ArticlePDF AvailableLiterature Review

The Neurobiological Grounding of Persistent Stuttering: from Structure to Function

Authors:

Abstract and Figures

Neuroimaging and transcranial magnetic stimulation provide insights into the neuronal mechanisms underlying speech disfluencies in chronic persistent stuttering. In the present paper, the goal is not to provide an exhaustive review of existing literature, but rather to highlight robust findings. We, therefore, conducted a meta-analysis of diffusion tensor imaging studies which have recently implicated disrupted white matter connectivity in stuttering. A reduction of fractional anisotropy in persistent stuttering has been reported at several different loci. Our meta-analysis revealed consistent deficits in the left dorsal stream and in the interhemispheric connections between the sensorimotor cortices. In addition, recent fMRI meta-analyses link stuttering to reduced left fronto-parieto-temporal activation while greater fluency is associated with boosted co-activations of right fronto-parieto-temporal areas. However, the physiological foundation of these irregularities is not accessible with MRI. Complementary, transcranial magnetic stimulation (TMS) reveals local excitatory and inhibitory regulation of cortical dynamics. Applied to a speech motor area, TMS revealed reduced speech-planning-related neuronal dynamics at the level of the primary motor cortex in stuttering. Together, this review provides a focused view of the neurobiology of stuttering to date and may guide the rational design of future research. This future needs to account for the perpetual dynamic interactions between auditory, somatosensory, and speech motor circuits that shape fluent speech.
Content may be subject to copyright.
Preprint submitted to Current Neurology and Neuroscience Reports (2015).
Published in final edited form as:
Curr Neurol Neurosci Rep. 2015 Sep; 15(9):579. DOI: 10.1007/s11910-015-0579-4
The neurobiological grounding of persistent stuttering:
From structure to function
Nicole E. Neef
1,
*, Alfred Anwander
1
, and Angela D. Friederici
1
1
Max Planck Institute for Human Cognitive and Brain Sciences,
Department of Neuropsychology, Leipzig, Germany.
* Corresponding author:
Nicole E. Neef
Max Planck Institute for Human Cognitive and Brain Sciences
Department of Neuropsychology
Stephanstr. 1a, 04103 Leipzig, Germany
Phone: +49 341 9940 2230
http://www.cbs.mpg.de/~neef
Preprint submitted to Curr Neurol Neurosci Rep (2015). Final draft: 06/2015
The neurobiological grounding of persistent chronic stuttering: From structure to function
2
Abstract
Neuroimaging and transcranial magnetic stimulation provide insights into the neuronal
mechanisms underlying speech disfluencies in chronic persistent stuttering. In the
present paper, the goal is not to provide an exhaustive review of existing literature, but
rather to highlight robust findings. We, therefore, conducted a metaanalysis of diffusion
tensor imaging studies which have recently implicated disrupted white matter
connectivity in stuttering. A reduction of fractional anisotropy in persistent stuttering
has been reported at several different loci. Our metaanalysis revealed consistent
deficits in the left dorsal stream and in the interhemispheric connections between the
sensorimotor cortices. In addition, recent fMRI metaanalyses link stuttering to reduced
left frontoparietotemporal activation while greater fluency is associated with boosted
coactivations of right frontoparietotemporal areas. However, the physiological
foundation of these irregularities is not accessible with MRI. Complementary,
transcranial magnetic stimulation (TMS) reveals local excitatory and inhibitory
regulation of cortical dynamics. Applied to a speech motor area TMS revealed reduced
speechplanningrelated neuronal dynamics at the level of the primary motor cortex in
stuttering. Together, this review provides a focused view of the neurobiology of
stuttering to date, and may guide the rational design of future research. This future
needs to account for the perpetual dynamic interactions between auditory,
somatosensory, and speech motor circuits that shape fluent speech.
Keywords
Persistent developmental stuttering, Metaanalysis, Speech production, Diffusion tensor
imaging, Transcranial magnetic stimulation, Diffusion MRI tractography
Keywords
AF arcuate fasciculus
ALE activation likelihood estimation
DTI diffusion tensor imaging
FA fractional anisotropy
FDR false discovery rate
IFG inferior frontal gyrus
IPL inferior parietal lobe
M1 primary motor cortex
MEP motor evoked potential
MFG middle frontal gyrus
MTG middle temporal gyrus
SLF superior longitudinal fasciculus
SMA supplementary motor area
SMG supramarginal gyrus
SPL superior parietal lobe
STG superior temporal gyrus
TBSS tractbased spatial statistics
TMS transcranial magnetic stimulation
VBS Voxel based statistics
The neurobiological grounding of persistent chronic stuttering: From structure to function
3
Introduction
Stuttering is a speech disorder which most often occurs between the age of 3 and 6 years
[1]. Lifespan incidence is higher than 5 %, with high rates of recovery (5287 %) [2, 3].
Lifespan prevalence is 0.72 % with a sex ratio of 2.3 [4]. Neither etiology nor
pathogenesis is known [5]; thus, stuttering is characterized by its symptoms. The
hallmark signs of stuttering are involuntary sound and syllable repetitions, sound
prolongations, and speech blocks [6]. In some cases, additional facial and limb
movements such as grimacing, hand tapping, or stamping with one’s foot accompany
these speech motor signs. Strategies to avoid stuttering include word substitutions,
sentence reordering, but also to fall silent in certain situations. Failure in communication
provokes negative emotions such as fear and embarrassment. The course of stuttering
varies across individuals and distinct phenotypes emerge. Depending on severity,
stuttering critically compromises quality of life [7].
Similar to other behaviourally defined disorders, the cause of stuttering is multifactorial
and is associated with various genetic and environmental risk factors. The large
presence of familial stuttering and the high concordance rate in twins support a genetic
role in stuttering [8]. To date, few linkage studies have nominated contributing genes [9,
10]. Genome–wide significance [10] still awaits replication [11••] and more genome
wide association studies are required [12]. It remains to be seen whether future efforts
will demonstrate the polygenetic basis of stuttering and thus shed light on the questions
of involved transmission models, chromosomes, genes, or sex factors.
The phenomenon of stuttering has given rise to manifold theories, each shaped by the
perspective of a certain field such as for example analytic psychology [13], speech and
language pathology [1, 5, 14], psychology [15, 16], linguistics [1719], biomechanics
[2022] and neuroscience [2327]. Neurosciencebased hypotheses have included an
aberrant dominant hemisphere structure [2830], basal ganglia dysfunction [23], a
disconnection syndrome [31], altered brain timing networks [25, 26, 32, 33], or an
altered sensorimotor integration [20, 34, 35], mostly interrelating with each other. This
multiplicity of causes is plausible due to the fact that a broad assortment of linguistic,
cognitive, and sensorimotor processes are involved in speech production. Speech is a
very complex sensorimotor action, and its intimate connection to language, a defining
feature of human cognition, makes speech and stuttering a very complicated field of
study for neuroscientists and neurologists. In the last 30 years, studies on the
neurobiology of stuttering have improved our understanding of potential mechanisms,
but there are still fundamental questions open. Here, we will summarize the main
neuroscientific findings on chronic persistent stuttering.
The continuous speech stream
The ultimate readout of language planning and speech motor control is articulation that
results in an audible, smooth, continuous stream of speech. Articulation is a demanding
coordinative challenge because it requires the orchestration of respiratory, laryngeal,
The neurobiological grounding of persistent chronic stuttering: From structure to function
4
and supralaryngeal structures by using approximately 100 muscles [36]. The
respiratory system regulates the outflow of air during speech and thus provides energy
for the acoustic targets of speech. The laryngeal system generates the quasiperiodic and
tonelike sound fundamental for pitch modulation, vowels, and voiced consonants (e.g.,
[b], [z], and [m]). Voiceless and aspirated consonants (e.g., [p], [s], and [h]) require
timely voice offsets transmitted by short transient glottal abductions. The
supralaryngeal system comprises the pharyngeal, oral, and nasal cavities whose
architecture and configuration shape the timbre and sound of the generated acoustic
signal. The supralaryngeal system, also called the vocal tract, can be constricted at
different places, for example via lip closure; lip protrusion; tongue tip or body elevation,
or retraction; and velum elevation. Characteristic sound features of speech vowels are
generated by articulatory gestures such as jaw lowering, tongue body elevation, and lip
protrusion. In contrast, distinct acoustic features of consonants are generated by the
magnitude of obstruction, resulting in bursts due to closure and frictionlike noise due
to finetuned constriction.
During speaking, our articulators are continuously in motion [37]. Our thoughts are
transformed into coupled articulatory patterns that carry specific melodies and
rhythms. Prosody and articulation are built upon motor units that act on multiple
timescales. Their execution happens simultaneously, in an overlapping or subsequent
manner continuously adapting to everchanging contexts due to changes in speaking
rate, coarticulation, or emotional load. Imagine a machine buildup of all necessary
effectors and degrees of freedom enabling the spatiotemporal dynamics of sound
production. Why would such a machine only produce scattered sounds but not smooth,
fluid speech? One aspect is the unsolved problem of prosodic modeling in speech
synthesis [38]. The other problem is a missing feedback system in current speech
synthesis programs. Human speech production is closely coupled to its perception. The
key to fluent speech is a productionperception interaction. The timely sequencing and
contextdependent binding of speech units are constantly monitored and adjusted by an
effective sensorimotor integration [39]. Feedbackrelated control couples not only
perception and production processes but also internal models that closely relate to the
sound envelop of a corresponding utterance [40] possibly translating auditory targets
into motor commands. For this reason, it is necessary to consider the output and input
systems as well as internal models, interfaces, and monitors to comprehensively
elucidate the neurobiology of stuttering.
Neural underpinnings of persistent stuttering: From structure to
function
Chronic persistent stuttering is highly heterogeneous with regard to symptoms,
avoidance behavior, applied strategies to overcome disfluencies, and severity. Therefore,
it is not surprising that imaging studies have produced diverse, puzzling, and sometimes
contradictory results [41]. It has been suggested that the “core” of the stuttered
The neurobiological grounding of persistent chronic stuttering: From structure to function
5
response may have nothing to do with changes in functional imaging observed at rest,
during speech, or following therapy [42]. This review will not outline the diverging
neuroimaging findings of the last 30 years. In fact, we rather have concentrated on
published findings from diffusion tensor imaging (DTI) in an informative metaanalysis
to obtain the most robust white matter changes in persistent stuttering currently
reported. Subsequently, we relate these structural findings to irregular brain function as
described in two recent activation likelihood estimation (ALE) meta–analyses [43••,
44••]. To account for the fact that the neural organization of speaking employs recurrent
networks working at a high temporal resolution, we complement the view by reviewing
results of those few transcranial magnetic stimulation (TMS) studies available.
