SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.
Specific GROWTH projects being offerred for summer 2019 can be found here.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor.
Announcements for external summer programs are listed here.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||fMRI and intracranial recordings in epileptic patients during movie watching|
|Disciplines:||Computer Science, Neurobiology|
Bren Professor, (BBE),
|Mentor URL:||emotion.caltech.edu (opens in new window)|
|AO Contact:||Ralph Adolphs, firstname.lastname@example.org|
|Background:||Over the past 2.5 years, in collaboration with Dr. Rutishauser at Cedars-Sinai Medical Center, we have been collecting neural data while patients with intractable focal epilepsy watch an edited version of the TV episode "Bang You're Dead" by Alfred Hitchcock (1961). Specifically, we first record functional MRI data at Caltech. Then, typically several weeks later, the patients are admitted to the hospital and implanted with intracranial depth electrodes (this is the clinical procedure used to localize the focus of the seizures, and better plan surgical resection); during their stay at the hospital we record from their brains while they watch the same movie again (intracranial field potentials, and extracellular single-unit recordings). For each patient, we thus have fMRI and intracranial data while they watch the same dynamic, naturalistic stimulus. We have completed this full protocol with 10 patients, and plan to release this precious dataset to the wider neuroscience community.|
|Description:||The student will be tasked with helping organize the data (neural recordings, as well as eye-tracking and movie annotations) according to emerging standards in the field (BIDS: Brain Imaging Data Structure), as well as generate demo code from our existing codebase to perform simple analyses (e.g: responses to faces; repeatability of the signals in each modality; comparison of intracranial electrophysiology and functional MRI). The data and code will be published in a data repository (such as OpenNeuro.org), accompanied by a data release publication describing the dataset in detail.|
* about the data sharing movement in neuroscience
Eickhoff, S., Nichols, T. E., Van Horn, J. D., & Turner, J. A. (2016). Sharing the wealth: Neuroimaging data repositories. NeuroImage, 124(Pt B), 1065–1068. http://doi.org/10.1016/j.neuroimage.2015.10.079
Naci, L., Cusack, R., Anello, M., & Owen, A. M. (2014). A common neural code for similar conscious experiences in different individuals. Proceedings of the National Academy of Sciences of the United States of America, 111(39), 14277–14282. http://doi.org/10.1073/pnas.1407007111
* large neuroscience project also using this stimulus
Taylor, J. R., Williams, N., Cusack, R., Auer, T., Shafto, M. A., Dixon, M., … Henson, R. N. (2015). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage. http://doi.org/10.1016/j.neuroimage.2015.09.018
* example data sharing project (possible template)
* Brain Imaging Data Structure
http://bids.neuroimaging.io/ (developed for fMRI; see also extension proposal 10: intracranial electrophysiology)
* Tool for version-controlled data sharing
* (Required) Strong motivation: this is a demanding project which requires the ability to maintain interest and focus throughout
* (Required) Attention to detail: a data release to the community must be as flawless as possible. Demo code must work well, and be tested on all platforms that it may be run on by end users
* (Required) fluency in Python language (libraries: numpy, scipy, pandas)
* (Required) fluency with Unix environment
* (Suggested) familiarity with nilearn, mne
* (Suggested) familiarity with containers (docker, singularity)
This AO can be done under the following programs:
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