Kristina Rewin Ciesielski

Associate Professor

kciesielski@mgh.harvard.edu

617-724-1845

MGH Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General
Hospital, HMS, 149-13th Street, 4th Floor, Boston, MA 02-129, USA

Tagged:Brain, Brain Mapping, Clinical, Data Science, EEG, fMRI, FreeSurfer, Integrative models of neuroimaging/neurocognitive/psychosocial markers of anxiety, Machine Learning, MEG, MRI, Preclinical, Probe Development, Spectroscopy

EDUCATION: • PhD in Biological Brain Sciences, Nencki Institute of Experimental Biology, Polish Science Academy, Warsaw • Certified Clinical Psychologist & Graduate M., Clinical Division, British Psychological Society; Member of HCPC (Royal British Charter) UK POST-DOCTORAL: • UMIST, University of Manchester, UK • UCSD, Department of Neurosciences & Children’s Hospital, San Diego, USA • University College London, Institute of Child Health, London, UK

RESEARCH: Multimodal neuroimaging and neuropsychological research conducted by Dr. Kristina Rewin Ciesielski focuses on developmental etiology of anxiety and OCD spectrum disorders. The conceptual target of the investigations are developmental mechanisms of the neural inhibitory control subserving sensorimotor and cognitive information processing within the brain dorsal and ventral visual networks. The main research procedure involves an integration of the MEG, EEG, strMRI, fMRI, MR spectroscopy and neuropsychological measurements to identify early childhood biological brain/behavior markers of subclinical anxiety. The crucial role of child-mother system in early anxiety is emphasized. The specific aim is to translate the above integrative insight into the design of early neuroimaging-informed preventative interventions, such as a child-friendly Brain-MEG Neurofeedback.

Associated Lab

The David Cohen MEG Laboratory, Department of Radiology, MGH, Charlestown, MA, USA

Associated Lab(s) Sites

Pediatric Neuroscience Laboratory, Department of Psychology, PCNC, University of New Mexico, ABQ, NM, USA

Education

PhD, AMBPS CD

Select Publications

• Ciesielski K.T.R., Hämäläinen MS, Lesnik PG, Geller DA, Ahlfors SP. (2005). Increased MEG activation in OCD reflects a compensatory mechanism specific to the phase of a visual working memory task. NeuroImage 24: 1180-119. (1st recorded MEG study in OCD).
• Ciesielski, KTR., Stern, E.M., Diamond, A., Sheraz, K., Busa E.A., Goldsmith, T.E., van der Kouwe A., Fischl, B., Rosen, B.R. (2019). Maturational Changes in Human Dorsal and Ventral Visual Networks. Cerebral Cortex, 30 March, 5131-5149. DOI: 10.1093/cercor/bhz053
• Ciesielski K.T.R, Bouchard C., Solis I., Coffman B.A., Tofighi D., Pesko J.C. (2021). Posterior brain sensorimotor recruitment for inhibition of delayed responses in children. Experimental Brain Research. 239: 3221 – 3242.
• Hodgdon E.A., Anderson R., Azzawi A.H., Wilson T.W., Calhoun V.D., Wang Y-P., Solis I., Greve D.N.,Stephen J.M., Ciesielski K.T.R. (2024). MRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children. Developmental Cognitive Neuroscience, 67, 101385.

Highlights

• Early maturation of the dorsal brain architecture including dorsal visual and default brain networks.
• Significance of a model of cognitive inhibitory control as the key for unraveling etiology of anxiety disorders.
• Critical role of early visual networks plasticity for prevention of anxiety and obsessive-compulsive spectrum.

Heidi Jacobs

Associate Professor

Principal Investigator of the Jacobs Lab (Blue spotters)

617 – 909 – 7679
hjacobs@mgh.harvard.edu

Tagged:Aging, Alzheimer's Disease, Brain, Brain Mapping, Brainstem, Clinical, Cognition, Data Science, Diffusion MRI, fMRI, High-field Imaging, MRI, Multimodal Imaging, neuropsychology, Neurotransmission, PET, PET-MRI

The Jacobs Lab aims to detect the earliest brain changes that contribute to cognitive decline and behavioral changes associated with the earliest stages of Alzheimer’s disease. Our focus is on neuroimaging method development, biomarker evaluation and testing new preventive interventions targeting the neuromodulatory subcortical systems (e.g. noradrenergic locus coeruleus, serotonergic raphe nucleus, cholinergic basal forebrain, orexinergic hypothalamus..).

Dr. Jacobs is a clinical neuropsychologist and neuroscientist with a broad expertise in cognitive evaluations in aging and dementia, neuroimaging (3T, 7T MRI and PET) and statistical modeling. She is also the chair of the neuromodulatory subcortical systems professional interest area of the Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment. She is also a guest associate professor in clinical neuroimaging at Maastricht University (the Netherlands).

Education

PhD

Select Publications

Van Egroo M., Koshmanova E., Vandewalle G., Jacobs H.I.L. (2022). Importance of the locus coeruleus-norepinephrine system in sleep-wake regulation: implications for aging and Alzheimer’s disease. Sleep Medicine Reviews, 62:101592; PMID: 35124476

Jacobs H.I.L., Becker J.A., Kwong K., Engels-Dominguez N., Prokopiou P.C., Papp K.V., Properzi M., Hampton O.L., d’Oleire Uquillas F., Sanchez J.S., Rentz D.M., El Fakhri G., Normandin M.D., Price J.C., Bennett D.A., Sperling R.A., Johnson K.A. (2021) In vivo and neuropathology data support locus coeruleus integrity as indicator of Alzheimer’s disease pathology and cognitive decline. Science Translational Medicine, 13(612):eabj2511, PMID: 34550726.

Jacobs H.I.L., Hedden T., Schultz A.P., Sepulcre J., Perea R.D., Amariglio R.E., Papp K.V., Rentz D.M., Sperling R.A. & Johnson K.A. (2018). Structural tract alterations predict down-stream tau accumulation in amyloid positive older individuals. Nature Neuroscience, 21(3), 424-431; PMCID: PMC5857215

Highlights

2012: International Junior Investigator award from the International College of Geriatric Psychoneuropharmacology

2018: Development of the first 7T MRI sequence to visualize the locus coeruleus in vivo

2022: de Leon award for best Neuroimaging paper of the year by the Alzheimer’s Association

Website

The Blue Spotters

Sava Sakadzic

Associate Professor

sava.sakadzic@mgh.harvard.ed
Optics@Martinos
617-724-4730

Tagged:Aging, Alzheimer's Disease, Brain Mapping, Cardiovascular Disease / Disorders, fMRI, Molecular Imaging, Multimodal Imaging, Optical Imaging, Preclinical, Spectroscopy, Stroke, TBI

Dr. Sakadzic obtained his Ph.D. with Dr. Lihong Wang at Texas A&M University and completed his postdoctoral training with Dr. David Boas at the Massachusetts General Hospital. His group is developing state-of-the-art optical microscopy imaging technologies and utilizing them to better understand oxygen delivery and consumption on microvascular scales. The goal of these research efforts is to better understand neurovascular coupling in health and disease, to facilitate improvement and development of treatments of brain conditions, and to enable new imaging biomarkers and more quantitative interpretation of macroscopic imaging modalities.

