Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy

Voxel-based and surface-based cortical morphometric MRI applications for identifying the epileptogenic zone: A narrative review

Approximately 40% of patients with drug-resistant epilepsy referred for surgical evaluation have no epileptogenic lesion on MRI (MRI-negative). MRI-negative epilepsy is associated with poorer seizure freedom prognosis and has therefore motivated the development of structural post-processing methods to "convert" MRI-negative to MRI-positive cases. In this article, we review the principles, advances, and challenges of voxel- and surface-based cortical morphometric MRI techniques in detecting the epileptogenic zone. The ground truth for the presumed epileptogenic zone in imaging studies can be classified into lesion-based (MRI lesion mask or histopathology) or epileptogenicity-based ground truth (anatomical-electroclinical correlations or resections that lead to seizure freedom). Voxel-based techniques are reported to have a 13%-97% concordance rate, while surface-based techniques have 67%-92% compared to lesion-based ground truths. Epileptogenicity-based ground truth may be more relevant in the case of MRI-negative cases; however, the sensitivity and concordance rate (voxel-based technique 7.1%-66.7%, and surface-based technique 62%) are limited by the reliance on scalp EEG and qualitative analysis of seizure-onset pattern. The use of stereo-EEG and quantitative EEG analysis may fill this gap to evaluate the correlation between cortical morphometry results and electrophysiological epileptogenic biomarkers of the epileptogenic zone and help improve the yield of structural post-processing tools. PLAIN LANGUAGE SUMMARY: Locating the epileptogenic zone (the brain area that is responsible for seizure generation) is important to diagnose and plan epilepsy treatments. An abnormal brain imaging (MRI) result can help clinical decision-making; however, around 40% of patients have normal MRI results (MRI-negative). We are reviewing the potential of two advanced MRI methods (voxel- and surface-based cortical morphometry) to localize the epileptogenic zone in the presence or absence of visible MRI abnormalities. We also describe the current challenge of applying the above methods in daily clinical practice and propose using advanced brain recording analysis to aid this translation process.

Brain gray matter changes in children at risk for sudden unexpected death in epilepsy

BACKGROUND

Potential failing adult brain sites, stratified by risk, mediating Sudden Unexpected Death in Epilepsy (SUDEP) have been described, but are unknown in children.

METHODS

We examined regional brain volumes using T1-weighted MRI images in 21 children with epilepsy at high SUDEP risk and 62 healthy children, together with SUDEP risk scores, calculated from focal seizure frequency. Gray matter tissue type was partitioned, maps normalized, smoothed, and compared between groups (SPM12; ANCOVA; covariates, age, sex, and BMI). Partial correlations between regional volumes and seizure frequency were examined (SPM12, covariates, age, sex, and BMI); 67% were at high risk for SUDEP.

RESULTS

The cerebellar cortex, hippocampus, amygdala, putamen, cingulate, thalamus, and para-hippocampal gyrus showed increased gray matter volumes in epilepsy, and decreased volumes in the posterior thalamus, lingual gyrus, and temporal cortices. The cingulate, insula, and putamen showed significant positive relationships with focal seizure frequency indices using whole-brain voxel-by-voxel partial correlations. Tissue volume changes in selected sites differed in direction from adults; particularly, cerebellar sites, key for hypotensive recovery, increased rather than adult declines.

CONCLUSION

The volume increases may represent expansion by inflammatory or other processes that, with sustained repetitive seizure discharge, lead to tissue volume declines described earlier in adults.

IMPACT

Children with epilepsy, who are at risk for Sudden Unexplained Death, show changes in brain volume that often differ in direction of change from adults at risk for SUDEP. Sites of volume change play significant roles in mediating breathing and blood pressure, and include areas that serve recovery from prolonged apnea and marked loss of blood pressure. The extent of volume changes correlated with focal seizure frequency. Although the underlying processes contributing to regional volume changes remain speculative, regions of tissue swelling in pediatric brain areas may represent transitory conditions that later lead to tissue loss in the adult condition.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches

PURPOSE

Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods.

METHODS

We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa.

RESULTS

The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use.

CONCLUSION

A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

MP2RAGE vs. MPRAGE surface-based morphometry in focal epilepsy

In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE-a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI-for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised 'novelty detection' machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).

Expedited epilepsy surgery prior to drug resistance in children: a frontier worth crossing?

Epilepsy surgery is an established safe and effective treatment for selected candidates with drug-resistant epilepsy. In this opinion piece, we outline the clinical and experimental evidence for selectively considering epilepsy surgery prior to drug resistance. Our rationale for expedited surgery is based on the observations that, 1) a high proportion of patients with lesional epilepsies (e.g. focal cortical dysplasia, epilepsy associated tumours) will progress to drug-resistance, 2) surgical treatment of these lesions, especially in non-eloquent areas of brain, is safe, and 3) earlier surgery may be associated with better seizure outcomes. Potential benefits beyond seizure reduction or elimination include less exposure to anti-seizure medications (ASM), which may lead to improved developmental trajectories in children and optimize long-term neurocognitive outcomes and quality of life. Further, there exists emerging experimental evidence that brain network dysfunction exists at the onset of epilepsy, where continuing dysfunctional activity could exacerbate network perturbations. This in turn could lead to expanded seizure foci and contribution to the comorbidities associated with epilepsy. Taken together, we rationalize that epilepsy surgery, in carefully selected cases, may be considered prior to drug resistance. Lastly, we outline the path forward, including the challenges associated with developing the evidence base and implementing this paradigm into clinical care.