Abnormal brain gamma oscillations in response to auditory stimulation in Dravet syndrome

European Journal of Paediatric Neurology xxx (xxxx) xxx

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European Journal of Paediatric Neurology

Original article

Abnormal brain gamma oscillations in response to auditory stimulation in Dravet syndrome Rocio Sanchez-Carpintero a, b, *, 1, Elena Urrestarazu b, c, Sofía Cieza c, Manuel Alegre b, c, Julio Artieda b, c, Nerea Crespo-Eguilaz a, Miguel Valencia b, d, **, 1 a

Pediatric Neurology Unit. Department of Pediatrics. Clínica Universidad de Navarra, Pamplona, Spain IdiSNA, Navarra Institute for Health Research, Pamplona, Spain Neurophysiology Service, Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain d University of Navarra, Neuroscience Program, CIMA, Pamplona, Spain b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 July 2019 Received in revised form 31 October 2019 Accepted 6 December 2019

Objective: To evaluate the capability of children with Dravet syndrome to generate brain g-oscillatory activity in response to auditory steady-state stimulation. Methods: Fifty-one subjects were included: 13 with Dravet syndrome with SCN1A gene alterations, 26 with non-Dravet epilepsies and 12 healthy controls. Responses to auditory steady-state stimulation elicited with a chirp-modulated tone between 1 and 120 Hz were collected in subjects and compared across groups. Results: Subjects with Dravet syndrome showed weak or no responses in the 1e120 Hz frequency range. Healthy controls showed oscillatory responses following the frequency of the modulation that were maximal in the low (30e70 Hz) and high (80e120) g-ranges both, in the power and inter-trial coherence estimates. Non-Dravet epileptic children showed differences in the auditory responses when compared with the healthy controls but were able to generate oscillatory evoked activities following the frequencyvarying stimulation. Conclusions: The ability to generate brain g-oscillatory activity of children with Dravet in response to a chirp-modulated auditory stimulus is highly impaired, is not due to epilepsy and is consistent with the Nav1.1 channel dysfunction affecting interneuron activity seen in Dravet mouse models. Significance: The reported deficits in the brain oscillatory activity evoked by chirp modulated tones in children with Dravet is compatible with Dravet syndrome disease mechanisms and constitutes a potential biomarker for future disease-modifying interventions. © 2019 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Keywords: Dravet syndrome Epileptic encephalopathy Oscillatory gamma activity Auditory evoked potentials Inhibitory interneurons

1. Introduction Dravet syndrome (DS) is characterized by lifelong refractory epilepsy, increased risk of sudden unexpected death, and disabling cognitive, behavioral and motor disturbances [1]. The genetic mechanism behind DS in most cases is haploinsufficiency caused by mutations, deletions or duplications of the SCN1A gene, which

* Corresponding author. Pediatric Neurology Unit. Department of Pediatrics. Clínica Universidad de Navarra Avenida Pio XII, 36 31080, Pamplona, Spain. ** Corresponding author. Systems Neuroscience Laboratory, University of Navarra, Neuroscience Program, CIMA, Avda. Pío XII 55, 31080, Pamplona, Spain. E-mail addresses: [email protected] (R. Sanchez-Carpintero), mvustarroz@ unav.es (M. Valencia). 1 These authors coordinated equally this work.

encodes the a-subunit of the voltage-gated sodium channel Nav1.1 [2e4]. Nav1.1 channels play a key role in neuron depolarization and are essential for the functionality of GABAergic inhibitory interneurons expressing parvalbumin (PVþ) or somatostatin (STþ) [5e7]. Recent studies suggest that most manifestations of DS are related to the dysfunction of Nav1.1 channels rather than consequences of the epilepsy. Selective knockdown of Nav1.1 expression in certain regions in mice leads to hippocampal q-oscillation dysregulation and spatial memory deficits, without promoting the occurrence of spontaneous seizures [8]. Oscillations in the g-range have not been explored thoroughly in DS. It has been suggested that g-oscillations reflect the synchronized activation of fast-spiking PV þ inhibitory interneurons interacting with excitatory input to pyramidal cells [9,10] Therefore,

https://doi.org/10.1016/j.ejpn.2019.12.004 1090-3798/© 2019 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Please cite this article as: R. Sanchez-Carpintero et al., Abnormal brain gamma oscillations in response to auditory stimulation in Dravet syndrome, European Journal of Paediatric Neurology, https://doi.org/10.1016/j.ejpn.2019.12.004