Education

PhD in Biomedical Engineering, Texas A&M University, College Station, Texas

Select Publications

1. Sakadzic S, Roussakis E, Yaseen MA, Mandeville ET, Srinivasan VJ, Arai K, Ruvinskaya S, Devor A, Lo EH, Vinogradov SA, Boas DA. Two-photon high-resolution measurement of partial pressure of oxygen in cerebral vasculature and tissue. Nat Methods. 2010 Sep; 7(9):755-9. PMID: 20693997.

2. Li B, Esipova TV, Sencan I, Kiliç K, Fu B, Desjardins M, Moeini M, Kura S, Yaseen MA, Lesage F, Østergaard L, Devor A, Boas DA, Vinogradov SA, Sakadžic S. More homogeneous capillary flow and oxygenation in deeper cortical layers correlate with increased oxygen extraction. Elife. 2019 07 15; 8. PMID: 31305237.

3. Sencan I, Esipova T, Kiliç K, Li B, Desjardins M, Yaseen MA, Wang H, Porter JE, Kura S, Fu B, Secomb TW, Boas DA, Vinogradov SA, Devor A, Sakadžic S. Optical measurement of microvascular oxygenation and blood flow responses in awake mouse cortex during functional activation. J Cereb Blood Flow Metab. 2022 03; 42(3):510-525. PMID: 325156

Website

Optics@Martinos

Matthew Sacchet

Associate Professor

Director of the Meditation Research Program

meditationadministration@mgh.harvard.edu

Tagged:Diffusion MRI, EEG, fMRI, MEG, MRI, Multimodal Imaging, PET, PET-MRI, Spectroscopy

Dr. Matthew D. Sacchet, Ph.D., is an Associate Professor and the Director of the Meditation Research Program at Harvard Medical School and Massachusetts General Hospital (Mass General). Dr. Sacchet and his team study advanced meditation: skills, states, stages, and endpoints of contemplative practice that unfold with mastery and time. Since 2012, he has authored more than 135 publications, presented more than 150 times at international, national, regional and local venues including at Cambridge, Harvard, Oxford, Princeton, Stanford, and Yale Universities, and the United Nations, and been cited more than 9,500 times. He has received generous support from numerous foundations and repeat awards from federal funding bodies in the United States, including NIH and NSF. His work has been covered by many major media outlets, including CBC, CBS, Forbes, Men’s/Women’s Health, NBC, NPR, Scientific American, TIME, Vox, and Wall Street Journal, and Forbes named him one of its “30 Under 30.” Dr. Sacchet is an Associate Editor of the leading meditation academic journal Mindfulness, and a Research Fellow of the Mind & Life Institute.

The mission of the Meditation Research Program at Mass General and Harvard is to establish a scientific understanding of, and also share, advanced meditation. The Program’s research spans and integrates diverse fields across clinical science and medicine, computer/computational science, engineering, epidemiology, neuroscience, philosophy, psychology, and religious studies. For example, the Program’s studies include investigation of meditative development and meditative endpoints toward a more comprehensive understanding of the trajectories and outcomes of advanced meditation. The Program has published landmark studies in a number of domains including contributing first insights into scientific understanding of advanced absorption (“jhana”) and insight meditation, and meditative endpoints (cessations of consciousness), and the epidemiology and public health implications of altered states of consciousness. This research promises to contribute to improving individual well-being and the collective health of society by informing the development of meditation training and meditation-based interventions that are more impactful. Toward this end, the Program develops advanced meditation training and educational materials for broad dissemination.

Education

Ph.D., Neurosciences, Stanford University School of Medicine; Sc.B., Contemplative Studies, Brown University

Select Publications

1. https://www.ncbi.nlm.nih.gov/myncbi/1x9tsgj0kLI57/bibliography/public/

2. https://scholar.google.com/citations?user=ckejHQkAAAAJ&hl=en

Highlights

2021: R01 National Institute of Mental Health (NIMH)

2021: Anne M. Cataldo Excellence in Mentoring Award Nominee, McLean Hospital

2021: Excellence in Mentoring Awards Nominee, Harvard Medical School

Websites

Meditation Research Program

Dania Daye

Associate Professor

Precision Interventional and Medical Imaging (PIMI) Research Group (Director)

ddaye@mgh.harvard.edu

Tagged:Adrenal, Algorithm Development, Artificial Intelligence, Cancer, Cancer Imaging, Clinical, Colorectal, Data Science, Kidneys, Liver, Machine Learning, MRI, Pancreas, Vascular, Vascular Disease

Dania Daye, MD, PhD is an Assistant Professor of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School. She is the co-director of IR Research at MGH and Director of the Precision Interventional and Medical Imaging (PIMI) Research Group. Her research centers around the applications of machine learning and computer vision for precision medicine.

For her research, Dr. Daye is the recipient of many awards that include the Association of American Physicians Stanley J. Korsmeyer Young Investigator Award, the Association of University Radiologists (AUR) Memorial Award, and the 40 under 40 MedTech Boston Healthcare Innovators. Dr. Daye previously served as the President of the American Physician Scientists Association and currently serves on the board of directors. For her local and national leadership roles, Dr. Daye was the recipient of the American Medical Association (AMA) Foundation Leadership Award.

Dr. Daye is a graduate of the MD/PhD program at the University of Pennsylvania, where she completed her PhD in Bioengineering as an HHMI-NIBIB Interfaces Scholar.

Education

MD, PhD, University of Pennsylvania

Select Publications

1. Ashraf AB, Daye D, Gavenonis S, Mies C, Feldman M, Rosen M, Kontos D. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology. 2014 Aug;272(2):374-84. doi: 10.1148/radiol.14131375. Epub 2014 Apr 4. PMID: 24702725; PMCID: PMC4564060.

2. Daye D, Staziaki PV, Furtado VF, Tabari A, Fintelmann FJ, Frenk NE, Shyn P, Tuncali K, Silverman S, Arellano R, Gee MS, Uppot RN. CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept. Cardiovasc Intervent Radiol. 2019 Dec;42(12):1771-1776. doi: 10.1007/s00270-019-02336-0. Epub 2019 Sep 5. PMID: 31489473.

3. Daye D, Tabari A, Kim H, Chang K, Kamran SC, Hong TS, Kalpathy-Cramer J, Gee MS. Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer. Eur Radiol. 2021 Aug;31(8):5759-5767. doi: 10.1007/s00330-020-07673-0. Epub 2021 Jan 16. PMID: 33454799.

