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AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:446-54. eCollection 2016.

Comparative Evaluation for Brain Structural Connectivity Approaches: Towards Integrative Neuroinformatics Tool for Epilepsy Clinical Research.

Author information

1
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH.
2
Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH; Department of Neurology, Case Western Reserve University, Cleveland, OH.
3
Department of Statistics, University of Nevada, Las Vegas, NV.
4
Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH.

Abstract

Recent advances in brain fiber tractography algorithms and diffusion Magnetic Resonance Imaging (MRI) data collection techniques are providing new approaches to study brain white matter connectivity, which play an important role in complex neurological disorders such as epilepsy. Epilepsy affects approximately 50 million persons worldwide and it is often described as a disorder of the cortical network organization. There is growing recognition of the need to better understand the role of brain structural networks in the onset and propagation of seizures in epilepsy using high resolution non-invasive imaging technologies. In this paper, we perform a comparative evaluation of two techniques to compute structural connectivity, namely probabilistic fiber tractography and statistics derived from fractional anisotropy (FA), using diffusion MRI data from a patient with rare case of medically intractable insular epilepsy. The results of our evaluation demonstrate that probabilistic fiber tractography provides a more accurate map of structural connectivity and may help address inherent complexities of neural fiber layout in the brain, such as fiber crossings. This work provides an initial result towards building an integrative informatics tool for neuroscience that can be used to accurately characterize the role of fiber tract connectivity in neurological disorders such as epilepsy.

PMID:
27570685
PMCID:
PMC5001773

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