DTI The left dorsal stream and interhemispheric somatosensory
connections are affected in stuttering
Fractional anisotropy (FA) is the most frequently reported parameter of DTI. It
measures the directionality of water molecule mobility on a submillimeter scale. This
directedness is especially high along the myelinated axons of the white matter, though
orientation distribution of axons and the degree of myelination are not the only
influencing factors. Axon diameter distribution and the axonal tissue fraction or density
affects the magnitude of the FA as well. Moreover, the macroscopic geometrical
arrangement of white matter bundles such as crossing or fanning fibers comes into play
especially at the low resolutions of 23 mm³ usually employed in human diffusion
weighted MRI. However, a reduced FA is commonly interpreted as less coherent white
matter structure [45]. Group comparisons of neuroimaging parameters are not trivial, as
individually shaped brains need to be aligned to a common space. To render DTI group
statistics possible, this normalization is most often achieved by the projection of voxels
with the highest FA in the center of each gyrus or white matter tract to a skeleton that
represents a common tractbased template for the studied group (tractbased spatial
statistics, TBSS [46]).
To date, nine DTI studies have reported wholebrain FA reductions from white matter
regions in cases with persistent stuttering (Table 1). Sixty widespread loci result from
the seven studies that examined subjects older than 14. Loci number and variability
increase when adding studies in children (aged 3 to 12) as well (Fig. 1a). To reduce
dimensionality, we calculated an informative metaanalysis of the coordinates of
decreased FA using the ALE method. This method was introduced for the metaanalysis
of functional MRI activation maps and detects threedimensional conjunctions of
coordinates, weighted by sample size [47]. The 60 loci that were included were from
seven studies which interrogated 121 persons who stutter and 124 fluent speakers aged
14 to 52 years. Higher FA values in persons who stutter were not considered because
increases are infrequently reported. The current analysis yielded three clusters of lower
FA values in persons who stutter (p<0.001; FDR q<0.05; Fig. 1b), located in the left
hemisphere and in the corpus callosum.
The neurobiological grounding of persistent chronic stuttering: From structure to function
6
Subsequent deterministic DTI tractography served to estimate the course of the white
matter connections passing through the significant clusters of the current meta
analysis. The chosen highquality diffusion tensor image of a representative single
young healthy subject has an isotropic resolution of 1 mm acquired on an ultrahigh
field MRI scanner using 60 diffusion directions and 4 averages [48, 49]. The first cluster
was located in the left superior longitudinal fasciculus (SLF III, 344 mm³ centered at
{41, 53, 42}; Fig. 1c left) of the inferior parietal lobe (IPL) adjacent to the angular
gyrus and the posterior division of the supramarginal gyrus (SMG). Reconstructed
connections terminated in the postcentral gyrus, in the ventral premotor cortex, and in
the posteriorventral area of the inferior frontal gyrus (IFG) pars opercularis as part of
Broca’s area. The second cluster was located below the fundus of the left central sulcus
in the left SLF but this time also including fibers of the arcuate fasciculus (AF, 280 mm³
centered at {38, 22, 30}; Fig. 1c middle). Connections terminated frontally in the
ventral motor cortex, in the ventral premotor cortex, and in the posterior part of Broca’s
area, the IFG pars opercularis; parietal terminations reached the SMG and the angular
gyrus; and temporal terminations reached the posterior superior temporal gyrus (STG)
and in the middle temporal gyrus (MTG). The third cluster was placed in the posterior
midbody of the corpus callosum (240 mm³ centered at {3, 22, 25}; Fig. 1c right) where
interhemispheric fibers pass through and terminate at the postcentral and precentral
gyri close to the vertex.
Our current metaanalysis related the most robust white matter changes in stuttering to
the left dorsal language stream. This is in line with diffusion tractography studies
reporting a reduced FA in these streams [50], the absence of streamlines in a large
portion of the left AF [51], as well as a reduced tractography density of the left SLF III
[52] in persons who stutter compared to fluent speakers. The four branches of the SLF
and AF are the prominent fiber bundles mediating the interaction between frontal,
parietal, and temporal regions [5355] also evident in the current tractography
results.
Another robust outcome of the current metaanalysis was the reduced FA in the
interhemispheric fibers of the posterior midbody of the corpus callosum. Our
deterministic fiber tracking showed that the disrupted callosal connections most likely
connect sensorimotor regions (Fig. 1c right). The reconstructed pathways link medial
regions of the post and precentral gyri, but the more lateral regions that are known to
control orofacial structures were not involved. Before drawing conclusions on this
restricted course, one should consider that transcallosal fibers are massively crossed by
orthogonal association and projection fibers. The DTI tractography algorithm used is
influenced by these crossing fiber populations, and the reconstruction of all callosal
connections cannot be easily solved [56]. No diffusion tractography study on stuttering
has fully reconstructed transcallosal connections. This may be due to these
methodological difficulties. Hence, the current tractogram does not allow ruling out an
involvement of fibers terminating in ventral sites of the sensorimotor cortex.
The neurobiological grounding of persistent chronic stuttering: From structure to function
7
The interpretation of the reduced FA values is not trivial. Particularly, the dorsal stream
is affected by crossing fibers from transcallosal as well as corticospinal and
corticothalamic connections or other subcortical loops. Whether FA reductions result
from a weakened intrahemispheric connectivity, a strengthened interhemispheric
connectivity, or both remains to be shown. Ultrahighfield imaging [48] in combination
with a sophisticated tracking algorithm [56] might disentangle macroanatomyrelated
changes. In contrast, FA is not affected by crossing fibers within the corpus callosum;
fibers run exclusively in one direction, reducing the number of variables that influence
FA to the degree of myelination, axon diameter distribution, and axon population
density. The axons with the largest diameter reside in the posterior midbody of the
corpus callosum [57] in healthy human subjects. From this, it follows that the
interhemispheric transmission is fastest and most efficient in this area which is capable
of transmitting reliable, precisely timed neuronal coupling. Hence, it is plausible that the
frequencyspecific interhemispheric correlation structure of spontaneous oscillatory
neuronal activity is nested in the highest frequency range (3245 Hz) between the
sensorimotor cortices compared to the temporal lobes (46 Hz) and the lateral parietal
areas (823 Hz) [58]. Largediameter axon fibers may also determine the degree of
interhemisphericcorrelated fMRI restingstate activity which is again highest in the
somatosensory cortices [59]. In stuttering, the reduced FA could be related to either
reduced myelination or altered axonal diameter distribution [60] in the affected area.
However, these two possibilities could have different outcomes: While reduced
myelination would cause a deficient interhemispheric interaction, increased density of
largediameter axon fibers could result in a strengthened interhemispheric interaction.
For this reason, it would be desirable to employ advanced methods that better resolve
the axon diameter distribution “in vivo” [61, 62].
To summarize, non–invasive “in vivo” DTI provides the most important insights into
connectivity changes of brain networks in stuttering. Short and longrange widely
integrated, parallel, and often redundant neuronal subcircuits supply speech fluency. It
is likely that connectivity changes of speechrelevant perisylvian brain areas lead to
disruption of speech functions. Our metaanalysis emphasized the important role of left
hemisphere corticocortical connections, namely the SLF and the AF, and transcallosal
connections of the posterior midbody for fluent speech production. However, right
hemisphere connectivity [50, 51, 63••, 64–66] as well as axons of the corticospinal tract
[50, 63–68], thalamic [64, 67], and cerebellar [50, 63••, 64, 65] connections have also
been reported to show irregularities in stuttering. Similar to other behavioral and
cognitive processes, fluent speech production depends on the embedding of various
areas in the human connectome [69••]. The following section of functional imaging
changes in stuttering mainly summarizes the altered recruitment of cortical and
subcortical areas suggesting irregular input and output operations within the speech
related connectome.
The neurobiological grounding of persistent chronic stuttering: From structure to function
8
Table 1 Diffusion tensor imaging studies published between August 2002 and May 2015
DTI/DSI Study
Method
PWS
Ctr
Gender
Age
range
p value
TBSS/VBS
Sommer et al. [31]
VBS
15
15
M/F
18 44
0.001
Watkins et al. [65]
TBSS
17
13
M/F
14 27
0.0025
Chang et al. [64]
TBSS
9
12
M
9 12
0.001
Kell et al. [106]
TBSS
13
13
M
18 44
0.001
0.05*
Connally et al. [50]
TBSS
29
37
M/F
14 45
0.002**
Cai et al. [66]
TBSS
20
18
M/F
18 47
0.002**
Cykowski [125]
TBSS
13
14
M
Nan
0.05*
Civier et al. [67]
TBSS
14
14
M/F
19 52
0.001
0.05
#
Chang et al. [63••]
TBSS
37
40
M/F
3 10
0.001
Fibertracking
Affected tracts
Connally et al. [50]
probablistic
29
37
L corticospinal tract, L & R AF
Chang et al. [52]
probablistic
15
14
L SLF, L AF
Cieslak et al. [51]
deterministic,
DSI
8
8
L & R AF, L temporalstriatal tract
KronfeldDuenias
et al. [68]
deterministic
15
19
L & R frontal aslant tract, L corticospinal
tract
VBS = voxel based statistics, TBSS = tractbased spatial statistics, PWS = persons who stutter, Ctr
= controls, M = male, F = female, SLF = superior longitudinal fasciculus, AF = arcuate fasciciulus
*Corrected p values; **k ≥ 10;
#
Corrected p value (one-tailed)
fMRI Right frontal overactivation characterizes stuttering while
right parietotemporal coactivation characterizes greater fluency
So far, we have only elaborated on structural imaging, focusing particularly on white
matter integrity and thus the connectome. A lot is already known about the underlying
function of the connections that come into focus here. Predominantly, left dorsal paths
subserve linguistic as well as speech motor functions. The AF, the medial part of the
dorsal stream, connects the IFG pars opercularis to the STG and mediates complex
syntax [70, 71] and phonology [72•]. Sublexical repetition of speech [73], speech
planning [72•], and articulation [74] map to the lateral part of the dorsal stream and the
indirect anterior portion of the SLF connecting the precentral gyrus to the SMG and the
The neurobiological grounding of persistent chronic stuttering: From structure to function
9
STG [55]. Articulatory phonetic skills rely on an effective auditorymotor integration
partly mediated by the recurrent networks of these dorsal streams [53, 75••, 76].
