Structural and Quantitative MRI in Epilepsy

Blackmon, Karen; Thesen, Thomas

doi:10.1007/978-0-387-92826-5_7

Cited by 1 publication

(1 citation statement)

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“…In fact, the International League Against Epilepsy new terminology and classification adopts a “network perspective” to better define focal versus generalized seizures . Even though visual inspection of MRI can reveal signs of pathologic abnormalities such as hippocampal sclerosis or malformations of cortical development, it is not possible to measure global network organization by qualitative analyses of imaging data alone . For this reason, studies that have reconstructed the connectome from DTI or fMRI at the individual level have begun to identify anomalous pathways that relate with different clinical symptoms.…”

Section: Future Clinical Translationmentioning

confidence: 99%

Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy

2015

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SUMMARYThe assessment of neural networks in epilepsy has become increasingly relevant in the context of translational research, given that localized forms of epilepsy are more likely to be related to abnormal function within specific brain networks, as opposed to isolated focal brain pathology. It is notable that variability in clinical outcomes from epilepsy treatment may be a reflection of individual patterns of network abnormalities. As such, network endophenotypes may be important biomarkers for the diagnosis and treatment of epilepsy. Despite its exceptional potential, measuring abnormal networks in translational research has been thus far constrained by methodologic limitations. Fortunately, recent advancements in neuroscience, particularly in the field of connectomics, permit a detailed assessment of network organization, dynamics, and function at an individual level. Data from the personal connectome can be assessed using principled forms of network analyses based on graph theory, which may disclose patterns of organization that are prone to abnormal dynamics and epileptogenesis. Although the field of connectomics is relatively new, there is already a rapidly growing body of evidence to suggest that it can elucidate several important and fundamental aspects of abnormal networks to epilepsy. In this article, we provide a review of the emerging evidence from connectomics research regarding neural network architecture, dynamics, and function related to epilepsy. We discuss how connectomics may bring together pathophysiologic hypotheses from conceptual and basic models of epilepsy and in vivo biomarkers for clinical translational research. By providing neural network information unique to each individual, the field of connectomics may help to elucidate variability in clinical outcomes and open opportunities for personalized medicine approaches to epilepsy. Connectomics involves complex and rich data from each subject, thus collaborative efforts to enable the systematic and rigorous evaluation of this form of "big data" are paramount to leverage the full potential of this new approach.

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Section: Future Clinical Translationmentioning

confidence: 99%