Weak language lateralization affects both verbal and spatial skills: An fMRI study in 297 subjects

Neuropsychologia 65 (2014) 56–62

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Weak language lateralization affects both verbal and spatial skills: An fMRI study in 297 subjects E Mellet a,b,c,n, L Zago a,b,c, G Jobard a,b,c, F Crivello a,b,c, L Petit a,b,c, M Joliot a,b,c, B Mazoyer a,b,c, N Tzourio-Mazoyer a,b,c a

Université de Bordeaux, GIN, UMR 5296, F-33000 Bordeaux, France CNRS, GIN, UMR 5296, F-33000 Bordeaux, France c CEA, GIN, UMR 5296, F-33000 Bordeaux, France b

art ic l e i nf o

a b s t r a c t

Article history: Received 17 June 2014 Received in revised form 8 October 2014 Accepted 10 October 2014 Available online 22 October 2014

The present study reappraised the relationship between hemispheric specialization strength and cognitive skills in a sample of 297 individuals including 153 left-handers. It additionally assessed the interaction with manual laterality factors, such as handedness, asymmetry of hand motor skills, and familial sinistrality. A Hemispheric Functional Lateralization Index (HFLI) for language was derived from fMRI. Through mixture Gaussian modeling, three types of language hemispheric lateralization were defined: typical (left hemisphere dominance with clear positive HFLI), ambilateral (no dominant hemisphere with HFLI values close to 0), and strongly-atypical (right-hemisphere dominance with clear negative HFLI values). Three cognitive scores were derived from 12 tests covering various aspects of verbal and spatial cognition. Compared to both typical and strongly-atypical participants, those ambilateral for language production had lower performances in verbal and non-verbal domains, indicating that hemispheric specialization and cognitive skills are related in adults. Furthermore, this relationship was independent from handedness and asymmetry for motor skills, as no interaction was observed between these factors. On the other hand, the relationship between familial sinistrality and cognitive skills tended to differ according to language lateralization type. In contrast to previous reports in children, in the present adult population, we found no linear correlation between HFLI and cognitive skills, regardless of lateralization type. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Hemispheric specialization Cognitive skills Handedness Familial sinistrality

1. Introduction Hemispheric specialization refers to a hemisphere-dependent relationship between a cognitive function and a set of brain structures (Hervé et al., 2013). Although this feature is not uniquely human, it is often considered a result of strong selection pressure and is thus seen as an evolutionary advantage strongly related to the apparition of language, which is emblematic of hemispheric specialization in humans (Bishop, 2013). However, the nature and even the existence of this advantage continue to be debated. The manner used to assess hemispheric specialization crucially affects the outcome and its interpretations. For example, the impacts on cognitive abilities of handedness, manual preference and strength, and asymmetry of manual skills have been considered to reflect variations of brain lateralization for language (Annett, 2002; Leask and

n Corresponding author at: Université de Bordeaux, GIN, UMR 5296, F-33000 Bordeaux, France. Tel.: þ 33 5 47 30 44 01; fax: 33 5 47 30 43 94. E-mail address: [email protected] (E. Mellet).

http://dx.doi.org/10.1016/j.neuropsychologia.2014.10.010 0028-3932/& 2014 Elsevier Ltd. All rights reserved.

Crow, 2006). In this framework, Crow et al. (1998) suggested an association between equal skill with the right and left hand and lower abilities in both the verbal and mathematical domains, supporting an advantage of hemispheric differentiation. However, it is now established that relationships between handedness and hemispheric specialization for language are far from univocal (Knecht et al., 2000; Mazoyer et al., 2014; Szaflarski et al., 2012). Using divided visual field presentation (Hellige, 1990), a series of behavioral studies in healthy humans have addressed the potential advantage of hemispheric lateralization in the language and visuospatial domains (Boles et al., 2008; Chiarello et al., 2009; Hirnstein et al., 2010). In these paradigms, a difference in performance following stimuli presentation in the left hemifield (right hemisphere) or right hemifield (left hemisphere) is interpreted as an index reflecting the hemisphere's superiority for a given process. These studies reported divergent outcomes relating to the language domain. Some emphasized a positive correlation between reading skills and index of lateralization word recognition tasks, while correlations were not significant for the semantic tasks (Chiarello et al., 2009). In contrast, others showed that high degrees of lateralization are detrimental to

