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Pathological patterns of hippocampal sclerosis may predict post surgical seizure outcome in intractable temporal lobe epilepsy

Elliott, KJ; (2007) Pathological patterns of hippocampal sclerosis may predict post surgical seizure outcome in intractable temporal lobe epilepsy. Doctoral thesis , UCL (University College London). Green open access

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Abstract

Medically intractable temporal lobe epilepsy is associated with hippocampal sclerosis. Following surgical removal, the hippocampi of patients suffering from intractable temporal lobe epilepsy can be pathologically evaluated. The surgical outcome of the epilepsy surgery can also be assessed. Whether or not there are patterns of hippocampal sclerosis (HS) that predict a better or worse surgical outcome is the question explored in this study. We found that using a qualitative method of HS pattern determination, that typical patterns of classical HS and total HS had better post surgical seizure outcome then more atypical patterns. The atypical patterns of HS included end folium sclerosis and CA1 predominant neuronal loss. We could not confirm this finding with a second quantitative HS pattern method, using the Histometrix analyzer. The quantitative method did confirm the better outcomes with the typical HS patterns, however. This quantitative method required manual sketching of each HS subfield that was followed by a laborious manual neuronal count of 10 high power microscopic fields from within each HS subfield drawn. From this count, a neuronal density was determined by the Histometrix analyzer. We also evaluated granule cell dispersion by measuring the width of the granule cell layer as well as by identifying nine different qualitative patterns. Severity of amount of GCD did not seem to predict seizure outcome.

Type: Thesis (Doctoral)
Title: Pathological patterns of hippocampal sclerosis may predict post surgical seizure outcome in intractable temporal lobe epilepsy
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Thesis digitised by ProQuest.
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
URI: https://discovery.ucl.ac.uk/id/eprint/1569666
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