Stuttered and Fluent Speech Production: An ALE Meta-Analysis of Functional Neuroimaging Studies - PDF

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Human Brain Mapping 25: (2005) Stuttered and Fluent Speech Production: An ALE Meta-Analysis of Functional Neuroimaging Studies Steven Brown, 1 Roger J. Ingham, 2 Janis C. Ingham, 2 Angela R. Laird,
Human Brain Mapping 25: (2005) Stuttered and Fluent Speech Production: An ALE Meta-Analysis of Functional Neuroimaging Studies Steven Brown, 1 Roger J. Ingham, 2 Janis C. Ingham, 2 Angela R. Laird, 1 and Peter T. Fox 1 1 Research Imaging Center, University of Texas Health Science Center at San Antonio, Texas 2 Department of Speech and Hearing Sciences, University of California, Santa Barbara, California Abstract: This study reports an activation likelihood estimation (ALE) meta-analysis of imaging studies of chronic developmental stuttering in adults. Two parallel meta-analyses were carried out: (1) stuttered production in the stutterers; (2) fluent production in the control subjects. The control subjects data replicated previous analyses of single-word reading, identifying activation in primary motor cortex, premotor cortex, supplementary motor area, Rolandic operculum, lateral cerebellum, and auditory areas, among others. The stuttering subjects analysis showed that similar brain areas are involved in stuttered speech as in fluent speech, but with some important differences. Motor areas were over-activated in stuttering, including primary motor cortex, supplementary motor area, cingulate motor area, and cerebellar vermis. Frontal operculum, Rolandic operculum, and anterior insula showed anomalous rightlaterality in stutterers. Auditory activations, due to hearing one s own speech, were essentially undetectable in stutterers. The phenomenon of efference copy is proposed as a unifying account of the pattern activation revealed within this ALE meta-analysis. This provides the basis for a stuttering system model that is testable and should help to advance the understanding and treatment of this disorder. Hum Brain Mapp 25: , Wiley-Liss, Inc. Key words: ALE; developmental stuttering; brain imaging; efference copy INTRODUCTION Speech is the most distinguishing and complex motor activity that humans engage in, requiring smooth coordination of processes related to respiration, phonation, and articulation. Syllable production, in particular, involves rapid and precisely controlled transitions between open and closed configurations of the vocal tract. Speech requires fine control of physiological processes extending from the lungs *Correspondence to: Roger J. Ingham, Department of Speech and Hearing Sciences, University of California Santa Barbara, Santa Barbara, CA Received for publication 7 February 2005; Accepted 8 February 2005 DOI: /hbm Published online in Wiley InterScience (www.interscience.wiley. com). to the lips, made all the more complicated because components of the vocal system also serve critical functions unrelated to speech (e.g., breathing, feeding, and facial expression). Like any complex motor activity, speech is subject to disruptions at many levels due to both congenital and acquired deficits, including those leading to syndromes like dysarthria, apraxia, dysphonia, and stuttering [Kent, 2000]. Chronic developmental stuttering is a speech disorder characterized by involuntary syllable repetitions and prolongations, especially during connected speech, thereby impairing normally fluent speech. This disorder provides a fascinating disease model of speech production not only because of its high prevalence in the population (approximately 1%) but because of its marked gender ratio (3:1 ratio of men:women), probable genetic basis, and responsiveness to environmental stimuli [Bloodstein, 1995]. There is a high rate of recovery in 2005 Wiley-Liss, Inc. Brown et al. children, but stuttering that persists into adolescence or adulthood is much more resistant to recovery [Ingham, 2001a]. Although the core pathology underlying developmental stuttering remains poorly understood, much research has effectively excluded the peripheral vocal system as the cause of the disorder and has instead placed the focus on the central nervous system (CNS). One of the main pieces of evidence for this is that stuttering can be eliminated almost immediately although temporarily by simple manipulations that have no direct effect on the vocal system itself but that almost certainly affect a central planning mechanism. These manipulations, known as fluency-inducing conditions, include oral reading along with another speaker (so-called chorus reading), auditory masking, singing, reading to the accompaniment of a real or imagined rhythmic stimulus, among several others [Bloodstein, 1995]. Importantly, the most effective fluency-inducing mechanisms involve either auditory stimulation or changes to the customary speech pattern [Ingham, 1984]. The fact that simple manipulations like hearing another speaker say the words to be read are so effective in eliminating stuttering strongly