scholarly journals ANALYSIS OF ELECTROENCEPHALOGRAPHIC RECORDINGS FROM EPILEPSY PATIENTS USING RESTRICTED GRANGER CAUSALITY MEASURES

2015 ◽  
Author(s):  
Elsa Siggiridou ◽  
Vasilios Kimiskidis ◽  
D. Kugiumtzis
Keyword(s):  
Granger Causality ◽  
Brain Connectivity ◽  
Brain Regions ◽  
World Population ◽  
Var Models ◽  
Vector Autoregressive ◽  
Epileptiform Discharges ◽  
Eeg Recordings ◽  
Generalized Partial Directed Coherence ◽  
Patients With Epilepsy

Epilepsy is a chronic disorder of the brain that affects 1% of world population. The occurrence of epileptiform discharges (ED) in electroencephalographic (EEG) recordings of patients with epilepsy signifies a change in brain dynamics and particularly brain connectivity. In the last decade, many linear and nonlinear measures have been developed for the analysis of EEG recordings to detect the direct causal effects between brain regions. In many cases the number of EEG channels (the time series variables) is large and the analysis is based on short time intervals, resulting in unstable estimation of vector autoregressive models (VAR models) and subsequently unreliable Granger causality measure. For this, restricted VAR models have been proposed and in our recent study it was found that optimal restriction of VAR for the estimation of Granger causality was obtained by the backward-in-time selection method (BTS). We use the concept of restricted VAR models in measures both in time and frequency domain, namely restricted conditional Granger causality and restricted generalized partial directed coherence. We test the two measures in their ability of detecting changes in brain connectivity during an epileptiform discharge from multi-channel scalp electroencephalograms (EEG).

scholarly journals Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  
Keyword(s):  
Normal Subjects ◽  
Intracranial Eeg ◽  
Zero Crossing ◽  
Temporal Precision ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

scholarly journals Forced Normalization Revisited: New Concepts About a Paradoxical Phenomenon

2021 ◽  
Vol 15 ◽  
Author(s):  
José Augusto Bragatti
Keyword(s):  
Imaging Techniques ◽  
Brain Regions ◽  
Experimental Models ◽  
Psychotic Symptoms ◽  
Resting State Networks ◽  
Epileptiform Discharges ◽  
New Concepts ◽  
Forced Normalization ◽  
Electrical Stimuli ◽  
Patients With Epilepsy

The phenomenon of Forced Normalization (FN) was first described by Landolt in 1953, who described the disappearance of epileptiform discharges in the EEG of patients with epilepsy, concomitant with the development of psychotic symptoms. Later, Tellenbach coined the term “alternative psychosis” referring specifically to the alternation between clinical phenomena. Finally, in 1991, Wolf observed a degenerative process involved in the phenomenon, which he called “paradoxical normalization.” Initially, FN was explained through experimental models in animals and the demonstration of the kindling phenomenon, in its electrical and pharmacological subdivisions. At this stage of research on the epileptic phenomenon, repetitive electrical stimuli applied to susceptible regions of the brain (hippocampus and amygdala) were considered to explain the pathophysiological basis of temporal lobe epileptogenesis. Likewise, through pharmacological manipulation, especially of dopaminergic circuits, psychiatric comorbidities began to find their basic mechanisms. With the development of new imaging techniques (EEG/fMRI), studies in the area started to focus on the functional connectivity (FC) of different brain regions with specific neuronal networks, which govern emotions. Thus, a series of evidence was produced relating the occurrence of epileptic discharges in the limbic system and their consequent coactivation and deactivation of these resting-state networks. However, there are still many controversies regarding the basic mechanisms of network alterations related to emotional control, which will need to be studied with a more homogeneous methodology, in order to try to explain this interesting neuropsychiatric phenomenon with greater accuracy.

Dynamics of Epileptiform Discharges Induced by Transcranial Magnetic Stimulation in Genetic Generalized Epilepsy

2017 ◽  
Vol 27 (07) ◽  
pp. 1750037 ◽  
Author(s):  
Dimitris Kugiumtzis ◽  
Christos Koutlis ◽  
Alkiviadis Tsimpiris ◽  
Vasilios K. Kimiskidis
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Network ◽  
Brain State ◽  
Epileptiform Discharges ◽  
Eeg Recordings ◽  
Generalized Epilepsy ◽  
The Brain