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cognitive performance in a word-matching and face-decision task (Hirnstein et al., 2010). While, to some extent, the divided visual field techniques can reportedly reveal the dominant hemisphere for language production (Van der et al., 2011), it remains an indirect estimation of hemispheric lateralization for language. Some studies have directly assessed hemispheric dominance using functional transcranial Doppler sonography (fTCD) or functional magnetic resonance imaging (fMRI). However, these methods have also produced contradictory results regarding the relationship with performance. One study in adults derived a functional asymmetry index using fTCD during an orthographic fluency task, and found no relationship between hemispheric asymmetry and either verbal fluency, number of fluently spoken foreign languages, academic achievements, or practice of artistic activities (Knecht et al., 2001). The authors additionally assessed general IQ in a sub-group of 21 participants, and found no relationship between functional hemispheric asymmetry and IQ. However, such an association has been reported in developmental studies. Studies in children have demonstrated that stronger left lateralization during language production, semantic, or phonological tasks is accompanied by better performance in the language domain (Everts et al., 2009; Groen et al., 2012). Among right-handed children, verbal IQ was found to be positively associated with a laterality index in the right superior temporal gyrus —indicating stronger right-hemispheric than left-hemispheric activation in individuals with higher vs. lower verbal IQ (Lidzba et al., 2011). Based on these findings, one might speculate that functional asymmetry is associated with cognitive skills among children but that this relationship is no longer present in adults. Critically, previous studies have focused on relationships between hemispheric lateralization and performance, without also investigating the interaction of manual laterality factors, such as handedness, manual preference strength, or familial sinistrality. In fact, prior studies have included few or no left handers. We recently showed that manual laterality factors impact performance in verbal and spatial domains through complex interactions (Mellet et al., 2014). It has also been suggested that hemispheric lateralization is related to manual preference strength and familial sinistrality (Tzourio-Mazoyer et al., 2010). Thus, it seems important to investigate these manual laterality factors and whether they interact with hemispheric lateralization or are independently associated with performances. Our brief review of behavioral and neuroimaging studies highlights that the association between cognitive skills and degree of language hemispheric lateralization remains unclear in adults, and that its relationship with manual laterality factors is unknown. Here we aimed to address these issues by using the BIL&GIN (Petit et al., 2012), a database especially designed for the study of hemispheric lateralization, which includes a sample 297 healthy individuals, balanced for sex and handedness. The BIL&GIN participants were repeatedly scanned with fMRI while executing an extensive battery of cognitive tasks, including language production. The goal of this study was to establish whether there was a relationship between hemispheric lateralization for language and various aspects of verbal and spatial domains. We also investigated whether the interaction between manual laterality factors and language hemispheric lateralization impacted cognitive performance. A lack of effect would mean that these two factors were independently associated with cognitive performance.

2. Materials and methods 2.1. Participants Participants were recruited within the framework of the BIL&GIN project, a multimodal imaging/psychometric/genetic database specifically designed for studying the structural and

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functional neural correlates of brain lateralization (Petit et al., 2012). A total of 297 adult healthy participants (145 women) provided informed written consent to participate in the study. The sample included 144 self-reported right-handers (RH) and 153 left-handers (LH). The mean score to the Edimburgh Handedness Inventory was 93.5 (SD, 11.5) for RH and  63.2 (SD, 39.6) for LH. The mean age was 25.2 years (SD, 6.4 years) and the mean education level was 15.6 years (SD, 2.3 years) of schooling after primary school. All participants were free of any neurological history. The study was approved by the BasseNormandie local Ethics Committee.

2.2. Data collection All participants were examined in three sessions. The first session included anatomical MRI acquisition and the first portions of the laterality and cognitive assessments. The cognitive assessments were completed at the second session, which was conducted a few days after the first. All tests were administered individually under the supervision of a trained psychologist. At the third session, fMRI was used to acquire functional imaging data.

