TY - JOUR
T1 - Connectivity strength, time lag structure and the epilepsy network in resting-state fMRI
AU - Bandt, S. Kathleen
AU - Besson, Pierre
AU - Ridley, Ben
AU - Pizzo, Francesca
AU - Carron, Romain
AU - Regis, Jean
AU - Bartolomei, Fabrice
AU - Ranjeva, Jean Philippe
AU - Guye, Maxime
N1 - Funding Information:
This work was supported by the following funding sources: 7T-AMI ANR-11-EQPX-0001, A*MIDEX-EI-13-07-130,115-08.38-7T-AMISTART, A*MIDEX ANR-11-IDEX-0001-02, CNRS (Centre National de la Recherche Scientifique) and The American Association of Neurological Surgeons.
Funding Information:
This work was supported by the following funding sources: 7T-AMI ANR-11-EQPX-0001 , A*MIDEX-EI-13-07-130,115-08.38-7T-AMISTART , A*MIDEX ANR-11-IDEX-0001-02 , CNRS ( Centre National de la Recherche Scientifique ) and The American Association of Neurological Surgeons .
Publisher Copyright:
© 2019 The Authors
PY - 2019
Y1 - 2019
N2 - The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.
AB - The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.
KW - Epilepsy network
KW - Functional connectivity
KW - Resting state networks
UR - http://www.scopus.com/inward/record.url?scp=85075262523&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075262523&partnerID=8YFLogxK
U2 - 10.1016/j.nicl.2019.102035
DO - 10.1016/j.nicl.2019.102035
M3 - Article
C2 - 31795065
AN - SCOPUS:85075262523
SN - 2213-1582
VL - 24
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
M1 - 102035
ER -