Institut de Neurosciences des Systèmes
Interictal epileptic networks in MEG and SEEG
Christian Bénar Institut de Neurosciences des Systèmes, Marseille
Workshop on Scale-free Dynamics and Networks in
Neurosciences, October 21-24, 2013, Montréal
Institut de Neurosciences des Systèmes
Overview • Context
• Characterization of networks
– Spike versus oscillations – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
Institut de Neurosciences des Systèmes
Overview • Context
• Characterization of networks
– Spike versus oscillations – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
Institut de Neurosciences des Systèmes
Presurgical Evaluation • The goal of presurgical evaluation is to remove
the epileptogenic zone « the minimum amount of cortex to be removed in order to render the
patient seizure-free » H. Lüders « the zone of primary organization of seizures » Talairach and Bancaud
MEG/EEG SEEG
(intracerebral EEG)
Semiology
Phase 1 Phase 2 Surgery
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Magnetoencephalography
Marseille MEG platform, 248 sensors
1 2 3
4 5 6
L R
EEG
L R 1 2 3
4 5 6
MEG
Gavaret et al Brain Topogr in press
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Stereotaxic EEG (SEEG)
Barbeau et al 2008: evoked activity during face recognition
SEEG implantation in deep regions
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Objectives
MEG/EEG SEEG (intracerebral EEG)
Phase 1 Phase 2 Surgery
Optimize signal processing strategies by confronting MEEG with SEEG
Guide SEEG implantation
Avoid SEEG in some cases
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Seizure start: focus or network ?
Bartolomei et al 2001 Start of seizure mesial + neocortical
Bartolomei et al 2008 Epileptogenicity index
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What about interictal activity? • Non-invasive tools mostly investigate interictal
discharges (i.e., between seizures)
• Simpler propagation patterns than seizures
• Complex relationship with seizure (Gotman 1989)
• Present network dynamic patterns: indication on propagation pathways?
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Existence of extended interictal networks in SEEG
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« preictal build up »
Schwartz et al 2011
The interictal networks fluctuate in time
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Gavaret et al J Clin Neurophysiol 2006
time (s)
frequ
ency
(Hz)
2 2.05 2.1 2.15 2.2 2.25 2.3 2.35 2.4 2.45 2.510
15
20
25
30
35
40
45
50
55
60
MEG discharge at 30 Hz
Interictal spikes and oscillations Average spike
Interictal activity can manifest as spikes, oscilation or a mixture of both (« moustache ») Transient patterns (not 1/f !!)
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Overview • Context
• Characterization of networks
– Spike versus oscillations – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
Institut de Neurosciences des Systèmes
Rationale • Characterize interictal epileptic networks, leading
region(s) with non-invasive tools
• Investigate differences betwen spikes and oscillations
• Strategies
– Time-frequency /time-scale analysis – Source localization – Independent component analysis (ICA)
Institut de Neurosciences des Systèmes
Overview • Context
• Characterization of networks
– Spike vs oscillations: methodological points – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
Institut de Neurosciences des Systèmes
Point 1: frequency overlap
-100
0
100
Original Signalam
plitu
de (u
V)
-50
0
50Filtered Signal (8-12 Hz)
time (s)
freq
uenc
y (H
z)
Time-Frequency Analysis
200 400 600 800 1000 1200 1400248
153058
Transients and oscillations overlap in frequency: difficulty of extracting « pure » oscillatory part
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« False ripples » (1) : transients
Bénar et al 2010
Filtering spikes or artefacts gives « false » oscillations
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« False ripples » (2): harmonics
Filtering signals with harmonics gives also « false » oscillations
Bénar et al 2010
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Separation of oscillations and transients Oscillations
Jmail et al 2010
In real data, dependence on fit between atoms and data Current collaboration with S Hitziger, T Papadopoulo, M Clerc (INRIA Sophia Antipolis): definition of atoms from data
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Point 2: multi-trial aspects
Benar et al 2009
« consensus matching pursuit » -> Taking into account interevent variability in atomic decomposition
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Point 3: Interactions background/activity
Indication of phase resetting in primary visual cortex
Krieg et al 2010
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Overview • Context
• Characterization of networks
– Spike versus oscillations – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
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Spike networks vs oscillation networks
N. Jmail
Institut de Neurosciences des Systèmes
Spike networks vs oscillation networks
Nawel Jmail
-non-invasive results concordant with SEEG: 89 % for spikes , 75 % for oscillations). -Only partial overlap between networks (38 % of total number of regions in MEG, 50% in SEEG). -Current directions: automatize extraction of networks, increase number of patients
Institut de Neurosciences des Systèmes
Overview • Context
• Characterization of networks
– Spike versus oscillations – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
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ICA on continuous MEG
Malinowska et al, Hum Brain Mapp 2013
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Co-occurrence testing
Similar principle than Bourien et al 2005
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Co-occurrence graphs
• significant direction
• spike co-occurrences (significance with non-parametric technique)
Malinowska et al, Hum Brain Mapp 2013
• leaders: delays or conditional probability
Source localisation on components
ICA1
ICA7
ICA6
ICA1
ICA6
ICA7
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Resultats (1): good concordance
L
MEG
SEEG
Malinowska et al, Hum Brain Mapp 2013
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L
MEG
SEEG
L
Results (2): partial concordance
Malinowska et al, Hum Brain Mapp 2013
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Comparison with ictal networks Interictal, SEEG Ictal, SEEG
Malinowska et al, Hum Brain Mapp 2013
Institut de neurosciences des systèmes
Networks: discussion • One can reliably extract interictal networks from
non-invasive MEG data
• One must be cautious on possible undetectable leaders: need to investigate depth/surface relationships
• Need to validate clinically on larger population of patients (seizure freedom)
• Future direction: use of virtual brain simulations
Institut de Neurosciences des Systèmes
Overview • Context
• Characterization of networks
– Spike versus oscillations – Strategy 1: Source localization – Strategy 2: ICA
• Simultaneous recordings of EEG/MEG/SEEG
Institut de Neurosciences des Systèmes
Rationale
• Previous comparison MEEG/SEEG done on separate recordings
• Not guaranteed to visualize exact same activity (temporal fluctuations)
• Cannot use single trial fluctuations as source of information (see simultaneous EEG-fMRI)
• Need to record simultaneous data
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Recording and Artefacts
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Simultaneous MEG-EEG-SEEG
Dubarry et al, under review
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Source localization
Dubarry et al, under review
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Single trial analysis
Dubarry et al, under review
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Simultaneous recordings: discussion
• We have shown the feasibility of simultaneous EEG/MEG/intracerebral EEG - Average - Single trial analysis
• Issue of artefacts: need further work on denoising
• Future directions: mapping of sensitivity of measures according to location/extent of activity
Acknowledgments
Neurologists P Chauvel F Bartolomei M Gavaret A Trébuchon Da Fonseca
Neuropsychology C Liegeois-Chauvel
Engineers JM Badier S Chen B Colombet M Woodman B Giusiano
Students, postdocs U Malinowska N Jmail AS Dubarry
Neurosurgeons J Régis R Caron
Collaborations: T Papadopoulo, M Clerc (INRIA Sophia); X Alario (LNC, Marseille); F Wendling (LTSI, Rennes) M Guye, JP Ranjeva (CEMEREM, Marseille), C Grova, JM Lina (Montreal)
Funding: Agence National de la Recherche (ANR Multimodel) Fondation pour la recherche médical (FRM)
