Arcuate fasciculus asymmetry has a hand in language function but not handedness

Abstract

The importance of relationships between handedness, language lateralization and localization, and white matter tracts for language performance are unclear. The goal of the study was to investigate these relationships by examining arcuate fasciculus (AF) structural asymmetry (DTI) and functional asymmetry (fMRI) in language circuits, handedness, and linguistic performance. A large sample of right-handed (n=158) and atypical-handed (n=82) healthy adults underwent DTI at 3T to assess number of streamlines and fractional anisotropy (FA) of the AF, and language fMRI. Language functions were assessed using standard tests of vocabulary, naming, verbal fluency and complex ideation. Laterality indices (LIs) illustrated degree of asymmetry and lateralization patterns for the AF (streamlines and FA) and verb generation fMRI. Both handedness groups showed leftward lateralization bias for streamline and fMRI LIs and symmetry for FA LI. The proportion of subjects with left, right or symmetric lateralization were similar between groups if based on AF LIs, but differed if based on fMRI LIs (p=0.0016). Degree of right-handedness was not associated with AF lateralization, but was associated with fMRI language lateralization (p=0.0014). FA LI was not associated with performance on language assessments, but streamline LI was associated with better vocabulary and complex ideation performance in atypical-handed subjects (p=0.022 and p=0.0098, respectively), and better semantic fluency in right-handed subjects (p=0.047); however, these did not survive multiple comparisons correction. We provide evidence that AF asymmetry is independent of hand preference, and while degree of right-handedness is associated with hemispheric language lateralization, the majority of atypical-handed individuals are left-lateralized for language.

Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines

“…Here, we test across all these methods and datasets, specifically evaluating whether arcuate macrostructure laterality index scores were influenced by subject sex (Tables A1 and A2) or age (Figure A17 and Table A3). We detected no significant effects, and this is consistent with the literature (Allendorfer et al, ; Budisavljevic et al, ; Lebel & Beaulieu, ). We also tested whether subjects' arcuate laterality index scores with one methodology correlated with their laterality index scores from another.…”

“…Previous research describes arcuate macrostructure laterality as a function of sex or age, mainly finding no significant impact of either (Allendorfer et al, ; Budisavljevic et al, ; Lebel & Beaulieu, ). Here, we test across all these methods and datasets, specifically evaluating whether arcuate macrostructure laterality index scores were influenced by subject sex (Tables A1 and A2) or age (Figure A17 and Table A3).…”

“…In Figure 5, we show that both the subject profiles of individual estimates and the group laterality index profiles exhibit a variance in space (from the arcuate's frontal end to its temporal end). Previous studies identified an average leftward asymmetry in the deterministic arcuate in the microstructure index FA (Allendorfer et al, 2016;Buchel et al, 2004;Lebel & Beaulieu, 2009). We replicate this average FA asymmetry ( Figure A25).…”

“…This includes deterministic tracking or the use of the older and simpler tensor model (Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, 2000;Mori, Crain, Chacko, & van Zijl, 1999). Indeed, for the case of arcuate laterality, we find more studies that employ a more limited deterministic tractography (Allendorfer et al, 2016;Budisavljevic et al, 2015;Takao, Hayashi, & Ohtomo, 2013) than we do studies that employ more permissive probabilistic methods (De Santis, Drakesmith, Bells, Assaf, & Jones, 2014;Zhao, Thiebaut de Schotten, Altarelli, Dubois, & Ramus, 2016). Instead of false-positive errors, however, these limited deterministic methods may be more prone to false-negative errors, as true streamlines may be aborted or prematurely terminated due to smaller sensitivity in deterministic tracking or the tensor model.…”

TRAFIC: fiber tract classification using deep learning

“…9 The role of this fiber bundle is still uncertain and current evidence suggests that it has an important role in language development. 10 We used 8 different brain atlases of neonate infants of 1–2 years old to build our data set as follows.

Training Set: 403676 negative single fibers + 13832 positive single fibers

Validation Set: 44852 negative single fibers + 1536 positive single fibers

n pt : 50

n feat : 7 (5 landmarks + curvature + torsion)

…”