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insufficient Nav1.1 function in DS would be expected to result in altered patters of g-oscillations. Steady-state responses constitute an easy and consistent way to examine the ability of cortical networks to synchronize neural responses to oscillating frequencies of sensory input [11]. Here we evaluated the auditory evoked responses elicited by chirpmodulated tones [12] in a group of DS patients. We predicted that, in comparison to healthy controls and children with other epilepsies also under antiepileptic drug (AED) treatment, DS patients would show impairment in following the chirp stimulus. 2. Subjects and methods 2.1. Standard protocol approvals, and subject consents All procedures were approved by the institutional ethics committee of the hospital. Parents or legal tutors were informed in detail and gave their written consent. Children over seven years of age were given information in accessible language, and those who were able to understand, gave their assent. 2.2. Participants Fifty-one children and young adults were included in the study. Thirteen DS patients were recruited prospectively in their first or follow-up visit to the Pediatric Neurology Unit in our hospital. Children were diagnosed with DS and therefore recruited if their first seizure -whether clonic generalised or hemiclonic, and whether febrile or afebrile-occurred in the first 12 months of life but they otherwise demonstrated normal development, and then later seizures recurred in relation to body temperature changes, were resistant to AED treatment and there was stagnation of cognitive development from the second year of life onwards. Another inclusion criterion for DS patients in the present study was SCN1A gene alteration, either known to produce DS or de novo. For all patients with DS cognitive development was tested objectively. Twenty-six subjects with non-DS epilepsy were also recruited during their visits to the Pediatric Neurology Unit. Patients with non-DS epilepsy were included if their epilepsy phenotype was clearly distinct from DS, i.e. their epilepsy features did not fulfil ILAE description for DS [13]. Cognitive development was tested objectively in those individuals underachieving at school. Twelve healthy control children were recruited from the families of hospital employees. 2.3. Electrophysiological recordings 2.3.1. Stimulation and recording procedures Auditory stimulation and EEG recording procedures were conducted as previously described in Ref. [12]. Auditory stimuli were implemented in the Matlab environment (Mathworks, USA), and delivered through the STIM module of the Neuroscan system (NeuroSoft, El Paso, USA). Bilateral intracanalicular earphones were used. The stimulus consisted of a 1200 Hz tone modulated in amplitude (90% depth, 85 dB SPL) by a time-dependent signal consisting of a linearly increasing frequency sinusoid (chirp). This type of stimulus enables exploration of the auditory evoked response in a continuous frequency range of interest. The evoked responses obtained do not depend on subject's cooperation [14] and have been proved useful in other pathologies [15e17]. Here we explored the range between 1 and 120 Hz. In the human auditory pathway, the highest amplitudes in the responses are found at stimulation frequencies in the grange, around 40 Hz and 80 Hz [18,19]. The length of the sound was set to 1.6 s, the inter-stimulus interval to 0.4 s, resulting in a total length of 2 s per sweep (see

Supplementary Fig. 1). Nineteen channels of continuous EEG were recorded in monopolar montage referred to both earlobes. Electrodes were located according to the 10e20 international system, using a commercial electrode cap (Electro-CAP, USA) and BrainAmps amplifiers (Brain Products, Germany). EEG and auditory stimuli were synchronized by means of TTL pulses delivered by the STIM® module. The EEG signal was amplified x20000, filtered 1e300 Hz, digitized at 1000 Hz and stored in a PC for offline analysis using the Brain Vision Recorder software (Brain Products, Germany). A minimum of 500 responses per subject was acquired. Total recording time was approximately 20 min divided into two separate blocks of 10 min. To encourage children to collaborate, they were allowed to watch muted cartoons during the test. 2.3.2. Signal analysis Continuous EEG signals were segmented into 2 s length sweeps synchronized to the beginning of the stimuli (pre-stimulus 0.4 s). Responses were reviewed offline and sweeps with visible artifacts or EEG signs of drowsiness were manually excluded (for an example, see Supplementary Fig. S2). The final number of trials averaged in the control group was between 251 and 519 (mean ¼ 381.7, std ¼ 100.0), between 223 and 572 in the DS group (mean 381.2, std:102.1) and between 266 and 698 in the nDS group (mean: 481.7, std:106.9). Auditory evoked responses (chirp-evoked potentials) were obtained by averaging artefact-free sweeps from each subject and channel. Then, the complex Gabor transform was used to estimate the time-frequency content of the averaged signal [20]. Healthy subjects show a time-frequency pattern characterized by a diagonal band of energy corresponding to the time-dependent increase in the frequency of modulation at each time instant [12]. Accordingly, here we characterized the response to the auditory stimulus by extracting the average value of the time-frequency transform across the diagonal band defined from t ¼ 0, f ¼ 1 to t ¼ 1.6, f ¼ 120, within time-frequency patches of 0.12 s by 6 Hz (for a schematic representation of the procedure, see Supplementary Fig. S3). Sleepiness, drugs and attention can affect the auditory responses [21,22]. Indeed, some studies suggest that attention may jeopardize auditory steady-state clinical testing using amplitude values [23], and that phase measures may be preferable in this context [24]. To deal with periods of lack of attention shown by some of the DS patients, we estimated the intertrial coherence (ITC) across trials [25]. The ITC is a measure of the variance in response across the multiple trials; it assesses the trial-to-trial EEG phase coherency at a channel, time bin, and frequency range. In the context of auditory evoked potentials (as it is the case here), the ITC measures the event-related 'timing' of the EEG evoked signals in response to the repeated presentation of the chirp stimuli. To compute it, a Gabor transform was applied to each individual sweep and the ITC was assessed according to:





1 X N
iFk ðt;f Þ
ITCðt; f Þ ¼
e


N k¼1

where Fk ðt; f Þ is the time-frequency varying ‘phase of the complex Gabor transform’ of the kth response and N is the total number of trials. The obtained ITCðt; f Þ ranges from 0 to 1; 0 for purely nonphase-locked activity and 1 for strictly phase-locked activity [26]. With this measure, also called “phase-locking factor” (PLF) [27], even very low-amplitude signals can be detected providing they are phase-locked across trials. As in the previous analysis, a diagonal band was selected from the time-frequency ITC estimate, representing the ITC values of the

Please cite this article as: R. Sanchez-Carpintero et al., Abnormal brain gamma oscillations in response to auditory stimulation in Dravet syndrome, European Journal of Paediatric Neurology, https://doi.org/10.1016/j.ejpn.2019.12.004

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amplitude-modulation-following response (see Supplementary Fig. S4). 2.4. Statistical analysis For each subject, the channel with the best signal to noise ratio in the power and ITC estimates was selected for statistical comparisons; typically Cz and Fz (in those subjects where artifacts precluded to select Fz or Cz, we took the closest channel with the best SNR). This choice is motivated by the fact that previous studies using the same EEG set up (10:20 system, with ear reference, bilateral stimulation) show that the chirp responses have a diffuse topography over the medial-central areas (see Supplementary Fig. S6). Prior to any statistical analysis, power and ITC values were corrected to account for the individual differences in the number of trials averaged. There is some evidence of increased spontaneous blink rate in Dravet syndrome [28] and given that trials with artifacts (including blinks) were excluded, an increase in blinking in one group could affect the number of trials per subject used per group. This is important because on the one hand, for the ITC, a lower number of trials increase the possibility of detecting phase coherence by chance and for the power of the averaged potential, the SNR increases as the number of trials increases (as the noise decreases as 1/sqrt(number of trials)). As a result, and in order to minimize any possible effects of the number of trials, we corrected the ITC for trial count using the formula sqrt[1/(number of trials) *log(0.5)] and the power with sqrt(number of trials). In order to assess the absence of baseline differences in the prestimulus period across the three groups, we estimated the mean and standard deviation (STD) of the (corrected) ITC and power values within the pre-stimulus period ([-0.1 0] seconds) for each subject. Kruskal-Wallis statistical analysis with group factor failed to detect any significant group effect (p > 0.05) in any of the parameters. Statistical differences in the responses across the ITC and power diagonal band were assessed by means of cluster-based nonparametric permutation analyses (ANOVA, P < 0.05, n ¼ 200 permutations, with group factor) followed by a Tukey multiple comparison post-hoc analysis to detect pair wise differences. The cluster-based permutation test is a type of non-parametric statistical test suitable to deal with the multiple comparisons problem (MCP). As the MCP arises from the fact that the effect of interest (difference of ITC/Power between groups of subjects here) is evaluated in a wide range of frequencies, the large number of statistical comparisons (one per frequency of interest) does not allow to control the family-wise error rate (FWER) by means of the standard statistical procedures (such as Bonferroni correction). Here, the cluster-based permutation test not only offers a straightforward way to solve the MCP problem, but it also permits to “detect” the frequency ranges where groups show significant differences without an aprioristic definition of such frequencies [29].All data analyses and statistical procedures were carried out by means of custom-made scripts coded in Matlab® (Mathworks, Natick, MA, USA). 3. Results 3.1. Participants Clinical, EEG and molecular features of the 13 DS subjects are summarized in Table 1. There were 5 females. The median age of children with DS at testing was 7 years and 2 months (ranging from 3 years 3 monthse17 years 11 months). Children in the non-DS group, 11/26 females, had a variety of