Objective: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. Methods: EEG recordings from two GGE patients were analyzed. For hypothesis (a), the characteristics of the brain dynamics at the early ED stage are measured with univariate and multivariate EEG measures and the dependence of the ED duration on these measures is evaluated. For hypothesis (b), effective connectivity measures are combined with network indices so as to quantify the brain network characteristics and identify changes in brain connectivity. Results: A number of measures combined with specific channels computed on the first EEG segment post-TMS correlate with the ED duration. In addition, brain connectivity is altered from pre-ED to ED and post-ED and statistically significant changes were also detected across stages within the ED. Conclusion: ED duration is not purely stochastic, but depends on the dynamics of the post-TMS brain state. The brain network dynamics is significantly altered in the course of EDs.

scholarly journals Pervasive false brain connectivity from electrophysiological signals

2021 ◽  
Author(s):  
Roberto D. Pascual-Marqui ◽  
Peter Achermann ◽  
Pascal Faber ◽  
Toshihiko Kinoshita ◽  
Kieko Kochi ◽  
...  
Keyword(s):  
Granger Causality ◽  
Time Series Data ◽  
Brain Connectivity ◽  
Estimation Method ◽  
Brain Regions ◽  
Reproducible Research ◽  
Series Data ◽  
Coupling Coefficients ◽  
Causality Inference ◽  
Observation Noise

1.AbstractSignals of brain electric neuronal activity, either invasively measured or non-invasively estimated, are commonly used for connectivity inference. One popular methodology assumes that the neural dynamics follow a multivariate autoregression, where the autoregressive coefficients represent the couplings among regions. If observation noise is present and ignored, as is common in practice, the estimated couplings are biased, affecting all forms of Granger-causality inference, both in time and in frequency domains. Significant nonsense coupling, i.e., nonsense connectivity, can appear when in reality there is none, since there is always observation noise in two possible forms: measurement noise, and activity from other brain regions due to volume conduction and low spatial resolution. This problem is critical, and is currently not being addressed, calling into question the validity of many Granger-causality reports in the literature. An estimation method that accounts for noise is based on an overdetermined system of high-order multivariate Yule-Walker equations, which give reduced variance estimators for the coupling coefficients of the unobserved signals. Simulation-based comparisons to other published methods are presented, demonstrating its adequate performance. In addition, simulation results are presented for a zero connectivity case with noisy observations, where the new method correctly reports no connectivity while classical analyses (as found in most software packages) report nonsense connectivity. For the sake of reproducible research, the supplementary material includes, in human readable format, all the time series data used here.

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

scholarly journals The prognostic value of electroencephalography in epilepsy: a long-term follow-up study

2010 ◽  
Vol 2 (2) ◽  
pp. 18 ◽  
Author(s):  
Stylianos Gatzonis ◽  
Nikolaos Triantafyllou ◽  
Maria Kateri ◽  
Anna Siatouni ◽  
Elias Angelopoulos ◽  
...  
Keyword(s):  
Social Adjustment ◽  
Significant Proportion ◽  
Favourable Outcome ◽  
Epileptiform Discharges ◽  
Long Term Follow Up ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
The Relationship

Predicting the evolution of epilepsy is of obvious importance for patients and their families. Value of electroencephalography (EEG) is extensively used in the diagnosis of epilepsy yet its role as a prognostication method remains unclear. The aim of the present retrospective study is to investigate the relationship between serial EEG recordings and long-term clinical and social outcomes in a cohort of patients with epilepsy. Thirty-nine epileptic patients were monitored clinically and with repeat EEG recordings for more than 15 years. All patients who initially had epileptiform discharges ended up with poor or moderate seizure control whereas more than half of the patients with normal initial recordings had good clinical outcomes and satisfactory social adjustment. Deterioration of the recordings over time was associated with unfavourable results in a significant proportion of patients (90 %), while stable or improved EEG findings predicted a favourable outcome. It is concluded that serial EEG recordings can be used in the prognostic evaluation of epilepsy.

scholarly journals Synchrony in Normal and Focal Epileptic Brain: The Seizure Onset Zone is Functionally Disconnected

2010 ◽  
Vol 104 (6) ◽  
pp. 3530-3539 ◽  
Author(s):  
Christopher P. Warren ◽  
Sanqing Hu ◽  
Matt Stead ◽  
Benjamin H. Brinkmann ◽  
Mark R. Bower ◽  
...  
Keyword(s):  
Focal Epilepsy ◽  
Brain Regions ◽  
Seizure Onset ◽  
Neuronal Synchrony ◽  
Seizure Onset Zone ◽  
Paradoxical Finding ◽  
Eeg Recordings ◽  
Learning And Cognition ◽  
Patients With Epilepsy ◽  
And Control

