brain imaging in stuttering: where next?

Journal of Fluency Disorders28 (2003) 265–272

Discussion

Brain imaging in stuttering:where next?

Peter T. Fox∗

Research Imaging Center, University of Texas Health Science Center at San Antonio,San Antonio, TX 78229-3900, USA

Received 18 August 2003; accepted 21 August 2003

Keywords: Stuttering; Brain imaging; PET; TMS; fMRI

As the articles comprising this special issue amply demonstrate, brain functionalimaging is having a significant impact on research in persistent developmental stut-tering (PDS). Specifically, voxel-wise statistical parametric images (SPI) demon-strating differences in the brain activation patterns evoked by fluent versus stutteredspeech are having an impact. The first such report (Fox et al., 1996), used positronemission tomography (PET) and overt paragraph reading to show that, in broadstrokes, stuttering was characterized by overactivity of the right inferior premotorcortex (operculum and insula) and underactivity of auditory cortex, abnormalitieswhich were remediated acutely by fluency induction (chorus reading). With theseveral papers in this issue (and numerous intervening papers), considerable con-sensus about these findings has emerged. The findings have been replicated with arange of immediately and temporarily effective fluency inductions as well as withsustained improved fluency produced by behavioral treatments. They have beenextended from paragraph reading to spontaneous speech and to single word tasks.They have been extended from overt speech to imagined speech and speech prepa-ration tasks. They have been extended from PET to functional magnetic resonanceimaging (fMRI). Despite significant methodological differences, the basic obser-vations stand. Given this, the question becomes, “Where next?” How best shouldthe PDS research community capitalize on the reliability of these findings? Howdo we use this new insight into stuttering? Do we try more and more task variants,to winnow our findings down to the finest, most replicable effects? Do we apply the

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266 P.T. Fox / Journal of Fluency Disorders 28 (2003) 265–272

latest advances in functional imaging to improve our signal to noise? Do we debateendlessly as to which of the observed effects are causal and which compensatory?Do we attempt to shoehorn the imaging data into one or another time-honoredtheories of stuttering? I would suggest that while each of these activities will makesome slow, steady progress, none would advance our understanding of this disorderin a fundamental way. What is needed is a new view, a quantum leap.

1. Structural imaging

One way for functional imaging to advance would be to discover relationshipsbetween abnormalities of functional organization and developmental abnormal-ities of brain structure. Clear demonstration of structural brain abnormalities—particularly if confined to the left hemisphere—would suggest that the right-lateralized brain activation observed with functional imaging in stuttering mightbest be interpreted as developmental plasticity. Functional imaging has alreadydemonstrated inter-hemispheric transfer of functions following acquired lesions(Weiller, Chollet, Friston, Wise, & Frackowiak, 1992; Weiller, Ramsay, Wise,Friston, & Frackowiak, 1993). PDS might well be another example. Whether de-velopmental stuttering is characterized by structural brain abnormalities remainsunclear. Abnormalities of gyral anatomy in the left and right frontal operculumhave been reported (Foundas, Bollich, Corey, Hurley, & Heilman, 2001) but notyet independently confirmed. If aberrant gyral patterns are present, their functionalsignificance will need careful consideration. While neuronal migration failures andpolymicrogyria can be detected by MRI and are strongly associated with abnor-malities of function, these have not been reported in developmental stuttering. Thephysiological significance of variations in surface folding patterns, if present, isunclear. Folding patterns of secondary and tertiary sulci are highly variable (Ono,Kubik, & Abernathy, 1990) and this variability seems to have little or no predic-tive value regarding functional organization (Hasnain, Fox, & Woldorff, 2001).On the other hand, differences in cortical thickness and total cortical volume areheritable (Thompson et al., 2001) and, if present in stuttering, would be a strongerindication of functionally significant pathology than would gyral folding patterns.Detection of these more subtle, but likely more significant anomalies, is best per-formed using fully automated methods for analysis of structural images, suchas deformation-field morphometry (Lancaster et al., 2003). In a recent study fromour group, deformation-field morphometry was applied to magnetic resonance im-ages to detect differences in cortical thickness between natively English-speakingCaucasians and natively Chinese-speaking Asians (Kochunov et al., 2003). Thecortical differences between these two groups were limited to specific loci in thefrontal, temporal and parietal lobes that were known through functional imagingstudies to differentiate Chinese speakers from English speakers. We interpret theseanatomical differences as evidence of neural plasticity shaped by the process oflanguage acquisition during childhood. A deformation-field morphometric study

P.T. Fox / Journal of Fluency Disorders 28 (2003) 265–272 267

comparing persons who do stutter with persons who do not is presently underwayin our laboratory and should give a definitive answer to the question of whethercortical anatomical abnormalities exist and, if present, how their locations relateto functional abnormalities.

