Instructor | Martinos Center

Gabriel Ramos Llorden

  • Instructor

gramosllorden@mgh.harvard.edu

Tagged:Aging, Brain Mapping, Clinical, Diffusion MRI, MRI, Preclinical
Home / Role: Instructor

Dr. Gabriel Ramos Llorden (1988, Spain) is a biomedical imaging scientist and engineer with more than 12 years of experience advancing medical imaging across multiple modalities, including MRI, ultrasound, and CT. He has deep expertise in MRI acquisition, reconstruction, and AI-driven analysis, complemented by broad training in signal processing, quantitative modeling, and image analysis. He earned his PhD in Medical Physics at the University of Antwerp, Belgium, where his research focused on
improving MRI relaxometry through statistical signal processing.

Following his PhD, Dr. Ramos Llorden pursued postdoctoral training at Brigham and Women’s Hospital and later at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital. His research during this time focused on advancing image acquisition and reconstruction technologies for in vivo and ex vivo human brain mapping using diffusion MRI and next-generation high-performance MRI scanners, such as the Connectome 2.0 system. He is currently supported by a prestigious NIH
BRAIN Initiative (NINDS) K99/R00 Award, through which he is developing high-resolution functional and diffusion MRI methods to map hippocampal neuroplasticity during spatial memory, with the goal of establishing early imaging biomarkers of memory impairment.

Education

PhD in Medical Physics, University of Antwerp, Belgium; Telecommunications Engineer, University of Valladolid, Spain

Select Publications

1. Ramos-Llordén, G., Lee, HH., Davids, M. et al. Ultra-high gradient connectomics and microstructure MRI scanner for imaging of human brain circuits across scales. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01457-x [doi.org]

2. Ramos-Llordén G, Park DJ, Kirsch JE, et al. Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring. Magn Reson Med. 2024; 91: 541–557. doi: 10.1002/mrm.29873

3. Ramos-Llordén G, Ning L, Liao C, et al. High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR). Magn Reson Med. 2020; 84: 1781–1795. https://doi.org/10.1002/mrm.28232 [doi.org]

Highlights

2023 Junior Fellow of the ISMRM
2024 ISMRM AMPC selection
2024 Brain Initiative K99/R00 Award (NINDS)

 

Associated Lab(s)

Connectome 2.0

 

  • Instructor

Kaisu Lankinen

  • Instructor

KLANKINEN@mgh.harvard.edu

Tagged:Brain, Brain Mapping, EEG, fMRI, High-field Imaging, Machine Learning, MEG, MRI, TMS
Home / Role: Instructor

Dr. Kaisu Lankinen is an Instructor at the Athinoula A. Martinos Center for Biomedical Imaging at
Massachusetts General Hospital and Harvard Medical School. She is a neuroscientist with a background in biomedical engineering, specializing in advanced neuroimaging techniques such as 7 T fMRI, MEG/EEG, TMS, and computational modeling.

Her recent research focuses on the functions of the auditory cortex, particularly the feedforward and
feedback processes involved in sensory-specific and cross-sensory processing. Additionally, she has explored the relationship between speech production and perception.


In her ECR R21 award project, she investigates the connection between the hippocampus and auditory cortex in noisy speech perception. Her ultimate goal is to understand how age-related hearing loss may relate to dementias.

 

Education

PhD in Biomedical Engineering, Aalto University, Finland

Select Publications

Lankinen K, Ahlfors SP, Mamashli F, Blazejewska AI, Raij T, Turpin T, Polimeni JR, Ahveninen J. Cortical depth profiles of auditory and visual 7 T functional MRI responses in human superior temporal areas. Hum Brain Mapp. 2023 Feb 1;44(2):362- 372.

Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex. J Neurosci. 2024 Apr 24;44(17). 


Lankinen K, Ahveninen J, Uluç I, Daneshzand M, Mareyam A, Kirsch JE, Polimeni JR, Healy BC, Tian Q, Khan S, Nummenmaa A, Wang QM, Green JR, Kimberley TJ, Li S. Role of articulatory motor networks in perceptual categorization of speech signals: a 7T fMRI study. Cereb Cortex. 2023 Dec 9;33(24):11517-11525.

Highlights

ECR R21 Award (NIDCD)

 

  • Instructor

Sheng Shen

  • Instructor

sshen6@mgh.harvard.edu

Tagged:Brain, Breast, Cardiovascular System, Low-field Imaging, MRI, Preclinical, Stroke
Home / Role: Instructor

Dr. Shen’s work focuses on advancing ultra-low field (ULF) MRI hardware, quantitative imaging techniques and ultra-low field MRI applicaiotns. He has developed novel approaches for T1 and T2 mapping at 6.5 mT to enable accessible MRI and quantitative evaluation for breast and brain. In addition, he explores the use of superparamagnetic iron oxide nanoparticles (SPIONs) to enhance contrast in ULF MRI, and applies AI-driven image post-processing to improve SNR and spatial resolution low field MRI. Looking ahead, his work aims to establish quantitative MRI biomarkers for cerebrovascular diseases assessment and monitoring, drive hardware innovations for breast cancer screening, and develop data-driven post-processing methods to enhance image quality at low field.

Dr. Shen received his PhD in Electrical Engineering from Chongqing University and completed joint training at the Martinos Center as a visiting PhD student before returning as a postdoctoral fellow. His contributions span RF and gradient coil design, electromagnet development, and topology optimization for MRI hardware systems.

 

Education

Ph.D.

Select Publications

1. Shen S, Koonjoo N, Ogier SE, Boele T, Saksena MA, Keenan KE, et al. B1-corrected breast T1 mapping at ultralow field. Magnetic Resonance in Medicine. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.30602
2. Shen S, Koonjoo N, Boele T, Lu J, Waddington DEJ, Zhang M, et al. Enhancing organ and vascular contrast in preclinical ultra-low field MRI using superparamagnetic iron oxide nanoparticles. Commun Biol. 2024 Sep 28;7(1):1–13.
3. Shen S, Xu Z, Koonjoo N, Rosen MS. Optimization of a Close-Fitting Volume RF Coil for Brain Imaging at 6.5 mT Using Linear Programming. IEEE Trans Biomed Eng. 2021 Apr;68(4):1106–14.

