The authors have declared that no competing interests exist.
Conceived and designed the experiments: BM LZ GJ FC MJ EM LP NTM. Performed the experiments: BM LZ GJ FC MJ GP EM LP NTM. Analyzed the data: BM NTM. Wrote the paper: BM LZ GJ FC MJ EM LP NTM.
Hemispheric lateralization for language production and its relationships with manual preference and manual preference strength were studied in a sample of 297 subjects, including 153 left-handers (LH). A hemispheric functional lateralization index (HFLI) for language was derived from fMRI acquired during a covert sentence generation task as compared with a covert word list recitation. The multimodal HFLI distribution was optimally modeled using a mixture of 3 and 4 Gaussian functions in right-handers (RH) and LH, respectively. Gaussian function parameters helped to define 3 types of language hemispheric lateralization, namely “Typical” (left hemisphere dominance with clear positive HFLI values, 88% of RH, 78% of LH), “Ambilateral” (no dominant hemisphere with HFLI values close to 0, 12% of RH, 15% of LH) and “Strongly-atypical” (right-hemisphere dominance with clear negative HFLI values, 7% of LH). Concordance between dominant hemispheres for hand and for language did not exceed chance level, and most of the association between handedness and language lateralization was explained by the fact that all Strongly-atypical individuals were left-handed. Similarly, most of the relationship between language lateralization and manual preference strength was explained by the fact that Strongly-atypical individuals exhibited a strong preference for their left hand. These results indicate that concordance of hemispheric dominance for hand and for language occurs barely above the chance level, except in a group of rare individuals (less than 1% in the general population) who exhibit strong right hemisphere dominance for both language and their preferred hand. They call for a revisit of models hypothesizing common determinants for handedness and for language dominance.
Two prominent behavioral characteristics of humans, as compared to non-human primates, are the preponderance of right-handedness and the capacity to acquire language. Pioneer studies of language hemispheric lateralization using Wada test
Author [reference] | Task | Reference | Method | Lateralization index | MP (ES score) | RH | LH | RH | LH | |
ROI/threshold | RH | LH | (N) | (N) | %typical | %typical | ||||
Knecht |
covert verbal fluency | none | fCTD | Hemisphere/0 | >25 | <25 | 195 | 131 | 94 | 77 |
Rosch |
covert verbal fluency | none | fCTD | Hemisphere/0 | ? | 20 | 75 | |||
Stroobant |
covert verbal fluency | none | fCTD | Hemisphere/0 | >70 | 30 | 90 | |||
Whitehouse |
covert verbal fluency | none | fCTD | Hemisphere/3 cat | >40 | <40 | 45 | 30 | 80 | 67 |
Badzakova |
covert verbal fluency | cross fixation | fMRI | Fr ROI/0 | >0 | <0 | 107 | 48 | 95 | 81 |
Berl |
semantic decision | reverse speech+tone | fMRI | Fr+Te ROI/9 cat | >40 | 118 | 98 | |||
Bethmann |
semantic decision | letter string matching | fMRI | Fr+Te ROI/20, 3 cat | >33 | <−47 | 26 | 92 | ||
Hung-Georgiadis |
semantic encoding | word spacing | fMRI | Fr ROI/10 | SR | SR | 17 | 17 | 94 | 53 |
Pujol |
covert verbal fluency | none | fMRI | Fr ROI/5 cat | SR | SR | 50 | 50 | 96 | 76 |
Springer |
semantic decision | tone decision | fMRI | Hemisphere/20 | >50 | 50 | 94 | |||
Szaflarski |
covert verbal fluency | finger tapping | fMRI | Combined ROI/20 | <52 | 50 | 78 | |||
ROI: region of interest used for lateralization index computation (Fr: Frontal, Te: Temporal). threshold: lateralization index value used to segregate typical from atypical language lateralization. MP: Manual Preference. ES score: Edinburgh inventory Score of right- (RH) and left-handers (LH). SR: Self-reported manual preference. N: sample size. %typical: fraction of individuals exhibiting typical left-hemisphere dominance for language. Summary statistics (means weighted by sample sizes, in bold italic) are provided for the set of reports using each method (fTCD or fMRI).
