311 research outputs found

    Narrative review of epilepsy: getting the most out of your neuroimaging

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    Neuroimaging represents an important step in the evaluation of pediatric epilepsy. The crucial role of brain imaging in the diagnosis, follow-up and presurgical assessment of patients with epilepsy is noted and has to be familiar to all neuroradiologists and trainees approaching pediatric brain imaging. Morphological qualitative imaging shows the majority of cerebral lesions/alterations underlying focal epilepsy and can highlight some features which are useful in the differential diagnosis of the different types of epilepsy. Recent advances in MRI acquisitions including diffusion-weighted imaging (DWI), post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection during the last decades. Functional MRI (fMRI) can be really useful and helps to identify cortical eloquent areas that are essential for language, motor function, and memory, and diffusion tensor imaging (DTI) can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. Also positron emission tomography (PET), single photon emission computed tomography (SPECT), simultaneous electroencephalogram (EEG) and fMRI, and electrical and magnetic source imaging can be used to assess the exact localization of epileptic foci and help in the design of intracranial EEG recording strategies. The main role of these “hybrid” techniques is to obtain quantitative and qualitative informations, a necessary step to evaluate and demonstrate the complex relationship between abnormal structural and functional data and to manage a “patient-tailored” surgical approach in epileptic patients

    The Value of Seizure Semiology in Epilepsy Surgery: Epileptogenic-Zone Localisation in Presurgical Patients using Machine Learning and Semiology Visualisation Tool

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    Background Eight million individuals have focal drug resistant epilepsy worldwide. If their epileptogenic focus is identified and resected, they may become seizure-free and experience significant improvements in quality of life. However, seizure-freedom occurs in less than half of surgical resections. Seizure semiology - the signs and symptoms during a seizure - along with brain imaging and electroencephalography (EEG) are amongst the mainstays of seizure localisation. Although there have been advances in algorithmic identification of abnormalities on EEG and imaging, semiological analysis has remained more subjective. The primary objective of this research was to investigate the localising value of clinician-identified semiology, and secondarily to improve personalised prognostication for epilepsy surgery. Methods I data mined retrospective hospital records to link semiology to outcomes. I trained machine learning models to predict temporal lobe epilepsy (TLE) and determine the value of semiology compared to a benchmark of hippocampal sclerosis (HS). Due to the hospital dataset being relatively small, we also collected data from a systematic review of the literature to curate an open-access Semio2Brain database. We built the Semiology-to-Brain Visualisation Tool (SVT) on this database and retrospectively validated SVT in two separate groups of randomly selected patients and individuals with frontal lobe epilepsy. Separately, a systematic review of multimodal prognostic features of epilepsy surgery was undertaken. The concept of a semiological connectome was devised and compared to structural connectivity to investigate probabilistic propagation and semiology generation. Results Although a (non-chronological) list of patients’ semiologies did not improve localisation beyond the initial semiology, the list of semiology added value when combined with an imaging feature. The absolute added value of semiology in a support vector classifier in diagnosing TLE, compared to HS, was 25%. Semiology was however unable to predict postsurgical outcomes. To help future prognostic models, a list of essential multimodal prognostic features for epilepsy surgery were extracted from meta-analyses and a structural causal model proposed. Semio2Brain consists of over 13000 semiological datapoints from 4643 patients across 309 studies and uniquely enabled a Bayesian approach to localisation to mitigate TLE publication bias. SVT performed well in a retrospective validation, matching the best expert clinician’s localisation scores and exceeding them for lateralisation, and showed modest value in localisation in individuals with frontal lobe epilepsy (FLE). There was a significant correlation between the number of connecting fibres between brain regions and the seizure semiologies that can arise from these regions. Conclusions Semiology is valuable in localisation, but multimodal concordance is more valuable and highly prognostic. SVT could be suitable for use in multimodal models to predict the seizure focus