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causes for their epilepsy (Table 2). Their age ranged from 3 years 3 monthse18 years and were recruited by matching them by age to the Dravet group. Antiepileptic drug treatment differed from the DS group (Table 2). None of the non-DS subjects were treated with stiripentol or with topiramate, both indicated in DS, and several of them were treated with lamotrigine or eslicarbazepine, that are contraindicated in DS. Seizure frequency in both groups with epilepsy is displayed in Tables 1 and 2 Fifty eight percent of children in the non-DS epilepsy group had their epilepsy controlled for the year before testing versus one percent in the Dravet group. Polytherapy was the rule in DS in contrast with 39% in the non-Dravet group. Regarding cognitive abilities, 45%, of children with Dravet and 65% in the non-Dravet group had average or borderline scores. Mild or moderate learning disability occurred in 16% of subjects with DS and in 27% of patients with non-DS, whereas 39% of subjects with DS had severe learning disability versus 8% of the non-DS subjects. Control children were healthy and were progressing normally at school without academic difficulties. Seven were females and age ranged from 4 years 11 monthse12 years 11 months. Exclusion criteria were having any chronic disease or receiving pharmacological treatment at the recording time. No significant differences in age (Kruskal-wallis ANOVA Chi2(2) ¼ 4.2809, p ¼ 0.1176) nor gender (Freeman-Halton extension of the Fisher exact probability test, p ¼ 0.94) were detected among the three groups. 3.2. Chirp evoked oscillatory responses All controls displayed an oscillatory response at the frequency of the modulation, with two areas of maximal power and inter-trial coherence (phase-locking): one between 30 and 70 Hz (low gamma), and the other between 80 and 120 Hz (high gamma). Subjects in the Dravet group gave weak responses or no response at all on the diagonal, while non-Dravet epileptic subjects tended to have a diagonal response characterized by larger power signal at lower frequencies. Control and non-Dravet epileptic patients had the characteristic harmonic diagonal at twice the frequency of the main response. No harmonic responses were found in the Dravet subjects. Fig. 1 shows the grand-averaged evoked potential, the median response of the power and inter-trial coherence of chirp evoked responses for each of the three groups (see Supplementary Fig. S5 for an example of a representative individual response for each of the three groups). Fig. 2 shows the mean and confidence interval of the power/ phase-locking values along the diagonal for the three groups: control (blue), Dravet (red) and non-Dravet epileptic (green). By visual comparison of the curves, the power values in Dravet patients (in red) in the two principal areas of the response were lower than in controls and non-DS epileptic subjects. Non-DS epileptic patients gave a weaker response in the high-g-range when compared with the control group. Direct comparison of the averaged response across the diagonal, detected group effects in the power estimates of the responses (Fig. 2 left, red line; cluster-based nonparametric permutation ANOVA, P < 0.05, n ¼ 200 permutations) in the [37 71], [92 99] and [115 120] Hz ranges. In the case of ITC measurements (Fig. 2 right; cluster-based nonparametric permutation ANOVA, P < 0.05, n ¼ 200 permutations), group effects were found for the [27 74], and [114 124] Hz frequency ranges. Pairwise comparison between groups detected that in the low g-range, the response in Dravet patients was significantly weaker when compared with that of controls or non-Dravet epileptic patients. Tukey multiple comparisons post-hoc test detected significant differences (p < 0.05) in both cases: in the [43 71] Hz range for control vs Dravet patients (Fig. 2-left, blue bar: ctrl vs DS) and in the

Please cite this article as: R. Sanchez-Carpintero et al., Abnormal brain gamma oscillations in response to auditory stimulation in Dravet syndrome, European Journal of Paediatric Neurology, https://doi.org/10.1016/j.ejpn.2019.12.004

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Table 1 Clinical, seizure, cognitive and molecular features of children with Dravet Syndrome. Patient Age at seizure onset

