Granger causality: Cortico-thalamic interdependencies during absence seizures in WAG/Rij rats
Introduction
Over the years electroencephalography is widely used in clinical practice for the investigation, classification and diagnosis of epileptic disorders. The electroencephalogram (EEG) provides valuable information in patients with typical and atypical epileptic syndromes and offers also important prognostic information.
Absence epilepsy, previously known as petit mal, is classically considered as non-convulsive generalized epilepsy (classification of the International League Against Epilepsy, ILAE) of unknown etiology (refs. in Panayiotopoulos, 1997). Clinically, absence seizures occur abrupt, last several seconds up to a minute and are accompanied by a brief decrease of consciousness that interrupts normal behavior. Absences may either have or have no facial automatisms, e.g. minimal jerks and twitches of facial muscles, and eye blinks. In humans, EEGs during typical absence seizures are characterized by the occurrence of generalized 3–5 Hz spike-wave complexes which have an abrupt on- and offset (Bosnyakova et al., 2007; Panayiotopoulos, 1997). Similar EEG paroxysms, spike-and-wave discharges (SWD) appear in rat strains with a genetic predisposition to absence epilepsy, such as GAERS (Genetic Absence Epilepsy Rats from Strasbourg; Vergnes, 1987) and WAG/Rij (Wistar Albino Glaxo from Rijswijk, Coenen and van Luijtelaar, 2003; see also http://www.socsci.ru.nl/wagrij/info.0.html). The EEG waveform and duration (1–30 s, mean 5 s) of SWD in rats and in humans are comparable, but the frequency of SWD in rats is higher, 8–11 Hz (Midzianovskaia et al., 2001, Sitnikova and van Luijtelaar, 2007, van Luijtelaar and Coenen, 1986).
Several modern computational techniques and advanced methods of EEG analysis have been developed to extract “hidden” information from the EEG in order to localize the region of onset and to anticipate the onset of ‘absences’ as early as possible (in rats, Meeren et al., 2002; Refs. for human data in Mormann et al., 2007). Our experiments are carried out in WAG/Rij rats (van Luijtelaar and Coenen, 1986). Every subject of this strain has typical absence seizures that are accompanied with spontaneous SWD in the EEG. Previously we used EEG coherence to measure neuronal synchrony between populations of thalamic and cortical neurons (Sitnikova and van Luijtelaar, 2006). We found that the onset of SWD was characterized by area-specific increase of coherence and supports the idea that the cortico-thalamo-cortical circuitry is primarily involved in the initiation and propagation of SWD (Meeren et al., 2005, Steriade, 2005). Coherence is a traditional measure of linear correlations between two EEG channels in the frequency domain, but it does not assume directionality of inter-channel interactions and does not provide temporal (time-domain) information of the EEG signals (Challis and Kitney, 1991). Granger causality compensates for these limitations (Ancona et al., 2004, Feldmann and Bhattacharya, 2004, Granger, 1969, Hlavackova-Schindler et al., 2007, Pereda et al., 2005). Granger causality concept can be used to determine directional coupling characteristics between recording sites in intracranial EEG and to reveal active abnormal causal relationships in epileptogenic networks (Chávez et al., 2003, Kaminski et al., 2001). Usually, Granger causality estimations are performed in long time intervals that include a dozen or even more, basic periods. The pairwise analysis is based on the construction of vector autoregressive (AR) models from bivariate data that estimates how well the current measure of one process can improve the prediction of the future of another process (Granger, 1969). Here we apply Granger causality to measure the strength of bidirectional functional interactions between neuronal assembles in thalamus and frontal cortex.
The present work aims to measure bidirectional (feedforward and feedback) network interdependences between local field potentials recorded simultaneously from the specific thalamus and the frontal cortex. Granger causality concept will be used to characterize the strength of functional connectivity between the cortical and thalamic EEGs for both directions before, during, and after SWD in rats.
Section snippets
Animals and EEG data acquisition
Experiments were performed in five male 11–12-month old WAG/Rij rats. The recordings were done at the Department of Biological Psychology, Radboud University Nijmegen in accordance with the European Communities Council Directive (86/609/EEC). Experiments were approved by the Ethical Committee on Animal Experimentation of Radboud University Nijmegen. Distress and suffering of animals were minimal.
EEGs were recorded from brain areas in which seizure activity is known to be the most robust: in the
Results
All SWD were detected automatically in the full length EEG recordings (6–7 h). In total, 53, 111, 34, 33 and 63 epileptic discharges in five rats were detected and analyzed.