2.3. Determination of the participant's language lateralization type using fMRI 2.3.1. Imaging session of language production We evaluated hemispheric dominance for language production using an index of asymmetry derived from t-maps obtained by contrasting a sentence generation task to the production of a list of words. These two conditions are fully described elsewhere (Mazoyer et al., 2014). During 1 s, subjects were presented white-line drawings on a black screen, which were either cartoons depicting a scene or randomly distributed pieces of white lines. Immediately after picture presentation, if the subject saw a cartoon, they were instructed to covertly generate a sentence (SENT) related to the depicted scene and to press the pad when the sentence was completed. This enabled recording of the time that each participant took to generate each sentence. On the other hand, if the participant saw scrambled lines, they were instructed to covertly generate a list (LIST) of the months of the year and to press the pad at the end. Immediately after the session, participants were asked to recall each sentence that they covertly generated during the fMRI session, with the support of the pictures they saw. This procedure enabled evaluation of the average number of words of covertly generated sentences for each participant. 2.3.2. Image acquisition and analysis Imaging was performed on a Philips Achieva 3 Tesla MRI scanner. The structural MRI protocol comprised a localizer scan, a high-resolution 3D T1-weighted volume (sequence parameters: TR¼ 20 ms; TE ¼4.6 ms; flip angle ¼101; inversion time ¼800 ms; turbo field echo factor¼ 65; sense factor¼ 2; field of view¼256  256  180 mm3; isotropic voxel size ¼1 mm3), and Tn2-weighted multi-slice image acquirement (Tn2-weighted fast field echo (Tn2-FFE), sequence parameters: TR ¼3500 ms; TE ¼ 35 ms; flip angle ¼901; sense factor¼ 2; 70 axial slices; isotropic voxel size¼2 mm3). Functional images were acquired with wholebrain Tn2-weighted echo planar images acquisition (Tn2-EPI, sequence parameters: 192 volumes; TR¼ 2 s; TE ¼ 35 ms; flip angle ¼801; 31 axial slices; isotropic voxel size ¼3.75 mm3) covering the same field of view as the Tn2-FFE acquisition. Image analysis was performed using SPM5 software. The T1weighted scans were normalized to a site-specific template

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(T-80TVS) matching the MNI space, using the SPM5 “segment” procedure with otherwise default parameters, which enabled identification of gray matter, white matter, and cerebrospinal fluid for each subject. The brain volume was calculated as the sum of these three components. To correct the subjects' motion during fMRI runs, 192 EPI-BOLD scans were realigned using a rigid-body registration. The EPI-BOLD scans were then registered rigidly to the structural T2-weighted image, which was itself registered to the T1-weighted scan. This combination of registration matrices allowed warping of the EPIBOLD functional scans to the standard space with a trilinear interpolation. Once in the standard space, a 6-mm FWHM Gaussian filter was applied. Finally, for each participant, we computed the BOLD signal difference map and the associated t-map corresponding to the map “SENT minus LIST” contrast. 2.3.3. Determination of the Hemispheric Functional Lateralization Index (HFLI) For each participant, we computed a Hemispheric Functional Lateralization Index (HFLI) for language production, using the LItoolbox applied to the “SENT minus LIST” individual t-map (Wilke and Lidzba, 2007). HFLIs were computed with a bootstrap algorithm from the individual “SENT minus LIST” t-maps, thresholded at t ¼0 (positive t-map) with a lower bootstrap sample of 5 voxels and higher sample size of 1000 voxels, with a resample ratio of k ¼0.25. The weighted mean HFLI values were reported. HFLIs were calculated within the anatomical template mask used for fMRI data normalization, excluding the cerebellum. The resulting values ranged between  100 and þ 100, with  100 being purely right lateralization and þ 100 purely left lateralization. Hemispheric rather than regional HFLI (i.e. frontal) was used because of its better sensitivity to assess hemispheric lateralizetion for language (Dym et al., 2011). 2.3.4. Definition of language lateralization types using Gaussian mixture modeling of HFLI distribution for language production This procedure has been fully described elsewhere (Mazoyer et al., 2014). Briefly, models of n Gaussian function mixture (n ranging from 1 to 5) were fit to the HFLI data distribution, and the optimal model was selected based on the corrected Akaike information criterion (AICc) (Akaike, 1974; Hurvich and Tsai, 1989). The optimal model resulted in four Gaussian functions. There was considerable overlap between the two Gaussian components with the highest means and, thus, we pooled the latter two components, yielding a total of three types of lateralization (Fig. 1). Individuals with a HFLI value superior to 18 were classified as “typical” (N ¼250, 88% of RH, 78% of LH), those with an HFLI value between  50 and 18 were considered “ambilateral” (N ¼37, 12% of RH, 15% of LH), and those with HFLI values below  50 were classified as “strongly-atypical” (N ¼10, no RH, 7% of LH). Table 1 presents the characteristics of the three so-defined groups. The three groups did not differ in age (F(2,294) ¼1.88, p ¼0.16) or educational level (F(2,294) ¼1.04, p¼ 0.35). We also observed no difference between the three groups regarding the number of words per sentence generated during the production task (typical: 12.4 72.0, ambilateral: 12.0 71.9, strongly-atypical: 13.4 73.0; p ¼0.14), or the time spent to generate a sentence (typical: 5608 7916 ms, ambilateral: 5576 71015 ms, strongly-atypical: 56767 1238 ms; p¼ 0.95).

