Resection of high frequency oscillations predicts seizure outcome in the individual patient.
TL;DR: The resection of the prospectively defined HFO area proved to be highly specific and reproducible in 13/13 patients with seizure freedom, while it may have improved the outcome in 4/7 patients with recurrent seizures.
Abstract: High frequency oscillations (HFOs) are recognized as biomarkers for epileptogenic brain tissue. A remaining challenge for epilepsy surgery is the prospective classification of tissue sampled by individual electrode contacts. We analysed long-term invasive recordings of 20 consecutive patients who subsequently underwent epilepsy surgery. HFOs were defined prospectively by a previously validated, automated algorithm in the ripple (80–250 Hz) and the fast ripple (FR, 250–500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR over several five-minute time intervals designated the HFO area. The HFO area was fully included in the resected area in all 13 patients who achieved seizure freedom (specificity 100%) and in 3 patients where seizures reoccurred (negative predictive value 81%). The HFO area was only partially resected in 4 patients suffering from recurrent seizures (positive predictive value 100%, sensitivity 57%). Thus, the resection of the prospectively defined HFO area proved to be highly specific and reproducible in 13/13 patients with seizure freedom, while it may have improved the outcome in 4/7 patients with recurrent seizures. We thus validated the clinical relevance of the HFO area in the individual patient with an automated procedure. This is a prerequisite before HFOs can guide surgical treatment in multicentre studies.
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TL;DR: It is found that persistent neural activity in the hippocampus participated in working memory processing that is specific to memory maintenance, load sensitive and synchronized to the cortex.
Abstract: The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. To investigate the influence of load on working memory, we asked human subjects to maintain sets of letters in memory while we recorded single neurons and intracranial encephalography (EEG) in the medial temporal lobe and scalp EEG. Along the periods of a trial, hippocampal neural firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects’ behavior during retrieval. During maintenance, neuronal firing was synchronized with intracranial hippocampal EEG. On the network level, synchronization between hippocampal and scalp EEG in the theta-alpha frequency range showed workload dependent oscillatory coupling between hippocampus and cortex. Thus, we found that persistent neural activity in the hippocampus participated in working memory processing that is specific to memory maintenance, load sensitive and synchronized to the cortex.
66 citations
TL;DR: The modulation index, a summary measure of the strength of phase‐amplitude coupling between high‐frequency activity and the phase of slow waves, would serve as a useful interictal biomarker for epilepsy presurgical evaluation.
Abstract: OBJECTIVE We hypothesized that the modulation index (MI), a summary measure of the strength of phase-amplitude coupling between high-frequency activity (>150 Hz) and the phase of slow waves (3-4 Hz), would serve as a useful interictal biomarker for epilepsy presurgical evaluation METHODS We investigated 123 patients who underwent focal cortical resection following extraoperative electrocorticography recording and had at least 1 year of postoperative follow-up We examined whether consideration of MI would improve the prediction of postoperative seizure outcome MI was measured at each intracranial electrode site during interictal slow-wave sleep We compared the accuracy of prediction of patients achieving International League Against Epilepsy class 1 outcome between the full multivariate logistic regression model incorporating MI in addition to conventional clinical, seizure onset zone (SOZ), and neuroimaging variables, and the reduced logistic regression model incorporating all variables other than MI RESULTS Ninety patients had class 1 outcome at the time of most recent follow-up (mean follow-up = 57 years) The full model had a noteworthy outcome predictive ability, as reflected by regression model fit R2 of 0409 and area under the curve (AUC) of receiver operating characteristic plot of 0838 Incomplete resection of SOZ (P < 0001), larger number of antiepileptic drugs at the time of surgery (P = 0007), and larger MI in nonresected tissues relative to that in resected tissue (P = 0020) were independently associated with a reduced probability of class 1 outcome The reduced model had a lower predictive ability as reflected by R2 of 0266 and AUC of 0767 Anatomical variability in MI existed among nonepileptic electrode sites, defined as those unaffected by magnetic resonance imaging lesion, SOZ, or interictal spike discharges With MI adjusted for anatomical variability, the full model yielded the outcome predictive ability of R2 of 0422, AUC of 0844, and sensitivity/specificity of 086/076 SIGNIFICANCE MI during interictal recording may provide useful information for the prediction of postoperative seizure outcome
65 citations
TL;DR: In this article, the authors presented a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing intracranial EEG (iEEG) from epilepsy patients for the detection of high frequency oscillations (HFO), which are a biomarker for epileptogenic brain tissue.
Abstract: The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies
52 citations
TL;DR: The authors discuss the ongoing shift towards personalized treatments for specific epilepsy aetiologies, which has led to the development of diagnostic biomarkers for epilepsy and the redefinition of some epileptic syndromes to incorporate aetiology.
