Presurgical epilepsy localization with interictal cerebral dysfunction

Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy

Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.

Characterizing the hyper- and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole-brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [¹⁸ F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region-of-interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo- and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm

Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS.

Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy.

Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm³) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions.

Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.

Medical Risk and Resilience in Adolescents and Young Adults With Epilepsy: The Role of Self-Management Self-Efficacy

Objective

Medical factors that put adolescents and young adults (AYA) with epilepsy at risk for poor health-related quality of life (HRQOL) are well-established. Less known is whether medical risk is associated with decreases in global psychological well-being and how self-management self-efficacy might contribute to resilience. The current study seeks to (a) examine the relationship between medical risk and both HRQOL and psychological well-being in AYA with epilepsy and (b) investigate the potential moderating role of self-management self-efficacy.

Methods

A sample of 180 AYA with epilepsy, aged 13–24 years, was recruited from clinic and community settings and completed questionnaires. A medical risk gradient composed of seizure frequency, antiepileptic drugs, and other health problems was created. HRQOL, psychological well-being, and self-management self-efficacy were assessed.

Results

Medical risk was negatively associated with HRQOL, such that youth with greater risk scores reported lower HRQOL (r = −0.35, p < .01). However, there was no significant relationship between medical risk and psychological well-being (r = −0.08, p = .31). Self-efficacy was positively correlated with HRQOL and well-being (r = 0.50, p < .01; r = 0.48, p < .01). A moderation effect was detected, such that the positive effect of self-efficacy on HRQOL differed across medical risk levels.

Implications

Cultivating psychological strengths, as opposed to solely addressing medical problems, may be a promising intervention target when treating AYA with epilepsy, including those navigating healthcare transitions. Self-efficacy predicted HRQOL at most levels of risk, suggesting an important modifiable intrinsic factor that may promote resilience.

The predictive value of hypometabolism in focal epilepsy: a prospective study in surgical candidates

PURPOSE

FDG PET is an established tool in presurgical epilepsy evaluation, but it is most often used selectively in patients with discordant MRI and EEG results. Interpretation is complicated by the presence of remote or multiple areas of hypometabolism, which leads to doubt as to the true location of the seizure onset zone (SOZ) and might have implications for predicting the surgical outcome. In the current study, we determined the sensitivity and specificity of PET localization prospectively in a consecutive unselected cohort of patients with focal epilepsy undergoing in-depth presurgical evaluation.

METHODS

A total of 130 patients who underwent PET imaging between 2006 and 2015 matched our inclusion criteria, and of these, 86 were operated on (72% with a favourable surgical outcome, Engel class I). Areas of focal hypometabolism were identified using statistical parametric mapping and concordance with MRI, EEG and intracranial EEG was evaluated. In the surgically treated patients, postsurgical outcome was used as the gold standard for correctness of localization (minimum follow-up 12 months).

RESULTS

PET sensitivity and specificity were both 95% in 86 patients with temporal lobe epilepsy (TLE) and 80% and 95%, respectively, in 44 patients with extratemporal epilepsy (ETLE). Significant extratemporal hypometabolism was observed in 17 TLE patients (20%). Temporal hypometabolism was observed in eight ETLE patients (18%). Among the 86 surgically treated patients, 26 (30%) had hypometabolism extending beyond the SOZ. The presence of unilobar hypometabolism, included in the resection, was predictive of complete seizure control (p = 0.007), with an odds ratio of 5.4.

CONCLUSION

Additional hypometabolic areas were found in one of five of this group of nonselected patients with focal epilepsy, including patients with "simple" lesional epilepsy, and this finding should prompt further in-depth evaluation of the correlation between EEG findings, semiology and PET. Hypometabolism confined to the epileptogenic zone as defined by EEG and MRI is associated with a favourable postoperative outcome in both TLE and ETLE patients.

The Role of Synapsins in Neurological Disorders

Synapsins serve as flagships among the presynaptic proteins due to their abundance on synaptic vesicles and contribution to synaptic communication. Several studies have emphasized the importance of this multi-gene family of neuron-specific phosphoproteins in maintaining brain physiology. In the recent times, increasing evidence has established the relevance of alterations in synapsins as a major determinant in many neurological disorders. Here, we give a comprehensive description of the diverse roles of the synapsin family and the underlying molecular mechanisms that contribute to several neurological disorders. These physiologically important roles of synapsins associated with neurological disorders are just beginning to be understood. A detailed understanding of the diversified expression of synapsins may serve to strategize novel therapeutic approaches for these debilitating neurological disorders.

Current and future diagnostic tools for traumatic brain injury: CT, conventional MRI, and diffusion tensor imaging

Brain imaging plays a key role in the assessment of traumatic brain injury. In this review, we present our perspectives on the use of computed tomography (CT), conventional magnetic resonance imaging (MRI), and newer advanced modalities such as diffusion tensor imaging. Specifically, we address assessment for immediately life-threatening intracranial lesions (noncontrast head CT), assessment of progression of intracranial lesions (noncontrast head CT), documenting intracranial abnormalities for medicolegal reasons (conventional MRI with blood-sensitive sequences), presurgical planning for post-traumatic epilepsy (high spatial resolution conventional MRI), early prognostic decision making (conventional MRI with diffusion-weighted imaging), prognostic assessment for rehabilitative planning (conventional MRI and possibly diffusion tensor imaging in the future), stratification of subjects and pharmacodynamic tracking of targeted therapies in clinical trials (specific MRI sequences or positron emission tomography (PET) ligands, e.g., diffusion tensor imaging for traumatic axonal injury). We would like to emphasize that all of these methods, especially the newer research approaches, require careful radiologic-pathologic validation for optimal interpretation. We have taken this approach in a mouse model of pericontusional traumatic axonal injury. We found that the extent of reduction in the diffusion tensor imaging parameter relative anisotropy directly correlated with the number of amyloid precursor protein (APP)-stained axonal varicosities (r(2)=0.81, p<0.0001, n=20 injured mice). Interestingly, however, the least severe contusional injuries did not result in APP-stained axonal varicosities, but did cause reduction in relative anisotropy. Clearly, both the imaging assessments and the pathologic assessments will require iterative refinement.