Fig. 1. Distribution of SENT HFLI and corresponding classification. Three groups of individuals were distinguished based on the distribution of Hemispheric Lateralization Indices (HFLI) during sentence production minus list of words production (SENT minus LIST): 250 strong leftward asymmetrical individuals (typical), 37 with moderate leftward or rightward asymmetry (ambilateral) and 10 individuals with strong rightward asymmetry (strongly-atypical).

Table 1 Characteristics of the three groups derived from Gaussian mixture modeling.

Sex (F/M) Handedness (LH/RH) Edinburgh score (SD) Motor asymmetry index FS (FSþ /FS-/no info) HFLI

Typical

Ambilateral

Strongly-atypical

123/127 120/130 20.1 (82.1) 2.5 7 5.9 90/158/2 60.3 7 13.1

16/21 23/14  9.6 (86.0)  0.3375.4 17/20  6.6 7 17.5

6/4 10/0  87.4 (17.9)  5.4 75.0 5/5  63.7 75.4

and 153 left-handed. The groups were balanced for sex (χ2(1) ¼ 0.16, p¼ 0.69, Chi-square test). This variable is referred to as “handedness” in the rest of the article. 2.4.2. Hand motor performance asymmetry index We used the finger tapping test to assess the lateralization of hand motor performance. Each subject was instructed to hit the button of a small counter with their left or right index finger as many times as possible during 10 s, keeping their wrist on the table. Each measurement was repeated thrice for each side, and the results were averaged for each finger. An asymmetry index was computed as follows: (RFT  LFT/(RFTþ LFT)n100, where RFT and LFT represented the average scores for the right- and left-hand finger tapping, respectively. Group mean index values were 6.3 (SD, 4.3) in RH and  2.3 (SD, 4.1) in LH. The relationship between hand motor performance asymmetry index and self-reported handedness was strongly significant (t(295) ¼17.6; p o0.001, Student's t test). 2.4.3. Familial sinistrality Positive familial sinistrality (FS) was defined as the presence of at least one LH individual among a subject's parents or siblings. Our sample included 112 subjects with positive FS (FS þ) and 183 with negative FS (FS  ). Two subjects were unable to report on their parents' handedness.