Highlights

NMR Probe research highlighted as the cover article in Journal of Magnetic Resonance (2019)
First Place, Abstract Competition – Low Field MRI Study Group, ISMRM (2025)

 

  • Instructor

Chiara Maffei

  • Instructor

cmaffei@mgh.harvard.edu

Home / Role: Instructor

Dr. Chiara Maffei, Ph.D., obtained her Master and Ph.D. in Neuroscience from the University of Trento in Italy, for which she received the Best Ph.D. thesis award in 2019. Her scientific interests focus on the optimization, validation, and clinical translation of automated diffusion MRI tractography tools for the accurate delineation and quantification of white matter structures in healthy subjects, subjects with depression, and patients with traumatic brain injury. Her postdoctoral work focused on combining anatomical information obtained at different scales (high-resolution ex vivo MRI, optical imaging, and animal tracer data) to validate tractography algorithms and develop novel white matter atlases of the human brain. Her more recent work focuses on using high-resolution dMRI acquired on the MGH Connectome 2.0 scanner to reconstruct very small subcortical pathways in vivo with exceptional anatomical accuracy and replicate results from anatomic tracer in NHPs. She received the Best Research of the Year Award by the IamBrain society and a Young Investigator Award by the Fetal, Infant and Toddler Imaging group. She was recently awarded a NARSAD Young Investigator Award by the Brain & Behavior Research Foundation to work on the development of ultra-high-resolution tractography to investigate the small pathways of the sub-cortical depression circuit.

Education

Ph.D. Neuroscience, Trento University, Italy

Select Publications

1. 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. doi: 10.1016/j.neuroimage.2021.118706. Epub 2021 Nov 12. PMID: 34780916; PMCID: PMC8835483.
2.Maffei C, Girard G, Schilling KG, Aydogan DB, Adluru N, Zhylka A, Wu Y, Mancini M, Hamamci A, Sarica A, Teillac A, Baete SH, Karimi D, Yeh FC, Yildiz ME, Gholipour A, Bihan-Poudec Y, Hiba B, Quattrone A, Quattrone A, Boshkovski T, Stikov N, Yap PT, de Luca A, Pluim J, Leemans A, Prabhakaran V, Bendlin BB, Alexander AL, Landman BA, Canales-Rodríguez EJ, Barakovic M, Rafael-Patino J, Yu T, Rensonnet G, Schiavi S, Daducci A, Pizzolato M, Fischi-Gomez E, Thiran JP, Dai G, Grisot G, Lazovski N, Puch S, Ramos M, Rodrigues P, Prčkovska V, Jones R, Lehman J, Haber SN, Yendiki A. Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. Neuroimage. 2022 Aug 15;257:119327. doi: 10.1016/j.neuroimage.2022.119327. Epub 2022 May 26. PMID: 35636227; PMCID: PMC9453851.
3.Maffei C, Gilmore N, Snider SB, Foulkes AS, Bodien YG, Yendiki A, Edlow BL. Automated detection of axonal damage along white matter tracts in acute severe traumatic brain injury. Neuroimage Clin. 2023;37:103294. doi: 10.1016/j.nicl.2022.103294. Epub 2022 Dec 13. PMID: 36529035; PMCID: PMC9792957.

Highlights

2022 Best Research of the Year Award IamBrain 2022 Annual Meeting “Submillimiter dMRI protocol optimization for accurate in-vivo reconstruction of deep-brain circuitry.”

2023 Brain & Behavior Research Foundation (BBRF) Young Investigator NARSAD Award https://bbrfoundation.org/sites/default/files/2023-11/2023-yi-booklet_1.pdf

2024 Invited OHBM Oral Presentation “Multi-modal, multi-scale imaging shows that long-association systems are made of short relay fibers”

 

Associated Labs

Laboratory for NeuroImaging of Coma and Consciousness (NICC)
Laboratory for Computational Neuroimaging (LCN)

Associated Lab(s) Sites

https://lcn.martinos.org; https://www.comarecoverylab.org

  • Instructor

Isil Uluc

  • Instructor

iuluc@mgh.harvard.edu

149 13th St, Boston, MA 02129

Tagged:Brain, Cognitive Neuroscience, EEG, fMRI, TMS, Working Memory
Home / Role: Instructor

My research focuses on working memory and its underlying neuronal processes in humans using fMRI, MEG, EEG, TMS. We also validate our results using intracranial human EEG data recorded from human participants with epilepsy during presurgical monitoring. Recently, my research focus expanded into understanding the cerebellar processes with MEG source localization and TMS/EEG using visually guided saccades. My mission is further the neuroscientific research by leveraging advanced and innovative techniques to obtain more precise anatomical, temporal, and spectral measurements of human higher brain functions. My current major interest is to investigate working memory mechanisms using multiple imaging modalities, machine learning and non-invasive brain stimulation. I also use MEG and TMS with event related analyses and source localization to map and cerebellar processes related to visually guided saccadic eye movement in individuals as my second major interest. My third interest is advancing fine-grained high-temporal resolution imaging of human cognition. We use advanced neuroimaging techniques including MEG to understand human cognitive processes. I am an instructor at MGH Martinos Center. I worked in several NIH-funded projects for the last six years. At MGH Martinos Center, I also have supervised several research assistants over the course of my postdoctoral fellowship. In addition to research, I organized and hosted Brainmap Seminar Series of Martinos center for two years.

Education

Ph.D.

