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High b-value diffusion tractography: Abnormal axonal network organization associated with medication-refractory epilepsy

Diffusion magnetic resonance imaging (dMRI) tractography has played a critical role in characterizing patterns of aberrant brain network reorganization among patients with epilepsy. However, the accuracy of dMRI tractography is hampered by the complex biophysical properties of white matter tissue. H...

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Detalles Bibliográficos
Autores principales: Gleichgerrcht, Ezequiel, Keller, Simon S., Bryant, Lorna, Moss, Hunter, Kellermann, Tanja S., Biswas, Shubhabrata, Marson, Anthony G., Wilmskoetter, Janina, Jensen, Jens H., Bonilha, Leonardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872809/
https://www.ncbi.nlm.nih.gov/pubmed/34974117
http://dx.doi.org/10.1016/j.neuroimage.2021.118866
Descripción
Sumario:Diffusion magnetic resonance imaging (dMRI) tractography has played a critical role in characterizing patterns of aberrant brain network reorganization among patients with epilepsy. However, the accuracy of dMRI tractography is hampered by the complex biophysical properties of white matter tissue. High b-value diffusion imaging overcomes this limitation by better isolating axonal pathways. In this study, we introduce tractography derived from fiber ball imaging (FBI), a high b-value approach which excludes non-axonal signals, to identify atypical neuronal networks in patients with epilepsy. Specifically, we compared network properties obtained from multiple diffusion tractography approaches (diffusion tensor imaging, diffusion kurtosis imaging, FBI) in order to assess the pathophysiological relevance of network rearrangement in medication-responsive vs. medication-refractory adults with focal epilepsy. We show that drug-resistant epilepsy is associated with increased global network segregation detected by FBI-based tractography. We propose exploring FBI as a clinically feasible alternative to quantify topological changes that could be used to track disease progression and inform on clinical outcomes.