The functional anatomy underlying stuttering has mostly been studied with positron
emission tomography [7782] and functional magnetic resonance imaging [8387]. Two
activation likelihood meta–analyses on stuttering were recently published [43••, 44••].
The metaanalyses considered 23 functional imaging studies published over the past 30
years; these included 213 [44••], and 222 [43••] persons who stutter and 186 [44••],
and 188 [43••] control subjects, and Fig. 1d, e summarizes their outcome.
These metaanalyses highlight the neurofunctional hallmark signs of persistent chronic
stuttering. What is striking is the consistent overactivation of the frontal motor areas of
the right hemisphere encompassing the primary motor cortex, the premotor cortex, the
presupplementary motor area (preSMA), the supplementary motor area (SMA), the
IFG, the insula, and the frontal and the rolandic operculum [43••, 44••] (Fig. 1d). An
opposite pattern of cortical activity emerges in the left hemisphere. Here, frontal regions
show no overactivation but instead a reduced activation of the M1 larynx area
combined with reduced activity in the planum temporale and the middle temporal
gyrus. The left cerebellar vermis and the left red nucleus also display robust imaging
changes that emerge from a comparison of speechrelated hemodynamic differences
between persons who stutter and fluent speakers. The only region that shows a higher
activation is the right parietal lobe. Stronger activations are located in the anterior
intraparietal sulcus and in the IPL PFcm. The remarkable right hemisphere over
activation in stuttering suggests an imbalanced hemispheric lateralization [28, 29]. It is
not yet clear whether this imbalance causes stuttering, whether it is the result of
impeded left frontoparietotemporal signal processes, or if it reflects compensatory
mechanisms [31, 78, 84, 8890].
Every investigation of stuttering tries to find out how fluency of speech production can
be attained. Therefore, imaging contrasts that relate brain activations to greater fluency
in persons who stutter are of special interest. In the right hemisphere, such contrasts
show a shift of activation patterns to parietal areas spanning several loci in the IPL,
heavy involvement of the temporal lobe (Heschl’s gyrus, planum temporale, and STG)
and the cerebellum. Greater fluency is associated with the recruitment of superior
temporal and inferior parietal regions in both hemispheres, whereas severe stuttering is
associated with dysfunction of a distributed network of classical motor areas engaging
sensorimotor regions amongst the central sulcus including the left and right
somatosensory cortex, the left larynx motor cortex, extended regions of the IFG
including the left pars opercularis, the left pars triangularis and right pars orbitalis,
bilateral SMA, and the cerebellum (Fig. 1e). Fluencyrelated activations in unimodal and
heteromodal association areas of the parietal and right temporal lobe, the right pars
opercularis, and the posterior ventral part of right Broca’s region strongly suggest an
important role of internal models and feedforward and feedback relevant control
mechanisms during speaking. In fluent speakers, lateralization of speech production
seems to start in the left temporal and parietal regions [91], namely the somatosensory
The neurobiological grounding of persistent chronic stuttering: From structure to function
10
cortex, the auditor cortex, and the planum temporale which might be the source of the
early sound feature–related cortical entrainment observed in left Broca’s area and the
left premotor cortex even ahead of external speech production [40]. Equivalent studies
in stuttering are missing, leaving the question open as to whether right lateralization
already occurs in the planning stage. However, one TMS study has indeed observed
missing lateralization at an early stage.
Figure 1 DTI (ac) and fMRI (d, e) imaging changes associated with persistent
chronic stuttering (a) Loci of reduced FA as reported in multiple studies were shown
on a transparent isosurface of the MNI brain. Red spheres indicate foci from studies of
persons aged 14 to 52 who stutter, and orange spheres indicate loci from children aged
The neurobiological grounding of persistent chronic stuttering: From structure to function
11
3 to 10 who stutter [63]. (b) Blue illustrates clusters of reduced FA in persistent
stuttering as derived from a metaanalysis using the activation likelihood estimation
(ALE) method (p<0.001; FDR q<0.05). (c) Diffusion tractography derived from full brain
deterministic fiber tracking [125] in an ultrahighresolution DTI data set of a single
subject [49]. Tracts are shown that cross a sphere with a diameter of 5 mm surrounding
the MNI coordinates of the metaanalysis after a linear registration to the subject’s
native space. (d) Trait stuttering is captured by contrasts between persons who stutter
and fluent speakers. Therefore, it reveals brain areas that are either more active (red
and orange dots) or less active (blue and light blue dots) in persons who stutter
compared to fluent speakers. Right hemisphere overactivations reside in the precentral
gyrus, lip motor cortex, rolandic operculum, insula, IFG pars opercularis, IFG pars
orbitalis, preSMA, middle frontal gyrus, IPL and SPL. Left hemisphere overactivations
reside in the SMA and in the SPL. Left hemisphere underactivations are located in the
left larynx motor cortex, left MTG, left superior temporal sulcus, cerebellar vermis, and
the red nucleus [43••, 44••]. Trait stuttering contrasts enlighten brain abnormalities
that cause stuttering or that compensate for it. (e) Supplementary, state stuttering
analyses capture withingroup contrasts which enlighten areas in the brain that are
more active when fluency is enhanced (green and light green dots) compared to areas
that are more active when fluency is worse (purple and violet dots). Disfluency related
activations reside in Broca’s area in the right IFG pars orbitalis, the left IFG pars
opercularis and pars triangularis, bilaterally in the SMA, the somatosensory cortex, and
the cerebellum, and in the left precuneus and the left globus pallidus. Fluencyrelated
activations reside mostly in the right hemisphere, namely the Heschl gurus, the planum
temporale, the posterior STG, MTG, SMG, IPL, IFG pars opercularis and the MFG. Left
hemisphere correlates are in the IPL [43••, 44••]. State stuttering contrasts might reveal
causes of stuttering events, attempts to compensate for stuttering, or the correlates of
stuttering as a motor act [44••].
TMS indicates a restricted range of neuronal dynamics at the level of
the primary motor cortex in stuttering
Both DTI and fMRI elucidate the spatial distribution of largescale neuronal dysfunction
in persistent stuttering, but its physiological basis remains unclear. Nonlinear neuronal
dynamics consist of excitatory and inhibitory activation, but these cannot be
discriminated with in vivo neuroimaging. The only noninvasive technique that allows
to measure excitatory and inhibitory brain function in healthy humans is TMS [92]. A
TMS pulse induces currents in conductive tissue such as the human cortex. When
applied to the motor cortex, neurons are stimulated and evoke motor potentials (MEP)
serving as a readout measure of excitability dynamics of local circuits. Statedependent
excitability regulation is quantified by comparing baseline MEP amplitudes with
amplitudes resulting under test conditions. Fortunately, the primary motor cortex is the
final overarching cortical output region [93] that generates speech behavior. Almost all
The neurobiological grounding of persistent chronic stuttering: From structure to function
12
dysfunctional computations accumulate at this site, making it an attractive target for
stuttering research even in the nonspeech domain [9496].
Pairedpulse TMS protocols are suitable for testing intracortical inhibitory and
excitatory circuits [97]. Compared to singlepulse responses, MEP amplitudes are
reliably reduced when a subthreshold pulse is followed by a superthreshold pulse with a
short interstimulus interval of 2 to 3 ms. This inhibition is likely caused by excited
GABAergic interneurons [98, 99]. In stuttering, ipsilateral and contralateral tongue
representations in the left and right hemisphere showed a delayed inhibition of
intracortical circuits [100]. The opposite phenomenon, intracortical facilitation, can be
generated when applying paired pulses with longer interstimulus intervals of 10 to 15
ms. In this case, MEP amplitudes are amplified driven by the sensory input on excitatory
motor circuits [101••]. In stuttering, this facilitation is remarkably reduced in the
primary motor tongue area of both hemispheres [100]. Thus, intracortical excitability
regulation is hampered in an area that controls one of the main effectors of articulation.
The combined reductions of intracortical inhibition and facilitation indicate a restricted
range of neuronal dynamics at rest.
Although orofacial midline muscles such as the tongue are bilaterally innervated from
corticobulbar projections of both hemispheres, speech motor plans are primarily
encoded in the left hemisphere motor cortex. However, this functional asymmetry
towards the left orofacial motor cortex is missing in stuttering [102••], suggesting that a
lack of a speechmotorplanninginduced facilitation of the left orofacial motor cortex is
a major pathophysiological cause of disfluent speech production. This lack might be
related to the underactivation of this area [32] as frequently reported in neuroimaging
studies [44••] implicating a fallible transmission or integration of speechplanning
related feedforward signals [20, 33, 103•]. Conversely, given the regularly reported
overactivation of the right primary motor cortex in stuttering [77, 85, 87, 104106],
one might expect to see a speechplanninginduced facilitation of this site, but this
pattern was not noted [102••].
The right hemisphere is known to play a dominant role in prosody perception and
production [107110]. One theory on stuttering suggests a misalignment of segmental
(phonemic) and suprasegmental (prosodic) phonetic features [111]. While consonantal
voice onsets and offsets act on a fast temporal scale with a resolution of 20 to 50 ms
[112], features such as rhythm, stress, and melody patterns span the temporal frame of a
whole utterance. The underlying auditorytoarticulatory alignment requires a precise
temporal coupling at multiple timescales. Fast auditory signals are preferentially
integrated in the left auditory cortex, while slow auditory signals are preferentially
integrated in the right auditory cortex [113]. Supposing the sensorimotor control of
slower suprasegmental features to be lateralized to the right hemisphere, and slow
auditory targets such as melody and stress mainly arise from the right frontal motor
regions. This would suggest speechplanninginduced facilitation of the right larynx
area rather than the right tongue area. Especially prosodic features are regulated at the
The neurobiological grounding of persistent chronic stuttering: From structure to function
13
laryngeal level, and notably the right primary motor larynx area shows increased
hemodynamic responses in persons who stutter [44••].
