Dataset Information

Multimodality imaging in the surgical treatment of children with nonlesional epilepsy.

ABSTRACT:

Objectives

To evaluate the diagnostic value of individual noninvasive presurgical modalities and to study their role in surgical management of nonlesional pediatric epilepsy patients.

Methods

We retrospectively studied 14 children (3-18 years) with nonlesional intractable focal epilepsy. Clinical characteristics, surgical outcome, localizing features on 3 presurgical diagnostic tests (subtraction peri-ictal SPECT coregistered to MRI [SISCOM], statistical parametric mapping [SPM] analysis of [18F] FDG-PET, magnetoencephalography [MEG]), and intracranial EEG (iEEG) were reviewed. The localization of each individual test was determined for lobar location by visual inspection. Concordance of localization between each test and iEEG was scored as follows: 2=lobar concordance; 1=hemispheric concordance; 0=discordance or nonlocalization. Total concordance score in each patient was measured by the summation of concordance scores for all 3 tests.

Results

Seven (50%) of 14 patients were seizure-free for at least 12 months after surgery. One (7%) had only rare seizures and 6 (43%) had persistent seizures. MEG (79%, 11/14) and SISCOM (79%, 11/14) showed greater lobar concordance with iEEG than SPM-PET (13%, 3/14) (p<0.05). SPM-PET provided hemispheric lateralization (71%, 10/14) more often than lobar localization. Total concordance score tended to be greater for seizure-free patients (4.7) than for non-seizure-free patients (3.9).

Conclusions

Our data suggest that MEG and SISCOM are better tools for lobar localization than SPM analysis of FDG-PET in children with nonlesional epilepsy. A multimodality approach may improve surgical outcome as well as selection of surgical candidates in patients without MRI abnormalities.

SUBMITTER: Seo JH

PROVIDER: S-EPMC3030221 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Publications

Multimodality imaging in the surgical treatment of children with nonlesional epilepsy.

Seo J H JH Holland K K Rose D D Rozhkov L L Fujiwara H H Byars A A Arthur T T DeGrauw T T Leach J L JL Gelfand M J MJ Miles L L Mangano F T FT Horn P P Lee K H KH

Neurology 20110101 1

<h4>Objectives</h4>To evaluate the diagnostic value of individual noninvasive presurgical modalities and to study their role in surgical management of nonlesional pediatric epilepsy patients.<h4>Methods</h4>We retrospectively studied 14 children (3-18 years) with nonlesional intractable focal epilepsy. Clinical characteristics, surgical outcome, localizing features on 3 presurgical diagnostic tests (subtraction peri-ictal SPECT coregistered to MRI [SISCOM], statistical parametric mapping [SPM] a ...[more]

PMID: 21205694

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Project description:BackgroundQuantitative neuroimaging analyses have demonstrated gray and white matter abnormalities in group comparisons of different types of non-lesional partial epilepsy. It is unknown to what degree these type-specific patterns exist in individual patients and if they could be exploited for diagnostic purposes. In this study, a two-level multi-modality imaging Bayesian network approach is proposed that uses information about individual gray matter volume loss and white matter integrity to classify non-lesional temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial-temporal sclerosis and frontal lobe epilepsy (FLE).Methods25 controls, 19 TLE-MTS, 22 TLE-no and 14 FLE were studied on a 4T MRI and T1 weighted structural and DTI images acquired. Spatially normalized gray matter (GM) and fractional anisotropy (FA) abnormality maps (binary maps with voxels 1 SD below control mean) were calculated for each subject. At the first level, each group's abnormality maps were compared with those from all the other groups using Graphical-Model-based Morphometric Analysis (GAMMA). GAMMA uses a Bayesian network and a Markov random field based contextual clustering method to produce maps of voxels that provide the maximal distinction between two groups and calculates a probability distribution and a group assignment based on this information. The information was then combined in a second level Bayesian network and the probability of each subject to belong to one of the three epilepsy types calculated.ResultsThe specificities of the two level Bayesian network to distinguish between the three patient groups were 0.87 for TLE-MTS and TLE-no and 0.86 for FLE, the corresponding sensitivities were 0.84 for TLE-MTS, 0.72 for TLE-no and 0.64 for FLE.ConclusionThe two-level multi-modality Bayesian network approach was able to distinguish between the three epilepsy types with a reasonably high accuracy even though the majority of the images were completely normal on visual inspection.

Dataset Information

Multimodality imaging in the surgical treatment of children with nonlesional epilepsy.

Objectives

Methods

Results

Conclusions

Publications

Multimodality imaging in the surgical treatment of children with nonlesional epilepsy.

Similar Datasets

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