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Combining Sodium MRI, Proton MR Spectroscopic Imaging and Intracerebral EEG in Epilepsy

August 2022

August 2022

DOI:10.1101/2022.08.03.22278332

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Authors:

Julia Makhalova

Assistance Publique Hôpitaux de Marseille

Wafaa Zaaraoui

French National Centre for Scientific Research

Samuel Medina Villalon

Assistance Publique Hôpitaux de Marseille

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Whole brain ionic and metabolic imaging has potential as a powerful tool for the characterization of brain diseases. In this study we combined sodium MRI ( ²³ Na MRI) and ¹ H-MR Spectroscopic Imaging ( ¹ H-MRSI) and compared ionic/metabolic changes probed by this multimodal approach to intracerebral stereotactic-EEG (SEEG) recordings. We applied a multi-echo density adapted 3D projection reconstruction pulse sequence at 7T ( ²³ Na MRI) and a 3D echo planar spectroscopic imaging sequence at 3T ( ¹ H-MRSI) in 19 patients suffering from drug-resistant focal epilepsy who underwent presurgical SEEG. We investigated ²³ Na MRI parameters including total sodium concentration (TSC) and the sodium signal fraction associated of with the short component of T 2 * decay ( f ), alongside the level of metabolites N-acetyl aspartate (NAA), choline compounds (Cho) and total creatine (tCr). All measures were extracted from spherical regions of interest (ROIs) centered between two adjacent SEEG electrode contacts and z-scored against the same ROI in controls. Group comparison showed a significant increase in f only in the epileptogenic zone (EZ) compared to controls and compared to patients propagation zone (PZ) and non-involved zone (NIZ). TSC was significantly increased in all patients’ regions compared to controls. Conversely, NAA levels were significantly lower in patients compared to controls, and lower in the EZ compared to PZ and NIZ. Multiple regression analyzing the relationship between sodium and metabolites levels revealed significant relations in PZ and in NIZ but not in EZ. Our results are in agreement with the energetic failure hypothesis in epileptic regions associated with widespread tissue reorganization.

Spectrogram from a healthy control cortical voxel. Here we highlighted the three metabolites we focused on in this study, namely total Choline (Cho), N-Acetyl Aspartate (NAA) and total creatine (tCr).

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Patient clinical demography

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Summary of multiple linear regression analysis for TSC and f.

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Combining Sodium MRI, Proton MR Spectroscopic

Imaging and Intracerebral EEG in Epilepsy

Mikhael Azilinon1,2,3, Julia Scholly3,4, Wafaa Zaaraoui1,3, Samuel Medina Villalon2,4 , Patrick

Viout1,3, Tangi Roussel1,3, Mohamed Mounir El Mendili1,3, Ben Ridley5, Jean-Philippe

Ranjeva1,3, Fabrice Bartolomei2,4 ,Viktor Jirsa2and Maxime Guye1,3

Abstract

Whole brain ionic and metabolic imaging has potential as a powerful tool for the

characterization of brain diseases. In this study we combined sodium MRI (23Na MRI) and

1H-MR Spectroscopic Imaging (1H-MRSI) and compared ionic/metabolic changes probed by

this multimodal approach to intracerebral stereotactic-EEG (SEEG) recordings.

We applied a multi-echo density adapted 3D projection reconstruction pulse sequence at 7T

(23Na MRI) and a 3D echo planar spectroscopic imaging sequence at 3T (1H-MRSI) in 19

patients suffering from drug-resistant focal epilepsy who underwent presurgical SEEG. We

investigated 23Na MRI parameters including total sodium concentration (TSC) and the

sodium signal fraction associated of with the short component of T2* decay (f), alongside the

level of metabolites N-acetyl aspartate (NAA), choline compounds (Cho) and total creatine

(tCr). All measures were extracted from spherical regions of interest (ROIs) centered between

two adjacent SEEG electrode contacts and z-scored against the same ROI in controls.

Group comparison showed a significant increase in fonly in the epileptogenic zone (EZ)

compared to controls and compared to patients propagation zone (PZ) and non-involved zone

(NIZ). TSC was significantly increased in all patients’ regions compared to controls.

Conversely, NAA levels were significantly lower in patients compared to controls, and lower

in the EZ compared to PZ and NIZ. Multiple regression analyzing the relationship between

sodium and metabolites levels revealed significant relations in PZ and in NIZ but not in EZ.

Our results are in agreement with the energetic failure hypothesis in epileptic regions

associated with widespread tissue reorganization.

Author affiliations:

1 Aix Marseille Univ, CNRS, CRMBM, Marseille, France.

2 Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.

3 APHM, Timone Hospital, CEMEREM, Marseille, France.

4 APHM, Timone Hospital, Epileptology Department, Marseille, France.

5 IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy

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is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 3, 2022. ; https://doi.org/10.1101/2022.08.03.22278332doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Correspondence to: Prof Maxime Guye, MD, PhD,

Centre de Résonance Magnétique Biologique et Médicale

Faculté des Sciences Médicales et paramédicales

27, Bd Jean Moulin, 13385 Marseille Cedex 5, France

Email: maxime.guye@univ-amu.fr

Running title: Sodium MRI and spectroscopy in focal epilepsy

Keywords : Sodium MRI ; 1H-Spectroscopic Imaging ; Epilepsy ; Intracranial EEG ;

Multimodal Imaging ; 7T MRI

Abbreviations:

AC-PC = Anterior Commissure - Posterior Commissure; ASL = Achieved Significance

Level; Cho = Choline Compounds; CRLB = Cramer-Rao Lower Bound; DNET =

Dysembryoplastic Neuroepithelial Tumor; EAAT = Glutamate Transporter; ECV =

Extracellular Volume; EI = Epileptogenicity Index; EZ = Epileptogenic Zones; f = fraction of

sodium signal with short T2* decays; FCD = Focal Cortical Dysplasia ; FDR = False

Discovery Rate; 18F-FDG-PET = Fluorodeoxyglucose PET; FOV = Field of View; GRAPPA

= GeneRalized Autocalibrating Partial Parallel Acquisition; 1H = Hydrogen; HC = Healthy

Controls; MIDAS = Metabolite Imaging and Data Analyses System; MNI = Montreal

Neurological Institute; MP2RAGE = Magnetization Prepared 2 Rapid Acquisition Gradient

Echoes; MPRAGE = Magnetization Prepared Rapid Acquisition Gradient Echoes; EPSI =

Echo Planar Spectroscopic Imaging; 23Na = Sodium; NAA = N-Acetyl Aspartate; NaLF =

apparent concentrations of sodium with long T2* decays; NaSF = apparent concentrations of

sodium with short apparent T2* decay; NBC =Bicarbonate Sodium Cotransporter; NCX =

Sodium Calcium Exchanger; NHE = Sodium Hydrogen Antiporter; NIZ = Non Involved

Zones; Na+/K+ pump = Sodium Potassium pump; NKCC1 = Sodium Potassium Chloride

cotransporter; pH = Potential of Hydrogen; PZ = Propagation Zones; QED = Quality

Electrodynamics; ROI = Region of Interest; SEEG = Stereotactic EEG; T2*long = Sodium

longT2* relaxation time; T2*short = Sodium short T2* relaxation time; tCr = Total Creatine; TE

= Echo Time; TI = Inversion Time; TR = Repetition Time; TSC = Total Sodium

Concentration; UTE = Ultrashort Echo Time; VGNC = Voltage Gated Sodium Channels

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1. Introduction

Recent advances in neuroimaging have challenged the concept of focal epilepsy as a brain

disorder strictly limited to the regions responsible for seizure generation and propagation

(Larivière et al., 2021). Various MRI modalities (i.e. structural, functional, metabolic) have

consistently demonstrated structural and functional alterations that extend beyond the

epileptogenic regions and affect areas non-involved in ictal discharges or interictal

epileptiform activity. These complex changes affect both structure and function at different

spatial and temporal scales and can reflect seizure-induced alterations, neuronal plasticity as

well as the underlying etiology. These factors, and the precise anatomical location of

generators of epileptiform activity, are subject to variation even between individuals with the

same epileptic syndrome. Thus, a systematic comparison of imaging data with the gold

standard of electrophysiological data derived from intracerebral

stereoelectroencephalography (SEEG) recording is essential to test the potential contribution

of any imaging modality towards better definition of the epileptogenic zone (EZ) in patients

suffering from drug-resistant focal epilepsy who are candidates for surgery. Moreover, the

need to characterize the variable manifestations of pathology suggest a clear need to exploit

multimodal imaging, combining metrics from different modalities. Insight into alterations of

ionic homeostasis and metabolic function can be probed by sodium (23Na) MRI and 1H-MR

spectroscopic imaging (MRSI), respectively.

