Neuroimaging in epilepsy

PURPOSE OF REVIEW: Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. RECENT FINDINGS: The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance. Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy. SUMMARY: Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy

Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features

Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value.Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis.Results: The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair).Conclusion: Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring

Neuroimaging for Epilepsy Diagnosis and Management

This chapter will cover the neuroimaging techniques and their application to the diagnostic work up and management of adults and children with new onset or chronic epilepsy. We will focus on the specific indications and requirements of different imaging techniques for the diagnosis and pre-surgical work up of pharmacoresistant focal epilepsies. We will discuss the sensitivity, specificity and prognostic value of imaging features, benign variants and artefacts, and the possible diagnostic significance of non-epileptogenic lesions. This chapter is intended to be relevant for day-to-day practice in average clinical circumstances, with emphasis on MRI and most commonly used functional neuroimaging techniques

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions

Quantitative positron emission tomography-guided magnetic resonance imaging postprocessing in magnetic resonance imaging-negative epilepsies

Objective: Detection of focal cortical dysplasia (FCD) is of paramount importance in epilepsy presurgical evaluation. Our study aims at utilizing quantitative positron emission tomography (QPET) analysis to complement magnetic resonance imaging (MRI) postprocessing by a morphometric analysis program (MAP) to facilitate automated identification of subtle FCD. Methods: We retrospectively included a consecutive cohort of surgical patients who had a negative preoperative MRI by radiology report. MAP was performed on T1-weighted volumetric sequence and QPET was performed on PET/computed tomographic data, both with comparison to scanner-specific normal databases. Concordance between MAP and QPET was assessed at a lobar level, and the significance of concordant QPET-MAP(+) abnormalities was confirmed by postresective seizure outcome and histopathology. QPET thresholds of standard deviations (SDs) of -1, -2, -3, and -4 were evaluated to identify the optimal threshold for QPET-MAP analysis. Results: A total of 104 patients were included. When QPET thresholds of SD = -1, -2, and -3 were used, complete resection of the QPET-MAP(+) region was significantly associated with seizure-free outcome when compared with the partial resection group (P = 0.023, P <0.001, P = 0.006) or the no resection group (P = 0.002, P <0.001, P = 0.001). The SD threshold of -2 showed the best combination of positive rate (55%), sensitivity (0.68), specificity (0.88), positive predictive value (0.88), and negative predictive value (0.69). Surgical pathology of the resected QPET-MAP(+) areas revealed mainly FCD type L Multiple QPETMAP(+) regions were present in 12% of the patients at SD = -2. Significance: Our study demonstrates a practical and effective approach to combine quantitative analyses of functional (QPET) and structural (MAP) imaging data to improve identification of subtle epileptic abnormalities. This approach can he readily adopted by epilepsy centers to improve postresective seizure outcomes for patients without apparent lesions on MRI.Peer reviewe

A Patch-Wise Generative Adversarial Network for PET-MR Image Generation with Feature Attribution for Detection of Focal Cortical Dysplasia

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022More than 50 million people worldwide suffer from epilepsy with a third of those being diagnosed with drug-resistant epilepsy where the seizures cannot be treated through pharmacotherapy. In these cases, surgical removal of the epileptic brain tissue in patients is presented as an effective solution for treatment. However, for surgery success, it is vital that the accurate location of epileptic regions in the brain are known. Neuroimaging, specifically magnetic resonance imaging (MRI) and positron emission tomography (PET), commonly are the doctor’s allies in identifying these lesions’ locations responsible for the seizures. Focal cortical dysplasias (FCDs) are the most common type of cortical lesions respon sible for drug-resistant epilepsy in children. These lesions have highly heterogeneous masses, occur in different brain regions and result in different levels of visibility, corresponding to the second most in tractable type of lesion in adults with epilepsy. Moreover, among drug-resistant epilepsy cases, a third of these lesions cannot be correctly identified by neuroimaging experts, resulting in unsuccessful surgical planning and consequently ineffective treatment for patients. Recently, Generative Adversarial Networks (GANs) have demonstrated their value in neuroimaging anomaly detection. Therefore, this work pro poses the application of two different GAN methods – WGAN and CycleGAN - for anomaly detection of FCDs, in PET-MRI data of epileptic patients. A 3D patch-basis anomaly detection approach was therefore developed, inspired by previous works, to detect FCDs location by deconfounding acquisition noise and normal cortical variabilities in PET-MR brain scans of epilepsy patients. Therefore, the GAN models applied two different approaches for lesion detection: detection through reconstruction (WGAN) and detection through translation (CycleGAN). Moreover, the combination of PET and MR modalities was studied and compared to training the networks with individual imaging modalities instead. Through the results, it was possible to understand and correct some issues GAN models have when training with multimodal 3D data. However, both methods for anomaly detection were able to detect diseased brain areas in patients with very visible FCDs, although failing to identify them in patients with very subtle lesions. Recent studies will be briefly discussed in the conclusion, which propose new approaches and architectures for multimodality training, with great potential to improve the performance of the networks for anomaly detection in future works

A Hybrid PET/MRI Brain Connectivity Approach for Improving Epilepsy Surgical Evaluation

Hybrid PET/MRI can non-invasively improve epileptic focus (EF) localization prior to surgical resection in drug-resistant epilepsy (DRE), especially when MRI is negative. In this thesis, we developed an 18F-fluorodeoxyglucose (FDG) PET-guided diffusion tractography (PET/DTI) approach to assess white matter (WM) integrity in MRI-negative DRE and evaluated its potential impact on epilepsy surgical planning. To validate the potential of PET/MRI, we first evaluated the diagnostic competence of PET/MRI in DRE and found that PET/MRI provides similar diagnostic information as PET/CT (current clinical standard). For the PET/DTI approach, we used asymmetry index (AI) mapping of FDG-PET to guide WM fiber tractography around glucose hypometabolic brain regions (potential EF). Fiber tractography was repeated in the contralateral brain region (opposite to EF), which served as a control for this study. WM fibers were quantified by calculating the fiber count, mean fractional anisotropy (FA), mean fiber length, and mean cross-section of each fiber bundle. WM integrity was assessed through fiber visualization and by normalizing ipsilateral fiber measurements to contralateral fiber measurements. The added value of PET/DTI in clinical decision-making was assessed by an experienced epileptologist. In over 60% of the patient cohort (n = 14), AI mapping findings were concordant with clinical reports on seizure-onset zone. Mean FA, fiber count, and mean fiber length were decreased in 14/14 (100%), 13/14 (93%), and 12/14 (86%) patients, respectively. PET/DTI improved diagnostic confidence in 10/14 (71%) patients and indicated surgical candidacy be reassessed in 3/6 (50%) patients who had not undergone surgery. FDG-PET coupled with diffusion tractography can be a powerful tool for detecting EF and assessing WM integrity around EF in MRI-negative epilepsy. PET/DTI could further enhance clinical decision-making in epilepsy surgery