Edited by: Christos Papadelis, Harvard Medical School, USA
Reviewed by: Danilo Bzdok, Research Center Jülich, Germany; Banu Ahtam, Boston Children’s Hospital, USA
This article was submitted to the journal Frontiers in Human Neuroscience.
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The neural causes of stuttering remain unknown. One explanation comes from neuroimaging studies that have reported abnormal lateralization of activation in the brains of people who stutter. However, these findings are generally based on data from adults with a long history of stuttering, raising the possibility that the observed lateralization anomalies are compensatory rather than causal. The current study investigated lateralization of brain activity in language-related regions of interest in young children soon after the onset of stuttering. We tested 24 preschool-aged children, half of whom had a positive diagnosis of stuttering. All children participated in a picture-naming experiment whilst their brain activity was recorded by magnetoencephalography. Source analysis performed during an epoch prior to speech onset was used to assess lateralized activation in three regions of interest. Activation was significantly lateralized to the left hemisphere in both groups and not different between groups. This study shows for the first time that significant speech preparatory brain activation can be identified in young children during picture-naming and supports the contention that, in stutterers, aberrant lateralization of brain function may be the result of neuroplastic adaptation that occurs as the condition becomes chronic.
Stuttering is a disorder of speech fluency that presents itself between the ages of 2 and 4 years. In the preschool population, the incidence is approximately 5% and the prevalence in the general population is 1%.
An early and influential theory of the brain basis of stuttering holds that its underlying cause is anomalous hemispheric lateralization of the speech control centers. Specifically, Orton (
Despite such findings, the theory that abnormal speech control lateralization drives stuttering still has currency in the general discourse around stuttering (e.g., Kushner,
Such compensatory plasticity has a well-established precedent in the lesion literature where transference of function between hemispheres has been observed in (e.g., Weiller et al.,
There is a missing piece in this puzzle that might help adjudicate between causal and reactive origins for hemispheric activation anomalies in stuttering. Given that stuttering emerges most commonly in the preschool years, observation of normal hemispheric laterality of brain activity during speech production would support the thesis that increases in the right hemispheric activation in adults who stutter are the result of compensatory mechanisms developed over a lifetime of stuttering. At present, there is no functional brain imaging evidence from children near the age of onset of stuttering. The present study was designed to provide such evidence.
This study was conducted with the approval of the Macquarie University Human Ethics Committee #HE29MAY2009-R06572. Preschool children who stutter (CWS) were recruited by newspaper advertisement. All were examined by a highly experienced speech pathologist (Elisabeth Harrison) who has more than 20 years of experience in the diagnosis and treatment of stuttering, prior to their inclusion in the study. Twelve children who were positively diagnosed as stutterers (CWS) were included in the study. The stutterers as a group were typical of the wider population of preschool age CWS in terms of the severity of their stuttering, i.e., all were in the range of mild–moderately severe with severity ratings between 3 and 6 (1 = no stuttering, 2 = extremely mild stuttering, 10 = extremely severe stuttering). This was expected since the distribution of stuttering severity is positively skewed in both children and adults (Bloodstein and Ratner,
Subjects performed a picture-naming task based on that presented in Levelt et al. (
Word | Age of acquisition (years) |
---|---|
Ear | 2.13 |
Dog | 2.23 |
Hand | 2.24 |
Sun | 2.34 |
House | 2.41 |
Bed | 2.42 |
Sock | 2.44 |
Spoon | 2.45 |
Cat | 2.5 |
Door | 2.55 |
Cup | 2.68 |
Box | 2.69 |
Shoe | 2.72 |
Cake | 2.73 |
Car | 2.73 |
Book | 2.79 |
Fish | 2.84 |
Bird | 2.87 |
Hat | 2.9 |
Duck | 2.93 |
Each subject received one training block to get acquainted with the procedure and to maximize name agreement across items. Subjects lay supine on a plinth in the magnetically shielded room and were presented with the picture-naming stimuli projected via a mirror onto a screen that was situated directly in the participant’s line of sight. The experimental presentation was controlled by the Presentation software package (Presentation 14.4, Neurobehavioral Systems, Albany, NY, USA).
