performing electrophysiological measurements in humans inside magnetic resonance imaging scanners;...

Performing electrophysiological measurements in humans inside Magnetic

Resonance Imaging scanners; applications in Epilepsy research and other areas

Louis Lemieux

Department of Clinical and Experimental EpilepsyUCL Institute of Neurology, Queen Square, London

&MRI Unit, Epilepsy Society

Chalfont St Peter, BuckinghamshireUK

Lemieux – ACES / Europe Dublin 2015

Outline

• Epilepsy

• Multimodal neuroimaging in humans

– EEG and functional MRI (fMRI)

– Mapping epileptic events using scalp EEG-fMRI

• Going deeper: intracranial EEG-fMRI

– Technique implementation

– An epileptic seizure

• Conclusions


Rely on accurate anatomical targeting

Epilepsy• Epilepsy is the most common serious chronic neurological

condition affecting all ages– 50 million people affected in the world– Economic costs (~€20 billion/year for Europe).

• 30% of all people with epilepsy have seizures that do not respond to medical treatment, leading to:– Cognitive decline– Poor quality of life– Significantly increased mortality– High societal costs

• Need for improved treatment– Surgery– Drug delivery

• Need for improved localisation of the epileptogenic areas


• fMRI

– Allows tomographic visualisation of haemodynamic changes associated with

brain activity

– Has better temporal resolution than PET (…for epileptic spikes)

– Is non-invasive (BOLD)

• EEG

– Important observable of brain activity in humans

– Reflects neuronal signal generation and synchronisation

– Important clinical tool in epilepsy (epileptic spikes, seizures, etc)

– Non-invasive (scalp) & cheap

Basic principles: EEG & fMRI


LF= 0.5 Hz HF= 30 Hz

1 sec.

25 uV

10:36:33 10:36:34 10:36:35 10:36:36

Fp2-F8

F8-T4

T4-T6

T6-O2

Fp1-F7

F7-T3

T3-T5

T5-O1

ECG1-ECG2

Epileptic spikes

• Spatially linked to the epileptic focus

• Brief (<100ms), unpredictable

• Sub-clinical: can only be seen on EEG

• Epileptic spike source localisation– Inverse problem of EEG…

– Can fMRI better localise epileptic spike generators?

– Clinical utility?


EEG-fMRI in epilepsyData acquisition strategy

Subject at restSimultaneous EEG-MRI

• fMRI:− Echo-planar imaging (EPI) fMRI

scanning sequences

− Whole-brain coverage

• EEG:− 64-channel cap on scalp

− MR-compatible amplifier and digitiser

− Digital signal transmitted to recording laptop outside the scanner room

Louis Lemieux

Amps placed at far end of scanner bore.


EEG-fMRI of epileptic spikes

Continuous EEG and fMRI:

EEG-based GLM

Spike-related BOLD

[Krakow et al, 1999; Lemieux et al, 2001]

1 sec.

25 uV

10:36:33 10:36:34 10:36:35 10:36:36

Fp2-F8

F8-T4

T4-T6

T6-O2

Fp1-F7

F7-T3

T3-T5

T5-O1

ECG1-ECG2


Fp1-Pz

F7 – Pz

T3 – Pz

T5 – Pz

O1 – Pz

Fp2 – Pz

F8 – Pz

T4 – Pz

T6 – Pz

O2 – Pz

Fp1 – F7

F7 – T3

T3 – T5

T5 – O1

Fp2 – F8

F8 – T4

T4 – T6

T6 – O2

ECG

OSC

1 Second LF = 0.5 Hz HF = 45 Hz50uV

The intra-MRI EEG artefact problem


Fp1-Pz

F7 – Pz

T3 – Pz

T5 – Pz

O1 – Pz

Fp2 – Pz

F8 – Pz

T4 – Pz

T6 – Pz

O2 – Pz

Fp1 – F7

F7 – T3

T3 – T5

T5 – O1

Fp2 – F8

F8 – T4

T4 – T6

T6 – O2

ECG

OSC


[Allen et al, 2000]



Fp1-Pz

F7 – Pz

T3 – Pz

T5 – Pz

O1 – Pz

Fp2 – Pz

F8 – Pz

T4 – Pz

T6 – Pz

O2 – Pz

Fp1 – F7

F7 – T3

T3 – T5

T5 – O1

Fp2 – F8

F8 – T4

T4 – T6

T6 – O2

ECG

OSC


[Allen et al, 1999]



