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Investigator

Peter A Bandettini, Ph.D.

Section on Functional Imaging Methods

Director, Functional Magnetic Resonance Imaging Core Facility (FMRIF)
Building 10 Room 1D80
10 Center Drive MSC1148
Bethesda MD 12345
Office: (301) 402-1333

Fax: (301) 402-1370
bandettini@nih.gov

Dr. Bandettini received his B.S. in Physics from Marquette University in 1989 and his Ph.D. in Biophysics in 1994 at the Medical College of Wisconsin and carried out his post-doc at the Massachusetts General Hospital NMR Center. Since 1999, he has been the Director of the Functional MRI Facility which is jointly supported by NINDS and NIMH, and Chief of the Section on Functional Imaging Methods in the Laboratory of Brain and Cognition. In 2017 he initiated two new teams to help investigators throughout the NIH. These are the Machine Learning Team and the Data Science and Sharing Team. At this time, he also became the founding Director of the Center for Multimodal. He was Editor in Chief of the journal, NeuroImage from 2011-2017. His research focus since 1991 has been on developing fMRI acquisition methods, brain activation strategies, and processing approaches to more effectively extract neuronal and physiologic information from fMRI data toward the goals of understanding the human brain and increasing the fMRI's clinical efficacy.



Functional MRI (fMRI) is a technique that utilizes time-series collection of rapidly-obtained magnetic resonance images that are sensitive to brain activation-induced changes in blood flow, oxygenation, and volume. The most common functional contrast used in fMRI is blood oxygenation level-dependent (BOLD) contrast. Since this technique was developed in 1992, it has continued to advance in methodology sophistication, sensitivity, temporal and spatial resolution, interpretability, and applications.

My Section on Functional Imaging Methods (SFIM), aims to develop and refine novel approaches to fMRI acquisition, brain activation paradigms, neuronal modulation, and processing methods that leverage in-depth and nuanced understanding functional MRI (fMRI) signal and noise so that more precise and interpretable information about human neuronal function and physiology can be extracted. Our ultimate goals are to advance multimodal neuroimaging to both gain insight about the brain and to contribute to health care approaches.

Major subfields of fMRI that my group focus on are ultra-high-resolution fMRI, time-series dynamics, resting-state fMRI, and naturalistic stimuli approaches.

Our Functional MRI Core Facility was started in 1999 and has grown to include 5 MRI scanners, one of which is a 7T scanner for high resolution and high sensitivity functional imaging. Over 30 Principle Investigators throughout the NIH utilize the center.

Our Data Sharing and Science Team supports investigators within the NIMH intramural program in creating, distributing, and leveraging large, open datasets to accelerate discovery. They provide tools and training to help the IRP embrace open science and stay on the cutting edge of novel datasets, tools, and platforms.

Our Machine Learning Team supports researchers in the NIMH intramural research program who want to address research problems in clinical and cognitive neuroscience using machine learning approaches. They do this by consulting with individual researchers and guiding them in the use of the appropriate tools and methods, or by taking on the analysis process themselves. In parallel, they develop new methods and analysis approaches, motivated by the needs of researchers or by the practical possibilities arising from advances in the field.

The Center for Multimodal Neuroimaging brings together NIMH neuroimaging, processing, and neuromodulation expertise so that communication channels are further enhanced between cores, sections, units, individual staff scientists, and, importantly, users. The goals of this level of integration are to foster collaborative bridges, create a centralized view of what is available to users, generate questions that lend themselves to cross-modal investigation and integration, and allow efficient exchange of practical information and new ideas.

More information can be found here for:

Section on Functional Imaging Methods https://fim.nimh.nih.gov
Functional MRI Facility https://fmrif.nimh.nih.gov
Data Sharing and Science Team https://cmn.nimh.nih.gov/dsst
Machine Learning Team https://cmn.nimh.nih.gov/mlt
Center for Multimodal Neuroimaging https://cmn.nimh.nih.gov

