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Review
. 2019 Mar:123:127-136.
doi: 10.1016/j.nbd.2018.05.026. Epub 2018 Jun 1.

Big data sharing and analysis to advance research in post-traumatic epilepsy

Dominique Duncan  1 , Paul Vespa  2 , Asla Pitkänen  3 , Adebayo Braimah  4 , Niina Lapinlampi  3 , Arthur W Toga  4
Affiliations
Review

Big data sharing and analysis to advance research in post-traumatic epilepsy

Dominique Duncan et al. Neurobiol Dis. 2019 Mar.

Abstract

We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.

Keywords: Biomarkers; EEG; Epilepsy; Epileptogenesis; Informatics; MRI; Neuroimaging; TBI.

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Figures

Figure 1
Figure 1
The EpiBioS4Rx portal supports heterogeneous data and provides essential features including upload/ingest, search/visualize, link/co-register, and analyze/annotate for users.
Figure 2
Figure 2
The major elements and functions of our data ingestion and archive.
Figure 3
Figure 3
Example of analysis performed using the LONI Pipeline, including MRI and DTI data with group analysis over 46 patients depicting common locations of hemorrhages across these patients and physiological group differences in PTE, making use of both modular and automated LONI Pipeline techniques.
Figure 4
Figure 4
Rat (male, 2 month-old Sprague Dawley rat, 300g weight, courtesy of University of Eastern Finland, 7T/16cm Bruker Pharmascan) T1 MRI on the left and corresponding DTI on the right for a control rat in the first 2 images and a rat (left parietal LFPI model, 5mm, severe injury, on a male, 2 month-old Sprague Dawley rat, 300g weight, courtesy of University of Eastern Finland, 7T/16cm Bruker Pharmascan) in the third and fourth images (decreased FA map intensity circled in red); FA map used deterministic fiber tracking algorithm, anisotropy threshold was randomly selected, angular threshold was selected from 15-90, and fiber trajectories were smoothed by averaging propagation direction with percentage of previous direction. The images are in radiological orientation, so right and left are flipped. Colors correspond with the direction of the water/fluid flow in the WM tracts, in which blue is superior-inferior direction, red is right –left (lateral), and green is anterior-posterior.
Figure 5
Figure 5
These images show an example of human pre-ictal scalp EEG raw data on the left, courtesy of UCLA with acquisition settings described above, an embedding into a 3-dimensional space using the 3rd, 5th, and 6th eigenvectors in the center (color represents time), and the Euclidean distance plotted of each point in the embedding to the origin on the right.
Figure 6
Figure 6
Data flow process.
See this image and copyright information in PMC

References

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