suggests that the pathology can be neither with the vocal organ itself nor with the proximal motor mechanism but instead at a locus closer to the level of vocal planning and initiation. Finally, stuttering is distinct from other speechmotor disorders in being more or less specific for speech, in comparison to syndromes such as dysarthria that tend to be part of generalized syndromes affecting motor control throughout much of the body [Kent, 2000]. The cause of chronic developmental stuttering remains unknown, resulting in a plethora of competing theories [Ingham, 2001a]. Neuroimaging studies have provided focus to the debate regarding the causation of stuttering by identifying functional and structural differences between the brains of stutterers and nonstutterers. Three general classes of functional neuroimaging findings have emerged: (1) overactivation of cortical motor areas, such as the primary motor cortex and supplementary motor area; (2) anomalous lateralization, such that speech-related brain areas that typically have lefthemisphere dominance in fluent speakers are active bilaterally or with right-hemisphere dominance in stutterers; and (3) auditory suppression such that primary and secondary auditory areas that are normally active during speech production are not activated [Fox, 2003; Ingham, 2001b]. Finally, anatomical imaging methods have pointed to structural abnormalities in the left hemisphere of developmental stutterers occurring in regions such as the superior temporal gyrus [Foundas et al., 2001] and Rolandic operculum [Sommer et al., 2002], again supportive of suggestions that stuttering may have a genetic basis. Stuttering can therefore provide a unique opportunity for understanding the neural basis of speech production by permitting the examination of correlations between speech production, brain activity, and brain anatomy [Fox, 2003]. Meta-analysis is an important means of examining the concordance of results across a corpus of studies and extracting the most significant and best-supported findings from these studies. Imaging studies of stuttering have been relatively few in number and have been mainly restricted to the oral reading of sentences or paragraphs rather than the types of spontaneous speech behaviors that prompt stuttering in everyday situations. Ingham [2001b] attempted to find regional commonalities among five positron emission tomography (PET) studies using a traditional tabulation of label-reported regional activations and deactivations from these studies. This analysis found partially overlapping abnormal activations in three of five studies in the supplementary motor area (SMA) and anterior insula, as well as abnormal deactivations in auditory association areas. A second meta-analysis, that included performance-correlation analyses of PET studies and more restrictive comparison criteria [Ingham, 2004], found partial overlap in these regions but greater agreement when task and image-analysis methods were matched across studies. Both studies were limited methodologically being tabular, label-based meta-analyses. Tabular meta-analyses suffer from poor spatial precision and high variability in labeling brain regions in different publications [Laird et al., 2005b]. Coordinate-based, voxelwise meta-analysis [Chien et al., 2002; Turkeltaub et al., 2005; Wager et al., 2003] offers a powerful alternative to label-based meta-analyses by deriving statistical wholebrain images of convergence across a corpus of studies. These methods have been applied to normal speech production [Chien et al., 2002; Turkeltaub et al., 2002], but have not been applied previously to studies of abnormal subjects and more specifically have not been applied in stuttering. We apply the activation likelihood estimation (ALE) method to stuttered speech production and concurrently to fluent speech production, using data published on normal control subjects in the stuttering literature. None of the normal-subject data had been utilized previously in meta-analyses of speech production [Fiez and Petersen, 1998; Indefrey and Levelt, 2000, 2004; Turkeltaub et al., 2002], offering a replication of these meta-analyses and a within-study control for stuttering subjects. The objective of these parallel analyses is to understand the neurophysiological basis of stuttering by reference to normal speech. An additional, more technical reason for carrying out voxel-wise meta-analyses of stuttered and fluent speech production is to use the high spatial resolution of these methods (compared to label-based meta-analyses) to define volumes of interest (VOIs) that can then be used to constrain network models of these systems. By limiting the data sets to data-driven VOIs, network-oriented analytical techniques can be applied to raw data (e.g., using structural equation modeling) [McIntosh and Gonzalez- Lima, 1994] and to coordinate-based meta-data (e.g., using replicator dynamics and related methods) [Neumann et al., this issue; Lancaster et al., this issue]. This has special relevance for pathological conditions such as stuttering [Fox, 2003] in which the breakdown of function most likely occurs at the level of functional systems rather than at the level of individual brain areas. 