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Journal of Speech, Language, and Hearing Research : JSLHR logoLink to Journal of Speech, Language, and Hearing Research : JSLHR
. 2021 Mar 9;64(6 Suppl):2317–2324. doi: 10.1044/2020_JSLHR-20-00325

Association Between Gray Matter Volume Variations and Energy Utilization in the Brain: Implications for Developmental Stuttering

Nathaniel Boley a,b, Sanath Patil b,c, Emily O Garnett d, Hua Li b, Diane C Chugani e, Soo-Eun Chang d,f,g, Ho Ming Chow b,e,✉
PMCID: PMC8740693  PMID: 33719533

Abstract

Purpose

The biological mechanisms underlying developmental stuttering remain unclear. In a previous investigation, we showed that there is significant spatial correspondence between regional gray matter structural anomalies and the expression of genes linked to energy metabolism. In the current study, we sought to further examine the relationship between structural anomalies in the brain in children with persistent stuttering and brain regional energy metabolism.

Method

High-resolution structural MRI scans were acquired from 26 persistent stuttering and 44 typically developing children. Voxel-based morphometry was used to quantify the between-group gray matter volume (GMV) differences across the whole brain. Group differences in GMV were then compared with published values for the pattern of glucose metabolism measured via F18 fluorodeoxyglucose uptake in the brains of 29 healthy volunteers using positron emission tomography.

Results

A significant positive correlation between GMV differences and F18 fluorodeoxyglucose uptake was found in the left hemisphere (ρ = .36, p < .01), where speech-motor and language processing are typically localized. No such correlation was observed in the right hemisphere (ρ = .05, p = .70).

Conclusions

Corroborating our previous gene expression studies, the results of the current study suggest a potential connection between energy metabolism and stuttering. Brain regions with high energy utilization may be particularly vulnerable to anatomical changes associated with stuttering. Such changes may be further exacerbated when there are sharp increases in brain energy utilization, which coincides with the developmental period of rapid speech/language acquisition and the onset of stuttering during childhood.

Supplemental Material

https://doi.org/10.23641/asha.14110454

Persistent stuttering is a neurodevelopmental disorder with a strong genetic underpinning. Recent findings from neuroimaging and genetic studies on stuttering have advanced our understanding of the neurological and cellular bases of the disorder (Chang et al., 2019; Frigerio-Domingues & Drayna, 2017). Gross brain abnormalities are not typically found in people who stutter, but subtle differences between people who stutter and their fluent peers have been found in gray matter volume (GMV; Beal et al., 2013; Chang et al., 2008; Chow et al., 2020), cortical thickness (Garnett et al., 2018; Whitaker et al., 2016), white matter diffusivity (Chow & Chang, 2017; Neef et al., 2015), metabolic rate (Wu et al., 1995), concentration of brain metabolites (O'Neill et al., 2017), brain activation during speech production (Braun et al., 1997; Fox et al., 1996), and functional magnetic resonance imaging (fMRI)–based functional connectivity (Chang et al., 2018; Lu et al., 2009). In terms of genetic studies, genetic variations in four genes—GNPTAB, GNPTG, NAGPA, and AP4E1—have been identified to be associated with stuttering (Kang et al., 2010; Raza et al., 2015). Interestingly, these genes are involved in intracellular trafficking of lysosomal enzymes, which are essential for degrading and recycling cellular waste (Kang et al., 2010). To further understand the effects of these gene mutations in the brain, knock-in mice with GNPTAB mutations found in humans were created. A series of studies of these mice have shown that, compared to wild-type mice with the same genetic background, the vocalization duration of these knock-in mice is reduced (Barnes et al., 2016). Moreover, a recent study of a gnptab knock-in mice strain has shown that the mutation is associated with reduced astrocyte density and volume of the corpus collosum (Han et al., 2019). However, the biological mechanism of these changes remains unknown.

Despite the advances in our understanding of both genetic and neural bases of stuttering, we still know very little about how genetic variants affect cellular functions, which in turn, disrupt the speech-motor system in the brain, leading to stuttering-like behaviors. One puzzling question is how gene mutations responsible for lysosomal enzyme trafficking, a vital function for the health of all cells, seem to disrupt fluent speech production specifically but leave other cognitive functions intact. One possible explanation for this specificity is that the genes associated with stuttering may have different levels of expression across various brain regions and thus the effects of the gene mutations vary among regions. In general, the effect of a gene mutation scales with the level of expression of that gene. Hence, expression patterns of genes associated with a genetic disease in the healthy brain can serve as a proxy of regional vulnerability (i.e., regions with relatively high expression of a neurological disease or risk gene). Applying this approach, several previous studies demonstrated spatial correlations between patterns of MRI anomalies in patients and expression patterns of risk genes associated with neurological diseases, including schizophrenia (Romme et al., 2017), Alzheimer's disease (Grothe et al., 2018), and Huntington's disease (McColgan et al., 2017).