Abstract: Over the last decade, advances in genetics, neuroimaging and EEG have enabled the aetiology of epilepsy to be identified earlier in the disease course than ever before. At the same time, progress in the study of experimental models of epilepsy has provided a better understanding of the mechanisms underlying the condition and has enabled the identification of therapies that target specific aetiologies. We are now witnessing the impact of these advances in our daily clinical practice. Thus, now is the time for a paradigm shift in epilepsy treatment from a reactive attitude, treating patients after the onset of epilepsy and the initiation of seizures, to a proactive attitude that is more broadly integrated into a 'P4 medicine' approach. This P4 approach, which is personalized, predictive, preventive and participatory, puts patients at the centre of their own care and, ultimately, aims to prevent the onset of epilepsy. This aim will be achieved by adapting epilepsy treatments not only to a given syndrome but also to a given patient and moving from the usual anti-seizure treatments to personalized treatments designed to target specific aetiologies. In this Review, we present the current state of this ongoing revolution, emphasizing the impact on clinical practice.
50 citations
TL;DR: In this paper, the authors developed and retrospectively validated a new electroencephalogram (EEG) marker-neural fragility-in a retrospective analysis of 91 patients by using neural fragility of the annotated seizure onset zone (SOZ) as a metric to predict surgical outcomes.
Abstract: Over 15 million patients with epilepsy worldwide do not respond to drugs. Successful surgical treatment requires complete removal or disconnection of the seizure onset zone (SOZ), brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30 and 70% because no clinically validated biological marker of the SOZ exists. We develop and retrospectively validate a new electroencephalogram (EEG) marker-neural fragility-in a retrospective analysis of 91 patients by using neural fragility of the annotated SOZ as a metric to predict surgical outcomes. Fragility predicts 43 out of 47 surgical failures, with an overall prediction accuracy of 76% compared with the accuracy of clinicians at 48% (successful outcomes). In failed outcomes, we identify fragile regions that were untreated. When compared to 20 EEG features proposed as SOZ markers, fragility outperformed in predictive power and interpretability, which suggests neural fragility as an EEG biomarker of the SOZ.
45 citations
References
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TL;DR: The current diagnostic techniques used in the definition of these cortical zones, such as video-EEG monitoring, MRI and ictal single photon emission computed tomography, are discussed and possible future developments that might lead to a more direct definition of the epileptogenic zone are presented.
Abstract: An overview of the following six cortical zones that have been defined in the presurgical evaluation of candidates for epilepsy surgery is given: the symptomatogenic zone; the irritative zone; the seizure onset zone; the epileptogenic lesion; the epileptogenic zone; and the eloquent cortex. The stepwise historical evolution of these different zones is described. The current diagnostic techniques used in the definition of these cortical zones, such as video-EEG monitoring, MRI and ictal single photon emission computed tomography, are discussed. Established diagnostic tests are set apart from procedures that should still be regarded as experimental, such as magnetoencephalography, dipole source localization and spike-triggered functional MRI. Possible future developments that might lead to a more direct definition of the epileptogenic zone are presented.
1,416 citations
TL;DR: This poster presents a poster presented at the annual meeting of the American Academy of Neurology, where it was presented for the first time with a focus on the treatment of seizure-like symptoms in patients with epilepsy.
Abstract: *Neurology Clinic, Epileptology & EEG, University Hospital, Zurich, Switzerland; †Department of Neurology, University Hospital, London, Ontario, Canada; ‡The National Hospital for Neurology and Neurosurgery, Queen Square, London, England; §Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, New York, U.S.A.; Department of Neurology, University of Essen, Essen, Germany; ¶Department of Neurology, Medical College of Georgia, Augusta, Georgia; **Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania; and ††Department of Neurology, The Cleveland Clinic Foundation, Cleveland, Ohio, U.S.A.
837 citations
TL;DR: The major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals with specific quantifiers derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials.
780 citations
TL;DR: Two similar types of high‐frequency field oscillations recorded from the entorhinal cortex and hippocampus of patients with mesial temporal lobe epilepsy are described, which are found in the epileptogenic region and may reflect pathological hypersynchronous population spikes of bursting pyramidal cells.
Abstract: Ripples are 100–200 Hz short-duration oscillatory field potentials that have recently been recorded in rat hippocampus and entorhinal cortex. They reflect fast IPSPs on the soma of pyramidal cells, which occur during synchronous afferent excitation of principal cells and interneuron networks. We now describe two similar types of high-frequency field oscillations recorded from the entorhinal cortex and hippocampus of patients with mesial temporal lobe epilepsy. The first type appears be the human equivalent of normal ripples in the rat. The second, which we have termed fast ripples (FR), are in the frequency range of 250–500 Hz. FR are found in the epileptogenic region and may reflect pathological hypersynchronous population spikes of bursting pyramidal cells. Hippocampus 1999;9:137–142. © 1999 Wiley-Liss, Inc.
656 citations
TL;DR: The reasoning behind permutation methods for exact inference is discussed and situations when they are exact and distribution-free are described.
Abstract: The use of permutation methods for exact inference dates back to Fisher in 1935. Since then, the practicality of such methods has increased steadily with computing power. They can now easily be employed in many situations without concern for computing difficulties. We discuss the reasoning behind these methods and describe situations when they are exact and distribution-free. We illustrate their use in several examples.
640 citations