Clinical relevance: EEG-fMRI of epileptic spikes inFocal Cortical Dysplasia

ANN NEUROL 2011;70:822–837

Conclusion:Scalp EEG-fMRI of epileptic spikes may predict less promising surgical cases and therefore avoid unnecessary invasive interventions


icEEG: Subdural grids and depth electrodes• Surgically placed on cortex• Sampling similar to high-density scalp EEG• Used to map epileptogenic tissue in relation to eloquent

cortex• 6x8 array of 3mm diameter Pt-Ir disk contacts

[Fried et al, 1999]

• Surgically inserted within brain• Used to detect epileptogenicity and propagation in deep

cortex/lesions• Sensitivity profile very different from scalp EEG and grids:

‘tunnel vision’ [see Cosandier et al 2007; Church et al, 1985]

• ‘Spencer probe’ commercial design• Pt-Ir cylindrical contacts & Ni-Cr terminations and wires

contained in polyurethane


[Carmichael et al, 2009; 2012]

• Health hazards• RF-induced heating• Induced voltages (stimulation)• Factors considered:• Field strength: 1.5T and 3T• RF transmit coil type: head and body• Electrodes: depths and grids • EEG wires

• Geometry and placement• Length• Termination

Simultaneous icEEG-fMRI:Safety tests in phantoms


[Carmichael et al, 2009, 2012]

• RF-induced heating greatest health risk• Excessive heating observed• Body coil• 3T• But for certain realistic conditions:

Max ∆T = 0.9ºC (@1.5T, SAR=2.4W/Kg, 6mins)

icEEG-fMRI possible without excessive additional health risk under certain conditions

Site-specific assessment necessary

Intra-cranial EEG-fMRI safety study results:Heating tests


1) Use likely safest regime2) Strict protocol:

- 1.5T Siemens Avanto / head (quad.) Tx/Rx coil- 90cm cables with 10cm fold along scanner central Z xis- Foam insert designed for exact positioning

- Position EEG system and cables reproducibly- Low SAR sequences:

- T1 volume, gradient echo EPI [TE=40ms], B0 map- 3-4% of 3.2 W/Kg- max duration 10 minutes

3) Close monitoring and documenting of patient responses, images, appearance of brain surface, histology

icEEG-fMRI: Implementation


icEEG-fMRI image quality:EPI signal degradation around electrodes

[Carmichael et al, 2012]


Feasibility study case report:• Seizures: R hand stiffening• MRI normal• No spikes during scalp EEG-fMRI• icEEG implantation: grids and strips over

left frontal lobe• Two 10-minute resting-state icEEG-fMRI

sessions:• 100’s of L fronto-central spikes

L

icEEG-fMRI

MEG: IED onset

L

L

MEG: IED propagation

L

L

irritative zone

Demonstration of spike-correlated icEEG-fMRI

[Vulliemoz/Carmichael et al, 2010]


Conclusions

EEG-fMRI allows:• Haemodynamic mapping of events with specific EEG features• ‘Extends’ EEG: whole-brain coverage, source complexity-independent

Intracranial EEG-fMRI:• Fascinating, complex data• Exquisite electrophysiological sensitivity• Image data quality is an issue:

− Electrode composition is suboptimal for MR imaging− Clinical impact: Limits ability to locate the electrodes in relation to the

anatomy− Research impact: Reduces the amount of fMRI signal available for

analysis


Team / collaborators / funding

David W. CarmichaelUmair ChaudharyAna-Carolina Coan (Campinas)Alessio De Ciantis (Firenze)Beate DiehlJohn S. DuncanMadeline GradeMarco LeiteAndrew McEvoyTeresa MurtaIrene PappalardoSuejen Perani (King’s)Sofia Markoula (Ioannina)Roman RodionovCatherine ScottNiraj SharmaRachel C. ThorntonAndré van GraanAnna Vaudano (Modena)Matthew C. WalkerBritta Wandschneider+ The Clinical Neurophysiology and

Neuroradiology teams at the National Hospital for Neurology and Neurosurgery, Queen Square