Synchronization of local and distributed neuronal assemblies is thought to underlie fundamental brain processes such as perception, learning, and cognition. In neurological disease, neuronal synchrony can be altered and in epilepsy may play an important role in the generation of seizures. Linear cross-correlation and mean phase coherence of local field potentials (LFPs) are commonly used measures of neuronal synchrony and have been studied extensively in epileptic brain. Multiple studies have reported that epileptic brain is characterized by increased neuronal synchrony except possibly prior to seizure onset when synchrony may decrease. Previous studies using intracranial electroencephalography (EEG), however, have been limited to patients with epilepsy. Here we investigate neuronal synchrony in epileptic and control brain using intracranial EEG recordings from patients with medically resistant partial epilepsy and control subjects with intractable facial pain. For both epilepsy and control patients, average LFP synchrony decreases with increasing interelectrode distance. Results in epilepsy patients show lower LFP synchrony between seizure-generating brain and other brain regions. This relative isolation of seizure-generating brain underlies the paradoxical finding that control patients without epilepsy have greater average LFP synchrony than patients with epilepsy. In conclusion, we show that in patients with focal epilepsy, the region of epileptic brain generating seizures is functionally isolated from surrounding brain regions. We further speculate that this functional isolation may contribute to spontaneous seizure generation and may represent a clinically useful electrophysiological signature for mapping epileptic brain.

scholarly journals Efficacy of levetiracetam monotherapy in epilepsy associated with benign epileptiform discharges of childhood

2019 ◽  
Vol 11 (1) ◽  
pp. 63-68 ◽  
Author(s):  
N. A. Borovkova ◽  
A. G. Malov
Keyword(s):  
Valproic Acid ◽  
Pediatric Patients ◽  
Slow Wave Sleep ◽  
Epileptiform Activity ◽  
Functional Tests ◽  
Antiepileptic Treatment ◽  
Epileptiform Discharges ◽  
First Choice ◽  
Eeg Recordings ◽  
Patients With Epilepsy

Aim. To analyze efficacy of levetiracetam monotherapy in patients with epilepsy associated with benign epileptiform discharges of childhood (BEDC). Materials and methods. We examined 29 pediatric patients with idiopathic and symptomatic BEDC-associated epilepsy, including continuous spike-and-wave epileptiform activity during slow-wave sleep (CSWS) in the stage of clinical remission. Of those, 12 children received antiepileptic treatment with valproic acid, and 17 children received levetiracetam. The examination included passive awake EEG recordings (with functional tests) as well as daytime sleep EEG recordings (within 60 minutes). Results. Levetiracetam was no less efficient in monotherapy of BEDC-associated epilepsy (including the CSWS patterns) than the traditionally used valproic acid, especially in idiopathic forms of epilepsy. Conclusion. Levetiracetam can be recommended for the first-choice basic anti-epileptic monotherapy treatment.

Fraud and Bank Performance in Nigeria – Var Granger Causality Analysis

e-Finanse ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 20-26
Author(s):  
Taiwo A. Muritala ◽  
Muftau A. Ijaiya ◽  
Olatanwa H. Afolabi ◽  
Abdulrasheed B. Yinus
Keyword(s):  
Control Systems ◽  
Granger Causality ◽  
Internal Control ◽  
Banking Sector ◽  
Bank Performance ◽  
Causality Analysis ◽  
Vector Autoregressive ◽  
Granger Causality Analysis ◽  
Long Run ◽  
Multivariate Cointegration

AbstractThis paper examines the causality between fraud and bank performance in Nigeria over the period 2000-2016 for quarterly financial data using Johansen’s Multivariate Cointegration Model and Vector Autoregressive (VAR) Granger Causality analysis. The results show a long-run relationship between the variables. Bank performance was found to be linked to Granger fraud variables and vice versa at 10% significant level. This study reveals that there was a direct causal relationship between bank performance and fraud because increase in fraudulent activities in the banking sector leads to reduction in bank performance. Hence, this study recommends that internal control systems of banks should be strengthened so as to detect and prevent fraud. In this way, bank assets would be protected.

Detection of Intracranial Signatures of Interictal Epileptiform Discharges from Concurrent Scalp EEG

2016 ◽  
Vol 26 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Loukianos Spyrou ◽  
David Martín-Lopez ◽  
Antonio Valentín ◽  
Gonzalo Alarcón ◽  
Saeid Sanei
Keyword(s):  
Detection Algorithm ◽  
The Other ◽  
Intracranial Eeg ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Scalp Electroencephalogram ◽  
Patients With Epilepsy ◽  
Electrical Activities ◽  
The Brain

Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject’s detection algorithm is based on the other patients’ data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.

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