In order to better describe the relationship between MP and language lateralization, several studies have considered MP as a continuous rather than a binary variable, but such an approach has given inconsistent findings. Assessing MP strength (MPS) with the Edinburgh inventory for example, some authors reported a linear relationship between MPS and either occurrence of atypical subjects
The goal of the present study was thus to establish the distribution of language lateralization in a large sample of LH in order to 1- investigate whether two groups of atypical subjects could be identified, 2- compare this distribution to that of RH, and 3- examine its relationship with the MP strength distribution.
We recruited 153 LH and 144 RH healthy volunteers, measured their manual preference strength (MPS), and evaluated their hemispheric lateralization for language with fMRI during covert production of sentences and word lists. Note that the sample of participants of this study is not representative of the general population, as it was deliberately enriched in left-handers aiming at a 50/50 ratio.
Participants were recruited within the framework of the BIL&GIN project, a multimodal imaging/psychometric/genetic database specifically designed for studying the structural and functional neural correlates of brain lateralization
Participants were asked to report whether they considered themselves as right- or left-handed: 144 declared themselves RH (including 72 women), and 153 LH (including 73 women). Among the latter, 4 women declared themselves as converted RH. Note that all individuals who declared themselves as RH used their right hand for writing. Note also that during the fMRI tasks, LH subjects were free to choose the hand they preferred for using the response pad, and that 135 used their right hand and 18 their left-hand (including 5 women). Self-reported LH were 2.6 years younger than RH (RH: 26.5±6.2 years, LH: 23.9±8.3 years, p<0.0005, Student-t test), and had 1 year of education less than RH (RH 16.1±2.2 years, LH 15.1±2.3 years, p = 0.0002, Student-t test).
MPS was quantified using the score at the Edinburgh inventory
Self-reported left- (resp. right-) handers correspond light (resp. dark) grey bars.
In order to compare our results with previous studies, MPS was transformed as an ordinal variable having either 3 or 7 levels, named MPS3 and MPS7, respectively. For MPS3, we used thresholds as close as possible to the 1st and 2nd terciles of MPS distribution. For MPS7, we used the same MPS category thresholds as previously defined by others (Knecht et al., 2000b). Values of thresholds and occurrences for each category and each variable are shown in
MPS3 | [−100, −55] | [−54, 99] | 100 | ||||
N | 99 | 100 | 98 | ||||
% | 33.3 | 33.7 | 33.0 | ||||
N | 52 | 31 | 42 | 24 | 12 | 38 | 98 |
% | 17.5 | 9.1 | 15.5 | 7.7 | 4.4 | 12.8 | 33.0 |
We evaluated hemispheric dominance for language production using an index of asymmetry derived from functional MR maps contrasting covert production of sentences (SENT) with covert recitation of a list of overlearned words, namely the months of the year from January to December (LIST).
Subjects were presented white line drawing pictures on a black screen which were either cartoons depicting a scene involving characters, or a scrambled version of these pictures (
Subjects were presented during 1(left part) or a cartoon depicting a scene (right part). Right after presentation of a picture, the subject had to covertly generate either the list of the months of the year (right part) or a sentence describing the cartoon (left part). During this generation period, participants had to fixate a white-cross displayed at the center of the screen and to press the pad with their index finger when they had finished. Note that a reference task followed each event, consisting in sustaining visual fixation on the cross and pressing the pad when the fixation cross was switched to a square.
