Dataset Information

A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.

ABSTRACT:

Background

Quantitative 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).

Methods

25 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.

Results

The 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.

Conclusion

The 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.

SUBMITTER: Mueller SG

PROVIDER: S-EPMC3619666 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.

Mueller Susanne G SG Young Karl K Hartig Miriam M Barakos Jerome J Garcia Paul P Laxer Kenneth D KD

NeuroImage 20130124

<h4>Background</h4>Quantitative 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 integri ...[more]

PMID: 23353601

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Project description:A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.

Dataset Information

A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.

Background

Methods

Results

Conclusion

Publications

A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.

Similar Datasets

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