Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Detecting Epileptic Regions Based on Global Brain Connectivity Patterns

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, USA.
2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
3Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
Sep-2013
Volume Number:
16
Issue Number:
Pt 1
Pages:
98-105
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 1):98-105.
PubMed ID:
24505654
PMCID:
PMC3960920
Appears in Collections:
NAC, NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
NSF CAREER Grant 0642971
MIT Lincoln Lab Collaboration
Generated Citation:
Sweet A., Venkataraman A., Stufflebeam S.M., Liu H., Tanaka N., Madsen J., Golland P. Detecting Epileptic Regions Based on Global Brain Connectivity Patterns. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 1):98-105. PMID: 24505654. PMCID: PMC3960920.
Downloaded: 733 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.

Additional Material
1 File (163.987kB)
Sweet-MICCAI2013-fig3.jpg (163.987kB)