For SENT, subjects were instructed to generate sentences each having the same structure, starting with a subject (The little boy, The gentleman…) and a complement (with his satchel… in shorts… with glasses…), followed by a verb describing the action taking place, ending with another complement of place (in the street… in the playground… on the beach…) or of manner (with happiness… nastily…). During this generation period, participants had to fixate a white-cross displayed at the center of the screen and to press the pad with their index finger when they had finished enouncing the sentence covertly. For LIST, participants had to covertly recite the ordered list of months of the year and to press the pad when they had finished.
Note that a low-level reference task followed each event (SENT or LIST), consisting in sustaining visual fixation on the central cross and pressing the pad when the fixation cross was switched to a square (both stimulus covering a 0.8°×0.8° visual area). This second part of the trial, that lasted at least half the total trial duration, aimed at refocusing the participant attention on a non-verbal stimulus and to control for the manual motor response.
Each trial was 18 sec long, the time limit for response being 9 sec including the 1-sec picture display. A 12-sec presentation of a fixation crosshair preceded and followed the first and last trial of each run. This slow-event related experimental paradigm randomly alternated 10 trials of sentence generation with 10 trials of recitation of a list of months. Overall, the fMRI run lasted 6 min 24 sec, response time in reciting each list of words or generating each sentence being recorded using a fiber optic pad.
In order to ensure proper execution of both tasks, participants were trained outside the scanner, in the hour preceding the fMRI session. Training included both overt and covert generation of sentences and word lists, using sets of drawings that were different from those used during the fMRI session.
Right after the session, participants were asked to rate the difficulty of the task on a 5-level scale, and to recall each sentence they covertly generated during the fMRI session with the support of the pictures they saw. This makes it possible to evaluate the average number of words of covertly generated sentences for each participant. Note that the average time for sentence generation in the magnet was positively correlated with the average sentence number of words measured during debriefing (r = 0.18, p<0.0001).
Imaging was performed on a Philips Achieva 3Tesla MRI scanner. The structural MRI protocol consisted of a localizer scan, a high resolution 3D T1-weighted volume acquisition (TR = 20 ms; TE = 4.6 ms; flip angle = 10°; inversion time = 800 ms; turbo field echo factor = 65; sense factor = 2; matrix size = 256×256×180; 1 mm3 isotropic voxel size) and a T2*-weighted multi-slice acquisition (T2*-FFE sequence, TR = 3,500 ms; TE = 35 ms; flip angle = 90°; sense factor = 2; 70 axial slices; 2 mm3 isotropic voxel size). Functional volumes were acquired with a T2*-weighted echo planar imaging acquisition (192 volumes; TR = 2 s; TE = 35 ms; flip angle = 80°; 31 axial slices; 3.75 mm3 isotropic voxel size) covering the same field of view than the T2*-FFE acquisition.
Image analysis was performed using the SPM5 software (
In order to correct for motion during the fMRI run, each of the 192 EPI-BOLD scans was realigned to the first one using a rigid-body registration. The participant EPI-BOLD scans were then rigidly registered to his structural T2*-weighted image, which was itself registered to his T1-weighted scan. The combination of all registration matrices allowed each EPI-BOLD functional scan to be warped into the standard MNI space using a tri-linear interpolation, with subsequent smoothing using a 6-mm FWHM Gaussian filtering.
We then computed for each participant the BOLD signal difference map and associated t-map corresponding to the “SENT minus LIST” contrast.
In the present study, we have used a language production task (SENT) and a reference task (LIST) somewhat different from those used by previous investigators in the field (see
For each individual, we computed a Hemispheric Functional Lateralization Index (HFLI) for language production (HFLI) using the LI-toolbox applied to the “SENT minus LIST” individual t-map
All statistical procedures were conducted using the JMP11 Pro software package, (
HFLI probability density function was modeled separately for LH and RH. Because of its multimodal aspect for either handedness group, a phenomenon previously noticed by others
For both LH and RH, the optimal model, and corresponding optimal number of Gaussian functions (
In order to compare our results, a Gaussian mixture model was also fit to the HFLI distribution observed over the entire sample of subjects.