Using similar methods as applied in these previous studies, we tested whether the expression patterns of genes associated with stuttering are spatially correlated with GMV and fMRI-based resting-state connectivity differences found in people who stutter relative to their matched controls (Benito-Aragón et al., 2019; Chow et al., 2020). The patterns of between-group differences for both GMV and fMRI-based resting-state connectivity were significantly and positively correlated with the expression of one of the four known genes associated with stuttering (GNPTG) in the brain. Among all other protein coding genes in the genome, the spatial correlations with GNPTG were within the highest 2.5%. While these correlational results prevent us from drawing conclusions about any direct, causal relationship between brain anomaly patterns associated with stuttering and gene expression, such a relationship is a possible explanation for the observed spatial correlation.

Since both of our previous studies indicate that the patterns of brain anomalies in children with persistent stuttering (pCWS) are associated with the expression pattern of a stuttering risk gene, studying other protein-coding genes whose expression patterns are also highly correlated with brain anomaly patterns might give us insight into the biological process of the disorder. Using gene set enrichment analysis, we examined the top 2.5% of genes whose expression patterns were highly correlated with the patterns of GMV differences in pCWS (Chow et al., 2020). This analysis showed that the gene set contains a significantly larger proportion of genes responsible for energy metabolism as compared to the proportion of these group genes in the whole genome, indicating that energy metabolism may play a role in the development of GMV differences in children who stutter (CWS).

In the current study, we sought to further examine the potential relationship between energy metabolism and stuttering that was implicated in our previous study (Chow et al., 2020). We compared regional GMV differences between pCWS and controls to patterns of glucose metabolism across the brain as measured by F18 fluorodeoxyglucose (FDG) uptake data reported in a previous positron emission tomography (PET) study (Stender et al., 2015). The uptake of FDG provides a quantitative measure of glucose metabolism, which reflects the level of energy utilization in the brain (Vansteenkiste et al., 2016). Based on our gene set enrichment analysis, we hypothesized that the GMV differences between pCWS and controls would be correlated with the pattern of energy utilization reflected by FDG uptake. We further expected that this relationship would exist in the left, but not in the right hemisphere because accumulating evidence suggests that stuttering is associated with the disruption of the left speech-motor network (Neef et al., 2015).

Method

Participants Included in Quantifying GMV Differences

The GMV data used in this study are the same as those reported in our previous study, where we examined the relationship between GMV differences and gene expression in the brain (Chow et al., 2020). A detailed description of the participants (i.e., recruitment procedures, demographic information, inclusion/exclusion criteria, behavioral test results) can be found in Chow et al. (2020). In brief, the final data set used in the current study included 87 scans from 26 pCWS (eight girls and 18 boys) and 139 scans from 44 typically developing children (23 girls and 21 boys) collected from an ongoing longitudinal neuroimaging study at Michigan State University. Each subject participated in one to four visits that were approximately twelve months apart; MRI scans were collected at each visit as detailed in Chow et al. (2020). The mean age at the first visit was 6.5 years (SD = 1.9) for pCWS and 6.5 years (SD = 2.0) for controls (see Table 1). The ages at the time of each scan are listed for participants in Supplemental Material S1. The groups did not differ in age, sex, handedness, or socioeconomic status based on the mother's education level. The persistence of stuttering was determined according to percent stuttering-like disfluencies (SLDs; Yairi et al., 2005) and the Stuttering Severity Instrument (SSI-4; Riley, 2009) scores collected at each annual visit. Participants were considered persistent when the most recent and an additional previous assessment exhibited at least 3% SLDs and an SSI-4 score greater than 10 (mild). Expressed parent concern and clinician reports were also considered. Inclusion criteria for controls included no history of speech disorder at any time, no family history of stuttering, SLDs less than 3% and SSI composite score below very mild. None of the participants had a history of developmental, psychological, neurological, or speech disorders, except for stuttering. All participants scored above −2 SD of the mean on a series of standardized assessments, including the Wechsler Preschool and Primary Scale of Intelligence–Third Edition for children 2:6–7:3 (WPPSI-III; Wechsler, 2002), Wechsler Abbreviated Scale of Intelligence for children 7 years and up (Wechsler, 2001), Peabody Picture Vocabulary Test–Fourth Edition for receptive vocabulary ability (Dunn & Dunn, 2007), Expressive Vocabulary Test (Williams, 2007), and the Goldman-Fristoe Test of Articulation–Second Edition (Goldman & Fristoe, 2000). The standardized test results are listed in Table 1. All research procedures were approved by the Michigan State University Institutional Review Board.

