Sinergia Consortium Brain Communication Pathways
- Home
- People
- Software
- Publications
Publications
Excellent! Next you can
create a new website with this list, or
embed it in an existing web page by copying & pasting
any of the following snippets.
JavaScript
(easiest)
PHP
iFrame
(not recommended)
<script src="https://bibbase.org/show?bib=https://sinergiaconsortium.bitbucket.io/resources/bibtex/publications.bib&jsonp=0&authorFirst=1&nocache=1&groupby=year&simplegroups=1&jsonp=1"></script>
<?php
$contents = file_get_contents("https://bibbase.org/show?bib=https://sinergiaconsortium.bitbucket.io/resources/bibtex/publications.bib&jsonp=0&authorFirst=1&nocache=1&groupby=year&simplegroups=1");
print_r($contents);
?>
<iframe src="https://bibbase.org/show?bib=https://sinergiaconsortium.bitbucket.io/resources/bibtex/publications.bib&jsonp=0&authorFirst=1&nocache=1&groupby=year&simplegroups=1"></iframe>
For more details see the documention.
This is a preview! To use this list on your own web site
or create a new web site from it,
create a free account. The file will be added
and you will be able to edit it in the File Manager.
We will show you instructions once you've created your account.
To the site owner:
Action required! Mendeley is changing its API. In order to keep using Mendeley with BibBase past April 14th, you need to:
- renew the authorization for BibBase on Mendeley, and
- update the BibBase URL in your page the same way you did when you initially set up this page.
2021
(6)
Rubega, M.; Formaggio, E.; Di Marco, R.; Bertuccelli, M.; Tortora, S.; Menegatti, E.; Cattelan, M.; Bonato, P.; Masiero, S.; and Del Felice, A.
Cortical correlates in upright dynamic and static balance in the elderly.
Scientific Reports, 11(1): 1–15. 2021.
link bibtex
link bibtex
@article{rubega2021cortical, title={Cortical correlates in upright dynamic and static balance in the elderly}, author={Rubega, Maria and Formaggio, Emanuela and Di Marco, Roberto and Bertuccelli, Margherita and Tortora, Stefano and Menegatti, Emanuele and Cattelan, Manuela and Bonato, Paolo and Masiero, Stefano and Del Felice, Alessandra}, journal={Scientific Reports}, volume={11}, number={1}, pages={1--15}, year={2021}, publisher={Nature Publishing Group} }
Formaggio, E.; Rubega, M.; Rupil, J.; Antonini, A.; Masiero, S.; Toffolo, G. M.; and Del Felice, A.
Reduced Effective Connectivity in the Motor Cortex in Parkinson’s Disease.
Brain Sciences, 11(9): 1200. 2021.
link bibtex
link bibtex
@article{formaggio2021reduced, title={Reduced Effective Connectivity in the Motor Cortex in Parkinson’s Disease}, author={Formaggio, Emanuela and Rubega, Maria and Rupil, Jessica and Antonini, Angelo and Masiero, Stefano and Toffolo, Gianna Maria and Del Felice, Alessandra}, journal={Brain Sciences}, volume={11}, number={9}, pages={1200}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }
Rué-Queralt, J.; Glomb, K.; Pascucci, D.; Tourbier, S.; Carboni, M.; Vulliémoz, S.; Plomp, G.; and Hagmann, P.
The connectome spectrum as a canonical basis for a sparse representation of fast brain activity.
NeuroImage, 244: 118611. 2021.
Paper doi link bibtex abstract 2 downloads
Paper doi link bibtex abstract 2 downloads
@article{RUEQUERALT2021118611, title = {The connectome spectrum as a canonical basis for a sparse representation of fast brain activity}, journal = {NeuroImage}, volume = {244}, pages = {118611}, year = {2021}, issn = {1053-8119}, doi = {https://doi.org/10.1016/j.neuroimage.2021.118611}, url = {https://www.sciencedirect.com/science/article/pii/S1053811921008843}, author = {Joan Rué-Queralt and Katharina Glomb and David Pascucci and Sébastien Tourbier and Margherita Carboni and Serge Vulliémoz and Gijs Plomp and Patric Hagmann}, abstract = {The functional organization of neural processes is constrained by the brain’s intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.} }
The functional organization of neural processes is constrained by the brain’s intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.
Pascucci, D.; Rubega, M.; Rué-Queralt, J.; Tourbier, S.; Hagmann, P.; and Plomp, G.
Structure supports function: informing directed and dynamic functional connectivity with anatomical priors.
bioRxiv. 2021.
link bibtex
link bibtex
@article{pascucci2021structure, title={Structure supports function: informing directed and dynamic functional connectivity with anatomical priors}, author={Pascucci, David and Rubega, Maria and Ru{\'e}-Queralt, Joan and Tourbier, Sebastien and Hagmann, Patric and Plomp, Gijs}, journal={bioRxiv}, year={2021}, publisher={Cold Spring Harbor Laboratory} }
Pascucci, D.; Tourbier, S.; Rue-Queralt, J.; Carboni, M.; Hagmann, P.; and Plomp, G.
Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes.
bioRxiv. 2021.
link bibtex
link bibtex
@article{pascucci2021source, title={Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes}, author={Pascucci, David and Tourbier, Sebastien and Rue-Queralt, Joan and Carboni, Margherita and Hagmann, Patric and Plomp, Gijs}, journal={bioRxiv}, year={2021}, publisher={Cold Spring Harbor Laboratory} }
Glomb, K.; Kringelbach, M. L; Deco, G.; Hagmann, P.; Pearson, J.; and Atasoy, S.
Functional harmonics reveal multi-dimensional basis functions underlying cortical organization.
Cell Reports, 36(8): 109554. 2021.
link bibtex
link bibtex
@article{glomb2021functional, title={Functional harmonics reveal multi-dimensional basis functions underlying cortical organization}, author={Glomb, Katharina and Kringelbach, Morten L and Deco, Gustavo and Hagmann, Patric and Pearson, Joel and Atasoy, Selen}, journal={Cell Reports}, volume={36}, number={8}, pages={109554}, year={2021}, publisher={Elsevier} }
2020
(5)
Glomb, K.; Queralt, J. R.; Pascucci, D.; Defferrard, M.; Tourbier, S.; Carboni, M.; Rubega, M.; Vulliemoz, S.; Plomp, G.; and Hagmann, P.
