating the relationship between tau deposition and neurovascular healthy in patients with Alzheimer’s disease. In collaboration with industry, they are developing imaging techniques to monitor treatment effect in sickle cell disease animal models to aid in drug development.

Education

PhD in Nuclear Physics, University of Minnesota, Minneapolis

Select Publications

1. Coghill RC, McHaffie JG, Yen YF. Neural correlates of interindividual differences in the subjective experience of pain. Proc Natl Acad Sci U S A. 2003 Jul 8;100(14):8538-42. doi: 10.1073/pnas.1430684100. Epub 2003 Jun 24. Erratum in: Proc Natl Acad Sci U S A. 2017 Nov 20;:. Yen, Ye-Fen [corrected to Yen, Yi-Fen]. PMID: 12824463; PMCID: PMC166264.

2. Yen YF, Kohler SJ, Chen AP, Tropp J, Bok R, Wolber J, Albers MJ, Gram KA, Zierhut ML, Park I, Zhang V, Hu S, Nelson SJ, Vigneron DB, Kurhanewicz J, Dirven HA, Hurd RE. Imaging considerations for in vivo 13C metabolic mapping using hyperpolarized 13C-pyruvate. Magn Reson Med. 2009 Jul;62(1):1-10. doi: 10.1002/mrm.21987. PMID: 19319902; PMCID: PMC2782538.

3. Albers MJ, Bok R, Chen AP, Cunningham CH, Zierhut ML, Zhang VY, Kohler SJ, Tropp J, Hurd RE, Yen YF, Nelson SJ, Vigneron DB, Kurhanewicz J. Hyperpolarized 13C lactate, pyruvate, and alanine: noninvasive biomarkers for prostate cancer detection and grading. Cancer Res. 2008 Oct 15;68(20):8607-15. doi: 10.1158/0008-5472.CAN-08-0749. PMID: 18922937; PMCID: PMC2829248.

Highlights

Awarded an NIH S10 High-end Instrumentation grant for a dissolution-DNP hyperpolarizer for pre-clinical and clinical research

Served on the Annual Meeting Program Committee of International Society of Magnetic Resonance in Medicine from 2016 to 2019

Named a Partners Healthcare Innovation Fellow, 2018

Website

https://yenlab.martinos.org

Jingyuan Chen

Assistant Professor

jechen@mgh.harvard.edu

Tagged:Brain, Brain Mapping, Data Science, EEG, fMRI, High-field Imaging, MRI, Multimodal Imaging, PET, PET-MRI, Preclinical

Dr. Chen’s research lies at the interface of neuroimaging technology, signal processing and neuroscience. She is interested in integrating state-of-the-art fMRI and multi-modal imaging techniques to achieve novel, comprehensive insights into our brain’s function and physiology. One line of her current research is to harness the cortical-depth-dependent information uncovered by high-resolution fMRI to improve the neuronal specificity and sensitivity of BOLD fMRI measures; and to exploit the depth-dependent patterns of resting-state brain functional architecture. A second line of her research focuses on integrating state-of-the-art fast PET technology and fMRI to probe the neuronal, vascular, energetic and neuromodulatory mechanisms underlying brain functional dynamics.

Education

PhD in Electrical Engineering (major) and Statistics (minor), Stanford University

Select Publications

1. Chen JE, Glover GH. BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz. Neuroimage. 2015 Feb 15;107:207-218.

2. Chen JE, Polimeni JR, Bollmann S, Glover GH. On the analysis of rapidly sampled fMRI data. Neuroimage. 2019 Mar;188:807-820.

3. Chen JE, Lewis LD, Chang C, Tian Q, Fultz NE, Ohringer NA, Rosen BR, Polimeni JR. Resting-state “physiological networks”. Neuroimage. 2020 Jun;213:116707.

Highlights

K99/R00 Pathway to Independence Award (National Institute of Neurological Disorders and Stroke)

Anne Klibanski Visiting Scholars Award (MGH)

Christopher Nguyen

Assistant Professor

Nguyen Lab (Director)

christopher.nguyen@mgh.harvard.edu
617-643-7838

Tagged:Aging, Algorithm Development, Body, Cardiovascular Disease / Disorders, Cardiovascular System, Clinical, Diffusion MRI, Fibrosis, fMRI, Hardware Development, High-field Imaging, Machine Learning, MRI, MRI Pulse Sequence Design, Multimodal Imaging, Optical Imaging, PET-MRI, Preclinical, Software Development

The Nguyen lab focuses on the development and clinical application of novel imaging techniques to characterize the cardiovascular system including MRI, optical, and PET. Our primary research interests fall into four general areas: (1) bio- inspired design for heart assisted devices (Science Robotics 2020), (2) developing novel cardiovascular imaging techniques leveraging advanced physics and engineering (Nature BME 2018), (3) validating imaging techniques in preclinical models (JACC BTS 2018), and (4) rapid clinical translation of imaging techniques to patients (Radiology 2020). The ultimate goal of our research is to empower scientists and clinicians with novel imaging technologies to answer fundamental questions in cardiovascular biology and pathophysiology.

