Multimodal assessment of hemispheric lateralization for language and its relevance for behavior

    Multimodal assessment of hemispheric lateralization for language and its relevance for behavior C. Piervincenzi, A. Petrilli, A. Marini, M. Caulo, G. Committeri, C. Sestieri PII: DOI: Reference:

S1053-8119(16)30396-2 doi: 10.1016/j.neuroimage.2016.08.018 YNIMG 13376

To appear in:

NeuroImage

Received date: Accepted date:

2 March 2016 9 August 2016

Please cite this article as: Piervincenzi, C., Petrilli, A., Marini, A., Caulo, M., Committeri, G., Sestieri, C., Multimodal assessment of hemispheric lateralization for language and its relevance for behavior, NeuroImage (2016), doi: 10.1016/j.neuroimage.2016.08.018

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance

Multimodal assessment of hemispheric lateralization for language and its relevance for behavior

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C. Piervincenzi1, A. Petrilli1, A. Marini2, M. Caulo1, G. Committeri1, C. Sestieri1 1

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Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies (ITAB), G. d’Annunzio University, Via dei Vestini 33, 66013, Chieti, Italy. 2 Department of Languages and Literatures, Communication, Education, and Society, University of Udine, Via Margreth, 3-33100 Udine, Italy; IRCCS Eugenio Medea, San Vito al Tagliamento, Pordenone, Italy.

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Keywords: Hemispheric asymmetry, task-evoked activity, functional connectivity, diffusion tensor tractography, language functions

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*Corresponding author:

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Running Title: Multimodal hemispheric language lateralization

Claudia Piervincenzi

Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies (ITAB) G. d’Annunzio University Via dei Vestini 33 66013, Chieti, Italy Tel: +39-328-6928501 E-mail: [email protected]

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Abstract. Although different MRI-based techniques have been proposed to assess the hemispheric

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lateralization for language (HLL), the agreement across methods, and its relationship with language abilities, are still a matter of debate. In the present study we obtained measures of HLL using both

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task-evoked activity during the execution of three different protocols and task-free methods of

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functional [resting state functional connectivity (rs-FC)] and anatomical [diffusion tensor imaging (DTI) tractography] connectivity. Regional analyses focusing on the perisylvian language network

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were conducted to assess the consistency of HLL across techniques. In addition, following a

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multimodal approach, we identified macro-factors of lateralization and examined their relationship with language performance. Our findings indicate the existence of a negative relationship between the structural asymmetry of the direct segment of the arcuate fasciculus (AF) and the inter-

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hemispheric rs-FC of key nodes of the perisylvian network. Instead, despite all the language tasks exhibited a leftward pattern of asymmetry, measures of HLL derived from task-evoked activity did

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not show a direct relationship with those obtained with the two task-free methods. Furthermore, a robust brain-behavioral relationship was observed only with a specific macro-factor that combined HLL measures derived from all MRI techniques. In particular, general language performance was positively related to more symmetrical structural organization, stronger inter-hemispheric communication at rest but more lateralized activation of Wernicke’s territory during production tasks. Our findings, while not supporting the existence of a direct relationship between indices of hemispheric lateralization for language derived from different MRI techniques, indicate that general language performance can be indexed using combined MRI measures. The same approach might prove successful for likewise complex human behaviours.

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Introduction. Hemispheric lateralization for language (HLL) has long been a focus of scientific interest. Since

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Broca’s and Wernicke’s findings, the loss of language abilities has been generally associated to a left hemisphere damage. For years, functional HLL has been assessed using invasive methods that

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determine the role played by each hemisphere in language functions (Wada, 1949). A risk-free

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assessment only became possible with the development of non-invasive imaging techniques, like functional MRI (fMRI) (Binder et al., 1995; Price, 2000), which measures task-related changes in

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blood oxygenation level-dependent (BOLD) signal. FMRI not only allows the assessment of HLL

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but also the individual localization of language-related areas (Binder et al., 1996; Desmond et al., 1995), which is critical for pre-surgical mapping. Different methods for a quantitative assessment of HLL using fMRI have been proposed, from Lateralization Indices (LIs; (Caulo et al., 2011; Nagata