Table 1.

Demographics, intelligence quotient (IQ), and language test scores of the participants included in the voxel-based morphometry study.

Variable Controls, n = 44 (21 boys)
 Persistent, n = 26 (18 boys)
M (SD) Range M (SD) Range
Average age across scans 7.6 (2.0) 4.0–11.2 7.7 (2.1) 4.9–12.3
Age at the first scan (years) 6.5 (2.0) 3.3–10.8 6.5 (1.9) 3.6–10.3
Numbers of scans 3.2 (1.0) 1–4 3.4 (0.8) 2–4
IQ a 114 (14.1) 84–144 106 (15.5) 81–138
PPVT-4 b 119 (12.7) 95–141 110 (13.5) 86–146
EVT-2 c 115 (11.8) 93–142 106 (12.2) 86–138
GFTA-2 d 104 (6.6) 84–115 102 (4.2) 92–110
SSI-4 — — 21 (8.3) 12–48
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a

Em dashes indicate data not applicable. Wechsler Scale of Intelligence (IQ; Wechsler Abbreviated Scale of Intelligence or Wechsler Preschool and Primary Scale of Intelligence–Third Edition). No significant difference between groups (t tests, p > .05)

b

The Peabody Picture Vocabulary Test–Fourth Edition (PPVT-4). Scores significantly higher in control than the persistent groups (two-sample t tests, p < .05).

c

The Expressive Vocabulary Test–Second Edition (EVT-2). Scores significantly higher in control than the persistent groups (two-sample t tests, p < .05).

d

The Goldman-Fristoe Test of Articulation–Second Edition (GFTA-2). Scores significantly higher in control than the persistent groups (two-sample t tests, p < .05).

Quantifying Regional Between-Group Differences in GMV

Voxel-based morphometry analysis was used to quantify the GMV according to participants' T1-weighted images (1-mm isotropic resolution). In the current study, the voxel-based morphometry procedures and statistical modeling for longitudinal GMV data were identical to that used in our previous study (Chow et al., 2020; Good et al., 2001). The details of scanning parameters, structural images preprocessing, and the statistical modeling can be found in Chow et al. (2020). Voxel-wise t-statistics of between-group GMV differences were converted to absolute values and averaged within each region defined by the Desikan Harvard–Oxford Atlas (Desikan et al., 2006; Stender et al., 2015), generating a map GMV differences of 54 regions in each hemisphere (48 cortical and six subcortical regions; see Supplemental Material S2). Consistent with our previous study, absolute t-statistics were used because the relationship with stuttering risk gene expression patterns only exist when absolute t-statistics were used.

GMV and FDG Uptake Correlation

Normative FDG uptake data were obtained from a previous PET study conducted by Stender et al. (2015), which included 41 vegetative patients and 29 healthy volunteers (10 females, 19 males; 44±16 years of age). Only the FDG data obtained from healthy volunteers were used in the current study. The FDG uptake values were first averaged within each region defined by the Desikan Harvard–Oxford Atlas and then across participants (Desikan et al., 2006; Stender et al., 2015). Spearman's rank correlation (ρ) was calculated between GMV between-group differences in terms of absolute t-statistics and FDG uptake across 54 regions (see Supplemental Material S2). The procedure is illustrated in Figure 1. Because we hypothesized that the relationship between FDG uptake and GMV difference would exist in the left hemisphere, the primary correlation analysis of the two measures were conducted separately for left and right hemisphere regions. Since there was a nonsignificant trend between two groups in sex, χ2(1, N = 70) = 3.09, p = .079, to rule out that the results were driven by sex difference, we repeated the primary analysis with male pCWS and their fluent controls. For all correlation analyses, the threshold for significance was set at p < .05.

Figure 1.

Figure 1.

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Overview of the data analysis procedure for quantifying spatial relationship between gray matter volume differences and F18 fluorodeoxyglucose (FDG) uptake in the brain. (A) The voxel-wise t-statistics representing the gray matter volume differences between children with persistent stuttering and their matched peers were obtained from the VBM analysis. (B) The Desikan Harvard–Oxford Atlas, where each color highlights a different region. (C) The average absolute t-statistics across voxels in each region in the atlas. (D) The average FDG uptake by region obtained from a previous positron emission tomography (PET) study.