Also, in order to compare with previous studies, we used the classical 2-category language lateralization classification obtained using a zero threshold on HFLI distribution, subjects having a positive (resp. negative) HFLI value being declared typical (resp. atypical).
Performances in the two tasks completed during the fMRI acquisitions were compared between groups of different handedness and language hemispheric lateralization types using an ANOVA of the response time for sentence and list generation, as well as of the mean number of words generated in sentences. Age, educational level, and sex were included as confounding factors. In order to ensure that the report made by the subject was consistent, we computed the correlation between the mean number of words per sentence and the mean time taken for their generation.
As emphasized in the Introduction section, various statistical approaches have been used for assessing the relationship between lateralization for language and manual preference, depending on whether these variables were considered as continuous, ordinal, or nominal. In the present study, we have implemented these different statistical analyses in order to be able to compare our findings with those of previous investigators and to demonstrate their robustness.
An ANOVA examined the effect of handedness on HFLI value, including sex, age, level of education and skull perimeter as confounding variables. This analysis, similar to that performed by previous investigators
Using the categorical transformation of HFLI, we also examined an association between language lateralization type and handedness (Fisher exact test). This analysis, also implemented by others
Finally, we implemented an original approach based on the kappa statistic
In order to assess the impact of Strongly-atypical subjects on the results, all analyses were repeated after excluding this subgroup from the sample.
Here again, different statistical analyses were implemented with the same motivations as in the previous subsection 4.3.
First, we studied the correlation between MPS and HFLI values as was done by others
Considering the MPS3 categorical version of MPS, an ANOVA examined the effect of MPS3 on HFLI, including sex, age, level of education and skull perimeter as confounding variables. Similar analyses have been reported by others
Conversely, an ANOVA examined the effect of language lateralization types (defined using the 3-category version of HFLI) on MPS values, including the same confounding variables as in b).
Then, considering categorical versions of both HFLI and MPS, we examined an association between language lateralization and MPS3, an approach similar to that of Isaacs et al.
In addition, we also used the original approach of the kappa statistic for measuring the degree of agreement between the 3-level ordinal variables language lateralization and MPS3.
Finally, in order to compare our results, we also conducted the same analysis than that of Knecht et al
As before, all analyses, except c) and e), were performed either with or without including Strongly-atypicals.
The activation probability map (
3D renderings of the probabilistic map of the individual SENT minus LIST contrast t-map after applying a t-threshold set at 1.96 (p<0.05, uncorrected) superimposed on the Caret anatomical template. L: left, R: right. The scale starts with 50% of overlap and the red areas correspond to a proportion larger than 80% of right-handers showing a significant activation.
Distributions of HFLI values in LH and RH and corresponding mixture of Gaussian fits are shown in
Solid lines are fits of these distributions by models of mixture of n Gaussian distributions (n = 3 for RH, n = 4 for LH and whole sample).
Optimal fits of the HFLI distributions were obtained with mixtures of 3 and 4 Gaussian functions for the RH and LH groups, respectively (
n | G1 | G2 | G3 | G4 | G5 | AICc | Likelihood |
µ1,σ1 | µ2,σ2 | µ3,σ3 | µ4,σ4 | µ5,σ5 | |||
π1 | π2 | π3 | π4 | π5 | |||
53.5, 22.7 | 1311.0 | 10−24 | |||||
100% | |||||||
60.0, 12.0 | 2.5, 17.1 | 1247.4 | 0.22 | ||||
89% | 11% | ||||||
65.5, 7.9 | 43.3, 6.9 | 5.0, 7.0 | −29.3, 7.1 | 1253.9 | 3.3×10−4 | ||
66% | 24% | 8% | 2% | ||||
65.5, 7.9 | 43.3, 6.9 | 13.3, 2.9 | −2.0, 2.6 | −29.3, 7.0 | 1252.4 | 1.5×10−3 | |
66% | 24% | 3% | 4% | 2% | |||
42.3, 40.0 | 1566.5 | 10−60 | |||||
100% | |||||||
62.2, 12.2 | −16.2, 33.0 | 1442.1 | 10−6 | ||||
75% | 25% | ||||||
61.3, 12.9 | −7.1, 17.4 | −63.6, 5.8 | 1434.7 | 2.0×10−3 | |||
78% | 16% | 6% | |||||
− |
− |
||||||
63.8, 10.4 | 34.8, 3.6 | 10.3, 4.0 | −19.2, 9.9 | −63.6, 5.2 | 1433.2 | 9.1×10−3 | |
71% | 8% | 5% | 10% | 6% |
Each model is the sum of N Gaussian functions, each Gaussian function being characterized by its mean (µi), standard deviation (σi) and fractional contribution (πi) for i = 1…N. AICc is the corrected Akaike’s Information Criterion (AICc), the optimal model having the lowest AICc value. The last column indicates the relative likelihood of each model as compared to the optimal one.