106 Stuttered and Fluent Speech Neuroimaging TABLE I. Studies included in the meta-analysis Reference Modality n Gender Vocal task Control Stutter Fox et al., 1996 PET 10/10 M Paragraph reading Rest Yes Braun et al., 1997 PET 18/20 M/F Spontaneous narrative Sentence Orolaryngeal control Yes construction Correlations w/dysfluency Yes Fox et al., 2000 PET 10/10 M Correlations w/stutter rate Yes De Nil et al., 2000 PET 10/10 M Word reading Silent reading No De Nil et al., 2003 PET 13/10 M Word reading Visual baseline No Neumann et al., 2003 fmri 16/16 M Sentence reading Visual baseline No Preibisch et al., 2003 fmri 16/16 M Sentence reading Visual baseline No Ingham et al., 2004 PET 10/10 F Correlations w/stutter rate Yes Eight studies were included in the two ALE meta-analyses. For n, the first number represents the number of stutterer subjects, and the second number represents the number of fluent control subjects. All studies except that of Braun et al. [1997] had subjects of one gender. Three studies [Braun et al., 1997; Fox et al., 2000; Ingham et al., 2004] include performance correlations with stuttering/dysfluency rate. Only half of the studies elicited stuttering in the stuttering subjects. Those happened to be the ones that employed the more extensive reading/speaking tasks, such as paragraph reading or spontaneous narration. For Braun et al. [1997] and Ingham et al. [2004], the correlation data contributes exclusively to the stuttering meta-analysis. For Fox et al. [2000], positive correlations with syllable rate are used for the control subjects as well. MATERIALS AND METHODS Inclusion Criteria for Articles Two parallel meta-analyses of eight studies were carried out using ALE analysis, one with the stutterer subjects and one with the control subjects (Table I). The same set of tasks and contrasts was used for both groups, making the two analyses overall comparable (but see caveats in following paragraph). None of these studies had been included in the three previous meta-analyses of speech production [Fiez and Petersen, 1998; Indefrey and Levelt, 2000, 2004; Turkeltaub et al., 2002]. Although the stuttering literature is quite small, several articles were excluded from the meta-analysis. Our inclusion criteria were that: (1) the studies presented coordinate-based analyses of the data; (2) all or most of the brain was imaged; and (3) overt speech was used as part of the task. Using these criteria, the following stuttering articles had to be excluded: Wu et al. [1995] and Van Borsel et al. [2003] because neither reported spatial coordinates for brain locations; De Nil et al. [2001], because only a fraction of the brain was imaged; and Ingham et al. [2000], because only covert speech was employed. As an aside, gender was not a factor in this meta-analysis. Most articles looked at male subjects in both groups (see Table I), and so the metaanalysis has a disproportionate emphasis on male brains. As stuttering mechanisms seem quite variable across the genders [Ingham et al., 2004], it will be important that future studies address gender effects in greater detail. In addition to including foci for brain activations, the metaanalyses include voxels showing positive correlations with either stuttering rate (stutterers) or syllable rate (controls) during connected speech. For Fox et al. [2000], comparable correlation data was present for both groups. For Braun et al. [1997] and Ingham et al. [2004], correlation data was presented only for the stutterers. Because Braun et al. [1997] included activation data (but not performance correlations) for the controls, it contributed coordinates to the analysis of the controls. The study of Ingham et al. [2004], based on correlations only, was the one article that contributed coordinates exclusively to stutterers and not controls. Finally, no deactivations or negative correlations were examined in this study, mainly because the number of foci across the eight studies was insufficient to do a reliable analysis. ALE Analysis Coordinates from conditional contrasts or performance correlations were taken from the original publications. Montreal Neurological Institute (MNI) coordinates were converted to Talairach coordinates using the Brett transform [Brett, 1999]. ALE meta-analysis was carried out on this data as described by Turkeltaub et al. [2002], using a full-width at half-maximum (FWHM) of 10 mm as based on a modification of Laird et al. [2005b]. Statistical significance was determined using a permutation test of randomly distributed foci. Five thousand permutations were computed using the same FWHM value and the same number of foci used in computing the ALE values. The test was corrected for multiple comparisons using the false discovery rate (FDR) method [Genovese et al., 2002]. All data processing was carried out using an in-house Java version of ALE developed at the Research Imaging Center (available