Connectome spectral analysis to track EEG task dynamics on a subsecond scale.
NeuroImage, 221: 117137. 2020.
link bibtex
link bibtex
@article{glomb2020connectome, title={Connectome spectral analysis to track EEG task dynamics on a subsecond scale}, author={Glomb, Katharina and Queralt, Joan Rue and Pascucci, David and Defferrard, Micha{\"e}l and Tourbier, Sebastien and Carboni, Margherita and Rubega, Maria and Vulliemoz, Serge and Plomp, Gijs and Hagmann, Patric}, journal={NeuroImage}, volume={221}, pages={117137}, year={2020}, publisher={Elsevier} }
Glomb, K.; Mullier, E.; Carboni, M.; Rubega, M.; Iannotti, G.; Tourbier, S.; Seeber, M.; Vulliemoz, S.; and Hagmann, P.
Using structural connectivity to augment community structure in EEG functional connectivity.
Network Neuroscience, 4(3): 761–787. 2020.
link bibtex
link bibtex
@article{glomb2020using, title={Using structural connectivity to augment community structure in EEG functional connectivity}, author={Glomb, Katharina and Mullier, Emeline and Carboni, Margherita and Rubega, Maria and Iannotti, Giannarita and Tourbier, Sebastien and Seeber, Martin and Vulliemoz, Serge and Hagmann, Patric}, journal={Network Neuroscience}, volume={4}, number={3}, pages={761--787}, year={2020}, publisher={MIT Press} }
Glomb, K.; Cabral, J.; Cattani, A.; Mazzoni, A.; Raj, A.; and Franceschiello, B.
Computational models in Electroencephalography.
arXiv preprint arXiv:2009.08385. 2020.
link bibtex
link bibtex
@article{glomb2020computational, title={Computational models in Electroencephalography}, author={Glomb, Katharina and Cabral, Joana and Cattani, Anna and Mazzoni, Alberto and Raj, Ashish and Franceschiello, Benedetta}, journal={arXiv preprint arXiv:2009.08385}, year={2020} }
Pascucci, D.; Rubega, M.; and Plomp, G.
Modeling time-varying brain networks with a self-tuning optimized Kalman filter.
PLoS computational biology, 16(8): e1007566. 2020.
link bibtex
link bibtex
@article{pascucci2020modeling, title={Modeling time-varying brain networks with a self-tuning optimized Kalman filter}, author={Pascucci, David and Rubega, Maria and Plomp, Gijs}, journal={PLoS computational biology}, volume={16}, number={8}, pages={e1007566}, year={2020}, publisher={Public Library of Science San Francisco, CA USA} }
Tourbier, S.; Aleman-Gomez, Y.; Mullier, E.; Griffa, A.; Bach Cuadra, M.; and Hagmann, P.
Connectome Mapper 3: a software pipeline for multi-scale connectome mapping of multimodal MR data.
In 26th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Online, June 2020.
Paper link bibtex 1 download
Paper link bibtex 1 download
@inproceedings{tourbier2020ohbm, address = {Online}, title = {Connectome Mapper 3: a software pipeline for multi-scale connectome mapping of multimodal MR data}, booktitle = {26th Annual Meeting of the Organization for Human Brain Mapping (OHBM)}, author = {Tourbier, S. and Aleman-Gomez, Y. and Mullier, E. and Griffa, A. and Bach Cuadra, M. and Hagmann, P.}, year = {2020}, month = {June}, url = {https://github.com/connectomicslab/cmp3-ohbm2020/blob/master/abstract/tourbier_ohbm2020_cmp3_abstract.pdf} }
2019
(8)
Pizzolato, M.; Yu, T.; Canales-Rodriguez, E. J.; and Thiran, J.
Robust T2 relaxometry with Hamiltonian MCMC for myelin water fraction estimation.
In In IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy, Venice, Italy, 2019.
link bibtex abstract
link bibtex abstract
@inproceedings{pizzolato2019isbi1, address = {Venice, Italy}, title = {Robust T2 relaxometry with Hamiltonian MCMC for myelin water fraction estimation}, abstract = {We present a voxel-wise Bayesian multi-compartment T2 relaxometry fitting method based on Hamiltonian Markov Chain Monte Carlo (HMCMC) sampling. The T2 spectrum is modeled as a mixture of truncated Gaussian components, which involves the estimation of parameters in a completely data-driven and voxel-based fashion, i.e. without fixing any parameters or imposing spatial regularization. We estimate each parameter as the expectation of the corre- sponding marginal distribution drawn from the joint posterior obtained with Hamiltonian sampling. We validate our scheme on synthetic and ex vivo data for which histology is available. We show that the proposed method enables a more robust parameter estimation than a state of the art point estimate based on differential evolution. Moreover, the proposed HMCMC-based myelin water fraction calculation reveals high spatial correlation with the histological counterpart.}, booktitle = {In IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy}, author = {Pizzolato, Marco and Yu, Thomas and Canales-Rodriguez, Erick Jorge and Thiran, Jean-Philippe}, year = {2019} }
We present a voxel-wise Bayesian multi-compartment T2 relaxometry fitting method based on Hamiltonian Markov Chain Monte Carlo (HMCMC) sampling. The T2 spectrum is modeled as a mixture of truncated Gaussian components, which involves the estimation of parameters in a completely data-driven and voxel-based fashion, i.e. without fixing any parameters or imposing spatial regularization. We estimate each parameter as the expectation of the corre- sponding marginal distribution drawn from the joint posterior obtained with Hamiltonian sampling. We validate our scheme on synthetic and ex vivo data for which histology is available. We show that the proposed method enables a more robust parameter estimation than a state of the art point estimate based on differential evolution. Moreover, the proposed HMCMC-based myelin water fraction calculation reveals high spatial correlation with the histological counterpart.
Pizzolato, M.; Deriche, R.; Canales-Rodriguez, E. J.; and Thiran, J.
