Neocortical pathological high-frequency oscillations are associated with frequency-dependent alterations in functional network topology

2013 ◽  
Vol 110 (10) ◽  
pp. 2475-2483 ◽  
Author(s):  
George M. Ibrahim ◽  
Ryan Anderson ◽  
Tomoyuki Akiyama ◽  
Ayako Ochi ◽  
Hiroshi Otsubo ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Phase Locking ◽  
Critical Role ◽  
Intractable Epilepsy ◽  
Network Connectivity ◽  
Relative Increase ◽  
High Frequencies ◽  
High Frequency Oscillations ◽  
Cortical Regions

Synchronization of neural oscillations is thought to integrate distributed neural populations into functional cell assemblies. Epilepsy is widely regarded as a disorder of neural synchrony. Knowledge is scant, however, regarding whether ictal changes in synchrony involving epileptogenic cortex are expressed similarly across various frequency ranges. Cortical regions involved in epileptic networks also exhibit pathological high-frequency oscillations (pHFOs, >80 Hz), which are increasingly utilized as biomarkers of epileptogenic tissue. It is uncertain how pHFO amplitudes are related to epileptic network connectivity. By calculating phase-locking values among intracranial electrodes implanted in children with intractable epilepsy, we constructed ictal connectivity networks and performed graph theoretical analysis to characterize their network properties at distinct frequency bands. Ictal data from 17 children were analyzed with a hierarchical mixed-effects model adjusting for patient-level covariates. Epileptogenic cortex was defined in two ways: 1) a hypothesis-driven method using the visually defined seizure-onset zone and 2) a data-agnostic method using the high-frequency amplitude of each electrode. Epileptogenic cortex exhibited a logarithmic decrease in interregional functional connectivity at high frequencies (>30 Hz) during seizure initiation and propagation but not at termination. At slower frequencies, conversely, epileptogenic cortex expressed a relative increase in functional connectivity. Our findings suggest that pHFOs reflect epileptogenic network interactions, yielding theoretical support for their utility in the presurgical evaluation of intractable epilepsy. The view that abnormal network synchronization plays a critical role in ictogenesis and seizure dynamics is supported by the observation that functional isolation of epileptogenic cortex at high frequencies is absent at seizure termination.

scholarly journals Exploring short period oscillations in filaments

2007 ◽  
Vol 3 (S247) ◽  
pp. 178-181 ◽  
Author(s):  
D. Y. Kolobov ◽  
N. I. Kobanov ◽  
S. A. Chupin
Keyword(s):  
Data Reduction ◽  
High Frequency ◽  
Entrance Slit ◽  
Doppler Velocity ◽  
High Frequencies ◽  
Spectrograph Slit ◽  
High Frequency Oscillations ◽  
Short Period ◽  
False Signal ◽  
Image Shifting

AbstractThe authors analyse sources of false Doppler velocity signals of high frequencies (10 mHz and higher) in observations of filaments. In ground-based observations, spectrograph noise and image shifting at the spectrograph entrance slit are the main causes of the false signal. It is shown that using differential methods and telluric lines as reference lines significantly reduces the influence of the first factor. Periodical image shifting along the spectrograph slit can be compensated for during data reduction. In some cases detected high-frequency oscillations appear to be real.

scholarly journals The use of high frequency oscillations to guide neocortical resections in children with medically-intractable epilepsy: How do we ethically apply surgical innovations to patient care?

Seizure ◽  
2012 ◽  
Vol 21 (10) ◽  
pp. 743-747 ◽  
Author(s):  
George M. Ibrahim ◽  
Aria Fallah ◽  
O. Carter Snead ◽  
James M. Drake ◽  
James T. Rutka ◽  
...  
Keyword(s):  
Patient Care ◽  
High Frequency ◽  
Intractable Epilepsy ◽  
High Frequency Oscillations

scholarly journals Thalamocortical dysrhythmia in intraoperative recordings of focal epilepsy

2019 ◽  
Vol 121 (6) ◽  
pp. 2020-2027 ◽  
Author(s):  
Daniel J. Martire ◽  
Simeon Wong ◽  
Mirriam Mikhail ◽  
Ayako Ochi ◽  
Hiroshi Otsubo ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Large Scale ◽  
Focal Epilepsy ◽  
Critical Role ◽  
Epileptogenic Zone ◽  
Thalamocortical Dysrhythmia ◽  
High Frequency Activity ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