Select Publications

1. Uluç I, Daneshzand M, Jas M, Kotlarz P, Lankinen K, Fiedler JL, Mamashli F, Pajankar N, Turpin T, Navarro de Lara L, Sundaram P, Raij T, Nummenmaa A, and Ahveninen J, Decoding auditory working memory content from EEG responses to auditory-cortical TMS. Brain Stimul, 2025. 18: p. 649-658. PMCID: PMC11838191.
2. Uluç, I, Schmidt, TT, Wu, Y-H, Blankenburg, F. Content-specific codes of parametric auditory working memory in humans. NeuroImage, 2018. 183: p. 254–262. PMID: 30107259.
3. Uluç I, Turpin T, Kotlarz P, Lankinen K, Mamashli F, and Ahveninen J, Comparing auditory and visual aspects of multisensory working memory using bimodally matched feature patterns. Experimental Brain Research, 2024. 243: p. 38. PMCID: PMC11848833.

Highlights

MGH Postdoctoral Association Travel Award
DAAD Award – German Academic Exchange Service

Associated Labs

ACLab
Sundaram Lab
Raij Lab
TMS lab

  • Instructor

Neel Dey

  • Instructor

ndey@mgh.harvard.edu

Laboratory for Computational Neuroimaging

149 13th St, Boston, MA 02129

Tagged:Aging, Algorithm Development, Autism, Brain, Brain Mapping, Diffusion MRI, Fetal, Fetal MRI, Machine Learning, MRI, Pediatric Imaging, Pediatrics, Placental, Spectroscopy
Home / Role: Instructor

Neel Dey is an Instructor at the A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School. He is broadly interested in building robust machine learning methods to analyze biomedical images, with a particular emphasis on approaches for automatic generalization to unseen datasets and messy clinical contexts. He was previously a postdoctoral associate at MIT CSAIL and received his Ph.D. in Computer Science from NYU.

Education

Ph.D.

Select Publications

Dey, N., Billot, B., Wong, H.E., Wang, C.J., Ren, M., Grant, P.E., Dalca, A.V. and Golland, P., 2025. Learning
General-Purpose Biomedical Volume Representations using Randomized Synthesis. International Conference on Learning Representations (ICLR).

Elaldi, A., Gerig, G. and Dey, N., 2024. Equivariant spatio-hemispherical networks for diffusion MRI
deconvolution. Advances in Neural Information Processing Systems (NeurIPS).


Dey, N., Abulnaga, M., Billot, B., Turk, E.A., Grant, E., Dalca, A.V. and Golland, P., 2024. AnyStar: Domain
randomized universal star-convex 3D instance segmentation. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

 

Highlights

– Received nine Outstanding Reviewer (or equivalent) awards from major conferences
– Pearl Brownstein Doctoral Research Award from NYU CSE for “doctoral research which shows the greatest promise” (equivalent to best departmental Ph.D. thesis)
– Deborah Rosenthal, MD Award for “Outstanding Performance on the Ph.D. Qualifying Exam” (given to 1–2 qualifying students each year in NYU CSE)

Associated Labs

Laboratory for Computational Neuroimaging

Associated Lab(s) Sites

https://www.neeldey.com

Michele Scipioni

  • Instructor

mscipioni@mgh.harvard.edu

149 13th St, Boston, MA 02129

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, Cancer, Clinical, Data Science, fMRI, Hardware Development, High-field Imaging, Machine Learning, Molecular Imaging, MRI, Multimodal Imaging, PET, PET-MRI, Software Development
Home / Role: Instructor

Dr. Michele Scipioni is dedicated to advancing our understanding of biological and neurochemical processes through the development of integrated PET/MRI technologies. With several years of experience, Dr. Scipioni offers a multidisciplinary perspective, drawing from mechanical engineering, computer science, medical imaging, and medical physics.

During his doctoral studies, Dr. Scipioni’s research focused on developing software tools and algorithms for PET image reconstruction and kinetic modeling. This work included a particular interest in leveraging quantitative PET for diagnosing cardiac amyloidosis, and combining DCE-MRI and PET imaging for liver cancer diagnosis.

At the “Athinoula A. Martinos” Center for Biomedical Imaging, Dr. Scipioni’s work has primarily revolved around the design and manufacturing of a next-generation, 7T MR-compatible, brain-dedicated PET scanner: NeuroSphere PET. In this context, he contributed to the innovative mechanical design required to achieve its unconventional spherical geometry, developed PET detector characterization setups, and implemented cooling and RF shielding solutions to optimize system performance and integration with the pre-existing MRI host system. This advanced system is anticipated to open new avenues for in-depth neuroscience research, offering unprecedented insights into brain function and disease.

Education

Ph.D. in Biomedical Engineering (Medical Imaging), M.Sc. in Biomedical Engineering, B.Sc. in Biomedical Engineering

Select Publications

Scipioni M, Corbeil J, Allen MS, Byars L, Schmidt FP, Galve P, Mareyam A, Kapusta M, Valcayo FA, Zhang X-M, Herraiz JL, Ambartsoumian G, Kirsch J, Udías JM, Rosen B, Wald LL, Judenhofer M, Catana C. “Design and Development of the Human Dynamic NeuroChemical Connectome Scanner,” 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), Vancouver, BC, Canada, 2023 11 04-11, pp. 1-2, doi: 10.1109/NSSMICRTSD49126.
2023.10337891

Scipioni M, Pedemonte S, Santarelli MF, Landini L. “Probabilistic Graphical Models for Dynamic PET: A Novel Approach to Direct Parametric Map Estimation and Image Reconstruction”. IEEE Trans Med Imaging. 2020 01; 39(1):152-160. PMID: 31199257

Fuin N, Catalano OA, Scipioni M, Canjels LPW, Izquierdo-Garcia D, Pedemonte S, Catana C. “Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced-MRI Using a Compressed Sensing Approach”. J Nucl Med. 2018 09; 59(9):1474-1479. PMID: 29371404

Genovesi D, Vergaro G, Giorgetti A, Marzullo P, Scipioni M, Santarelli MF, Pucci A, Buda G, Volpi E, Emdin M. “[18F]-Florbetaben PET/CT for Differential Diagnosis Among Cardiac Immunoglobulin Light Chain, Transthyretin Amyloidosis, and Mimicking Conditions”. JACC Cardiovasc Imaging. 2021 01; 14(1):246-255. PMID: 32771577

