Data Sharing

The De-identification Toolbox – A data sharing tool for neuroimaging studies
The De-identification Toolbox (formerly DeID) is a Java program that users can use to remove identifying information in neuroimaging datasets. This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols. The software runs with Windows, Linux, and Mac operating systems.

Song, X., Wang, J., Wang, A., Meng, Q., Prescott, C., Tsu, L., Eckert, M.A. (2015). DeID–a data sharing tool for neuroimaging studies. Frontiers in Neuroscience, 9.



Communication Value Task
The code and files used to perform the study described in Eckert et al. (2017) can be accessed at the link below.  This download includes the index.html, jsPsych related files, and images used to run the Communication Value Task experiment in any browser.

Eckert, M.A., Teubner-Rhodes, S., Vaden, K.I., Bentzley, B. (2017). A Novel Communication Value Task Demonstrates Evidence of Response Bias in Cases with Presbyacusis. Scientific Reports. 7:16512. DOI:10.1038/s41598-017-16673-y

  CVT (11.7 MiB, 8 hits)


Neuroimaging Analyses

Locus Coeruleus Map
Users can overlay a probabilistic spatial map of the human locus coeruleus to determine the extent to which imaging findings fall within the locus coeruleus.

Keren, N., Lozar, C., Harris, K.C., Morgan, P., Eckert, M.A.  (2009).  In vivo mapping of the
human locus coeruleus.  Neuroimage, 47(4): 1261-1267.


MultImpute: Group Level Imputation of Statistic Maps (Developed by Kenny Vaden)
Group Level Imputation of Statistic Maps (version 1.0) is a toolkit that performs multiple imputation for group level, single sample t-tests. Whole brain group level statistic maps from fMRI rarely cover the entire brain as a result of missing data. Missingness between subjects in fMRI datasets can result from susceptibility artifacts, bounding box (acquisition parameters), and small differences in post-normalized morphology. The toolkit consists of several interactive command line scripts that guide the user to map the spatial distribution of missing data across contrast images, calculate spatial neighborhood averages that help impute values, perform conventional and multiple imputed t-statistics, save the results to brain maps, and create result tables. The toolkit contains an instruction manual (pdf), two Matlab scripts and one R-Statistics script, which depend on functions defined in the popular SPM toolbox and functions defined in the MICE package for [R].

Vaden, KI. Jr, Gebregziabher, M., Kuchinsky, S.E., Eckert, M.A. (2012). Multiple imputation of missing fMRI data in whole brain analysis. Neuroimage, 60(3), 1843-1855.


PythagorasPythagorean Displacement and Motion Regressors (Developed by Kenny Vaden)
This Matlab script uses the Pythagorean Theorem to calculate head motion and position, while preserving degrees of freedom. The motion parameters output by SPM (rp*.txt) estimate head position relative to the first volume in 3D translation and 3D rotation, which are often entered as a nuisance regressor during individual-level statistics. Regressing the total displacement and relative position can potentially explain more variance in voxel-level BOLD signals that is related to head movement during an fMRI experiment.

Vaden, K.I. Jr, Kuchinsky S.E., Keren, N.I., Harris, K.C., Ahlstrom, J.B., Dubno, J.R., Eckert, M.A. (2012) Inferior frontal sensitivity to common speech sounds is amplified by increasing word intelligibility. Neuropsychologia, 49(13), 3563-3572.