Structural correlates of spoken language abilities: A surface-based region-of interest morphometry study
Introduction
Language is a multifaceted faculty that we use every day to comprehend and communicate complex ideas and emotions. Functional magnetic resonance imaging (fMRI) studies have shown that a distributed network of cortical and subcortical regions is used to accomplish even the simplest language tasks, which demonstrates that the complexity of the language system translates into a complex neural architecture (for a review, see for example Indefrey and Levelt, 2004, Price, 2010). While the relation between brain functioning and language processes has been studied in some detail, little is known about the relation between brain anatomy and language skills. Interestingly, if the results of functional and structural imaging are sometimes convergent, suggesting a close relationship between brain structure and function (Maguire et al., 2000, Richardson et al., 2010), structural imaging studies can also offer novel insights by identifying regions not typically identified using fMRI.
One of the most widely studied aspects of human brain anatomy is cortical thickness (CT), which can be assessed using magnetic resonance imaging (MRI). The human cerebral cortex is composed of highly folded horizontal layers of neurons; the thickness of this neuronal sheet varies across brain regions and individuals, and ranges from 1 to 4.5 mm, with an average of approximately 2.5 mm (Zilles, 1990). Changes in CT are of great interest in both normal brain maturation and aging as well as in a variety of neurodegenerative and psychiatric disorders (Fischl & Dale, 2000). Recent neuroimaging studies have revealed that differences in gray matter architecture are also associated with differences in performance in healthy adults in a number of cognitive and motor tasks (Kanai and Rees, 2011, May and Gaser, 2006, Tomassini et al., 2011). For example, positive correlations have been found between GM architecture and proficiency in sports, in regions involved in motor planning, execution and learning including the bilateral inferior frontal (IFG) and mid-temporal gyrus, left precentral and middle frontal gyri (MFG), cerebellum, as well as regions involved in visual and spatial association processes such as the left inferior parietal (IPL), left superior temporal sulcus and right parahippocampal gyrus (Di Paola et al., 2013, Draganski et al., 2004, Jacini et al., 2009, Wei et al., 2011).
However, only a limited number of studies have used structural MRI to study language skills, including vocabulary acquisition (Lee et al., 2007), second language proficiency (Hosoda et al., 2013, Mechelli et al., 2004), and speech perception and production (Bilodeau-Mercure et al., 2014, Grogan et al., 2009, Tremblay et al., 2013). The study of spoken language production is complex because it depends upon a very large number of sensorimotor and cognitive processes. To express conceptual ideas, word forms must first be retrieved, converted into a phonological code, sequenced and articulated, while unintended words need to be suppressed and the output need to be monitored (see for example Guenther et al., 2006, Price, 2010 for a review). Commensurate with this complex picture, fMRI studies of speech production have identified a large number of regions involved in producing language including the cerebellum, M1, the basal ganglia, IFG and MFG, the inferior parietal lobe, the prefrontal cortex, and the superior and middle temporal gyri (e.g. Adank, 2012, Blank et al., 2002, Bohland et al., 2010, Bohland and Guenther, 2006, Ghosh et al., 2008, Peeva et al., 2010, Riecker et al., 2002, Riecker et al., 2005, Tremblay and Gracco, 2009, Tremblay and Gracco, 2010, Tremblay and Small, 2011b, Turkeltaub et al., 2002, Whitney et al., 2009, Wildgruber et al., 2001, Wise et al., 1999). The functional importance of anatomical variations within these regions, however, is largely unknown, and so is their importance for the different cognitive and motor stages of spoken language production.