Highlights

AUR Memorial Award, Association of University Radiologists

Stanley J. Korsmeyer Young Investigator Award, Association of American Physicians

Junior Fellow, International Society of Magnetic Resonance in Medicine

40 under 40 MedTech Boston Healthcare Innovator Award

RSNA Roentgen Research Award, Radiological Society of Northern America

Website

Precision Interventional and Medical Imaging (PIMI) Research Group

Jodi Gilman

Associate Professor

Center for Addiction Medicine (Director of Neuroscience)

jgilman1@mgh.harvard.edu
617-643-7293

Tagged:Brain, Clinical, Data Science, fMRI, MRI, Optical Imaging, PET

Dr. Gilman is an Associate Professor at Harvard Medical School (HMS) Department of Psychiatry and the Director of Neuroimaging at the Massachusetts General Hospital (MGH) Center for Addiction Medicine. Her research uses multi-modal imaging, behavioral and cognitive testing to understand the biological, psychological, and clinical aspects of addiction. Specifically, she uses brain imaging and cognitive and behavioral methods to understand different stages of substance use, from initiation to maintenance to recovery.

She is interested in the effects of cannabis on the brain and on cognition, and has been PI on a several NIDA-funded grants, including a K01 to study the neuroscience of peer influences and cannabis use in college students, a K02 to use big data to understand the brain changes in addiction in a large longitudinal dataset, and three R01s to study medicinal properties of cannabinoids.

Education

PhD in Neuroscience, Brown University

Select Publications

Paul RH, Cho K, Belden A, Carrico AW, Martin E, Bolzenius J, Luckett P, Cooley SA, Mannarino J, Gilman JM, Miano M, Ances BM. Cognitive Phenotypes of HIV Defined Using a Novel Data-driven Approach. J Neuroimmune Pharmacol. 2022 Jan 4. doi: 10.1007/s11481-021-10045-0. Epub ahead of print. PMID: 34981318.
Cooke ME, Clifford JS, Do EK, Gilman JM, Maes HH, Peterson RE, Prom-Wormley EC, Evins AE, Schuster RM; Spit for Science Working group. Polygenic score for cigarette smoking is associated with ever electronic-cigarette use in a college-aged sample. Addiction. 2021 Oct 11. doi: 10.1111/add.15716. Epub ahead of print. PMID: 34636095.
Cooke ME, Gilman JM, Lamberth E, Rychik N, Tervo-Clemmens B, Evins AE, Schuster RM. Assessing Changes in Symptoms of Depression and Anxiety During Four Weeks of Cannabis Abstinence Among Adolescents. Front Psychiatry. 2021 Jul 1;12:689957. doi: 10.3389/fpsyt.2021.689957. PMID: 34276449; PMCID: PMC8280499.

Highlights

Dr. Gilman uses functional and structural neuroimaging, combined with clinical trial designs, to understand how drugs affect the brain. Using multimodal imaging, her goal is to conduct research that can inform decision-making among the public, patients, and clinicians regarding evidence-based decisions to use or not use cannabis for recreational or medical purposes. Specifically, she is studying:

(1) cannabis use and brain/cognitive changes among adolescents and vulnerable populations such as those with psychiatric illnesses;

(2) potential medicinal properties of components of the cannabis plant and specifically of cannabidiol (CBD), a much-needed area of research considering the interest among the public surrounding CBD.

Dr. Gilman has received two NIDA Career Development Awards; a K01 to develop novel neuroimaging paradigms to investigate the effect of social influence and drug use on brain function, and a K02 to learn big data techniques and complete a series of analyses investigating impulsivity in adolescent drug use using the Adolescent Brain Cognitive Development (ABCD) dataset.

She has also been awarded a NIDA grant (R01) to investigate the role of medical cannabis on escalation to addiction, neurocognitive effects, and functional brain changes, and a second NIDA grant (R01) to investigate the role of cannabidiol on neuroinflammation using PET scanning in patients with low back pain. She is also Co-investigator on a project using neuroimaging to characterize impaired driving after acute administration of THC or placebo.

Website

Center for Addiction Medicine

Xin Yu

Associate Professor

xyu9@mgh.harvard.edu

Tagged:Alzheimer's Disease, Brain Mapping, fMRI, High-field Imaging, MRI, Multimodal Imaging, Optical Imaging, Preclinical, RF Coil Development, Stroke, TBI

Xin Yu studied Neuroscience and Biophysics at New York University, USA. During his Ph.D. training in Dan Turnbull’s lab, he implemented Manganese-enhanced MRI to study the auditory midbrain plasticity and mid-hindbrain development. Meanwhile, he was trained by Dan Sanes to target the inferior colliculus and collaborated with Rodolfo Llinas’s lab to trace inferior olive with in vivo electrophysiology and Manganese-enhanced MRI (MEMRI). After obtained his Ph.D. in 2007, he moved to Bethesda and worked as a research fellow in Alan Koretsky’s lab in the National Institutes of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH). He combined MEMRI and BOLD-fMRI with electrophysiological recordings to study the system-to-synaptic brain plasticity. Using high-field fMRI (11.7T scanner), he made a significant effort to push the spatiotemporal limit of fMRI so as to better understand the neurovascular coupling mechanism of fMRI. In 2014, he moved to Germany and build a Translational Neuroimaging and Neural Control research group at the Max Planck Institute for Biological Cybernetics. The group specializes in developing advanced fMRI methods under high field (14T for animals, and 9.4T for humans) and implementing optogenetics and fiber-optic mediated calcium recording methods to build up multi-modal fMRI platform to study different brain states (arousal, anesthetized, and sleep). His group also focuses on investigating the neurovascular dynamic changes underlying the coma induction and reemergence in rodent models. In 2018, Dr. Yu moves to Martinos Center and serves as the Director of the Small Animal MR Scanning Facility.

Education

PhD in Biophysics, Physiology, and Neuroscience, New York University

Select Publications

1. Drew PJ, Mateo C, Turner KL, Yu X, Kleinfeld D. Ultra-slow Oscillations in fMRI and Resting-State Connectivity: Neuronal and Vascular Contributions and Technical Confounds. Neuron. 2020 Aug 06. PMID: 32791040.

2. Chen Y, Sobczak F, Pais-Roldán P, Schwarz C, Koretsky AP, Yu X. Mapping the Brain-Wide Network Effects by Optogenetic Activation of the Corpus Callosum. Cereb Cortex. 2020 Jun 18. PMID: 32556241.

3. Pais-Roldán P, Takahashi K, Sobczak F, Chen Y, Zhao X, Zeng H, Jiang Y, Yu X. Indexing brain state-dependent pupil dynamics with simultaneous fMRI and optical fiber calcium recording. Proc Natl Acad Sci U S A. 2020 03 24; 117(12):6875-6882. PMID: 32139609.

