Reply: Temporal plus epilepsy is a major determinant of temporal lobe surgery failures

A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizures

BACKGROUND

Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood.

METHODS

We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed.

RESULTS

We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients.

CONCLUSION

Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy

OBJECTIVES

Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE.

METHODS

Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks."

RESULTS

SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs.

CONCLUSIONS

IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE.

KEY POINTS

• ICA of resting-state fMRI carries disease-specific information about epilepsy. • Machine learning can classify these components with 97.5% accuracy. • "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.

Proton MR Spectroscopy in Patients with Nonlesional Insular Cortex Epilepsy Confirmed by Invasive EEG Recordings

BACKGROUND AND PURPOSE

Recent studies suggest that a nonnegligible proportion of drug-resistant epilepsy surgery candidates have an epileptogenic zone that involves the insula. We aimed to examine the value of proton magnetic resonance spectroscopy (¹ H-MRS) in identifying patients with insular cortex epilepsy.

METHODS

Patients with possible nonlesional drug-refractory insular epilepsy underwent a voxel-based ¹ H-MRS study prior to an intracranial electroencephalographic (EEG) study. Patients were then divided into two groups based on invasive EEG findings: the insular group with evidence of insular seizures and the noninsular group with no evidence of insular seizures. Sixteen age-matched healthy controls were also scanned for normative data.

RESULTS

Twenty-two epileptic patients were recruited, 12 with insular seizures and 10 with extra-insular seizures. Ipsilateral and contralateral insular N-acetyl-aspartate concentrations ([NAA]) and NAA/Cr ratios were found to be similar in both patient groups. No significant differences in [NAA] or NAA/Cr ratios were found between the insular group, noninsular group, and healthy controls. [NAA] and NAA/Cr asymmetry indices correctly lateralized the seizure focus in only 16.7% and 0% of patients, respectively.

CONCLUSIONS

Our preliminary findings suggest that ¹ H-MRS fares poorly in identifying patients with nonlesional insular epilepsy.