In the MGH/HST Martinos Center for Biomedical Imaging, our lab designs and implements in-house imaging technologies on cutting-edge MRI scanners. We study both large animal models and patients on human clinical systems for immediate clinical translation

Dr. Nguyen received his PhD in Biomedical Engineering from the University of California Los Angeles in 2015 as a NIH Ruth L. Kirschstein NRSA pre-doctoral fellow. This led to his postdoctoral training at Cedars-Sinai Medical Center and affiliated postdoctoral fellowship at MGH. Subsequently in early 2017, he was promoted to faculty at Cedars-Sinai Medical Center in the Department of Biomedical Sciences and Biomedical Imaging Research Institute. In October 2017, Dr. Nguyen joined the Martinos Center and Cardiovascular Research Center faculty after receiving the early career NIH NIBIB Trailblazer Award.

Education

PhD in Biomedical Engineering, UCLA

Select Publication

Nguyen CT, Dawkins J, Bi X, Marbán E, and Li D. Diffusion Tensor Cardiac Magnetic Resonance Reveals Exosomes From Cardiosphere-Derived Cells Preserve Myocardial Fiber Architecture After Myocardial Infarction. JACC Basic Transl Sci. 2018 Feb;3(1):97-109. PMID: 29600288.

Akerberg AA, Burns CE, Burns CG, Nguyen C. Deep learning enables automated volumetric assessments of cardiac function in zebrafish. Dis Model Mech. 2019 Oct 25;12(10):dmm040188. doi: 10.1242/dmm.040188. PMID: 31548281; PMCID: PMC6826023.

Park C, Fan Y, Gregor H, Yuk H, Singh M, Rojas A, Hameed A, Saeed M, Vasilyev N, Steele T, Zhao X, Nguyen CT**, and Roche ET**. “An Organosynthetic Dynamic Heart Model with Enhanced Biomimicry Guided by Cardiac Diffusion Tensor Imaging.” Science Robotics 5, no. 38 (January 29, 2020). https://doi.org/10.1126/scirobotics.aay9106.

Highlights

2017 NIH NIBIB Trailblazer Award

2019 SCMR / NIBIB Plenary Lecturer

2020 Organosynethic heart development highlighted in BBC https://www.sciencefocus.com/news/robotic-hybrid-heart-beats-like-a-real-organ/

Lab Site

Nguyen Lab

Shasha Li

Assistant Professor

shasha.li@mgh.harvard.edu
617-642-0449

Tagged:Auditory Communication Disorders, Brain Mapping, Cardiovascular Disease / Disorders, Data Science, Diffusion MRI, EEG, fMRI, FreeSurfer, High-field Imaging, Machine Learning, MEG, MRI, Multimodal Imaging, Preclinical, Stroke, TMS

Shasha Li, M.D., Ph.D., joined the faculty at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) with the goal of improving the understanding of the physiological implications of altered neural networks in neurological diseases. She has made substantial contributions to the field of neurorehabilitation through the application of her specialized knowledge in neuromodulation (primarily transcranial magnetic stimulation, TMS, and transcranial direct current stimulation, tDCS) and cutting-edge neuroimaging. Specifically, her lab’s research focuses on the development of novel insights into brain recovery, particularly in investigating brain recovery prediction among a broad clinical population of neurological diseases in correlation with the mechanism of the non-invasive brain stimulation (TMS and tDCS) combined with multi-modal MR techniques in individualized, precise therapeutic neuromodulation.

Education

MD, Sichuan University, Chengdu, China; PhD in Physical Medicine & Rehabilitation, Sichuan University, Chengdu, China

BSN, MGH Institute of Health Professions

Select Publications

1. Shasha Li, Cheng Luo, Bo Yu, Bo Yan, Qiyong Gong, Chengqi He, Li He, Xiaoqi Huang, Dezhong Yao, Su Lui, Hanhe Tang, Qin Chen, Yan Zeng, Dong Zhou. Functional magnetic resonance imaging study on dysphagia after unilateral hemispheric stroke: a preliminary study. Journal of Neurology, Neurosurgery, and Psychiatry, 2009;80(12):1320-1329

2. Shasha Li, Marziye Eshghi, Sherza Khan, Qiyuan Tian, Juho Joutsa, Yangming Ou, Qing Mei Wang, Jian Kong, Bruce Robert Rosen, Jyrki Ahveninen, Aapo Nummenmaa. Localizing central swallowing functions by combining non-invasive brain stimulation with neuroimaging. Brain Stimulation, 2020;13(5):1207-1210

3. Loukas G. Astrakas, Shasha Li, Sabrina Elbach, A Aria Tzika. The severity of sensorimotor tracts degenerations may predict motor performance in chronic stroke patients, while brain structural network dysfunction may not. Frontiers in Neurology, 2022;13:813763

Highlights

Postdoctoral Fellowship of the American Heart Association

K23: Mentored Patient-Oriented Research Career Development Award NIH/NIDCD

Course Director: HMS LN701 Intermediate Medical Mandarin

Website

Translational Neuroimage, Neuromodulation, and Neurorehabilitation Lab (TN³ Lab)