Overview
The Yale Low-Resolution Controls (Yale Lowres) comprises 100 healthy individuals and was collected with the purpose of assessing the intrinsic organization of the human brain at rest. For each subject, eight functional scans (48 min total) and two anatomical scans (MPRAGE and FLASH) were acquired. The complete dataset includes:
The complete dataset includes:
- 800 functional scans (100 subjects x 8 runs/subject)
- 200 anatomical scans (100 subjects x 2 scans/subject)
Click here (pdf) for scan parameters.
Experimental Protocol
Subjects were instructed to keep their eyes open, stay awake, remain still, try to relax, and try not to think about anything in particular. No visual stimulation was provided; subjects were shown a black screen.
Data Release Download
Click here to get the demographics.
Click here to access the Yale Low-Resolution Controls Dataset.
Data are also available for download as files in an Amazon Web Services S3 bucket.
Each file in the S3 bucket can only be accessed using HTTP (i.e., no ftp or scp ). You can obtain a URL for each desired file and then download it using an HTTP client such as a web browser, wget, or curl. Each file can only be accessed using its literal name - wildcards (i.e. "*") will not work.
There are file transfer programs that can handle S3 natively and will allow you to navigate through the data using a file browser. Cyberduck is one such program that works with Windows and Mac OS X (New Cyberduck version might not work, please try version 5.03.). Cyberduck also has a command line version that works with Windows, Mac OS X, and Linux. Instructions for using the Cyberduck program are as follows:
- Open Cyberduck and click on Open Connection.
- Set the application protocol in the dropdown menu to S3 (Amazon Simple Storage Service).
- Set the server to s3.amazonaws.com.
- Check the box labelled Anonymous Login.
- Expand the More Options tab and set Path to fcp-indi/data/Projects/INDI/YALE/folder/lowres.
- Click Connect.
Personnel
- Fuyuze Tokoglu1
- Dustin Scheinost1
- Michelle Hampson1,2,3
- Xilin Shen1*
- R. Todd Constable1,4,5
*please send any correspondence to Xilin Shen (xilin[dot]shen[at]yale[dot]edu)
1Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.
2Department of Psychiatry, Yale School of Medicine, New Haven, CT 06520, USA.
3Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA.
4Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.
5Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA.
Data Sharing License
Creative Commons – Attribution-NonCommercial Share Alike (CC-BY-NC-SA): Standard INDI data sharing policy. Prohibits use of the data for commercial purposes.
Acknowledgments
We would additionally like to acknowledge our MR technicians—Hedwig Sarofin, Terry Hickey, Karen Martin, and Cheryl McMurray—for their tireless work and our subjects, without whom this work would not be possible.
Publications
- Scheinost D, Tokoglu F, Shen X, Finn ES, Noble S, Papademetris X, Constable RT. Fluctuations in Global Brain Activity Are Associated With Changes in Whole-Brain Connectivity of Functional Networks. IEEE Trans Biomed Eng. 2016 Dec;63(12):2540-2549. Epub 2016 Aug 16.
- Scheinost D, Finn ES, Tokoglu F, Shen X, Papademetris X, Hampson M, Constable RT. Sex differences in normal age trajectories of functional brain networks. Hum Brain Mapp. 2015 Apr;36(4):1524-35. doi: 10.1002/hbm.22720. Epub 2014 Dec 18.
- Lee HW, Arora J, Papademetris X, Tokoglu F, Negishi M, Scheinost D, Farooque P, Blumenfeld H, Spencer DD, Constable RT. Altered functional connectivity in seizure onset zones revealed by fMRI intrinsic connectivity. Neurology. 2014 Dec 9;83(24):2269-77. doi: 10.1212/WNL.0000000000001068. Epub 2014 Nov 12.
- Roth JK, Johnson MK, Tokoglu F, Murphy I, Constable RT. Modulating intrinsic connectivity: adjacent subregions within supplementary motor cortex, dorsolateral prefrontal cortex, and parietal cortex connect to separate functional networks during task and also connect during rest. PLoS One. 2014 Mar 17;9(3):e90672. doi: 10.1371/journal.pone.0090672. eCollection 2014.
- Shen X, Tokoglu F, Papademetris X, Constable RT. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage. 2013 Nov 15;82:403-15. doi: 10.1016/j.neuroimage.2013.05.081. Epub 2013 Jun 4.
- Hampson M, Tokoglu F, Shen X, Scheinost D, Papademetris X, Constable RT. Intrinsic brain connectivity related to age in young and middle aged adults. PLoS One. 2012;7(9):e44067. doi: 10.1371/journal.pone.0044067. Epub 2012 Sep 11.
- Zhang X, Tokoglu F, Negishi M, Arora J, Winstanley S, Spencer DD, Constable RT. Social network theory applied to resting-state fMRI connectivity data in the identification of epilepsy networks with iterative feature selection. J Neurosci Methods. 2011 Jul 15;199(1):129-39. doi: 10.1016/j.jneumeth.2011.04.020. Epub 2011 May 5.
- Shen X, Papademetris X, Constable RT. Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data. Neuroimage. 2010 Apr 15;50(3):1027-35. doi: 10.1016/j.neuroimage.2009.12.119. Epub 2010 Jan 7.