23Na-MRI provides a unique opportunity to non-invasively image sodium signal in the brain

(Madelin et al., 2014). To date, the only 23Na-MRI study performed in a group of human focal

epilepsy, has demonstrated an increase of the total sodium concentration (TSC) in patients’

brain compared to controls, which was greater in the epileptogenic zone (EZ) compared to

the propagation zone (PZ) and the non-involved regions (NIZ) (Ridley et al., 2017). The

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usefulness of TSC as a potential epileptogenicity marker may remain limited due to its

limited specificity, as it likely reflects different underlying phenomena at the cellular level,

such as changes in intracellular sodium concentration, changes in extracellular volume and

cells density and/or organization among others.

For 23Na-MRI the increase in signal to noise available at 7T allows novel approach based on a

3D-multi-echoes density-adapted radial sequence which exploits the biexponential T2* decay

of the 23Na MR signal (Ridley et al., 2018). This approach permits a multiparametric

investigation of variation in T2* decay behavior related to the quadrupolar interactions of the

3/2 spin of 23Na with the electric field gradient of surrounding molecules (Rooney &

Springer, 1991), as an indicator of tissue organization and molecular environment. The

biexponential fit model can be used as a probe to determine the motional regimes of sodium

nuclei within the surrounding environment. Thus, by quantifying the sodium signal fraction

with the short T2* decay component (f) this approach may offer a more relevant metric for

studying tissue alterations and potentially provide a better link between sodium homeostasis

and neuronal excitability in human epilepsy. In the present study, we implemented this

multiparametric approach of sodium MRI for the first time in the assessment of profiles

within and outside the epileptogenic and propagation networks in focal drug-resistant

epilepsy.

As a further objective, we assessed metabolic alterations accompanying sodium concentration

changes. To do so, we explored metabolic status by using whole-brain 1H-Echo Planar

spectroscopic imaging (1H-EPSI) in the same subjects at 3T. Three main metabolites were

quantified : (i) N-acetyl Aspartate (NAA) reflecting neuronal viability, mitochondrial

dysfunction or neuronal loss (Moffett et al., 2013; Stefano et al., 1995), (ii) Choline

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compounds (Cho) reflecting membrane turnover and inflammatory processes (Achten, 1998;

Urenjak et al., 1993), and (iii) total-creatine compounds (tCr) reflecting intracellular energy

states, and energy-dependent systems in the brain (Kreis et al., 1992; Kreis & Ross, 1992),

and considered as a cellularity index (Kreis et al., 1993). Importantly, NAA has been

consistently shown to be decreased in the epileptic brain, particularly in the EZ and PZ,

compared to non-involved regions, and is thus considered a potential epileptogenicity marker

in focal epilepsy (Guye et al., 2002, 2005; Hugg et al., 1993; Kuzniecky et al., 1998;

Lundbom et al., 2001; Simister et al., 2002). Furthermore, it has been hypothesized that the

observed increase in total sodium concentration would mainly reflect energetic failures due to

mitochondrial dysfunction affecting the Na+/K+pump activity (Ridley et al., 2017; Stys et al.,

1992). Thus, measuring both sodium and NAA in the same regions provides clues with

regard to the mitochondrial defect hypothesis (Donadieu et al., 2019; Paling et al., 2011).

However, the use of this metabolite has been limited by poor resolution and spatial coverage

of routinely performed 1H-MRSI, as well as by its insufficient specificity. In this study, we

benefited from the whole brain coverage with a relatively high spatial resolution allowing

comparison between multimodal MRI and electrophysiological metrics.

Therefore, through this trimodal approach, we aimed to characterize ionic and metabolic

changes within epileptogenic networks in comparison with electrophysiologically normal

appearing brain networks. For this purpose, we analyzed differences in 23Na-MRI (TSC and f)

and 1H-MRSI (Cho, NAA and tCr) metrics between patients and controls as well as between

regions of interest (ROI) defined by quantitative SEEG signal analysis (Figure 1). We then

investigated the association between 23Na-MRI and 1H-MRSI metrics in EZ, PZ and NIZ in

order to link the homeostatic and metabolic mechanisms to the SEEG recorded electrical

alterations.

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2. Materials and Methods

2.1. Subjects

Among all patients who underwent stereotactic intracerebral EEG (SEEG) recording in the

context of presurgical evaluation for drug-resistant focal epilepsy at our center between

January 2017 and February 2020, 19 consecutive patients with available 3D 1H-MRSI and

23Na-MRI were retrospectively included (Table 1). All patients had detailed non-invasive

presurgical evaluation including medical history, neurological examination,

neuropsychological assessment, 18F-FDG-PET, high-resolution structural 7T and 3T MRI,

and a long-term scalp-video-EEG. All these steps were necessary for patient enrollment. The

SEEG was indicated in all patients to localize the EZ and to precisely determine its relation

with eloquent areas. SEEG was performed as a part of the routine clinical management in line

with the French national guidelines on stereoelectroencephalography (SEEG) (Isnard et al.,

2018). SEEG implantation was planned individually for each patient, according to

anatomo-electro-clinical hypotheses about the localization of the EZ based on non-invasive

investigations. All SEEG explorations were bilateral and systematically sampled temporal,

insular, frontal, and parietal regions of at least one hemisphere. Follow-up information was

collected from a review of the medical records.

MRI data were always acquired before SEEG implantation. After quality checks (see below),

seventeen spectroscopic 1H-MRSI datasets, and fifteen 23Na-MRI datasets were used for

further analysis. Thirteen out of the nineteen patients fulfilled data quality in sufficient ROIs

for both modalities. For 1H-MRSI, we used a control database of 25 healthy controls (HC;

mean age 30.5 ± 9.7 years, range 20-60 years, 14 women). For 23Na-MRI, we used a control

database of 18 HC for (mean age 30.5 ± 8.36 years, range 21-54 years, 10 women).

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Participants provided informed consent in compliance with the ethical requirements of the

Declaration of Helsinki and the protocol was approved by the local Ethics Committee

(Comité de Protection des Personnes sud Méditerranée 1).

2.2. MRI Acquisitions

The protocol was conducted for all subjects on the two same MR scanners. The

Spectroscopic imaging protocol on a 3-Tesla Magnetom Verio MR system (Siemens,

Erlangen, Germany) and the 23Na MRI protocol on a whole-body 7-Tesla Magnetom Step 2

MR system (Siemens, Erlangen, Germany).

At 3T, 1H-MRI and 1H-EPSI were performed with a thirty-two channel phased-array head

coil and included a sagittal high resolution 3D-MPRAGE protocol (TE/TR/TI = 3/2300/900

ms, 160 sections, 256×256 mm², FOV 256×256 matrix, resolution = 1 mm³). Whole brain 3D

1H-EPSI was acquired as described in (Lecocq et al., 2015) using two axial acquisitions with

two different orientations that are the AC-PC plane and the AC-PC + 15° plane (TE/TR/TI =

20/1710/198 ms, nominal voxel size = 5.6 × 5.6 × 10 mm3, FOV = 280 × 280 × 180 mm3, flip

angle = 73°, 50 × 50 × 18 k-space points, GeneRalized Autocalibrating Partial Parallel

Acquisition (GRAPPA) factor = 2, acquisition time ≈ 17 minutes). The two angles of EPSI

orientations were chosen in order to obtain good quality spectra on as large brain area as

possible on at least one acquisition with a reasonable angulation to permit accurate automatic

normalization procedure.