Trials began with a white fixation cross appearing in the center of a black background. The duration of the fixation cross was randomly varied between 3000 and 4000 ms after which time, a picture appeared in the center of the screen. The subject was instructed to respond to the picture by naming it as quickly as possible. Vocal responses triggered a voice key connected to a directional microphone positioned on the ceiling of the magnetically shielded room above the subject’s head. Timestamps thus collected were used to determine vocal onset reaction times. Trials were terminated as soon as the voice key was triggered. The active response period was limited to 3000 ms. Stimuli were presented in blocks of 20 trials. A single block contained all of the 20 stimuli randomly shuffled prior to the start of the block. Subjects participated in one or two recording sessions.
Brain magnetic fields were measured during picture-naming using a custom built pediatric 64-channel whole-head gradiometer MEG system. A detailed description of specifications of this device is available in Johnson et al. (
Before subjects entered the magnetically shielded room for MEG data acquisition, their head shapes were recorded using a digitizing pen (Polhemus Fastrack, Colchester, VT, USA); approximately 200 randomly selected points were recorded for each subject’s head surface. The 3D locations of the five head position indicator (HPI) coils attached to a tightly fitting elastic cap, and the locations of three cardinal landmarks (the nasion and bilateral preauricular points) were also digitized. Each subject’s head position in the MEG dewar was measured at the start and end of each recording block from the five HPI coils.
Continuous data were acquired at a sampling rate of 1000 Hz and filtered online between 0.03 and 250 Hz. Fieldtrip (Oostenveld et al.,
In order to test whether stuttering status affected the evoked response to picture-naming stimuli, we used topological inference to search the entire sensor space for differences between groups. Based on the random field theory, topological inference for MEG data has been implemented in SPM8 (Litvak et al.,
Source analysis was performed in Matlab (2013b; MathWorks, Inc., Natick, MA, USA) using the SPM8 toolbox for M/EEG. A canonical cortical mesh derived from the MNI template was co-registered and warped, in a non-linear manner, to match the participant’s digitized headshape. Leadfields were computed using a single sphere volume conductor model. Source localization was then performed using a group inversion with multiple sparse priors (Friston et al.,
In order to minimize the potential for movement and EMG artifacts distorting the source estimation, trials were discarded in which the subject’s vocal reaction time was shorter than 700 ms. Based on the approach using MEG to measure language laterality developed by Tanaka et al. (
3D volumetric source maps were smoothed with a full width at half maximum (FWHM) smoothing kernel and passed to a second level SPM analysis. A paired
In order to test whether there was any effect of group or hemisphere on any of the activations within the ROIs, we performed a multivariate, repeated measures ANOVA on the between subjects factor Group (CWS or TD) and the within subjects factor Hemisphere (left or right) across the three ROIs, which were included as separate variates. This analysis was performed using IBM® SPSS® Statistics version 21.
In order to assess the degree of lateralization of the speech preparatory process, ROI masks for both hemispheres were constructed using the AAL atlas (Tzourio-Mazoyer et al.,
The average total number of trials contributing to the analysis was 190 for the PWS and 160 for the TD. There was no significant difference between the two groups in terms of trial numbers (
Following the onset of the picture-naming stimulus, sensor level waveforms were characterized by an m100/200 complex, which was largest over occipital areas – consistent with early visual activation. A later component, peaking around 450 ms, was evident bilaterally in temporal areas and in the left frontal region. This pattern of activation is illustrated in the grand mean sensor plots in Figure
Compared to baseline, there were six significant activation clusters in the brain during the epoch 300–600 ms after the onset of the naming stimulus (Figure
Cluster size (voxels) | Lobe | Area | Hemisphere | Brodmann areas | Peak intensity | MNI coordinates at peak (mm) |
---|---|---|---|---|---|---|
718 | Parietal | Precuneus, superior parietal lobule, inferior parietal lobule, paracentral lobule | L | 7, 5, 40, 3, 2 | 6.6 | −18 −52 58 |
Postcentral gyrus | ||||||
214 | Frontal | Middle frontal gyrus, inferior frontal gyrus | L | 9, 46, 45 | 5.6 | −44 22 30 |
107 | Parietal | Inferior parietal lobule, supramarginal gyrus | R | 40 | 6.0 | 54 −42 36 |
100 | Frontal | Precentral gyrus, middle frontal gyrus | R | 6 | 5.8 | 34 −10 54 |
30 | Temporal | Middle temporal gyrus | L | 39 | 5.7 | −52 −62 6 |
10 | Frontal | Middle frontal gyrus | L | 6 | 5.4 | −32 4 50 |
In the right hemisphere, there were two significant clusters: the largest was in the SMG intersecting with Brodmann area 40. The other significant cluster was within the SMA Brodmann area 6. There were no significant activation differences between groups.
