What happens to the inhibitory control functions of the right inferior frontal cortex when this area is dominant for language?

  1. Esteban Villar-Rodríguez  Is a corresponding author
  2. Cristina Cano-Melle
  3. Lidón Marin-Marin
  4. Maria Antònia Parcet
  5. César Avila  Is a corresponding author
  1. Neuropsychology and Functional Neuroimaging; Jaume I University, Spain

Abstract

A low number of individuals show an atypical brain control of language functions that differs from the typical lateralization in the left cerebral hemisphere. In these cases, the neural distribution of other cognitive functions is not fully understood. Although there is a bias towards a mirrored brain organization consistent with the Causal hypothesis, some individuals are found to be exceptions to this rule. However, no study has focused on what happens to the homologous language areas in the right frontal inferior cortex. Using an fMRI-adapted stop-signal task in a healthy non right-handed sample (50 typically lateralized and 36 atypically lateralized for language production), our results show that atypical lateralization is associated with a mirrored brain organization of the inhibitory control network in the left hemisphere: inferior frontal cortex, presupplementary motor area, and subthalamic nucleus. However, the individual analyses revealed a large number of cases with a noteworthy overlap in the inferior frontal gyrus, which shared both inhibitory and language functions. Further analyses showed that atypical lateralization was associated with stronger functional interhemispheric connectivity and larger corpus callosum. Importantly, we did not find task performance differences as a function of lateralization, but there was an association between atypical dominance in the inferior frontal cortex and higher scores on schizotypy and autistic spectrum traits, as well as worse performance on a reading accuracy test. Together, these results partially support the Causal hypothesis of hemispheric specialization and provide further evidence of the link between atypical hemispheric lateralization and increased interhemispheric transfer through the corpus callosum.

eLife assessment

This study has important implications for theoretical proposals concerning how language lateralization affects the lateralization of other cognitive functions. The methods are solid, with an appropriate selection of cognitive control tasks that share homotopic regions of the brain with language, comparing participants with typical and atypical organization of language. The participants included in the study were mainly bilinguals, a population previously reported to have a more bilateral organization of cognitive control regions than monolinguals, limiting the generalizability of the results to the general population. Despite this limitation, the results will be of interest to researchers working of brain plasticity and development, in addition to those interested in language and cognitive control.

https://doi.org/10.7554/eLife.86797.3.sa0

Introduction

One of the oldest findings in human neuroscience is that the brain control of language is lateralized to the left hemisphere (Broca, 1861; Wernicke, 1874; Price, 2012). However, not all individuals present a typical organization of this function. Some left-handers (22–24%) show an atypical lateralization of language – right or ambilateral (Pujol et al., 1999; Szaflarski et al., 2002; Mazoyer et al., 2014). Also, this atypical organization is observed in some neurodevelopmental disorders such as schizophrenia, dyslexia, or autistic spectrum (Bishop, 2013; Sommer et al., 2001; Eyler et al., 2012).

How these atypical individuals organize the rest of their lateralized functions has been an extensively researched question. Historically, two different hypotheses have been proposed to explain the hemispheric specialization of the brain. The Causal hypothesis (Kosslyn, 1987; Hellige, 1990) argues that rapid activities requiring the implementation of sequences of cognitive processes are better performed from a single, unilateral control. As a result, these activities are innately programmed to be lateralized, and, according to this model, some functions will be performed better if they are controlled by different hemispheres. This evolutionary pressure engenders a complementary relationship between these lateralized functions. For example, language production predominantly lateralizes to the left hemisphere, while visuospatial attention typically favors the right hemisphere. As per the Causal hypothesis, if language were to atypically lateralize to the right hemisphere, visuospatial attention would, in turn, shift to the left hemisphere. The Statistical hypothesis (Bryden et al., 1983), on the other hand, postulates that each cognitive function lateralizes independently from the others. In this view, the likelihood of encountering atypical hemispheric dominance is essentially a statistical phenomenon, with no inherent interrelationship between various lateralized functions. The evidence supporting each theory in left-handers is mixed. Some studies have obtained data favoring the Statistical hypothesis when analyzing language, visuospatial attention, and face processing (Whitehouse and Bishop, 2009; Badzakova-Trajkov et al., 2010; Rosch et al., 2012). Most investigations, however, have demonstrated a mirrored organization of praxis (Vingerhoets et al., 2013; Gerrits et al., 2020), visuospatial attention (Badzakova-Trajkov et al., 2010; Gerrits et al., 2020; Cai et al., 2013), face recognition (Badzakova-Trajkov et al., 2010; Gerrits et al., 2020; Gerrits et al., 2019), and emotional prosody (Gerrits et al., 2020) in most individuals with atypical lateralization of language. Nonetheless, it should be noted that this reversed pattern is not observed in all atypical participants, and all the studies found specific cases in favor of both theories (Gerrits et al., 2020).

All these studies have employed a production task to investigate language lateralization in the inferior frontal cortex (IFC), classifying left-handed participants into typical and atypical groups. The rest of the lateralized cognitive functions have been examined using several tasks, but they all mainly depended on posterior parts of the brain. This may have limited the results, given that they were not dealing with directly homotopic functions, which would have been the most sensitive scenario to the proposed advantages of hemispheric specialization (Rogers, 2000; Vallortigara and Rogers, 2020). To directly address the language production function, which is typically dependent on the left IFC, we would have to use a cognitive task that is typically dependent on the homotopic right IFC. According to previous literature, this function would be inhibitory control. Extensive results support the notion that stopping an ongoing response involves a rightward network that includes the IFC, presupplementary motor area (preSMA), and subthalamic nucleus (STN) (Aron and Poldrack, 2006; Xue et al., 2008; Aron et al., 2014; Hannah and Aron, 2021). Thus, inhibitory control and language production together are the ideal candidates to investigate the Causal vs. Statistical disjunctive. But surprisingly, no investigation to date has analyzed this.

What causes language function to typically or atypically lateralize also remains unclear. Left-handedness appears to be, at best, a predisposing factor (Mazoyer et al., 2014). In clinical populations, atypical lateralization of language has been found to be a defining characteristic among patients of dyslexia (Bishop, 2013), schizophrenia (Sommer et al., 2001), and autism spectrum disorders (Eyler et al., 2012). However, aside from exhibiting poorer language performance (Mellet et al., 2014; Powell et al., 2012), no study to date has identified subclinical markers of these neurodevelopmental conditions among healthy individuals with atypical lateralization. Regarding the neural basis of language lateralization, the corpus callosum hypothesis (Gazzaniga, 2000)— which suggests that typical hemispheric specialization relies on the proper development of this structure — has garnered attention in recent years due to several noteworthy findings: (1) regional functional lateralization is directly linked to the size reduction of its interconnecting corpus callosum segments (Karolis et al., 2019); and (2) atypical lateralization of language has been associated with a larger corpus callosum and increased interhemispheric transfer at rest in language-relevant regions (Labache et al., 2020).

The primary aim of this study was to investigate the relationship between the lateralization of language production and inhibitory control. To achieve this, we divided non-right-handed participants into typically lateralized and atypically lateralized for language using the verb generation task. These two groups then completed the stop-signal task. In alignment with the predictions of the Causal hypothesis, we expected that: (1) the atypical group would exhibit a mirrored brain organization — particularly in the IFC, preSMA, and STN — during the stop-signal task; and (2) no individual would demonstrate lateralization of both language production and inhibitory control in the IFC of the same hemisphere. The secondary aim of this study was to explore certain potentially causal correlates of the lateralization of both functions. To test the presence of correlates supporting the corpus callosum hypothesis of lateralization, we analyzed the callosal volume and the interhemispheric functional connectivity at rest of the IFC, preSMA, and STN. To test the relation between atypical lateralization and several neurodevelopmental disorders (dyslexia, schizophrenia, and autism), we analyzed subclinical traits of these conditions among our healthy participants.