At 7T, a high-resolution proton MRI 3D-MP2RAGE (TR = 5000 ms/TE = 3 ms/TI1 = 900

ms/TI2 = 2750 ms, 256 slices, 0.6 mm isotropic resolution, acquisition time = 10 min) was

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obtained using a 32-element (32Rx/1Tx) 1H head coil (Nova Medical). 23Na-MRI was

acquired using a dual-tuned 23Na/ 1H QED birdcage coil and a multi-echo density adapted 3D

projection reconstruction pulse sequence (TR = 120 ms, 5000 spokes, 384 radial samples per

spoke, 3 mm nominal isotropic resolution, 24 echoes (8 per run with 3 runs, acquisition time

10 min per run (30 min in total)). Three dimension 23Na MRI volumes were obtained at 24

different TEs ranging from 0.2 ms to 70.78 ms (Run 1: 0.2 - 9.7 - 19.2 - 28.7 - 38.2 - 47.7 -

57.2 - 66.7 ms; Run 2: 1.56 - 11.06 - 20.56 - 30.06 - 39.56 - 49.06 - 58.56 - 68.06 ms; Run 3:

4.28 - 13.78 - 23.28 - 32.78 - 42.28 - 51.78 - 61.28 - 70.78 ms). This ensured a sufficient

number and distribution of TEs while taking into account the 5 ms readout of the sequence,

especially for measuring 23Na signal with short T2*. For quantitative calibration of brain

sodium concentrations we used as external reference six tubes (80mm length, 15mm

diameter) filled with a mixture of 2% agar gel and sodium at different concentrations: two

tubes at 25mM, one at 50mM, two at 75mM and one at 100mM. Tubes were positioned in the

field of view in front of the subject’s head and maintained using a cap.

2.3. MRI Data Processing

Three dimension 1H-EPSI images were post-processed with the Metabolite Imaging and Data

Analyses System (MIDAS, Trac, MRIR, Miami) (Maudsley et al., 2006). This software

ensures B0map correction, lipid suppression, tissue volume fraction through T1segmentation,

spectral fitting, exclusion of outlier voxels based on Cramer Rao lower bounds (CRLB) and

signal normalization with the interleaved water signal acquired. MIDAS provided AC-PC and

AC-PC 15° oriented maps, including metabolite maps (NAA, Cho, tCr), quality maps, CRLB

maps and linewidth maps among others that were used in further processing and quality

check steps. In this study we only analyzed NAA, tCr and Cho maps because m-Ino

(Myo-Inositol) and Glx (for glutamate, glutamine and glutathione) maps did not fulfill CRLB

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criteria in the majority of the ROIs (Figure 2). ROI selection process is detailed in the next

paragraph. The maps that we used for the ROIs signal extraction are the average maps of

realigned AC-PC and AC-PC 15° oriented maps (Donadieu et al., 2019; Lecocq et al., 2015)

(SPM12, (Statistical Parametric Mapping: The Analysis of Functional Brain Images - 1st

Edition, n.d.)).

Metabolic profiles of each ROI (for details on ROI definition see section 2.6) were

determined by extracting water signal normalized values (arbitrary unit) from corrected

quantitative NAA, tCr and Cho maps derived from MIDAS. We applied quality assurance

criteria for spectra in each ROI based on a combination of Cramer-Rao minimum variance

bound (CRLB < 15%, or CRLB lower than half of all HC), and water peak linewidth (<16

Hz) thresholds. Supplementary Fig.1. illustrates the spectra that are in the permitted range or

not. If, after ROIs rejection in a patient, there was no ROI in EZ, the patient was entirely

discarded from further analyzes.

The 23Na-MRI data were processed according to (Grimaldi et al., 2021) using a homemade

adjusted procedure. After brain extraction on ²³Na-MRI images (ANTS, (Avants et al.,

2011)), a denoising filter was applied to the resulting 23Na-MRI volumes (Aja-Fernandez et

al., 2008; Rajan et al., 2010). The first echo time (TE) volumes from 23Na MRI were used as

reference for coregistration of the other TE volumes to correct for potential motion between

acquisitions. Hand drawn ROIs were placed in the center of each agar tube to extract signal

intensities from the twenty-four TE volumes of 23Na-MRI acquisitions for signal calibration

procedure. Linear fitting of 23Na signal decay from tubes ROI (Matlab) provided the slope (a)

and intercept (b), which is used for calibration purposes (see below).

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For each ROI (for details on ROI definition see section 2.6), we fitted the mean signal

intensity across each of the 24 TEs with a biexponential model using the equation:

where Ais an amplitude scaling term, fis the sodium signal fraction of the short T2* decay

component and Ric refers to a Rician noise-related scaling parameter (Ridley et al., 2017).

From the model we estimated a sodium signal fraction for short (f) and long (1-f) T2* decay.

Note that these short and long fractions of the sodium signal are only present when the Na+

ions are present in an organized non-isotropic molecular environment. Indeed, in these

environments, the electric quadrupolar moment of the sodium nucleus interacts strongly with

the surrounding electric field gradient leading to a residual quadrupolar interaction

responsible for the bi-exponential T2relaxation with a short T2* and a long T2* depending on

the motional regime of the environment. In contrast, in an isotropic non-restricted

environment such as the cerebrospinal fluid, there is no residual quadrupolar interaction and

all energetic transitions are equal and lead to a monoexponential decay with only one (long)

T2* (Burstein & Springer, 2019). We calculated magnetization (M0) corresponding to the

signal fraction estimated by the model in terms of the intercepts of the signal fraction

components of the model, obtaining M0SF = A·fand M0LF = A·(1-f).Then, NaSF and NaLF

were calculated with raw M0 signal values and the linear fit estimated over the tube

phantoms, i.e. slope (a) and intercept (b):

Finally, we calculated the total sodium concentration (TSC) in each ROI as follows:

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Each ROI had to be almost totally (> 99.9%) included in the brain mask of the individual

patient and in the brain masks of at least half of all HC. Finally, to limit the partial volume

effect related to CSF, patient ROIs with high CSF contains - estimated with structural image

segmentation - relative to HC (|Z-scoreCSF| > 1.96) were also discarded. If after this final step

a patient had no remaining EZ ROIs, the patient was entirely discarded from the analyzes,

reducing the number of investigated patients from 22 to 19.

2.4. SEEG Recordings

Recordings were performed using intracerebral multiple contact electrodes (10–18 contacts

with length 2 mm, diameter 0.8 mm, and 1.5 mm apart, Alcis, France). The electrodes were

implanted using a stereotactic surgical robot ROSA™. Cranial CT scan was performed to

verify the absence of any complication and the spatial accuracy of the implantation. CT/MRI

data co-registration and 3D-reconstructions of patients' brains with electrodes was performed

using an in-house open-source software (EpiTools, (Medina Villalon et al., 2018)) to

automatically localize the position of each electrode contact and display the results of signal

analysis in each patient's anatomy.

Signals were recorded on a 256-channel Natus system, sampled at 512 Hz and saved on a

hard disk (16 bits/ sample) using no digital filter. Two hardware filters were present in the

acquisition procedure: a high-pass filter (cut-off frequency equal to 0.16 Hz at -3 dB), and an

anti-aliasing low-pass filter (cut-off frequency equal to 170 Hz at 512Hz).

2.5. SEEG-signal analysis

All signal analyzes were performed in a bipolar montage and computed using the

open-source AnyWave software (Colombet et al., 2015) available at

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https://meg.univ-amu.fr/wiki/AnyWave.The epileptogenicity of different brain structures was

assessed by quantitative SEEG-signal analysis using the Epileptogenicity Index (EI)

(Bartolomei et al., 2008).31 The EI combines analysis of both spectral and temporal features

of SEEG signals, respectively, related to the propensity of a brain area to generate fast

discharges (12.4 – 127 Hz), and to how early this area becomes involved in seizure. A

normalized EI value is used, ranging from 0 to 1. If there is no involvement of the brain

structure, the EI = 0 (no epileptogenicity) whereas if the brain structure generates a rapid

discharge and the time to seizure onset is minimal, the EI = 1 (maximal epileptogenicity).

In each patient, maximal EI values from at least three representative seizures were computed.

We labeled each pair of bipolar SEEG contacts as belonging to the EZ, propagation zone (PZ)

or non-involved zone (NIZ), as defined by EI, based on previous studies (Aubert et al., 2009;

Lagarde et al., 2019). An EI value of 0.4 and higher was set as a threshold to define a

structure as belonging to the EZ. The PZ was defined as brain areas with 0.1 < EI < 0.4, with

sustained discharge during the seizure course. The NIZ was defined as all other brain

structures.