K Friston (UCL)M Guye / F Bartolomei / P Chauvel / JP Ranjeva (Marseille)S Vulliemoz / C Michel (Geneva)P Figueiredo (Lisbon)M Papadopoulou / D Marinazzo (Ghent)Radhakrishnan A / Chandrasekharan K / Sreedharan S (Trivandrum)R Quian Quiroga / C Pedreira (Leicester)V Kokkinos (Thessaloniki)S Meletti (Modena)F Cendes (Campinas)K Mullinger / R Bowtell (Nottingham)KR Muller / V Samek / D Blythe (Berlin)H Laufs (Frankfurt)J Daunizeau (Paris)K Whittingstall (Sherbrooke)E Formisano / F De Martino (Maastricht)M Torkmani Azar (Konya)

Funding:Action Medical ResearchBrain Research Trust / James Tudor FoundationNIHR (UK Department of Health)Medical Research CouncilSupport in kind:Brain Products

Thank you


The problem of presurgical test validation• Validation of localisation tests in epilepsy is fundamentally limited• Gold standard is not golden:

• Surgical resection localisation and outcome data

• Ictiogenic ‘source’ must be considered a network in most patients a priori

• Elements of a possible solution• Improved characterisation / Modelling

• Functional connectivity networks

• Effective connectivity (“DCM”)

• Validation / Interventions:• Surgical: connection disruptions (functional connectivity sufficient?)

• More sophisticated: stimulation (effective connectivity necessary?)


What else the EEG-fMRI can tell us?

Psychophysiologial interaction (PPI)

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-1.5

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-0.5

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LFdl activity

LFp

resp

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Psychophysiologic Interaction

IED

seed ROI

Vaudano et al., 2013-Frontiers in Neurology

2. Connectivity analysis2. Connectivity analysis


Interictal EEG

Intracranial EEG-fMRI:Incidental Studies of Other Brain

Phenomena in Humans


EEG oscillations: their functional roles and brain state correlates

Classical EEG oscillation bands• Delta (0.5-3.5Hz):

– deep sleep, learning, motivational processes and reward system• Theta (4-7 Hz):

– working memory, emotional arousal and fear conditioning• Alpha (8-12 Hz):

– cortical operations during the awake resting-state in the absence of sensory inputs, disengagement of task-irrelevant brain areas, working memory and short-term-memory retention

– Rolandic Alpha / ‘Mu rhythm’ (9-11 Hz)• Suppressed during task

• Beta (13-30 Hz):– Vigilence and attention– Rolandic Beta

• Linked to motor functions• Inhibited by motor imagery

• Gamma (> 30 Hz):– feature integration, attention, etc


Rolandic Alpha (‘Mu’) and Beta on Scalp EEG

• What are their respective roles?

• They fluctuate similarly but are not perfectly correlated

• Based on MEG, their spatial distributions seem to differ:

• Rolandic Alpha: post-central (primary somatosensory)

• Rolandic Beta: pre-central (primary motor)

• fMRI-based localisation?...


fMRI of Rolandic Alpha and Beta on scalp EEG: Ritter et al, 2009

• Bilateral hand motor task• 15 healthy subjects• Two main fMRI models:

• Band/channel• Blind source separation

Beta – BOLD correlation:

Conclusions

Complex data quality correction and modelling methodology

BOLD of Rolandic alpha and beta rhythms differ: postcentral gyrus (SI) for alpha and precentral gyrus (MI) for beta;

Negative Rolandic alpha and beta rhythms - BOLD correlation in the pericentral cortex


BOLD Mapping of Rolandic Alpha and Beta oscillations on ECoG

Tim

e

HRF

Band Averaging

(around peak)

0.5mV

M1

S1

8,9…

30Hz

M1

S1

Identification of patient-specific

band peak

Spatial PCA:1st PC

[Perani et al, in preparation]

Grid placed over left motor cortexicEEG-fMRI:

• Rest (epileptic activity mapping)• Alternating finger tapping task (no rest)

For each band: Alpha and Beta


L

L

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L


BOLD Mapping of Rolandic Alpha and Beta on ECoG:Task data

BOLD increases

BOLD decreases BOLD decreases

BOLD increases

Alpha (Mu) Beta


Intra-cranial EEG-fMRI of interictal spikesSummary BOLD maps across all IZ1 IED

[Chaudhary - submitted]

7 cases had concordant maps: better outcome (ILAE 1 & 3)

5 cases had discordant maps:worse outcome (ILAE 4 & 5; 5 cases)

Case # 3 (ILAE class 1)

Case # 13 (ILAE class 4)

SPM{F} contrast across all IZ1 IED-related effects

Summary measure for comparison:Relationship of BOLD clusters with the presumed, icEEG-derived EZ


Conclusions (2)

• EEG-fMRI (+video) to have increased importance in neuroscience• Intracranial EEG-fMRI

• Analysis is complex: additional layer on top of icEEG analysis & interpretation• Abundance of activity -> more reliable maps than scalp EEG-fMRI• Interpretation: compare it against what?