Results

Inhibitory control shifts its lateralization according to language production

Inhibitory control components during the stop-signal task were defined using four Regions Of Interest (ROIs) and subsequently analyzed through a MANOVA. This analysis included Hemisphere and Region as within-subject factors and Group (typical/atypical) as a between-subject factor. Examination of the simple effects across each level of Region revealed that all the tested structures displayed a statistically significant Hemisphere × Group interaction (pars opercularis F1,84=25.59, p<0.001; pars triangularis F1,84=19.95, p<0.001; preSMA F1,84=7.63, p=0.028; STN F1,84=14.24, p<0.001; p values Bonferroni-adjusted; see Figure 1a). In other words, depending on the lateralization group, all four regions appeared to exhibit a leftward (atypical group) or rightward (typical group) lateralization of their BOLD signal during the ‘correct stop >correct go’ condition of the stop-signal task.

Hemispheric lateralization of inhibitory control according to language lateralization.

(a) ROI analysis of the main components of the inhibitory control network. Graphs depict adjusted mean BOLD signal during successful inhibitions on the stop-signal task (‘stop >go’ contrast) for both hemispheres and both groups (n=50 typical and 36 atypical). All four structures showed significant Hemisphere × Group interactions in a repeated-measures MANOVA (p<0.05). Error bars represent one standard error. pOper = pars opercularis, pTri = pars triangularis, preSMA = presupplementary motor area, and STN = subthalamic nucleus. (b) Voxel-wise whole-brain analysis of functional asymmetry. Significance maps (voxel-wise p<0.001; FWE cluster-corrected at p<0.05; color bar represents t value) are displayed in three-dimensional reconstructions plus coronal and transversal slices using MNI space. (c) Mean BOLD values of the significant regions found in the voxel-wise whole-brain analysis. Graphic depicts BOLD values for every region, hemisphere, and group. IFC = inferior frontal cortex, preSMA = presupplementary motor area, AngG = angular gyrus, MTG = middle temporal gyrus, Thal = thalamus, STN = subthalamic nucleus, Caud = caudate. (d) Functional overlap between language production and inhibitory control in the IFC of ambilateral participants. Overlapping maps for inhibition (voxel-wise p<0.001; FWE cluster-corrected at p<0.05) and language (voxel-wise p<0.05; uncorrected) are displayed in coronal, sagittal and transversal slices using MNI space. VGT = verb generation task, SST = stop-signal task.

A voxel-wise whole-brain analysis of the functional asymmetry maps during the stop-signal task was performed to complement the ROI results. Two-sample t-test comparisons of the typical and atypical groups during the ‘correct stop >correct go’ condition showed a differential asymmetry pattern in all the ROI-corresponding areas, while also revealing differences in other cortical and subcortical regions, including the angular gyrus, middle temporal gyrus, and caudate (Figure 1b). Specifically, the typical group presented a rightward activity in these regions, whereas the atypical group exhibited a leftward shift (Figure 1c).

Finally, the Pearson’s correlation between the frontal LIs (pars opercularis + pars triangularis) from the verb generation task and the stop-signal task revealed a strong inverse correlation (r84 = −0.58; two-tailed p<0.001; R2=0.34) (Figure 2). Out of 50 typically lateralized for language, 33 (66%) presented a typical right lateralization for inhibitory control, and 17 (34%) showed an atypical (left – ambilateral) lateralization. Among the 36 individuals from the atypical group, only 8 (22.2%) presented a typical lateralization during the stop-signal task, whereas 28 (77.8%) showed an atypical lateralization. Importantly, no participant presented a leftward + leftward or rightward + rightward segregation pattern for language production and response inhibition. Strong segregation of both functions into different hemispheres was observed in 38 participants (44.2%). The remaining 48 individuals (55.8%) presented an ambilateral representation of one or both functions, with an important level of spatial overlap in their activations (see Figure 1d). In this ambilateral group, language production presented an overlap in 32.3% of its voxels in the left hemisphere, and in 58.6% of its voxels in the right hemisphere. Inhibitory control showed an overlap in 57.2% of its voxels in the left hemisphere, and in 22.8% of its voxels of the right hemisphere.

Correlation between the LIs of the verb generation and stop-signal tasks.

r = −0.583, two-tailed p<0.001, R2=0.339. Negative values indicate rightward lateralization, whereas positive values indicate leftward lateralization. Segregated and ambilateral phenotypes are also depicted according to the background color. The green area corresponds to segregated individuals (both functions strongly lateralized), and the red area corresponds to integrated individuals (at least one function ambilaterally controlled). Numbers inside each quadrant denote the number of individuals (n) contained in it. Each individual datapoint is symbolized according to its functional organization: 〇=typical segregation; ⬤=reversed segregation; △=ambilateral inhibition; ▽=ambilateral language; ◇=ambilateral language and inhibition.

Laterality indexes correlate to interhemispheric functional connectivity, callosal volume and preclinical markers

Correlational analyses revealed certain links between the functional lateralization of both tasks and behavioral, neuroanatomical and connectivity measures (Table 1).

Table 1
Spearman’s partial correlations between task LIs and neuroanatomical plus behavioral variables.
Verb generation task LIStop-signal task LI
Spearman’s ρpSpearman’s ρp
Callosal genu volume−0.2490.021*0.2510.02*
Callosal body volume−0.2580.017*0.1320.224
Callosal splenium volume−0.2490.021*0.1180.279
Pars Opercularis VMHC−0.0620.5840.1480.192
Pars Triangularis VMHC−0.1190.2950.2580.022*
preSMA VMHC0.0520.6470.0370.748
STN VMHC0.0330.7720.2170.054
‘Go’ reaction time0.1250.251−0.0410.71
‘Go’ accuracy−0.0760.4890.0390.724
SSRT0.1730.111−0.0380.726
Reading length accuracy−0.2370.028*0.2140.047*
Reading familiarity accuracy−0.2320.032*0.3010.005**
Reading length time0.0540.622−0.1840.068
Reading familiarity time0.0910.403−0.1980.091
SPQ−0.1940.0780.2470.023*
AQ−0.2470.023*0.0750.498
  1. General intelligence and age were included as covariates of no interest. Callosal volume correlations were additionally corrected for total intracranial volume.

First, we studied how the inter-hemispheric functional connectivity and callosal size behaved in relation to LI. Volume in the genu, body and splenium of the corpus callosum was inversely related with language LI (respectively: ρ83 = −0.25, p=0.02; ρ83 = −0.26, p=0.02; and ρ83 = −0.25, p=0.02). However, only the callosal genu was found related with inhibition LI (ρ83=0.25, p=0.02). That is, the volume of the callosal genu increased as functional organization of the IFC became more atypical, extending this effect to the whole corpus callosum when considering exclusively language lateralization. On the same line, ROI interhemispheric functional connectivity analyses at rest revealed that VMHC of the pars triangularis also increased as a function of inhibition LI (ρ77=0.26, p=0.02), but a similar relation failed to be found for language LI (ρ77 = −0.12, p=0.3). No statistically significant linear relationships were found when exploring the VMHC of the pars opercularis, the preSMA, or the STN. It should be mentioned, however, that the association between VMHC of the STN and inhibition LI was close to significance (ρ77=0.22, p=0.054).