2.6. ROI Definition

GARDEL software (Medina Villalon et al., 2018) provided the MRI voxel coordinates of

electrode contacts allowing the definition of spherical ROIs for sodium and metabolite

quantification in the patient’s native space. As SEEG recordings processed layout

corresponds to the signal differential of two adjacent electrode contacts, a five mm radius

spherical ROIs were positioned with a center between two adjacent electrode contacts (Ridley

et al., 2017). ROIs corresponding to a poor SEEG signal quality - those located in white

matter for instance - were discarded by expert (J.S.) inspection. To deal with possible

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contamination of the brain sodium signal by an over-representation of CSF in patients, we

discarded ROIs with overly high Z-scoreCSF (see section 2.3. MRI Data Processing).

Anatomical references were the MPRAGE volumes for 1H-MRSI (3T) and the MP2RAGE

volumes for 23Na-MRI (7T). ANTS brain extraction function was applied to all anatomical

and 23Na-MRI images. Each subject’s (patients and controls) anatomical images were

coregistered onto their respective 1H-MRSI and 23Na-MRI maps. Each individual anatomical

volume was also spatially normalized onto the MNI 152 template to obtain the direct and

reverse spatial transforms for each subject. ROIs were projected from the patient native space

to MNI space, and back-projected from MNI to each HC native space (ANTS). This

procedure allowed us to extract normative values of 23Na MRI and 1H-MRSI data from the

control group for each ROI defined in each patient. Details about the numbers of ROIs for

each category were summarized in supplementary Table 1.

2.7. Statistical Analysis

We performed a group comparison of sodium and metabolites levels across the three ROI

classes, EZ, PZ and NIZ, to decipher subtle and specific homeostatic and metabolic

modifications among patients compared to healthy controls. To account for brain sodium and

metabolite variability across participants, sodium and metabolite levels had to be normalized

across participants. Thus, each patient’s sodium concentrations and metabolites levels in each

ROI were expressed as z-scores with respect to the same mean quantities from the

corresponding ROI in healthy controls. Normalization of healthy controls’ sodium

concentrations and metabolites levels was done using a leave-one-out procedure, z-scoring

control’s quantities relative to the same ROI in other controls’ sodium concentrations and

metabolites levels.

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We compared patients and healthy controls, looking for different sodium concentration and/or

metabolic change profiles between them, and between different region categories (described

in ‘SEEG-signal analysis’). Significant group differences of z-scores were analyzed using a

bootstrap two-tailed Welch’s t-test procedure (Efron & Tibshirani, 1993). Achieved

significance levels (ASL) (equivalent to exact p-values) were obtained after one million

random samplings, then corrected for multiple comparisons using false-discovery rate (FDR)

(Benjamini & Hochberg, 1995).

In order to investigate the relationship between sodium concentrations and metabolites levels,

we evaluated how well sodium concentration can be predicted from metabolite profiles.

Hence, we performed a multiple linear regression analysis (Jobson, 1991) using Statsmodels

(Seabold & Perktold, 2010) on all ROIs, with NAA, Cho and tCr z-scores as predictors to

compute linear model for each 23Na-MRI measures (fand TSC) z-scores.

3. Results

3.1. Clinical features

Patients’ clinical characteristics are summarized in Table 1. Mean age at epilepsy onset was

13.2 years (range 0.1-40), mean duration of epilepsy was 18 years (range 4-31). Mean seizure

frequency was 26 per month (range 2-120). Anatomical MRI was normal in fifteen (71%)

and showed a structural abnormality in four (29%) cases. Thirteen out of twenty-one patients

underwent curative surgical procedure following SEEG exploration. From the remaining

eight, six patients were recused from surgery because of bilaterality of the epileptogenic zone

or of a high risk of post-surgical functional impairment, one became seizure-free after

SEEG-guided thermocoagulations and one refused surgery. The post-surgical outcome was

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favorable in eleven (Engel class I (seizure-free) and class II (almost seizure-free), n=11, 85%)

and with worthwhile improvement in two cases (Engel class III, 15%). Epilepsy etiology,

according to histopathological findings, was: focal cortical dysplasia (FCD) type I

(non-detectable on MRI) in six patients, FCD type II in two, and slight gliosis in two. One

patient underwent laser-guided interstitial thermosurgery for an MRI-diagnosed DNET, with

no histology available.

From the initial sample of nineteen patients and according to the quality thresholds

previously described, data analyzes were conducted in fifteen patients for 23Na-MRI and

seventeen patients for 1H-MRSI (Table 1). Patients and healthy controls (HC) did not

significantly differ in terms of age and sex, neither in the 23Na-MRI associated patient group

(Wilcoxon rank sum, w = -0.36, p = 0.72; χ2(1, N=33) = 0.26, p = 0.88) nor 1H-MRSI

associated patient group (Wilcoxon rank sum, w = 0.58, p = 0.56; χ2(1, N=42) = 0.038, p =

0.98).

3.2. Ion homeostasis and metabolic profiles in patients

TSC was increased in epileptic patients relative to corresponding ROIs in healthy controls in

all types of electrophysiologically defined regions, (i.e. EZ , PZ and NIZ) with no significant

differences between these three region categories within patients (see Table 2 and Figure

3.A). The short fraction fwas significantly increased in EZ relative to corresponding ROIs in

healthy controls, but was not significantly different compared to healthy controls in regions

corresponding to PZ and NIZ. In patients, fwithin EZ was also significantly increased

relative to PZ and to NIZ.

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In patients, NAA levels were significantly decreased in all types of regions relative to

controls (see Table 2 and Figure 3.B). In addition, NAA was significantly decreased in EZ

compared to PZ and NIZ. In patients relative to controls, tCho levels were significantly

increased in EZ, decreased in NIZ and not significantly different in PZ; tCr levels were not

significantly different in EZ and PZ but significantly decreased in NIZ.

3.3. Association between ionic and metabolic parameters

To study the relationship between sodium concentrations and metabolite levels, a multiple

linear regression analysis was conducted. We evaluated the prediction of each 23Na-MRI

measure from all 1H-MRSI measures in each region category, namely EZ, PZ and NIZ,

setting Bonferroni corrected p for coefficient t-test was set at 0.05/ 2*3*3 = 0.0028.

Significant regression equations were found for the model predicting TSC from metabolites

predictors alone in PZ (F(3, 80) = 10.76, p < 0.0028) and in NIZ (F(3, 283) = 16.25, p <

0.0028) but not in EZ (F(3, 34) = 1.29, p = 0.295). As mentioned previously, the significant

regressions only partially thinly explained variations in TSC, with R² of .29 in PZ and a R² of

.15 in NIZ. We observed a negative association between TSC and NAA in PZ (β = -0.87, p <

0.0028) and in NIZ (β = -0.45, p < 0.0028). We also found a negative association between

TSC and Cho and a trend towards a positive association with tCr in NIZ (Table 3). Multiple

linear regression analysis of fdoes not provide significant results in any ROI.

4. Discussion

This work aimed to explore both in vivo ion homeostasis and metabolic alterations in focal

drug-resistant epilepsy investigated by SEEG. We observed global brain impact in patients

reflected by consistent multimodal alterations including an increase of TSC as well as

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decreases in NAA levels within all categories of electrically defined regions, namely EZ, PZ

and NIZ. Interestingly, the EZ showed a characteristic pattern with significantly higher f, as

well as significantly lower NAA and higher Cho levels. TSC exhibits strong association with

metabolites, especially in the PZ and NIZ. These imaging features likely reflect both

hyperexcitability and tissue alterations with a specific pattern of ffor the epileptogenic

tissues.

4.1. Deciphering sodium homeostasis processes with 7T 23Na MRI

Accumulation of TSC is a feature described in a number of neurological diseases including

multiple sclerosis (Maarouf et al., 2017; Zaaraoui et al., 2012), amyotrophic lateral sclerosis

(Grapperon et al., 2019), Hungtington’s disease (Reetz et al., 2012) and epilepsy (Ridley et

al., 2017). Attempts to model processes involved in TSC increases have been proposed

considering in vitro experiments showing increases of intracellular sodium concentrations in

multiple sclerosis (Waxman, 2006). In this model, an increase in TSC – the total sodium

concentration - is usually associated with an elevation of intracellular sodium concentration

due to an influx of sodium resulting from a dysfunction of the sodium potassium pump

(Na+/K+ pump) (Pike et al., 1985). However, though sensitive, TSC is not specific to altered

homeostasis between sodium compartments.