Highlights

AMPC member (Annual Meeting of Program Committee) of ISMRM (2018-2021)

Junior Fellow of the ISMRM (2011)

Website

https://tnnc.mgh.harvard.edu/

Andre van der Kouwe

Associate Professor

Laboratory for Computational Neuroimaging

andre@nmr.mgh.harvard.edu

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Diffusion MRI, EEG, fMRI, FreeSurfer, HIV, MRI, Pulse Sequence Development, Software Development, TBI

Dr. van der Kouwe does research in the field of MRI pulse sequence design and image analysis. He supports neuroscience research at the MGH and collaborating institutions by improving acquisition methods, providing techniques such as high-reliability imaging for quantitative brain morphometry with FreeSurfer, automatic slice prescription and prospective on-scanner motion and shim correction for anatomical imaging and spectroscopy. He developed the original Siemens ?AutoAlign? prototype. This technology improves clinical workflow and reliability in brain imaging. He shares sequences for bandwidth matched morphometry, such as MEMPRAGE, with other research sites. The MPRAGE and T2-SPACE sequences with vNav (volumetric navigator) based prospective motion correction are used at multiple sites in the ABCD and HCP studies and elsewhere. For the last decade, Dr. van der Kouwe has collaborated with the University of Cape Town and Stellenbosch University on techniques for imaging HIV infected children and adolescents with Drs. Meintjes, Laughton and Holmes, and their students, and fetal alcohol spectrum disorder with Drs. Sandra and Joseph Jacobson.

Education

PhD in Biomedical Engineering, The Ohio State University

Select Publications

van der Kouwe AJW, Benner T, Salat DH, Fischl B. Brain morphometry with multiecho MPRAGE. Neuroimage. 2008 Apr 1;40(2):559-569. doi: 10.1016/j.neuroimage.2007.12.025. Epub 2008 Feb 1. PMID: 18242102; PMCID: PMC2408694.

Tisdall MD, Hess AT, Reuter M, Meintjes EM, Fischl B, van der Kouwe AJ. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med. 2012 Aug;68(2):389-99. doi: 10.1002/mrm.23228. Epub 2011 Dec 28. PMID: 22213578; PMCID: PMC3320676.

Frost R, Wighton P, Karahanoğlu FI, Robertson RL, Grant PE, Fischl B, Tisdall MD, van der Kouwe A. Markerless high-frequency prospective motion correction for neuroanatomical MRI. Magn Reson Med. 2019 Jul;82(1):126-144. doi: 10.1002/mrm.27705. Epub 2019 Feb 28. PMID: 30821010; PMCID: PMC6491242.

Highlights

2007: Elected to senior member of the IEEE

2014: Recognized on Thomson Reuters list of top 1% most cited researchers in neuroscience

2016: Honorary Associate Professor, Department of Human Biology, University of Cape Town

Website

Laboratory for Computational Neuroimaging

Kawin Setsompop

Associate Professor

kawin.setsompop@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Diffusion MRI, fMRI, Functional Magnetic Resonance Imaging, Magnetic Resonance Imaging, MRI, Software Development

Dr. Setsompop is an Associate Professor of Radiology at Harvard Medical School and an affiliated faculty member at Harvard-MIT Division of Health Sciences and Technology (HST). He received his Master’s degree in Engineering Science from Oxford University and his PhD in Electrical Engineering and Computer Science from MIT.

Over the past decade, Dr. Setsompop has been a pioneer in the development of a number of MRI acquisition technologies, including parallel transmission and simultaneous multi-slice imaging. In particular, his blipped-CAIPI technology has been distributed to more than 200 research and clinical sites and is now a clinical product on Siemens, GE and Phillips MRI scanners worldwide. Such technology is changing how diffusion, perfusion and functional MRI are being performed today. Beyond his research and mentoring role within his laboratory, Dr. Setsompop also has had a number of leadership roles in the organization of workshops, training courses and seminars for the MRI community. In addition to these, he has taken on a number of formal teaching roles at Harvard and MIT, serving as a course director as well as giving numerous guest lectures.

Education

PhD in Electrical Engineering and Computer Science, EECS, Massachusetts Institute of Technology (MIT)

Select Publications

1. Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med. 2012 May;67(5):1210-24.

2. Setsompop K, Fan Q, Stockmann J, Bilgic B, Huang S, Cauley SF, Nummenmaa A, Wang F, Rathi Y, Witzel T, Wald LL. High-resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: Simultaneous multislice (gSlider-SMS). Magn Reson Med. 2018 Jan;79(1):141-151.

3. 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.

Highlights

K99/R00 NIH Career development award 2010

NIBIB New Horizon plenary lecture, ISMRM 2016

Berkin Bilgic

Associate Professor

MRI Acquisition Group
Magnetic Resonance – Physics & Instrumentation Group

bbilgic@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Clinical, Data Science, Diffusion MRI, fMRI, High-field Imaging, Machine Learning, MRI, Software Development, Spectroscopy

MRI has demonstrated ability to provide exquisite contrast for non-invasive imaging. What limits its efficiency and sensitivity are the tradeoffs between scan time, resolution and signal-to-noise ratio. Dr. Bilgic’s research is devoted to breaking this stalemate by developing new acquisition and reconstruction methods that synergistically exploit MR physics, signal processing algorithms and hardware capabilities.

He pursues this goal on three fronts:

(i) Rapid comprehensive brain exam: Development of the Wave-CAIPI acquisition trajectory to fully harness the encoding capability of high-channel count receivers and provide an order of magnitude faster imaging. This enabled the development of a 6-minute, multi-contrast whole-brain exam at 1 mm isotropic resolution.

(ii) Efficient quantitative imaging: Spearheaded the development of Quantitative Susceptibility Mapping (QSM), a novel contrast mechanism that probes the magnetic properties of tissues to provide a biomarker strongly correlated with tissue iron concentration. Dr. Bilgic’s technical developments in MR Fingerprinting (MRF), a new quantitative imaging concept, have provided the fastest whole-brain MRF acquisition, with the highest resolution T1 and T2 relaxation maps to date.

(iii) Next generation imaging with joint reconstruction: Exploitation of similarities across multiple contrasts routinely acquired in MRI exams with our joint parallel imaging strategies. While these already allow >10-fold speed-up in conventional sequences, they can be combined with Wave-CAIPI trajectory to provide >20× faster clinical scans.

Education

PhD in Electrical and Computing Science, Massachusetts Institute of Technology (MIT)

Select Publications

1: Bilgic B, Pfefferbaum A, Rohlfing T, Sullivan EV, Adalsteinsson E. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage. 2012 Feb 1;59(3):2625-35.

2: Bilgic B, Goyal VK, Adalsteinsson E. Multi-contrast reconstruction with Bayesian compressed sensing. Magn Reson Med. 2011 Dec;66(6):1601-15.

3. Bilgic B, Gagoski BA, Cauley SF, Fan AP, Polimeni JR, Grant PE, Wald LL, Setsompop K. Wave-CAIPI for highly accelerated 3D imaging. Magn Reson Med. 2015 Jun;73(6):2152-62.