Age at testing

Cognitive impairment

Number of seizures

Treatment

SCN1A mutation

1 2

8mo 6mo

7y 0mo 6y 4mo

1 >20

0 >20

3 4

6mo 6mo

7y 5mo 17y 11mo

Moderate None (low average) Severe Severe

16 >20

5 6

8mo 8mo

4y 7mo 5y 9mo

Mild Borderline

7

6mo

4y 8mo

8

3mo

9 10 11 12 13

Protein location

0 >20

VPA þ TPM VPA þ BMD

c.992dup; p.L331Pfs *9 DI S5eS6 c.2417T > C; p.L806P DII S2

De novo De novo

0 10

0 3

VPA þ TPM VPA þ STP þ CLB

c.2730G > C; p.Q910H DII S5 c.2140 A > G; p.M714V DI-DII

2 >20

0 >20

0 >20

DII S5eS6 DIII S5eS6

Severe

>20

8

2

DIII S5

De novo

7y 2mo

Borderline

0

0

0

DIV S4

De novo

4mo 6mo

8y 0mo 3y 5mo

Severe None (average)

15 12

0 0

0 0

DI S5eS6 DIV S2

De novo De novo

7mo 4mo 4mo

5y 4mo 10y 6mo 3y 3mo

Borderline Severe Borderline

>20 10 >20

>20 0 2

>20 0 0

VPA þ TPM c.1146 C > A; p.D382E LVT þ CLB þ VPA þ ESM c.4168 G > A; p.V1390M VPA þ STP þ CLB c.4073G > A; p.W1358* VPA þ TPM c. 4942 C > A; p.R1648S VPA þ STP þ CLB c.829 T > G; p.C277G TPM þ STP c.4694dup; p. T; p.D382Y VPA þ TPM c.4649 T > G; p.L550R VPA þ TPM þ LVT c.760-763delACTG p.Thr254Cysfs*4

De novo Paternally inherited De novo De novo

DI S5eS6 DIV S1 DI S5

De novo De novo De novo

Yearly Monthly Weekly

Pattern of Inheritance

BMD: bromide; CLB: Clobazam; ESM: Ethosuximide; KD: ketogenic diet; LVT: levetiracetam; mo: months; STP: stiripentol; TPM: topiramate; VPA: Valproic Acid; y: years; ZNS: zonisamide.

Compared to the control group, DS patients showed weaker power responses in the [91 99], [104 108] and [113 123] Hz ranges (Tukey multiple comparisons post-hoc test, p < 0.05; [113 124] for the ITC values). Control subjects showed stronger responses (ITC) than non-Dravet epileptic patients in the [119 124] Hz range (Tukey multiple comparisons post-hoc test, p < 0.05) and no differences in the responses of Dravet/non-Dravet patients were found for this range. No seizures were recorded during the acquisition of the evoked responses neither in DS group nor in the non-DS epilepsy patients.

[37 55] range for epileptic non-Dravet vs Dravet patients (Fig. 2-left, green bar: non-DS vs DS). In addition, differences between control and non-Dravet epileptic patients’ responses were also found, but in the upper segment of the low- g-range ([64 70] Hz, Tukey multiple comparisons post-hoc test, p < 0.05, Fig. 2-left, light blue bar: ctrl vs non-DS). With regard to ITC values, generally the same differences were found (Fig. 2-right). In the high-g-range, paired analyses showed essentially similar intervals when computing power vs ITC parameters. Both measurements detected differences centred around 95 and 118 Hz.

Table 2 Clinical, seizure, cognitive and molecular features of children with non-Dravet epilepsy. Patient

Age at seizure onset

Age at testing

Cognitive impairment

Number of seizures Yearly

Monthly

Weekly

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

14y 4y8mo 10y 8mo 3y 10mo 12y 9 mo 2y 3mo 3y 9mo 8y 1 mo 3y 3y 6 mo 4y 10 mo 2y 2y 6 mo 2y 3 mo 6y 8y 7 mo 19 days 9y 2y 11mo 2y 2y 5mo 11y 9 mo 11y 5mo 9y 11mo 7mo 3y 3mo

14y7mo 9y 9mo 13y 6 mo 11y 16y6 10y 4 mo 12y 6mo 18y 6y 8mo 4y 10mo 5y 8mo 5y 5mo 3y 3mo 4y 9y 2mo 11y 2mo 7y 8mo 15y 6mo 8y 1mo 3y 7mo 5y 4mo 17y 1mo 13y 11mo 9y 1mo 11y 8mo 6y