Conclusion and Outlook
Speech is regulated by coactivated neuronal circuits of largescale dynamic networks
[114] and their dysfunction results in persistent stuttering. Reduced speechrelated
dynamics in the left hemisphere and augmented right hemisphere involvement are
cardinal neuronal signs possibly caused by imbalanced wiring. This review lacks a
detailed description of subcortical contributions to stuttering behavior, although there is
converging evidence for cerebellar, thalamic, as well as basal ganglia irregularities [23,
50, 65, 86, 115118]. We attach importance to the cortical dynamics within the speech
related connectome as a result of new metaanalyses offering a condensed view of
imaging changes associated with chronic persistent stuttering. This is by no means
intended to scale down the importance of neuroimaging findings derived from every
individual study. Quite the contrary is true; it elucidates that current methods are not
sensitive enough to fully disentangle the brain dynamics of stuttering. However, our
review provides a focused view on the brain deficits of persons affected with persistent
stuttering, which might open the gate for a rethinking of how best to proceed. Future
studies employing TMS, deep brain stimulation [118], sophisticated neuroimaging
techniques [119, 120], and selected animal studies [121, 122•, 123•, 124•] may advance
mechanistic models [75] and may eventually guide success in therapeutic efforts aiming
to facilitate fluency. The following questions are of particular interest: What are the
interhemispheric interactions that allow fluent speech production and how do they
change in stuttering? Which brain dynamics characterize single acts of stuttering and
would it be possible to interfere with those sudden interruptions of the integrity of the
speech motor network? Is it possible to employ special hearing aids to facilitate the
maturation of temporoparietofrontal interactions necessary for stable sensorimotor
integration? Which neuromodulatory interventions could strengthen the left fronto
parietotemporal network to overcome the problem that only fluencyenhancing
techniques such as chorus speaking or speaking to the rhythm of a metronome
unburden the computational load of the frontal motor network [116] and bypass the
IFG, precentral gyrus, insula, putamen, nucleus caudatus, and globus pallidus?
Acknowledgments
Nicole E. Neef was supported by the Deutsche Forschungsgemeinschaft (NE1841/11).
The neurobiological grounding of persistent chronic stuttering: From structure to function
14
References
Papers of particular interest, published recently, have been highlighted as:
• Of importance
•• Of major importance
1. Yairi E, Ambrose NG. Early childhood stuttering. Austin, TX: ProEd.; 2005.
2. Howell P, Davis S. Predicting persistence of and recovery from stuttering by the
teenage years based on information gathered at age 8 years. Journal of
Developmental & Behavioral Pediatrics. 2011;32:196205.
3. Dworzynski K, Remington A, Rijsdijk F, Howell P, Plomin R. Genetic etiology in cases of
recovered and persistent stuttering in an unselected, longitudinal sample of young
twins. American Journal of SpeechLanguage Pathology. 2007;16:169.
4. Craig A. Epidemiology of stuttering in the community across the entire life span.
Journal of Speech, Language, and Hearing Research. 2002;45:1097105.
5. Bloodstein O, Ratner NB. A handbook on stuttering. 6th ed. Clifton Park, NY: Delmar
Learning; 2008.
6. Wingate ME. A standard definition of stuttering. Journal of Speech and Hearing
Disorders. 1964;29:484.
7. Yaruss JS. Assessing quality of life in stuttering treatment outcomes research. Journal
of Fluency Disorders. 2010;35:190202.
8. Kraft SJ, Yairi E. Genetic bases of stuttering: The state of the art, 2011. Folia
Phoniatrica et Logopaedica. 2012;64:3346.
9. Suresh R, Ambrose N, Roe C, Pluzhnikov A, WittkeThompson JK, Ng MCY, et al. New
complexities in the genetics of stuttering: significant sexspecific linkage signals.
The American Journal of Human Genetics. 2006;78:55463.
10. Riaz N, Steinberg S, Ahmad J, Pluzhnikov A, Riazuddin S, Cox NJ, et al. Genomewide
significant linkage to stuttering on chromosome 12. Am J Hum Genet.
2005;76:64751.
11.•• Yairi E, Ambrose N. Epidemiology of stuttering: 21st century advances. Journal of
Fluency Disorders. 2013;38:6687.
This article is a comprehensive review on epidemiological factors in stuttering
also critically evaluating the current biological research of gene identification.
12. Kraft SJ. Genomewide association study of persistent developmental stuttering.
University of Illinois at Urbana Champaign; 2010.
13. Damsté PH, Zwaan EJ, Schoenaker TJ. Learning principles applied to the stuttering
problem. Folia Phoniatrica et Logopaedica. 1968;20:32741.
14. Riper CV. The nature of stuttering. PrenticeHall; 1971.
The neurobiological grounding of persistent chronic stuttering: From structure to function
15
15. Smith A, Kelly E. Stuttering: A dynamic multifactorial model. In: Curlee R, Siegel G,
editors. Nature and treatment of stuttering: new directions. 2nd ed. Needham
Heights, MA: Allyn & Bacon; 1997. p. 20417.
16. Starkweather CW, Gottwald SR. The demands and capacities model II: Clinical
applications. Journal of Fluency Disorders. 1990;15:14357.
17. Coulter CE, Anderson JD, Conture EG. Childhood stuttering and dissociations across
linguistic domains: A replication and extension. Journal of Fluency Disorders.
2009;34:25778.
18. Howell P. Assessment of some contemporary theories of stuttering that apply to
spontaneous speech. Contemporary Issues in Communication Science and
Disorders. 2004;31:12239.
19. Postma A, Kolk H. The covert repair hypothesis: prearticulatory repair processes in
normal and stuttered disfluencies. Journal of Speech Language and Hearing
Research. 1993;36:47287.
20. Civier O, Tasko SM, Guenther FH. Overreliance on auditory feedback may lead to
sound/syllable repetitions: Simulations of stuttering and fluencyinducing
conditions with a neural model of speech production. Journal of Fluency Disorders.
2010;35:24679.
21. Namasivayam AK, van Lieshout P, McIlroy WE, De Nil L. Sensory feedback
dependence hypothesis in persons who stutter. Human Movement Science.
2009;28:688707.
22. Van Lieshout P, Hulstijn W, Peters H. Searching for the weak link in the speech
production chain of people who stutter: A motor skill approach. In: Maassen B,
Kent R, Peters HFM, van Lieshout P, Hulstijn W, editors. Speech Motor Control in
Normal and Disordered Speech. New York: Oxford University Press; 2004.
23. Alm PA. Stuttering and the basal ganglia circuits: A critical review of possible
relations. Journal of Communication Disorders. 2004;37:32569.
24. Büchel C, Sommer M. What causes stuttering? PLoS Biology. 2004;2:e46.
25. Etchell AC, Johnson BW, Sowman PF. Beta oscillations, timing, and stuttering.
Frontiers in Human Neuroscience. 2015;8:1036.
26. Kent RD. Research on speech motor control and its disorders: A review and
prospective. Journal of Communication Disorders. 2000;33:391428.
27. Ludlow CL. Stuttering: dysfunction in a complex and dynamic system. Brain.
2000;123:19834.
28. Travis LE. The cerebral dominance theory of stuttering: 19311978. Journal of
Speech and Hearing Disorders. 1978;43:278281.
29. Orton ST, Travis L. Studies in stuttering: IV. Studies of action currents in stutterers.
Archives of Neurology & Psychiatry. 1929;21:618.
The neurobiological grounding of persistent chronic stuttering: From structure to function
16
30. Foundas AL, Bollich AM, Corey DM, Hurley M, Heilman KM. Anomalous anatomy of
speechlanguage areas in adults with persistent developmental stuttering.
Neurology. 2001;57:20715.
31. Sommer M, Koch MA, Paulus W, Weiller C, Büchel C. Disconnection of speech
relevant brain areas in persistent developmental stuttering. The Lancet.
2002;360:3803.
32. Ludlow CL, Loucks T. Stuttering: A dynamic motor control disorder. Journal of
Fluency Disorders. 2003;28:273295.
33. Salmelin R, Schnitzler A, Schmitz F, Freund HJ. Single word reading in
developmental stutterers and fluent speakers. Brain. 2000;123:1184202.
34. Hickok G, Houde J, Rong F. Sensorimotor integration in speech processing:
computational basis and neural organization. Neuron. 2011;69:40722.
35. Foundas AL, Bollich AM, Feldman J, Corey DM, Hurley M, Lemen LC, et al. Aberrant
auditory processing and atypical planum temporale in developmental stuttering.
Neurology. 2004;63:16406.
36. Ackermann H. Cerebellar contributions to speech production and speech perception:
psycholinguistic and neurobiological perspectives. Trends in Neurosciences.
2008;31:26572.
37. Goldstein L, Pouplier M. The temporal organization of speech. In: Goldrick M,
Ferreira VS, Miozzo M, editors. The Oxford Handbook of Language Production.
Oxford University Press; 2014.
38. Batliner A, Möbius B, Möhler G, Schweitzer A, Nöth E. Prosodic models, automatic
speech understanding, and speech synthesis: towards the common ground. In:
Proceedings of the 7th European Conference on Speech Communication and
Technology, 2001;22858.
39. Vry MS, Saur D, Rijntjes M, Umarova R, Kellmeyer P, Schnell S, et al. Ventral and
dorsal fiber systems for imagined and executed movement. Experimental Brain
Research. 2012;219:20316.
40. Magrassi L, Aromataris G, Cabrini A, AnnovazziLodi V, Moro A. Sound
representation in higher language areas during language generation. Proceedings
of the National Academy of Sciences of the United States of America.
2015;112:186873.
41. Wymbs NF, Ingham RJ, Ingham JC, Paolini KE, Grafton ST. Individual differences in
neural regions functionally related to real and imagined stuttering. Brain and
Language. 2013;124:15364.
42. Rosenfield DB. Neural anomaly and reorganization in speakers who stutter: A short
term intervention study. Neurology. 2013;80:1538.
43.•• Budde KS, Barron DS, Fox PT. Stuttering, induced fluency, and natural fluency: A
hierarchical series of activation likelihood estimation metaanalyses. Brain and
The neurobiological grounding of persistent chronic stuttering: From structure to function
17
Language. 2014;139:99107.
Together with the ALE metaanalysis by Belyk and colleagues this article
concentrates the most robust findings of functional neuroimaging in stuttering
of the last 30 years.
44.•• Belyk M, Kraft SJ, Brown S. Stuttering as a trait or state an ALE metaanalysis of
neuroimaging studies. European Journal of Neuroscience. 2015;41:27584.
Together with the ALE metaanalysis by Budde and colleagues this article
concentrates the most robust findings of functional neuroimaging in stuttering
of the last 30 years.
45. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)based white matter mapping in
brain research: A review. Journal of Molecular Neuroscience. 2007;34:5161.
46. Smith SM, Jenkinson M, JohansenBerg H, Rueckert D, Nichols TE, Mackay CE, et al.
Tractbased spatial statistics: Voxelwise analysis of multisubject diffusion data.
NeuroImage. 2006;31:1487505.
47. Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinatebased
activation likelihood estimation metaanalysis of neuroimaging data: A random
effects approach based on empirical estimates of spatial uncertainty. Human Brain
Mapping. 2009;30:290726.
48. Heidemann RM, Anwander A, Feiweier T, Knösche TR, Turner R. kspace and q
space: Combining ultrahigh spatial and angular resolution in diffusion imaging
using ZOOPPA at 7 T. NeuroImage. 2012;60:96778.
49. Jeon HA, Anwander A, Friederici AD. Functional network mirrored in the prefrontal
cortex, caudate nucleus, and thalamus: Highresolution functional imaging and
structural connectivity. The Journal of Neuroscience. 2014;34:920212.
50. Connally EL, Ward D, Howell P, Watkins KE. Disrupted white matter in language and
motor tracts in developmental stuttering. Brain and Language. 2014;131:2535.
51. Cieslak M, Ingham RJ, Ingham JC, Grafton ST. Anomalous white matter morphology in
adults who stutter. Journal of Speech Language and Hearing Research.
2015;58:26877.
52. Chang SE, Horwitz B, Ostuni J, Reynolds R, Ludlow CL. Evidence of left inferior
frontalpremotor structural and functional connectivity deficits in adults who
stutter. Cerebral Cortex. 2011;21:250718.
53. Friederici AD, Gierhan SM. The language network. Current Opinion in Neurobiology.
2013;23:2504.
54. Makris N, Kennedy DN, McInerney S, Sorensen AG, Wang R, Caviness VS, et al.
Segmentation of Subcomponents within the Superior Longitudinal Fascicle in
Humans: A Quantitative, In Vivo, DTMRI Study. Cerebral Cortex. 2005;15:85469.
55. Martino J, Hamer PCDW, Berger MS, Lawton MT, Arnold CM, de Lucas EM, et al.
Analysis of the subcomponents and cortical terminations of the perisylvian
The neurobiological grounding of persistent chronic stuttering: From structure to function
18
superior longitudinal fasciculus: A fiber dissection and DTI tractography study.
Brain Structure and Function. 2012;218:10521.
56. Schreiber J, Riffert T, Anwander A, Knösche TR. Plausibility tracking: A method to
evaluate anatomical connectivity and microstructural properties along fiber
pathways. NeuroImage. 2014;90:16378.
57. Aboitiz F, Scheibel AB, Fisher RS, Zaidel E. Fiber composition of the human corpus
callosum. Brain Research. 1992;598:14353.
58. Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK. Largescale cortical correlation
structure of spontaneous oscillatory activity. Nature Neuroscience. 2012;15:884
90.
59. Stark DE, Margulies DS, Shehzad ZE, Reiss P, Kelly AMC, Uddin LQ, et al. Regional
variation in interhemispheric coordination of intrinsic hemodynamic fluctuations.
The Journal of Neuroscience. 2008;28:1375464.
60. Barazany D, Basser PJ, Assaf Y. In vivo measurement of axon diameter distribution in
the corpus callosum of rat brain. Brain. 2009;132:121020.
61. Assaf Y, BlumenfeldKatzir T, Yovel Y, Basser PJ. AxCaliber: A method for measuring
axon diameter distribution from diffusion MRI. Magnetic Resonance in Medicine.
2008;59:134754.
62. Alexander DC, Hubbard PL, Hall MG, Moore EA, Ptito M, Parker GJM, et al.
Orientationally invariant indices of axon diameter and density from diffusion MRI.
NeuroImage. 2010;52:137489.
63.•• Chang SE, Zhu DC, Choo AL, Angstadt M. White matter neuroanatomical
differences in young children who stutter. Brain. 2015;138:694711.
This article reports the neuroanatomical connectivity changes in
developmental stuttering measured with diffusion MRI in the youngest and
largest cohort published to date.
64. Chang SE, Erickson KI, Ambrose NG, HasegawaJohnson MA, Ludlow CL. Brain
anatomy differences in childhood stuttering. NeuroImage. 2008;39:133344.
65. Watkins KE, Smith SM, Davis S, Howell P. Structural and functional abnormalities of
the motor system in developmental stuttering. Brain. 2008;131:509.
66. Cai S, Tourville JA, Beal DS, Perkell JS, Guenther FH, Ghosh SS. Diffusion imaging of
cerebral white matter in persons who stutter: evidence for networklevel
anomalies. Frontiers in Human Neuroscience. 2014;8:54.
67. Civier O, KronfeldDuenias V, Amir O, EzratiVinacour R, BenShachar M. Reduced
fractional anisotropy in the anterior corpus callosum is associated with reduced
speech fluency in persistent developmental stuttering. Brain and Language.
2015;143:2031.
The neurobiological grounding of persistent chronic stuttering: From structure to function
19
68. KronfeldDuenias V, Amir O, EzratiVinacour R, Civier O, BenShachar M. The frontal
aslant tract underlies speech fluency in persistent developmental stuttering. Brain
Structure and Function. 2014;117.
69.•• Friederici AD, Singer W. Grounding language processing on basic
neurophysiological principles. Trends in Cognitive Sciences. 2015;19: 32938.
This review is in line with a current paradigm shift in cognitive neuroscience
emphasizing the view that cognitive functions depend on distributed
computations in specialized cortical areas forming largescale dynamic
recurrent networks.
70. Wilson SM, Galantucci S, Tartaglia MC, Rising K, Patterson DK, Henry ML, et al.
Syntactic processing depends on dorsal language tracts. Neuron. 2011;72:397
403.
71. Friederici AD. The cortical language circuit: from auditory perception to sentence
comprehension. Trends in Cognitive Sciences. 2012;16:2628.
72.• Sarubbo S, De Benedictis A, Merler S, Mandonnet E, Balbi S, Granieri E, et al.
Towards a functional atlas of human white matter. Human Brain Mapping. 2015;
36:311736.
This original paper reports results from cortical and subcortical
electrostimulations in 130 patients under awake surgery for glioma, providing
comprehensive subcortical functional maps of left and right hemisphere
connections.
73. Bizzi A, Nava S, Ferrè F, Castelli G, Aquino D, Ciaraffa F, et al. Aphasia induced by
gliomas growing in the ventrolateral frontal region: Assessment with diffusion MR
tractography, functional MR imaging and neuropsychology. Cortex. 2012;48:255
72.
74. Duffau H, Gatignol P, Denvil D, Lopes M, Capelle L. The articulatory loop: study of the
subcortical connectivity by electrostimulation. Neuroreport. 2003;14:20058.
75.•• Guenther FH, Hickok G. Chapter 9 Role of the auditory system in speech
production. In: Aminoff MJ, Boller F, Swaab DF, editors. Handbook of Clinical
Neurology. Elsevier; 2015. p. 16175.
Guenther and Hickok set out to advance mechanistic models of speech
production summarizing and comparing their approaches for the first time in
this most recent article.
76. Rauschecker JP, Scott SK. Maps and streams in the auditory cortex: nonhuman
primates illuminate human speech processing. Nature Neuroscience.
2009;12:71824.
77. Fox PT, Ingham RJ, Ingham JC, Hirsch TB, Downs JH, Martin C, et al. A PET study of
the neural systems of stuttering. Nature. 1996;382:15862.
78. Braun AR, Varga M, Stager S, Schulz G, Selbie S, Maisog JM, et al. Altered patterns of
cerebral activity during speech and language production in developmental
The neurobiological grounding of persistent chronic stuttering: From structure to function
20
stuttering. An H2(15)O positron emission tomography study. Brain.
1997;120:76184.
79. Ingham RJ, Fox PT, Costello Ingham J, Zamarripa F. Is overt stuttered speech a
prerequisite for the neural activations associated with chronic developmental
stuttering? Brain and Language. 2000;75:16394.
80. Ingham RJ, Grafton ST, Bothe AK, Ingham JC. Brain activity in adults who stutter:
Similarities across speaking tasks and correlations with stuttering frequency and
speaking rate. Brain and Language. 2012;122:1124.
81. De Nil LF, Kroll RM, Kapur S, Houle S. A positron emission tomography study of silent
and oral single word reading in stuttering and nonstuttering adults. Journal of
Speech Language and Hearing Research. 2000;43:103853.
82. De Nil LF, Kroll RM, Lafaille SJ, Houle S. A positron emission tomography study of
short and longterm treatment effects on functional brain activation in adults
who stutter. Journal of Fluency Disorders. 2003;28:35780.
83. Neumann K, Preibisch C, Euler HA, von Gudenberg AW, Lanfermann H, Gall V, et al.
Cortical plasticity associated with stuttering therapy. Journal of Fluency Disorders.
2005;30:2339.
84. Preibisch C, Neumann K, Raab P, Euler HA, von Gudenberg AW, Lanfermann H, et al.
Evidence for compensation for stuttering by the right frontal operculum.
NeuroImage. 2003;20:135664.
85. Nil LFD, Beal DS, Lafaille SJ, Kroll RM, Crawley AP, Gracco VL. The effects of simulated
stuttering and prolonged speech on the neural activation patterns of stuttering and
nonstuttering adults. Brain and Language. 2008;107:11423.
86. Giraud AL, Neumann K, BachoudLevi AC, von Gudenberg AW, Euler HA,
Lanfermann H, et al. Severity of dysfluency correlates with basal ganglia activity in
persistent developmental stuttering. Brain and Language. 2008;104:1909.
87. Chang SE, Kenney MK, Loucks TMJ, Ludlow CL. Brain activation abnormalities
during speech and nonspeech in stuttering speakers. NeuroImage. 2009;46:201
12.
88. Neef NE, Jung K, Rothkegel H, Pollok B, von Gudenberg AW, Paulus W, et al. Right
shift for nonspeech motor processing in adults who stutter. Cortex. 2011;47:945
54.
89. Kikuchi Y, Ogata K, Umesaki T, Yoshiura T, Kenjo M, Hirano Y, et al. Spatiotemporal
signatures of an abnormal auditory system in stuttering. NeuroImage.
2011;55:8919.
90. Krishnan G, Nair RP, Tiwari S. Clinical evidence for the compensatory role of the right
frontal lobe and a novel neural substrate in developmental stuttering: A single case
study. Journal of Neurolinguistics. 2010;23:50110.
The neurobiological grounding of persistent chronic stuttering: From structure to function
21
91. Kell CA, Morillon B, Kouneiher F, Giraud AL. Lateralization of speech production
starts in sensory cortices A possible sensory origin of cerebral left dominance for
speech. Cerebral Cortex. 2011;21:9327.
92. Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55:18799.
