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      Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions

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          Abstract

          To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1–2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80–250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250–600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively ( p < 0.01). HFO frequency was also affected ( p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ ( p < 0.01). However, the increases were larger during the sleep recordings ( p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates ( p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients ( p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

            One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
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              High-frequency oscillations: The state of clinical research.

              Modern electroencephalographic (EEG) technology contributed to the appreciation that the EEG signal outside the classical Berger frequency band contains important information. In epilepsy, research of the past decade focused particularly on interictal high-frequency oscillations (HFOs) > 80 Hz. The first large application of HFOs was in the context of epilepsy surgery. This is now followed by other applications such as assessment of epilepsy severity and monitoring of antiepileptic therapy. This article reviews the evidence on the clinical use of HFOs in epilepsy with an emphasis on the latest developments. It highlights the growing literature on the association between HFOs and postsurgical seizure outcome. A recent meta-analysis confirmed a higher resection ratio for HFOs in seizure-free versus non-seizure-free patients. Residual HFOs in the postoperative electrocorticogram were shown to predict epilepsy surgery outcome better than preoperative HFO rates. The review further discusses the different attempts to separate physiological from epileptic HFOs, as this might increase the specificity of HFOs. As an example, analysis of sleep microstructure demonstrated a different coupling between HFOs inside and outside the epileptogenic zone. Moreover, there is increasing evidence that HFOs are useful to measure disease activity and assess treatment response using noninvasive EEG and magnetoencephalography. This approach is particularly promising in children, because they show high scalp HFO rates. HFO rates in West syndrome decrease after adrenocorticotropic hormone treatment. Presence of HFOs at the time of rolandic spikes correlates with seizure frequency. The time-consuming visual assessment of HFOs, which prevented their clinical application in the past, is now overcome by validated computer-assisted algorithms. HFO research has considerably advanced over the past decade, and use of noninvasive methods will make HFOs accessible to large numbers of patients. Prospective multicenter trials are awaited to gather information over long recording periods in large patient samples.
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                Author and article information

                Contributors
                Michael.sperling@jefferson.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 November 2021
                1 November 2021
                2021
                : 11
                : 21388
                Affiliations
                [1 ]GRID grid.262863.b, ISNI 0000 0001 0693 2202, Department of Neurology, , State University of New York Downstate, ; Brooklyn, NY 11203 USA
                [2 ]GRID grid.262863.b, ISNI 0000 0001 0693 2202, Department of Physiology and Pharmacology, , State University of New York Downstate, ; Brooklyn, NY 11203 USA
                [3 ]GRID grid.422616.5, ISNI 0000 0004 0443 7226, Department of Neurology, , New York City Health + Hospitals/Kings County, ; Brooklyn, NY USA
                [4 ]Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Rochester, USA
                [5 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Department of Physiology and Biomedical Engineering, , Mayo Clinic, ; Rochester, MN 55905 USA
                [6 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Neurology, , David Geffen School of Medicine at UCLA, ; Los Angeles, CA 90095 USA
                [7 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Neurobiology, , David Geffen School of Medicine at UCLA, ; Los Angeles, CA 90095 USA
                [8 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Psychiatry and Biobehavioral Sciences, , David Geffen School of Medicine at UCLA, ; Los Angeles, CA 90095 USA
                [9 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Brain Research Institute, , David Geffen School of Medicine at UCLA, ; Los Angeles, CA 90095 USA
                [10 ]GRID grid.265008.9, ISNI 0000 0001 2166 5843, Department of Neurology and Neuroscience, , Thomas Jefferson University, ; 901 Walnut St. Suite 400, Philadelphia, PA 19107 USA
                [11 ]GRID grid.265008.9, ISNI 0000 0001 2166 5843, Department of Neurosurgery, , Thomas Jefferson University, ; Philadelphia, PA 19107 USA
                [12 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Penn Image Computing & Science Lab, , University of Pennsylvania, ; Philadelphia, PA 19143 USA
                Article
                894
                10.1038/s41598-021-00894-3
                8560764
                34725412
                cef46691-3fa8-44dd-9b79-15728c13b9fa
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 September 2021
                : 19 October 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: K23 NS094633
                Award ID: R01 NS106958
                Award ID: R01 NS033310
                Award Recipient :
                Categories
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                © The Author(s) 2021

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                epilepsy,diagnostic markers
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                epilepsy, diagnostic markers

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