Highlights

2017: Outstanding Emerging Investigator, University of Utah

2015: ISMRM Junior Fellow

2012: MICCAI Young Scientist Award Finalist

Websites

Berkin Bilgic
MRI Acquisition Group
Magnetic Resonance – Physics & Instrumentation Group

Jean Augustinack

Associate Professor

Histology Laboratory (Director)
Laboratory for Computational Neuroimaging (LCN)

jaugustinack@mgh.harvard.edu
617-724-0429

Tagged:Aging, Alzheimer's Disease, Brain, Brain Mapping, FreeSurfer, High-field Imaging, Histology, Histopathology, Magnetic Resonance Imaging, MRI, Parkinson's Disease, Preclinical, TBI, Traumatic Brain Imaging

The main goal of Dr. Augustinack’s research is to validate neuroimaging, such as MRI, with ground truth histologic analyses and to advance neuroanatomical and pathological biomarkers for in vivo imaging. Her laboratory bridges the domains of ground truth histologic staining and MRI tissue properties. The laboratory focuses both on the methods for combining ex vivo (post mortem) MRI and histology and on establishing diagnostics markers to evaluate neurodegenerative disease with neuroimaging.

Dr. Augustinack is a neuroanatomist and neuroscientist with broad background in human neuroanatomy and brain mapping. She teaches and contributes to the Health Science and Technology (HST130, block II) neuroanatomy course at Harvard Medical School and serves as the Co-Director of the graduate course Neuroanatomy and Neuropathology at Harvard Medical School. She has special interest in the neuroanatomy and the neuropathology of the medial temporal lobe in the preclinical stages of Alzheimer’s disease. In all of her projects, she applies her neuroanatomy knowledge to better understand the structure-function relationship in disease using MRI modeling.

Education

PhD in Anatomy and Cell Biology, University of Iowa

Select Publications

1. Saygin ZM, Kliemann D, Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M, Stevens A, Van Leemput K, McKee A, Frosch MP, Fischl B, Augustinack JC; Alzheimer’s Disease Neuroimaging Initiative. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage. 2017 Jul 15;155:370-382.

2. Augustinack JC, Huber KE, Postelnicu GM, Kakunoori S, Wang R, van der Kouwe AJ, Wald LL, Stein TD, Frosch MP, Fischl B. Entorhinal verrucae geometry is coincident and correlates with Alzheimer’s lesions: a combined neuropathology and high-resolution ex vivo MRI analysis. Acta Neuropathol. 2012 Jan;123(1):85-96.

3. Augustinack JC, van der Kouwe AJ, Blackwell ML, Salat DH, Wiggins CJ, Frosch MP, Wiggins GC, Potthast A, Wald LL, Fischl BR. Detection of entorhinal layer II using 7Tesla [corrected] magnetic resonance imaging. Ann Neurol. 2005 Apr;57(4):489-94.

Highlights

Dr. Augustinack has taught neuroanatomy at Harvard Medical School for 15 years.

Website

Laboratory for Computational Neuroimaging

Kenneth Kwong

Associate Professor

kwong@nmr.mgh.harvard.edu

Kenneth Kwong, PhD, has been conducting magnetic resonance imaging (MRI) research for more than 30 years with expertise in diffusion (R.1), functional imaging (R.2) and perfusion imaging (R.3). He was one of the earliest researchers to explore MR diffusion imaging of healthy subjects and patients. His team presented the first MR human diffusion anisotropy result in a conference paper, in 1988 at the annual meeting of the Society of Magnetic Resonance (R.1). He conducted the first successful human functional magnetic resonance imaging (fMRI) experiment utilizing intrinsic blood oxygenation level dependent (BOLD) signal (R.2). He was the first to apply MRI selective and nonselective inversion pulses for arterial spin labeling for perfusion imaging (R.3). He maintained a longstanding collaboration with Dr. Kathleen Hui on neuroimaging of peripheral nerve stimulation such as acupuncture/needling intervention. His current work includes non-invasive measurement of heme oxygenase-1 for autoimmune and inflammatory markers; a collaboration with Dr. Chan on brain-body interaction of respiratory gas exchange metrics; and collaborations with different teams on developing new imaging techniques to discover new brain markers for traumatic brain injury, atrial fibrillation and many other challenging disorders.

R.1 First mapping of white matter tract anisotropy by MRI diffusion (1988)

MR diffusion mapping of anisotropy of white matter tracts at the internal capsule, an accidental discovery when Dr. Kwong was imaged with head prone as well as lying on his side. Anisotropy discovery was reported by Dr. Daisy Chien in an ISMRM abstract in 1988. This anisotropy itself is the fundamental principle underlying the modern method of MRI tractography and structural connectomics (the in vivo visualization the axonal fibers that connect neurons in the brain). MR diffusion anisotropy is an extremely powerful tool to characterize brain tissue deficits in all sorts of neuronal disorders. Our team including Drs. Daisy Chien and Ferdinando Buonanno used MRI diffusion to measure chronic results of ischemic stroke.

Chien D, Buxton RB, Kwong KK, Brady TJ, Rosen BR. MR diffusion imaging of the human brain. J Comput Assist Tomogr. 1990 Jul-Aug;14(4):514-20.

R.2 First discovery of intrinsic contrast of deoxyhemoglobin to be used for measurement of brain functions

First discovery of non-invasive in-flow weighted perfusion measurement of fMRI, launching the use of arterial spin labeling in functional imaging (1991). That discovery started a new era of non-invasive functional MRI of the brain.

Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R, et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A. 1992 Jun 15;89(12):5675-9.

R.3. First application of MRI selective and nonselective inversion pulses for arterial spin labeling for perfusion imaging

Kwong KK, Chesler DA, Weisskoff RM, Rosen BR. Perfusion MR Imaging, in “Proc., SMR, 2nd Annual Meeting, San Francisco, 1994,” p. 1005.

Kwong KK. Functional magnetic resonance imaging with echo planar imaging. Magn Reson Q. 1995 Mar;11(1):1-20. Review.

Kwong KK, Chesler DA, Weisskoff RM, Donahue KM, Davis TL, Ostergaard L, Campbell TA, Rosen BR. MR perfusion studies with T1-weighted echo planar imaging. Magn Reson Med. 1995 Dec;34(6):878-87.

Education

PhD in High Energy Physics, University of California, Riverside

Select Publications

1. Kwong KK, McKinstry RC, Chien D, Crawley AP, Pearlman JD, Rosen BR. CSF-suppressed quantitative single-shot diffusion imaging. Magn Reson Med. 1991 Sep;21(1):157-63.

2. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R, et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A. 1992 Jun 15;89(12):5675-9.

3. Kwong KK, Chesler DA, Weisskoff RM, Donahue KM, Davis TL, Ostergaard L, Campbell TA, Rosen BR. MR perfusion studies with T1-weighted echo planar imaging. Magn Reson Med. 1995 Dec;34(6):878-87.

Highlights

More recent work includes bioluminescence imaging of heme oxygenase-1 upregulation in transgenic mice with the Gua Sha procedure.