Behaviorally, performance (RT, SSRT, and accuracy) during the scanner stop-signal task did not correlate with language and inhibition LIs. Regarding the reading test, accuracy for word length and word familiarity were found correlated with both LIs (length and language: ρ84 = −0.24, p=0.03; length and inhibition: ρ84=0.21, p=0.04; familiarity and language: ρ84 = −0.23, p=0.03; familiarity and inhibition: ρ84=0.3, p=0.01). That is, an atypical organization was related with a higher rate of errors when reading long words and unfamiliar words. No significant correlations were found when exploring reading speed. SPQ score was found significantly related with inhibition LI (ρ82=0.25, p=0.02), but it did not reach statistical significance when paired with language LI (ρ84 = −0.19, p=0.08). AQ, on the other hand, presented an association with language LI (ρ84 = −0.25, p=0.02) but not with inhibition LI (ρ84=0.0.8, p=0.5).

Discussion

The present study employed the stop-signal task to investigate brain areas involved in the inhibitory control function in two different groups of individuals: typically and atypically lateralized for language production. As expected, the atypical participants showed, as a group, a mirrored brain organization of the inhibitory control function compared to the typical group. This leftward organization affected the entire inhibitory control network, including the IFC, preSMA, and STN. However, some participants manifested a clear overlap in the control of language and inhibitory functions. Our results also demonstrate that atypical organization of language production is associated with an increased white matter volume of the corpus callosum, and that atypical lateralization of inhibitory control is related with a higher interhemispheric functional coupling of the IFC. Behaviorally, atypical lateralization was not associated with performance on the task, but an association existed with worse reading accuracy, and more schizotypy and autistic traits.

Our first relevant result is that the group of individuals with atypical lateralization of language presented a mirrored brain organization during the stop-signal task compared to the typically lateralized group. Remarkably, the hemispheric asymmetry differences obtained affected the entire inhibitory control brain network (Hannah and Aron, 2021). The inferior frontal cortex, or IFC, associated with the initiation of the stop command and the main cortical area of this system, showed a clear lateralization effect, with the typical group almost exclusively using the right part and the atypical group mostly activating the left part. The presupplementary motor area, or pre-SMA, related to the implementation of the stop command, was more bilaterally involved in both groups, although atypicals activated the left part more and typicals activated the right part more. Importantly, the hemispheric reversal of organization also affected the subcortical structures participating in this network, namely the STN and thalamus. These structures displayed a lateralization pattern similar to the preSMA, with a more pronounced bilateral engagement but higher involvement of the right part in typicals and of the left part in atypicals. Thus, this is the first demonstration that an entire cerebral network controlling a cognitive function shows a completely flipped organization in an atypical population for language, which supports the Causal hypothesis of lateralization (Kosslyn, 1987; Hellige, 1990). Moreover, this result reveals that hemispheric lateralization goes beyond cerebral cortices, and even interhemispheric connectivity, through the corpus callosum, given that it also involves frontal-subcortical circuits. Additionally, the specificity of this result to the areas of the proposed network is another element that supports the relevance of this system in inhibitory control.

It should be noted that previous studies have demonstrated a significantly higher involvement of the left IFC during cognitive control tasks in bilingual individuals when compared to monolinguals (Garbin et al., 2010; Rodríguez-Pujadas et al., 2013). However, this could not be directly considered in the current study due to the limited number of monolinguals participants (n=8), which would not allow for statistically robust tests. So, we cannot rule out the possibility that bilingualism could be acting as an enabling mechanism among atypically lateralized participants for the shift of the inhibitory control network to the left hemisphere. Therefore, the findings of this study should be extrapolated to the monolingual population with caution.

Remarkably, this pattern of reversed specialization for inhibitory control was observed in the absence of a relation between functional lateralization and performance differences during the ‘go’ or ‘stop’ conditions. Even though the ‘go’ condition involves a lexical decision requiring language processing, and the ‘stop’ condition consists of inhibiting a linguistic process, the functional organization of the IFC during the tasks did not affect the response speed (RT and SSRT) or the ‘go’ accuracy in either case. Thus, brain organization for language and inhibition, as initially defined in our study, and cognitive efficiency are not directly related, based on our data. This finding is consistent with prior studies highlighting that: (1) a densely segregated functional organization, rather than a reversed organization, is linked to general (Gerrits et al., 2020) and specific (Mellet et al., 2014) cognitive deficits; and (2) specific cognitive deficits appear to be confined to the domains of verbal and spatial skills (Mellet et al., 2014).

Importantly, despite observing a complimentary relationship between language production and inhibitory control at the group level, not all the individuals presented a strong and segregated distribution of these two functions. The vast majority of hemispherically segregated individuals showed the typical segregation of language in the left hemisphere and inhibitory control in the right hemisphere, whereas only a low percentage displayed the opposite segregated organization. The rest, in agreement with similar reports on other functions (Gerrits et al., 2019), lacked a clear hemispheric lateralization of language production, inhibitory control, or both. In all cases, this implied some level of overlapping or sharing of the same area of the inferior frontal cortex. Both the low ratio of atypical segregation and the high ratio of bilateral inhibitory control could be explained by the fact that typical right hemispheric functions (such as inhibition) seem to be more bilaterally represented than typical left functions (such as language; Gotts et al., 2013). Some conclusions can be extracted from this: (1) Although infrequent, a strong atypical segregation of the two functions is possible; (2) Our data have not revealed any case with a segregation of both frontal cognitive functions in the same hemisphere; and (3) A large percentage of participants with a strong left lateralization of language presented a bilateral control of inhibition, showing that a typical dominance for language does not imply a strongly lateralized organization of other cognitive functions. The existence of exceptions to the group pattern of mirrored brain organization requires us to use caution when interpreting these data as supporting the causal hypothesis. Although the negative correlation between the lateralization indexes during the verb generation task and the stop-signal task is strong at the group level, some individual data support the statistical hypothesis (Bryden et al., 1983) instead. Given that we found no cases of both cognitive functions completely sharing a hemisphere (see Figure 2), we propose that causal-supporting vs. statistical-supporting results reflects two different pathways to cognitive control. On the one hand, the segregated pathway (in line with the Causal hypothesis) develops due to the cerebral bias towards lateralizing certain cognitive functions in a single hemisphere. On the other hand, the integrated pathway (in line with the Statistical hypothesis), appears when ontogenetic development towards strong lateralized control fails in some way.

These findings could have significant implications when studying neonatal lesions that facilitate the development of left-handedness (Dinomais et al., 2017), or when assessing the consequences of brain lesions in the left-handed population (Newport et al., 2022). For example, a left-sided lesion in an individual with rightward language lateralization may result in inhibitory control and social deficits (Mosch et al., 2005). It also raises questions about neuroplasticity, particularly how functional reorganization following a brain lesion (such as a left-to-right reorganization of language) would impact this hemispheric relationship between language and inhibitory control, and the cognitive consequences for inhibitory control after such a reorganization (Bates et al., 2001). These results could also offer valuable insights for cognitive rehabilitation procedures in patients with psychiatric disorders that seem to be linked to a higher prevalence of left-handedness and alterations in language lateralization (Webb et al., 2013).

The corpus callosum is the main cerebral structure in interhemispheric connectivity, with the genu area being responsible for the connectivity between the two inferior frontal gyri (Hofer and Frahm, 2006). Phylogenetic data have demonstrated a negative association between the corpus callosum volume and hemispheric specialization, suggesting that the ontogenetic development of the brain is supported by a decrease in interhemispheric connectivity to potentiate and establish intrahemispheric connectivity and hemispheric specialization (Tzourio-Mazoyer, 2016). There is evidence of this mechanism in language development (Tzourio-Mazoyer and Seghier, 2016) and in agenesis of the corpus callosum, which has been associated with lower lateralization of language and worse language performance (Hinkley et al., 2016; Bartha-Doering et al., 2021). In line with this proposal, we have replicated the relation between callosal enlargement and atypical functional organization found in a previous study (Labache et al., 2020). Moreover, in our data, this structural correlation was accompanied by an association between atypical lateralization of inhibitory control and strength of interhemispheric functional connectivity in the pars triangularis of the IFC.

