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.
ObjectivesHigh frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.MethodsWe propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2–5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.ResultsThe detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80–500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors.ConclusionsCompared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.
Objectives: Since the pioneering work of Parkinson, several studies have described the microsurgical anatomy and surgical procedures involving the cavernous sinus (CS). A proposed geometric construct has been adopted as nomenclature for the region by many neurosurgeons. However, authors differ in naming and describing some of these triangular spaces. The purpose of this study is to present the anatomy and measure the dimensions of the 10 triangles in and around this region. Materials and Methods: Eighteen CS of five cadaveric heads and four skull bases fixed in formalin were dissected using 3 Â to 40 Â magnification of the surgical microscope. The heads and skull bases were injected with colored silicone and the sides and area of the triangles were measured. Each cadaveric head was placed in a Sugita head-holder and a cranio-orbitozygomatic approach and a combined extra-and intradural approach were performed. The last step was the detachment of the brain from the skull base and measurement of the inferolateral paraclival and inferomedial paraclival triangles. Results: The measurements of the medial border, lateral border, and base of each triangle as well as the standard deviation and area are presented. The posteromedial middle fossa triangle was the largest and the clinoidal triangle the smallest. Conclusions: The normal anatomy of the CS triangle and its areas are important in the approach of the CS lesions because these spaces are natural corridors through which the lesions can be reached. The same concept must be used for the triangles around this space. Whenever these geometric spaces might be distorted by pathology or surgical maneuvers, the surgeon must have precise knowledge about their normal sizes.
We suggest prospective validation of a score ("STAMPE2") based on clinical findings, EEG, and brain-imaging measures to estimate postoperative seizure risk and guide anticonvulsant treatment in meningioma patients.
The surgical outcome in terms of seizures was rewarding in the majority of patients, particularly in those who exhibited the following irregularities on preoperative investigations: regular local dysfunctions on electroencephalography, dysmorphic changes in the mesiobasal temporal parenchyma on MR imaging studies, and hypometabolism in the anterior third of the temporal lobe on PET studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.