Lilla Zollei

Associate Professor

Laboratory for Computational Neuroimaging
Fetal-Neonatal Neuroimaging and Developmental Science, Boston Children’s Hospital (P.E. Grant, MD)

lzollei@nmr.mgh.harvard.edu
617-643-7791

Tagged:Algorithm Development, Brain Development, Brain Mapping, Data Science, Diffusion MRI, FreeSurfer, Magnetic Resonance Imaging, MRI, Multimodal Imaging, Perinatal Imaging, Postmortem Imaging, SIDS, Software Development

Dr. Lilla Zollei’s research focuses on the design and development of quantitative analysis tools for neuroscientific problems. She builds and tests computational tools that can be used for both individual and group studies and investigate, through the combination of image information from multiple imaging modalities, how structural and functional organization of the brain influence one another. Through her collaboration with medical experts, she is also keen on ensuring that these tools are used to improve the study and diagnosis of both research and clinical subjects.

Her scientific investigation at present addresses challenges of pediatric MRI imaging and developing computational tools that are capable of exploring the dynamic aspect of perinatal neurodevelopment. As the fundamental principles on which many adult analysis technologies are built – a static central tendency with small alterations – are less appropriate in the case of infants, She is working on alternative approaches to benefit this population. Recently, she has also extended her research agenda to acquiring and analyzing high-resolution postmortem infant brain images. With collaborators from Neurology and Pathology, she and colleagues have proposed to map the structural connectome in developing ex vivo human fetal and infant brains.

Education

PhD in Computer Science, Massachusetts Institute of Technology (MIT)

Select Publications

1. Ferradal SL, Gagoski B, Jaimes C, Yi F, Carruthers C, Vu C, Litt JS, Larsen R, Sutton B, Grant PE, Zöllei L. System-Specific Patterns of Thalamocortical Connectivity in Early Brain Development as Revealed by Structural and Functional MRI. Cereb Cortex. 2019 Mar 1;29(3):1218-1229.

2. Kolasinski J, Takahashi E, Stevens AA, Benner T, Fischl B, Zöllei L, Grant PE. Radial and tangential neuronal migration pathways in the human fetal brain: anatomically distinct patterns of diffusion MRI coherence. Neuroimage. 2013 Oct 1;79:412-22.

3. Postelnicu G, Zollei L, Fischl B. Combined volumetric and surface registration. IEEE Trans Med Imaging. 2009 Apr;28(4):508-22.

Highlights

2005: Chateaubriand Fellowship, Embassy of France in the United States of America, Academic Excellence

2015: Science Without Borders, Brazilian Federal Government, Special Visiting Researcher

2018: Scholarly Writing Award, Center for Faculty Development’s Office for Women’s Careers, MGH (Award to facilitate working on a manuscript while caring for dependent)

Website

Laboratory for Computational Neuroimaging

Eva-Maria Ratai

Associate Professor

Ratai Lab (Director)
The Quantitative Translational Imaging in Medicine Lab (Collaborator)

eratai@mgh.harvard.edu

Tagged:Alzheimer's Disease, Autism, Brain, Cancer, Clinical, Data Science, Kidney, Liver, Magnetic Resonance Imaging, MRI, Pain, Spectroscopy, Spine, TBI, Traumatic Brain Injury

Dr. Eva-Maria Ratai has been Director of Clinical Magnetic Resonance Spectroscopy at Massachusetts General Hospital (MGH) since 2003. After completing her doctoral thesis in physical chemistry at the University of Münster in Germany in 2000, and her first post-doctoral position in the Department of Chemistry at UC Davis, she accepted a postdoctoral fellowship with the Department of Radiology at MGH in 2002. After completing her fellowship, she joined the faculty of Harvard Medical School (HMS) in 2004 and is currently an associate professor of radiology at HMS. As Director of Clinical Spectroscopy, her clinical activities at MGH include the development, implementation and optimization of new MRI and MR spectroscopy pulse sequences and protocols, as well as data processing tools to improve patient diagnosis, guide therapy, and enhance clinical throughput. In addition to her clinical duties, Dr. Ratai has been conducting clinical research related to neoplasm and neuropathic pain as well as neurodegenerative diseases and neuro-developmental disorders including amyotrophic lateral sclerosis and spinocerebellar ataxia.

Education

PhD in Physical Chemistry, University of Münster

Select Publications

1. Ratai EM, Alshikho MJ, Zürcher NR, Loggia ML, Cebulla CL, Cernasov P, Reynolds B, Fish J, Seth R, Babu S, Paganoni S, Hooker JM, Atassi N. Integrated imaging of [(11)C]-PBR28 PET, MR diffusion and magnetic resonance spectroscopy (1)H-MRS in amyotrophic lateral sclerosis. Neuroimage Clin. 2018 Aug 9;20:357-364.

2. Ratai EM, Zhang Z, Fink J, Muzi M, Hanna L, Greco E, Richards T, Kim D, Andronesi OC, Mintz A, Kostakoglu L, Prah M, Ellingson B, Schmainda K, Sorensen G, Barboriak D, Mankoff D, Gerstner ER; ACRIN 6684 trial group. ACRIN 6684: Multicenter, phase II assessment of tumor hypoxia in newly diagnosed glioblastoma using magnetic resonance spectroscopy. PLoS One. 2018 Jun 14;13(6):e0198548.

3. González RG, Fell R, He J, Campbell J, Burdo TH, Autissier P, Annamalai L, Taheri F, Parker T, Lifson JD, Halpern EF, Vangel M, Masliah E, Westmoreland SV, Williams KC, Ratai EM. Temporal/compartmental changes in viral RNA and neuronal injury in a primate model of NeuroAIDS. PLoS One. 2018 May 11;13(5):e0196949.

Highlights

Director of Clinical MR Spectroscopy Massachusetts General Hospital

Charter member of the NIH Imaging Technology Development [ITD] study section

Partners in Excellence Award

Websites

Ratai Lab
The Quantitative Translational Imaging in Medicine Lab

Thomas Deisboeck

Associate Professor

deisboec@helix.mgh.harvard.edu

Tagged:Algorithm Development, Brain, Cancer, Data Science, Lungs, Multimodal Imaging

Tom Deisboeck has spent over 25 years in life sciences research. He holds an MD (Dr. med) from the Technical University of Munich as well as an MBA from Massachusetts Institute of Technology (MIT). Dr. Deisboeck is an Associate Professor of Radiology (PT) at Massachusetts General Hospital and Harvard Medical School. Working for both the NIH/NCI and the EU, Dr. Deisboeck’s teams have been involved in pioneering research in the field of computational cancer systems biology.

Education

MD, Technical University of Munich
MBA, Massachusetts Institute of Technology

Select Publications

1. Deisboeck TS, Zhang L, Yoon J, Costa J. In silico cancer modeling: is it ready for prime time? Nat Clin Pract Oncol. 2009;6(1):34-42.

