# Impaired auditory sampling in dyslexia: further evidence from combined fMRI and EEG

Katia Lehongre, Benjamin Morillon, Anne-Lise Giraud, Franck Ramus

► To cite this version:

Katia Lehongre, Benjamin Morillon, Anne-Lise Giraud, Franck Ramus. Impaired auditory sampling in dyslexia: further evidence from combined fMRI and EEG. Frontiers in Human Neuroscience, 2013, 7, pp.454. 10.3389/fnhum.2013.00454. hal-01557287

HAL Id: hal-01557287

https://hal.sorbonne-universite.fr/hal-01557287

Submitted on 6 Jul 2017

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Impaired auditory sampling in dyslexia: further evidence from combined fMRI and EEG

Katia Lehongre<fnref n="1" />, Benjamin Morillon<fnref n="2" />, Anne-Lise Giraud<fnref n="3" /> and Franck Ramus<fnref n="4" />*

**Edited by:**

Usha Goswami, University of
Cambridge, UK
Andrea Facoetti, Università di
Padova, Italy
Marie Lallier, Basque Center on
Cognition Brain and Language, Spain
Alan J. Power, University of
Cambridge, UK

**Reviewed by:**

Jarmo Hamalainen, University of
Jyvaskyla, Finland
Hanne Poelmans, KU Leuven,
Belgium

*Correspondence:

Franck Ramus, Laboratoire de
Sciences Cognitives et
Psycholinguistique, Ecole Normale
Supérieure, 29 rue d'Ulm, 75005
Paris, France
e-mail: franck.ramus@ens.fr

The aim of the present study was to explore auditory cortical oscillation properties in developmental dyslexia. We recorded cortical activity in 17 dyslexic participants and 15 matched controls using simultaneous EEG and fMRI during passive viewing of an audiovisual movie. We compared the distribution of brain oscillations in the delta, theta and gamma ranges over left and right auditory cortices. In controls, our results are consistent with the hypothesis that there is a dominance of gamma oscillations in the left hemisphere and a dominance of delta-theta oscillations in the right hemisphere. In dyslexics, we did not find such an interaction, but similar oscillations in both hemispheres. Thus, our results confirm that the primary cortical disruption in dyslexia lies in a lack of hemispheric specialization for gamma oscillations, which might disrupt the representation of or the access to phonemic units.

**Keywords:** dyslexia, auditory sampling, phonemic processing, gamma oscillation, theta oscillation, delta oscillation, EEG-fMRI

## INTRODUCTION

Developmental dyslexia is a specific difficulty in the acquisition of reading skills that is not accounted for by mental age, visual acuity deficit or inadequate schooling (WHO, 2011). It is thought to affect between 3 and 7% of the population (Lindgren et al., 1985), although estimates vary widely depending on cut-off criteria on reading performance scales.

While there remains a great diversity of theoretical outlooks on dyslexia (Ramus and Ahissar, 2012), it is now widely agreed that the majority of dyslexic individuals share difficulties in one or several aspects of phonological processing, including paying attention to and mentally manipulating speech sounds (phonological awareness), storing phonological material for a few seconds (verbal short-term memory), and rapidly retrieving long-term phonological representations (Wagner and Torgesen, 1987; Vellutino et al., 2004). Debates persist on whether this phonological deficit (i) follows from an underlying primary auditory processing deficit (Tallal, 1980; Goswami et al., 2011), (ii) arises from a degradation of phonological representations or in difficulties accessing them (Ramus and Szenkovits, 2008), and (iii) is sufficient by itself to account for learning disability or merely a risk factor (Pennington, 2006).

At the anatomo-functional level, while the issue remains debated (Richlan et al., 2011), the vast majority of findings from functional and structural brain imaging and post-mortem dissection converge on a disruption of left perisylvian cortical networks, which are involved in speech processing and recruited

for reading acquisition (Galaburda et al., 1985; Eckert, 2004; Richardson and Price, 2009; Linkersdorfer et al., 2012). Genetic variations associated with dyslexia further point to structural and functional variations in the perisylvian language network (Darki et al., 2012; Pinel et al., 2012; Giraud and Ramus, 2013).

A new theoretical framework for the cortical organization of speech processing sheds a new light on the neural basis of developmental dyslexia. According to “asymmetric sampling in time” (AST) theory (Poeppel, 2003), left and right auditory cortices show cortical oscillations at different preferred rates: low gamma (25–45 Hz) in the left hemisphere and delta-theta (1–7 Hz) in the right. Gamma and theta oscillations are assumed to play a role in the segmentation of the sound stream into units of the corresponding sizes, which may be optimal for the analysis of phonemes by the left auditory cortex and syllables and prosodic cues by the right one (around 25 and 200 ms, respectively). While this hypothesis has received empirical support from neuroimaging and neurophysiological studies (Giraud et al., 2007; Abrams et al., 2008; Telkemeyer et al., 2009; Morillon et al., 2010), its implications for developmental dyslexia are not so clear. On the one hand, Giraud and Poeppel (2012) have hypothesized that dyslexic individuals might not show the typical left-hemisphere specialization for the gamma rate, which would explain their specific difficulties with the processing of phonemic units. On the other hand, Goswami (2011) has hypothesized that dyslexic individuals might rather show atypical cortical oscillations in the theta

<footnote n="1">¹ Research Center of the Brain and Spine Institute (CRICM), Hôpital de la Pitié-Salpêtrière, Paris, France</footnote>

<footnote n="2">² Department of Psychiatry, Columbia University Medical Center, New York, NY, USA</footnote>

<footnote n="3">³ Department of Neuroscience, University of Geneva, Geneva, Switzerland</footnote>

<footnote n="4">⁴ Laboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supérieure, EHESS, CNRS, Paris, France</footnote>
(4–7 Hz) or in the delta (1–4 Hz) frequency band, thereby leading to processing deficits at the syllable and more generally at the prosodic level, which are also proposed to be causally linked to reading disability (Goswami et al., 2010).