Abnormalities of myelin structure have been found in the left rolandic oper-culum of persons with PDS (Sommer, Koch, Paulus, Weiller, & Büchel, 2002).The functional interpretation of this finding appears more straightforward: a dis-connection of superior temporal and inferior frontal language regions of the lefthemisphere underlies stuttering. Disordered inter-regional connectivity in stutter-ing has also been suggested by magnetoencephalographic studies (Salmelin et al.,1998) showing that the temporal sequence of speech-associated activation is ab-normal in stuttering speakers. In regards to the functional imaging evidence ofexcess right inferior premotor activation, this might well indicate that a compen-satory shift of language processes into the non-dominant hemisphere has occurred.A fundamental difficulty for accepting a causal link between structural and func-tional abnormalities, however, is the normalization of functional activation patternsseen during fluency induction and following treatment. If function has migratedaway from a structural lesion, why would function shift back into a structurallyabnormal region? If the shift to right-hemispheric processing evolves over yearsduring development, how can it be so readily reversed? Taken more generally,congruence between functional and structural observations may be more difficultto achieve than one might naively presume.

2. Epidemiology and genetics

It is now well established that stuttering is a heritable disorder (Ambrose, Cox,& Yairi, 1997). Another potential avenue for brain imaging to advance our under-standing of stuttering would be to use functional (and structural) imaging character-istics as additional phenotypes to facilitate pedigree analyses and linkage mapping.Functional imaging patterns during memory tasks have been demonstrated to bereliably associated with the APOE-4 gene, which transmits risk for Alzheimer’sdisease (Bookheimer et al., 2000). Structural abnormalities in speech-motor cortexand subcortical nuclei have been demonstrated to be reliably associated with theFOXP2 gene, which transmits risk for speech dyspraxia (Watkins et al., 2002).If the abnormal patterns of brain activation which are now reliably detected ingroup-average images of persons with PDS can be quantified on a per-subject ba-sis, and if they are present in lesser degrees in non-stuttering relatives of stutteringprobands, they could prove to be a substantial aid in disambiguating the inheritancepattern of stuttering and, through DNA linkage analyses, in identifying candidateloci for in-depth study. However, even if (when) the genetics underlying devel-opmental stuttering are elucidated, there is an additional level of explanation thatmust be addressed. How do the genetic traits result in the behavior we recognizeas stuttering?


3. Neural system’s modeling

Ultimately, what is needed are explanations at the neural system level as to howspeech production is organized and executed, how the speech system is dysregu-lated so as to produce the execution errors collectively termed stuttering, and howfluency inductions and treatments achieve behavioral normalization. This level ofexplanation is well beyond descriptions of brain activation patterns or structuralabnormalities. It is well beyond box-and-arrow models of the connections amongbrain regions implicated in languageà lá Jürgens (2002), even when accompaniedby verbal descriptions of the functions of each boxà lá Petersen, Fox, Posner,Mintun, and Raichle (1989). What is needed is a computational model of the neu-ral systems of speech—a model that describes the system as a whole and quantifiesboth the functioning of individual regions and the functional interactions amongregions. How do we begin to build such a model? Can it be built from functionalimaging data?

Control-system models, neural network models, and realistic models of neu-ronal conduction properties have been applied to great advantage to invasively ac-quired electrophysiological recordings. Heuristic, non-quantitative models (e.g.,Jürgens, 2002; Mayberg et al., 2000) have been developed from functional imag-ing data. Heuristic models can be used as general guides for experimental designbut not for data modeling and model-based analysis. Mathematical modeling tech-niques that add more rigor to functional imaging have been developed in recentyears, but remain quite limited in scope.

Functional connectivity modeling—inter-regional covariances in functional ac-tivation levels during task performance—was introduced to functional imaging asan explicit technology transfer from multi-electrode electrophysiology (Friston,1994; Grafton, Sutton, Couldwell, Lew, & Waters, 1994), in an attempt to impartmodeling rigor to functional imaging. Using functional connectivity to demonstratediscrete paths of information exchange in the motor system,Liu, Gao, Liotti, Pu,and Fox (1999)showed high information exchange between supplementary motorarea and the dentate nucleus and between the primary motor cortex and the externalsegment of the globus pallidus, externa, but little information exchange betweenthese two subsystems. In the speech motor system, functional connectivity wasused to model differences in inter-regional information flow between speech andtongue movement (He et al., 2003). Functional connectivity modeling, however,has two serious limitations. First, it models information exchange but not infor-mation flow. That is, the covariance between two regions is expressed as a single,bi-directional value, rather than separate values for each direction. Second, data areobtained during task performance, making them task-specific. Thus, connectivityvalues obtained during any given task likely won’t apply to any other task. Further,changes over time in connectivity values (i.e., treatment effects) are almost cer-tainly contaminated by alterations in task strategy (i.e., which brain areas are re-cruited and to what degree) and is not an unambiguous measure of changes in syna-ptic efficiency (receptor density), the most likely mechanism of motor learning.