Highlights

2019: Best Doctoral Thesis Award [GNB – National Bioengineering Group]

Associated Labs

Integrated MR-PET Imaging Laboratory

Nikolaos Efthymiou

  • Instructor

neftymiou@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Clinical, Fibrosis, Lungs, Multimodal Imaging, PET, PET-MRI, whole-body
Home / Role: Instructor

Nikos Efthimiou is a non-clinical researcher at the Athinoula A. Martinos Center for Biomedical Research with more than ten years of experience in Medical Imaging, focused on Positron Emission Tomography. Nikos’ expertise spans detector technologies such as Silicon Photomultipliers, front-end electronics, system and detector modeling, statistical image reconstruction, absolute quantification, motion, and scatter correction.
Thanks to his interdisciplinary background, he has a successful record of accomplishments in academia. In his current position, Nikos investigates synergistic motion and attenuation correction in PET/MR scanners. Also, has developed energy-based scatter correction algorithms for Long Axial FOV PET scanners. Also, he has explored other emerging technological challenges, such as BGO-Cherenkov detectors for PET scanners. Nikos has lived in three countries and speaks English and Greek. His colleagues describe him as analytical, thorough, committed, and hard-working.

Education

PhD in Medical Physics, University of Patras

Select Publications

1. N. Efthimiou, J.S. Karp, S. Surti, “Data-driven, Energy-based method for estimation of Scattered events in Positron Emission Tomography”, Phys. Med. Biol 67 095010.
2. D.P. Watts, J. Bordes, J.R. Brown, A. Cherlin, R. Newton, J. Allison, M. Bashkanov, N. Efthimiou, N.A. Zachariou, “Photon quantum entanglement in the MeV regime and its application in PET imaging”, Nat Commun 12, 2646 (2021).
3. N. Efthimiou, N. Kratochwil, S. Gundacker, A. Polesel, M. Salomoni, E. Auffray, and M. Pizzichemi, “TOF-PET image reconstruction with multiple timing kernels applied on Cherenkov radiation in BGO”, IEEE TRPMS vol. 5, no. 5, pp. 703-711.

Highlights

* 2019 EMIM Poster Award “Monte Carlo simulation of a Total Body PET scanner based on the PENN PET”, Novel Nuclear Medicine technologies session
* 2017 The Allam Lecture First place: Oral presentation Award for “Benefits on Positron Emission Tomography from Ultra-Fast Time-of-Flight detectors.”
* 2008 State Scholarships Foundation’s (ΙΚΥ) scholarship for postgraduate studies on Telecommunication applications applied to Medicine.
* Member of the IEEE Nuclear and Plasma Sciences Society
* Developer at SIRF, the Collaborative Computational Platform for Synergistic Reconstruction for PET/MR
* Developed and maintainer of STIR: PET image reconstruction toolkit

Associated Labs

The Caravan Lab,
Catana Group

Maria Hakonen

  • Instructor

mhakonen@mgh.harvard.edu

Tagged:Aging, Alzheimer's Disease, Brain, Brain Mapping, brain networks, EEG, fMRI, FreeSurfer, functional connectivity, High-field Imaging, Machine Learning, MEG, MNE Python, MRI, tinnitus
Home / Role: Instructor

Dr. Maria Hakonen is an Instructor (Research Faculty) at Massachusetts General Hospital (MGH) and Harvard Medical School. She has extensive experience studying human brain function using 3T/7T fMRI, MEG, and EEG. She is specifically interested in individual differences in brain functional networks and how these differences relate to individual characteristics in both health and disease.

Her recent work has centered on mapping the functional connectivity of the auditory cortex. In her K99/R00 project, she investigates whether tinnitus can be classified into subtypes based on brain network patterns. In addition, she is working on a project exploring the relationship between age-related hearing loss and Alzheimer’s disease using advanced functional network analyses.

Education

PhD in Biomedical Engineering, Aalto University, Finland

Select Publications

Hakonen, M., Dahmani, L., Blazejewska, A., Cui, W., Kotlarz, P., Lankinen, K., Li, M., Polimeni, J., Ren, J., Turpin, T., Wang, D., Liu, H., Ahveninen J. (2025). Individual connectivity-based parcellations reflect functional properties of human auditory cortex. Imaging Neuroscience.

Hakonen, M., May, P. J., Jääskeläinen, I. P., Jokinen, E., Sams, M. & Tiitinen, H. (2017). Predictive processing increases intelligibility of acoustically distorted speech: Behavioral and neural correlates. Brain and Behavior. 7.9.

Hakonen, M., Nurmi, T., Vallinoja, J., Jaatela, J., & Piitulainen, H. (2022). More comprehensive proprioceptive stimulation of the hand amplifies its cortical processing. Journal of neurophysiology, 128(3), 568-581.

Highlights

NIH Pathway to Independence Award (K99/R00)

Yohan Jun

  • Instructor

yjun@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Cancer, Clinical, Data Science, Diffusion MRI, High-field Imaging, Machine Learning, MRI, Multimodal Imaging, Software Development
Home / Role: Instructor

Dr. Yohan Jun’s research focuses on developing novel MR acquisition and reconstruction techniques using MR physics and machine/deep-learning-based algorithms to accelerate MRI scans while achieving high-fidelity images. He works on the following research topics:

i. Rapid high-resolution quantitative imaging: Zero-shot self-supervised learning combined with subspace reconstruction technique (Zero-DeepSub) enables the acquisition of quantitative T1, T2, and proton density maps with high-fidelity using 3D-QALAS sequence. The self-supervised-learning-based mapping technique (SSL-QALAS) can also accelerate the quantitative mapping process without any external dataset or explicit dictionary.

ii. Highly accelerated distortion-free diffusion imaging: Phase Reversed Interleaved Multi-Echo acquisition (PRIME) enables distortion-free diffusion MRI by inserting an additional echo without prolonging TR, when generalized slice dithered enhanced resolution (gSlider) RF encoding is used for volumetric acquisition.

iii. Acceleration of structural MRI: Deep model-based MR image reconstruction algorithms (Joint-ICNet, DPI-net), which combine MR physical and deep-learning models, allow fast MRI up to 8-fold for TOF MRA, T1/T2-weighted, FLAIR, and post-contrast imaging.

iv. Automatic diagnosis of brain disorders: Artificial intelligence algorithms allow us to support the diagnosis of brain disorders, including brain metastases, meningioma, and glioblastoma, by using deep-learning-based diagnosis algorithms for automatic detection, segmentation, and grading.