TSC alterations in humans in vivo have largely been demonstrated at 3T. Here, to improve

our understanding of the dysregulation mechanisms affecting the sodium ion homeostasis in

epilepsy, we used 7T 23Na MRI. Ultra-high field offers the opportunity to study the

complexity of the T2*decays of the 23Na MR signal influenced by the quadrupolar relaxation.

Indeed, multi-exponential relaxations of this three-half spin reflect the time-dependent

relative position of the quadrupolar moments of sodium nuclei and the electric field gradients

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of charged molecules at their vicinity. The multi-TE density adapted radial 23Na, ultra-short

echo time (UTE) approach used here permits the characterization of the signal decay

components through a bi-exponential fit. While related, the signal fraction of the short T2*

decay component (f), and the total sodium concentration (TSC) may reflect different

consequences of ion homeostasis dysregulation (Ridley et al., 2018).

Normal physiological neuronal activity has been observed to dynamically alter sodium signal

across different TEs, in a manner consilient with fMRI and consistent with physiological

mechanisms that should dominate at different points of ‘activation’ (Bydder et al., 2019)

Using dynamic sodium MRI acquisition compared to BOLD functional MRI, variations in

sodium signals recorded at different TE (0.2ms, 10ms, 19ms) during a right finger tapping

task showed a slightly increased TSC (TE=0.2ms) in the activated left contralateral motor

area interpreted as increased cerebral blood volume, and a more drastic signal decrease at

longer TEs considered, at least in part, to a decrease in extracellular contributions due a

reduced extracellular volume fraction (Antonio et al., 2016; Dietzel et al., 1982; Lux et al.,

1986), while reverse signal variations were observed in the deactivated motor area ipsilateral

to movement. The fmetric, reflecting the variations in the ratio of apparent short and long

T2* sodium signal decays, enables to pool in a single parameter these effects seen at different

TEs, and has the potential to locate regions with abnormal excitability as shown in the present

study.

4.2. Sodium changes in epilepsy

In the context of epilepsy, abnormal TSC increase has been found in EZ and to a lesser extent

also in other regions of the brain at 3T (Ridley et al., 2017). In addition, rat models of

acquired epilepsy have reported persistent TSC increases in affected cortices in response to

kainate-induced epileptogenesis (Mori et al., 2000; Y. Wang et al., 1996). TSC may

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incorporate various processes including both structural reorganization and dysregulation of

ionic homeostasis. Indeed, several structural modifications can impact 23Na-MRI signals,

particularly changes in the extracellular volume (ECV). Cell shrinkage and cerebral atrophy

could lead to ECV increase, and subsequently to increases in TSC. Both have been reported

in epilepsy, localized in the epileptic areas, but also often extended to the non-epileptogenic

regions (Bernhardt et al., 2009; Dingledine et al., 2014; Liu et al., 2005; Voets et al., 2017).

Thus, even though excessively high CSF levels (Z-scoreCSF > 1.95) led to the removal of a

ROI from consideration, its contribution cannot be entirely ruled out. In addition, ECV

increase was recently shown to be related to neuronal excitability (Colbourn et al., 2019).

Moreover, increases of perivascular space were also reported in epilepsy (Feldman et al.,

2018, 2019), and could contribute to TSC accumulation due to the CSF surrounding the

vessels. Recently, venous blood was also demonstrated to have an impact on total sodium

signal (Driver et al., 2020), leading to overestimation of sodium concentration measures in

case of atrophy. In addition, reactive astrogliosis and microgliosis (Devinsky et al., 2013;

Seifert & Steinhäuser, 2013; Sofroniew & Vinters, 2010) can be associated to epilepsy

implying changes of astrocytes proportion, size (Boscia et al., 2016) and ion homeostasis,

leading to surrounding cell homeostasis disruption (Karus et al., 2015). Interestingly, TSC

was also shown to correlate with conductivity at 3T (Liao et al., 2019).

Beyond general explanations for alterations of TSC in epilepsy, cortices subject to different

epileptiform manifestations (EZ, PZ; NIZ) are likely to differ in underlying pathological

mechanisms, something that was probed in the current work through the use of a

multiparametric approach. While keeping in mind that both sodium signal fractions (i.e. short

and long) will contain contributions from extracellular sodium – albeit with potentially

different weightings – a plausible reason for the concomitant EZ-specific increase relative to

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both HC and other cortices of both fand TSC is an increase of intracellular sodium

concentration. This would be consistent with a range of known mechanisms associated with

the EZ while TSC increase outside the EZ in the absence of changes in f may could be related

to structural changes combined with preserved perfusion, which is usually decreased in the

EZ during the interictal period (Kojan et al., 2021; Y.-H. Wang et al., 2018).

Sodium homeostasis dysregulation in the EZ could be induced by several mechanisms

affecting ion channels such as (i) alterations in type II and III voltage gated sodium channel

(VGNC) properties (Bartolomei et al., 1997; Gastaldi et al., 1997; Gorter et al., 2010;

Lombardo et al., 1996), (ii) incomplete inactivation of sodium channels and a consequent

increase in persistent sodium currents (Mantegazza et al., 2010; Oliva et al., 2012) and (iii)

the reduced efficiency of clearance by the Na+/K+ pump induced by a lack in ATP supplies

(Folbergrová & Kunz, 2012; Grisar et al., 1992; Kovac et al., 2017).

Other mechanisms secondary to hyperexcitability or energy failure affecting astrocytes could

also lead to an alteration of sodium homeostasis and an increase in intracellular sodium

(Gerkau et al., 2017; Kirischuk et al., 2012; Rose & Karus, 2013). Indeed, as a consequence

of hyperexcitability, astrocytes may uptake sodium through various

tranporters(sodium-potassium-chloride co-transporter (NKCC1), sodium-bicarbonate

co-transporter (NBC), sodium-proton exchanger (NHE) and sodium-calcium exchanger

(NCX)) increasing the intracellular sodium concentrations. Heightened intracellular sodium

concentrations reduce the Electrochemical gradient for glutamate uptake (Karus et al., 2015)

by the excitatory amino acid transporter (EAAT), a transmembrane transporter of sodium and

glutamate from peri-synaptic extracellular space into astrocytes, ensuring ion and

neurotransmitter clearance. Then extracellular glutamate concentration increases and

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eventually downregulates EAAT (Rose & Karus, 2013); as astrocytes store energy supplies,

astrocytic energy failure is critical for ion homeostasis of both astrocytes and the surrounding

neurons. The reduced availability of ATP associated with increases in intracellular sodium

also leads to the dysregulation of homeostasis for other ions such as potassium, calcium and

proton, resulting in excitotoxicity (Gerkau et al., 2017). During hyperexcitability, intracellular

pH decreases which leads to sodium uptake via NBC, VGNC or Na+/K+ pump among

others. This reduced intracellular pH was suggested to result from lack of NHE1 (Zhao et al.,

2016). The relationship between reduced intracellular pH and increased intracellular sodium

was also shown in epilepsy during hepatic encephalitis (Kelly et al., 2009) with ammonium

intoxication. The intoxication of cerebral tissue promotes intracellular pH increase (in

particular in astrocytes) resulting in intracellular sodium increase.

4.3. Metabolic changes

The multimodal nature of our investigation provides further evidence of an alteration in ionic

homeostasis in epilepsy due to a disruption of metabolic energy supply. The decreased NAA

we observed has been consistently associated with neuronal death, mitochondrial dysfunction

(Stefano et al., 1995) and subsequent ATP decrease (Vagnozzi et al., 2007). NAA decrease in

the EZ has been widely reported since the 90’s (Guye et al., 2005; Hugg et al., 1993;

Kuzniecky et al., 1998; Petroff et al., 2003; Simister et al., 2002; van der Hel et al., 2013).

While more recent research indicates that decreases in NAA extends to other regions, those

involved in seizures remain the most affected (Guye et al., 2005; Lundbom et al., 2001;

Mueller et al., 2011) as is the case for TSC as well as NAA in this study. This finding is in

line with our energetic failure hypothesis, however, it should be noted we were unable to

observe a significant association between these two measures when explored by multiple

linear regression.

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Increased Cho has also been reported in temporal lobe epilepsy (Achten et al., 1997; Simister

et al., 2009) and is frequently associated with cellular proliferation and increased membrane

turnover (Miller, 1991; Urenjak et al., 1993). These processes are likely to be linked to

structural changes associated with epileptogenic lesions such as tumors, tumor-like tissue and

gliosis (van der Hel et al., 2013). The Cho decrease in NIZ was rather unexpected and has not

to our knowledge been previously reported. It could result from reduced cellular density or

neuronal loss extending beyond the EZ.