Structural equation modeling (SEM), also termed Path Analysis, was intro-duced to functional imaging byMcIntosh and Gonzalez-Lima (1991), and Grafton(Grafton & DeLong, 1997; Grafton et al., 1994). SEM addresses (some of) theshortcomings of functional connectivity analysis. As applied to functional imag-ing data, SEM (a technology transfer from social psychology) adds directionality tothe model by assuming/postulating specific anatomical connections and omittingothers. The “raw” inter-regional covariances are then modeled onto this assumedconnectivity, to derive unidirectional path weights. SEM, however, still has severelimitations. Perhaps most glaring is the need to postulate the existence of specificconnections and the non-existence of others, with such assumptions being basedon animal work in all SEM studies reported to date. There are many systems (e.g.,the speech system) in which such assumptions are highly problematic. Second,like functional connectivity analysis, SEM remains task-specific, in that the datafrom which covariances are computed are obtained during a task.

Task-independent methods of measuring inter-regional connectivity have beenestablished as an important technical objective in several laboratories specificallyto address this shortcoming of functional connectivity modeling and SEM model-ing. Fox et al. (1997)andPaus et al. (1997)introduced the conjoined applicationof transcranial magnetic stimulation (TMS) and PET, to measure inter-regionalconnectivity in a task-free manner. TMS directly depolarizes neurons in corteximmediately beneath coil face. This local depolarization physiologically propa-gates to remote regions connected to the target region. The TMS-induced changesin neural activity, both local and remote, can be readily imaged using PET. Fur-ther, TMS/PET provides path-weighting values (r-values) for the strengths of eachconnection. As it is unreasonable to expect that treatments will cause new physicalconnections between regions (new fiber pathways), changes in TMS/PET-derivedpath weights are best interpreted as changes in synaptic weighting of existingpathways, i.e., as synaptic plasticity. TMS/PET has been used to demonstrateTMS-induced synaptic plasticity in persons with PDS (Tandon et al., 2000). Todate, however, no laboratory has used TMS/PET data to constrain a SEM offunctional imaging data, nor to test the mechanisms of action of a non-TMStherapy.

Resting-state fMRI is being explored as an alternative to TMS/PET for task-independent measurement of inter-regional connectivity (Xiong, Gao, & Fox,1999). In the resting state, regional neuronal firing rates are periodically variable.Regions that are anatomically connected show temporal covariation at rest. Thetime scale of this periodicity is sufficiently slow (seconds) that it can be detectedby functional MRI performed with short repetition time (TR= 1 s). Resting-statefMRI connectivity measures have the advantage of providing connectivity informa-tion for any and all brain regions imaged, rather than being limited to a stimulatedarea, as with PET/TMS. Resting-state fMRI is not yet a well-validated or widelyaccepted method. Still, it has the potential to supplement (possibly even supplant)TMS/PET for measuring inter-regional connectivity, testing for synaptic plasticity,and for providing anatomical constraints for SEM.


Functional imaging research sorely needs the rigor provided by mathemati-cal modeling. A modeling framework—structural equation modeling—has beendeveloped which appears appropriate for modeling human imaging data in amanner which captures some (but certainly not all) of the important character-istics of the neural systems of speech. SEM is most rationally applied whentask-independent (i.e., “resting state”) anatomical connectivity data are used, in ad-dition to task-induced regional covariations. To date, this has not been reported byany laboratory. A reasonable next step would be to apply these advanced imagingand modeling methods to a treatment trial in developmental stuttering. If success-ful, such a study has the potential of defining a new strategy by which functionalimaging is applied to disease pathophysiology and treatment mechanisms of actionand should provide fundamental insights into the nature of stuttering.

4. Developmental stuttering as a proving ground for imaging methods

From the perspective of a scientist heavily involved in the development ofbrain imaging strategies, persons with persistent developmental stuttering offera truly unique resource. This is a disorder whose symptoms can be rapidly andbriefly eliminated (by fluency induction), allowing subjects to be imaged with andwithout stuttering in a single imaging session. Further, it is a disorder for whichreasonably effective treatments exist, allowing the mechanisms of action of ther-apy to be studied with imaging before and after treatment. Subjects with PDS aremedically, psychiatrically and neurologically unimpaired, making it much eas-ier for them to participate in research than for persons with virtually any otherneural system disorder. Evidence, both from behavioral studies and from imag-ing studies, indicates that PDS is quite restricted in the neural systems compro-mised and non-progressive. This makes PDS far easier to study than degenerativedisorders (e.g., Parkinson’s disease, Alzheimer’s disease). All of these factorscombine to make PDS an extremely attractive model system within which todevelop and validate imaging methods for modeling neural systems and neuralsystem disorders and within which to learn to use imaging as a phenotype forgenetic analyses. Using PDS as a vehicle for prototyping new imaging strategiesclearly benefits persons with PDS, but should also bring benefits to persons witha wide range of brain disorders in whom these new techniques subsequently areapplied.

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brain imaging in stuttering: where next?

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