Elsevier

Brain and Language

Volume 126, Issue 3, September 2013, Pages 263-278
Brain and Language

Computational modeling of stuttering caused by impairments in a basal ganglia thalamo-cortical circuit involved in syllable selection and initiation

https://doi.org/10.1016/j.bandl.2013.05.016Get rights and content

Highlights

  • We examine the roles of dopamine excess and atypical white matter in stuttering.

  • We simulate “neurally impaired” versions of the neurocomputational model GODIVA.

  • Each abnormality causes stuttering by affecting the same BG–thalamus–vPMC circuit.

  • The circuit selects/initiates the next syllable too late, resulting in dysfluency.

  • The results account for brain imaging findings during dysfluent speech production.

Abstract

Atypical white-matter integrity and elevated dopamine levels have been reported for individuals who stutter. We investigated how such abnormalities may lead to speech dysfluencies due to their effects on a syllable-sequencing circuit that consists of basal ganglia (BG), thalamus, and left ventral premotor cortex (vPMC). “Neurally impaired” versions of the neurocomputational speech production model GODIVA were utilized to test two hypotheses: (1) that white-matter abnormalities disturb the circuit via corticostriatal projections carrying copies of executed motor commands and (2) that dopaminergic abnormalities disturb the circuit via the striatum. Simulation results support both hypotheses: in both scenarios, the neural abnormalities delay readout of the next syllable’s motor program, leading to dysfluency. The results also account for brain imaging findings during dysfluent speech. It is concluded that each of the two abnormality types can cause stuttering moments, probably by affecting the same BG–thalamus–vPMC circuit.

Introduction

Developmental stuttering is a speech disorder characterized by frequent repetitions of syllables or parts of syllables (sound/syllable repetitions), and by audible and inaudible prolongations of articulatory positions (prolongations and blocks, respectively). Although approximately 1% of the population stutters (Van Riper, 1982), the etiology of the disorder is still unknown. The primary goal of this paper is to enhance our understanding of the underlying mechanisms by using a computational model to explore alternative neurological bases of stuttering.1 This approach provides detailed explanatory accounts for already published data, and frames testable hypotheses to guide future experiments. The model developed here extends recent instantiations of the DIVA (Directions into Velocities of Articulators) and GODIVA (Gradient Order DIVA) neurocomputational models of speech production (Bohland et al., 2010, Golfinopoulos et al., 2010, Guenther, 1995, Guenther et al., 2006, Guenther et al., 1998, Nieto-Castanon et al., 2005; Tourville, Reilly, & Guenther, 2008).2

Brain imaging studies of developmental stuttering have disclosed various abnormalities in the brains of persons who stutter (PWS). In this paper we discuss two discoveries: a structural abnormality in white matter fibers (WMF) beneath the left precentral gyrus (Chang et al., 2008, Cykowski et al., 2010, Kell et al., 2009, Sommer et al., 2002, Watkins et al., 2008),3 and evidence that dorsal striatum has markedly elevated dopamine (DA, see Wu et al., 1997), which is released by fibers from the substantia nigra pars compacta (SNc, see Watkins et al., 2008). Although both abnormalities have been suggested as possibly contributing to stuttering, it is unclear which functional neural circuits are implicated in the white matter impairment or the elevated dopamine levels. Also unknown are the functional consequences of these abnormalities, and how they lead to moments of stuttering (for selected proposals, see the aforementioned papers and Brown et al., 2005, Giraud et al., 2008, Max et al., 2004). Lastly, an open question is whether the two abnormalities need to exist simultaneously for stuttering to emerge.

We believe that each of these abnormalities may separately lead to the dysfluencies that characterize stuttering. The location of the structural abnormality in the deep perisylvian operculum (Cykowski et al., 2010) that lies beneath the left precentral gyrus suggests that the abnormality is close to the ventral primary motor cortex, or vMC (Sommer et al., 2002). In accordance with this view, we follow the hypothesis that the impairment is in corticostriatal projections that originate in vMC and indicate the current motor state, and that the resulting transmission errors prevent the basal ganglia (BG) from detecting the proper motor context for shifting between syllables, i.e., terminating the previous syllable and initiating the motor program – a stored sequence of motor commands – for the next syllable (Alm, 2004, p. 358). Concerning the second abnormality, we build upon the claim that dopamine excess may adversely affect the striatum (Maguire, Yu, Franklin, & Riley, 2004), and we hypothesize that a specific part of the striatum, the putamen, would become dysfunctional. This dysfunction could hamper the BG’s ability to bias cortical competition (among motor programs for similar syllables) in favor of the motor program appropriate for the next syllable (cf. Alm, 2004, Lu et al., 2010, Watkins et al., 2008).