None (average) None (average) Mild Moderate None (average) Severe Mild Borderline None (average) None (average) None (average) None (average) Mild None (average) None (average) Mild None (average) None (average) None (average) moderate None (average) None (average) None (average) None (average) Mild None (average)

10 1 >20 0 0 >20 >20 0 0 0 0 0 1 0 >20 0 0 0 0 >20 0 0 >20 >20 0 0

1 0 >20 0 0 >20 10 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 5 0 0 0

0 0 >20 0 0 >20 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

Treatment

Cause of the epilepsy/type of seizures

VPA LVT RFM þ LTG þ ESM þ CLB VPA þ LVT þ CLB VPA þ LTG VPA þ LTG þ ESM VPA þ ZNS þ LTG LTG LVT VPA þ ESM VPA þ ESM þ LVT LVT LVT VPA LVT VPA VPA LVT VPA VPA þ ESM þ LTG LVT LVT LTG þ ESLICBZ VPA VPA LVT

Unknown/Focal temporal Perinatal ischemic damage/focal Lissencephaly/multifocal Polymicrogyria/multifocal Unknown/Focal temporal Doose syndrome/generalised Perinatal stroke/multifocal Unknown/focal Idiopathic/generalised Doose syndrome/generalised Idiopathic/POCS-focal Postnatal Haemorrhage/focal Unknown/multifocal Unknown/focal Unknown/focal Idiopathic focal Unknown/generalised Idiopathic/juvenile myoclonic Perinatal damage/multifocal Idiopathic/focal-POCS Idiopathic/generalised Idiopathic/generalised Idiopathic/focal Childhood Absence/generalised Sturge-Weber/focal Idiopathic/generalised

CLB: Clobazam; ESLCBZ: eslicarbazepine acetate; ESM: Ethosuximide; LTG: lamotrigine; LVT: levetiracetam; mo: months; RFM: rufinamide TPM: topiramate; VPA: Valproic Acid; y: years; ZNS: zonisamide.

Please cite this article as: R. Sanchez-Carpintero et al., Abnormal brain gamma oscillations in response to auditory stimulation in Dravet syndrome, European Journal of Paediatric Neurology, https://doi.org/10.1016/j.ejpn.2019.12.004

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Fig. 1. Time-frequency representation of the auditory evoked responses Example of a representative single-subject chirp-evoked potential for each of the three groups (top row). Median of the chirp evoked responses (power, second row; inter-trial coherence, bottom line) for the control (ctr, Left), Dravet (DS, Middle) and non-Dravet epileptic (non-DS, Right) groups. Responses were normalized (z-scored) respect to the pre-stimulus interval previous to median computation. Note the presence of two main areas of response (higher intensity of power/ITC responses) in the diagonal band and the weaker/lack of response in the Dravet group.

Nevertheless, we wanted to further investigate the effect of seizure frequency on the oscillatory activity measured by chirp responses. To do that, we first stratified the patients in five groups as having diary, weekly, monthly, yearly or no seizures. Then, a Kruskal-Wallis non parametric ANOVA was used to check if seizure frequency (5 levels) had an effect in the mean power and ITC values computed in the whole range of frequencies (20e130 Hz) and within the low and

high granges where differences were found ([37 71] and [92 120] Hz respectively). None of the analyses detected significant effects of the seizure-frequency classification factor on the power nor in the ITC values both, in the DS or non-DS groups. Alternatively, we created an additional classification by segregating the patients into two groups: patients with more or less than 20 seizures in the last year. Again, statistical analyses performed in the DS and nDS groups