2.4. Manual laterality variables

2.5. Assessment of cognitive skills: tests and factorial analysis

2.4.1. Self-reported handedness Participants answered the question “Are you a left-hander or a right-hander?”, with 144 declaring themselves to be right-handed

2.5.1. Tests Participants' verbal abilities were evaluated with the following battery of seven tests (Table 1): 1) a recall test of an 18-word list

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(Rey, 1958) for verbal memory evaluation; 2) a recall test of a list of 18 pseudo-words for verbal memory evaluation with minimal semantic associations; 3) a verb generation test for semantic verbal fluency exploration; 4) a synonym finding test for estimating vocabulary extent (Binois and Pichot, 1956); 5) a listening span test based on spoken sentences; 6) a reading span test based on read sentences for verbal working memory assessment (Daneman and Carpenter, 1980; Desmette et al., 1995); and 7) a rhyming test on visually presented pseudo-words for evaluation of graphophonemic conversion ability. Visuospatial abilities were assessed with the following five tests: 1) The mental rotation test, which estimates the ability to rotate and spatially manipulate mental images (Vandenberg and Kuse, 1978); 2) the Corsi Block Test, which evaluates visuospatial short-term memory abilities (Della Sala et al., 1999); 3) a homemade 3D maze test for evaluating topographic orientation skills; 4) a canceling task for evaluating visuospatial attention and visual exploration abilities (Weintraub and Mesulam, 1985); and 5) the raven matrix for assessing non-verbal reasoning (Raven, 1956). Table 2 presents the scores on the 12 tests for each group. 2.5.2. Factorial analysis Principal component analysis (PCA; Promax rotation) was performed to reduce the data matrix of standardized scores from the 12 verbal and visuospatial tests. We used the Kaiser criterion to determine the number of factors to include (i.e. eigenvalue 41). This resulted in a set of three components that explained 52% of the total variance. The first was a spatial component (SPA) that aggregated the mental rotations test, the maze test, the Corsi Block Test, and the Raven matrix (loading factors: 0.75, 0.71, 0.42, and 0.61, respectively). Although spatial by nature, the canceling task was only marginally represented in this component (loading factor: 0.16). Secondly, there was a language component (LANG) that included the vocabulary assessment, the verbal fluency task, the reading and listening span tests, and the rhyme judgment task (loading factors: 0.58, 0.62, 0.47, 0.48, and 0.39, respectively). Thirdly, there was a memory component (MEM), including the recall tests of list of words and pseudo-words (loading factor: 0.50 and 1.0, respectively). The reading and the listening span tests had small loading factors on the MEM component (0.11 and 0.08, respectively). 2.6. Relationships between SPA, LANG, MEM, and language lateralization type and laterality factors

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lateralization type by FS, and language lateralization type by motor asymmetry index. Age, education level, sex, and brain volume were included as confounding factors. Subjects with unknown FS status were excluded from this analysis. Statistical analyses were performed with JMP software (SAS, Cary, USA, version 11.0). Other investigators have previously observed significant correlations between HFLI and cognitive performance among children (Everts et al., 2009; Lidzba et al., 2011). Thus, we investigated the correlation between three components and HFLI values through a MANCOVA performed separately for each group of language lateralization type. The interaction of HFLI with handedness was entered as a factor in the typical and ambilateral subgroups. Each MANCOVA included the same confounding factors as in the first statistical analysis.

3. Results The main effects of laterality factors on cognitive scores in an extended sample have been described elsewhere (Mellet et al., 2014). Accordingly, the present report will focus on the effects (main and interactions) of language lateralization type on cognitive scores. 3.1. Relationships between SPA, LANG, MEM, and language lateralization type and laterality factors We found a main effect of language lateralization type (F (2,282) ¼3.96; p ¼0.020) that did not differ between the three PCA components (F(2,282) ¼1.14; p¼ 0.32, Fig. 2). Fig. S1 (see Supplementary material) displays the normalized scores at the tests level. Although language lateralization type had only a small effect (partial η2 ¼0.03), it was consistent for all scores. Post-hoc analysis revealed that ambilateral individuals performed worse than either typical (F(1,282) ¼6.18; p ¼0.013) or strongly-atypical subjects (F(1,282) ¼ 3.88; p ¼0.05). Scores did not differ between typical and strongly-atypical subjects (F(1,282) ¼ 1.53; p ¼0.22). We found no significant interaction of motor asymmetry index by language lateralization type (F(1,282) ¼ 0.49; p¼ 0.61), with no difference in this interaction between the PCA components (F(2,282) ¼1.34; p ¼0.26). Our results showed an interaction between FS and language lateralization type (F (2,282) ¼2.97, p ¼0.05). Post-hoc analysis evealed that the effect of FS was similar between typical and ambilateral groups (F