These two dysfluency mechanisms – a failure to cancel the activation of the previous syllable, and a failure to bias cortical competition in favor of the next syllable – might be considered specific cases of the two common accounts of any perseveratory phenomenon: the failure to inhibit previous, and the failure to activate next (see, for example, Fischer-Baum & Rapp, 2012); both accounts were already proposed in the context of stuttering (Howell, 2007, MacKay and MacDonald, 1984). However, in contrast with perseveration which is characterized by repetition of whole units, stuttering is mostly characterized by prolongations, blocks, and sound/syllable repetitions that do not respect segmental (phonemic) boundaries (Conture, 2001, p. 6). Therefore, we hypothesize that each of the two mentioned dysfluency mechanisms, rather than inducing repetition of the previous syllable, may lead to abnormally slow activation of all but the earliest part (see Howell, 2007) of the next syllable’s motor program (cf. Brown et al., 2005, Packman et al., 2007). All dysfluencies in stuttering may result from such delayed syllable activation, whereas the type of dysfluency (sound/syllable repetition, prolongation, block, etc.) depends on additional factors, including the reaction of the speaker to the delayed activation.

Both mechanisms described above involve the basal ganglia whose relation with stuttering is demonstrated by various lines of evidence (see Alm, 2004). This group of nuclei, which takes part in speech production (Crosson, 1992, Gracco and Abbs, 1987, Guenther, 2007), was first linked to stuttering indirectly: the association was made due to the BG’s interconnection with the supplementary motor area (SMA), which may also be involved in the disorder (Caruso, Abbs, & Gracco, 1988). Basal ganglia lesions are associated with the presence of acquired (neurogenic) stuttering following strokes (Theys, De Nil, Thijs, van Wieringen, & Sunaert, 2012) and traumatic brain injuries (Ludlow, Rosenberg, Salazar, Grafman, & Smutok, 1987), and several case studies showed that such a scenario (acquired stuttering after a BG lesion) leads to speech disturbances similar to developmental stuttering in various behavioral and clinical dimensions (Heuer et al., 1996, Koller, 1983, Krishnan and Tiwari, 2011, Tani and Sakai, 2011). The BG also have critical role in sequence skill learning, which is deficient in PWS (e.g., Smits-Bandstra & De Nil, 2007).

Perhaps the strongest evidence for BG involvement in stuttering comes from pharmacological studies. The BG’s striatum receives the densest dopamine innervation in the brain, and in repeated findings, drugs that block type D2 dopamine receptors (D2Rs) have been shown to be effective in reducing stuttering (Brady, 1991, Maguire et al., 2004, Stager et al., 2005, Tran et al., 2008). Although other parts of the brain also have D2Rs, it is likely that a major part of the ameliorative action of the D2R blockers was in the BG. Opponent processing in the BG, in which the D1R (D1 dopamine receptors) expressing striatal projection neurons promote action whereas the D2R-expressing striatal projection neurons oppose action (see Section 2.2), is fundamental to forebrain control of action in all jawed vertebrates (Reiner, 2009), and imbalances of D1R vs. D2R processing are associated with a wide range of disorders of movement and decision making. These include schizophrenia, bipolar disorder, and Parkinson’s disease. The use of D2R blockers to treat stuttering was motivated, for example, by similarities between stuttering and Tourette’s syndrome. Both begin in childhood, and both occur more frequently in males than in females. Unfortunately, treatment of stuttering with most D2 antagonists incurs problematic side effects (see Maguire et al., 2004).

A role for BG in stuttering is also supported by functional imaging studies (Braun et al., 1997; Brown et al., 2005, Chang et al., 2009, Giraud et al., 2008, Ingham et al., 2004, Lu et al., 2010, Watkins et al., 2008, Wu et al., 1995). However, BG nuclei are small in size and/or complex in shape, so it is difficult to determine which BG nuclei are contributing to activation changes (e.g., Watkins et al., 2008). This may explain why, in a recent meta-analysis of functional imaging studies (Brown et al., 2005), PWS showed only one reliably detected abnormality in the BG: they lacked the weak but reliable above-baseline activation observed in the left globus pallidus of control subjects during speech.

Despite their limitations in highlighting problems in the BG, imaging studies are very useful in detecting cortical abnormalities. In the Brown et al. (2005) meta-analysis, the activation in the left precentral gyrus of fluent speakers extended from the vMC to the ventral premotor cortex (vPMC), whereas the activation in the left precentral gyrus of PWS was restricted to the vMC. That PWS fail to properly activate the left vPMC was confirmed by a Watkins et al. (2008) functional imaging experiment (see also Chang et al., 2011, Salmelin et al., 2000). The activation-failures reported by Watkins et al. were observed under conditions in which PWS were dysfluent, suggesting that the degree of deactivation of the vPMC may be causally related to actual moments of stuttering, and not solely to an underlying deficiency. The correlation of vPMC deactivation with actual stuttering is also consistent with observations that the left inferior lateral premotor cortex, which largely coincides with vPMC, is the only left hemisphere motor region in which the activation level for PWS of both sexes is inversely correlated with stutter rate (Ingham et al., 2004). Also relevant is Neumann et al.’s (2003) finding of abnormally low activation in the left precentral cortex (of which the vPMC is part). In that study, the only dysfluencies produced by the subjects were sentence-initial blocks.