G1 | G2 | G3 | G4 | |
µ1 |
µ2 |
µ3 |
µ4 |
|
65.3 [63.7, 66.9] | 43.9 [42.0, 45.8] | 4.4 [−3.8, 12.7] | – | |
71.5 [69.9, 73.0] | 52.0 [49.4, 54.6] | −8.2 [−14.9, −1.6] | −63.6 [−67.0, −60.3] | |
σ1 |
σ2 |
σ3 |
σ4 |
|
8.1 [6.7, 9.8] | 5.3 [3.8, 7.4] | 17.7 [12.1, 26.0] | – | |
6.0 [4.6, 7.7] | 10.6 [8.4, 13.4] | 16.4 [11.6, 23.2] | 5.4 [2.7, 10.8] | |
π1 |
π2 |
π3 |
π4 |
|
67.4 [52.9, 79.2] | 20.3 [13.9, 28.7] | 12.2 [7.5, 19.1] | – | |
36.7 [27.7, 46.7] | 41.3 [31.7, 51.7] | 15.4 [10.1, 22.7] | 6.5 [3.4, 11.9] |
Each Gaussian function is characterized by its mean (µi), standard deviation (σi) and fractional contribution (πi) for i = 1…n. [95% CI] is the 95% confidence interval.
Except for these 10 subjects and the associated G4 component, HFLI distribution was characterized by 3 components for either RH or LH. The first two components (G1 and G2) gathered subjects with largely positive HFLI values, thus having a typical left lateralization for language production. These two Gaussian components, that accounted for 87.7% and 78.0% of the RH and LH probability density function, respectively, had moments that differed between the two groups. Indeed, G1 and G2 means were slightly but significantly larger in LH than in RH (71.5 versus 65.3, and 52.0 versus 43.9, respectively, p<0.05 in both cases). Meanwhile, G1 accounted for a larger fraction of the whole distribution in RH than in LH (67% versus 37%), the reverse being observed for G2 (20% for RH versus 41% in LH). As for the variance of these components, they were comparable for G1, and larger in LH than in RH for G2.
The third component (G3) appeared to have similar parameter values in both groups: a mean parameter close to 0 (4.4 in RH and −8.2 in LH), and a standard deviation around 17. This component concerned subjects with either weak or no lateralization for language production, who will be referred to as Ambilateral. Interestingly, this component represented similar fractions of the overall distribution in RH and LH (12.2% in RH and 15.4% in LH).