2. Deisboeck TS, Wang Z, Macklin P, Cristini V. Multiscale cancer modeling. Annu Rev Biomed Eng. 2011;13:127-55.

3. Wang Z, Deisboeck TS. Mathematical modeling in cancer drug discovery. Drug Discov Today. 2014;19(2):145-50.

Highlights

Other notable publications include:

1. Guiot C, Degiorgis PG, Delsanto PP, Gabriele P, Deisboeck TS. Does tumor growth follow a “universal law”? J Theor Biol. 2003;225(2):147-51.

2. Sole RV, Deisboeck TS. An error catastrophe in cancer? J Theor Biol. 2004;228(1):47-54.

3. Chen LL, Blumm N, Christakis NA, Barabasi AL, Deisboeck TS. Cancer metastasis networks and the prediction of progression patterns. Br J Cancer. 2009;101(5):749-58.

4. Deisboeck TS, Couzin ID. Collective behavior in cancer cell populations. Bioessays. 2009;31(2):190-7.

5. Lucia U, Deisboeck TS. The importance of ion fluxes for cancer proliferation and metastasis: A thermodynamic analysis. J Theor Biol. 2018;445:1-8.

Christian Farrar

Associate Professor

cfarrar@nmr.mgh.harvard.edu

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Cancer, Cardiovascular System, Fibrosis, High-field Imaging, Liver, Machine Learning, Magnetic Resonance Imaging, Molecular Imaging, MRI, Multimodal Imaging, PET-MRI, Preclinical, Probe Development, Stroke

The Farrar group’s research program is focused on the development of novel Magnetic Resonance molecular imaging contrast agents and methods and on the development of innovative Magnetic Resonance Imaging (MRI) methods for characterizing vascular structure and function. The tools being developed in the lab are being applied to studies of a wide range of diseases, including Alzheimer’s disease, cancer, fibrosis and stroke. The group’s recent molecular imaging efforts have been focused on the development of chemical exchange saturation transfer (CEST) MRI reporter genes for imaging oncolytic virotherapy, CEST magnetic resonance fingerprinting (MRF) methods for rapid and quantitative CEST imaging, collagen-targeted MRI contrast agents for imaging liver fibrosis, and RNA aptamer probes for imaging amyloid-β plaques. Its vascular imaging efforts have focused on developing MRI methods for measuring average vessel caliber, vascular reactivity and transendothelial water exchange and their application to studies of novel brain tumor therapies and stroke.

Education

PhD in Chemistry, Harvard University

Select Publications

1. Cohen O, Huang S, McMahon MT, Rosen MS, Farrar CT. Rapid and quantitative chemical exchange saturation transfer (CEST) imaging with magnetic resonance fingerprinting (MRF). Magn Reson Med. 2018;80(6):2449-63.

2. Farrar CT, Buhrman JS, Liu G, Kleijn A, Lamfers ML, McMahon MT, et al. Establishing the Lysine-rich Protein CEST Reporter Gene as a CEST MR Imaging Detector for Oncolytic Virotherapy. Radiology. 2015;275(3):746-54.

3. Farrar CT, DePeralta DK, Day H, Rietz TA, Wei L, Lauwers GY, et al. 3D molecular MR imaging of liver fibrosis and response to rapamycin therapy in a bile duct ligation rat model. J Hepatol. 2015;63(3):689-96.

Highlights

CEST MR fingerprinting work highlighted in January 2019 Magn Reson Med

CEST reporter gene work highlighted in Radiology

Website

Farrar Research Group

Giorgio Bonmassar

Associate Professor

The Analog Brain Imaging Lab (Principal Investigator)

giorgio.bonmassar@mgh.harvard.edu
617-726-0962

Tagged:Brain, EEG, Electroencephalography, Epilepsy, fMRI, Functional MRI, Hardware Development, Parkinson's Disease, Stroke, TMS, Transcranial Magnetic Stimulation

The unifying theme of Dr. Bonmassar’s academic career has been the basic science development and pre-clinical testing of novel methods for performing MRI/CT compatible neuro-electrophysiological measurements and stimulations. Specifically, his major goals have been both to improve the healthcare of patients with neural implants and to better understand human brain function using data from multiple varied biomedical monitoring technologies, such as functional MRI (fMRI) and electroencephalography (EEG).

Education

PhD in Electrical Engineering, University of Rome “La Sapienza”
PhD in Biomedical Engineering, Boston University

Select Publications

1. Ekstrom LB, Roelfsema PR, Arsenault JT, Bonmassar G, Vanduffel W. Bottom-up dependent gating of frontal signals in early visual cortex. Science. 2008;321(5887):414-7.

2. Bonmassar G, Lee SW, Freeman DK, Polasek M, Fried SI, Gale JT. Microscopic magnetic stimulation of neural tissue. Nat Commun. 2012;3:921.

3. Serano P, Angelone LM, Katnani H, Eskandar E, Bonmassar G. A novel brain stimulation technology provides compatibility with MRI. Sci Rep. 2015;5:9805.

Highlights

2005: PCT/US2005/042401 Apparatuses and Methods For Electrophysiological Signal Delivery and Recording During MRI

2013: PCT/US2013/053959 System and Method Employing the Stochastic Gabor Function and Dual Energy Pulses for Electrical Impedance Spectroscopy

2019: 14/850,229 MRI Compatible Leads For A Deep Brain

Seppo Ahlfors

Associate Professor

The David Cohen MEG Laboratory

seppo@nmr.mgh.harvard.edu
617-726-0663

Tagged:Algorithm Development, Brain, Brain Mapping, Data Science, EEG, Electroencephalography, fMRI, Functional Magnetic Resonance Imaging, Machine Learning, Magnetic Resonance Imaging, Magnetoencephalography, MEG, MRI, Multimodal Imaging, Preclinical

Dr. Ahlfor’s research focuses on non-invasive neuroimaging — in particular, the analysis and interpretation of magnetoencephalography (MEG) signals. He has developed methodologies for multimodal integration of MEG, electroencephalography (EEG) and structural and functional magnetic resonance imaging (MRI) data. He and colleagues, in the David Cohen MEG Laboratory and elsewhere, are currently using effective connectivity analysis of combined MEG, EEG and MRI data to identify neural mechanisms that regulate phonological structure during human language processing, and MEG and 7T-fMRI to identify feedforward and feedback influences in cortical activation associated with multisensory processing. He also collaborates with clinical and cognitive scientists in applications of MEG to studies of normal as well as various clinical populations, including patients with epilepsy, aphasia, obsessive compulsive disorder, dyslexia and autism.

Education

PhD in Technical Physics and Biomedical Engineering, Helsinki University of Technology

Select Publications

1. Ahlfors SP, Jones SR, Ahveninen J, Hamalainen MS, Belliveau JW, Bar M. Direction of magnetoencephalography sources associated with feedback and feedforward contributions in a visual object recognition task. Neurosci Lett. 2015;585:149-54.