In a previous study, we used magnetoencephalography (MEG) to measure auditory steady-state responses (ASSRs) to an amplitude-modulated white noise in dyslexic and control individuals. We found the expected left dominance of ASSRs in the *planum temporale* in the gamma range (25–35 Hz) in controls, but not in dyslexic participants who displayed no or a reversed asymmetry (Lehongre et al., 2011). These results, that are supported by another study that also measured ASSRs [using electroencephalography (EEG)] and found group differences at 20 Hz in the left hemisphere (Poelmans et al., 2012), were consistent with the hypothesis of a left-hemisphere-based gamma oscillation disruption, but did not explicitly test the delta/theta alteration hypothesis. In the delta/theta range, one study found no group difference at 4 Hz using EEG-ASSR (Poelmans et al., 2012), and another one exploring MEG-ASSRs at 2, 4, 10 and 20 Hz found a right-hemisphere dominance at 2 Hz in controls but not in dyslexic participants and no further group difference at any other frequency (Hamalainen et al., 2012). Using more naturalistic speech stimuli, an EEG study found that poor readers, in contrast to good readers, did not present the natural dominance of the right hemisphere to follow the speech envelope. They presented a more bilateral pattern, but this was the case only when speech was compressed to 50% of its original duration (Abrams et al., 2009). Finally, anomalies in the alpha rhythm have also been found in children with dyslexia or with language impairments (Heim et al., 2011; Babiloni et al., 2012; Han et al., 2012).

In the context of these conflicting results, the goals of the present study were twofold: Firstly, to replicate and extend the results of our previous study, using more naturalistic stimuli and an entirely different methodology; secondly, to directly compare the predictions of the two main theories by testing cortical oscillations in the gamma, theta and delta ranges in auditory cortices. For this purpose, rather than using a simple amplitude-modulated noise, we exposed participants to an audiovisual documentary movie featuring more ecological conversational speech, thereby including the whole range of amplitude modulations relevant to language.

We used simultaneous EEG and functional magnetic resonance imaging (fMRI) to measure the activity of the auditory cortex at both high temporal (millisecond) and spatial (millimeter) resolution. Changes in Blood-oxygen-level-dependent (BOLD) are associated with changes in the spectral profile of neuronal activity (Rosa et al., 2010b), and EEG and fMRI do not capture exactly the same signals. Only a small fraction of the BOLD signal reflects synaptic activity involving pyramidal cells (Lee et al., 2010; Logothetis, 2010), which we hypothesize here to work as a sampling device. By combining delta, theta and low-gamma power variations and BOLD activity we expect to capture the part of the BOLD activity that is explained out by circuits involving pyramidal cells, which generate these rhythms. Furthermore, simultaneous EEG/fMRI recordings are well suited to study oscillations in continuous recordings with uncontrolled states as it has been demonstrated in several studies

[see for review: (Herrmann and Debener, 2008; Rosa et al., 2010a; Laufs, 2012)].

# MATERIALS AND METHODS

## PARTICIPANTS

Thirty-two normal-hearing, French-native-speakers participated in a simultaneous EEG and fMRI experiment (with approval of the local ethics committee; biomedical protocol C08-39). Seventeen participants reported a history of reading disability, scored at or below the expected level for ninth graders in a standardized reading test, and at least 90 in non-verbal IQ. The remaining 15 participants were normal readers matching dyslexic participants with respect to age, gender, handedness, and non-verbal IQ, but scoring above the ninth grade reading level. Demographic and psychometric data, as well as the results of a larger battery (Soroli et al., 2010) of literacy and phonological tests are reported in Table 1.

## BEHAVIORAL TEST BATTERY

Non-verbal intelligence was assessed in all participants using Raven's matrices (Raven et al., 1998). Their receptive vocabulary was assessed with the EVIP test (Dunn et al., 1993), a French adaptation of the Peabody picture vocabulary scale. They were included on the basis of performance on the Alouette test (Lefavrais, 1967), a meaningless text that assesses both reading accuracy and speed, yielding a composite measure of reading fluency. Orthographic skills were assessed using a computerized orthographic choice task, and a spelling-to-diction test. Phonological tests: we used the WAIS digit span as a measure of verbal working memory (Wechsler, 2000). Phonological awareness was assessed using a computerized spoonerism task, in which participants heard pairs of words, and had to produce them swapping the initial phonemes. Finally, rapid automatized naming was assessed using the object and digit sheets from the PhAB (Frederickson et al., 1997) and two custom-made color sheets modeled on the object sheets, with five different colors repeated 10 times each, one replacing each object. The dependent variable is the total time taken to name all items on each sheet, irrespective of errors.

