Focal extratemporal epilepsy [Neurosurgery Wiki]

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====== Focal extratemporal epilepsy ======

Extra-temporal [[seizure surgery]] constitutes about a quarter of the surgical [[procedure]]s for [[epilepsy]] and includes [[resection]] of the [[frontal lobe]]s, [[parietal lobe]]s or [[occipital lobe]]s. These [[resection]]s are guided by localization from invasive [[subdural electrode]]s and, if necessary, detailed cortical functional mapping. Extra-temporal resections are individualized to the seizure onset focus, the type of seizure or syndrome, and the functional mapping which defines a safe resection boundary. Motor and sensory cortex and language cortex localization is performed and greatly minimizes neurological deficits from surgery. 

[[Insular lobe epilepsy]] (ILE) is an under-recognized cause of extratemporal epilepsy

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A 'Virtual [[resection]]' consists of computationally simulating the effect of an actual resection on the brain. Demuru et al. validated two [[functional connectivity]] based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings.

A [[nonlinear relationship]] association index was applied to pre-resection recordings from 11 Focal [[extratemporal epilepsy]] patients. They computed two virtual [[resection]] strategies: first, a 'naive' one obtained by simply removing from the connectivity matrix the [[electrode]]s that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. They validated the virtual resections with two analysis: 

1) They tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; 2) they tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test.

The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings.

The findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection.

They pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making
((Demuru M, Zweiphenning W, van Blooijs D, Van Eijsden P, Leijten F, Zijlmans M, Kalitzin S. Validation of virtual resection on intraoperative interictal data acquired during epilepsy surgery. J Neural Eng. 2020 Oct 21. doi: 10.1088/1741-2552/abc3a8. Epub ahead of print. PMID: 33086212.)).