Fitting HFLI distribution of the entire sample of LH and RH gave results very consistent with those reported above: the optimal fit was obtained with a mixture of 4 Gaussian functions with estimated means, variance and proportions equal to (67, 46, −4, and −63), (7.9, 8.4, 19.3, and 5.3) and (57.5%, 25.7%, 13.3%, and 3.3%), respectively. Note, in particular, that, the fourth component was identical to the G4 component observed when fitting the HFLI distribution of the LH subsample (see
Using local minima of the optimal Gaussian mixture model function, thresholds could be easily identified for segregating the Gaussian components having the lowest HFLI means (G4 from G3 in LH, and G3 from G2 for both RH and LH, see
GMM | RH (N = 144) | LH (N = 153) | ||||
Typical | Atypical | Strongly-atypical | Typical | Atypical | Strongly-atypical | |
130 (90.3%) | 14 (9.7%) | 0 (0.0%) | 120 (78.4%) | 23 (15.0%) | 10 (6.5%) | |
59±13 | −2±17 | – | 61±13 | −9±17 | −63±5 | |
93±11 | 94±12 | – | −59±42 | −73±29 | −87±18 | |
136 (94.4%) | 8 (5.6%) | 128 (83.7%) | 25 (16.3%) | |||
57±16 | −13±15 | 58±18 | −37±24 | |||
93±11 | 95±14 | −59±41 | −82±21 |
Typical, Ambilateral and Strongly-atypical subjects did not significantly differ as regards their performances on tasks executed during fMRI acquisition, whether considering the number of words generated per sentence or the response time for sentence or for word list generation (see
Behavioral control during fMRI | RH | LH | p | Typical | Ambilateral | Strongly-atypical | p |
2.79 (0.09) | 2.67 (0.09) | 0.57 | 2.71 (0.07) | 2.71 (0.18) | 3.20 (0.34) | 0.36 | |
12.3 (0.17) | 12.5 (0.17) | 0.41 | 12.4 (0.12) | 12.0 (0.32) | 13.4 (0.94) | 0.17 | |
5597 (90) | 5618 (77) | 0.85 | 5608 (58) | 5576 (167) | 5676 (391) | 0.94 | |
5228 (96) | 5234 (93) | 0.98 | 5219 (70) | 5363 (198) | 4974 (428) | 0.69 |
p value corresponds to the results of an ANOVA entering sex, age, SP and educational level as covariates (RT: response time, N: number). Note that there was no difference between the LH groups using different response hand.
Average HFLI values were larger in RH (53.5±22.7, mean ± S.D.) than in LH (43.2±40.0), the difference being significant (p = 0.001, ANOVA). Note that HFLI variance was larger in LH than in RH (p<10−4). There was no effect of sex (p = 0.28), age (p = 0.08), educational level (p = 0.9) or skull perimeter (p = 0.12). After exclusion of the 10 Strongly-atypical subjects, average HFLI for LH was 49.8 (S.D. = 29.4) and did not significantly differ from that of RH (p = 0.11, ANOVA).
Language lateralization type | ||||
Handedness | Typical | Ambilateral | Strongly-atypical | All |
130 (0.44) | 14 (0.047) | 0 (0.00) | 144 (0.49) | |
120 (0.40) | 23 (0.077) | 10 (0.033) | 153 (0.51) | |
250 (0.84) | 37 (0.12) | 10 (0.033) | 297 (1.00) |
Language lateralization types were derived from Gaussian mixture modeling of the probability density function of hemispheric functional lateralization index measured with fMRI. Handedness was self-reported by the subjects. RH: right-handed; LH: left-handed. Each cell contains the number of subjects and corresponding fraction of the total sample size in parentheses.
The kappa statistic was low but significantly different from 0 when considering the entire sample (κ = 0.11, π = 0.006), but not when excluding the 10 Strongly-atypicals (κ = 0.063, π = 0.11), meaning that agreement between hemispheric dominance for hand preference and for language was barely above the chance level. Using a 0-threshold for defining language lateralization categories gave very similar results whether including Strongly-atypicals or not (κ = 0.105, π = 0.005, and κ = 0.049, π = 0.13, respectively).