93. Weiler N, Wood L, Yu J, Solla SA, Shepherd GMG. Topdown laminar organization of
the excitatory network in motor cortex. Nature Neuroscience. 2008;11:3606.
94. Busan P, D’Ausilio A, Borelli M, Monti F, Pelamatti G, Pizzolato G, et al. Motor
excitability evaluation in developmental stuttering: A transcranial magnetic
stimulation study. Cortex. 2013;49:78192.
95. Alm PA, Karlsson R, Sundberg M, Axelson HW. Hemispheric lateralization of motor
thresholds in relation to stuttering. PLoS ONE. 2013;8:e76824.
96. Sommer M, Knappmeyer K, Hunter EJ, Gudenberg AW, Neef N, Paulus W. Normal
interhemispheric inhibition in persistent developmental stuttering. Movement
Disorders. 2009;24:76973.
97. Kujirai T, Caramia MD, Rothwell JC, Day BL, Thompson PD, Ferbert A, et al.
Corticocortical inhibition in human motor cortex. The Journal of Physiology.
1993;471:50119.
98. Fisher RJ, Nakamura Y, Bestmann S, Rothwell JC, Bostock H. Two phases of
intracortical inhibition revealed by transcranial magnetic threshold tracking.
Experimental Brain Research. 2002;143:2408.
99. Hanajima R, Furubayashi T, Iwata NK, Shiio Y, Okabe S, Kanazawa I, et al. Further
evidence to support different mechanisms underlying intracortical inhibition of the
motor cortex. Experimental Brain Research. 2003;151:42734.
100. Neef NE, Paulus W, Neef A, von Gudenberg AW, Sommer M. Reduced intracortical
inhibition and facilitation in the primary motor tongue representation of adults
who stutter. Clinical Neurophysiology. 2011;122:180211.
101.•• Cash RFH, Isayama R, Gunraj CA, Ni Z, Chen R. The influence of sensory afferent
input on local motor cortical excitatory circuitry in humans. The Journal of
Physiology. 2015;593:166784.
This work suggests that the sensory input on excitatory motor cortical
circuitry plays an important role in sensorimotor integration and motor
control.
102.•• Neef NE, Hoang TNL, Neef A, Paulus W, Sommer M. Speech dynamics are coded in
the left motor cortex in fluent speakers but not in adults who stutter. Brain.
2015;138:71225.
Neef et al. verify the proposed uncoupling of motor output cells from motor
plan cells in left primary motor cortex in fluent speech, and reveal its
disruption in stuttering.
The neurobiological grounding of persistent chronic stuttering: From structure to function
22
103.• Civier O, Bullock D, Max L, Guenther FH. Computational modeling of stuttering
caused by impairments in a basal ganglia thalamocortical circuit involved in
syllable selection and initiation. Brain and Language. 2013;126:26378.
Civier et al. employ the most sophisticated mechanistic model of speech
production to simulate stuttering and at the same time accounting for brain
imaging findings.
104. Sakai N, Masuda S, Shimotomai T, Mori K. Brain activation in adults who stutter
under delayed auditory feedback: An fMRI study. International Journal of Speech
Language Pathology. 2009;11:211.
105. den Ouden DB, Montgomery A, Adams C. Simulating the neural correlates of
stuttering. Neurocase. 2014;20:43445.
106. Kell CA, Neumann K, von Kriegstein K, Posenenske C, von Gudenberg AW, Euler H,
et al. How the brain repairs stuttering. Brain. 2009;132:274760.
107. Weintraub S, Mesulam M, Kramer L. Disturbances in prosody a righthemisphere
contribution to language. Archives of Neurology. 1981;38:7424.
108. Wildgruber D, Ackermann H, Klose U, Kardatzki B, Grodd W. Functional
lateralization of speech production at primary motor cortex: A fMRI study.
Neuroreport. 1996;7:27915.
109. Friederici AD, Alter K. Lateralization of auditory language functions: A dynamic
dual pathway model. Brain and Language. 2004;89:26776.
110. Meyer M, Alter K, Friederici AD, Lohmann G, von Cramon DY. FMRI reveals brain
regions mediating slow prosodic modulations in spoken sentences. Human Brain
Mapping. 2002;17:7388.
111. Karniol R. Stuttering, language, and cognition: A review and a model of stuttering as
suprasegmental sentence plan alignment (SPA). Psychological Bulletin.
1995;117:10424.
112. Giraud AL, Poeppel D. Cortical oscillations and speech processing: emerging
computational principles and operations. Nature Neuroscience. 2012;15:5117.
113. Giraud AL, Kleinschmidt A, Poeppel D, Lund TE, Frackowiak RSJ, Laufs H.
Endogenous cortical rhythms determine cerebral specialization for speech
perception and production. Neuron. 2007;56:112734.
114. Duffau H. Stimulation mapping of white matter tracts to study brain functional
connectivity. Nature Reviews Neurology. 2015;11:25565.
115. Wu JC, Maguire G, Riley G, Lee A, Keator D, Tang C, et al. Increased dopamine
activity associated with stuttering. Neuroreport. 1997;8:76770.
116. Toyomura A, Fujii T, Kuriki S. Effect of external auditory pacing on the neural
activity of stuttering speakers. NeuroImage. 2011;57:150716.
The neurobiological grounding of persistent chronic stuttering: From structure to function
23
117. Toyomura A, Fujii T, Kuriki S. Effect of an 8week practice of externally triggered
speech on basal ganglia activity of stuttering and fluent speakers. NeuroImage.
2015;109:45868.
118. Bhatnagar S, Buckingham H. Neurogenic stuttering: Its reticular modulation.
Current Neurology and Neuroscience Reports. 2010;10:4918.
119. Wald LL. The future of acquisition speed, coverage, sensitivity, and resolution.
NeuroImage. 2012;62:12219.
120. Huber L, Goense J, Kennerley AJ, Trampel R, Guidi M, Reimer E, et al. Cortical
laminadependent blood volume changes in human brain at 7 T. NeuroImage.
2015;107:2333.
121. Kubikova L, Bosikova E, Cvikova M, Lukacova K, Scharff C, Jarvis ED. Basal ganglia
function, stuttering, sequencing, and repair in adult songbirds. Scientific Reports.
2014;4:6590.
122.• Fukushima M, Margoliash D. The effects of delayed auditory feedback revealed by
bone conduction microphone in adult zebra finches. Scientific Reports.
2015;5:8800.
This work demonstrates that a transient stuttering period can be induced by
delayed auditory feedback (DAF) and that neither DAFinduced stuttering nor
its recovery occurs instantaneously but depends on longterm tuning of
involved circuits.
123.• Palmer LM, Schulz JM, Murphy SC, Ledergerber D, Murayama M, Larkum ME. The
cellular basis of GABABmediated interhemispheric inhibition. Science.
2012;335:98993.
Palmer et al. discovered a mechanistic principle of how interhemisphereic
inhibition could be implemented in the brain.
124.• Manita S, Suzuki T, Homma C, Matsumoto T, Odagawa M, Yamada K, et al. A top
down cortical circuit for accurate sensory perception. Neuron. 2015;86:113.
Manita et al. demonstrate the impact of recurrent interaction between primary
and secondary motor and somatosensory circuits on perception.
125. Cykowski MD, Fox PT, Ingham RJ, Ingham JC, Robin DA. A study of the
reproducibility and etiology of diffusion anisotropy differences in developmental
stuttering: A potential role for impaired myelination. NeuroImage. 2010;52:1495
504.
126. Fillard P, Pennec X, Arsigny V, Ayache N. Clinical DTMRI Estimation, Smoothing,
and Fiber Tracking With LogEuclidean Metrics. IEEE Transactions on Medical
Imaging. 2007;26:147282.
... The rationale for research grounded within the medical model typically asserts that clarifying the potential causes of stuttering can yield treatments that effectively lessen or stop the overt behaviors, thus leading to diminished adverse impacts on PWS. This line of research has been fruitful in that it has begun to shed light on the neurological (Chow, Garnett, Etchell, & Ho Ming, 2018;Etchell, Civier, Ballard, & Sowman, 2018;Garnett, Chow, & Chang, 2019;Neef, Anwander, & Friederici 2015) and genetic (Frigerio-Domingues & Drayna, 2017;Kollbrunner, Wedell, Zimmerman, & Seifert, 2014) underpinnings of stuttering, but these findings have not yet directly influenced stuttering treatment. ...
... Of the sub-themes embedded within internal factors, participants highlighted physical and mental exhaustion as significant contributors to increased stuttering across contexts. Although this particular factor has been mentioned in the literature on stuttering, and frequently emerges in anecdotal accounts of stuttering variability, little to no research has been conducted to investigate how fatigue may interact with the neural vulnerabilities associated with stuttering (Chow et al., 2018;Kollbrunner et al., 2014;Neef et al., 2015). By the same token, the ease with which PWS can often swear fluently, or speak with increased fluency while experiencing strongly altered emotional states (e.g., anger, rage) has not been scientifically explored. ...
Thesis
Stuttering is a neurologically based speech impairment often defined by listener-oriented parameters (i.e., its overt characteristics). These fail to encompass contextual variability and anticipation, two facets of the speaker’s experience which, though frequently encountered by people who stutter (PWS), remain poorly understood and largely under-researched. To better understand the subjective underpinnings of these phenomena, as well as how PWS conceptualize and relate to their stuttering, the present study sought to explore a) the experiences of PWS with the unpredictable and/or variable nature of their stuttering, as well as their beliefs surrounding potential contributors to its variability; b) the experiences of PWS with anticipation, and whether they believe that anticipation has a role in the variability of their stuttering across contexts; and c) the ways in which experiences of contextual variability and/or the anticipation of stuttering may impact levels of self-acceptance, quality of life, and life satisfaction of PWS.
... The SLF III on the other hand is involved in fluent speech production (Bonilha et al., 2019). Early diffusion tensor imaging (DTI) studies associated persistent developmental stuttering with a weak left SLF/AF as quantified via an activation likelihood estimation (ALE) metaanalysis across experiments using tract-based spatial statistics (TBSS) (Neef, Anwander, & Friederici, 2015). More recent studies used tractography to investigate white matter microstructure and morphological characteristics of tracts. ...
... So, although left SLF FA was not reduced in PWS compared to controls, those PWS+ who had lower left SLF FA values and thus a weaker SLF, showed less success in improving their speech fluency, whereas those PWS+ who had the highest FA and thus the strongest SLF showed a more successful implementation of fluency-shaping. This relationship is in line with the widely accepted view that the left SLF is implicated in stuttering (Chang, Garnett, Etchell, & Chow, 2019;Neef et al., 2015;Watkins, Chesters, & Connally, 2016). In general, the SLF III is postulated to mediate the conversion of auditory input into phonological and articulatory forms supported by verbal working memory areas of the SMG. ...