Education

PhD in Electrical and Electronic Engineering, Yonsei University, South Korea

Select Publications

1. Jun, Y., Arefeen, Y., Cho, J., Fujita, S., Wang, X., Grant, P.E., Gagoski, B., Jaimes, C., Gee, M.S. and Bilgic, B., 2024. Zero‐DeepSub: Zero‐shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D‐QALAS. Magnetic Resonance in Medicine, 91(6), pp.2459-2482.

2. Jun, Y., Cho, J., Wang, X., Gee, M., Grant, P.E., Bilgic, B. and Gagoski, B., 2023. SSL‐QALAS: Self‐Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D‐QALAS. Magnetic Resonance in Medicine, 90(5), pp.2019-2032.

3. Jun, Y., Shin, H., Eo, T., Kim, T. and Hwang, D., 2021. Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method. Medical Image Analysis, 70, p.102017.

Highlights

2024 – ISMRM Junior Fellow

2024 – NIBIB R21 Trailblazer Award

2017-2024 – ISMRM 4 Summa Cum Laude & 2 Magna Cum Laude Merit Awards

Teppei Matsubara

  • Instructor

tmatsubara@mgh.harvard.edu

Clinical MEG Lab (Steven Stufflebeam)

Tagged:Algorithm Development, Brain, Brain Mapping, Clinical, Data Science, MEG, MRI, Software Development
Home / Role: Instructor

Since joining the Department of Radiology at Massachusetts General Hospital in 2019, Teppei Matsubara has worked under Dr. Steven Stufflebeam at the Martinos Center, where he applies his expertise as a board-certified neurosurgeon and epileptologist to clinical neuroimaging, particularly with MEG and MRI. He has played a key role in diagnosing epilepsy, assessing approximately 100 patients annually, while also advancing research on neurophysiological functions in metabolic disorders, specifically studying the effects of elevated GABA levels on cognitive functions. His research, supported by grants from the Japan Society for the Promotion of Science and the Nakatani Foundation, aims to identify neurophysiological biomarkers and enhance imaging protocols. Teppei is also dedicated to training emerging researchers in MEG and MRI, co-founding YES-Japan, an ILAE-affiliated network to support young epilepsy researchers, and is committed to expanding his collaborative network to advance neurological sciences.

Education

MD, PhD

Select Publications

Matsubara T, Khan S, Sundaram P, Stufflebeam S, Aygun D, Dibacco M, Roullet JB, Pearl P, Okada Y: Delays in latencies of median-nerve evoked magnetic fields in patitents with succinic semialdehyde dehydrogenase deficiency. Clin Neurophysiol. 2024 161:52–8.

Matsubara T, Ahlfors SP, Mima T, Hagiwara K, Shigeto H, Tobimatsu S, Goto Y, Stufflebeam S. Bilateral representation of sensorimotor responses in benign adult familial myoclonus epilepsy: An MEG study. Front Neurol. 2021 Oct 26;12:759866.

Matsubara T, Hironaga N, Uehara T, Chatani H, Tobimatsu S, Kishida K. A novel method for extracting interictal epileptiform discharges in multi-channel MEG: Use of fractional type of blind source separation. Clin Neurophysiol. 2020 Feb;131(2):425–36.

Highlights

Overseas Research Fellowship for Young Scientists, Japan Society for the Promotion of Science (2022-2024)

The Nakatani Foundation for advancement of measuring technologies in biomedical engineering (2019-2022)

Research Fellowship for Young Scientists, Japan Society for the Promotion of Science (2020-2022)

Jian (Andrew) Li

  • Instructor

jli112@mgh.harvard.edu

Laboratory for Computational Neuroimaging

Tagged:Aging, Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, Data Science, EEG, fMRI, FreeSurfer, Machine Learning, MRI, Software Development, TBI
Home / Role: Instructor

Jian (Andrew) Li is an Instructor at the A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School. His research interests lie in the application of statistical signal and image processing and machine learning theory to modeling and analysis of neuroimaging data. Currently, he focuses on the development of computational methods for functional brain mapping and functional registration.

Education

Ph.D. in Electrical Engineering, University of Southern California

Select Publications

Li, J., Tuckute, G., Fedorenko, E., Edlow, B.L., Dalca, A.V. and Fischl, B., 2024. JOSA: Joint surface-based registration and atlas construction of brain geometry and function. Medical Image Analysis, 98, p.103292.

Li, J., Liu, Y., Wisnowski, J.L. and Leahy, R.M., 2023. Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain. Neuroimage, 270, p.119944.

Li, J., Curley, W.H., Guerin, B., Dougherty, D.D., Dalca, A.V., Fischl, B., Horn, A. and Edlow, B.L., 2021. Mapping the subcortical connectivity of the human default mode network. Neuroimage, 245, p.118758.