As hypothesized, when considering the functional lateralization during both tasks, atypical organizations were found linearly related with higher scores on a schizotypy test (only for inhibitory control), higher scores on an autistic spectrum test (only for language production), and a higher rate of reading errors (both language production and inhibitory control). The first result is consistent with previous structural, functional, and behavioral data that have shown reduced language lateralization in schizophrenia patients (Sommer et al., 2001; Li et al., 2007). In the case of schizotypy, the evidence for reduced lateralization of cognitive functions is mixed and may respond to differences in the methodology employed (Park and Waldie, 2017). However, consistently with the present results, this condition has been previously associated to impaired behavioral and neural processing during the stop-signal task (Jia et al., 2021), and to an abnormal frontal functional asymmetry (Le et al., 2020). The second result relates atypical language lateralization with the presence of autistic traits, a result consistent with data obtained in autism (Jouravlev et al., 2020). The third result shows that atypical organization was related with a higher rate of reading errors when considering word length and word familiarity. The length effect has been considered a pathognomonic symptom of reading disorders such as developmental dyslexia (Provazza et al., 2019) and pure alexia (Roberts et al., 2013). These disorders have also been characterized by a weak language lateralization (Bishop, 2013). Thus, presented results support the hypothesis that atypical hemispheric specialization is related with worse cognitive performance in the linguistic domain, and even preclinical traits of some neurodevelopmental disorders among healthy population.

In conclusion, our study demonstrates a strong connection between the lateralization of language production in the IFC and the hemispheric specialization of the inhibitory control network. This functional interrelationship is apparent through both regional and voxel-wise analyses and impacts all the primary cerebral hubs of inhibitory control. Furthermore, the atypical lateralization of these two cognitive functions shows a linear correlation with increased inter-hemispheric functional connectivity in the IFC, a larger corpus callosum, the presence of subclinical markers of schizotypy and autism, and decreased reading accuracy. Consequently, our research provides compelling evidence in support of the causal and corpus callosum hypotheses of lateralization among left-handers, and offers valuable insights into the pathogenic mechanisms underlying atypical lateralization in healthy population.

Materials and methods

Participants

Eighty-six participants were included in the present study. They were selected following a functional magnetic resonance imaging (fMRI) language lateralization assessment via a verb generation task. Hence, 50 were typically lateralized for language – left-dominant – (mean ± SD age=22.4 ± 3.6 years; 24 male, 26 female) and 36 were atypically lateralized (mean ± SD age=23.4 ± 4 years; 16 male, 20 female).

All the participants were non-right-handed, according to the Edinburgh Handedness Inventory (Bryden, 1977; Oldfield, 1971). There were no significant between-group differences in age (t84=1.28; p=0.2), sex (χ2=0.11; p=0.74), or general intelligence by WAIS-IV matrix reasoning subtest (Wechsler, 2012; t84=1.8; p=0.08). The participants had no history of any neurological or psychiatric disorders or head injury with loss of consciousness. Written informed consent was obtained from all participants following a protocol approved by Universitat Jaume I. All methods were carried out in accordance with approved guidelines and regulations.

Regarding their bilingual status, we found three categories: Spanish monolinguals (n=6 typical and 2 atypical), Spanish-Catalan bilinguals (n=27 typical and 23 atypical); and Spanish-Catalan passive bilinguals (n=17 typical and 11 atypical). Passive bilingualism refers to the fact that some residents of this region (Valencian Community) understand Spanish and Catalan, but their frequency of use of one language is extremely low when compared to the other. No significant between-group differences were found in bilingual status when comparing the typical and atypical groups (χ2=1.2; p=0.55).

Experimental design

Participants were recruited via multiple advertisements across Castellón and Valencia universities (bulletin boards, mass emailing, etc.) asking for the collaboration of left-handers in an fMRI brain study. Persons older than 36 years or younger than 16 years were discarded. Valid participants were cited for a first fMRI session, in which they completed the verb generation task and the resting-state acquisition. During this session, we used the BrainWave software (GE HealthCare Technologies Inc) to visualize real-time data and roughly categorize participants as potentially typical or potentially atypical. Forty-three participants were found potentially atypical for language lateralization, and were invited to a second fMRI session, along with 43 potentially typical participants. During this second session, participants completed the stop-signal task (scanner) and a reading skill test (out of scanner). Also, data regarding schizotypy personality and autistic spectrum was gathered via self-questionnaires. Note that classification of participants as potentially typical or potentially atypical was used for screening purposes, and it did not completely match the final assessment.

Verb generation task

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Expressive language function was measured via fMRI during a computerized verb generation task (Sanjuán et al., 2010; Villar-Rodríguez et al., 2020) that consists of a two-block design paradigm with intercalating activation and control blocks. In the activation blocks, participants are asked to overtly say the first verb that comes to mind when visually presented with a concrete noun. In the control blocks, they have to read visually-presented letter pairs aloud. This task lasted for 6 min, with a block duration of 30 s (6 activation blocks and 6 control blocks), a stimulus duration of 1500 ms, and a blank inter-stimulus interval of 1500 ms. Before entering the scanner, participants practiced with a different version of the task for 1 min. Stimuli were presented using MRI-compatible goggles (VisuaStim Digital, Resonance Technology Inc), and responses were recorded with a noise-cancelling microphone (FOMRI III+, Optoacoustics Ltd.) to verify that each participant was engaged correctly in the task.

Stop-signal task

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Response inhibition was measured via fMRI during a computerized stop-signal task adapted from another study (Xue et al., 2008). The task consists of an event-related design paradigm with ‘Go’ and ‘Stop’ trials. In the ‘Go’ trials, participants are asked to manually answer by pressing a button if the visually presented string is a word (has a meaning) or a pseudoword (mimics typical word structure but has no meaning). Words require an index button press, whereas pseudowords require a thumb button press. However, on the ‘Stop’ trials, the string is followed by a ‘beep’ noise that signals to the participants to inhibit their response and refrain from pressing any button. The instructions emphasized the importance of going correctly and stopping correctly. However, participants were asked to respond as quickly and accurately as possible and avoid withholding their responses in anticipation of a possible ‘beep’.

This task took 13 min and 28 s, and it was divided into two runs that lasted 6 min and 14 s each, separated by a 1 min rest. Each run consisted of 135 trials, of which 32 (23.7%) were ‘Stop’ trials. All the trials randomly presented words or pseudowords. The trial structure consisted of a fixation crosshair (500ms), a word or pseudoword (1000ms, during which the ‘beep’ may or may not be presented at some point), and a blank inter-stimulus interval (ranging from 500 to 4000ms, sampled from an exponential distribution truncated at 4000ms, with mean of 1000ms). The Stop Signal Delay or SSD (the amount of time after the onset of the word or pseudoword when the ‘beep’ is presented during ‘Stop’ trials) changed dynamically during the task after each ‘Stop’ trial, depending on whether inhibition was successful (+25ms) or unsuccessful (−25ms), with a minimum of 100ms and a maximum of 800ms. The lower the SSD, the easier it is to inhibit the response, and vice versa. Hence, a dynamic SSD normalizes the task’s difficulty across all participants based on their performance, aiming at a 50% successful inhibition rate. The SSD used for the first ‘Stop’ trial was estimated for each participant based on their practice session before entering the scanner. This practice session lasted 6 min and used a different set of words and pseudowords. Stimuli were presented using MRI-compatible goggles and headset (VisuaStim Digital, Resonance Technology Inc), and responses were recorded via an MRI-compatible response-grip (ResponseGrips, NordicNeuroLab). The ‘beep’ volume was kept at a comfortable level and was constant across participants.

