Peter A. Bandettini
National Institutes of Health
279 Papers
1.6K Citations
Peter A. Bandettini is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Computer science & Functional magnetic resonance imaging. The author has an hindex of 84, co-authored 261 publications. Previous affiliations of Peter A. Bandettini include Medical College of Wisconsin & Harvard University.
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Papers
Principles of BOLD Functional MRI
Seong-Gi Kim,Peter A. Bandettini +1 more
- 01 Jan 2011
TL;DR: Various fMRI techniques, the sources of the BOLD fMRI signals, improvement of BOLD techniques, (4) contrast-to-noise consideration, and (5) spatial and temporal resolution are discussed, opening a window of basic and clinical neuroscience research.
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Untangling the Relatedness among Correlations, Part III: Inter-Subject Correlation Analysis through Bayesian Multilevel Modeling for Naturalistic Scanning
Gang Chen,Paul A. Taylor,Xianggui Qu,Peter J. Molfese,Peter A. Bandettini,Robert W. Cox,Emily S. Finn +6 more
TL;DR: A Bayesian multilevel (BML) framework for ISC data analysis that integrates all regions of interest into one model that improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting is proposed.
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Functional MRI: A confluence of fortunate circumstances.
TL;DR: The desired goal of this review is to convey the field of fMRI from the perspective of what was critically important before, during and after its inception and how things might have been if these circumstances would have been different.
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Frequency-dependent tACS modulation of BOLD signal during rhythmic visual stimulation
TL;DR: It is demonstrated that fMRI could localize the tACS effect on stimulus‐induced brain rhythms, which could lead to a new approach for understanding the high‐level cognitive process shaped by the ongoing oscillatory signal.
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Effects of image contrast on functional MRI image registration.
Javier Gonzalez-Castillo,Kristen N. Duthie,Ziad S. Saad,Carlton Chu,Peter A. Bandettini,Wen-Ming Luh +5 more
TL;DR: This work shows that the use of low FAs and the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coils data.
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