Results

For each hemisphere, we examined the regional correspondence between previously reported average FDG uptake (Stender et al., 2015) and the between-group GMV differences across 54 regions using the Spearman's rank correlation (ρ). The patterns of these two measures are illustrated in Figure 2. A significant positive correlation was found, indicating a significant spatial correspondence between the level of energy utilization and group differences in regional GMV, in the left hemisphere (ρ = .36, p < .01), but not in the right hemisphere (ρ = .05, p = .70). The relationship between GMV differences and FDG uptake in the left and right hemisphere are illustrated in Figure 3. In the male-only, follow-up analysis, the results were very similar to those in the primary analyses containing both males and females. A significant positive correlation was found in the left hemisphere (ρ = .36, p < .01), but not in the right hemisphere (ρ = .20, p = .15), indicating that the observed relationship was not likely driven by sex differences.

Figure 2.

Figure 2.

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Between-group gray matter volume (GMV) differences (upper panel), and F18 fluorodeoxyglucose (FDG) uptake (lower panel) in the left hemispheric regions defined in the Desikan Harvard–Oxford Atlas. The levels of the two measures were projected onto a single subject anatomical image in Montreal Neurological Institute space.

Figure 3.

Figure 3.

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Scatter plots of between-group gray matter volume (GMV) differences and F18 fluorodeoxyglucose (FDG) uptake in the left (left panel) and right (right panel) hemispheric regions defined in the Desikan Harvard–Oxford Atlas. The colors of the markers indicate the approximate locations of the regions. The values of each region are listed in the Supplemental Material S2.

Discussion

While the genetic bases of stuttering have begun to be identified, the biological mechanisms that lead to the behavioral phenotype of the disorder are still poorly understood. We previously found that the pattern of structural anomalies reflected by left hemisphere GMV differences in pCWS was positively correlated with the expression patterns of a disproportionally high number of genes involved in energy metabolism (Chow et al., 2020). The current study was designed to follow up and extend this previous finding. Consistent with our hypothesis, the results of the current study indicated a potential connection between energy metabolism and stuttering (i.e., the higher the energy utilization of a region, the larger the GMV differences between pCWS and controls). Furthermore, as hypothesized, this relationship was only significant in the left but not in the right hemisphere. This lateralization further supports the predominant role of the left hemisphere in stuttering as demonstrated in previous imaging studies of brain function and structure (e.g., Chang et al., 2011; Chow & Chang, 2017; Desai et al., 2017; Foundas et al., 2001; Garnett et al., 2018; Kell et al., 2009; Kronfeld-Duenias et al., 2016; Walsh et al., 2017; Watkins et al., 2008).

How might energy metabolism play a role in the development of neurological anomalies associated with stuttering? Metabolic dysfunction is known to be involved in several neurological disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and amyotrophic lateral sclerosis (Chaturvedi & Beal, 2013; Schapira, 2006). Furthermore, lysosomes, which are associated with stuttering related genes, play an important role in modulating metabolic functions (McKenna et al., 2018; Todkar et al., 2017; Wong et al., 2018). The known genes related to stuttering (GNPTAG, GNPTG, NAGPA, AP4E1) are involved in transporting lysosomal enzymes from the endosomes to lysosomes (cell organelles) for the degradation of macromolecules, including damaged mitochondria (Plotegher & Duchen, 2017). If there is a deficiency in lysosomal function, fragments of damaged mitochondria may accumulate, increasing oxidative stress and negatively impacting neurological development (de la Mata et al., 2016; Kiselyov et al., 2010). The effect of this potential vulnerability may be exacerbated in brain regions with relatively high energy utilization, especially during preschool age, a period of sharp increases in brain metabolism (Chugani et al., 1987; Goyal et al., 2014). Homozygous mutations in the genes linked to stuttering (GNPTAG and GNPTG) are known to cause lysosomal storage diseases Mucolipidosis Types II and III. Consistent with our postulation, a previous study showed that increased mitochondrial fragmentations was observed in Mucolipidosis Types II, III, and IV(Jennings et al., 2006). While most people who stutter carry a heterozygous mutation (Kang et al., 2010) that does not lead to the detrimental symptoms of Mucolipidosis, a previous study showed that enzyme activity associated with NAGPA is partially compromised by the mutations found in people who stutter (Lee et al., 2011). However, whether mitochondrial function is affected by a partial deficiency of the enzyme involved in lysosomal enzyme trafficking found in stuttering remains unclear. Future imaging genetic studies on persistent stuttering may be able to provide direct evidence to support our hypothesized relationship between stuttering and energy metabolism.