Resting-state

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Functional connectivity was measured via fMRI during a resting-state session (Biswal et al., 1995). In this paradigm, participants were presented with a fixation crosshair and instructed to just lie in the scanner with their eyes open and try not to sleep or think about anything in particular. This session lasted for 7 min. Seven participants did not complete this session due to time constraints, and they were subsequently removed from the functional connectivity analyses. It is important to highlight that the exclusion of these seven participants across all analyses does not notably impact the overall results.

Behavioral measures

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Individual inhibition speed was estimated by calculating the Stop-Signal Reaction Time (SSRT; Verbruggen et al., 2019). SSRT was computed as the difference between the median reaction time (RT) on correct ‘go’ trials and the mean SSD on the stop-signal task. We chose to employ the median RT instead of the mean RT, in accordance with the approach used in the study from which we adapted our stop-signal task (Xue et al., 2008). This choice was made for two specific reasons: (1) when working with RT, using the median helps reduce the impact of outlier responses during the task; and (2) using the median RT allowed our sample’s SSRT to align more closely with a normal distribution. It should be noted that technical problems involving the MRI-compatible response-grip invalidated the SSD and RT data of four participants, and so their SSRT was estimated using the data from the practice session instead. This practice session, which lasted for 6 min, took place outside of the scanner in a different room, but using an identical set of response-grip. So, for these four participants, the resulting SSD and RT after those 6 min of practice was used for their SSRT calculation.

Reading skill was evaluated using the word reading subtest of the PROLEC-SE-R (Cuetos Vega et al., 2016), a battery that assesses reading processes in Spanish. Participants had to read four lists of words consisting, respectively, of short familiar words, long familiar words, short unfamiliar words, and long unfamiliar words. Responses were recorded with a microphone, and their accuracy and speed were subsequently measured by computing length (performance during short words – performance during long words) and familiarity (performance during familiar words – performance during unfamiliar words) indicators.

Schizotypal traits were explored with the SPQ (Raine and Raine, 1991), a preclinical self-report questionnaire modeled after DSM-III criteria (American Psychiatric Association, 1980). This questionnaire evaluates schizotypy based on the three-dimensional model: disorganized traits, cognitive-perceptual traits, and interpersonal traits. For two participants, these data were not collected due to time constraints.

Autistic spectrum traits were explored with the AQ (Baron-Cohen et al., 2001), a preclinical self-report questionnaire modeled after DSM-IV criteria (Association, 1994). This questionnaire evaluates autistic spectrum based on the ‘triad’ of impaired communication, impaired social skills, and a restricted and repetitive way of acting (Rutter, 1978), plus two sub-scales for imagination and attention to detail. For two participants, these data were not collected due to time constraints.

Image acquisition

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Images were acquired on a 3T General Electric Signa Architect magnetic resonance imaging (MRI) scanner using a 32-channel head coil. All slices were acquired in the sagittal plane. A 3D structural MRI was acquired for each subject using a T1-weighted magnetization-prepared rapid gradient-echo sequence (TR/TE = 8.5/3.3ms; flip angle = 12; matrix = 512 × 512 × 384; voxel size = 0.47 × 0.47 × 0.5). For the fMRI, a gradient-echo T2*-weighted echo-planar imaging sequence was used in the acquisition of 150 functional volumes on the verb generation task (TR/TE = 2500/30ms; flip angle = 70; matrix = 64 × 64 × 30; voxel size = 3.75 × 3.75 × 4), and a different sequence was used in the acquisition of 374 functional volumes during the stop-signal task and 210 functional volumes in the resting-state (TR/TE = 2000/30ms; flip angle = 70; matrix = 64 × 64 × 27; voxel size = 3.75 × 3.75 × 4.5).

Image processing

Task-based fMRI processing

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Task functional images were processed using the Statistical Parametric Mapping software package (SPM12; Wellcome Trust Centre for Neuroimaging, London, UK). Preprocessing followed the default pipeline and included: (a) alignment of each participant’s fMRI data to the AC‐PC plane by using the anatomical image; (b) head motion correction, where the functional images were realigned and resliced to fit the mean functional image; (c) co-registration of the anatomical image to the mean functional image; (d) re‐segmentation of the transformed anatomical image using a symmetric tissue probability map; (e) spatial normalization of the functional images to the MNI (Montreal Neurological Institute, Montreal, Canada) space with 3 mm3 resolution; and (f) spatial smoothing (FWHM = 4 mm).

The general linear model for the verb generation task was defined for each participant by contrasting activation  > control blocks. The general linear model for the stop-signal task was defined for each participant by contrasting correct ‘stop’ > correct ‘go’ trials. For both tasks, the BOLD (Blood‐Oxygen‐Level‐Dependent) signal was estimated by convolving the task’s block/trial onsets with the canonical hemodynamic response function. Six motion realignment parameters extracted from head motion preprocessing were included as covariates of no interest, and a high‐pass filter (128 s) was applied to the contrast images to eliminate low‐frequency components.

After that, we utilized the VOI analysis function within SPM12 to extract the first eigenvariate of the BOLD time courses from the stop-signal contrast images, which highly resembles the mean time course across the voxels. This extraction was carried out region-wise for the main components of the inhibitory control network: pars opercularis, pars triangularis, preSMA, and STN. Pars opercularis and pars triangularis of the IFC were defined following the criteria of the Harvard-Oxford atlas (Frazier et al., 2005; Desikan et al., 2006; Makris et al., 2006; Goldstein et al., 2007). For the PreSMA region, we used the SMA region, as defined by the same atlas, but including only voxels anterior to MNI Y=0 (Ruan et al., 2018). STN was defined by a 10 mm box centered at MNI coordinates 10,–15, −5 (Aron and Poldrack, 2006; Lucerna et al., 2002).

Lastly, we computed whole-brain voxel-wise functional asymmetry maps from the stop-signal contrast images. To do so, stop-signal contrast images were flipped at midline, inverting the right and left hemispheres, and subsequently subtracted from the original unflipped contrast images (Kurth et al., 2015).

Resting-state fMRI processing

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Resting-state functional images were processed using the Data Processing Assistant for the Resting‐State toolbox (DPARSFA; Chao-Gan and Yu-Feng, 2010), which is based on SPM and the Data Processing & Analysis of Brain Imaging toolbox (DPABI; Yan et al., 2016). Preprocessing steps included: (a) slice‐timing correction for interleaved acquisitions; (b) head motion correction (no participant had a head motion of more than 2 mm maximum displacement in any direction or 2° of any angular motion throughout the scan); (c) co-registration of the anatomical image with the mean functional image; (d) new segmentation to DARTEL; (e) removal of nuisance variance through linear regression: six parameters from the head motion correction, white matter signal, cerebrospinal fluid signal, and global mean signal; (f) spatial normalization to the MNI (3 mm3); (g) spatial smoothing (FWHM = 4 mm); (h) removal of the linear trend in the time series; (i) band‐pass temporal filtering (0.01–0.1); (j) normalization to a symmetric template; (k) Voxel-Mirrored Homotopic Computation (VMHC) (Zuo et al., 2010), calculated as the Pearson correlation coefficient of every voxel with its hemispheric counterpart; and (l) normalization of all voxel-wise time courses to Fisher z values. Finally, mean VMHC values were extracted for the pars opercularis, pars triangularis, preSMA, and STN. ROI definition can be found in the previous section.

Structural MRI processing

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Structural images were processed via voxel-based morphometry (VBM) using the CAT12 toolbox (Gaser and Kurth, 2016), which is based on SPM. Preprocessing steps followed the recommended pipeline and included: (a) segmentation into grey matter, white matter, and cerebrospinal fluid; (b) registration to the ICBM standard template; (c) modulated normalization of grey matter and white matter segments to the MNI template; (d) spatial smoothing (FWHM = 6 mm); and (e) extraction of Regions of Interest (ROIs) values from native space. Three ROIs were delimited by the voxels mapped as genu, body and splenium of the corpus callosum according to the Mori atlas (Oishi et al., 2009). Total intracranial volume was also extracted for use as covariate of no interest.