## MRI ACQUISITION

Subjects were either asked to rest with closed eyes or to pay attention to an audiovisual movie (Morillon et al., 2010). The movie, a scientific documentary on an ecological topic, included three speakers (2 men). Data were acquired in three sessions with session 1: 10 min of rest; sessions 2 and 3: 10 min of movie followed by 11 min of rest. The scanner produced a constant auditory signal during data acquisition presumed not to interfere with our analyzes, given that its modulation frequencies are outside our frequencies of interest (See supplemental data p. 1, Morillon et al., 2010).

The three sessions yielded 1560 echoplanar fMRI image volumes (Tim-Trio; Siemens, 40 transverse slices, voxel size = 3 × 3 × 3 mm; repetition time = 2 s; echo time = 50 ms; field of view = 192). A 7-min anatomical T1-weighted magnetization-prepared rapid acquisition gradient echo sequence (176 slices, field of view = 256, voxel size = 1 × 1 × 1 mm) was acquired at the end of scanning.
<table><caption>Table 1 | Summary of behavioral tests.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Controls (15 subjects)</th><th colspan="3">Dyslexics (17 subjects)</th></tr><tr><th>n</th><th>Mean</th><th>sd</th><th>n</th><th>Mean</th><th>sd</th></tr></thead><tbody><tr><td>Males</td><td>7</td><td></td><td></td><td>9</td><td></td><td></td></tr><tr><td>Right handed</td><td>10</td><td></td><td></td><td>12</td><td></td><td></td></tr><tr><td>Age</td><td>15</td><td>24.09</td><td>3.54</td><td>17</td><td>23.79</td><td>4.04</td></tr><tr><td>Non-verbal IQ</td><td>15</td><td>112.07</td><td>13.43</td><td>17</td><td>111.35</td><td>10.73</td></tr><tr><td>EVIP Vocabulary</td><td>15</td><td>123.07</td><td>2.94</td><td>15</td><td>120.67</td><td>5.26</td></tr><tr><td>Reading fluency (nb of correct words/min)***</td><td>15</td><td>196.16</td><td>36.98</td><td>17</td><td>113.53</td><td>24.70</td></tr><tr><td>Spelling (%)**</td><td>15</td><td>94.22</td><td>4.67</td><td>12</td><td>81.25</td><td>11.81</td></tr><tr><td>Orthographic choice accuracy (%)</td><td>14</td><td>95.07</td><td>5.47</td><td>15</td><td>89.60</td><td>9.82</td></tr><tr><td>RAN of objects (sec)**</td><td>15</td><td>61.80</td><td>10.23</td><td>17</td><td>78.37</td><td>17.85</td></tr><tr><td>RAN of digits (sec)***</td><td>15</td><td>31.87</td><td>6.56</td><td>17</td><td>44.78</td><td>12.34</td></tr><tr><td>RAN of colors (sec)**</td><td>15</td><td>54.33</td><td>9.15</td><td>17</td><td>71.21</td><td>18.80</td></tr><tr><td>Spoonerisms accuracy**</td><td>15</td><td>0.75</td><td>0.10</td><td>15</td><td>0.50</td><td>0.26</td></tr><tr><td>Digit span (%)***</td><td>15</td><td>10.73</td><td>1.58</td><td>17</td><td>7.35</td><td>2.85</td></tr></tbody></table>

As indicated by the number of participants, some values were missing for some tests.

Sec: seconds; Stars indicate significant differences between controls and dyslexics (independent t-test, ** for p < 0.01, *** for p < 0.001, uncorrected).

## MRI PREPROCESSING

We used statistical parametric mapping (SPM8; Wellcome Department of Imaging Neuroscience, UK; www.fil.ion.ucl.ac.uk) for fMRI standard preprocessing, which first involved realignment of each subject's functional images and coregistration with structural images. Structural images were segmented, spatially normalized to Montreal Neurological Institute stereotactic space to 3 × 3 × 3 mm and finally spatially smoothed with a 10-mm full-width half-maximum isotropic Gaussian kernel to compensate for residual variability after spatial normalization.

## EEG ACQUISITION

Continuous EEG data were recorded at 5 kHz from 62 scalp sites (Easycap electrode cap) using MR compatible amplifiers (BrainAmp MR and Brain Vision Recorder software; Brainproducts). Two additional electrodes (electro-oculograph, EOG and electrocardiograph, ECG) were placed under the right eye and on the collarbone. FCz was set as reference for the acquisition of all electrodes. Impedances were kept under 10 kΩ and EEG was time-locked with the scanner clock, which helps to have a very reproducible, more easily removed MRI artifact, thus resulting in higher EEG quality in the gamma band (Mandelkow et al., 2006).