Discussion
This study tackles a challenging problem of predictability of absence seizures in EEG. Using the concept of Granger causality, we measured bidirectional (straightforward and backward) linear interdependences between the thalamus and the cortex during absence seizures and obtained new yet comprehensive information about functional thalamo-cortical interactions during absence epilepsy. Traditional methods, such as cross-correlation analysis of unit activity in a model of generalized epilepsy in
Acknowledgements
We gratefully acknowledge the valuable help of Prof. G.D. Kuznetsova, Prof. A. Coenen and Dr. C. van Rijn. The technical assistance of H. Krijnen, S. Hermeling, E. Willems-van Bree, J. Dederen, G. van Oyen and Dr. ir. P.L.C. van den Broek is highly appreciated.
The work was supported by the RFBR (grant no. 07-02-00747) and the “Russian Science Support Foundation” (grant to Evgenia Sitnikova).
References (39)
- et al.
A robust method for detecting interdependences: application to intracranially recorded EEG
Physica D
(1999) - et al.
Cellular biology of epileptogenesis
Lancet Neurology
(2003) - et al.
Some peculiarities of time-frequency dynamics of spike-wave discharges in humans and rats
Clin Neurophysiol
(2007) - et al.
Statistical assessment of nonlinear causality: application to epileptic EEG signals
J Neurosci Methods
(2003) - et al.
EEG nonstationarity during intracranially recorded seizures: statistical and dynamical analysis
Clin Neurophysiol
(2005) - et al.
Causality detection based on information-theoretic approaches in time series analysis
Phys Rep
(2007) - et al.
Thalamic synchrony and dynamic regulation of global forebrain oscillations
Trends Neurosci
(2007) - et al.
Thalamic multiple-unit activity underlying spike-wave discharges in anesthetized rats
Brain Res
(1993) - et al.
Analysis of dynamic brain oscillations: methodological advances
Trends Neurosci
(2007) - et al.
Electrophysiological and pharmacological characteristics of two types of spike-wave discharges in WAG/Rij rats
Brain Res
(2001)
Nonlinear multivariate analysis of neurophysiological signals
Prog Neurobiol
Cortical and thalamic coherence during spike-wave seizures in WAG/Rij rats
Epilepsy Res
Sleep, epilepsy and thalamic reticular inhibitory neurons
Trends Neurosci
Two types of electrocortical paroxysms in an inbred strain of rats
Neurosci Lett
Global and focal aspects of absence epilepsy: the contribution of genetic models
Neurosci Biobehav Rev
Spontaneous spike and wave discharges in thalamus and cortex in a rat model of genetic petit mal-like seizures
Exp Neurol
Radial basis function approach to nonlinear Granger causality of time series
Phys Rev E
The thalamus and seizures
Arch Neurol
Biomedical signal processing (in four parts). Part 3. The power spectrum and coherence function
Med Biol Eng Comput
Cited by (58)
Corticomuscular coupling analysis based on improved LSTM and transfer entropy
2021, Neuroscience LettersSynchronous analysis of brain regions based on multi-scale permutation transfer entropy
2019, Computers in Biology and MedicineCitation Excerpt :Previous studies have employed this approach, including single and multi-scale permutation entropy, to analyze the coupling properties of EEG signals in healthy subjects as well as various patient groups, providing evidence that permutation mutual information can be used to enhance the calculation of mutual information of EEG signals [11–13]. To further analyze the directional relationship between physiological signals, measures of causality, including Granger causality and transfer entropy (TE), have been employed to assess the directional coupling between signals [14–16]. Unfortunately, Granger causality is a linear method that cannot effectively characterize the nonlinear and multi-scale properties of physiological signals [17,18].
Modeling spike-wave discharges by a complex network of neuronal oscillators
2018, Neural NetworksPredictive role of brain connectivity for resective surgery in Lennox–Gastaut syndrome
2016, Clinical NeurophysiologyMethods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models
2016, Journal of Neuroscience MethodsDynamics of directional coupling underlying spike-wave discharges
2016, Neuroscience
- 1
Tel.: +7 8452 498037; fax: +7 8452 272401.
- 2
Tel.: +7 8452 511180; fax: +7 8452 272401.
- 3
Tel.: +7 8452 511180/498037; fax: +7 8452 272401.
- 4
Tel.: +31 24 3615612; fax: +31 24 3616066.