Individual assessment of functional lateralization

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Individual functional lateralization was assessed by calculating the Laterality Index (LI) on the unflipped contrast images (Sanjuán et al., 2010; Villar-Rodríguez et al., 2020). We used the bootstrap method implemented in the LI-toolbox (Wilke and Lidzba, 2007), based on SPM. LI is a proportion of the brain activation between the two hemispheres, thus giving us information about the direction and degree of hemispheric specialization during a particular function in a single individual. LI ranges from +1 (totally leftward function) to −1 (totally rightward function). For both the verb generation task and the stop-signal task, we explored the LI of the inferior frontal region roughly corresponding to the classic Broca’s area: pars opercularis and pars triangularis of the inferior frontal gyrus, according to the Harvard-Oxford atlas (Frazier et al., 2005; Desikan et al., 2006; Makris et al., 2006; Goldstein et al., 2007). This region is critical for both language production (Price, 2012) and response inhibition (Aron et al., 2014), depending on the hemisphere. During the verb generation task, language production was classified as typically lateralized (LI higher than +0.4) or atypically lateralized (LI lower than +0.4). We used +0.4 as a cut-off point (contrary to the more traditional +0.2), based on previous findings that emphasized the importance of lateralization strength when grouping individuals (Mazoyer et al., 2014; Labache et al., 2020).

Statistical analyses

Task-based region-wise analysis

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We performed different analyses to investigate the hypothesis that the atypical group would show a mirrored brain organization during the stop-signal task. First, we conducted a region-wise analysis to compare the functional asymmetry of the primary components within the inhibitory control network between the typically and atypically lateralized groups. To achieve this, we employed a repeated-measures MANOVA, with Hemisphere (left/right) and Region (pars opercularis/pars triangularis/preSMA/STN) as within-subject factors and Group (typical/atypical) as the between-subject factor. Subsequently, to assess potential hemispheric differences in any of these regions between both groups, we contrasted the simple effects of the Hemisphere × Group interaction across the Region factor. All resulting p-values were adjusted using a Bonferroni correction.

Task-based voxel-wise analyses

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Previous analysis was complemented with whole-brain functional asymmetry contrast maps (extracted from the stop-signal task) that were compared in typically and atypically lateralized groups. To do so, we performed a whole-brain two-sample t-test (voxel-wise p<0.001; FWE cluster-corrected at p<0.05) via SPM12.

We also explored the functional overlap between both tasks in the subgroup of participants that presented an ambilateral organization of at least one function (n=48). To do so, we overlayed two one-sample t-tests restricted to the IFC: one for the stop-signal task (voxel-wise p<0.001; FWE cluster-corrected at p<0.05), and one for the verb generation task (voxel-wise p<0.05; uncorrected). Functional overlap was then expressed as the proportion of overlapped voxels for every function and hemisphere.

Correlational analyses

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The relationship between the hemispheric specialization of the verb generation task and the stop-signal task at the IFC was also examined linearly. More precisely, we conducted a Pearson’s correlation between the LI from the verb generation task and the LI from the stop-signal task used in the individual lateralization classification (p<0.05, two-tailed).

Using these LIs, we also tested if a linear relationship existed between the functional lateralization of an individual, and several behavioral tests and neuroanatomical measures. Partial Spearman’s correlations were computed between the LI of both tasks and: stop-signal task performance (RT, accuracy, and SSRT), SPQ derived-scores, AQ derived-scores, reading test performance (length and familiarity effects on speed and accuracy), functional connectivity measures (VMHC), and white matter volume of the callosal genu. Age and general intelligence were included as covariates in all analyses. Total intracranial volume was also included as covariate in the volumetric analysis.

Data availability

All data presented in this study are included in figshare.

The following data sets were generated
    1. Villar-Rodríguez E
    2. Cano-Melle C
    3. Marin-Marin L
    4. Parcet MA
    5. Avila C
    (2023) figshare
    Structural (VBM), functional (fMRI) and behavioral data relative to the research article "What happens to the inhibitory control functions of the right inferior frontal cortex when this area is dominant for language?".
    https://doi.org/10.6084/m9.figshare.22047299

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Peer review

Reviewer #1 (Public Review):

The authors' aim was to test to what extent atypical organization of language is associated with a mirrored brain organization of other cognitive functions. In particular, they focused on the inferior frontal gyri (IFG) by studying the inhibitory control network. This allowed them to directly test support for the Causal hypothesis of hemispheric specialization, arguing for fast sequences of cognitive processes being better performed by a single hemisphere, versus the Statistical hypothesis of lateralization, postulating an independent lateralization of each cognitive function.

Previous studies on this topic did not focus on functions involving homotopic language regions. This limitation is bypassed in this study by assessing inhibition with a Stop-Signal Task which also engages the IFG in the contralateral site to the verb generation task. By studying a combination of structural and functional information, in addition to the activation contrasts, the authors are able to test whether atypical organization is accompanied by stronger interhemispheric connectivity. Although relying mainly on correlations and lacking important methodological information that may be critical to understand the reported effects, the results are quite straightforward. However the bilingual/monolingual status and gender of the participants is not reported which might affect the relationship between language and inhibitory control.

The conclusions of the paper are supported by the data. With their design, the authors observed that, as a group, individuals with atypical organization show a mirror organization of the whole inhibitory network to the contralateral site, supporting the Causal hypothesis at the group level. However, individual data support the Statistical hypothesis, since the segregation between language and inhibition was not observed in all individuals and a variety of configurations in bilateral and bilateral organisation of language and inhibition were also observed.

The results of this study have important implications for our understanding of the independence of different cognitive functions, which is crucial when addressing brain damage and rehabilitation. This aspect also indirectly speaks to researchers interested in evolution and in bilingualism and its relation to cognitive control. These aspects are not discussed but incorporating them would broaden the interest of the paper beyond the current implications mentioned.

https://doi.org/10.7554/eLife.86797.3.sa1

Reviewer #2 (Public Review):

Language skills are traditionally associated with a network of brain regions in the left hemisphere. In this intriguing study, Esteban Villar-Rodríguez and collaborators examined whether atypical hemispheric lateralization for language determines the functional and structural organisation of the network for inhibitory control as well as its relationship with schizotypy and autistic spectrum traits. The results suggest that individuals who have atypical lateralisation of the language function have also an atypical (mirrored) lateralisation of the inhibitory control network, compared to the typical group (individuals with left-lateralised language function). Furthermore, the atypical organization of language production is associated with a greater white matter volume of the corpus callosum, and atypical lateralization of inhibitory control is related to a higher interhemispheric functional coupling of the IFC, suggesting a link between atypical functional lateralisation (language and inhibitory control) and structural and functional changes in the brain.

This study also provides interesting evidence on how atypical language lateralisation impacts some aspects of language behaviour (reading), i.e., atypical lateralization predicts worse reading accuracy. Furthermore, the results suggest an association between atypical lateralization and increased schizotypy and autistic traits.

https://doi.org/10.7554/eLife.86797.3.sa2

Author response

The following is the authors’ response to the original reviews.

Reviewer #2 (Recommendations For The Authors):

The evidence provided in this study reflects important discoveries on language lateralisation and most of the conclusions of this paper are supported by evidence. However, there are several areas regarding the characteristics of participants tested, hypotheses/predictions and the type of analysis, that need to be clarified and/or corrected.

1. There is a substantial disconnection between the introduction and the methods/results section.

One reason is because of lack of consistency. One example refers to the fact that, in the introduction, only IFC is mentioned. However, the analyses carried out to examine neural activity in different groups focused on IFC as well as other brain regions related to inhibitory control. However, these areas were not mentioned at all in the introduction.Second and related to the above, the rationale for conducting certain types of analyses is not specified. Some brain analyses focus on IFC only. Instead, other analyses focus on several areas.