## EEG PREPROCESSING

Gradient and pulse artifacts were first detected and marked using in-house software (wiki.cenir.org/doku.php/datahandler). Detection was achieved by correlating the data with automatically (for gradient) or manually (for pulse) defined templates. We used the raw signal of channel FC2 for gradient artifacts, and the raw ECG signal filtered between 0.5 and 10 Hz for pulse artifacts. In a second step, artifacts were corrected using two software: FASST v111017 (www.montefiore.ulg.ac.be/~phillips/FASST.html) for gradient artifacts and EEGlab v.9 (sccn.ucsd.edu/eeglab) for pulse artifacts. In both cases we used the principal component analyses methods implemented in the software. Blinks and vertical eye

movements were corrected using independent component analyses as described in EEGlab's tutorial. Data were subsequently down-sampled to 250 Hz and re-referenced to a common average reference. The original reference electrode was recalculated as FCz, generating a total of 63 cortical electrodes.

For each subject, periods with head movement artifacts were detected by visual inspection, for rejection purposes as described below. As our interest was in auditory activity, we focused the analyses described below on the temporal channels that best captured the activity of our ROIs, i.e., where we observed the highest correlation between EEG and BOLD: T7, T8, FT7, FT8, TP7, TP8, TP9, and TP10.

## ANALYZES

Analyzes are reported for the movie condition, as our interest was to explore brain oscillation properties during natural speech processing. Analyzes were also conducted on rest sessions but did not yield any significant result, in particular no group effect.

## TIME-FREQUENCY ANALYZIS

For each movie session, we performed time-frequency (TF) analyses with a Morlet wavelets approach using Fieldtrip (Oostenveld et al., 2011) at each channel for frequencies from 1 to 48 Hz bands, with a frequency step of 1 Hz and a time step of 0.1 s. For each frequency and channel, the previously detected periods of movement were rejected by replacing values by NaNs (Not A Number). The power time courses were converted to Z-scores, which, when larger than 4, were replaced by NaNs to remove residual artifacts. The transformed signal was then averaged over the eight temporal channels, Z-transformed a second time and averaged across frequency bands of interest: 1–3 Hz (delta), 4–7 Hz (theta) and 25–35 Hz (gamma). Finally NaNs were replaced by zeros. We removed (on average across all subjects) around 40 s of signal per movie condition (mean +/- s.e.m: controls: 36 +/- 8 s; dyslexics: 45 +/- 18 s; independent t-test: t = -0.44, p = 0.65).
## REGIONS OF INTEREST (ROI)

In accordance with AST theory we focused our analysis on the auditory cortex. We used three regions of interest (ROI) within Heschl's gyrus (Te1.0, Te1.1, and Te1.2), and the *planum temporale*: Te3 (Figure 1), all defined from cytoarchitectonic criteria using the SPM anatomy toolbox v.1.6. BOLD time courses were averaged over voxels using MarsBar (Brett et al., 2002).

## CORRELATION BETWEEN BOLD TIME COURSE OF ROIS AND EEG POWER SPECTRUM

For each ROI and frequency band (delta, theta and gamma), we built a design matrix to perform a partial correlation between the BOLD time course of the ROI and the EEG power. The EEG time courses (averaged across all eight electrodes) of each frequency band were convolved with the hemodynamic response function (HRF) and downsampled to 0.5 Hz, i.e., the fMRI sampling rate. This analysis was meant to capture the degree to which each ROI oscillates in each frequency band. A positive correlation indicates that the EEG power fluctuations in any given frequency band are reflected in the modulations of local synaptic activity as detected with fMRI (Laufs et al., 2003). Such correlations allow us to exploit the spatially detailed BOLD effect to precisely localize widespread EEG effects, resulting in a finer localization of oscillatory activity than with EEG only (Rosa et al., 2010a). We averaged over the whole set of temporal electrodes (left and right hemisphere) to avoid hemispheric biases in the EEG. Using this approach, asymmetries arise from specific correlations with the BOLD effect (Giraud et al., 2007). Our statistical model also included the motion parameters, their derivatives, the averaged signal of three brain compartments (white-matter gray-matter and CSF), and of all out-of-brain voxels, as nuisance covariates, as well as a regressor modeling the sessions. For normalization purposes the entire matrix was Z-transformed in time.

## GROUP LEVEL STATISTICS

A repeated-measures ANOVA (with Greenhouse-Geisser corrections when appropriate) was performed with SPSS (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0.

Armonk, NY), with the Fisher transformed partial correlation coefficients between EEG power and BOLD time courses as dependent variables, the 4 areas, 3 frequency bands and 2 hemispheres as within-subject factors, and group, sex and handedness as between-subject factors. All factors were entered as main effects in the model, as well as the 2nd, 3rd, and 4th order interactions of the 3 within-subject factors and group. Effect sizes were estimated for some measures of within or between group differences with Cohen's formula: $d = (M1 - M2)/SD_{pooled}$, with M1 = mean of group 1, M2 = mean of group 2, $SD_{pooled} = \sqrt{[(SD_1^2 + SD_2^2)/2]}$, $SD_1$ = standard deviation of group 1, $SD_2$ = standard deviation of group 2. All other analyses were done using Matlab (version 2011/2012) (The MathWorks Inc., Natick, Massachusetts, USA).

## RESULTS

The repeated-measures ANOVA yielded significant main effects of area and hemisphere, and a group × frequency × hemisphere interaction.