Highlights

2021: Highlight, Poster Playlist, Organization for Human Brain Mapping

2019: Fellow, American Epilepsy Society

2019: Runner-up, Image Processing Best Paper Award, SPIE Medical Imaging

Websites

Laboratory for NeuroImaging of Coma and Consciousness

Jian (Andrew) Li

Xiaoqing Alice Zhou

  • Instructor

xzhou27@mgh.harvard.edu

Tagged:Aging, Alzheimer's Disease, Autism, Brain, fMRI, High-field Imaging, MRI, Multimodal Imaging, Optical Imaging, Preclinical, Schizophrenia
Home / Role: Instructor

Dr. Zhou has over 10 years of experience in neuroscience and neuroimaging, utilizing advanced magnetic resonance imaging (MRI) and innovative neuro-techniques for translational studies in both animal and human brains. Early in her career, she investigated the degenerative brain functions related to cognitive impairment in Alzheimer’s disease and type 2 diabetes patients using high-field MRI. During her Ph.D. training under Prof. Perry Bartlett at the Queensland Brain Institute, Dr. Zhou focused on understanding the regulatory mechanisms behind the cognitive benefits of exercise in combating dementia and aging. Currently, at the Martinos Center for Biomedical Imaging, she is further developing novel MRI methodologies to study the mechanistic regulation of brain function in aging and neurodegeneration, collaborating with Dr. Xin Yu to optimize multimodal fMRI platforms, particularly focusing on single-vessel fMRI and astrocytic Ca2+ signaling in models of aging and degenerative diseases.

Education

PhD, University of Queensland

Select Publications

• Zhou XA, Zhang j, Chen Y, Ma T, Wang Y, Wang J, Zhang Z (2014). Aggravated cognitive and brain functional impairment in mild cognitive impairment patients with type 2 diabetes: a resting-state functional MRI study. Journal of Alzheimer’s Disease 41 (3), 925-935. PMID: 24705547
• Zhou XA, Blackmore DG, Zhuo J, Nasrallah FA, To XV, Kurniawan ND, Carlisle A, Vien KY, Chuang KH, Jiang T, Bartlett PF (2021). iScience, 103450. PMCID: PMC8633984
• Zhou XA, Jiang Y, Man W & Yu X (2023). Multimodal methods to help interpret resting-state fMRI. Advances in Resting-state Functional MRI: Methods, Interpretation, and Applications. Book chapter, paperback ISBN: 9780323916882

Highlights

AARFD Grant, Alzheimer’s Association
Summa Cun Laude Merit Award, ISMRM, 2024
Young Investigator Award, BBRF, 2024

Website

Translational Neuroimaging and Neural Control Lab

Ivan Coto Hernandez

  • Instructor

icotohernandez@mgh.harvard.edu

Tagged:Algorithm Development, fluorescence, Hardware Development, label-free, multiphoton, Optical Imaging, Peripheral nerve regeneration, Spectroscopy, Super-resolution
Home / Role: Instructor

Dr. Coto Hernandez is an instructor at Harvard Medical School and Massachusetts General Hospital. Throughout his career, he has developed multiple hardware and software methods able to enhance effective spatial resolution and imaging depth and achieve label-free imaging.

Education

PhD

Select Publications

1. I. Coto Hernández, M. Castello, L. Lanzanò, M. d’Amora, P. Bianchini, A. Diaspro and G. Vicidomini. (2016) Two-Photon Excitation STED Microscopy with Time-Gated Detection. Sci Rep, 6, 19419.

2. L. Rishøj*, I. Coto Hernández *, N. Jowett, S. Ramachandran. (2022) Multiharmonic Imaging of Human Peripheral Nerves using a 1300 nm Ultrafast Fiber Laser. J. Biomed. Opt. 27(5), 056501.

3. I. Coto Hernández, S. Mohan, S. Minderler, N. Jowett. (2022) Super-Resolved Fluorescence Imaging of Peripheral Nerve.” Sci Rep 12, 12450. This article was on the Top 100 Neuroscience papers published in 2022

Highlights

2023: Mentored Quantitative Research Career Development Award (K25)

Chieh-En (Jane) Tseng

  • Instructor

Zürcher Lab
Chemical Neuroscience Program (Director of Multimodal Imaging)

ctseng4@mgh.harvard.edu

Tagged:Aging, Autism, Brain, Brain Mapping, Clinical, Cognition, Data Science, Diffusion MRI, fMRI, MRI, Multimodal Imaging, neuropsychology, Neuroscience, PET, PET-MRI, Psychiatry
Home / Role: Instructor

I am an Instructor in Radiology with expertise in magnetic resonance imaging (MRI) and positron emission tomography (PET). My PhD focused on using structural, diffusion, and functional MRI to study cognitive functions and related neuroanatomy in very preterm born adults. I completed my postdoctoral training at the Martinos Center where I was trained in PET. My research interest is in using neuroimaging to better understand the structure and function of the of the brain and how alterations lead to behavioral differences in psychiatric disorders, with a focus on autism spectrum disorder (ASD). My efforts towards this goal include the first investigations of in vivo neuroepigenetics in adults with ASD and bipolar disorder. In the long term, I hope my research can contribute to improving the quality of life of individuals with psychiatric disorders.

Education

PhD in Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London

Select Publications

1. Tseng CJ, McDougle CJ, Hooker JM, Zürcher NR. (2022). Epigenetics of Autism Spectrum Disorder: Histone deacetylases. Biological Psychiatry 91(11):922-933.

2. Tseng CJ*, Gilbert TM*, Catanese MC, Hightower BG, Peters AT, Parmar AJ, Kim M, Wang C, Roffman JL, Brown HE, Perlis RH, Zürcher NR, Hooker JM. (2020). In Vivo Human Brain Expression of Histone Deacetylases in Bipolar Disorder. Translational Psychiatry 10(1):224.

3. Zürcher NR, Loggia ML, Mullett JE, Tseng C, Bhanot A, Richey L, Hightower BG, Wu C, Parmar AJ, Butterfield RI, Dubois JM, Chonde DB, Izquierdo-Garcia D, Wey HY, Catana C, Hadjikhani N, McDougle CJ, Hooker JM. (2021). [11C]PBR28 MR-PET imaging reveals lower regional brain expression of translocator protein (TSPO) in young adult males with autism spectrum disorder. Molecular Psychiatry 26: 1659–1669.

Highlights

Certificate of Merit, MGH 8th Annual Radiology Research Celebration Poster Session, 2019

Mainak Jas

  • Instructor

The David Cohen MEG Lab
mjas@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Data Science, EEG, Hardware Development, Machine Learning, MEG, Preclinical, Software Development
Home / Role: Instructor

Dr. Jas completed his PhD from Telecom ParisTech. His thesis focused on automating MEG/EEG analysis pipelines. He is a proponent of open and reproducible science. He has been a key contributor to several open source
neuroimaging tools: most notably MNE-Python, MNE-BIDS, and HNN-core. He developed Autoreject, a tool for automatic annotation and repair of artifactual MEG/EEG data.