Resonant interactions between the thalamus and cortex subserve a critical role for maintenance of consciousness as well as cognitive functions. In states of abnormal thalamic inhibition, thalamocortical dysrhythmia (TCD) has been described. The characteristics of TCD include a slowing of resting oscillations, ectopic high-frequency activity, and increased cross-frequency coupling. Here, we demonstrate the presence of TCD in four patients who underwent resective epilepsy surgery with chronically implanted electrodes under anesthesia, continuously recording activity from brain regions at the periphery of the epileptogenic zone before and after resection. Following resection, we report an acceleration of the large-scale network resting frequency coincident with decreases in cross-frequency phase-amplitude coupling. Interregional functional connectivity in the surrounding cortex was also increased following resection of the epileptogenic focus. These findings provide evidence for the presence of TCD in focal epilepsy and highlight the importance of reciprocal thalamocortical oscillatory interactions in defining novel biomarkers for resective surgeries. NEW & NOTEWORTHY Thalamocortical dysrhythmia (TCD) occurs in the context of thalamic dysfacilitation and is characterized by slowing of resting oscillations, ectopic high-frequency activity, and cross-frequency coupling. We provide evidence for TCD in focal epilepsy by studying electrophysiological changes occurring at the periphery of the resection margin. We report acceleration of resting activity coincident with decreased cross-frequency coupling and increased functional connectivity. The study of TCD in epilepsy has implications as a biomarker and therapeutic target.

Automated Detector of High Frequency Oscillations in Epilepsy Based on Maximum Distributed Peak Points

2017 ◽  
Vol 28 (01) ◽  
pp. 1750029 ◽  
Author(s):  
Guo-Ping Ren ◽  
Jia-Qing Yan ◽  
Zhi-Xin Yu ◽  
Dan Wang ◽  
Xiao-Nan Li ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
High Frequency ◽  
Intractable Epilepsy ◽  
Rank Correlation ◽  
Spearman’S Rank Correlation ◽  
High Frequency Oscillations ◽  
Spearman’S Rank Correlation Coefficient ◽  
Peak Points ◽  
Higher Sensitivity ◽  
Novel Algorithm

High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80–200[Formula: see text]Hz), fast ripples (FRs, 200–500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman’s rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.

scholarly journals High frequencies in TESS A–F main-sequence stars

2019 ◽  
Vol 487 (2) ◽  
pp. 2117-2132 ◽  
Author(s):  
L A Balona ◽  
D L Holdsworth ◽  
M S Cunha
Keyword(s):  
High Frequency ◽  
Driving Mechanism ◽  
Rotational Axis ◽  
Satellite Mission ◽  
High Frequencies ◽  
High Frequency Oscillations ◽  
Photometric Observations ◽  
F Stars ◽  
Ap Stars ◽  
Scuti Stars

Abstract The driving mechanism for high-frequency oscillations in some chemically peculiar Ap stars, the rapidly oscillating Ap stars (roAp stars), is not understood. The Transiting Exoplanet Survey Satellite mission (TESS) data provide an ideal opportunity to extend the number of roAp stars that might provide further clues to address this problem. From an examination of over 18 000 stars in TESS sectors 1–7, we have discovered high-frequency pulsations in 14 A–F stars, of which only 3 are classified as Ap stars. In addition to these new discoveries, we discuss the frequencies in nine previously known roAp stars. In one of these stars, HD 60435, we confirm a previous finding that the pulsations have lifetimes of only a few days. In another known roAp star, HD 6532, the relative amplitudes of the rotationally modulated sidelobes, which are generally used to estimate the inclination of the magnetic axis relative to the rotational axis, are significantly different from those found in ground-based B-band photometric observations. We also discuss four δ Scuti stars that appear to have independent frequencies similar to those of roAp stars.

scholarly journals A Connectomic Atlas of the Human Cerebrum—Chapter 1: Introduction, Methods, and Significance

2018 ◽  
Vol 15 (suppl_1) ◽  
pp. S1-S9 ◽  
Author(s):  
Cordell M Baker ◽  
Joshua D Burks ◽  
Robert G Briggs ◽  
Andrew K Conner ◽  
Chad A Glenn ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Network Connectivity ◽  
Cortical Function ◽  
Human Cortex ◽  
Functional Aspects ◽  
Human Connectome Project ◽  
Higher Cognitive Functions ◽  
Cortical Regions