Across groups, frequencies and hemispheres, there was a main effect of area [$F_{(2,26.8)} = 7.45, p = 0.001$]. Correlations between EEG and fMRI were overall strongest in area Te1.2 and weakest in area Te1.1. This suggests that different regions of the auditory cortex show differential strength of responses in the frequency bands of interest, however, the specific correlation pattern observed here does not suggest any obvious interpretation. We also observed a main effect of hemisphere, with overall higher correlations in the right than in the left hemisphere [$F_{(1,28)} = 8.28, p = 0.008$], an effect that seems to be carried by the delta and theta bands [$F_{(1,28)} = 10.15, p = 0.004$ and $F_{1,28} = 7.04, p = 0.013$ respectively, see Figure 2].

<figure><img src="image_3.png" /><figcaption>**FIGURE 1 | Regions of interest.** Cortical cytoarchitectonic areas defined by the anatomy toolbox (axial plane, MNI: z = +11). Focus on the left auditory cortex and the four ROIs: Te1.1, Te1.0, Te1.2, Te3. Te1 is Heschl's gyrus and Te3 is the *planum temporale*.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**FIGURE 2 | Group × Frequency × Hemisphere interaction.** Individual beta values were averaged over ROIs and plotted along the different dimensions of the interaction: Frequency × Hemisphere interaction in controls (A) and dyslexics (B) and Frequency × Group interaction in the gamma (C), delta (D) and theta (E) bands. Error bars represent the s.e.m.
\*indicates a significant interaction or difference with *p* < 0.05. \#indicates a marginally significant difference, *p* = 0.066.</figcaption></figure>
With respect to the two target hypotheses on dyslexia, we
find that dyslexic individuals' auditory cortical function dif-
fers from controls more in the left than in the right auditory
cortex (group × frequency interaction in the left hemisphere
only), and more for the gamma than for the delta and theta
bands (group × hemisphere interaction for the gamma band
only). The present results hence are consistent with our pre-
vious conclusion (Lehongre et al., 2011), that dyslexia reflects
a primary disruption of left-hemisphere based gamma band
oscillations.

Exploring low frequency oscillations in the delta (1–3 Hz) and theta ranges (4–7 Hz), we confirmed a right dominance for delta-theta oscillations in the auditory cortex of both controls and dyslexics (Poeppel, 2003). However, as there was neither a group × hemisphere interaction for the delta and theta bands, nor any hint of a group difference in the right hemisphere, the present results do not lend much support to the hypothesis of a right-hemisphere based, delta or theta oscillation disruption (Goswami, 2011).

One possibility to explain the conflicting results between stud-
ies might be a language difference. Indeed studies reporting group
differences in delta-theta frequency bands were carried out in
English only (Abrams et al., 2009; Hamalainen et al., 2012). It
might be that speakers of a stress-timed language like English
acquire stronger sensitivity to delta and theta rhythms than those
of a syllable-timed language like French. However, this hypothesis
is not consistent with the results obtained in (stress-timed) Dutch
speakers by Poelmans et al. (2012). Another parameter to take
into account is that the BOLD signal is described as mostly driven
by oscillations in the beta/gamma band (Magri et al., 2012). This
might induce both greater EEG/BOLD correlations and a better
signal/noise ratio for this frequency band than for the delta/theta
range, and therefore increase our statistical power to detect group
differences in the gamma band. However, we do not find in our
data any evidence of greater correlations in the gamma than in the
delta-theta bands.

Our study also differs from the other EEG/MEG studies, as we
did not analyze simple EEG power spectrum. The reason is that
our stimulus being conversational speech, there is no multiply-
repeated stimulus that enables a well-defined spectro-temporal
analyzis of the evoked response. We did not analyze the phase
locking with the speech envelope either, because this would pro-
vide information about low frequencies (Howard and Poeppel,
2012), but not the gamma band, and we wanted to compare
both frequency bands. Our analysis of correlations between the
EEG power time course and the BOLD signal allowed us to esti-
mate the degree to which activations in specific brain regions
reflect cortical oscillations in given frequency bands. The EEG
signal alone lacks spatial resolution, and the BOLD signal alone
reflects much more than cortical oscillations. Here we were able
to interrogate auditory cortex very precisely, as variations in
EEG/BOLD correlations presumably reflect variations in corti-
cal oscillations in the specific areas of interest, that are probably
related to the fluctuations in amplitude of the speech envelope
modulations.

To what extent do our results depend on the naturalistic audio-
visual stimuli used here, as opposed to amplitude-modulated

sounds used in previous studies? Our exclusive focus on primary
and secondary auditory cortex makes it unlikely that we should
observe direct responses to either the higher-level linguistic infor-
mation, or the visual information. However, it is plausible that
the access to semantic information and the presence of synchro-
nized visual information (lip movements) may have served to
enhance the responses to the auditory stimuli, thereby improving
the signal/noise ratio.

As described by other functional and anatomical studies,
many measures highlight a weak or reversed brain lateralization
related to language in dyslexics, including in the *planum temporale*, labeled Te3 here (Galaburda et al., 1985, 1987; Altarelli et al., submitted). The lack of hemispheric specialization for delta, theta, and gamma oscillations could affect the efficiency of the auditory processing based on a dual-scale temporal integration (Poeppel, 2003). One possible consequence of this deficit of low gamma oscillations in dyslexics' left hemisphere is to lead them to segment the auditory input into non-standard phonemic units, hence distorting the very format of their phonological representations. This would be consistent with the general understanding of the phonological deficit in dyslexia (Mody et al., 1997; Adlard and Hazan, 1998; Serniclaes et al., 2004; Vellutino et al., 2004). Another possibility would be that such cortical oscillations do not directly affect the format of phonological representations, but their salience or their availability for downstream cognitive processes. Thus, the disruption of phoneme-relevant cortical oscillations is also compatible with the view that dyslexics' phonological representations are essentially normal, but that phonemic units are more difficult to rapidly access, to pay attention to, and to manipulate for dyslexic individuals (Ramus and Szenkovits, 2008; Ramus and Ahissar, 2012; Giraud and Ramus, 2013).