Currently, he is focusing on the development of next-generation MEG using optically pumped magnetometers(OPMs) and their application to new neuroscience problems.

Education

PhD in Image and Signal Processing, Télécom ParisTech

Select Publications

1. Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A (2017). Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage, 159, 417-429.

2. Jas M, Thorpe R, Tolley N, Bailey C, Brandt S, Caldwell B, Cheng H, Daniels D, Pujol CF, Khalil M, Kanekar S, Kohl C, Kolozsvári O, Lankinen K, Loi K, Neymotin S, Partani R, Pelah M, Rockhill A, Sherif M, Hamalainen M, & Jones S (2023). HNN-core: a Python software for cellular and circuit-level interpretation of human MEG/EEG. Journal of Open Source Software, 8(92): 5848.

3. Jas, M., Larson, E., Engemann, D. A., Leppäkangas, J., Taulu, S., Hämäläinen, M., & Gramfort, A. (2018). A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good
practices. Frontiers in neuroscience, 12, 530.

Highlights

2018: MILA best poster award (Montreal AI and Neuroscience conference)
2023: Student award at Brain and Human Modeling Conference

Yuanyuan Jiang

  • Instructor

Translational Neuroimaging and Neural Control Laboratory

yjiang13@mgh.harvard.edu
617 417 6305
Building 149, 13th Street

Tagged:Brain, Brain Mapping, fMRI, High-field Imaging, Multimodal Imaging, Preclinical, Stroke
Home / Role: Instructor

Dr. Jiang’s research focuses on multi-modal fMRI brain imaging technology in rodent models. He is developing a novel multichannel fiber-optic mediated extracellular glutamate and intracellular calcium recording with high-filed MRI to study different brain states. Dr. Jiang’s work also includes developing novel single-vessel fMRI methods and investigating the neurovascular dynamic changes underlying coma induction and reemergence in rodent models. He is now on the faculty at the Martinos Center and a member of the Translational Neuroimaging and Neural Control Laboratory

Education

Education: PhD , Technical University of Munich, 2017

Select Publications

Jiang, Y., Pais‐Roldán, P., Pohmann, R., & Yu, X. (2024). High Spatiotemporal Resolution Radial Encoding Single‐Vessel fMRI. Advanced Science, 2309218.

Zeng H, Jiang Y.#, Hammer S., Yu. X. (2022) Awake mouse fMRI and pupillary recordings in the ultra-high magnetic field. Frontiers in Neuroscience, p.1028.

Chen X.*, Jiang Y.*, Choi S., Pohmann R., Scheffler K., et al. (2021) Assessment of single-vessel cerebral blood velocity by phase contrast fMRI. PLOS Biology, 19(9): e3000923

Highlights

2023 Heitman Young Investigator Career Development Award.
Magna Cum Laude Merit Award, ISMRM 2023
Summa Cum Laude Merit Award, ISMRM 2018

Roberta Sclocco

  • Instructor

Napadow Lab
Center for Integrative Pain Neuroimaging (CIPNi)
Center for Neurointestinal Health

rsclocco@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Brain-Gut Axis, Clinical, fMRI, High-field Imaging, MRI, Pain, Stomach, tVNS
Home / Role: Instructor

Dr. Sclocco has a background in bioengineering and signal processing, with specific training in non-invasive neuroimaging (e.g., fMRI, EEG) and peripheral autonomic data analyses. Since the beginning of her career, she have been interested in the interactions between the central and peripheral autonomic nervous systems. Thus, she built her expertise in autonomic neuroimaging, combining fMRI with peripheral measurements (e.g., heart rate variability, skin conductance), in order to better understand the central autonomic network – i.e., the brain circuitry controlling and responding to autonomic modulation. During her postdoctorate training, she also gained expertise in applying the techniques above to investigate potential mechanisms supporting autonomic-based electrical transcutaneous neuromodulation – i.e., respiratory-gated transcutaneous vagus nerve stimulation (tVNS), specifically using ultrahigh- and high-field fMRI to assess response in autonomic brainstem nuclei.

Recently, she has begun to explore the brain-gut axis, with new projects combining brain and stomach imaging to assess the effects of tVNS on gastric function. She has started developing an analysis pipeline for 4D gastric MRI data that allows the evaluation of multiple aspects of gastric function (e.g., emptying, motility) with a single, non-invasive test.

Education

PhD in Bioengineering, Politecnico di Milano, Italy

Select Publications

1. Sclocco R, Beissner F, Desbordes G, Polimeni JR, Wald LL, Kettner NW, Kim J, Garcia RG, Renvall V, Bianchi AM, Cerutti S, Napadow V, Barbieri R. Neuroimaging brainstem circuitry supporting cardiovagal response to pain: a combined heart rate variability/ultrahigh-field (7 T) functional magnetic resonance imaging study. Philos Trans A Math Phys Eng Sci. 2016 May 13;374(2067):20150189. doi: 10.1098/rsta.2015.0189. PMID: 27044996; PMCID: PMC4822448.

2. Sclocco R, Garcia RG, Kettner NW, Isenburg K, Fisher HP, Hubbard CS, Ay I, Polimeni JR, Goldstein J, Makris N, Toschi N, Barbieri R, Napadow V. The influence of respiration on brainstem and cardiovagal response to auricular vagus nerve stimulation: A multimodal ultrahigh-field (7T) fMRI study. Brain Stimul. 2019 Jul-Aug;12(4):911-921. doi: 10.1016/j.brs.2019.02.003. Epub 2019 Feb 10. PMID: 30803865; PMCID: PMC6592731.