• How precious is our ‘gold’ standard?• Surgical outcome + localisation of resected tissue: what about disruption of

wider networks?

• Effective connectivity: ‘the full multimodal generator model’ (DCM)• Improved biophysical models of epileptogenic networks being developed• Applicable to icEEG, then fMRI


Seizure propagation in hypothalamic hamartomas (HH)

• Seizures originate in HH, and control of seizures can be achieved by surgically removing the HH.

• Possible surgical alternative: disconnection of the underlying pathway.

• Different seizure propagation pathways have been described in HH [Leal et al. Epilepsia 2003; Kahane et al. Epileptic Disord. 2003]:

• Aim: identify the correct seizure propagation pathway in individual patients using DCM.

1.HH to temporal-occipital (posterior, PR) to frontal lobe (anterior, AR)

(through the fornix)

2.HH to frontal (anterior, AR) to temporal-occipital (posterior, PR) lobe

(through the mammillo-thalamo-cingulate pathway)

[Murta et al, 2012]


Seizure propagation in HH:Family of DCM models

• HH is the driving region• All structures consistent with each of 2 propagation hypotheses• Each connection: uni- or bi-directional (22=4 structures per hypothesis)• Each connection: linear (black) or bilinear (black and green) (24=8 models per hypothesis)

[Murta et al, 2012]


Seizure propagation in HH: Bayesian model comparison

[Murta et al, 2012]

Most likely model

Model 1:

HH → temp-occ. → frontal

Beyond mapping:Networks and causality(and a detour via IGE)


• Multi-modal imaging = Combinations of images or maps– from different sources (instruments)

or– that show different aspects (e.g. MR contrasts)

• Fundamental assumption: measurements relate to the same phenomenon– Location– Time

Multi-modal imaging: basics


Estimating a network of sources and their dynamics:Dynamic Causal Modelling

[Friston, 2009; David et al 2006]

Bi-linear model of effective connectivity

– Effect of activity in one region on activity in other: equations of motion

– Biophysical generative model

fMRI:EEG/MEG neural mass model:

+ Model comparison (Bayesian)


EEG fMRI of GSW - QS 1.5T seriesGroup analysis

IGE

N=18

Sup Post Par ↓

Front ↓

Post Cing ↓

Thalam ↑

[Hamandi et al., 2006]

Th

Th


Dynamic causal models ofeffective connectivity in GSW

Centrencephalic

Cortico/corticoreticular

Precuneus model

[Vaudano et al, 2009]

Data:EEG-fMRI of GSW in 7 patients with IGESignificant GSW-related BOLD: Thalamus, Precuneus and ventromedial prefrontal

Model of effective connectivity: DCM of fMRIGSW EEG onsets and offsets modelled as (endogenous) input3 models:

Best in 5/7 cases


Integrate and Fire Neurons

Mean Field Population Dynamics

DCM of focal seizuresA more realistic biophysical model of (fast) ictal activity

Time (s)

Freq

uenc

y (H

z)

5 10 15 20 25 30

8

16

32

64

128

Model: Coupled neural population dynamics

[M Leite, unpublished]


+10mV

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E vs. I

Pyr Int

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)

Phase (%) Phase (%)

Phase (%) Phase (%)

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pA)

Current (pA) Current (pA)

Pyr Int

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[M Leite; unpublished - In collaboration with D Kullmann and D Kuzmin]

A more realistic biophysical modelof (fast) ictal activity: Validation initial results


Resting-state BOLD

BOLD increases

BOLD decreases BOLD decreases

BOLD increases

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Alpha (Mu) Beta

performing electrophysiological measurements in humans inside magnetic resonance imaging scanners;...

Science

fmri of rolandic alpha

lemieux aces europe

epilepsy epilepsy

eeg fmri

beta negative rolandic

scalp eegfmri

epilepsy epileptic spikes

rolandic alpha mu