publication . Article . 2015

Neural Connectivity in Epilepsy as Measured by Granger Causality.

Coben, Robert; Mohammad-Rezazadeh, Iman;
Open Access English
  • Published: 01 Jul 2015 Journal: Frontiers in Human Neuroscience (issn: 1662-5161, Copyright policy)
  • Publisher: Frontiers Media S.A.
Abstract
Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in n...
Subjects
free text keywords: Epilepsy, Seizures, connectivity, connectivity analysis, Granger causality, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Hypothesis and Theory
41 references, page 1 of 3

Abeles M. (1991). Corticonics: Neural Circuits of the Cerebral Cortex. Cambridge, IN: Cambridge University Press.

Ahammad N. Fathima T. Joseph P. (2014). Detection of epileptic seizure event and onset using EEG. Biomed. Res. Int. 2014, 450573.10.1155/2014/450573 24616892 [OpenAIRE] [PubMed] [DOI]

Ahmadi M. E. Hagler D. J. Jr. McDonald C. R. Tecoma E. S. Iragui V. J. Dale A. M. (2009). Side matters: diffusion tensor i maging tractography in left and right temporal lobe epilepsy. AJNR Am. J. Neuroradiol. 30, 1740–1747.10.3174/ajnr.A1650 19509072 [OpenAIRE] [PubMed] [DOI]

Akalin Acar Z. Makeig S. (2013). Effects of forward model errors on EEG source localization. Brain Topogr. 26, 378–396.10.1007/s10548-012-0274-6 23355112 [OpenAIRE] [PubMed] [DOI]

Astolfi L. Cincotti F. Mattia D. Marciani M. G. Baccala L. A. de Vico Fallani F. (2007). Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum. Brain Mapp. 28, 143–157.10.1002/hbm.20263 16761264 [OpenAIRE] [PubMed] [DOI]

Bernhardt B. C. Chen Z. He Y. Evans A. C. Bernasconi N. (2011). Graph-theoretical analysis reveals disrupted small-world organization of cortical thickness correlation networks in temporal lobe epilepsy. Cereb. Cortex 21, 2147–2157.10.1093/cercor/bhq291 21330467 [OpenAIRE] [PubMed] [DOI]

Bhardwaj R. D. Mahmoodabadi S. Z. Otsubo H. Carter Snead O. I. I. I. Rutka J. T. Widjaja E. (2010). Diffusion tensor tractography detection of functional pathway for the spread of epileptiform activity between temporal lobe and rolandic region. Childs Nerv. Syst. 26, 185–190.10.1007/s00381-009-1017-1 19915854 [OpenAIRE] [PubMed] [DOI]

Bonilha L. Nesland T. Martz G. U. Joseph J. E. Spampinato M. V. Edwards J. C. (2012). Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures. J. Neurol. Neurosurg. Psychiatr. 83, 903–909.10.1136/jnnp-2012-302476 22764263 [OpenAIRE] [PubMed] [DOI]

Bressler S. L. Seth A. K. (2010). Wiener-Granger causality: a well established methodology. Neuroimage 58, 323–329.10.1016/j.neuroimage.2010.02.059 20202481 [OpenAIRE] [PubMed] [DOI]

Burnham K. P. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304.10.1177/0049124104268644 [OpenAIRE] [DOI]

Cadotte A. J. DeMarse T. B. He P. Ding M. (2008). Causal measures of structure and plasticity in simulated and living neural networks. PLoS ONE 3:e3355.10.1371/journal.pone.0003355 18839039 [OpenAIRE] [PubMed] [DOI]

Clemens B. Puskás S. Besenyei M. Spisák T. Opposits G. Hollódy K. (2013). Neurophysiology of juvenile myoclonic epilepsy: EEG-based network and graph analysis of the interictal and immediate preictal states. Epilepsy Res. 106, 357–369.10.1016/j.eplepsyres.2013.06.017 23886656 [OpenAIRE] [PubMed] [DOI]

Coben R. Mohammad-Rezazadeh I. Cannon R. L. (2014). Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity. Front. Hum. Neurosci. 8:45.10.3389/fnhum.2014.00045 24616679 [OpenAIRE] [PubMed] [DOI]

Constable R. T. Scheinost D. Finn E. S. Shen X. Hampson M. Winstanley F. S. (2013). Potential use and challenges of functional connectivity mapping in intractable epilepsy. Front Neurol. 4:39.10.3389/fneur.2013.00039 23734143 [OpenAIRE] [PubMed] [DOI]

David O. Guillemain I. Saillet S. Reyt S. Deransart C. Segebarth C. (2008). Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 6:e315.10.1371/journal.pbio.0060315 19108604 [OpenAIRE] [PubMed] [DOI]

41 references, page 1 of 3
Related research
Abstract
Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in n...
Subjects
free text keywords: Epilepsy, Seizures, connectivity, connectivity analysis, Granger causality, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Hypothesis and Theory
41 references, page 1 of 3

Abeles M. (1991). Corticonics: Neural Circuits of the Cerebral Cortex. Cambridge, IN: Cambridge University Press.