    Imaging in Seizure Patterns

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    AIMS AND OBJECTIVES: 1. To study the Neuroimaging findings in patients presenting with various patterns of Siezure disorder using Magnetic Resonance Imaging. 2. To measure the Hippocampal volume in MRI in seizure disorder patients with no structural lesions or any visually detectable changes on routine assessment. MATERIALS AND METHODS: Source of Data: This study was conducted at Mahatma Gandhi Memorial Government Hospital, Trichy in collaboration with Department of Radiology. Study Design: Descriptive study. Period of Study: January 2014 to September 2014. Inclusion Criteria: 1. Age > 12. 2. Documented history of convulsion, who have MRI brain done on them as Out-patient or inpatient. 3. Consent to the study (patient and /or patient’s legal guardian). Exclusion Criteria: 1. Age < 12. 2. Diabetic, chronic renal disease, suspected metabolic encephalopathy 3. Patients with convulsions with history of acute antecedent events like Trauma, Drugs, toxins, fever. Method: In this study ,56 participants aged >12 presenting with seizure as OUTPATIENT/INPATIENT in Medicine department between January 2014 and September 2014were studied after getting informed consent from patient and /or legal guardian. History taking and clinical examination was done and recorded in the form of a proforma. History included age, sex, duration of seizure, type of seizure, time, any predisposing factors, antecedent events if any, pork ingestion, contact with open case of tuberculosis etc. Detailed ,head to foot, examination including examination for any focal neurological deficit was done. Neuro imaging (MRI) was obtained after stabilization. In those imaging studies where no obvious visually detectable changes were found, hippocampal volumetry was done using these steps: • Acquisition of MRI slices (coronal) and evaluation in a DICOM viewer(radiant) and exporting them in the form of JPEG image. • Creating stacks of image slices IMAGE J software. • Marking REGION OF INTEREST on the stacked image and measuring the area of it. • Area is the multiplied with the number of slices stacked varying per viewer/and or patient. • Sum of these values per slice is used to calculate volume of 3D structure. • The acquired data is entered into a MICROSOFT EXCEL sheet and analysed. Statistical Analysis: Statistical analysis was done by using percentages, mean values, standard deviation, standard error, chi square tests. SPSS version 20 was used to analyse data. The level of significance used was 0.05 levels for the corresponding degree of freedom to draw the inference. A p-value 0.05 was considered to be not statistically significant. SUMMARY: 1. The most common type of seizure in adults is GTCS 2. The predominant type of seizure in a) 12-20 age group is partial seizure(simple and complex with or without secondary generalization); b) 21-40 age group shows a predominance in GTCS; c) 41 to 65 age group shows an equal prevalence of GTCS and Partial seizures. d) Absence seizure was reported only in 12-20 age group 3. Normal imaging in MRI is most commonly associated with GTCS type of seizure. 4. Focal and complex partial seizures are predominantly associated with neuroimaging abnormalities. 5. Most common MRI finding in SEIZURE patients is Normal study. 6. Every patient with epilepsy invariably needs an MRI for complete evaluation. 7. Tuberculomas are amongst the most common finding in IMAGE POSITIVE MRI results. 8. Hippocampal volumetry revealed gross volume regression in 2 subjects with one bilateral regression. 9. 8 cases out of the 19 with MRI NEGATIVE SEIZURES had significant variability in Interhippocampal volume difference from the mean IHD. 10. Correlation between IHD and duration of seizure is equivocal. 11. One subject showed a significant unilateral hippocampal regression on volumetry who may be a potential candidate for epilepsy surgery and needs further work up. 12. There is correlation between age and Total Hippocampal Volume with a peak Hippocampal volume in the 31-40 age range. CONCLUSION : 1. History and physical examination have no substitute. 2. MRI is more sensitive and specific for imaging in patients with seizure than CT. 3. Hippocampal volumetry is essential in patients with MRI negative seizures in setups where investigations like fMRI, SPECT and PET are unavailable. 4. More extensive studies with longer term and larger population of study are required before we can establish a clear set of guidelines. 5. Further investigation and information regarding an option of Epilepsy surgery should be offered to the patients with unilateral Hippocampal regression on volumetry as they may progress to Refractory Epilepsy. 6. Subjects with significant variability in Inter Hippocampal volume difference need regular follow up with imaging to rule out the development of Hippocampal regression

    Neuroimaging of Sudden Unexpected Death in Epilepsy (SUDEP)

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    BACKGROUND: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of premature death among people with epilepsy. The precise mechanisms underlying SUDEP remain elusive, though work so far demonstrates a potential centrally mediated event in which autonomic, respiratory and/or arousal processes fail to recover following a significant seizure. Neuroimaging enables non-invasive assessment of the structural and functional architecture among sites and networks involved in regulating such processes; damage or alterations may indicate a central predisposition in those at high-risk and who suffer SUDEP, and provide non-invasive biomarkers. // METHODS: In this thesis, structural and functional imaging techniques were employed to address this possibility. Both retrospective investigations of those who succumbed to SUDEP, and prospective studies of those at high-risk, were performed. Voxel-based morphometry, volumetry and resting-state functional magnetic resonance imaging (RS-fMRI) network analysis techniques were utilised to identify and characterise brain structural and functional alterations relative to low-risk subjects and controls. // RESULTS: Brain morphometric and volumetric alterations among sites involved in cardiorespiratory regulation and recovery were found in those who later suffered SUDEP and in matched, living individuals at high risk. Prospective work revealed similar, and additional, structural alterations in those at high-risk which were associated with the extent of seizure-related hypoxemia; notably among the thalamus, periaqueductal grey (PAG), medulla, vermis and hippocampus. Network analysis of functional imaging data revealed disturbed patterns of connectivity in high-risk temporal lobe epilepsy (TLE) patients, and altered functional organisation in confirmed cases of SUDEP, among regulatory brain sites as well as the whole brain. // CONCLUSIONS: Structural and resting state functional connectivity disturbances were found in patients who suffered SUDEP, and those at elevated risk. Injury and connectivity disturbances may indicate damage or dysfunction within sites and networks involved central regulatory processes, which could facilitate SUDEP. However, further work is required to elucidate the precise mechanisms of volume and functional connectivity alterations, and to provide firm links between centrally mediated autonomic and respiratory dysfunction, SUDEP and related imaging findings. A more immediate use for the imaging outcomes revealed here may rest with the development of non-invasive biomarkers, which may one day assist in identifying those at risk and evaluating individual risk for SUDEP based on injury to brain sites or altered functional networks