To evaluate the associations between cognitive skills, lateralization type, and laterality factors, the three PCA components were entered in a MANCOVA. The following factors and interactions were considered: language lateralization type, motor asymmetry index, FS, language Table 2 Scores of typical, ambilateral and strongly-atypical participants (Mean7 SD). Typical

Ambilateral

Strongly-atypical

Reading span (max: 6) 4.17 1.1 3.6 7 1.1 4.17 1.0 Listening span 4.8 7 1.1 4.4 7 1.2 4.25 7 1.3 Vocabulary (max: 44) 28.17 3.8 27.6 7 3.8 29.5 7 3.1 Rhyme judgment (max: 80) 67.8 7 5.5 66.77 5.4 67.6 7 4.3 Verbal fluency 47.6 7 9.8 46.0 7 9.5 47.7 7 8.1 List of word (max: 90) 65.6 7 7.2 64.47 9.2 63.17 9.2 List of pseudo-word (max: 90) 35.6 17 10.6 31.9 7 11.2 37.5 7 9.9 Mental rotations (max: 20) 10.9 7 4.5 10.17 4.2 11.4 7 6.3 Corsi block 5.9 7 1.0 5.6 7 1.1 6.0 7 1.2 Topographic orientation 6.3 7 2.5 5.5 7 2.5 5.0 7 3.1 Raven matrix 111.17 10.3 106.97 10.2 106.47 8.3 Canceling task (max: 60) 47.17 8.0 43.9 7 7.7 50.9 7 6.2

Fig. 2. Scores on the 3 components in the typical, ambilateral and strongly-atypical groups. Ambilaterals exhibited lower scores than both typicals and strong-atypicals (p ¼ 0.013 and p ¼0.05 respectively). Scores did not differ between typical and strongly-atypical subjects (p ¼ 0.22). Error bars represent the standard error of the mean. SPA: spatial component, LANG: language component, MEM: memory component.

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(1,282) ¼0.54; p ¼0.46), with FS  performing better than FSþ in both groups. On the other hand, the effect of FS was found to be in the opposite direction among strongly-atypical participants compared to both the typical (F(1,282) ¼5.13; p¼ 0.024) and ambilateral groups (F(1,282) ¼5.81; p ¼0.017) (Fig. 3). The effects of handedness could not be investigated in this analysis because all strongly-atypical participants were lefthanders. Thus, to assess these effects, we conducted an additional MANCOVA after pooling strongly-atypical and ambilateral individuals into a single group. The three components were entered as dependent variables, and lateralization type, handedness and language lateralization type by handedness were entered as independent variables. This analysis did not reveal any interaction between language lateralization type (two modalities) and handedness (F(1,291) ¼ 0.19; p ¼0.67). 3.2. Relationships between SPA, LANG, and MEM scores, and strength of cerebral lateralization for language (HFLI) in each language lateralization type group We found no significant associations between HFLI and components scores in the typical (F(1,242)¼0.80; p ¼0.37), ambilateral (F(1,29) ¼1.28; p ¼0.27), or strongly-atypical group (F(1,4) ¼1.70; p ¼0.26). Fig. 4 presents the post-hoc correlations. We found no significant difference between the three PCA components in any of the language lateralization groups [typical: F(2,241) ¼0.80, p ¼0.45; ambilateral: F(2,28) ¼0.66, p ¼0.52; and strongly-atypical: F(2,3) ¼0.45, p ¼0.57. Similarly, there was no interaction between HFLI and handedness on the PCA components in either typical (F(1,242)¼0.14; p¼ 0.71) or ambilateral individuals (F(1,29)¼ 0.63; p ¼0.43). This absence of interaction was similar among the three components (F(2,241) ¼ 2.34, p ¼0.10 in typicals; F(2,28) ¼0.25, p ¼0.78 in ambilaterals).