There was a significant main effect of Hemisphere for all ROIs [IFG:
For all subjects, activity was lateralized to the left for all ROIs (Table
Subject | IFG |
STG |
SMG |
|||
---|---|---|---|---|---|---|
CWS | TD | CWS | TD | CWS | TD | |
1 | 0.16 | 0.19 | 0.36 | 0.23 | 0.18 | 0.14 |
2 | 0.17 | 0.20 | 0.15 | 0.18 | 0.01 | 0.11 |
3 | 0.16 | 0.18 | 0.14 | 0.25 | 0.07 | 0.22 |
4 | 0.19 | 0.18 | 0.27 | 0.38 | 0.26 | 0.22 |
5 | 0.17 | 0.20 | 0.23 | 0.28 | 0.22 | 0.19 |
6 | 0.17 | 0.18 | 0.55 | 0.27 | 0.55 | 0.28 |
7 | 0.17 | 0.18 | 0.14 | 0.30 | 0.08 | 0.30 |
8 | 0.15 | 0.19 | 0.28 | 0.21 | 0.11 | 0.26 |
9 | 0.17 | 0.17 | 0.20 | 0.22 | 0.19 | 0.13 |
10 | 1.00 | 1.00 | 0.31 | 0.60 | 0.36 | 0.50 |
11 | 0.18 | 0.20 | 0.29 | 0.23 | 0.09 | 0.12 |
12 | 1.00 | 0.15 | 0.35 | 0.34 | 0.33 | 0.29 |
ROI | CWS | TD | Two-sample |
All subjects | One-sample |
---|---|---|---|---|---|
IFG | 0.31 ± 0.09 | 0.25 ± 0.07 | 0.28 ± 0.06 | ||
STG | 0.27 ± 0.03 | 0.29 ±0.03 | 0.28 ± 0.02 | ||
SMG | 0.21 ± 0.04 | 0.23 ± 0.03 | 0.22 ± 0.03 |
The current results are the first functional brain imaging data of overt speech production in preschool-aged CWS. This is an important contribution to a literature based on results from older children and adults, whose brain functions have had many years to develop compensatory strategies.
There is a long history of attributing the cause of stuttering to atypical laterality of speech/language function. The roots of this theory can be traced back to publications in the early twentieth century by Orton (
An important caveat to this conclusion comes from the fact that a significant proportion of those children who begin to stutter will spontaneously recover [up to 80% by some estimations (Yairi and Ambrose,
The possibility remains though that anomalous laterality of other speech or language-related brain functions might exist in the early stages of stuttering. Indeed, a recent study using near infrared spectroscopy suggests that this may be the case in regard to some aspects of auditory language processing (Sato et al.,
While the interpretation of child MEG data in source space must be considered in light of the inherent uncertainties that govern solutions to the inverse problem, the concordance between the results of the whole-brain analysis presented herein and previous MEG studies of speech in adult subjects suggests that these findings are robust. In the time after early visual processing, and consistent with articulatory planning for speech (Levelt et al.,
Our conclusions regarding the lack of laterality differences must be considered within the scope of the limited part of the speech planning process that we have examined. It is important to emphasize that the time window beyond 600 ms was not taken into inversion analysis and, given that the average vocal reaction time was longer than 1000 ms, there remains a significant epoch in which laterality differences might manifest. It is, however, important to note that articulatory mouth movement begins significantly earlier than the onset of overt speech – similar studies to the current one suggest this difference is in the order of 300 ms in adult subjects (Salmelin et al.,
A number of previous neuroimaging studies using hemodynamic techniques (PET, fMRI) have shown there to be differences between stutterers and non-stutterers in the activation strength of various cortical and subcortical sources (e.g., Fox et al.,
In conclusion, we have demonstrated that in the very early stages of stuttering development, the preparation for speech is not characterized by anomalous lateralization of brain activations. This evidence gives weight to the hypothesis that the right hemispheric biases in chronic stuttering are due to neuroplastic adaptations rather than being an underlying primary source of dysfunction.
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.
This work was funded by the National Health and Medical Research Council (#1003760) and was also supported by the Australian Research Council Centre of Excellence for Cognition and its Disorders (CE110001021) (
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