    Biomarkers of Sudden Unexpected Death in Epilepsy (SUDEP)

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    La SUDEP (Sudden Unexpected Death in Epilepsy) è una complicanza devastante dell’epilessia e rappresenta la più comune causa di mortalità prematura in epilessia. Studi volti alla definizione di fattori di rischio clinici hanno permesso di identificare gruppi ad alto rischio. Tuttavia al momento non esistono validati biomarkers genomici, elettrofisiologici o strutturali predittivi di aumentato rischio di SUDEP. Al fine di definire la base genetica della SUDEP, abbiamo condotto una analisi di sequenziamento esomico per esaminare la prevalenza di varianti con effetto deleterio in soggetti deceduti per SUDEP rispetto a pazienti epilettici non deceduti e controlli con altre patologie. Abbiamo riscontrato una prevalenza significativamente aumentata di varianti deleterie diffuse a livello dell’intero genoma nei soggetti deceduti per SUDEP in confronto agli altri gruppi. Un secondo studio di neuroimaging è stato dedicato alla valutazione di anomalie regionali del volume della sostanza grigia in soggetti deceduti per SUDEP, confrontati con soggetti epilettici viventi rispettivamente ad alto e basso rischio per SUDEP, e controlli sani. Abbiamo riscontrato un aumento del volume della sostanza grigia in emisfero destro a livello di amigdala, parte anteriore dell’ippocampo e paraippocampo nei soggetti deceduti per SUDEP e nei soggetti ad alto rischio, rispetto ai soggetti a basso rischio ed ai controlli. Sia il sequenziamento esomico sia il neuroimaging strutturale hanno fornito dati significativi per il profilo di rischio di SUDEP. La definizione dei meccanismi eziologici della SUDEP è fondamentale. La traslazione di tali dati in algoritmi predittivi di rischio individuale consente di promuovere la ‘medicina personalizzata’, allo scopo di adottare strategie preventive e ridurre il rischio individuale di SUDEP in pazienti con epilessia.SUDEP (Sudden Unexpected Death in Epilepsy) is the most devastating outcome in epilepsy and the commonest cause of epilepsy-related premature mortality. Studies of clinical risk factors have allowed identifying high-risk populations. However no genomic, electrophysiological or structural features have emerged as established biomarkers of an increased SUDEP risk. To elucidate the genetic architecture of SUDEP, we used an unbiased whole-exome sequencing approach to examine overall burden and over-representation of deleterious variants in people who died of SUDEP compared to living people with epilepsy and non-epilepsy disease controls. We found significantly increased genome-wide polygenic burden per individual in the SUDEP cohort when compared to epilepsy and non-epilepsy disease controls. The polygenic burden was driven both by the number of variants per individual, and overrepresentation of variants likely to be deleterious in the SUDEP cohort. To elucidate which brain regions may be implicated in SUDEP, we investigated whether regional abnormalities in grey matter volume appear in those who died of SUDEP, compared to subjects at high and low risk for SUDEP, and healthy controls. We identified increased grey matter volume in the right anterior hippocampus/amygdala and parahippocampus in SUDEP cases and people at high risk, when compared to those at low risk and controls. Compared to controls, posterior thalamic grey matter volume, an area mediating oxygen regulation, was reduced in SUDEP cases and subjects at high risk. It is fundamental to understand the range of SUDEP aetiological mechanisms. Our results suggest that both exome sequencing data and structural imaging features may contribute to generate SUDEP risk estimates. Translation of this knowledge into predictive algorithms of individual risk and preventive strategies would promote stratified medicine in epilepsy, with the aim of reducing an individual patient's risk of SUDEP

    Functional network correlates of language and semiology in epilepsy

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    Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs

    Multidelay ASL of the pediatric brain

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    Arterial spin labeling (ASL) is a powerful noncontrast MRI technique for evaluation of cerebral blood flow (CBF). A key parameter in single-delay ASL is the choice of postlabel delay (PLD), which refers to the timing between the labeling of arterial free water and measurement of flow into the brain. Multidelay ASL (MDASL) utilizes several PLDs to improve the accuracy of CBF calculations using arterial transit time (ATT) correction. This approach is particularly helpful in situations where ATT is unknown, including young subjects and slow-flow conditions. In this article, we discuss the technical considerations for MDASL, including labeling techniques, quantitative metrics, and technical artefacts. We then provide a practical summary of key clinical applications with real-life imaging examples in the pediatric brain, including stroke, vasculopathy, hypoxic-ischemic injury, epilepsy, migraine, tumor, infection, and metabolic disease
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