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et al., 2001)) to Statistical Lateralization Maps (SLMs; (Liegeois et al., 2002)). However, one limitation of such measures is that they depend on the execution of a particular task (Jansen et al.,

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2006), so that discrepancies across studies may reflect differences in input modality or task difficulty (Seghier, 2008). Moreover, those studies that compared the lateralization of different language tasks (Baciu et al., 2005; Binder et al., 2008; Jansen et al., 2006) did not perform a direct statistical comparisons of the corresponding HLL. This issue is also relevant when assessing the relationship between HLL and individual language abilities (Everts et al., 2009; van EttingerVeenstra et al., 2010). More recent imaging techniques, such as diffusion tensor imaging (DTI) tractography (Basser et al., 2000), have been used to obtain task-free measures of the asymmetry of language-related white matter (WM) tracts and their relation to language performance. In particular, Catani and colleagues (Catani et al., 2005) identified three branches of the Arcuate Fasciculus (AF) connecting the main nodes of the perisylvian language network (Broca’s, Wernicke’s and Geschwind’s). Importantly, 3

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance only the direct segment connecting Broca’s and Wernicke’s territories was found to be highly leftlateralized, although stronger asymmetry was associated with less efficient verbal recall (Catani et

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al., 2007). More recently, hemispheric lateralization has been explored using another task-free technique such as resting-state functional connectivity (rs-FC) MRI, which examines the pattern of

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synchronous spontaneous fluctuations of the BOLD signal (Biswal et al., 1995). However, the

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assessment of HLL and the choice of meaningful laterality indices using rs-FC is not trivial, as suggested by the number of different approaches that have been proposed (e.g. (Gee et al., 2011;

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Gotts et al., 2013; Liu et al., 2009; Zhu et al., 2014)). For example, lateralization does not

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necessarily imply a differential amount of left vs. right intra-hemispheric connectivity, as one would expect following a strong analogy with task-evoked LIs. In this respect, measures of interhemispheric connectivity might better reflect the amount of communication between homologue

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pairs of regions, and thus possible mechanisms of cross-hemispheric inhibition, as also suggested by the relevance of this measure in clinical studies (Carter et al., 2010; He et al., 2007; Pravatà et

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al., 2014)

The agreement between measures of HLL obtained from different MRI-based techniques is still a matter of debate. Previous studies have separately investigated the agreement between pairs of techniques with somewhat conflicting results. While some authors suggest that the structural asymmetry of the AF does not reflect task-related functional HLL (Vernooij et al., 2007), others support the presence of a positive correlation in right- (Powell et al., 2006) or left-handers (Propper et al., 2010). Notably, even when anatomical and functional connectivity methods have been directly compared (reporting no significant correlation), the assessment of FC was not task-free, as it was obtained during the execution of a language task (Lopez-Barroso et al., 2013). Concerning the agreement between functional methods, Doucet and colleagues (Doucet et al., 2015) have recently reported the first evidence that the degree of rs-FC of Broca’s territory can predict the 4

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance strength of HLL assessed with task-evoked activity in both epileptic and healthy individuals. Overall, a review of the literature indicates the need for a multimodal assessment of structural and

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functional measures of HLL, and, within the functional domain, of a comparison between measures obtained with task-free and task-related approaches, in order to better understand their complex

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relationship in the same group of subjects.

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Another main issue concerns how indices of HLL obtained with different functional or structural approaches relate to language performance. This question has traditionally been addressed by

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looking at the brain-behavior relationship within a single MRI technique. However, contrasting

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results are not only observed in studies investigating HLL through task-evoked activity (Everts et al., 2009; van Ettinger-Veenstra et al., 2010), but also in those using approaches that do not depend on the choice of a particular task. For example, evidence for a positive relationship between

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functional segregation of the left hemispheric rs-FC and verbal skills (Gotts et al., 2013) does not easily fit with findings of a negative relationship between leftward structural asymmetry and