4. Discussion The present work aimed to assess the relationships between hemispheric lateralization for language and performance in various aspects of verbal and spatial cognition. The originality of this study lies in several features. First, the sample included 297 healthy subjects balanced for handedness, thus comprising a much higher proportion and number of lefthanders than previous studies. This enabled us to account for the

Fig. 3. Interaction between language lateralization type and familial sinistrality. The effect of familial sinistrality (FS) was found to be in the opposite direction among strongly-atypical participants compared to both typical and ambilateral groups. The mean component score of the y-axis corresponds to the average of the values of SPA, LANG and MEM components. Errors bars represent the standard error of the mean.

Fig. 4. Correlation between HFLI and mean component score. No correlation was found in any group. Typicals: r ¼0.05, p ¼ 0.37, ambilaterals: r¼ 0.22, p¼ 0.18, strongly-atypicals: r ¼0.11, p ¼ 0.76. The mean component score of the y-axis corresponds to the average of the values of SPA, LANG and MEM components.

higher variability of language lateralization type among lefthanders. Secondly, the brain lateralization index for language was assessed using fMRI, an approach that is as accurate as the Wada test (Binder, 2011; Dym et al., 2011). The reliability of the lateralization index was further improved by contrasting a sentence production task with a high-level reference condition, such as the automatic production of a list of words, (Binder, 2011). Moreover, the classification of participants into three types of lateralization resulted from unsupervised Gaussian mixture modeling, while other studies have based such classification on an arbitrary threshold applied to the index distribution (Everts et al., 2009; Groen et al., 2012; Knecht et al., 2001; Lidzba et al., 2011). Thirdly, the present work included a large set of tests aimed at assessing cognitive abilities in both verbal and spatial domains. The principal components derived from these tests thus involved most aspects of these cognitive domains. Finally, in addition to manual preference, the present analysis included other laterality factors known to affect cognitive performance, such as asymmetry

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of motor performance and familial sinistrality (Mellet et al., 2014). This made it possible to unravel potential effects on cognitive performances of the interactions between these factors and the type of hemispheric lateralization for language. Our present results showed that subjects weakly lateralized for language production performed worse in the verbal, verbal memory, and spatial components. Strikingly, the effect on performance of weak or absent lateralization for language production was not confined to the verbal domain but also equally affected spatial cognition. Lateralization for language production can reportedly be correlated with skills in other verbal domains (Chiarello et al., 2012, 2009). Our results extend this knowledge, by suggesting that the index of lateralization for language production reflects variations in the global organization of the brain rather than solely in the organization of language. Researchers using hand skill asymmetry measurement as an indirect marker of brain lateralization have previously proposed the existence of a so-called hemispheric indecision point, around which participants had lower academic abilities (Crow et al., 1998; Peters et al., 2006), although this concept has been challenged by others (Mayringer and Wimmer, 2002). In this regard, our results provide the first evidence that near-zero values of brain activation asymmetry for language production are indeed associated with lower performance in various cognitive domains. This finding goes against the hypothesis that strongly lateralized individuals should exhibit lower performances than less lateralized ones (Annett and Manning, 1989). It must be noted that the present outcome does not agree with that of a previous fTCD study reporting no association between scores and strength of hemispheric lateralization (Knecht et al., 2001). This discrepancy may be explained by two aspects of the previous work. First, in the study by Knecht et al. lateralization types were derived from the standard error of the lateralization index, leading to a larger proportion of participants categorized as strongly-atypical (10% vs. 3% in the present study). This different definition of language lateralization type could have blurred the group differences. Second, most abilities assessed by Knecht et al. are hardly comparable with the cognitive evaluation implemented in the present work. The authors particularly focused on academic achievements and the number of fluently spoken foreign languages—characteristics that are more closely related to education level than to well-defined cognitive skills. In this regard, it is notable that our present results showed similar education levels among the typical, ambilateral, and strongly-atypical participants. Knecht et al. additionally measured IQ and linguistic processing speed in a sub-group of 21 subjects (7 subjects of each group), and found that these scores were not correlated with the strength of lateralization. However, these negative results could be due to a lack of sensitivity. A more recent study in 36 right-handed children did not reveal any association between verbal IQ and hemispheric lateralization during a covert word generation task (Lidzba et al., 2011). However, the asymmetry was assessed in only few and relatively small regions of interest located in the inferior frontal, precentral, and middle temporal gyri. In contrast, the present work considered the whole hemispheric asymmetry. Interestingly, a study that used larger regions of interest did report a positive correlation (Everts et al., 2009). A recent study found that hemispheric lateralization of verbal fluency and of visuospatial processing were not related to performances in verbal comprehension and perceptual organization (Powell et al., 2012). However, the authors reported an effect of the interaction between the two hemispheric lateralizations, with performances in both verbal and perceptual domains being lower when the verbal and visuospatial processes were associated in the same hemisphere. It is possible that this hemispheric organization concern preferentially ambilateral participants, what could partly