2. Ahlfors SP, Wreh C, 2nd. Modeling the effect of dendritic input location on MEG and EEG source dipoles. Med Biol Eng Comput. 2015;53(9):879-87.

3. Ahlfors SP, Han J, Belliveau JW, Hämäläinen MS. Sensitivity of MEG and EEG to source orientation. Brain Topogr. 2010;23(3):227-32.

Highlights

In his research, Dr. Ahlfors has demonstrated novel types of complementary properties of MEG and EEG, specifically by showing how signal cancellation and source orientation sensitivity leads to systematic differences between MEG and EEG signals for focal versus extended regions of cortical activation. Building on cognitive neuroscience theories and biophysical computational modeling, he has proposed a relationship between the direction of MEG and EEG source currents and the underlying feedforward or feedback type of activity between cortical areas.

Website

The David Cohen MEG Laboratory

Jyrki Ahveninen

Associate Professor

Director of MEG Neuroscience
jyrki@nmr.mgh.harvard.edu

Tagged:Brain, Brain Mapping, EEG, Electroencephalography, fMRI, FreeSurfer, Functional Magnetic Resonance Imaging, High-field Imaging, Magnetic Resonance Imaging, Magnetoencephalography, MEG, MNE, MNE Python, MRI, TMS, Transcranial Magnetic Stimulation

Dr. Ahveninen’s mission is to apply novel and improved techniques to achieve more accurate estimates of human brain function than previously achieved. His work focuses on neuroimaging of human auditory system, auditory working memory and higher-order auditory cognition using techniques including fMRI, MEG/EEG and TMS/EEG. In his initial work at the Martinos Center, he used these techniques to elucidate the functional subsystems of human auditory cortex and to examine how attention and information from other sensory systems affect auditory processing.

Currently, he leads a project that pursues neuronal mechanisms of auditory working memory using fMRI-guided MEG/EEG and simultaneous TMS/EEG in healthy subjects, as well as intracranial EEG recorded from human pre-surgical patients. His second project examines novel ways to infer feedback and feedforward influences from non-invasive neuroimaging data using ultra-high-resolution 7T fMRI and MEG/EEG. In his third major project, he and his colleagues use “model free” neuroimaging approaches to map the intrinsic functional organization of human AC in individual subjects. The work they conduct in these projects is supported by NIDCD. In addition to his primary projects, he contribute to the development of novel data analysis methods and clinical cognitive neuroimaging studies on brain disorders and dysfunctions.

Education

PhD in Psychology, University of Helsinki, Finland

Select Publications

1. Ahveninen J, Jaaskelainen IP, Raij T, Bonmassar G, Devore S, Hamalainen M, et al. Task-modulated “what” and “where” pathways in human auditory cortex. Proc Natl Acad Sci U S A. 2006;103(39):14608-13.

2. Ahveninen J, Hamalainen M, Jaaskelainen IP, Ahlfors SP, Huang S, Lin FH, et al. Attention-driven auditory cortex short-term plasticity helps segregate relevant sounds from noise. Proc Natl Acad Sci U S A. 2011;108(10):4182-7.

3. Ahveninen J, Huang S, Nummenmaa A, Belliveau JW, Hung AY, Jaaskelainen IP, et al. Evidence for distinct human auditory cortex regions for sound location versus identity processing. Nat Commun. 2013;4:2585.

Hakan Ay

Associate Professor

hay@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Cardiovascular System, Clinical, Data Science, Diffusion MRI, Machine Learning, Magnetic Resonance Imaging, MRI, Multimodal Imaging, Preclinical, Software Development, Stroke

Dr. Hakan Ay is an MD with residency training in Neurology and fellowship training in Vascular Neurology. He currently serves as an Associate Professor at Harvard Medical School with appointments in both departments of Neurology and Radiology at the Massachusetts General Hospital. Dr. Ay has 28 years of experience in patient care and research in basic and clinical neuroscience in academia. He is productive scholar with several high-impact scientific publications. He has served as a PI or co-PI in industry-sponsored and NIH-funded studies. Dr. Ay is an active member of American Heart Association where he serves as a member of the Stroke Council. He is a seasoned teacher committed to teaching and supporting student, residents, and fellows to succeed in their careers.

Dr Ay’s main research focus has been on developing automated decision-support systems for stroke. His systems extract the information in neuroimaging that is not visible to the human eye, harmonize it with clinical and laboratory information from a patient’s diagnostic work-up, and provide an output that enhances precision in diagnosis, risk stratification, and prognostication after acute stroke. Dr. Ay has a proven skillset to overcome the technical hurdles that prevents such technologies from being deployed, from generating well-curated discovery and validation datasets to guiding physicists and computer scientists in developing new machine learning tools.

Dr. Ay’s research program also focuses on identifying the brain’s intrinsic neuroprotective and anti-inflammatory circuits leveraged by external neurostimulation using advanced imaging techniques in experimental animals as well as in humans. His work has demonstrated that non-invasive vagus nerve stimulation can offer potential for treatment of stroke.

Lastly, Dr. Ay is a strong advocate of interdisciplinary collaboration and actively work with the faculty from other departments to integrate translational research into clinical practice. Dr. Ay runs a research program that brings vascular neurologists, internists, and radiologists together to explore the effects of brain injury such as stroke on internal organ systems such as the heart, lungs, and the immune system.

Education

Select Publications

1. Ay H, Furie KL, Singhal A, Smith WS, Sorensen AG, Koroshetz WJ. An evidence based causative classification system for acute ischemic stroke. Ann Neurol 2005; 58(5):688-97.

2. Ay H, Koroshetz WJ, Benner T, Vangel MG, Wu O, Schwamm LH, et al. Transient ischemic attack with infarction: a unique syndrome? Ann Neurol 2005;57(5):679-86.

3. Ay H, Koroshetz WJ, Benner T, Vangel MG, Melinosky C, Arsava EM, et al. Neuroanatomic Correlates of Stroke-Related Myocardial Injury. Neurology 2006;66:1325-1329.

4. Ay I, Nasser R, Simon B, Ay H. Transcutaneous Cervical Vagus Nerve Stimulation Ameliorates Acute Ischemic Injury in Rats. Brain Stimul. 2016;9(2):166-73.

5. Arsava EM, Kim G-M, Oliveria-Filho J, Gungor L, Noh HJ, Lordelo MJ, et al. Prediction of early recurrence after acute ischemic stroke. JAMA Neurol. 2016;73:396-401

Highlights

Causative Classification of Ischemic Stroke (CCS) System: This is a web-based, automated system licensed by the MGH that identifies the most likely cause of stroke based on clinical, laboratory, and imaging data available from typical stroke work-up.

Clinical and Imaging Based Prediction of Stroke Risk after TIA (CIP System): This is a web based automated system to estimate the 7-day risk of stroke after a TIA based on clinical and imaging characteristics of TIA.

Recurrence Risk Estimator-90 (RRE-90): This is a web-based automated system to estimate the 90-day risk of stroke after an ischemic stroke.