CONCLUSION

In this study, we used a combined EEG-fMRI paradigm to mea-
sure the oscillations of auditory cortical areas in response to
speech stimuli in dyslexic and in control adult participants,
in three frequency bands of interest: gamma (25–35 Hz), theta
(4–7 Hz) and delta (1–3 Hz). We first confirmed the presence
of an auditory sampling impairment in dyslexia, using natu-
ral speech listening conditions. Secondly, we further confirmed
that this impairment consisted mainly in a in a reduced respon-
siveness of left auditory cortex to gamma oscillations. On the
other hand, we found little evidence for a disruption in auditory
sampling in the delta and theta rates. Our results are consis-
tent with a large literature showing the difficulties of dyslexic
individuals with the representation, processing, or access to
phonemic units, and provide a possible brain basis for this
phenomenon.

ACKNOWLEDGMENTS

This work has been supported by the European Commission,
the Fondation pour la Recherche Médicale, the Neuropôle de
Recherche Francilien, the Fondation Bettencourt-Schueller, and
the Agence Nationale de la Recherche. We would like to thank
Nadège Villiermet for conducting the behavioral test battery on
the participants.
REFERENCES

Abrams, D. A., Nicol, T., Zecker, S., and Kraus, N. (2008). Right-hemisphere auditory cortex is dominant for coding syllable patterns in speech. *J. Neurosci.* 28, 3958–3965. doi: 10.1523/JNEUROSCI.0187-08.2008

Abrams, D. A., Nicol, T., Zecker, S., and Kraus, N. (2009). Abnormal cortical processing of the syllable rate of speech in poor readers. *J. Neurosci.* 29, 7686–7693. doi: 10.1523/JNEUROSCI.5242-08.2009

Adlard, A., and Hazan, V. (1998). Speech perception in children with specific reading difficulties (dyslexia). *Q. J. Exp. Psychol. A* 51, 153–177.

Altarelli, I., Monzalvo, K., Iannuzzi, S., Fluss, J., Billard, C., Ramus, F., et al. (2013). A functionally guided approach to the morphology of occipito-temporal regions in developmental dyslexia: evidence for differential effects in boys and girls. *J. Neurosci.* 10.1523/JNEUROSCI.5854-12.2013

Babiloni, C., Stella, G., Buffo, P., Vecchio, F., Onorati, P., Muratori, C., et al. (2012). Cortical sources of resting state EEG rhythms are abnormal in dyslexic children. *Clin. Neurophysiol.* 123, 2384–2391. doi: 10.1016/j.clinph.2012.05.002

Brett, M., Anton, J.-L., Valabregue, R., and Poline, J.-B. (2002). "Region of interest analysis using an SPM toolbox," in *Presented at the 8th International Conference on Functional Mapping of the Human Brain*, Vol. 16 (Sendai). Available on CD-ROM in NeuroImage, abstract 497.

Darki, F., Peyrard-Janvid, M., Matsson, H., Kere, J., and Klingberg, T. (2012). Three dyslexia susceptibility genes, DYX1C1, DCDC2, and KIAA0319, affect temporal-parietal white matter structure. *Biol. Psychiatry* 72, 671–676. doi: 10.1016/j.biopsych.2012.05.008

Dunn, L. M., Thériault, C. M., and Dunn, L. M. (1993). *Echelle de vocabulaire en images Peabody (EVIP)*. Toronto, ON: Psycan.

Eckert, M. (2004). Neuroanatomical markers for dyslexia: a review of dyslexia structural imaging studies. *Neuroscientist* 10, 362–371. doi: 10.1177/1073858404263596

Evans, T. M., Flowers, D. L., Napoliello, E. M., and Eden, G. F. (2013). Sex-specific gray matter volume differences in females with developmental dyslexia. *Brain Struct. Funct.* doi: 10.1007/s00429-013-0552-4. [Epub ahead of print].

Frederickson, N., Frith, U., and Reason, R. (1997). *Phonological Assessment Battery*. Windsor: nferNelson.