There are additional clues supporting the involvement of vPMC in stuttering. The first is that the most common speech elements repeated by PWS are complete syllables or their initial parts. This problem may be the result of a vPMC malfunction because this brain region codes for syllables, as was recently demonstrated using functional imaging techniques (Bohland and Guenther, 2006, Ghosh et al., 2008, Peeva et al., 2010). Another clue is the existence of vPMC-to-vMC projections (Barbas and Pandya, 1987, Passingham, 1993), which, according to the DIVA model, constitute feedforward or “well-learned” speech motor commands (Guenther et al., 2006). Simulations with the DIVA model showed that impaired readout of feedforward commands, which is one possible outcome of vPMC malfunction (see Section 2.2.3), may lead to sound/syllable repetitions (Civier, Tasko, & Guenther, 2010). Lastly, activation of the vPMC depends on reciprocal excitatory links with the thalamus (Hoover & Strick, 1993), and thalamic activation is gated by potent inhibitory outputs from BG. The vPMC is therefore likely to be affected by the BG problems discussed above. Because the vPMC both sends projections to (Takada, Tokuno, Nambu, & Inase, 1998), and (via the thalamus) receives projections from, the BG, we will refer to this closed circuit by the term BGvPMC loop.

Although suggestive of a role for the BG in stuttering, systemic drug effects cannot reveal exact mechanisms of any disorder. Similarly, imaging studies that suggest a role for vPMC in stuttering do not explicate how that role may relate to a BG dysfunction. Imaging studies can be used, though, to evaluate hypotheses concerning BG dysfunction. Unfortunately, this method cannot be applied to most past hypotheses as they cannot predict whether particular brain regions would be over- or under-activated during stuttering (e.g., Brown et al., 2005, p. 114; but see Giraud et al., 2008, Wu et al., 1995); without a modeling strategy that progressively incorporates the BG’s complex internal circuits, including its many inhibitory connections, it will remain very difficult to predict neural activation patterns (cf. Alm, 2004, p. 335).

To begin to overcome such problems, our method is to formulate and test hypotheses using an extended version of the GODIVA model that includes both cortical and subcortical sites. We first describe the extended model, with an emphasis on the newly-developed BG–vPMC loop module. After introducing each abnormality (white matter impairment or elevated dopamine levels), we then confirm that the model is capable of generating speech behaviors that account for dysfluencies. As the GODIVA model is both neurocomputational and biologically plausible (compare with other models reviewed in Civier, 2010, p. 3), it can also predict entire patterns of blood-oxygenation-level-dependent (BOLD) responses observable across the brain regions simulated, much as cerebral and cerebellar activation patterns during normal and perturbed speech were predicted based on simulations of the GODIVA model’s counterpart, the DIVA model (Golfinopoulos et al., 2010, Guenther et al., 2006, Tourville et al., 2008). To evaluate our hypotheses, we compare these predicted BOLD responses to published functional imaging data.

Section snippets

The original GODIVA model

The GODIVA model (Bohland et al., 2010) explains how arbitrary utterances that fall within a speaker’s language rules can be represented in the brain, and how such utterances can be produced from a finite library of learned motor programs (well-learned syllables) when activated in the proper order.

Results

Computer simulations of the extended GODIVA model producing its name (/godivə/) were performed to test our two hypotheses about the etiology of stuttering. One hypothesis involves elevated dopamine levels (affecting both the putamen D1R and D2R cells), and the other involves impaired white matter fibers (in corticostriatal projections originating in vMC). To assess each hypothesis, we used abnormal values for the critical parameters of the model that are listed either in the first or second row

Discussion

The simulations of the extended GODIVA model support two hypotheses of how neural abnormalities in stuttering prevent the SSM choice cell for the next syllable from being activated in a timely manner. In the case of elevated dopamine levels, the basal ganglia fail to bias cortical competition in favor of the SSM choice cell adequate for the next syllable, and in the case of white matter impairment, the BG fail to quickly cancel the activation of the SSM choice cell for the current syllable. It

Conclusions

Based on GODIVA, a recently developed model of connected-speech planning and sequencing, we investigate two independent hypotheses of the etiology of stuttering: the “DA hypothesis” proposes that stuttering is caused by elevated dopamine levels affecting the putamen, and the “WMF hypothesis” proposes that stuttering is caused by structural abnormality to corticostriatal white matter fibers that arise from vMC-to-brainstem projections. According to the GODIVA model simulations, which support our

Acknowledgments

This study is part of the PhD dissertation of Oren Civier at Boston University and was supported by NIH/NIDCD Grants R01 DC07683 and R01 DC02852 (P.I. Frank Guenther) and RO1DC007603 (P.I. Ludo Max). We are grateful to Jason Bohland for developing the GODIVA model code and helping with the simulations, to Jason Tourville for the extensive knowledge and guidance with the prediction of BOLD responses, and to Gerald Maguire and Per Alm whose novel hypotheses and suggestions paved the way for this

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