Another weakness is that there is not sufficient detail regarding the hypotheses/predictions and the specific types of analyses chosen to test these hypotheses/predictions. For example, there is no mention of resting state fMRI data in the introduction, but later we discover that this type of data was collected and analyzed. Even a brief mention of the inclusion of resting state data in the introduction would be beneficial. Along the same lines, by reading the methods section we find out that VBM analyses were conducted. But it is unclear why. What was the purpose of this data analysis? This should be clarified briefly in the introduction and then in the methods section. It remains unclear why resting state results would be particularly informative for addressing the research question of this study. Task-related brain connectivity seems a more appropriate choice. Additionally, it is not explained what comparisons and outcomes would be informative/expected to distinguish between the two mentioned competing hypotheses. This should be made clear.

Another aspect that lacks clarity is the authors' predictions when investigating the relationship "between the lateralization of both functions and inter-hemispheric structural-functional connectivity, as well as with behavioural markers of certain clinical conditions that have been related with atypical lateralization". The hypotheses are completely omitted in this section.

Thank you for bringing this to our attention. We concur with Reviewer #2 that our introduction was somewhat lacking in detail and assumed too much prior knowledge on the part of the reader. This, together with a lack of a clear presentation of our tested hypotheses, made the introduction have a poor connection with both the results and discussion sections, which hindered the understanding of the paper.

As a result, we have made some additions to enhance the exposition of the following areas: (1) the causal and statistical hypotheses of lateralization (Lines 55-65); and (2) the hypotheses regarding subclinical markers of neurological disorders and the corpus callosum (Lines 90-104).

Furthermore, we have extensively revised the final paragraph of the introduction (Lines 105-121) to provide a clearer and more coherent linkage between the drivers presented during the introduction, our hypotheses, and the subsequent analyses.

2. It is important to provide more information on the language background of the participants. Were the participants in this study Catalan-Spanish bilinguals? If so, it is crucial for the authors to mention this.

Language background of the participants has been added to the corresponding section (Lines 138-145).

In fact, previous studies, including several publications from the authors themselves (Garbin et al., 2010; Rodríguez-Pujadas et al., 2013; Anderson et al., 2018), have shown that there are qualitative differences between bilinguals and monolinguals in the neural circuitry underlying executive control. Across all these studies, it was consistently reported that bilingual individuals, when engaged in non-linguistic inhibitory control tasks, recruited a broader network of left-brain regions associated with language control, including the left IFC, in comparison to monolingual individuals. If the participants in this study were indeed bilinguals, it raises concern if the aim of the study is to generalize the conclusions on lateralization effects beyond the bilingual population.

Rodríguez-Pujadas, A., Sanjuán, A., Ventura-Campos, N., Román, P., Martin, C., Barceló, F., … & Ávila, C. (2013). Bilinguals use language-control brain areas more than monolinguals to perform non-linguistic switching tasks. PLoS One, 8(9), e73028.

Garbin, G., Sanjuan, A., Forn, C., Bustamante, J. C., Rodríguez-Pujadas, A., Belloch, V., ... & Ávila, C. (2010). Bridging language and attention: Brain basis of the impact of bilingualism on cognitive control. NeuroImage, 53(4), 1272-1278.

Anderson, J. A., Chung-Fat-Yim, A., Bellana, B., Luk, G., & Bialystok, E. (2018). Language and cognitive control networks in bilinguals and monolinguals. Neuropsychologia, 117, 352-363.

Indeed, we have thoroughly reported that, when compared to monolinguals, bilinguals exhibit a significant implication of left brain regions during switching and inhibition tasks. So, this is a legitimate concern. Unfortunately, the society from which our participants were drawn is primarily bilingual, encompassing both active and passive bilinguals. The monolingual sample in those previous studies consisted of university students originating from predominantly monolingual regions of Spain. Given this context, it is unsurprising that the current study has a rather limited number of monolinguals (n=8), with only 2 displaying atypical language lateralization. Thus, we cannot provide a reliable answer to the role of bilingualism status in our data. Consequently, we have included a comment on this limitation on the discussion (Lines 504-512).

3. Regarding the methods section, I have the following specific queries. The first is about the control condition in the verb generation task. I find it puzzling that the 'task' and 'control' conditions differ in terms of the number of words uttered. Could the authors please provide further clarification on this?

Thank you for raising this question. Regarding the control condition, it is important to note that the design of this task drew inspiration from previously published verb generation tasks for fMRI (Benson et al., 1999; Fitzgerald et al., 1997) and PET (Petersen et al., 1988). In the fMRI tasks, a fixation cross served as the control condition, while the PET study used word repetition as the control. We acknowledged that a mere fixation cross might not adequately control for the movement and visual-related activations inherent in the verb generation task. Conversely, word repetition could potentially engage the default mode network due to the repetition of the same simple task, which might not be suitable for a control condition, and it could be overly linguistic because it involves a word. Consequently, we aimed to strike a balance by employing a control condition that consisted of reading letters. This approach allowed us to control for movement and vision factors without invoking semantics. Thus, after careful consideration, we ultimately opted on the reading of two letters to equate the response to the vocalization length of generating a verb.

Although we understand the concern of single vs. two vocalizations, it is worth emphasizing that this version of the verb generation task had undergone prior testing to assess its suitability for determining language lateralization in both healthy and clinical populations (Sanjuan et al., 2010). In fact, this task has been an integral component of our lab’s standard presurgical assessment protocol, which has been used for nearly two decades in individually evaluating language function in over 500 patients with central nervous system lesions.

Benson, R. R., Fitzgerald, D. B., Lesueur, L. L., Kennedy, D. N., Kwong, K. K., Buchbinder, B. R., Davis, T. L., Weisskoff, R. M., Talavage, T. M., Logan, W. J., Cosgrove, G. R., Belliveau, J. W., & Rosen, B. R. (1999). Language dominance determined by whole brain functional MRI in patients with brain lesions. Neurology, 4(52), 798–809.

Fitzgerald, D. B., Cosgrove, G. R., Ronner, S., Jiang, H., Buchbinder, B. R., Belliveau, J. W., Rosen, B. R., & Benson, R. R. (1997). Location of Language in the Cortex: A Comparison between Functional MR Imaging and Electrocortical Stimulation. AJNR Am J Neuroradiol, 18, 1529–1539.

Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M., & Raichle, M. E. (1988). Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature, 331(18), 585–589.

Sanjuán, A., Bustamante, J. C., Forn, C., Ventura-Campos, N., Barrós-Loscertales, A., Martínez, J. C., Villanueva, V., & Ávila, C. (2010). Comparison of two fMRI tasks for the evaluation of the expressive language function. Neuroradiology, 52(5), 407–415. https://doi.org/10.1007/s00234-010-0667-8

Second, it is mentioned that some participants were excluded from different tasks due to technical issues or time constraints. It is important to ensure that all the results can be attributed to the exact same sample of participants across all tasks.

We absolutely agree that excluding participants can be problematic when presenting the results of multiple sets of analyses. Therefore, we repeated all analyses while excluding the 7 participants that lacked resting-state data. All results remained virtually identical, with a few minor exceptions:

1. Region-wise analysis of the stop-signal task: Hemisphere × Group effect in the preSMA region is significant (uncorrected P = 0.019), but it does not survive Bonferroni correction (corrected P = 0.076)

2. Voxel-wise analysis of the stop-signal task: The Thalamus + STN and Caudate clusters are significant at the voxel level, but do not survive the cluster-based FWE correction. They do survive FDR correction, though.