Ahammad N. Fathima T. Joseph P. (2014). Detection of epileptic seizure event and onset using EEG. Biomed. Res. Int. 2014, 450573.10.1155/2014/450573 24616892 [OpenAIRE] [PubMed] [DOI]

Ahmadi M. E. Hagler D. J. Jr. McDonald C. R. Tecoma E. S. Iragui V. J. Dale A. M. (2009). Side matters: diffusion tensor i maging tractography in left and right temporal lobe epilepsy. AJNR Am. J. Neuroradiol. 30, 1740–1747.10.3174/ajnr.A1650 19509072 [OpenAIRE] [PubMed] [DOI]

Akalin Acar Z. Makeig S. (2013). Effects of forward model errors on EEG source localization. Brain Topogr. 26, 378–396.10.1007/s10548-012-0274-6 23355112 [OpenAIRE] [PubMed] [DOI]

Astolfi L. Cincotti F. Mattia D. Marciani M. G. Baccala L. A. de Vico Fallani F. (2007). Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum. Brain Mapp. 28, 143–157.10.1002/hbm.20263 16761264 [OpenAIRE] [PubMed] [DOI]

Bernhardt B. C. Chen Z. He Y. Evans A. C. Bernasconi N. (2011). Graph-theoretical analysis reveals disrupted small-world organization of cortical thickness correlation networks in temporal lobe epilepsy. Cereb. Cortex 21, 2147–2157.10.1093/cercor/bhq291 21330467 [OpenAIRE] [PubMed] [DOI]

Bhardwaj R. D. Mahmoodabadi S. Z. Otsubo H. Carter Snead O. I. I. I. Rutka J. T. Widjaja E. (2010). Diffusion tensor tractography detection of functional pathway for the spread of epileptiform activity between temporal lobe and rolandic region. Childs Nerv. Syst. 26, 185–190.10.1007/s00381-009-1017-1 19915854 [OpenAIRE] [PubMed] [DOI]

Bonilha L. Nesland T. Martz G. U. Joseph J. E. Spampinato M. V. Edwards J. C. (2012). Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures. J. Neurol. Neurosurg. Psychiatr. 83, 903–909.10.1136/jnnp-2012-302476 22764263 [OpenAIRE] [PubMed] [DOI]

Bressler S. L. Seth A. K. (2010). Wiener-Granger causality: a well established methodology. Neuroimage 58, 323–329.10.1016/j.neuroimage.2010.02.059 20202481 [OpenAIRE] [PubMed] [DOI]

Burnham K. P. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304.10.1177/0049124104268644 [OpenAIRE] [DOI]

Cadotte A. J. DeMarse T. B. He P. Ding M. (2008). Causal measures of structure and plasticity in simulated and living neural networks. PLoS ONE 3:e3355.10.1371/journal.pone.0003355 18839039 [OpenAIRE] [PubMed] [DOI]

Clemens B. Puskás S. Besenyei M. Spisák T. Opposits G. Hollódy K. (2013). Neurophysiology of juvenile myoclonic epilepsy: EEG-based network and graph analysis of the interictal and immediate preictal states. Epilepsy Res. 106, 357–369.10.1016/j.eplepsyres.2013.06.017 23886656 [OpenAIRE] [PubMed] [DOI]

Coben R. Mohammad-Rezazadeh I. Cannon R. L. (2014). Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity. Front. Hum. Neurosci. 8:45.10.3389/fnhum.2014.00045 24616679 [OpenAIRE] [PubMed] [DOI]

Constable R. T. Scheinost D. Finn E. S. Shen X. Hampson M. Winstanley F. S. (2013). Potential use and challenges of functional connectivity mapping in intractable epilepsy. Front Neurol. 4:39.10.3389/fneur.2013.00039 23734143 [OpenAIRE] [PubMed] [DOI]

David O. Guillemain I. Saillet S. Reyt S. Deransart C. Segebarth C. (2008). Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 6:e315.10.1371/journal.pbio.0060315 19108604 [OpenAIRE] [PubMed] [DOI]

41 references, page 1 of 3
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