Like every technique, the relatively new method that we used to identify the potential connection between energy metabolism and stuttering has limitations. First, it is worth noting that this method is fundamentally different from conventional imaging studies, which are typically aimed at mapping brain functions or locating anomalous regions. In contrast, the method we used here compared patterns of various measures such as gene expression and GMV in the brain to establish linkage between those measures. The current method is analogous to comparing maps of light pollution and population density in the United States to show the relationship between light pollution and human activity, rather than identifying locations with severe light pollution (Falchi et al., 2016). Therefore, whether the GMV difference in a region was significantly different between groups or not was not the focus of the current study (see Chow et al., 2020, for those findings).

Because the spatial relationship with the expression of genes associated with stuttering only emerged when the absolute GMV differences were used (Chow et al., 2020), absolute values were also used in the current analysis. This implies that greater GMV differences between the groups could be due to either smaller or larger GMV in CWS for those brain regions with relatively high energy utilization. The biological mechanism(s) underlying this nondirectional relationship is yet to be identified. We suspect that the nondirectional relationship could be an interplay of two effects. First, many neurological disorders, such as ADHD and childhood onset schizophrenia, are associated with the reduction of GMV (Gogtay et al., 2004; Nakao et al., 2011). Like these neurological disorders, the effects of genetics and other factors in stuttering could directly impact the function of a brain region and be reflected in GMV reduction. Second, those effects may also delay the cortical developmental trajectories of gray matter in children who stutter. The normal developmental trajectories of GMV differ across different brain areas, with most areas showing general decreases with age (Ducharme et al., 2015; Lange, 2012). Thus, delays in GMV decreases during development may appear as increased GMV when compared with age-matched controls.

Another technical note about the method is that we used adult FDG data to approximate the regional energy utilization in the brains of pCWS. Ideally, FDG-PET data from age-matched children should be used. However, the FDG values available for children do not have sufficient regional detail required for this type of analysis (Chugani et al., 1987). Although energy utilization increases and decreases over in the course of brain development during childhood and adolescence, it has been shown that the pattern of FDG uptake (i.e., the relative levels among brain regions) in children in the age range of our study is highly similar to that in adults (Chugani et al., 1987). Therefore, adult FDG-PET data are a reasonable proxy of the pattern of energy utilization in children's brain. Lastly, it is important to emphasize that the results reported in this study are correlational and the underlying biological mechanisms of this observed correlation are yet to be identified.

In conclusion, the results of the current study further support the potential connection between energy metabolism and stuttering. This current study, together with our previous studies (Benito-Aragón et al., 2019; Chow et al., 2020), indicate the feasibility of using neuroimaging techniques to obtain insights into the neurobiology of stuttering, which will help us generate testable hypotheses to further understand the casual mechanism of the disorder.

Supplementary Material

Supplemental Material S1. Participants’ sex and age at each scan.
Click here for additional data file. (125.7KB, pdf)
Supplemental Material S2. Gray matter volume (GMV) differences in terms of absolute t-statistics (|t-stat|) and 18F-fluorodeoxyglucose (FDG) uptake (μmol/g/min) in each region.
Click here for additional data file. (98KB, pdf)

Acknowledgments

This work was supported by Award Numbers R21DC015853 (H. M. C.), R01DC011277 (S. C.) from the National Institute on Deafness and Other Communication Disorders (NIDCD), and the Matthew K. Smith Stuttering Research Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the National Institutes of Health. The authors wish to thank all the children and parents who participated in this study. We also thank Saralyn Rubsam, Megan Sheppard, Nasreen Al-Qadi, Chelsea Johnson, and Kristin Hicks for their assistance in participant recruitment, behavioral testing, and help with MRI data collection, Scarlett Doyle for her assistance in MRI data acquisition, and Ashley Diener for her assistance in speech data analyses.

Funding Statement

This work was supported by Award Numbers R21DC015853 (H. M. C.), R01DC011277 (S. C.) from the National Institute on Deafness and Other Communication Disorders (NIDCD), and the Matthew K. Smith Stuttering Research Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the National Institutes of Health.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material S1. Participants’ sex and age at each scan.
Click here for additional data file. (125.7KB, pdf)
Supplemental Material S2. Gray matter volume (GMV) differences in terms of absolute t-statistics (|t-stat|) and 18F-fluorodeoxyglucose (FDG) uptake (μmol/g/min) in each region.
Click here for additional data file. (98KB, pdf)

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