In our cohort, tCr was significantly decreased in NIZ only. Heighted tCr was previously

related to decreased intracellular energy status or reactive astrocytes in the litterature (Achten,

1998; Urenjak et al., 1993). This result also points to alterations beyond EZ potentially

accompanying structural changes and cognitive co-morbidities (Kreis et al., 1992; Kreis &

Ross, 1992).

4.4. Technical limitations

For both 23Na-MRI and 1H-MRSI, CSF signal contribution is critical as it can clearly bias the

data. MIDAS software handled this bias for 1H-MRSI data (Lecocq et al., 2015). For

23Na-MRI we corrected for partial volume effect and removed ROIs with remaining CSF after

segmentation. We designed a quality check procedure inspired by (Ridley et al., 2017) in

order to get rid of this CSF contribution. Despite this, it is impossible to totally delimit the

partial volume effect completely for the moment, neither for 1H-MRSI nor 23Na-MRI.

Another limitation is inherent to the SEEG procedure, which suffers from limited spatial

sampling, whereas MRI gives access to information across the entire brain. Conversely, the

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pathological specificity offered by SEEG is considered gold-standard and beyond what is

possible with anatomically defined and atlas derived ROIs.

4.5. Conclusion and Perspectives

An increase of the signal fraction of the short T2* decay component (f) was found to be

associated with the EZ whereas increased TSC was not limited to epileptogenic regions.

Taken together with the patterns of metabolite changes our results are in line with the

energetic failure hypothesis in epileptic regions associated with widespread tissue changes

beyond electrically abnormal areas. Here, a multimodal approach has allowed parallel

insights supporting pathological processes in epilepsy. This and additional combinations -

including for example 31P-MRSI, which provides information about energy metabolism via

ATP quantification and pH estimation - could further strengthen the delineation of the EZ as

this non-invasive information would complement the current presurgical evaluation in

patients suffering from drug resistant focal epilepsy.

Acknowledgements

The authors would like to thank L. Pini, C. Costes, and V. Gimenez for data acquisition and

study logistics. We would also like to thank A. Ivanov and C. Bernard for helpful discussions.

Funding

This work has received support from the French government under the “Programme

Investissements d’Avenir”, Excellence Initiative of Aix–Marseille University –A*MIDEX

(AMX-19-IET-004), 7TEAMS Chair, EPINOV (Grant ANR-17-RHUS-0004) and ANR

(ANR-17-EURE-0029); and from the European Union’s Horizon 2020 Framework Program

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain

Project SGA2) and No. 945539 (Human Brain Project SGA3)

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal

relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

The data that support the findings of this study are available on request from the

corresponding author. The data are not publicly available due to sensitive information that

could compromise the privacy of research participants.

CRediT authorship contribution statement

Mikhael Azilinon: Investigation, Methodology, Data Curation, Formal Analysis, Writing -

original draft. Julia Scholly: Resources, Data Curation, Formal Analysis, Writing - review

and editing. Wafaa Zaaraoui: Funding Acquisition, Writing - review and editing. Samuel

Medina Villalon: Methodology. Patrick Viout: Formal Analysis. Tangi Roussel:

Methodology, Writing - review and editing. Mohamed Mounir El Mendili: Methodology,

Writing - review and editing. Ben Ridley: Methodology, Writing - review and editing.

Jean-Philippe Ranjeva: Supervision, Validation, Methodology, Writing - original draft.

Fabrice Bartolomei: Funding Acquisition, Resources, Writing - review and editing. Viktor

Jirsa: Funding Acquisition, Supervision, Writing - review and editing. Maxime Guye:

Project Administration, Funding Acquisition, Resources, Conceptualization, Supervision,

Validation, Writing - original draft.

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Figures and Tables

Figure 1:3D representation of an example of SEEG electrode implantation scheme (top)

and f, TSC and NAA maps slices corresponding to the transversal green section on the

3D view. Red spherical ROIs correspond to regions belonging to the epileptogenic zone (EZ),

yellow ROIs correspond to regions belonging to the propagation zones (PZ), blue ROIs

correspond to regions non-involved by electrical abnormalities (NIZ) and black ROIs

correspond to regions not explored in this study. Black spheres correspond to regions

excluded from the analyses based on the SEEG signal. The red dot in the transverse planes

(i.e. T1w image and f, TSC and NAA maps) represents the ROI corresponding to electrode

OF’ (left orbitofrontal location) contact 5 and 6 (or bipolar contact 5-6). OF’5-OF’6 ROI is

circled in red in the implantation scheme. Red arrows are pointing to the mean ROI value for

each map.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

Figure 2: Spectrogram from a healthy control cortical voxel. Here we highlighted the

three metabolites we focused on in this study, namely total Choline (Cho), N-Acetyl

Aspartate (NAA) and total creatine (tCr).

Glx: glutamate, glutamine and glutathione; m-Ino: myo-Inositol.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

Figure 3: Violin plot of mean Z-scores of sodium (A) and metabolite estimates (B) observed

in patients within epileptogenic zones (EZ, red), propagation zones (PZ, yellow) and

non-involved zones (NIZ, black), compared to healthy controls (HC, gray). Asterisks indicate

significant differences between patients and controls when under violins, and between

regions within patients when above violins. *: p-uncorrected < 0.05. **: p-FDR < 0.05

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

Table 1. Patient clinical demography

Patient

Gender

Epilepsy

duration

(y)

Seizure

frequency

(per month)

Epilepsy type

Side

3T/7T anatomical MRI

Surgical procedure

Engel

class

Histology

23Na-MRI/

1H-MRSI

Temporal latero-mesial

Normal

TC, SR

FCD1

Both

Temporo-parietal mesial

R posterior cingular FCD

TC, VNS, contra-indication for SR

Both

Insulo-parietal

Normal

TC, VNS contra-indication for SR

Both

Temporal mesial

Normal

TC, SR (ATL)

FCD3a

Both

Temporo-insular

Normal

TC, SR

FCD1

Both

120

Temporal mesial

Normal

TC, SR

slight gliosis

Both

Temporal mesial

Normal

TC, SR

FCD1b

Both

Temporo-insular

Normal

TC, contra-indication for SR

Both

Temporal lateral

Normal

TC, SR

III

slight gliosis

Both

Temporal mesial

Normal

TC, SR (ATL)

FCD1

Both

Orbitofronto-insular

L orbitofrontal FCD

TC, SR

III

FCD2a

Both

L parietal mesial

Bilateral temporal mesial

R&L

L posterior cingular NDT

TC, LITT (aiming NDT)

III

Both

100

Bilateral temporal mesial,

R insulo-operculo-frontal

R>L

Normal

TC, contra-indication for SR

Both

Temporal latero-mesial

Normal

TC, SR

FCD2a

23Na-MRI

Temporo-parietal

L perisylvian PMG, schizencephaly, temporo-parietal

PNH, R posterior PNH

TC, contra-indication for SR

1H-MRSI

Temporal mesial

Normal

TC, patient refused SR

1H-MRSI

Bilateral temporal mesial

R&L

Normal

TC, DBS, contra-indication for SR

Both

Insulo-opercular

Normal

TC, seizure free

Both

Temporal mesial

Normal

TC, SR (ATL)

III

FCD1c

Both

Abbreviations: ATL, anterior temporal lobectomy; DBS, deep brain stimulation; DNET, dysembryoplastic neuroepithelial tumor; FCD, focal

cortical dysplasia; L, left; LITT, laser-guided interstitial thermal therapy; NA, not applicable; NDT, neuro-developmental tumor; PMG,

polymicrogyria; PNH, periventricular nodular heterotopia; R, right; SR, surgical resection; TC, thermocoagulation; VNS, vagus nerve

stimulation.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

Table 2. Bootstrapped t-value followed by p (or ASL, the bootstrapped achieved

significance level).

EZ Vs HC

PZ Vs HC

NIZ Vs HC

EZ Vs PZ

EZ Vs NIZ

PZ Vs NIZ

2.96, 3.8∙10-3

2.51, 0.012

2.43, 0.015

TSC

3.73, 2.5∙10-4

5.35, 1.0∙10-6

12.83, p < 1.0∙10-6

NAA

-6.03,p < 1.0∙10-6

-4.51, 8.0∙10-6

-9.46, p < 1.0∙10-6

-2.32, 0.022

Cho

3.31, 1.8∙10-3

-2.75, 6.4∙10-3

3.94, 1.3∙10-4

2.47, 0.015

tCr

-7.42, p < 1.0∙10-6

FDR-correction yielded p < 0.016 as threshold for tested 23Na-MRI measurements, and p <

0.022 for tested 1H-MRSI.