Spatially varying Monte Carlo SURE for the regularization of biomedical images.
In In IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy., Venice, Italy, 2019.
link bibtex abstract
link bibtex abstract
@inproceedings{pizzolato2019isbi2, address = {Venice, Italy}, title = {Spatially varying Monte Carlo SURE for the regularization of biomedical images}, abstract = {Regularization, filtering, and denoising of biomedical images requires the use of appropriate filters and the adoption of efficient regularization criteria. It has been shown that the Stein’s Unbiased Risk Estimate (SURE) can be used as a proxy for the mean squared error (MSE), thus giving an effective criterion for choosing the regularization amount as to that minimizing SURE. Often, due to the complexity of the adopted filters and solvers, this proxy must be calculated with a Monte Carlo method. In practical biomedical applications, however, images are affected by spatially-varying noise distributions, which must be taken into account. We propose a modification to the Monte Carlo method, called svSURE, that accounts for the spatial variability of the noise variance, and show that it correctly estimates the MSE in such cases.}, booktitle = {In IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy.}, author = {Pizzolato, Marco and Deriche, Rachid and Canales-Rodriguez, Erick Jorge and Thiran, Jean-Philippe}, year = {2019} }
Regularization, filtering, and denoising of biomedical images requires the use of appropriate filters and the adoption of efficient regularization criteria. It has been shown that the Stein’s Unbiased Risk Estimate (SURE) can be used as a proxy for the mean squared error (MSE), thus giving an effective criterion for choosing the regularization amount as to that minimizing SURE. Often, due to the complexity of the adopted filters and solvers, this proxy must be calculated with a Monte Carlo method. In practical biomedical applications, however, images are affected by spatially-varying noise distributions, which must be taken into account. We propose a modification to the Monte Carlo method, called svSURE, that accounts for the spatial variability of the noise variance, and show that it correctly estimates the MSE in such cases.
Canales-Rodriguez, E. J.; Pizzolato, M.; Piredda, G. F.; Kunz, N.; Kober, T.; Thiran, J.; Pot, C.; and Daducci, A.
Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points.
In Montreal, Canada, 2019.
link bibtex
link bibtex
@inproceedings{canales2019ismrm, address = {Montreal, Canada}, title = {Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points}, author = {Canales-Rodriguez, Erick Jorge and Pizzolato, Marco and Piredda, Gian Franco, and Kunz, Nicolas and Kober, Tobias and Thiran, Jean-Philippe and Pot, Caroline and Daducci, Alessandro}, year = {2019} }
van Mierlo, P.; Holler, Y.; Focke, N. K.; and Vulliemoz, S.
Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity.
Front Neurol, 10: 721. 2019.
Paper doi link bibtex abstract 1 download
Paper doi link bibtex abstract 1 download
@article{vanmierlo2019frontneu, author = {van Mierlo, P. and Holler, Y. and Focke, N. K. and Vulliemoz, S.}, title = {Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity}, journal = {Front Neurol}, volume = {10}, pages = {721}, abstract = {The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.}, issn = {1664-2295}, doi = {10.3389/fneur.2019.00721}, url = {http://www.ncbi.nlm.nih.gov/pubmed/31379703}, year = {2019} }
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.
Coito, A.; Michel, C. M.; Vulliemoz, S.; and Plomp, G.
Directed functional connections underlying spontaneous brain activity.
Hum Brain Mapp, 40(3): 879-888. 2019.
Paper doi link bibtex abstract 3 downloads
Paper doi link bibtex abstract 3 downloads
@article{coito2019hbm, author = {Coito, A. and Michel, C. M. and Vulliemoz, S. and Plomp, G.}, title = {Directed functional connections underlying spontaneous brain activity}, journal = {Hum Brain Mapp}, volume = {40}, number = {3}, pages = {879-888}, abstract = {Neuroimaging studies have shown that spontaneous brain activity is characterized as changing networks of coherent activity across multiple brain areas. However, the directionality of functional interactions between the most active regions in our brain at rest remains poorly understood. Here, we examined, at the whole-brain scale, the main drivers and directionality of interactions that underlie spontaneous human brain activity by applying directed functional connectivity analysis to electroencephalography (EEG) source signals. We found that the main drivers of electrophysiological activity were the posterior cingulate cortex (PCC), the medial temporal lobes (MTL), and the anterior cingulate cortex (ACC). Among those regions, the PCC was the strongest driver and had both the highest integration and segregation importance, followed by the MTL regions. The driving role of the PCC and MTL resulted in an effective directed interaction directed from posterior toward anterior brain regions. Our results strongly suggest that the PCC and MTL structures are the main drivers of electrophysiological spontaneous activity throughout the brain and suggest that EEG-based directed functional connectivity analysis is a promising tool to better understand the dynamics of spontaneous brain activity in healthy subjects and in various brain disorders.}, issn = {1097-0193}, doi = {10.1002/hbm.24418}, url = {http://www.ncbi.nlm.nih.gov/pubmed/30367722}, year = {2019} }
Neuroimaging studies have shown that spontaneous brain activity is characterized as changing networks of coherent activity across multiple brain areas. However, the directionality of functional interactions between the most active regions in our brain at rest remains poorly understood. Here, we examined, at the whole-brain scale, the main drivers and directionality of interactions that underlie spontaneous human brain activity by applying directed functional connectivity analysis to electroencephalography (EEG) source signals. We found that the main drivers of electrophysiological activity were the posterior cingulate cortex (PCC), the medial temporal lobes (MTL), and the anterior cingulate cortex (ACC). Among those regions, the PCC was the strongest driver and had both the highest integration and segregation importance, followed by the MTL regions. The driving role of the PCC and MTL resulted in an effective directed interaction directed from posterior toward anterior brain regions. Our results strongly suggest that the PCC and MTL structures are the main drivers of electrophysiological spontaneous activity throughout the brain and suggest that EEG-based directed functional connectivity analysis is a promising tool to better understand the dynamics of spontaneous brain activity in healthy subjects and in various brain disorders.
Coito, A.; Biethahn, S.; Tepperberg, J.; Carboni, M.; Roelcke, U.; Seeck, M.; van Mierlo, P.; Gschwind, M.; and Vulliemoz, S.
Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low-density EEG.
Epilepsia Open, 4(2): 281-292. 2019.
Paper doi link bibtex abstract 1 download
Paper doi link bibtex abstract 1 download
@article{coito2019epi, author = {Coito, A. and Biethahn, S. and Tepperberg, J. and Carboni, M. and Roelcke, U. and Seeck, M. and van Mierlo, P. and Gschwind, M. and Vulliemoz, S.}, title = {Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low-density EEG}, journal = {Epilepsia Open}, volume = {4}, number = {2}, pages = {281-292}, abstract = {Objective: Electrical source imaging (ESI) is used increasingly to estimate the epileptogenic zone (EZ) in patients with epilepsy. Directed functional connectivity (DFC) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high-density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ, based on low-density clinical electroencephalography (EEG). Methods: We selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE], 14 extra-TLE [ETLE]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient-specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger-causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure-free patients. Results: ESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher (P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE. Significance: ESI and DFC derived from low-density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.}, issn = {2470-9239}, doi = {10.1002/epi4.12318}, url = {http://www.ncbi.nlm.nih.gov/pubmed/31168495}, year = {2019} }
Objective: Electrical source imaging (ESI) is used increasingly to estimate the epileptogenic zone (EZ) in patients with epilepsy. Directed functional connectivity (DFC) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high-density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ, based on low-density clinical electroencephalography (EEG). Methods: We selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE], 14 extra-TLE [ETLE]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient-specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger-causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure-free patients. Results: ESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher (P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE. Significance: ESI and DFC derived from low-density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.
Tourbier, S.; Aleman-Gomez, Y.; Griffa, A.; Bach Cuadra, M.; and Hagmann, P.
Multi-Scale Brain Parcellator: a BIDS App for the Lausanne Connectome Parcellation.
In 25th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Roma, Italie, June 2019.
Paper link bibtex 1 download
Paper link bibtex 1 download
@inproceedings{tourbier2019ohbm, address = {Roma, Italie}, title = {Multi-Scale Brain Parcellator: a BIDS App for the Lausanne Connectome Parcellation}, booktitle = {25th Annual Meeting of the Organization for Human Brain Mapping (OHBM)}, author = {Tourbier, S. and Aleman-Gomez, Y. and Griffa, A. and Bach Cuadra, M. and Hagmann, P.}, year = {2019}, month = {June}, url = {https://ww5.aievolution.com/hbm1901/index.cfm?do=abs.viewAbs&abs=1714} }
Glomb, K.; Kringelbach, M. L; Deco, G.; Hagmann, P.; Pearson, J.; and Atasoy, S.
Functional harmonics reveal multi-dimensional basis functions underlying cortical organization.
bioRxiv,699678. 2019.
link bibtex
link bibtex
@article{glomb2019functional, title={Functional harmonics reveal multi-dimensional basis functions underlying cortical organization}, author={Glomb, Katharina and Kringelbach, Morten L and Deco, Gustavo and Hagmann, Patric and Pearson, Joel and Atasoy, Selen}, journal={bioRxiv}, pages={699678}, year={2019}, publisher={Cold Spring Harbor Laboratory} }
2018
(12)
Rubega, M.; Carboni, M.; Seeber, M.; Vulliemoz, S.; and Michel, C. M.
Estimating EEG source dipoles based on singular-value decomposition for connectivity analysis.
In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, 2018.
link bibtex
link bibtex
@inproceedings{rubega2018abim, title={Estimating EEG source dipoles based on singular-value decomposition for connectivity analysis}, author={Rubega, Maria and Carboni, Margherita and Seeber, Martin and Vulliemoz, Serge and Michel, Christoph M.}, booktitle={Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018}, year={2018}, }
Pascucci, D.; Rubega, M.; Carboni, M.; Vulliemoz, S.; Michel, C. M.; and Plomp, G.
A constrained Kalman filter approach to inform dynamic functional connectivity with anatomical priors: a simulation study.
In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, 2018.
link bibtex
link bibtex
@inproceedings{pascucci2018abim, title={A constrained Kalman filter approach to inform dynamic functional connectivity with anatomical priors: a simulation study}, author={Pascucci, David and Rubega, Maria and Carboni, Margherita and Vulliemoz, Serge and Michel, Christoph M. and Plomp, Gijs}, booktitle={Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018}, year={2018}, }
Carboni, M.; Rubega, M.; Toscano, G.; van Mierlo, P.; Pittau, F.; Seek, M.; Michel, C. M.; and Vulliemoz, S.
Increased network segregation as a bio-marker in focal epilepsy.
In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, 2018.
link bibtex
link bibtex
@inproceedings{carboni2018abim, title={Increased network segregation as a bio-marker in focal epilepsy}, author={Carboni, Margherita and Rubega, Maria and Toscano, Gianpaolo and van Mierlo, Pieter and Pittau, Francesca and Seek, Margitta and Michel, Christoph M. and Vulliemoz, Serge }, booktitle={Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018}, year={2018}, }
Seeber, M.; Rubega, M.; ; and Michel, C. M.
Leakage correction for EEG source space analyses on spatial resolution properties.
In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, 2018.
link bibtex
link bibtex
@inproceedings{seeber2018abim, title={Leakage correction for EEG source space analyses on spatial resolution properties}, author={Seeber, Martin and Rubega, Maria and and Michel, Christoph M.}, booktitle={Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018}, year={2018}, }
Tourbier, S.; Pizzolato, M.; Carboni, M.; Pascucci, D.; Rubega, M.; Vuillemoz, S.; Plomp, G.; Michel, C. M.; Thiran, J.; and Hagmann, P.
Adopting the Brain Imaging Data Structure in the Connectome Mapper.