Manual preference strength was assessed using the Edinburgh inventory, ranging from 100 (exclusive use of the right hand) to −100 (exclusive use of the left hand). Subjects also self-reported whether they consider themselves as right- handed (RH, squares) or left-handed (LH, circles). HFLI, an index of hemispheric functional lateralization for language measured with fMRI during covert generation of sentences compared to covert generation of list of words, was used for classifying subjects as « Typical » (HFLI>50, bright color symbols), « Ambilateral» (−20<HFLI<50, pale color symbols), or « Strongly-atypical » (HFLI<−20, open symbols).
Spearman rank correlation coefficient between MPS and HFLI did not significantly differ from 0, neither for RH (ρ = −0.069, p = 0.40), nor for LH (ρ = 0.059, p = 0.46), nor for the entire sample (ρ = 0.057, p = 0.32). Discarding the 10 Strongly-atypicals did not result in significant correlation (LH: ρ = −0.089, p = 0.91, RH+LH: ρ = −0.011, p = 0.84).
A significant effect of MPS3 on HFLI values was found (p = 0.0015, ANOVA), strong LH having lower HFLI average values than either strong RH (38.3 versus 51.9, p = 0.0016) or individuals with moderate MPS (38.3 versus 53.1, p = 0.0019). This effect vanished when discarding the 10 Strongly-atypicals from the sample (p = 0.37, ANOVA).
Conversely, a significant effect of language lateralization type on MPS values was observed (p<10−4, ANOVA), average MPS values of Strongly-atypicals (−87.3) being significantly different from that of either Typicals (20.0, p<10−4) or Ambilaterals (−9.6, p = 0.023), Typicals and Ambilaterals being also different (p = 0.01). Looking separately at RH and LH revealed that, Typical and Ambilateral RH subjects did not differ as regards their MPS average values (p = 0.60, see
Consistent findings were observed when looking at the relationships between language lateralization and MPS3 categorical variables.
As for the kappa statistic, it was again small and failed to reach significance (κ = 0.033, π = 0.055) indicating that agreement between language lateralization and MPS3 was weak and again not significantly different from the chance level.
Finally, similar to what was found by others
Language lateralization type | ||||
MPS3 | Typical | Ambilateral | Strongly-atypical | All |
87 (0.29) | 11 (0.037) | 0 (0.00) | 98 (0.33) | |
90 (0.30) | 9 (0.030) | 1 (0.003) | 100 (0.33) | |
73 (0.25) | 17 (0.057) | 9 (0.030) | 99 (0.33) | |
250 (0.84) | 37 (0.12) | 10 (0.033) | 297 (1.00) |
Language lateralization types were derived from Gaussian mixture modeling of the probability density function of hemispheric functional lateralization index measured with fMRI. Manual Preference Strength was measured with the Edinburgh inventory (MPS) and scored on a 3-level scale (MPS3). Strong R: MPS = +100, Moderate: −55<MPS<+100, Strong L: MPS<−55. Each cell contains the number of subjects and corresponding fraction of the total sample size in parentheses.
In a large sample of healthy individuals, balanced for handedness, Gaussian mixture modeling of the distribution of hemispheric functional asymmetries during sentence production identified 3 types of lateralization, namely Typical (left-lateralized), Ambilateral (no lateralization) and Strongly-atypical (right-lateralized), the last category being rare (less than 1% prevalence) and including only LH. Excluding these rare subjects, we measured a concordance between dominant hemispheres for language and for the preferred hand that was not above what could be expected to occur by chance only, this being true both for RH and for LH. In LH, a significant association was observed between the strength of lateralization for language and the strength of manual preference, but this relationship was largely explained by the existence of the small group of Strongly-atypical individuals who had both strong left hand preference and strong right hemisphere dominance for language.
Language production has long been known as the most lateralized language task as compared to speech listening that elicits smaller leftward asymmetries
In the present work we designed a language production paradigm that allowed for an investigation of inter-individual variability of hemispheric asymmetries of sentence processing areas. We chose to rely on a very familiar and overlearned list of words because it constitutes a high-level reference task in mother tongue that was balanced with the sentence task in terms of amount of verbal stimuli to be processed.