Article
Full-text available
Persistent stuttering is a prevalent neurodevelopmental speech disorder, which presents with involuntary speech blocks, sound and syllable repetitions, and sound prolongations. Affected individuals often struggle with negative feelings, elevated anxiety, and low self-esteem. Neuroimaging studies frequently link persistent stuttering with cortical alterations and dysfunctional cortico-basal ganglia-thalamocortical loops; dMRI data also point toward connectivity changes of the superior longitudinal fasciculus (SLF) and the frontal aslant tract (FAT). Both tracts are involved in speech and language functions, and the FAT also supports inhibitory control and conflict monitoring. Whether the two tracts are involved in therapy-associated improvements and how they relate to therapeutic outcomes is currently unknown. Here, we analyzed dMRI data of 22 patients who participated in a fluency-shaping program, 18 patients not participating in therapy, and 27 fluent control participants, measured 1 year apart. We used diffusion tractography to segment the SLF and FAT bilaterally and to quantify their microstructural properties before and after a fluency-shaping program. Participants learned to speak with soft articulation, pitch, and voicing during a 2-week on-site boot camp and computer-assisted biofeedback-based daily training for 1 year. Therapy had no impact on the microstructural properties of the two tracts. Yet, after therapy, stuttering severity correlated positively with left SLF fractional anisotropy, whereas relief from the social-emotional burden to stutter correlated negatively with right FAT fractional anisotropy. Thus, posttreatment, speech motor performance relates to the left dorsal stream, while the experience of the adverse impact of stuttering relates to the structure recently associated with conflict monitoring and action inhibition.
... A major aim of this study was to investigate how brain activity patterns in the auditory cortex during continuous speech production differ between AWS and controls. Overall, group differences in brain activity patterns observed in each condition were largely similar, showing the expected pattern in AWS of heightened activity in motor areas (right hemisphere premotor cortex and SMA) but decreased activity in auditory regions previously reported as neural signatures associated with stuttering (Brown et al., 2005;Belyk et al., 2015;Neef et al., 2015). In this way, the current results partially support our hypothesis that choral reading would attenuate the aberrant motor and auditory activity during speech in AWS relative to controls; however, these activity pattern differences were subtle. ...
Article
Full-text available
Previous neuroimaging investigations of overt speech production in adults who stutter (AWS) found increased motor and decreased auditory activity compared to controls. Activity in the auditory cortex is heightened, however, under fluency-inducing conditions in which AWS temporarily become fluent while synchronizing their speech with an external rhythm, such as a metronome or another speaker. These findings suggest that stuttering is associated with disrupted auditory motor integration. Technical challenges in acquiring neuroimaging data during continuous overt speech production have limited experimental paradigms to short or covert speech tasks. Such paradigms are not ideal, as stuttering primarily occurs during longer speaking tasks. To address this gap, we used a validated spatial ICA technique designed to address speech movement artifacts during functional magnetic resonance imaging (fMRI) scanning. We compared brain activity and functional connectivity of the left auditory cortex during continuous speech production in two conditions: solo (stutter-prone) and choral (fluency-inducing) reading tasks. Overall, brain activity differences in AWS relative to controls in the two conditions were similar, showing expected patterns of hyperactivity in premotor/motor regions but underactivity in auditory regions. Functional connectivity of the left auditory cortex (STG) showed that within the AWS group there was increased correlated activity with the right insula and inferior frontal area during choral speech. The AWS also exhibited heightened connectivity between left STG and key regions of the default mode network (DMN) during solo speech. These findings indicate possible interference by the DMN during natural, stuttering-prone speech in AWS, and that enhanced coordination between auditory and motor regions may support fluent speech.
... Developmental stuttering is a speech fluency disorder (ICD-11, World Health Organization, 2019) with neurophysiological corre-lates within the speech production network (Watkins et al., 2008). Models allocate speech preparation to the left inferior frontal cortex, movement initiation to the supplementary motor area within a basal-ganglia-thalamo-cortical loop, and speech execution to sensorimotor regions (Bohland et al., 2010;Guenther and Vladusich, 2012;Hickok, 2012;Kotz and Schwartze, 2010 adults with developmental stuttering (AWS), 1 these regions show abnormal structural connectivity (Kronfeld-Duenias et al., 2016;Neef et al., 2015;Sommer et al., 2002) and functional activity (Belyk et al., 2015;Budde et al., 2014). Imaging findings suggest a hyperdopaminergic state in the basal ganglia (Maguire et al., 2020;Metzger et al., 2018;Watkins et al., 2008;Wu et al., 1997) and dysregulated inhibitory and excitatory activity during speech production (Alm, 2004;Civier et al., 2013). ...
Article
Full-text available
Objective The neurophysiological dynamics of the occurrence of a stuttering event are largely unknown. This sensor-level EEG study investigated whether already the intention to speak alters the formation of the speech production network in stuttering. Methods We studied alpha (8-13 Hz), low beta (15-25 Hz) and high beta (25-30 Hz) power modulation in 19 adults with developmental stuttering (AWS) and 19 fluently speaking control participants during speech intention. Results Both groups show that the anticipation of overt reading coincides with broadband low-frequency suppression in posterior sensors, a common sign of network formation for speech production. Prior to fluent speech, frontotemporal alpha and low-beta power were weaker in AWS with mild stuttering but stronger in AWS with severe stuttering. These correlations were not significant prior stuttered speech. Further, post hoc comparisons confirmed the difference between AWS with mild and severe stuttering in low beta power. Conclusions AWS with more severe stuttering seem to show stronger maintenance of the current cognitive or sensorimotor state, as stuttering severity was associated with increased beta power. Increased beta power levels may influence subsequent speech preparation and execution processes. Significance Upcoming breakdowns of the speech production network as evident in actual stuttering are related to beta power during the intention to speak.
... However, recent brain mapping studies have shown that there are some structural and functional differences between people who stutter (PWS) and people who do not stutter (PWNS). For example, PWS show some auditory deficits and hypoactivity in the superior temporal gyrus, as compared to fluent speakers [3][4][5][6][7][8][9]. Another example is about some differences in cognitive mechanisms involved in sound perception in PWS [10,11]. ...
Article
Introduction: Over the last century, a variety of theories have been proposed to explain the etiology of stuttering, but the exact cause and neurological origin of it are still uncertain. The aim of the present study is to investigate the correlation between stuttering severity and ERP measures. Methods: 36 adults were recruited in this study. This population consisted of 12 adults with moderate and 12 adults with severe stuttering, as well as 12 fluent speakers as the control group. All participants were right-handed and native speakers of Farsi and they had normal hearing thresholds. ERPs were recorded during an auditory task in which subjects should listen attentively to acoustic stimuli and determine an oddball stimulus. The data were analyzed using a two-way analysis of variance (p < 0.05). In addition, Pearson’s correlation coefficient (r) was used to assess the relationship between the percentage of stuttered syllables (%SS) and ERP measures. Results: The result of MMN amplitude analysis revealed significant differences between severe stuttering and normal groups (p=0.006) as well as between moderate and severe stuttering groups (p=0.001). Moreover, the result showed there were significant differences between three study groups for P300 amplitude. In addition, there was a statistically significant correlation between %SS and ERP components amplitude. Discussion: The findings of the present study can suggest that the differences in auditory related areas of the cortex activity are existed not only between people who stutter and fluent speakers, but also between people with different levels of stuttering severity.
... The left pre-central region is often under-activated in functional studies when individuals who stutter produce fluent speech. [6][7][8]11,90,91 Interestingly, with the exception of the GP and Lobule VIII of the CBM, reduced connection strength was associated with brain regions associated with cognitive, memory and emotional processing rather than solely related to sensorimotor control. 92,93 The connectivity of the PoCG on the left with the right GP and CBM (Lobule X and Crus I, II) was reduced as well. ...
Article
Full-text available
Persistent developmental stuttering is a speech disorder that primarily affects normal speech fluency but encompasses a complex set of symptoms ranging from reduced sensorimotor integration to socioemotional challenges. Here, we investigated the whole brain structural connectome and its topological alterations in adults who stutter. Diffusion weighted imaging data of 33 subjects (13 adults who stutter and 20 fluent speakers) was obtained along with a stuttering severity evaluation. The structural brain network properties were analyzed using Network-based statistics and graph theoretical measures particularly focusing on community structure, network hubs and controllability. Bayesian power estimation was used to assess the reliability of the structural connectivity differences by examining the effect size. The analysis revealed reliable and wide-spread decreases in connectivity for adults who stutter in regions associated with sensorimotor, cognitive, emotional, and memory-related functions. The community detection algorithms revealed different subnetworks for fluent speakers and adults who stutter, indicating considerable network adaptation in adults who stutter. Average and modal controllability differed between groups in a subnetwork encompassing frontal brain regions and parts of the basal ganglia. The results revealed extensive structural network alterations and substantial adaptation in neural architecture in adults who stutter well beyond the sensorimotor network. These findings highlight the impact of the neurodevelopmental effects of persistent stuttering on neural organization and the importance of examining the full structural connectome and the network alterations that underscore the behavioral phenotype.
Article
Memory reflects the brain function in encoding, storage and retrieval of the data or information, which is a fundamental ability for any live organism. The development of approaches to improve memory attracts much attention due to the underlying mechanistic insight and therapeutic potential to treat neurodegenerative diseases with memory loss, such as Alzheimer’s disease (AD). Deep brain stimulation (DBS), a reversible, adjustable, and non-ablative therapy, has been shown to be safe and effective in many clinical trials for neurodegenerative and neuropsychiatric disorders. Among all potential regions with access to invasive electrodes, fornix is considered as it is the major afferent and efferent connection of the hippocampus known to be closely associated with learning and memory. Indeed, clinical trials have demonstrated that fornix DBS globally improved cognitive function in a subset of patients with AD, indicating fornix can serve as a potential target for neurosurgical intervention in treating memory impairment in AD. The present review aims to provide a better understanding of recent progresses in the application of fornix DBS for ameliorating memory impairments in AD patients.