Fig. 2. Statistical comparison of the evoked responses. Comparison of power (left) and ITC (right) estimates for the three groups: controls (blue), Dravet (red) and non-Dravet epileptic patients (green). Solid lines indicate mean values and shadowed areas represent the confidence interval of the mean. Top horizontal bars define frequency ranges showing significant differences of the differences between the three groups for both, power and ITC estimates (cluster-based nonparametric permutation ANOVA, P < 0.05, n ¼ 200 permutations. Blue bar: post-hoc test healthy control versus Dravet subjects, Tukey posthoc test, P < 0.05. Light blue post-hoc test healthy control versus non-Dravet epileptic subjects, Tukey posthoc test, P < 0.05. Red: post-hoc test non-Dravet epileptic/Dravet groups, Tukey posthoc test, P < 0.05.). ctrl: controls. DS: patients with Dravet syndrome.nonDS: epileptic patients but not Dravet syndrome. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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failed to detect any significant effect of the seizure frequency. Additional analyses were also run to assess the effect of cognitive impairment in the chirp responses. Following the same approach than above, we evaluated the effect of cognitive impairment on the mean power and ITC values computed in the whole range of frequencies (20e130 Hz) and within the low and high gamma ranges for the DS and non-DS patients. Again, no significant effects were detected in any of the parameters evaluated nor in any of the two groups (Kruskal-Wallis non-parametric ANOVA with one factor and three levels: none, borderline, moderate and severe). Finally, and motivated by some previous studies reporting age effects both in power and ITC [30], we investigated the presence of age effects in the mean power and ITC. Spearman correlation analyses were not able to detect any significant interactions of power/ age or ITC/age within any of the groups (p < 0.05). Interestingly, and for the nDS group, a trend in the ITC/age correlation was detected in the low gamma range (Spearman correlation; rho: 0.377, p ¼ 0.054). Although not significant, this finding warrants the necessity of performing further studies with larger sample sizes and optimized ages distributions to be in position to assess the presence of these interactions. 4. Discussion Our results show that the capacity of patients with Dravet syndrome to generate oscillatory g-activity in response to modulated-amplitude auditory stimulation is highly impaired. In both healthy adults and children chirp-modulated tones evoke oscillatory responses that are maximal around 40 and 80 Hz and resemble those obtained from steady-state evoked auditory responses (SSAR) [12,16,17]. Chirp-modulated tones allows to explore a continuous range of stimulation frequencies in a single test and thereby detect the resonance frequencies (those with maximal response) more accurately than a constantly modulated tone [12]. The recording time needed to collect consistent responses is about 20 min, and there is little need of subject collaboration, making this technique suitable to be used in clinical settings and with uncooperative patients such as children or young adults with DS. By using the chirp-modulated tone, we were able to obtain good quality data of evoked responses in children with DS, most of them with moderate or severe cognitive and behavioral impairments. Interpretation of the inability of children with DS to respond to chirp-evoked oscillatory needs clarification of the mechanisms underlying SSAR. Higher amplitudes in average evoked responses are due, in the case of chirp-modulated auditory stimulation, to a combination of: (i) the increase in the amplitude of oscillatory activities elicited by each individual stimulus and to (ii) an increase in the phase synchrony across trials due to phase resetting of ongoing oscillations [12]. Several studies have located the origin of the 40 Hz SSAR in the auditory cortex [31,32]. A study with H15 2 O-PET showed that the frequency of maximal regional cerebral blood flow SSAR is the same as that for EEG SSAR: 40 Hz [33]. These results suggest that the maximal 40 Hz SSAR is related to increased cortical synaptic activity in the primary auditory cortex [34]. Ongoing g-oscillations are difficult to detect with surface EEG. Here, by averaging the evoked responses elicited by an auditory stimulus, we provide evidence that the generation of g-activity is impaired in children with DS. A decrease in the power and ITC of auditory evoked responses, as found in the DS group in this study, suggests either that there are less neurons responding to the stimulus or, more probably, that there is a deficiency in the synchronization mechanisms needed to elicit a coherent response. Children with non-Dravet epilepsy and under AED treatment also manifested altered responses, but the differences were less pronounced relative to