Because most studies of language production have relied preferentially on voxel-based morphometry (VBM) (Amici et al., 2007, Beal et al., 2013, Golestani and Pallier, 2007, Grogan et al., 2009, Mechelli et al., 2004, Zhu et al., 2013) and no study has examined how other morphometric measures (cortical volume (VOL) and surface area (SA)) are associated with language abilities in healthy adults, the main objective of this study was to explore the relation between brain morphometry and language performance using two classic language production tasks (sentence generation task and semantic fluency) in healthy adults using surface-based morphometry (SBM). In SBM, morphometric measures are derived from geometric models of the cortical surface from which different metrics like CT, VOL or SA of brain regions at a subvoxel level resolution can be extracted (Dale et al., 1999, Fischl et al., 1999). In the present study, CT, VOL, and SA measures were computed and correlated with performance in these tasks, which involve different sets of processes. In the verbal fluency task, word retrieval is usually driven by association chains between clusters of words belonging to semantic subcategories. For example, for the category “animals”, people often begin with animals considered as pets and when this subcategory is exhausted, they switch to a different subcategory (Katzev et al., 2013, Wechsler-Kashi et al., 2014). Sentence generation, in contrast, involves a different series of cognitive stages that include object recognition, lexical retrieval of the element presented in the picture, access to the phonological word form, syntactic planning (DeLeon et al., 2007, Wechsler-Kashi et al., 2014). Because of these differences, we hypothesized that performance on the two language tasks would be correlated with distinct brain regions. For example, damage to the anterior insula (AI) has been associated with fluency and articulatory impairments (Baldo et al., 2011, Dronkers, 1996). The structure of the AI could then correlate with the performance on the semantic fluency task. Because the sentence generation task relies on the recognition of object pictures, performance on this task should instead correlate with the structure of regions involved in visual processing located in the inferior parietal lobe (Culham & Kanwisher, 2001). Several fMRI studies have also shown that manipulating response selection during word production modulates the pre-SMA, the inferior fontal gyrus (IFG), and the ventral premotor (PM) cortex (Alario et al., 2006, Crosson et al., 2001, Nagel et al., 2008, Thompson-Schill et al., 1997, Thompson-Schill et al., 1999, Thompson-Schill et al., 1998, Tremblay and Gracco, 2006, Tremblay and Gracco, 2009, Zhang et al., 2004). In view of these results, we were interested in examining if the structure of these regions would show a stronger relation to verbal fluency than to sentence generation due to the high demand on selection imposed by the fluency task.
Section snippets
Participants
21 right-handed adults (10 males, mean 25 ± 4.4 years, range 20–36 years), with a mean education level of 15.4 years (range = 12–22 years) participated in the experiment. The study sample consisted of Caucasian (85.7%), African American (9.5%) and Hispanic participants (4.7%). All participants were native speakers of standard American English and had normal pure tone thresholds and normal speech recognition scores (92.3% accuracy on the Northwestern University auditory test number 6). Participants were
Experimental procedure
A category fluency task was used to evaluate the capacity to spontaneously generate words. Participants were instructed to name as many animals and vegetables as possible during one minute, in two distinct trials. Participants’ responses were recorded and stored to disk for offline analysis. A research assistant naive to the purpose of the study transcribed all the responses. The total number of correct words generated in both categories was used as the measure of overall fluency. Participants
Behavioral data
The scores on the verbal fluency task ranged from 26.5 to 59 words, with a mean of 39.7 ± 9.6 words. The accuracy in the sentence generation task ranged from 67.5% to 100%, with a mean accuracy of 86.4 ± 7.9%. The generated sentences had an average length or 4.48 ± 0.18 words.
Correlations between verbal fluency and brain morphometry
The structure of several frontal, insular and parietal regions (the complete list is provided in Table 1 and represented in Fig. 2a) correlated with the ability to spontaneously name items of a specific category as evaluated by
Discussion
The aim of this study was to examine the relation between brain morphometry and performance on two classic measures of expressive language in a group of young healthy right-handed adults. The current findings demonstrate that inter-individual differences in the structure of several cortical regions correlate with measures of expressive language. First, this study highlights the presence of task-related differences in the relationship between brain morphometry and spoken language skills. Second,
Conclusion
In conclusion, the present findings provide important insights into the relationship between brain structure and spoken language production. Performance on the sentence generation and semantic fluency tasks were associated with different brain regions, suggesting that they relied, at least partly, on different cognitive and sensorimotor abilities. Moreover, by looking at CT, SA and VOL, we found different patterns of correlation that might reflect different neuronal plastic changes occurring
Acknowledgments
This study was performed at The University of Chicago (USA). We acknowledge the support of the Departments of Neurology and Psychology, and of the Pritzker School of Medicine during the time this study was conducted. The study was supported by a research grant from the NIDCD (R01 DC003378) to S.L. Small. Their support is gratefully acknowledged. The analyses reported in this article were conducted at the “Institut Universitaire en Santé Mentale de Québec”, Quebec City, supported by funding from
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