[Mnesic disorders caused by left temporal gliomas]

Episodic memory disorders are frequent in patients with temporal lesion. Verbal or visuo-spatial memory disorders depend on the location and the lateralization of the lesion. These disorders are well described in temporal epilepsy but rarely in population with cerebral tumor and especially not specifically focus on temporal glioma. The purpose of this study was to describe neuropsychological examination in patient with temporal glioma in the database of the regional memory centre of Besançon. Four patients were identified (all right-handed and with a left temporal glioma). Verbal episodic memory impairment and auditory-verbal short-term memory impairment were observed. One patient had also visual memory disorders. Therefore, further investigations showed an associated Alzheimer's disease. This finding modified the clinical management of this patient. Extensive neuropsychological assessment should be systematic initially to seek an associated pathology, especially in elderly patients, if the cognitive profile is unusual, during the follow-up to better understand cognitive evolution and the effect of therapies on cognition.

Surgical outcome in patients with MRI-negative, PET-positive temporal lobe epilepsy

PURPOSE

The purpose of this study was to determine the long-term surgical outcomes of magnetic resonance imaging (MRI)-negative, fluorodeoxyglucose positron emission tomography (FDG-PET)-positive patients with temporal lobe epilepsy (TLE) and compare them with those of patients with mesial temporal sclerosis (MTS).

METHODS

One hundred forty-one patients with TLE who underwent anterior temporal lobectomy were included in the study. The surgical outcomes of 24 patients with unilateral temporal hypometabolism on FDG-PET without an epileptogenic lesion on MRI were compared with that of patients with unilateral temporal hypometabolism on FDG-PET with MTS on MRI (n=117). The outcomes were compared using Engel's classification at 2 years after surgery. Clinical characteristics, unilateral interictal epileptiform discharges (IEDs), histopathological data and operation side were considered as probable prognostic factors.

RESULTS

Class I surgical outcomes were similar in MRI-negative patients and the patients with MTS on MRI (seizure-free rate at postoperative 2 years was 79.2% and 82% in the MRI-negative and MTS groups, respectively). In univariate analysis, history of febrile convulsions, presence of unilateral IEDs and left temporal localization were found to be significantly associated with seizure free outcome. Multivariate analysis revealed that independent predictors of a good outcome were history of febrile convulsions and presence of unilateral IEDs.

CONCLUSION

Our results suggest that epilepsy surgery outcomes of MRI-negative, PET positive patients are similar to those of patients with MTS. This finding may aid in the selection of best candidates for epilepsy surgery.

An appraisal of the new operational definition of epilepsy--then and now

The focus to define epilepsy in the newly proposed classification has shifted from the conceptual perspective to practical application thought to better reflect that which is happening to the patient. Within the new definition, a single unprovoked or reflex seizure can be considered as epilepsy if the recurrence risk is similar to that following two unprovoked seizures. Epilepsy is considered to be resolved if the individual had an age-dependent epilepsy syndrome and has passed the applicable age or if the person has remained seizure-free for the last ten years without seizure medications for the last five years. This new operational definition of epilepsy may change the epileptologist's approach regarding when and how long to treat patients with seizures. The new definition also has significant psychosocial and employment-related implications for the patients. With regard to etiology, the terms idiopathic, symptomatic, and cryptogenic have been replaced by genetic, structural/metabolic, and unknown. This reflects a better understanding of the underlying cause of epilepsy based on genetic tests and better neuroimaging. The terms 'simple partial' and 'complex partial' seizures have been replaced by 'focal motor/sensory' and 'focal dyscognitive' seizures, thereby ending the ambiguity associated with the former terms and the difficulty encountered with definitions of altered states of consciousness. These changes, reflective of a better insight into the pathogenesis of seizures and epilepsy, are expected to be more pragmatic and assist when managing patients with epilepsy.

Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69-90%] or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement - not replace - expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.

Neuroimaging for the prediction of response to medical and surgical treatment in epilepsy

INTRODUCTION

Approximately 30% of patients with epilepsy do not respond to adequate medication and are candidates for surgical treatment. Outcome predictors can improve the selection of more suitable treatment options for each patient. Therefore, the authors aimed to review the role of neuroimaging studies in predicting outcomes for both clinical and surgical treatment of epilepsy.

AREAS COVERED

This review analyzes studies that investigated different neuroimaging techniques as predictors of clinical and surgical treatment outcome in epilepsy. Studies involving both structural (i.e., T1-weighted images and diffusion tensor images) and functional MRI (fMRI) were identified, as well as other modalities such as spectroscopy, PET, SPECT and MEG. The authors also evaluated the importance of fMRI in predicting memory outcome after surgical resections in temporal lobe epilepsy.

EXPERT OPINION

The identification of reliable biomarkers to predict response to medical and surgical treatments are much needed in order to provide more adequate patient counseling about prognosis and treatment options individually. Different neuroimaging techniques may provide combined measurements that potentially may become these biomarkers in the near future.