control subjects than was the case with DS patients with SCN1A alterations. Consequently, the disruption to generation of g-oscillations shown by DS patients cannot be explained solely by the presence of epilepsy or by receiving AED treatment and, therefore, seems to be specifically related to DS. Our finding of altered g-responses in human DS subjects is consistent with the observation of selective impairment of the activity of PVþ and ST þ interneurons caused by insufficient Nav1.1 function in mice [5e7,35]. Nav1.1 channels localize to axons of PV þ inhibitory interneurons [5], exert perisomatic inhibition of pyramidal cells and drive the generation of hippocampal -and very likely cortical- g-oscillations [36]. Furthermore, interneuron spike times correlate consistently with the phase of g-oscillations [37]. Nav1.1 haploinsufficiency, and the consequent dysfunction of GABAergic inhibitory interneurons, results in impaired inhibition on pyramidal cells, a mechanism that, amongst others, may explain seizure susceptibility [38,39]. Moreover, evidence in a mouse model of Dravet that different behavioural phenotypes can be explained by impairment in distinct GABAergic interneuron populations, support the hypothesis that happloinsuficiency of Nav1.1 channels is also involved in some of the non-epilepsy features of DS [40]. Similarly, mice with focal knockdown of Nav1.1 expression, an experimental situation that does not induce seizure activity, show impairments in the generation of q-activity in the hippocampus, and this reduced q-activity correlates with deficits in spatial memory [8]. A H15 2 O-PET rCBF study demonstrated that normal subjects have increased synaptic activity of some cerebellar regions during auditory stimulation at 40 Hz [33], indicating that the cerebellum is involved in the generation or control of g-evoked responses. The connection between the cerebellum and g-evoked responses also provides a possible clue towards an explanation for the alterations detected in DS subjects in the current study, since Purkinje cells in the cerebellar cortex express Nav1.1 channels [41]. Despite the possible consistency of interpretation of the various strands of research that indicate a role of Nav1.1 channels in the features of DS it must be noted that the connections discussed above are rather inferential and that, to date, there is no direct proof of such involvement of the Nav1.1 channel. Impairment in brain oscillatory activity outside the g-range in DS children has been reported by using resting EEG [42]. To the best of our knowledge, activity in the g-range has not been studied in DS. Ongoing g-band oscillations of the local field potential synchronize neuronal response onset latencies to a sensory stimulus within and across hemispheres in visual cortex. This type of neuronal coherence is particularly important during cognitive processes that demand selective neuronal interactions as during states of expectation, attention, working memory, sensoryemotor integration and planning of actions [43]. Subjects with DS have heterogeneous cognitive impairments but there is some evidence of universal difficulties with tasks that require visuomotor coordination and deficits in attention and executive function [44]. We did not find evidences of a clear correlation between intelligence or developmental quotients and the average chirp-evoked potential responses in our samples. However, the sample size might not allow to reject the correlations with enough statistical power, and the unspecific nature of the cognitive indices we used might preclude the finding of such correlations. Further investigations using visuomotor coordination, attentional or executive function indices may be necessary to find associations with goscillatory impairments in DS subjects. The abnormal chirp-evoked responses we recorded in subjects with DS are relevant for indirect measurement in vivo of disease mechanisms and may serve as a marker of disease changes. Other study of intracortical inhibition in vivo in people with Dravet also

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found impairment by applying transcranial magnetic stimulation using short interval intracortical inhibition paradigms, but the technique was difficult to apply in uncooperative subjects [45]. Gene therapy is now a “real word” therapeutic option for neurological diseases in children [46] and non-invasive methods to detect disease changes are needed. Seizure control and cognitive or behavioral development are the only current clinical markers of DS progression. However, inter and intra-individual variations in the clinical expression of the syndrome require more objective measures and more mechanisms-of-disease related methods to follow the result of disease modifying therapies. The technique we propose is applicable in clinical settings and with no easily cooperative patients. A limitation of this study is the difference in antiepileptic drug regimens between the DS and the non-DS epilepsy groups. However, contraindicated medications in DS are those that block sodium channels and GABAergic antiepileptic drugs are preferred to treat DS epileptic seizures. If the underlying failure of GABAergic interneuron activity in DS is involved in the inability to produce goscillations, the difference in medication use between groups would have made more difficult to significantly distinguish them. Therefore, the disparity in drug regime is most probably not responsible for the differences found in the generation of chirp responses. In any case, future studies including larger sample sizes will allow to further clarify this. 5. Conclusions Gamma oscillatory activity evoked by modulated-amplitude auditory stimulation is highly impaired in children with DS known to have Nav1.1 channel impairment. The abnormal gamma band activity in Dravet children that we found is consistent with deficits in inhibitory GABAergic activity as found in Dravet mice models and cannot be explained by the fact of having epilepsy or receiving antiepileptic medication. Our findings are relevant for indirect measurement in vivo of disease mechanisms and may serve as biomarkers of disease change. Declaration of competing interest Dra. Rocio Sanchez-Carpintero serves as scientific advisor to the
n Síndrome de Dravet Espan ~ a without economic remunerFundacio ation. The rest of the authors do not have any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Acknowledgements We thank David Burdon for English proofreading. We also thank study participants for their collaboration. Appendix ASupplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.ejpn.2019.12.004. Funding
n Sindrome de This work was partially supported by Fundacio
n Desafía Dravet, Spain. Dravet, Spain and Asociacio

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