Galaburda, A. M., Corsiglia, J., Rosen, G. D., and Sherman, G. F. (1987). Planum temporale asymmetry: reappraisal since Geschwind and Levitsky. *Neuropsychologia* 25, 853–868. doi: 10.1016/0028-3932(87)90091-1

Galaburda, A. M., Sherman, G. F., Rosen, G. D., Aboitiz, F., and Geschwind, N. (1985). Developmental dyslexia: four consecutive patients with cortical anomalies. *Ann. Neurol.* 18, 222–233. doi: 10.1002/ana.410180210

Giraud, A. L., Kleinschmidt, A., Poeppel, D., Lund, T. E., Frackowiak, R. S., and Laufs, H. (2007). Endogenous cortical rhythms determine cerebral specialization for speech perception and production. *Neuron* 56, 1127–1134. doi: 10.1016/j.neuron.2007.09.038

Giraud, A. L., and Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. *Nat. Neurosci.* 15, 511–517. doi: 10.1038/nn.3063

Giraud, A. L., and Ramus, F. (2013). Neurogenetics and auditory processing in developmental dyslexia. *Curr. Opin. Neurobiol.* 23, 37–42. doi: 10.1016/j.conb.2012.09.003

Goswami, U. (2011). A temporal sampling framework for developmental dyslexia. *Trends Cogn. Sci.* 15, 3–10. doi: 10.1016/j.tics.2010.10.001

Goswami, U., Gerson, D., and Astruc, L. (2010). Amplitude envelope perception, phonology and prosodic sensitivity in children with developmental dyslexia. *Read. Writ.* 23, 995–1019. doi: 10.1007/s11145-009-9186-6

Goswami, U., Wang, H. L.S., Cruz, A., Fosker, T., Mead, N., and Huss, M. (2011). Language-universal sensory deficits in developmental dyslexia: English, Spanish, and Chinese. *J. Cogn. Neurosci.* 23, 325–337. doi: 10.1162/jocn.2010.21453

Hamalainen, J. A., Rupp, A., Soltesz, F., Szucs, D., and Goswami, U. (2012). Reduced phase locking to slow amplitude modulation in adults with dyslexia: an MEG study. *Neuroimage* 59, 2952–2961. doi: 10.1016/j.neuroimage.2011.09.075

Han, J., Mody, M., and Ahlfors, S.P. (2012). Gamma phase locking modulated by phonological contrast during auditory comprehension in reading disability. *Neuroreport* 23, 851–856. doi: 10.1097/WNR.0b013e32835818e1

Heim, S., Friedman, J.T., Keil, A., and Benasich, A. A. (2011).

Reduced sensory oscillatory activity during rapid auditory processing as a correlate of language-learning impairment. *J. Neurolinguistics* 24, 538–555. doi: 10.1016/j.jneuroling.2010.09.006

Herrmann, C. S., and Debener, S. (2008). Simultaneous recording of EEG and BOLD responses: a historical perspective. *Int. J. Psychophysiol.* 67, 161–168. doi: 10.1016/j.ijpsycho.2007.06.006

Howard, M. F., and Poeppel, D. (2012). The neuromagnetic response to spoken sentences: co-modulation of theta band amplitude and phase. *Neuroimage* 60, 2118–2127. doi: 10.1016/j.neuroimage.2012.02.028

Humphreys, P., Kaufmann, W. E., and Galaburda, A. M. (1990). Developmental dyslexia in women: neuropathological findings in three patients. *Ann. Neurol.* 28, 727–738. doi: 10.1002/ana.410280602

Laufs, H. (2012). A personalized history of EEG-fMRI integration. *Neuroimage* 62, 1056–1067. doi: 10.1016/j.neuroimage.2012.01.039

Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., et al. (2003). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. *Proc. Natl. Acad. Sci. U.S.A.* 100, 11053–11058. doi: 10.1073/pnas.1831638100

Lee, J. H., Durand, R., Gradinaru, V., Zhang, F., Goshen, I., Kim, D. S., et al. (2010). Global and local fMRI signals driven by neurons defined optogenetically by type and wiring. *Nature* 465, 788–792. doi: 10.1038/nature09108

Lefavrais, P. (1967). *Test de l'Alouette*, 2nd Edn. Paris: Editions du Centre de Psychologie Appliquée.

Lehongre, K., Ramus, F., Villiermet, N., Schwartz, D., and Giraud, A. L. (2011). Altered low-gamma sampling in auditory cortex accounts for the three main facets of dyslexia. *Neuron* 72, 1080–1090. doi: 10.1016/j.neuron.2011.11.002

Lindgren, S. D., De Renzi, E., and Richman, L. C. (1985). Cross-national comparisons of developmental dyslexia in Italy and the United States. *Child Dev.* 56, 1404–1417. doi: 10.2307/1130460

Linkersdorfer, J., Lonnemann, J., Lindberg, S., Hasselhorn, M., and Fiebach, C. J. (2012). Grey matter alterations co-localize with functional abnormalities in developmental dyslexia: an ALE meta-analysis. *PLoS ONE* 7:e43122. doi: 10.1371/journal.pone.0043122

Logothetis, N. K. (2010). Bold claims for optogenetics. *Nature* 468, E3–4; discussion: E4–E5.