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explain the presently observed relationship between hemispheric lateralization for verbal production and performances in the spatial domain. Although the small number of strong-atypical subjects calls for caution, our present results suggest that the relationship between lateralization for language production and cognitive performances is not directional. The strongly lateralized individuals performed better than weakly lateralized ones, regardless of the side of lateralization. This is in line with the conclusion of a metaanalysis of divided fields studies including various language tasks (Boles et al., 2008). Notably, the ambilateral participants had no history of specific language impairment. This underlines that although decreased leftward lateralization has been observed in dyslexic participants, ambilaterality could also be a normal variant of language lateralization (Bishop, 2013; Illingworth and Bishop, 2009). We found no association between HFLI and scores, regardless of lateralization type. This is in contrast to the linear relationship between hemispheric lateralization index and verbal and nonverbal performances that has been previously reported in developing children (Everts et al., 2009), This discrepancy may suggest that once brain maturation is completed and lateralization has reached a plateau (Szaflarski et al., 2006), there is no longer a correlation between performance and the degree of lateralization, although ambilateral subjects can still be distinguished from typical and strongly-atypical subjects. These two observations are not contradictory. It is indeed possible that these differences between groups reflect variability in the establishment of hemispheric lateralization during brain development. Later, the relationship between hemispheric lateralization and cognitive skills could have been altered by external factors (e.g. education) that have a more important effect on performance than behavioral manual laterality factors (Mellet et al., 2014). Our present results showed no interaction between hemispheric lateralization for language and either handedness or asymmetry in manual ability. Handedness-related literature typically confounds the effects of behavioral laterality – such as handedness and asymmetry of manual ability – with variations of hemispheric lateralization (Annett, 1992; Annett and Manning, 1989; Crow et al., 1998; Leask and Crow, 2001). However, our findings indicate that the association between language lateralization and cognitive skills is unrelated to behavioral manual laterality. This is consistent with the reportedly weak association between handedness and lateralization for language (Mazoyer et al., 2014). Here we found that familial sinistrality was the unique laterality factor that showed a significant interaction with the type of language lateralization, resulting in different effects in stronglyatypical compared to either typical or ambilateral participants. In the former group, familial sinistrality was not associated with weaker performances as previously reported (D’Andrea and Spiers, 2005; Mellet et al., 2014; Searleman et al., 1984). Obviously, this result must be interpreted cautiously given the small number of strongly-atypical participants. However, it emphasizes the fact that, although handedness is weakly heritable, familial sinistrality not only affect performances but also shapes some anatomical and functional features in the brain (Tzourio-Mazoyer et al., 2010, 2011). In summary, the present results suggest that the strength but not the direction of hemispheric specialization for language relates to cognitive skills in both verbal and spatial domains. The rationale behind this association remains speculative as there is no hint of a causal relationship. Better characterization of this relationship will require a study including more strongly-atypical subjects, and thus with a considerable number of participants, as they constitute less than 1% of the general population. Several developmental studies have

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