Table 3. Summary of multiple linear regression analysis for TSC and f.

Parameters

Coefficients

TSC

Const

0,43

0,191

Df Residuals

Cho

-0,05

0,837

Df Model

NAA

-0,59

0,0743

R²

0.1

tCr

0,39

0,229

F (p)

1.29 (0.295)

Const

0,86

8,89 ∙ 10-5

Df Residuals

Cho

-0,33

0,0787

Df Model

NAA

-0,87

9,02 ∙ 10-4*

R²

0.29

tCr

0,56

0,0408

F (p)

10.76 (5.13 ∙ 10-6)

NIZ

Const

0,94

1,60 ∙ 10-17

Df Residuals

283

Cho

-0,29

6,53 ∙ 10-3*

Df Model

NAA

-0,45

1,90 ∙ 10-6*

R²

0.15

tCr

0,25

0,0392

F (p)

16.25 (8.96 ∙ 10-10)

Const

0,51

6,15 ∙ 10-3

Df Residuals

Cho

-0,33

0,0114

Df Model

NAA

0,01

0,967

R²

0.18

tCr

0,24

0,17

F (p)

2.48 (0.0781)

Const

-0,12

0,448

Df Residuals

Cho

0,03

0,832

Df Model

NAA

-0,28

0,154

R²

0.03

tCr

0,12

0,572

F (p)

0.94 (0.425)

NIZ

Const

0,02

0,791

Df Residuals

283

Cho

0,11

0,158

Df Model

NAA

0,04

0,587

R²

0.01

tCr

-0,16

0,0615

F (p)

1.4 (0.244)

Bonferroni corrected p-values are highlighted in bold followed by an asterisk, while

uncorrected p-values are in bold without asterisk.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

ResearchGate has not been able to resolve any citations for this publication.

Increased Sodium Concentration in Substantia Nigra in Early Parkinson's Disease: A Preliminary Study With Ultra-High Field (7T) MRI

Article

Full-text available

Sep 2021

Pathophysiology of idiopathic Parkinson's disease (iPD) is complex and still misunderstood. At a time when treatments with disease-modifying potential are being developed, identification of early markers of neurodegeneration is essential. Intracerebral sodium accumulation could be one of them. Indeed, it may be in relation to the mitochondrial dysfunction that early exists in iPD. For the first time, we used brain sodium (²³Na) MRI to explore sodium concentration changes that have already been reported to be related to neurodegeneration in other diseases. We prospectively included 10 iPD patients (mean age 52.2 ± 5.9 years-old) with motor symptoms that started <36 months before inclusion and 12 healthy subjects (mean age 53 ± 6.4 years-old). Patients were scanned in OFF medication state by using proton (¹H) and ²³Na MRI at 7T. We then extracted quantitative Total Sodium Concentration (TSC) from five regions of interest known to be early impaired in iPD [substantia nigra (SN), putamen, caudate nucleus, pallidum, thalamus] and in one region supposed to be relatively spared in the first stages of the disease [cortical gray matter (neocortex)]. Potential atrophy in these structures was also investigated with ¹H MRI. Relative to healthy subjects, iPD patients showed higher TSC in the SN (43.73 ± 4.64 vs. 37.72 ± 5.62, p = 0.006 after Bonferroni correction). A trend of increase in sodium concentrations was found within the pallidum (45.80 ± 4.19 vs. 41.07 ± 4.94, p = 0.017), putamen (48.65 ± 4.58 vs. 43.66 ± 5.04, p = 0.041) and the cortical gray matter (56.34 ± 3.92 vs. 50.81 ± 5.50, p = 0.021). No significant brain atrophy was found in patients compared to controls. Thus, alteration of sodium homeostasis in the SN in the absence of atrophy could be considered as a potential early marker of cellular dysfunction in iPD.

Arterial Spin Labeling is a Useful MRI Method for Presurgical Evaluation in MRI-Negative Focal Epilepsy

Article

Full-text available

Jul 2021
BRAIN TOPOGR

Arterial spin labeling (ASL) is an MRI technique measuring brain perfusion using magnetically labeled blood as a tracer. The clinical utility of ASL for presurgical evaluation in non-lesional epilepsy as compared with the quantitative analysis of interictal [18F] fluorodeoxyglucose PET (FDG-PET) was studied. In 10 patients (4 female; median age 29 years) who underwent a complete presurgical evaluation followed by surgical resection, the presurgical FDG-PET and ASL scans were compared with the resection masks using asymmetry index (AI) maps. The positive predictive value (PPV) and sensitivity (SEN), were calculated from the number of voxels inside the mask (true positive), and outside the mask (false positive). The comparison of the PPVs showed better PPV in 6 patients using ASL and in 2 patients with PET. SEN was better in 4 patients using ASL and in 5 patients with PET. According to the Wilcoxon signed rank test for PPV (p = 0.74) and for SEN (p = 0.43), these methods have similar predictive power. ASL is a useful method for presurgical evaluation in non-lesional epilepsy. The main benefits of ASL over PET are that it avoids radiation exposure for patients, and it offers lower costs, higher availability, and better time efficiency.

Correlation of quantitative conductivity mapping and total tissue sodium concentration at 3T/4T

Article

Full-text available

May 2019
MAGN RESON MED

Purpose To investigate the correlation between electrical conductivity and sodium concentration, both measured in vivo, in the human brain. Methods Conductivity measurements were performed on samples with different sodium (Na⁺) and agarose concentrations using a dielectric probe, and the correlation between conductivity and Na+ content was evaluated. Subsequently, brain conductivity and total Na+ content maps were measured in 8 healthy subjects using phase‐based MREPT and sodium MRI, respectively. After co‐registration and spatial normalization to the 1 mm 152 MNI brain atlas, the relationship between conductivity and tissue sodium concentration (TSC) was examined within different brain regions. Results The conductivities of agarose gels increased linearly with NaCl concentration, while remaining almost independent of agarose content. When measured in healthy subjects, conductivities showed positive correlation with total tissue sodium concentration (R = 0.39, P < 0.005). The same trend was found in gray matter (R = 0.36, P < 0.005) and in white matter (R = 0.28, P < 0.05). Conclusion Tissue conductivity shows a positive correlation with total sodium concentration. Conductivity might serve as a novel technique to visualize the total tissue electrolyte concentration, although refinements in the consideration of e.g., tissue water content, would be necessary to improve the quantitative value.

7T MRI in epilepsy patients with previously normal clinical MRI exams compared against healthy controls

Article

Full-text available

Mar 2019
PLOS ONE

Objective To compare by 7 Tesla (7T) magnetic resonance imaging (MRI) in patients with focal epilepsy who have non-lesional clinical MRI scans with healthy controls. Methods 37 patients with focal epilepsy, based on clinical and electroencephalogram (EEG) data, with non-lesional MRIs at clinical field strengths and 21 healthy controls were recruited for the 7T imaging study. The MRI protocol consisted of high resolution T1-weighted, T2-weighted and susceptibility weighted imaging sequences of the entire cortex. The images were read by two neuroradiologists, who were initially blind to clinical data, and then reviewed a second time with knowledge of the seizure onset zone. Results A total of 25 patients had findings with epileptogenic potential. In five patients these were definitely related to their epilepsy, confirmed through surgical intervention, in three they co-localized to the suspected seizure onset zone and likely caused the seizures. In seven patients the imaging findings co-localized to the suspected seizure onset zone but were not the definitive cause, and ten had cortical lesions with epileptogenic potential that did not localize to the suspected seizure onset zone. There were multiple other findings of uncertain significance found in both epilepsy patients and healthy controls. The susceptibility weighted imaging sequence was instrumental in guiding more targeted inspection of the other structural images and aiding in the identification of cortical lesions. Significance Information revealed by the improved resolution and enhanced contrast provided by 7T imaging is valuable in noninvasive identification of lesions in epilepsy patients who are non-lesional at clinical field strengths.