In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, Champéry, 2018.
link bibtex abstract
link bibtex abstract
@inproceedings{tourbier2018abim, address = {Champéry}, title = {Adopting the {Brain} {Imaging} {Data} {Structure} in the Connectome Mapper}, abstract = {Connectome Mapper is an open-source software pipeline that has been developed since 2012 to help researchers through the tedious process of organizing, processing and analyzing diffusion MRI data to perform global brain connectivity analysis. At this time, there had been no standard tools to describe data and its organization on storage devices, despite initiatives such as the eXtensible Markup Language (XML)-based Clinical Experiment Data Exchange schema (XCEDE) or the openfMRI convention, which had been poorly adopted. This was mainly caused by the adoption of tools not trivial to be used for non-informatics experts, together with the lack of file format specifications (XCEDE), and by the lack of explicit support for a number of important data types such as diffusion MRI (openfMRI). Consequently, the Connectome Mapper adopted its own standard for description and organization of anatomical and diffusion MRI, which requires reorganization of open datasets and limits the inter-operability with other software. Last year, a standard, known as the Brain Imaging Data Structure (BIDS), has emerged and been increasingly used. BIDS gives specifications on data description based on simple text-based file formats, a simple and comprehensive organization, and the use of NIfTI for images. Such standard is indeed essential to guarantee data understanding for people not implicated in the acquisition, easy data sharing and re-using within or between the labs, and application of automated analysis workflows for enhanced reproducibility and efficiency. In this work, we present a new version of the Connectome Mapper that adopts BIDS as standard for datasets.}, booktitle = {Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018}, author = {Tourbier, Sebastien and Pizzolato, Marco and Carboni, Margherita and Pascucci, David and Rubega, Maria and Vuillemoz, Serge and Plomp, Gijs and Michel, Christoph M. and Thiran, J.-P. and Hagmann, Patric}, year = {2018} }
Connectome Mapper is an open-source software pipeline that has been developed since 2012 to help researchers through the tedious process of organizing, processing and analyzing diffusion MRI data to perform global brain connectivity analysis. At this time, there had been no standard tools to describe data and its organization on storage devices, despite initiatives such as the eXtensible Markup Language (XML)-based Clinical Experiment Data Exchange schema (XCEDE) or the openfMRI convention, which had been poorly adopted. This was mainly caused by the adoption of tools not trivial to be used for non-informatics experts, together with the lack of file format specifications (XCEDE), and by the lack of explicit support for a number of important data types such as diffusion MRI (openfMRI). Consequently, the Connectome Mapper adopted its own standard for description and organization of anatomical and diffusion MRI, which requires reorganization of open datasets and limits the inter-operability with other software. Last year, a standard, known as the Brain Imaging Data Structure (BIDS), has emerged and been increasingly used. BIDS gives specifications on data description based on simple text-based file formats, a simple and comprehensive organization, and the use of NIfTI for images. Such standard is indeed essential to guarantee data understanding for people not implicated in the acquisition, easy data sharing and re-using within or between the labs, and application of automated analysis workflows for enhanced reproducibility and efficiency. In this work, we present a new version of the Connectome Mapper that adopts BIDS as standard for datasets.
Marquis, R.; Van Mierlo, P.; Baud, M.; Mégevand, P.; Spinelli, L.; and Vulliémoz, S.
Conduction delays as estimated by cortico-cortical evoked potentials.
In Alpine Brain Imaging Meeting, Champéry, January 2018.
link bibtex abstract
link bibtex abstract
@inproceedings{marquis2018abim, address = {Champéry}, title = {Conduction delays as estimated by cortico-cortical evoked potentials}, abstract = {Functional brain connectivity derived from electroencephalography (EEG) provides important insights into epilepsy and the localization of the seizure onset zone. However, functional connectivity metrics are highly influenced by assumptions on conduction delays. Current models of functional connectivity based on Granger causality do not take into account the variability of conduction delay across the human brain. In this study, we applied cortico-cortical evoked potentials (CCEP) to estimate effective functional connectivity and derive conduction delays for early (0 to 50 ms post-stimulus onset) and late (50 to 500 ms post-stimulus onset) evoked responses in 1 patient with depth electrodes. We extracted the latency at the peak of the response, at the rising phase and at the response onset. Early responses showed an average latency of 7.21 ± 4.14, 11.79 ± 5.42 and 22.73 ± 8.62 ms at the onset of the response, at the rising phase and at the peak respectively. Late responses showed an average latency of 54.98 ± 13.92, 86.37 ± 39.93 and 128.25 ± 55.62 ms at response onset, rising phase and peak respectively. The variability of these latencies suggests that conduction delays contain valuable information that might improve functional brain connectivity models.}, booktitle = {Alpine {Brain} {Imaging} {Meeting}}, author = {Marquis, Renaud and Van Mierlo, Pieter and Baud, Maxime and Mégevand, Pierre and Spinelli, Laurent and Vulliémoz, Serge}, month = jan, year = {2018} }
Functional brain connectivity derived from electroencephalography (EEG) provides important insights into epilepsy and the localization of the seizure onset zone. However, functional connectivity metrics are highly influenced by assumptions on conduction delays. Current models of functional connectivity based on Granger causality do not take into account the variability of conduction delay across the human brain. In this study, we applied cortico-cortical evoked potentials (CCEP) to estimate effective functional connectivity and derive conduction delays for early (0 to 50 ms post-stimulus onset) and late (50 to 500 ms post-stimulus onset) evoked responses in 1 patient with depth electrodes. We extracted the latency at the peak of the response, at the rising phase and at the response onset. Early responses showed an average latency of 7.21 ± 4.14, 11.79 ± 5.42 and 22.73 ± 8.62 ms at the onset of the response, at the rising phase and at the peak respectively. Late responses showed an average latency of 54.98 ± 13.92, 86.37 ± 39.93 and 128.25 ± 55.62 ms at response onset, rising phase and peak respectively. The variability of these latencies suggests that conduction delays contain valuable information that might improve functional brain connectivity models.
Marquis, R.; Spinelli, L.; Baud, M.; Carboni, M.; Mégevand, P.; Michel, C.; Momjian, S.; Schaller, K.; Van Mierlo, P.; Seeck, M.; and Vulliémoz, S.
Localising scalp-invisible interictal epileptic spikes using simultaneous scalp and stereo-EEG.