Staff Image
  • Rasmus Birn, Ph.D.
    Staff Scientist
    (301) 402-1350

  • Anthony Boemio, Ph.D.
    Postdoctoral Fellow
    (301) 402-1379

  • Hauke Heekeren, M.D.
    Adjunct Investigator

  • David Knight, Ph.D.
    Postdoctoral Fellow
    (301) 402-1359

  • Nikolaus Kriegeskorte, Ph.D.
    Postdoctoral Fellow
    (301) 594-9195

  • Kay Kuhns, B.S.
    Program Specialist
    (301) 594-9191

  • Marta Maieron, Ph.D.
    Adjunct Investigator

  • Hanh Nguyen, B.S.
    Post baccalaureate Fellow
    (301) 402-7298

  • Natalia Petridou, M.S.
    Student

  • Douglass Ruff
    Post baccalaureate Fellow
    (301) 451-9582

  • Monica Smith, B.S.
    Post baccalaureate Fellow
    (301) 594-9197

  • August Tuan, B.S.
    Adjunct Investigator

  • Najah Waters, B.S.
    Post baccalaureate Fellow
    (301) 402-7299

  • 1) P. Kundu, V. Voon, P. Balchandani, M. V. Lombardo, B. A. Poser, P. Bandettini (2017)
  • Multi-Echo fMRI: A Review of Applications in fMRI Denoising and Analysis of BOLD Signals.
  • NeuroImage 154, pp. 59-80
  • 2) J. Gonzalez-Castillo, P. A. Bandettini (2018)
  • Task-based dynamic functional connectivity: recent findings and open questions.
  • NeuroImage, 180, pp. 526-533
  • 3) L. Huber, D. A. Handwerker, D. C. Jangraw, G. Chen, A. Hall, C. Stuber, J. Gonzalez-Castillo, D. Ivanov, S. Marrett, M. Guidi, J. Goense, B. A. Poser, P. A. Bandettini (2017)
  • High-resolution CBV-fMRI allows mapping of laminar activity and connectivity of cortical input and output in human M1.
  • Neuron, 96(6), pp. 1253-1267
  • 4) D. C. Jangraw, J Gonzalez-Castillo, D. A. Handwerker, M. Ghane, M. D. Rosenberg, P. Panwar, P. A. Bandettini (2018)
  • A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task.
  • NeuroImage, 166, pp. 99-109
  • 5) E. S. Finn, P. R. Corlett, G. Chen, P. A. Bandettini, R. T. Constable (2018)
  • Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative.
  • Nature Communications (9)
  • 6) L. Huber, E. S. Finn, D. A. Handwerker, M. Boenstrup, D. Glen, S. Kashyap, D. Ivanov, N. Petridou, S. Marrett, J. Goense, B. Poser, P. A. Bandettini (2020)
  • Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex.
  • NeuroImage, 116828
  • 7) J. Gonzalez-Castillo, C. Caballero-Gaudes, N. Topolski, D. Handwerker, F. Pereira, P. Bandettini (2019)
  • Imaging the spontaneous flow of thought: distinct periods of cognition contribute to dynamic functional connectivity during rest.
  • NeuroImage, 116129
  • 8) 8. E. S. Finn, L. Huber, D. C. Jangraw, P. A. Bandettini (2019)
  • Layer-dependent activity in human prefrontal cortex during working memory.
  • Nature Neuroscience, 22 (10), 10687-10695
  • 9) E. S. Finn, E. Glerean, A. Y. Kojandi, D. Nielson, P. J. Molfese, D. A. Handwerker, P. A. Bandettini (2020)
  • Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging.
  • NeuroImage, 116828
  • 10) Y. Chai, L. Li, L. Huber, B. A. Poser, P. A. Bandettini (2020)
  • Integrated VASO and perfusion contrast: a new tool for laminar functional MRI.
  • NeuroImage 116358
  • 11) R. M. Birn, R. W. Cox, P. A. Bandettini (2004)
  • Functional MRI experimental designs and processing strategies for studying brain activation associated with overt responses.
  • NeuroImage, 23, 1046-1058
  • 12) D. C. Knight, H. T. Nguyen, P. A. Bandettini (2003)
  • Expression of conditional fear with and without awareness.
  • Proc. Nat'l. Acad. Sci. USA, 100, 15280-15283
  • 13) Z. S. Saad, K. M. Ropella, E. A. DeYoe, P. A. Bandettini (2003)
  • The spatial extent of the BOLD response
  • NeuroImage, 19, 132-144
  • 14) P.S.F. Bellgowan, Z. S. Saad, P. A. Bandettini (2003)
  • Understanding neural system dynamics through task modulation and measurement of functional MRI amplitude, latency, and width.
  • Proc. Nat'l. Acad. Sci. USA, 100, 1415-1419
  • 15) R. M. Birn, R. W. Cox, P. A. Bandettini (2002)
  • Detection versus estimation in event-related fMRI: choosing the optimal stimulus timing
  • NeuroImage, 15, 262-264
  • 16) J. Bodurka, P. A. Bandettini (2002)
  • Toward direct mapping of neuronal activity: MRI detection of ultra weak transient magnetic field changes
  • Magn. Reson. Med., 47, 1052-1058
  • 17) J. C. Patterson II, L. G. Ungerleider, and P. A Bandettini (2002)
  • Task - independent functional brain activity correlation with skin conductance changes: an fMRI study
  • NeuroImage, 17, 1787-1806
  • 18) R. M. Birn, Z. S. Saad, P. A. Bandettini (2001)
  • Spatial heterogeneity of the nonlinear dynamics in the fMRI BOLD response
  • NeuroImage, 14, 817-826
  • 19) P. A. Bandettini, L. G. Ungerleider (2001)
  • From neuron to BOLD: new connections.
  • Nature Neuroscience, 4, 864-866
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