at The ALE maps presented in Figure 1 are shown overlaid onto an anatomical template generated by spatially normalizing the International Consortium for Brain Mapping (ICBM) template to Talairach space [Kochunov et al., 2002]. Between-Group ALE Comparison To create a comparison between the ALE maps for the stutterers and controls, their respective ALE maps were subtracted from one another and a permutation test was run on the subtracted maps to obtain the appropriate threshold 107 Brown et al. Axial slices demonstrating major ALE foci from the two metaanalyses. a: Major ALE foci for the fluent controls. Principal sites of activation are labeled; bilateral cortical activations are labeled on only one side of the brain. b: Major ALE foci for the stuttering subjects. The labels highlight activations seen uniquely in the stuttering group. c: Group comparison of the ALE foci from the two groups. For this panel only, orange indicates stutterers controls, and blue indicates controls stutterers (the latter seen only for the superior temporal sulcus). Labels highlight the vocal-motor areas shown by the meta-analysis to have large cross-laboratory Figure 1. concordance. The bilateral auditory areas, present in controls but absent in the stutterers, are below threshold in the group comparison. The Talairach coordinates for the slices are shown at the bottom of the figure. The same set of seven slices is shown in all three panels. The left side of a slice is the left side of the brain. The threshold for all analyses is P STS, superior temporal sulcus; RO, Rolandic operculum; M1, primary motor cortex; SMA, supplementary motor area; Vermis III, the medial portion of lobule III of the cerebellum; FO, frontal operculum/anterior insula; SMG, supramarginal gyrus; CMA, cingulate motor area. for significance (P 0.05), as described in Laird et al. [2005b]. Region-of-Interest Analysis of ALE Clusters Once the two ALE meta-analyses for the studies were complete, the BrainMap database ( was searched to determine the foci from the original datasets that were located within a region-of-interest (ROI) that was defined by the extent of various clusters from the two metaanalyses. Eleven clusters that showed interesting betweengroup differences were subjected to ROI analysis. The bounding box of the ROIs was obtained from the ALE map (P 0.05). Once the coordinates that fell within the bounding box were determined, they were inspected to verify the ones that actually fell within the appropriate cluster border. RESULTS Two ALE meta-analyses were carried out using activation data or performance correlations for the same tasks in both groups (but see caveats in the Methods section). In total, 154 foci were analyzed for stutterers and 73 for controls. This markedly larger number of foci for stutterers compared to that for controls is in agreement with virtually all imaging studies in the stuttering literature, showing more areas of activation and a wider distribution of these areas for stutterers relative to controls when performing the same tasks. Such differences are seen even when behavioral performance is equated across groups, such as when fluencyinducing manipulations (e.g., chorus reading and treatment programs) are employed to eliminate stuttering [Fox et al., 1996; Ingham et al., 2003; Neumann et al., 2003]. 108 Stuttered and Fluent Speech Neuroimaging TABLE II. Major ALE foci for the fluent control subjects Lobe Region x y z ALE ( 10 3 ) Size (mm 3 ) Frontal Left Primary motor cortex (4/6) ,128 Inferior frontal gyrus (47) Prefrontal cortex (10) Right Primary/premotor cortex (4/6) ,312 Rolandic operculum (4/43) SC Supplementary motor area (6) Temporal Left Superior temporal sulcus (22/21) Superior temporal gyrus (42) Right Superior temporal gyrus (22) SC Occipital Left Cuneus (17) Lingual gyrus (19) Lingual gyrus (18) Lingual gyrus (19) Right Lingual gyrus (17) Cerebellum Left Lobule VI ,096 Right Lobule VI ,128 Vermis VI The 17 principal ALE clusters derived from the analysis with the control subjects. After each anatomical name in the region column is the Brodmann area (BA) in parentheses. The columns labeled as x, y, and z are the Talairach coordinates for the weighted center of each cluster. The ALE score shown is the true value multiplied by The right column shows the size (in mm 3 ) of each cluster. The two righthemisphere clusters labeled as SC in the size column (namely, 57, 9, 20 and 62, 8, 8) are derived from the right primary motor cortex cluster at 54, 10, 34, having a cluster size of 2,312 mm 3. The Rolandic operculum is listed here in the frontal lobe, although it is listed for the stuttering subjects in the parietal lobe due to a slight difference in the location of the weighted center of the cluster. Fluent Controls ALE images for the fluent controls are presented in Figure 1a, and the ALE scores and cluster sizes for these locations are presented in Table II. The analysis shows that most core areas of the vocal-control system highlighted in the previous
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