In Schweizerische Gesellschaft für Klinische Neurophysiologie Congress, Aarau, Switzerland, May 2018.
link bibtex abstract
link bibtex abstract
@inproceedings{marquis_localising_2018, address = {Aarau, Switzerland}, title = {Localising scalp-invisible interictal epileptic spikes using simultaneous scalp and stereo-{EEG}}, abstract = {Background: Cortical generators of interictal epileptic spikes can be accurately localised using high-density scalp electroencephalography (EEG) and electrical source imaging (ESI), as validated by intracranial EEG (icEEG) or epilepsy surgery outcome. The detection of purely medial temporal spikes on scalp EEG remains controversial as well as their localisation using ESI. Methods: We acquired simultaneous icEEG (stereo-EEG) and 256-channels scalp EEG in one patient. We analysed the scalp voltage topography of 97 manually marked icEEG spikes presenting no relevant lateral temporal component and no visible spike on scalp. We selected the 25\% scalp segments showing highest correlation between the average and single spike topography (average correlation=0.7±0.1). We applied ESI to these segments with solution points in grey matter (ca. 5 mm spacing). Results: A typical temporal spike was visible on scalp EEG only after averaging. ESI maximum was 15-16 mm from the icEEG contacts with largest responses in the right posterior hippocampus, while contacts closest to ESI maximum (6-7mm) were in the right anterior hippocampus. Discussion: The localization accuracy was comparable to that previously reported in studies in which spikes are visible on the scalp. Future studies, including additional patients, will investigate performance of ESI methods.}, booktitle = {Schweizerische {Gesellschaft} für {Klinische} {Neurophysiologie} {Congress}}, author = {Marquis, Renaud and Spinelli, Laurent and Baud, Maxime and Carboni, Margherita and Mégevand, Pierre and Michel, Christoph and Momjian, Shahan and Schaller, Karl and Van Mierlo, Pieter and Seeck, Margitta and Vulliémoz, Serge}, month = may, year = {2018} }
Background: Cortical generators of interictal epileptic spikes can be accurately localised using high-density scalp electroencephalography (EEG) and electrical source imaging (ESI), as validated by intracranial EEG (icEEG) or epilepsy surgery outcome. The detection of purely medial temporal spikes on scalp EEG remains controversial as well as their localisation using ESI. Methods: We acquired simultaneous icEEG (stereo-EEG) and 256-channels scalp EEG in one patient. We analysed the scalp voltage topography of 97 manually marked icEEG spikes presenting no relevant lateral temporal component and no visible spike on scalp. We selected the 25% scalp segments showing highest correlation between the average and single spike topography (average correlation=0.7±0.1). We applied ESI to these segments with solution points in grey matter (ca. 5 mm spacing). Results: A typical temporal spike was visible on scalp EEG only after averaging. ESI maximum was 15-16 mm from the icEEG contacts with largest responses in the right posterior hippocampus, while contacts closest to ESI maximum (6-7mm) were in the right anterior hippocampus. Discussion: The localization accuracy was comparable to that previously reported in studies in which spikes are visible on the scalp. Future studies, including additional patients, will investigate performance of ESI methods.
Rubega, M.; Carboni, M.; Seeber, M.; Pascucci, D.; Tourbier, S.; Toscano, G.; Van Mierlo, P.; Hagmann, P.; Plomp, G.; Vulliemoz, S.; and Michel, C. M.
Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.
Brain Topography. Dec 2018.
Paper doi link bibtex abstract 1 download
Paper doi link bibtex abstract 1 download
@Article{rubega2018, author="Rubega, M. and Carboni, M. and Seeber, M. and Pascucci, D. and Tourbier, S. and Toscano, G. and Van Mierlo, P. and Hagmann, P. and Plomp, G. and Vulliemoz, S. and Michel, C. M.", title="Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis", journal="Brain Topography", year="2018", month="Dec", day="03", abstract="In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources ({\textasciitilde}{\thinspace}80{\%}) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.", issn="1573-6792", doi="10.1007/s10548-018-0691-2", url="https://doi.org/10.1007/s10548-018-0691-2" }
In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~þinspace80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.
Pizzolato, M.; Canales-Rodriguez, E. J.; Daducci, A.; and Thiran, J.
Multimodal microstructure imaging: combining relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties.
In In proceedings of ISMRM 26th Annual Meeting, Venice, Italy, 2018.
link bibtex abstract
link bibtex abstract
@inproceedings{pizzolato2018ismrm1, address = {Venice, Italy}, title = {Multimodal microstructure imaging: combining relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties}, abstract = {We propose a multimodal joint estimation that aims at exploiting the complementary information of diffusion and multi-echo spin echo data to disentangle the contributions and properties of the main tissue microstructure compartments. We recovered T2, diffusion coefficient, and volume fractions values of myelin, intracellular, extracellular, and cerebrospinal fluid compartments within an ex vivo spinal cord sample by means of diffusometry and relaxometry. A g-ratio map was also calculated.}, booktitle = {In proceedings of ISMRM 26th Annual Meeting}, author = {Pizzolato, Marco and Canales-Rodriguez, Erick Jorge and Daducci, Alessandro and Thiran, Jean-Philippe}, year = {2018} }
We propose a multimodal joint estimation that aims at exploiting the complementary information of diffusion and multi-echo spin echo data to disentangle the contributions and properties of the main tissue microstructure compartments. We recovered T2, diffusion coefficient, and volume fractions values of myelin, intracellular, extracellular, and cerebrospinal fluid compartments within an ex vivo spinal cord sample by means of diffusometry and relaxometry. A g-ratio map was also calculated.
Pizzolato, M.; Wassermann, D.; Deriche, R.; Thiran, J.; and Fick, R.
Orientation-Dispersed Apparent Axon Diameter via Multi-Stage Spherical Mean Optimization.
In Computational Diffusion MRI, MICCAI, Granada, Spain, Granada, Spain, 2018.