3. Correlation between SPQ score and LI of the stop-signal task: This correlation weakens just behind statistical significance, with a P value of 0.053.

4. Correlation between reading variables and LIs of both tasks: Severe drops in P values are evident between both LIs and reading length accuracy (P = .111 and .133), as well as between verb generation LI and reading familiarity accuracy (P = .111). However, the association between the stop-signal LI and the reading length time is now significant (r = −.229, P = .042).

According to this, we have included this statement in the methods section: (Lines 218-220).“It is important to highlight that the exclusion of these seven participants across all analyses does not notably impact the overall results.“

It is unclear how the authors have estimated the RTs results from the practice trials. This requires more explanation. Also, why was the median used for the Go Reaction Time instead of the mean, when calculating the individual SSRT?

We adapted the procedure used by Xue et al. (2008), implementing their approach to calculate SSRT. This has been elaborated further (Lines 227-230), together with the use of practice trials (Lines 233-236).

Xue, G., Aron, A.R., and Poldrack, R.A. (2008). Common Neural Substrates for Inhibition of Spoken and Manual Responses. Cerebral Cortex 18, 1923–1932. 10.1093/CERCOR/BHM220.

On a final note, information about the different types of pre-processing and data analysis is all reported in the same paragraph. I think using subsections would increase the intelligibility of the section.

Thank you for this suggestion. We have added subsections in both the ‘image processing’ and ‘statistical analyses’ sections.

4. Data analysis and Interpretation of the results. It is unclear how the mean BOLD signal was extracted to conduct ROI analysis (Marsbar?).

Thank you for ponting this out. Indeed, we were not very accurate in the description of this procedure. We extracted the first eigenvariate via the VOI function within SPM12. This has been included in Lines 291-293.

I feel uneasy about the way results are corrected for multiple comparisons. For instance, it is mentioned that in the ROI analysis, all p-values were FDR-corrected for four comparisons, but it is unclear why. The correct procedure for supporting conclusions about the effect of specific brain would be to have 'brain region' (n=4) as another within-subject factor. Furthermore, the one-tailed correlation is appropriate but only when testing for the possibility of a relationship in one direction and completely disregarding the possibility of a relationship in the other direction. However, this does not seem to be the case here (see Introduction), so a two-tailed correlation would be more appropriate.

We agree with Reviewer #2 that presenting this analysis as a single MANOVA that includes a ‘Region’ factor is a more accurate approach. Consequently, we have made the aforementioned correction in the methods section (Lines 357-364) and the results section (Lines 395-406). The LI-LI one-tailed correlation was also changed to a two-tailed correlation in the methods section (Line 383), the results section (Line 417), and Figure 2 (Line 886).

I am quite confused about using the term interhemispheric connectivity to refer to the volume of the genu, body and splenium of the corpus callosum. In fact, the volumes of genu, body and splenium of the corpus callosum do not reflect a measure of how strongly RH and LH IFC are connected to each other.

We agree that using the term ‘interhemispheric connectivity’ when referring to callosal volume may be somewhat misleading. We have replaced every instance of this terminology throughout the paper.

Furthermore, it is unclear why in a set of analyses (ROI and whole brain analyses) the authors focus on brain responses in different ROIs but instead, in connectivity measures the focus is only on IFC.

Our initial rationale was to focus on regions that are prominently involved in language, particularly the IFC, for examining inter-hemispheric connectivity at rest.

However, upon more careful consideration, it is true that the preSMA is also implicated in the language network (Labache et al., 2018), and certain studies have reported an impact of STN stimulation on specific language skills (for a review, see Vos et al., 2021). Consequently, we have incorporated these two regions into the resting-state analysis, along with subsequent correlations with LIs (Table 1 and Lines 118, 321-322 & 449-452).

Labache, L., Joliot, M., Saracco, J., Jobard, G., Hesling, I., Zago, L., Mellet, E., Petit, L., Crivello, F., Mazoyer, B., & Tzourio-Mazoyer, N. (2018). A SENtence Supramodal Areas AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic connectivity in 144 healthy right-handers. Brain Structure and Function 2018 224:2, 224(2), 859–882. https://doi.org/10.1007/S00429-018-1810-2

Vos, S. H., Kessels, R. P. C., Vinke, R. S., Esselink, R. A. J., & Piai, V. (2021). The Effect of Deep Brain Stimulation of the Subthalamic Nucleus on Language Function in Parkinson’s Disease: A Systematic Review. Journal of Speech, Language, and Hearing Research, 64(7), 2794–2810. https://doi.org/10.1044/2021_JSLHR-20-00515

Minor corrections/comments:

It is unclear why in figure caption 1, the conjunction maps are mentioned even if formal conjunction analysis was not conducted.

This poor choosing of words has been replaced to ‘overlapping maps’.

Line 382. VHMC should be VMHC.

Fixed. Thank you.

Line 334. This sentence and especially its relationship with the results is not clear at all. What do you mean by 'This finding is consistent with previous reports showing that cognitive deficits appear only in specific cognitive domains'?

This has been clarified (Lines 521-525).

https://doi.org/10.7554/eLife.86797.3.sa3

Article and author information

Author details

  1. Esteban Villar-Rodríguez

    Neuropsychology and Functional Neuroimaging; Jaume I University, Castellón de la Plana, Spain
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    esvillar@uji.es
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9691-3776
  2. Cristina Cano-Melle

    Neuropsychology and Functional Neuroimaging; Jaume I University, Castellón de la Plana, Spain
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  3. Lidón Marin-Marin

    Neuropsychology and Functional Neuroimaging; Jaume I University, Castellón de la Plana, Spain
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Maria Antònia Parcet

    Neuropsychology and Functional Neuroimaging; Jaume I University, Castellón de la Plana, Spain
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
  5. César Avila

    Neuropsychology and Functional Neuroimaging; Jaume I University, Castellón de la Plana, Spain
    Contribution
    Conceptualization, Supervision, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    avila@psb.uji.es
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5840-605X

Funding

Agencia Estatal de Investigación (PID2019- 108198GB-I00)

  • César Avila

Universitat Jaume I (UJI-B2021-11)

  • César Avila

Ministerio de Educación y Formación Profesional (FPU18/00687)

  • Esteban Villar-Rodríguez

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the Spanish State Research Agency (PID2019-108198GB-I00) and the Universitat Jaume I (UJI-B2021-11). EV-R was supported by a predoctoral graduate program grant from the Spanish Ministry of Education, Culture and Sports (FPU18/00687). We also thank all of our participants for their collaboration in this study, as well as the radiographers at the clinic ASCIRES Castellón for their technical support during data acquisition.

Ethics

All methods were carried out in accordance with guidelines and regulations approved by the Research Ethics Committee of the Universitat Jaume I (reference number: CD/34/2019). Written informed consent was obtained from all participants, following a protocol approved by the Universitat Jaume I, and they received monetary compensation for their participation.

Senior Editor

  1. Jonathan Roiser, University College London, United Kingdom

Reviewing Editor

  1. Ruth de Diego-Balaguer, Universitat de Barcelona, Spain

Version history

  1. Sent for peer review: March 9, 2023
  2. Preprint posted: March 11, 2023 (view preprint)
  3. Preprint posted: July 25, 2023 (view preprint)
  4. Preprint posted: January 8, 2024 (view preprint)
  5. Version of Record published: January 18, 2024 (version 1)

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.86797. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2023, Villar-Rodríguez et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Esteban Villar-Rodríguez
  2. Cristina Cano-Melle
  3. Lidón Marin-Marin
  4. Maria Antònia Parcet
  5. César Avila
(2024)
What happens to the inhibitory control functions of the right inferior frontal cortex when this area is dominant for language?
eLife 12:RP86797.
https://doi.org/10.7554/eLife.86797.3

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https://doi.org/10.7554/eLife.86797

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