Magri, C., Schridde, U., Murayama, Y., Panzeri, S., and Logothetis, N. K. (2012). The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies. *J. Neurosci.* 32, 1395–1407. doi: 10.1523/JNEUROSCI.3985-11.2012

Mandelkow, H., Halder, P., Boesiger, P., and Brandeis, D. (2006). Synchronization facilitates removal of MRI artefacts from concurrent EEG recordings and increases usable bandwidth. *Neuroimage* 32, 1120–1126. doi: 10.1016/j.neuroimage.2006.04.231

Mody, M., Studdert-Kennedy, M., and Brady, S. (1997). Speech perception deficits in poor readers: auditory processing or phonological coding? *J. Exp. Child Psychol.* 64, 199–231. doi: 10.1006/jecp.1996.2343

Morillon, B., Lehongre, K., Frackowiak, R. S. J., Ducorps, A., Kleinschmidt, A., Poeppel, D., et al. (2010). Neurophysiological origin of human brain asymmetry for speech and language. *Proc. Natl. Acad. Sci. U.S.A.* 107, 18688–18693. doi: 10.1073/pnas.1007189107

Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J. M. (2011). FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. *Comput. Intell. Neurosci.* 2011, 156869. doi: 10.1155/2011/156869

Pennington, B. F. (2006). From single to multiple deficit models of developmental disorders. *Cognition* 101, 385–413. doi: 10.1016/j.cognition.2006.04.008

Pinel, P., Fauchereau, F., Moreno, A., Barbot, A., Lathrop, M., Zelenika, D., et al. (2012). Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions. *J. Neurosci.* 32, 817–825. doi: 10.1523/JNEUROSCI.5996-10.2012

Poelmans, H., Luts, H., Vandermosten, M., Boets, B., Ghesquiere, P., and Wouters, J. (2012). Auditory steady state cortical responses indicate deviant phonemic-rate processing in adults with dyslexia. *Ear Hear.* 33, 134–143. doi: 10.1097/AUD.0b013e31822c26b9

Poeppel, D. (2003). The analysis of speech in different temporal integration windows: cerebral
lateralization as “asymmet-
ric sampling in time.” *Speech
Commun.* 41, 245–255. doi:
10.1016/S0167-6393(02)00107-3

Ramus, F., and Ahissar, M. (2012). Developmental dyslexia: the difficulties of interpreting poor performance, and the importance of normal performance. *Cogn. Neuropsychol.* 29, 104–122. doi: 10.1080/02643294. 2012.677420

Ramus, F., and Szenkovits, G. (2008). What phonological deficit? *Q. J. Exp. Psychol.* 61, 129–141. doi: 10.1080/17470210701508822

Raven, J., Raven, J. C., and Court, J.
H. (1998). *Advanced Progressive
Matrices*. Oxford: Oxford
Psychologists Press.

Richardson, F. M., and Price, C. J. (2009). Structural MRI studies of language function in the undamaged brain. *Brain Struct. Funct.* 213, 511–523. doi: 10.1007/s00429-009-0211-y

Richlan, F., Kronbichler, M., and Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. *Neuroimage* 56, 1735–1742. doi: 10.1016/j.neuroimage.2011.02.040

Rosa, M. J., Daunizeau, J., and Friston, K. J. (2010a). EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches. *J. Integr. Neurosci.* 9, 453–476. doi: 10.1142/S0219635210002512

Rosa, M. J., Kilner, J., Blankenburg, F., Josephs, O., and Penny, W. (2010b). Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI. *Neuroimage* 49, 1496–1509. doi: 10.1016/j.neuroimage.2009.09.011

Serniclaes, W., Van Heghe, S., Mousty, P., Carre, R., and Sprenger-Charolles, L. (2004). Allophonic mode of speech perception in dyslexia. *J. Exp. Child Psychol.* 87, 336–361. doi: 10.1016/j.jecp.2004.02.001

Soroli, E., Szenkovits, G., and Ramus,
F. (2010). Exploring dyslexics'
phonological deficit III: foreign
speech perception and produc-
tion. *Dyslexia* 16, 318–340. doi:
10.1002/dys.415

Tallal, P. (1980). Auditory temporary perception, phonics, and reading disabilities in children. *Brain Lang.* 9, 182–198. doi: 10.1016/0093-934X(80)90139-X

Telkemeyer, S., Rossi, S., Koch, S. P., Nierhaus, T., Steinbrink, J., Poeppel, D., et al. (2009). Sensitivity of newborn auditory cortex to the temporal structure of sounds. *J. Neurosci.* 29, 14726–14733. doi: 10.1523/JNEUROSCI.1246-09.2009

Vellutino, F. R., Fletcher, J. M., Snowling, M. J., and Scanlon, D. M. (2004). Specific reading disability (dyslexia): what have we learned in the past four decades? *J. Child Psychol. Psychiatry* 45, 2–40. doi: 10.1046/j.0021-9630. 2003.00305.x

Wagner, R. K., and Torgesen, J. K. (1987). The nature of phonological processing and its causal role in the acquisition of reading skills. *Psychol. Bull.* 101, 192–212. doi: 10.1037/0033-2909. 101.2.192

Wechsler, D. (2000). WAIS-III: Echelle de l'intelligence de Wechsler pour adultes. *Troisième Edn.* Paris: Editions du Centre de Psychologie Appliquée.

World Health Organization. (2011). *International Statistical Classification of Diseases and Related Health Problems*—10th

revision. Geneva: World Health
Organization.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 21 February 2013; accepted: 22 July 2013; published online: 09 August 2013.

Citation: Lehongre K, Morillon B, Giraud A-L and Ramus F (2013) *Impaired auditory sampling in dyslexia: further evidence from combined fMRI and EEG*. *Front. Hum. Neurosci.* 7:454. doi: 10.3389/fnhum.2013.00454

Copyright © 2013 Lehongre, Morillon, Giraud and Ramus. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.