ABSTRACT BACKGROUND As knowledge of the brain has increased, clinicians have learned that the cerebrum is composed of complex networks that interact to execute key functions. While neurosurgeons can typically predict and preserve primary cortical function through the primary visual and motor cortices, preservation of higher cognitive functions that are less well localized in regions previously deemed “silent” has proven more difficult. This suggests these silent cortical regions are more anatomically complex and redundant than our previous methods of inquiry can explain, and that progress in cerebral surgery will be made with an improved understanding of brain connectomics. Newly published parcellated cortex maps provide one avenue to study such connectomics in greater detail, and they provide a superior framework and nomenclature for studying cerebral function and anatomy. OBJECTIVE To describe the structural and functional aspects of the 180 distinct areas that comprise the human cortex model previously published under the Human Connectome Project (HCP). METHODS We divided the cerebrum into 8 macroregions: lateral frontal, motor/premotor, medial frontal, insular, temporal, lateral parietal, medial parietal, and occipital. These regions were further subdivided into their relevant parcellations based on the HCP cortical scheme. Connectome Workbench was used to localize parcellations anatomically and to demonstrate their functional connectivity. DSI studio was used to assess the structural connectivity for each parcellation. RESULTS The anatomy, functional connectivity, and structural connectivity of all 180 cortical parcellations identified in the HCP are compiled into a single atlas. Within each section of the atlas, we integrate this information, along with what is known about parcellation function to summarize the implications of these data on network connectivity. CONCLUSION This multipart supplement aims to build on the work of the HCP. We present this information in the hope that the complexity of cerebral connectomics will be conveyed in a more manageable format that will allow neurosurgeons and neuroscientists to accurately communicate and formulate hypotheses regarding cerebral anatomy and connectivity. We believe access to this information may provide a foundation for improving surgical outcomes by preserving lesser-known networks.

scholarly journals Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury

2016 ◽  
Vol 116 (4) ◽  
pp. 1840-1847 ◽  
Author(s):  
Ahmad Alhourani ◽  
Thomas A. Wozny ◽  
Deepa Krishnaswamy ◽  
Sudhir Pathak ◽  
Shawn A. Walls ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Mild Traumatic Brain Injury ◽  
Phase Locking ◽  
Brain Regions ◽  
Memory And Attention ◽  
Control Subjects ◽  
Cortical Regions ◽  
Phase Locking Value

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects ( n = 9) compared with age-matched control subjects ( n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

scholarly journals Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Guoping Ren ◽  
Yueqian Sun ◽  
Dan Wang ◽  
Jiechuan Ren ◽  
Jindong Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Slow Wave Sleep ◽  
Intractable Epilepsy ◽  
Wavelet Spectrum ◽  
Epileptogenic Zone ◽  
High Frequency Oscillations ◽  
Scale Transformations ◽  
Better Than

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

scholarly journals Decreased Resting-State Interhemispheric Functional Connectivity in Parkinson’s Disease

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
ChunYan Luo ◽  
XiaoYan Guo ◽  
Wei Song ◽  
Bi Zhao ◽  
Bei Cao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Critical Role ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Drug Naïve ◽  
And Gender ◽  
Cortical Regions

Background. Abnormalities in white matter integrity and specific functional network alterations have been increasingly reported in patients with Parkinson’s disease (PD). However, little is known about the inter-hemispheric interaction in PD.Methods. Fifty-one drug naive patients with PD and 51 age- and gender-matched healthy subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. We compared the inter-hemispheric resting-state functional connectivity between patients with PD and healthy controls, using the voxel-mirrored homotopic connectivity (VMHC) approach. Then, we correlated the results from VMHC and clinical features in PD patients.Results. Relative to healthy subject, patients exhibited significantly lower VMHC in putamen and cortical regions associated with sensory processing and motor control (involving sensorimotor and supramarginal cortex), which have been verified to play a critical role in PD. In addition, there were inverse relationships between the UPDRS motor scores and VMHC in the sensorimotor, and between the illness duration and VMHC in the supramarginal gyrus in PD patients.Conclusions. Our results suggest that the functional coordination between homotopic brain regions is impaired in PD patients, extending previous notions about the disconnection of corticostriatal circuit by providing new evidence supporting a disturbance in inter-hemispheric connections in PD.

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