Comparison between simultaneously acquired arterial spin labeling and 18 F-FDG PET in mesial temporal lobe epilepsy assisted by a PET/MR system and SEEG

Article

Full-text available

Jun 2018

Objective In the detection of seizure onset zones, arterial spin labeling (ASL) can overcome the limitations of positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG), which is invasive, expensive, and radioactive. PET/magnetic resonance (MR) systems have been introduced that allow simultaneous performance of ASL and PET, but comparisons of these techniques with stereoelectroencephalography (SEEG) and comparisons among the treatment outcomes of these techniques are still lacking. Here, we investigate the effectiveness of ASL compared with that of SEEG and their outcomes in localizing mesial temporal lobe epilepsy (MTLE) and assess the correlation between simultaneously acquired PET and ASL. Methods Between October 2016 and August 2017, we retrospectively studied 12 patients diagnosed with pure unilateral MTLE. We extracted and quantitatively computed values for ASL and PET in the bilateral hippocampus. SEEG findings and outcome were considered the gold standard of lateralization. Finally, the bilateral asymmetry index (AI) was calculated to assess the correlation between PET and ASL. Results Our results showed that hypoperfusion in the hippocampus detected using ASL matched the SEEG-defined epileptogenic zone in this series of patients. The mean normalized voxel value of ASL in the contralateral hippocampus was 0.97 ± 0.19, while in the ipsilateral hippocampus, it was 0.84 ± 0.14. Meanwhile, significantly decreased perfusion and metabolism were observed in these patients (Wilcoxon, p < 0.05), with a significant positive correlation between the AI values derived from PET and ASL (Pearson's correlation, r = 0.74, p < 0.05). Significance In our SEEG- and outcome-defined patients with MTLE, ASL could provide significant information during presurgical evaluation, with the hypoperfusion detected with ASL reliably lateralizing MTLE. This non-invasive technique may be used as an alternative diagnostic tool for MTLE lateralization.

Ionic mechanisms of anoxic injury in mammalian CNS white matter: role of Na+ channels and Na(+)-Ca2+ exchanger

Article

Full-text available

Feb 1992

White matter of the mammalian CNS suffers irreversible injury when subjected to anoxia/ischemia. However, the mechanisms of anoxic injury in central myelinated tracts are not well understood. Although white matter injury depends on the presence of extracellular Ca2+, the mode of entry of Ca2+ into cells has not been fully characterized. We studied the mechanisms of anoxic injury using the in vitro rat optic nerve, a representative central white matter tract. Functional integrity of the nerves was monitored electrophysiologically by quantitatively measuring the area under the compound action potential, which recovered to 33.5 +/- 9.3% of control after a standard 60 min anoxic insult. Reducing Na+ influx through voltage-gated Na+ channels during anoxia by applying Na+ channel blockers (TTX, saxitoxin) substantially improved recovery; TTX was protective even at concentrations that had little effect on the control compound action potential. Conversely, increasing Na+ channel permeability during anoxia with veratridine resulted in greater injury. Manipulating the transmembrane Na+ gradient at various times before or during anoxia greatly affected the degree of resulting injury; applying zero-Na+ solution (choline or Li+ substituted) before anoxia significantly improved recovery; paradoxically, the same solution applied after the start of anoxia resulted in more injury than control. Thus, ionic conditions that favored reversal of the normal transmembrane Na+ gradient during anoxia promoted injury, suggesting that Ca2+ loading might occur via reverse operation of the Na+)-Ca2+ exchanger. Na(+)- Ca2+ exchanger blockers (bepridil, benzamil, dichlorobenzamil) significantly protected the optic nerve from anoxic injury. Together, these results suggest the following sequence of events leading to anoxic injury in the rat optic nerve: anoxia causes rapid depletion of ATP and membrane depolarization leading to Na+ influx through incompletely inactivated Na+ channels. The resulting rise in the intracellular [Na+], coupled with membrane depolarization, causes damaging levels of Ca2+ to be admitted into the intracellular compartment through reverse operation of the Na(+)-Ca2+ exchanger. These observations emphasize that differences in the pathophysiology of gray and white matter anoxic injury are likely to necessitate multiple strategies for optimal CNS protection.

Distribution of brain sodium long and short relaxation times and concentrations: a multi-echo ultra-high field 23Na MRI study

Article

Full-text available

Mar 2018

Sodium (23Na) MRI proffers the possibility of novel information for neurological research but also particular challenges. Uncertainty can arise in in vivo23Na estimates from signal losses given the rapidity of T2* decay due to biexponential relaxation with both short (T2*short) and long (T2*long) components. We build on previous work by characterising the decay curve directly via multi-echo imaging at 7 T in 13 controls with the requisite number, distribution and range to assess the distribution of both in vivo T2*shortand T2*longand in variation between grey and white matter, and subregions. By modelling the relationship between signal and reference concentration and applying it to in vivo23Na-MRI signal,23Na concentrations and apparent transverse relaxation times of different brain regions were measured for the first time. Relaxation components and concentrations differed substantially between regions of differing tissue composition, suggesting sensitivity of multi-echo23Na-MRI toward features of tissue composition. As such, these results raise the prospect of multi-echo23Na-MRI as an adjunct source of information on biochemical mechanisms in both physiological and pathophysiological states.

Hippocampal MRS and subfield volumetry at 7T detects dysfunction not specific to seizure focus

Article

Full-text available

Nov 2017

Ultra high-field 7T MRI offers sensitivity to localize hippocampal pathology in temporal lobe epilepsy (TLE), but has rarely been evaluated in patients with normal-appearing clinical MRI. We applied multimodal 7T MRI to assess if focal subfield atrophy and deviations in brain metabolites characterize epileptic hippocampi. Twelve pre-surgical TLE patients (7 MRI-negative) and age-matched healthy volunteers were scanned at 7T. Hippocampal subfields were manually segmented from 600μm isotropic resolution susceptibility-weighted images. Hippocampal metabolite spectra were acquired to determine absolute concentrations of glutamate, glutamine, myo-inositol, NAA, creatine and choline. We performed case-controls analyses, using permutation testing, to identify abnormalities in hippocampal imaging measures in individual patients, for evaluation against clinical evidence of seizure lateralisation and neuropsychological memory test scores. Volume analyses identified hippocampal subfield atrophy in 9/12 patients (75%), commonly affecting CA3. 7/8 patients had altered metabolite concentrations, most showing reduced glutamine levels (62.5%). However, neither volume nor metabolite deviations consistently lateralized the epileptogenic hippocampus. Rather, lower subiculum volumes and glutamine concentrations correlated with impaired verbal memory performance. Hippocampal subfield and metabolic abnormalities detected at 7T appear to reflect pathophysiological processes beyond epileptogenesis. Despite limited diagnostic contributions, these markers show promise to help elucidate mnemonic processing in TLE.

Statsmodels: Econometric and Statistical Modeling with Python

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Quantification of Perivascular Spaces at 7 Tesla: A Potential MRI Biomarker for Epilepsy

Article

Nov 2017
SEIZURE-EUR J EPILEP

Purpose: 7T (7T) magnetic resonance imaging (MRI) facilitates the visualization of the brain with resolution and contrast beyond what is available at conventional clinical field strengths, enabling improved detection and quantification of small structural features such as perivascular spaces (PVSs). The distribution of PVSs, detected in vivo at 7T, may act as a biomarker for the effects of epilepsy. In this work, we systematically quantify the PVSs in the brains of epilepsy patients and compare them to healthy controls. Methods: T2-weighted turbo spin echo images were obtained at 7T on 21 epilepsy patients and 17 healthy controls. For all subjects, PVSs were manually marked on Osirix image analysis software. Marked PVSs with diameter≥0.5mm were then mapped by hemisphere and lobe. The asymmetry index (AI) was calculated for each region and the maximum asymmetry index (|AImax|) was reported for each subject. The asymmetry in epilepsy subjects was compared to that of controls, and the region with highest asymmetry was compared to the suspected seizure onset zone. Results: There was a significant difference between the |AImax| in epilepsy subjects and in controls (p=0.016). In 72% of patients, the region or lobe of the brain showing maximum PVS asymmetry was the same as the region containing the suspected seizure onset zone. Conclusion: These findings suggest that epilepsy may be associated with significantly asymmetric distribution of PVSs in the brain. Furthermore, the region of maximal asymmetry of the PVSs may help provide localization or confirmation of the seizure onset zone.

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