Article
Background Developmental stuttering is thought to be underpinned by structural impairments in the brain. The only way to support the claim that these are causal is to determine if they are present before onset. Materials and Methods Magnetic resonance imaging (MRI) was conducted on 18 neonates, aged 8–18 weeks, 6 of whom were determined to be genetically at risk of stuttering. Results With tract-based spatial statistics (TBSS) analysis, no statistically significant differences were found between the at-risk group and the control group. However, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) in the corpus callosum of the at-risk group were lower (uncorrected) than in the control group. Automated Fiber Quantification (AFQ) yielded lower FA in the at-risk group than in the control group in the medial section of the callosum forceps minor. Discussion The findings, albeit with a small number of participants, support the proposition that reduced integrity of white matter in the corpus callosum has a causal role in developmental stuttering. Longitudinal research to determine if children with this impairment at birth later start to stutter is needed to confirm this. The left arcuate fasciculus is thought to develop as speech develops, which likely explains why there were no abnormal findings in this area in our at-risk neonates so soon after birth. This is the first study to investigate the brains of children before the onset of stuttering and the findings warrant further research.
Article
Full-text available
Drug-induced stuttering (DIS) is a type of neurogenic stuttering (NS). Although DIS has not been reported as frequently as other cases of NS in the literature, it is not a negligible adverse drug reaction (ADR) which can significantly affect the quality of life if not treated. This literature review aims to evaluate the epidemiological and clinical characteristics of DIS and suggests some pathophysiological mechanisms for this ADR. Relevant English-language reports in Google Scholar, PubMed, Web of Science, and Scopus were identified and assessed without time restriction. Finally, a total of 62 reports were included. Twenty-seven drugs caused 86 episodes of stuttering in 82 cases. The most episodes of DIS were related to antipsychotic drugs (57%), mostly including clozapine, followed by central nervous system agents (11.6%) and anticonvulsant drugs (9.3%). The majority of the cases were male and between the ages of 31 and 40 years. Repetitions were the most frequent core manifestations of DIS. In 55.8% of the episodes of DIS, the offending drug was withdrawn to manage stuttering, which resulted in significant improvement or complete relief of stuttering in all cases. Based on the suggested pathophysiological mechanisms for developmental stuttering and neurotransmitters dysfunctions involved in speech dysfluency, it seems that the abnormalities of several neurotransmitters, especially dopamine and glutamate, in different circuits and areas of the brain, including cortico-basal ganglia-thalamocortical loop and white matter fiber tracts, may be engaged in the pathogenesis of DIS.
Article
Full-text available
Although diffusion tensor imaging (DTI) and postmortem dissections improved the knowledge of white matter (WM) anatomy, functional information is lacking. Our aims are: to provide a subcortical atlas of human brain functions; to elucidate the functional roles of different bundles; to provide a probabilistic resection map of WM. We studied 130 patients who underwent awake surgery for gliomas (82 left; 48 right) with electrostimulation mapping at cortical and subcortical levels. Different aspects of language, sensori-motor, spatial cognition, and visual functions were monitored. 339 regions of interest (ROIs) including the functional response errors collected during stimulation were co-registered in the MNI space, as well as the resections' areas and residual tumors. Functional response errors and resection areas were matched with DTI and cortical atlases. Subcortical maps for each function and a probability map of resection were computed. The medial part of dorsal stream (arcuate fasciculus) subserves phonological processing; its lateral part [indirect anterior portion of the superior longitudinal fascicle (SLF)] subserves speech planning. The ventral stream subserves language semantics and matches with the inferior fronto-occipital fascicle. Reading deficits match with the inferior longitudinal fascicle. Anomias match with the indirect posterior portion of the SLF. Frontal WM underpins motor planning and execution. Right parietal WM subserves spatial cognition. Sensori-motor and visual fibers were the most preserved bundles. We report the first anatomo-functional atlas of WM connectivity in humans by correlating cognitive data, electrostimulation, and DTI. We provide a valuable tool for cognitive neurosciences and clinical applications. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
Article
Full-text available
Vocal control and learning are critically dependent on auditory feedback in songbirds and humans. Continuous delayed auditory feedback (cDAF) robustly disrupts speech fluency in normal humans and has ameliorative effects in some stutterers; however, evaluations of the effects of cDAF on songbirds are rare. We exposed singing young (141–151 days old) adult zebra finch males to high-amplitude cDAF. cDAF exposure was achieved by the recording of bone-conducted sounds using a piezoelectric accelerometer, which resulted in high-quality song recordings that were relatively uncontaminated by airborne sounds. Under this condition of cDAF, birds rapidly (2–6 days) changed their song syllable timing. The one bird for which we were able to maintain the accelerometer recordings over a long period of time recovered slowly over more than a month after cDAF was discontinued. These results demonstrate that cDAF can cause substantial changes in the motor program for syllable timing generation over short intervals of time in adult zebra finches. S ongbirds are vocal learners and have been used as animal models for speech acquisition and production 1,2. Auditory feedback (AF) is required for normal speech development and maintenance in humans 3–5. In songbirds, AF is necessary for song development 6–8 and adult song maintenance as demonstrated by experiments with deafened birds 9,10. More recently, various real-time manipulations of AF have revealed the capability of the monitoring mechanism to adjust song morphology and sequences in the presence of altered sensory consequences of motor commands
Article
A fundamental issue in cortical processing of sensory information is whether top-down control circuits from higher brain areas to primary sensory areas not only modulate but actively engage in perception. Here, we report the identification of a neural circuit for top-down control in the mouse somatosensory system. The circuit consisted of a long-range reciprocal projection between M2 secondary motor cortex and S1 primary somatosensory cortex. In vivo physiological recordings revealed that sensory stimulation induced sequential S1 to M2 followed by M2 to S1 neural activity. The top-down projection from M2 to S1 initiated dendritic spikes and persistent firing of S1 layer 5 (L5) neurons. Optogenetic inhibition of M2 input to S1 decreased L5 firing and the accurate perception of tactile surfaces. These findings demonstrate that recurrent input to sensory areas is essential for accurate perception and provide a physiological model for one type of top-down control circuit. Copyright © 2015 Elsevier Inc. All rights reserved.
Article
In animal models the neural basis of cognitive and executive processes has been studied extensively at various hierarchical levels from microcircuits to distributed functional networks. This work already provides compelling evidence that diverse cognitive functions are based on similar basic neuronal mechanisms. More recent data suggest that even cognitive functions realized only in human brains rely on these canonical neuronal mechanisms. Here we argue that language, like other cognitive functions, depends on distributed computations in specialized cortical areas forming large-scale dynamic networks and examine to what extent empirical results support this view. Copyright © 2015 Elsevier Ltd. All rights reserved.
Article
Self-repairing of speech errors demonstrates that speakers possess a monitoring device with which they verify the correctness of the speech flow. There is substantial evidence that this speech monitor not only comprises an auditory component (i.e., hearing one's own speech), but also an internal part: inspection of the speech program prior to its motoric execution. Errors thus may be detected before they are actually articulated. In the covert repair hypothesis of disfluency, this internal error detection possibility has been extended with an internal correction counterpart. Basically, the covert repair hypothesis contends that disfluencies reflect the interfering side-effects of covert, prearticulatory repairing of speech programming errors on the ongoing speech. Internally detecting and correcting an error obstructs the concurrent articulation in such manner that a disfluent speech event will result. Further, it is shown how, by combining a small number of typical overt self-repair features such as interrupting after error detection, retracing in an utterance, and marking the correction with editing terms, one can parsimoniously account for the specific forms disfluencies are known to take. This reasoning is argued to apply to both normal and stuttered disfluency. With respect to the crucial question concerning what makes stuttering speakers so greatly disfluent, it is hypothesized that their abilities to generate error-free speech programs are disordered. Hence, abundant stuttering derives from the need to repeatedly repair one's speech programs before their speech motor execution.
Article
Despite advances in the new science of connectomics, which aims to comprehensively map neural connections at both structural and functional levels, techniques to directly study the function of white matter tracts in vivo in humans have proved elusive. Direct electrical stimulation (DES) mapping of the subcortical fibres offers a unique opportunity to investigate the functional connectivity of the brain. This original method permits real-time anatomo-functional correlations, especially with regard to neural pathways, in awake patients undergoing brain surgery. In this article, the goal is to review new insights, gained from axonal DES, into the functional connectivity underlying the sensorimotor, visuospatial, language and sociocognitive systems. Interactions between these neural networks and multimodal systems, such as working memory, attention, executive functions and consciousness, can also be investigated by axonal stimulation. In this networking model of conation and cognition, brain processing is not conceived as the sum of several subfunctions, but results from the integration and potentiation of parallel-though partially overlapping-subnetworks. This hodotopical account, supported by axonal DES, improves our understanding of neuroplasticity and its limitations. The clinical implications of this paradigmatic shift from localizationism to hodotopy, in the context of brain surgery, neurology, neurorehabilitation and psychiatry, are discussed.
Article
This chapter reviews evidence regarding the role of auditory perception in shaping speech output. Evidence indicates that speech movements are planned to follow auditory trajectories. This in turn is followed by a description of the Directions Into Velocities of Articulators (DIVA) model, which provides a detailed account of the role of auditory feedback in speech motor development and control. A brief description of the higher-order brain areas involved in speech sequencing (including the pre-supplementary motor area and inferior frontal sulcus) is then provided, followed by a description of the Hierarchical State Feedback Control (HSFC) model, which posits internal error detection and correction processes that can detect and correct speech production errors prior to articulation. The chapter closes with a treatment of promising future directions of research into auditory-motor interactions in speech, including the use of intracranial recording techniques such as electrocorticography in humans, the investigation of the potential roles of various large-scale brain rhythms in speech perception and production, and the development of brain-computer interfaces that use auditory feedback to allow profoundly paralyzed users to learn to produce speech using a speech synthesizer. © 2015 Elsevier B.V. All rights reserved.
Article
Developmental stuttering is a speech disorder that severely limits one's ability to communicate. White matter anomalies were reported in stuttering, but their functional significance is unclear. We analyzed the relation between white matter properties and speech fluency in adults who stutter (AWS). We used diffusion tensor imaging with tract-based spatial statistics, and examined group differences as well as correlations with behavioral fluency measures. We detected a region in the anterior corpus callosum with significantly lower fractional anisotropy in AWS relative to controls. Within the AWS group, reduced anisotropy in that region is associated with reduced fluency. A statistically significant interaction was found between group and age in two additional regions: the left Rolandic operculum and the left posterior corpus callosum. Our findings suggest that anterior callosal anomaly in stuttering may represent a maladaptive reduction in interhemispheric inhibition, possibly leading to a disadvantageous recruitment of right frontal cortex in speech production. Copyright © 2015 Elsevier Inc. All rights reserved.