This paradigm allowed for obtaining robust asymmetrical contrast maps at the individual level, and its reliability is evidenced by the proportion of RH having a HFLI >0 (94.4%) strongly concordant with existing literature, and independent of the method used or of the production paradigm applied. As a matter of fact, we observed a proportion of RH with positive HFLI identical to that observed by others who used fCTD during a word fluency task (
Definition of language lateralization categories in our study differs from previous works that were based on arbitrary thresholds (see
Because typical subjects represent 90% of the population, it is important to assess whether or not they constitute a homogeneous group with respect to hemispheric dominance. Gaussian mixture model suggests the existence two distinct subgroups of typical individuals, having strong and moderate left language lateralization, respectively, this holding both for RH and for LH. However, because of the overlap between the two Gaussian distributions associated to these two putative groups (G1 and G2), it was not possible to reliably assign Typical subjects to either group. As proposed by others, additional variables, including regional patterns of functional asymmetry, may be necessary for identifying these subgroups and the factors that explain their differences
Finally, it is worth mentioning that in the present studies we used two measures of handedness, namely self-report and hand preference inventory, for investigating the relationship between handedness and hemispheric dominance for language. Other measures could have been used, such as relative hand skill or performance at a reaching task. However, a recent report indicates that none of these different measures emerged as clearly superior to the others as regards their correlation with cerebral dominance for language
Using Gaussian mixture modeling-based classification, we found 90% of RH exhibiting typical language lateralization. This proportion increased to 94% with the usual binary classification based on a zero-threshold on HFLI. Such proportions are in agreement (see
Regarding LH, the 78% proportion of LH with typical language lateralization using the 3-group classification rose to 84% with the binary approach, identical to figures reported by Szaflarski et al. with fMRI during word production
Overall, atypical language lateralization was found more frequent in LH than in RH, in agreement with pioneer neuropsychological studies conducted by Hécaen
Among non left-dominant language lateralization individuals, Gaussian mixture modeling segregated a subgroup of individuals with right hemisphere language dominance, confirming the existence of this rare but normal variant of language organization
In our sample, right-hemisphere dominance was observed only in LH, in agreement with previous studies that reported no case of rightward dominance in healthy RH subjects during verb generation
An important finding of our study is that, when ignoring this group of rare Strongly-atypical individuals, we found no significant chance-corrected agreement between hemispheric dominance for hand and hemispheric dominance for language production. Given the 90% of right-handedness and 90% of left-hemisphere dominance in the general population, this result may at first sight seem counterintuitive. However, one should remember that, due to this joint high prevalence, a high level of agreement between these two traits is expected due to chance only, namely in about 81% of the subjects. Other studies have already pinpointed such a lack of agreement
Another key result of the present study is that occurrence of atypical individuals, as assessed by the 2-category classification, was found significantly correlated with strength of handedness. In a previous study, Knecht et al.
In order to exclude a possible dependence of this finding on the category boundaries of the MPS7 rating scale, we conducted the same analysis using a 3-level scale with almost the same number of individuals in each category (MPS3). Again, a significant relationship was found that vanished when the 10 Strongly-atypical individuals were disregarded, calling for a different interpretation of Knecht et al findings. First, one should note that the first four levels of the MP7 scale included only LH, the fifth included a majority of LH, and the last two classes included RH only (see
This study demonstrates that, except in a small sample of strong LH with rightward asymmetry, concordance of hemispheric dominance for hand and for language production occurs by chance. The present result thus questions the existence of a link between control of the hand and of language by the same hemisphere, while indicating that a rightward representation of language, although rare, is a normal variant of language lateralization.
(XLSX)
The authors are indebted to Isabelle Hesling for very helpful comments in preparing the manuscript, and to Nicolas Delcroix and Marie-Renée Boudou-Turbelin for their help in acquiring and processing the BIL&GIN database.