3. Sclocco R, Garcia RG, Kettner NW, Fisher HP, Isenburg K, Makarovsky M, Stowell JA, Goldstein J, Barbieri R, Napadow V. Stimulus frequency modulates brainstem response to respiratory-gated transcutaneous auricular vagus nerve stimulation. Brain Stimul. 2020 Jul-Aug;13(4):970-978. doi: 10.1016/j.brs.2020.03.011. Epub 2020 Mar 27. PMID: 32380448.

Highlights

2019: Young Investigator Forum, American Neurogastroenterology and Motility Society

2019: Poster of Distinction, American Gastroenterological Association

2016: Abstract Travel Award, National Science Foundation

Websites

Napadow Lab
Center for Integrative Pain Neuroimaging (CIPNi)

Noam Peled

  • Instructor

MEG core lab

Brain Aging and Dementia (BAnD) Lab

NPELED@MGH.HARVARD.EDU

(617) 959-0457

Tagged:Algorithm Development, Clinical, Data Science, EEG, fMRI, FreeSurfer, MEG, MRI, Multimodal Imaging, Software Development
Home / Role: Instructor

I have a broad background in neuroimaging, with specific training and expertise in analyzing and visualization of multi-modality neuroimaging datasets. I received my Ph.D. degree in machine learning and games theory from Bar-Ilan University in 2014, and in 2015 I started my position as a research fellow in the Athinoula A. Martinos Center for Biomedical Imaging, funded by the TRANSFORM-DBS program, which concluded in May 2019.

TRANSFORM-DBS (Transdiagnostic Repair of Affective Networks by Systematic, Function-Oriented, Real-time Modeling, and Deep Brain Stimulation) was a 5-year program sponsored by the Defense Advanced Research Projects Agency (DARPA). In my position, I had been responsible for building tools and algorithms for the group and visualize and analyze the highly complex collected datasets. For doing so, I had to collaborate with all the different labs and personal that are part of this project, deploy and install my tools in different environments and for different types of users, collect their feedback and suggestions to be able to improve my tools and algorithms.

As the leading developer of the multi-modality analysis and visualization tool (mmvt.mgh.harvard.edu), I laid the groundwork for developing a unique interactive platform to visualize and analyze highly complex multi-modalities neuroimaging datasets (e.g., EEG, MEG, fMRI, PET, and invasive electrodes). My research focuses mainly on developing new algorithms and analyzing epileptic activity using EEG, MEG, and invasive electrodes datasets based on source distribution, frequencies, and connectivity.

Most recently, I initiated a pilot with the director of functional neurosurgery at MGH, for using MMVT for analyzing new patients’ multi-modal datasets and preparing epileptic cases for the surgical planning session. By synergetic integrating the acquired multi-modal datasets, I will assist in the surgical planning sessions. I have also initiated such an initiative with the epilepsy centers in Boston Children’s Hospital and Texas Children’s Hospital.

Education

PhD in Neuroscience at Bar-Ilan University (Israel)

Selected Publications

Felsenstein O, Peled N*, Hahn E, et al. Multi-Modal Neuroimaging Analysis and Visualization Tool (MMVT). arXiv preprint arXiv:191210079. Published online 2019.

Peled N, Kraus S. A study of computational and human strategies in revelation games. Autonomous Agents and Multi-Agent Systems. 2015;29(1):73–97

Keren N, Peled N, Korngreen A. Constraining compartmental models using multiple voltage recordings and genetic algorithms. Journal of neurophysiology. Published online 2005

Highlights

Systems and Methods for Multi-Modal Bioimaging Data Integration

U.S. Patent Application No. 62/941,305

Suk-tak (Phoebe) Chan

  • Instructor

phoebe@nmr.mgh.harvard.edu
617-724-7194

Tagged:Aging, Autonomic Dysfunction, Brain, Brain Mapping, Cerebrovascular Disease, fMRI, High-field Imaging, Low-field Imaging, Machine Learning, MRI, Perfusion Imaging, Respiratory Physiology, Spine, Transcranial Doppler Ultrasound, Vascular Ultrasound
Home / Role: Instructor

Dr. Chan joined the Athinoula A. Martinos Center for Biomedical Imaging at MGH in 2010 as an instructor.  Her current research focuses on imaging of cerebrovascular responses using ultrasound and MRI. She uses natural breathing/gaseous challenge to measure cerebrovascular responses in healthy subjects, patients with episodic migraine, traumatic brain injury, Huntington’s disease, atrial fibrillation and chronic fatigue syndrome.  She has developed a robust physiological model for the assessment of cerebrovascular reactivity under brief breath hold challenge.  Her model has been tested on patients to localize subtle cerebrovascular changes for disease diagnosis and monitoring of disease progression.

Education

PhD in Radiography/Ultrasound, Hong Kong Polytechnic University

Select Publications

Chan ST, Brook F, Ahuja A, Brown B, Metreweli C. Relationship of thyroid blood flow to reproductive events in normal Chinese females. Ultrasound Med Biol. 1999 Feb;25(2):233-40. doi: 10.1016/s0301-5629(98)00145-8. PMID: 10320312.

Chan ST, Tam Y, Lai CY, Wu HY, Lam YK, Wong PN, Kwong KK. Transcranial Doppler study of cerebrovascular reactivity: are migraineurs more sensitive to breath-hold challenge? Brain Res. 2009 Sep 29;1291:53-9. doi: 10.1016/j.brainres.2009.07.057. Epub 2009 Jul 25. PMID: 19635466.

Chan ST, Evans KC, Song TY, Selb J, van der Kouwe A, Rosen BR, Zheng YP, Ahn A, Kwong KK. Cerebrovascular reactivity assessment with O2-CO2 exchange ratio under brief breath hold challenge. PLoS One. 2020 Mar 24;15(3):e0225915. doi: 10.1371/journal.pone.0225915. PMID: 32208415.

Highlights

Over 40 peer-reviewed journal articles

  1. 1
  2. 2
×

We use cookies and other tools to enhance your experience on our website and to analyze our web traffic. For more information about these cookies and the data collected, please refer to Mass General Brigham's Web Privacy Policy