Paper link bibtex abstract 1 download
Paper link bibtex abstract 1 download
@inproceedings{pizzolato2018cdmri, address = {Granada, Spain}, title = {Orientation-Dispersed Apparent Axon Diameter via Multi-Stage Spherical Mean Optimization}, abstract = {The estimation of the apparent axon diameter (AAD) via diffusion MRI is affected by the incoherent alignment of single axons around its axon bundle direction, also known as orientational dispersion. The simultaneous estimation of AAD and dispersion is challenging and requires the optimization of many parameters at the same time. We propose to reduce the complexity of the estimation with an multi-stage approach, inspired to alternate convex search, that separates the estimation problem into simpler ones, thus avoiding the estimation of all the relevant model parameters at once. The method is composed of three optimization stages that are iterated, where we separately estimate the volume fractions, diffusivities, dispersion, and mean AAD, using a Cylinder and Zeppelin model. First, we use multi-shell data to estimate the undispersed axon micro-environment’s signal fractions and diffusivities using the spherical mean technique; then, to account for dispersion, we use the obtained micro-environment parameters to estimate a Watson axon orientation distribution; finally, we use data acquired perpendicularly to the axon bundle direction to estimate the mean AAD and updated signal fractions, while fixing the previously estimated diffusivity and dispersion parameters. We use the estimated mean AAD to initiate the following iteration. We show that our approach converges to good estimates while being more efficient than optimizing all model parameters at once. We apply our method to ex-vivo spinal cord data, showing that including dispersion effects results in mean apparent axon diameter estimates that are closer to their measured histological values.}, booktitle = {Computational Diffusion MRI, MICCAI, Granada, Spain}, author = {Pizzolato, Marco and Wassermann, Demian and Deriche, Rachid and Thiran, Jean-Philippe and Fick, Rutger}, year = {2018}, url = {https://infoscience.epfl.ch/record/257227} }
The estimation of the apparent axon diameter (AAD) via diffusion MRI is affected by the incoherent alignment of single axons around its axon bundle direction, also known as orientational dispersion. The simultaneous estimation of AAD and dispersion is challenging and requires the optimization of many parameters at the same time. We propose to reduce the complexity of the estimation with an multi-stage approach, inspired to alternate convex search, that separates the estimation problem into simpler ones, thus avoiding the estimation of all the relevant model parameters at once. The method is composed of three optimization stages that are iterated, where we separately estimate the volume fractions, diffusivities, dispersion, and mean AAD, using a Cylinder and Zeppelin model. First, we use multi-shell data to estimate the undispersed axon micro-environment’s signal fractions and diffusivities using the spherical mean technique; then, to account for dispersion, we use the obtained micro-environment parameters to estimate a Watson axon orientation distribution; finally, we use data acquired perpendicularly to the axon bundle direction to estimate the mean AAD and updated signal fractions, while fixing the previously estimated diffusivity and dispersion parameters. We use the estimated mean AAD to initiate the following iteration. We show that our approach converges to good estimates while being more efficient than optimizing all model parameters at once. We apply our method to ex-vivo spinal cord data, showing that including dispersion effects results in mean apparent axon diameter estimates that are closer to their measured histological values.
Verhoeven, T.; Coito, A.; Plomp, G.; Thomschewski, A.; Pittau, F.; Trinka, E.; Wiest, R.; Schaller, K.; Michel, C.; Seeck, M.; Dambre, J.; Vulliemoz, S.; and van Mierlo, P.
Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.
Neuroimage Clin, 17: 10-15. 2018.
Paper doi link bibtex 1 download
Paper doi link bibtex 1 download
@article{verhoeven2018neuro, author = {Verhoeven, T. and Coito, A. and Plomp, G. and Thomschewski, A. and Pittau, F. and Trinka, E. and Wiest, R. and Schaller, K. and Michel, C. and Seeck, M. and Dambre, J. and Vulliemoz, S. and van Mierlo, P.}, title = {Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes}, journal = {Neuroimage Clin}, volume = {17}, pages = {10-15}, issn = {2213-1582}, doi = {10.1016/j.nicl.2017.09.021}, url = {http://www.ncbi.nlm.nih.gov/pubmed/29527470}, year = {2018} }
van Mierlo, P.; Lie, O.; Staljanssens, W.; Coito, A.; and Vulliemoz, S.
Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG.
Brain Topogr, 31(5): 753-766. 2018.
Paper doi link bibtex
Paper doi link bibtex
@article{vanmierlo2018braintop, author = {van Mierlo, P. and Lie, O. and Staljanssens, W. and Coito, A. and Vulliemoz, S.}, title = {Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG}, journal = {Brain Topogr}, volume = {31}, number = {5}, pages = {753-766}, issn = {1573-6792}, doi = {10.1007/s10548-018-0646-7}, url = {http://www.ncbi.nlm.nih.gov/pubmed/29700719}, year = {2018}, }
2017
(2)
van Mierlo, P.; Strobbe, G.; Keereman, V.; Birot, G.; Gadeyne, S.; Gschwind, M.; Carrette, E.; Meurs, A.; Van Roost, D.; Vonck, K.; Seeck, M.; Vulliemoz, S.; and Boon, P.
Automated long-term EEG analysis to localize the epileptogenic zone.
Epilepsia Open, 2(3): 322-333. 2017.
Paper doi link bibtex abstract
Paper doi link bibtex abstract
@article{vanmierlo2017epi, author = {van Mierlo, P. and Strobbe, G. and Keereman, V. and Birot, G. and Gadeyne, S. and Gschwind, M. and Carrette, E. and Meurs, A. and Van Roost, D. and Vonck, K. and Seeck, M. and Vulliemoz, S. and Boon, P.}, title = {Automated long-term EEG analysis to localize the epileptogenic zone}, journal = {Epilepsia Open}, volume = {2}, number = {3}, pages = {322-333}, abstract = {Objective: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. Methods: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. Results: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. Significance: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus.}, ISSN = {2470-9239}, doi = {10.1002/epi4.12066}, url = {http://www.ncbi.nlm.nih.gov/pubmed/29588961}, year = {2017} }
Objective: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. Methods: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. Results: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. Significance: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus.
Staljanssens, W.; Strobbe, G.; Van Holen, R.; Keereman, V.; Gadeyne, S.; Carrette, E.; Meurs, A.; Pittau, F.; Momjian, S.; Seeck, M.; Boon, P.; Vandenberghe, S.; Vulliemoz, S.; Vonck, K.; and van Mierlo, P.
EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy.
Neuroimage Clin, 16: 689-698. 2017.
Paper doi link bibtex abstract
Paper doi link bibtex abstract
@article{staljanssens2017neuro, author = {Staljanssens, W. and Strobbe, G. and Van Holen, R. and Keereman, V. and Gadeyne, S. and Carrette, E. and Meurs, A. and Pittau, F. and Momjian, S. and Seeck, M. and Boon, P. and Vandenberghe, S. and Vulliemoz, S. and Vonck, K. and van Mierlo, P.}, title = {EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy}, journal = {Neuroimage Clin}, volume = {16}, pages = {689-698}, abstract = {Electrical source imaging (ESI) from interictal scalp EEG is increasingly validated and used as a valuable tool in the presurgical evaluation of epilepsy as a reflection of the irritative zone. ESI of ictal scalp EEG to localize the seizure onset zone (SOZ) remains challenging. We investigated the value of an approach for ictal imaging using ESI and functional connectivity analysis (FC). Ictal scalp EEG from 111 seizures in 27 patients who had Engel class I outcome at least 1 year following resective surgery was analyzed. For every seizure, an artifact-free epoch close to the seizure onset was selected and ESI using LORETA was applied. In addition, the reconstructed sources underwent FC using the spectrum-weighted Adaptive Directed Transfer Function. This resulted in the estimation of the SOZ in two ways: (i) the source with maximal power after ESI, (ii) the source with the strongest outgoing connections after combined ESI and FC. Next, we calculated the distance between the estimated SOZ and the border of the resected zone (RZ) for both approaches and called this the localization error ((i) LEpow and (ii) LEconn respectively). By comparing LEpow and LEconn, we assessed the added value of FC. The source with maximal power after ESI was inside the RZ (LEpow = 0 mm) in 31% of the seizures and estimated within 10 mm from the border of the RZ (LEpow </= 10 mm) in 42%. Using ESI and FC, these numbers increased to 72% for LEconn = 0 mm and 94% for LEconn </= 10 mm. FC provided a significant added value to ESI alone (p < 0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy.}, issn = {2213-1582}, doi = {10.1016/j.nicl.2017.09.011}, url = {http://www.ncbi.nlm.nih.gov/pubmed/29034162}, year = {2017} }
Electrical source imaging (ESI) from interictal scalp EEG is increasingly validated and used as a valuable tool in the presurgical evaluation of epilepsy as a reflection of the irritative zone. ESI of ictal scalp EEG to localize the seizure onset zone (SOZ) remains challenging. We investigated the value of an approach for ictal imaging using ESI and functional connectivity analysis (FC). Ictal scalp EEG from 111 seizures in 27 patients who had Engel class I outcome at least 1 year following resective surgery was analyzed. For every seizure, an artifact-free epoch close to the seizure onset was selected and ESI using LORETA was applied. In addition, the reconstructed sources underwent FC using the spectrum-weighted Adaptive Directed Transfer Function. This resulted in the estimation of the SOZ in two ways: (i) the source with maximal power after ESI, (ii) the source with the strongest outgoing connections after combined ESI and FC. Next, we calculated the distance between the estimated SOZ and the border of the resected zone (RZ) for both approaches and called this the localization error ((i) LEpow and (ii) LEconn respectively). By comparing LEpow and LEconn, we assessed the added value of FC. The source with maximal power after ESI was inside the RZ (LEpow = 0 mm) in 31% of the seizures and estimated within 10 mm from the border of the RZ (LEpow
in press
(1)
Carboni, M; Rubega, M; Iannotti, G R; De Stefano, P; Toscano, G; Tourbier, S; Pittau, F; Hagmann, P; Momjian, S; Schaller, K; Seeck, M; Michel, C M; van Mierlo, P; and Vulliemoz, S
The network integration of epileptic activity in relation to surgical outcome.
Clinical Neurophysiology. in press.
link bibtex
link bibtex
@article{carboni2019clineu, author = {Carboni, M and Rubega, M and Iannotti, G R and De Stefano, P and Toscano, G and Tourbier, S and Pittau, F and Hagmann, P and Momjian, S and Schaller, K and Seeck, M and Michel, C M and van Mierlo, P and Vulliemoz, S}, title = {The network integration of epileptic activity in relation to surgical outcome}, journal = {Clinical Neurophysiology}, year = {in press} }
Project keywords
- Brain Connectomics
- Effective Connectivity
- Communication through Coherence
- Diffusion MRI
- Microstructural Imaging
- Electrical Source Imaging
- Functional Brain Dynamics
- Epilepsy
Principal investigators
Prof. Patric Hagmann (lead)
Radiology Research CenterDepartment of Radiology
Lausanne University Hospital (CHUV)
Rue Centrale 7, 4th floor
CH-1003 Lausanne
Switzerland
Prof. Serge Vulliémoz
EEG and Epilepsy UnitDepartment of Neurology
University Hospital of Geneva
Rue Gabrielle Perret-Gentil 4
CH-1205 Geneva
Switzerland
Prof. Christoph Michel
Faculty of MedicineFaculty of Medicine
University of Geneva
Campus Biotech
Chemin des Mines 9
CH-1202 Geneva Switzerland
Prof. Jean-Philippe Thiran
Signal Processing Laboratory (LTS5)EPFL-STI-IEL-LTS5
Station 11
CH-1015 Lausanne
Switzerland
Partners
Prof. Gijs Plomp
Department of PsychologyUniversity of Fribourg
Rue P.-A.-de-Faucigny 2
CH-1700 Fribourg
Switzerland
Prof. Pieter Van Mierlo
ELIS Electronics and Information SystemsGhent University
Technologiepark-Zwijnaarde 15
BE-9000 Gent
Belgium
Prof. Gustavo Deco
Computational Neuroscience Research GroupDepartment of Information and Communication Technologies
Universidad Pompeu Fabra / ICREA
Roc Boronat, 138
ES-08018 Barcelona
- Contact
- © 2017-2019 Brain Communication Pathways Sinergia Consortium all rights reserved
- Supported by SNF Sinergia grant CRSII5_170873