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performance ((Catani et al., 2007), see also (Lopez-Barroso et al., 2013)). In this respect, the development of multimodal indices of asymmetry might better capture complex patterns of brain organization related to inter-subject variability in language performance. The aim of the present study was to compare several functional and structural measures of HLL and analyze their relationship with language performance in a group of healthy right-handed participants. Voxelwise (Baciu et al., 2005; Liegeois et al., 2002; Seghier et al., 2011) and regional HLL measures of task-evoked activity were assessed during the execution of three language tasks commonly used in studies of hemispheric lateralization and covering multiple aspects (i.e. production, comprehension) of language functions: two covert tasks of word and verb generation (Jansen et al., 2006; Liegeois et al., 2002) and an overt task of sentence comprehension (Binder et al., 1997; Price, 2000, 2010) (see Figure 1 for details on experimental procedure and tasks used in 5

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance the present work). A data-driven approach for functional connectivity was used to identify the language network in the resting state and obtain seeds for the subsequent assessment of

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lateralization. In addition, a tractographic reconstruction of the three segments of the AF was performed to assess structural HLL (Catani et al., 2007; Catani et al., 2005). Finally, we combined

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information across MRI techniques to identify macro-factors of hemispheric language lateralization

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and examine their relationship with behavioral factors of language performance derived from a

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comprehensive neuropsychological assessment.

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Methods. Subjects

Twenty-four right-handed healthy young adults without psychiatric or neurological disorders

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participated in the experiment. They were all Italian native speakers and had normal or corrected-tonormal vision. Participants gave written informed consent to participate in the experiment, in

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accordance with guidelines set by Human Studies Committee of “G. D’Annunzio” University, Chieti, and ethical standards of the 1964 Declaration of Helsinki. Three subjects were excluded after the first MRI session due to the presence of evident anatomical asymmetries of the lateral ventricles that might bias the comparison between hemispheres. Another subject was excluded since she did not complete the whole experiment. Thus, the analyses were conducted in a group of twenty subjects (10 females, mean age = 25.33 years, s.d. = 2.79, range = 21-29). Procedure The dataset was collected over three days at the Institute of Advanced Biomedical Technologies (ITAB), G. d’Annunzio University, Chieti, Italy (see Figure 1A for an overview of the experimental protocol) and included neuropsychological measures of language abilities and multiple MRI measures for the assessment of HLL. The present dataset is part of a larger project aimed at 6

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance investigating the hemispheric asymmetry of several cognitive functions (e.g. spatial cognition). For this reason, the experimental procedure includes additional non-linguistic neuropsychological tests

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and further task-related MRI paradigms. On the first day subjects underwent neuropsychological testing (Figure 1A). Half of them received the WAIS-R (Wechsler, 1981); the other half received a

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selection of tasks from the battery for attentional performance (TAP; (Zimmermann and Fimm,

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2007). All subjects were further tested at the “Learning of Semantically Related and Unrelated Words” Test (LSRUW; (Mauri et al., 1997)). Handedness was assessed by the Edinburgh

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Handedness Inventory (Oldfield, 1971). On the second day subjects completed a fMRI session

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including resting state functional connectivity (BOLD rs-FC) scans, anatomical (T1-FFE) scans, and structural (DWI) scans for anatomical connectivity. The imaging session lasted for approximately 1 hour and was followed by a neuropsychological testing including the WAIS-R or

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the TAP (depending on which battery had been administered on the first day) and the second part of the LSRUW Test (Figure 1A). On the third day all subjects completed another fMRI session for the

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assessment of task-evoked activity. After the scanning session, which lasted for approximately 1 hour, they completed the Nine Hole Peg Test for the assessment of finger dexterity (9HPT; (Mathiowetz et al., 1985)) and a test for the assessment of narrative speech (Marini et al., 2011; Marini and Carlomagno, 2004) (Figure 1A). The interval between the first and the second day was always 24 hours, while the mean time interval between the second and the third day was approximately 6 months (SD=2 months). Neuropsychological Testing. WAIS-R: subjects were administered the WAIS-R battery. The following measures were selected for the purpose of the present study because of their well-established association with lateralized cognitive functions (Carroll, 1993; Gotts et al., 2013; Semel et al., 2003; Wallace et al., 2013;

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Warrington et al., 1986): Full Scale Total IQ, Verbal IQ, Performance IQ, along with vocabulary and block design subtests.

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Learning of Semantically Related and Unrelated Words Test (LSRUW): this verbal memory test evaluates the encoding and retrieval performance of heard words and the use of semantic

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association as a learning strategy. The measures of immediate and delayed total words recalled were

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selected based on previous evidence for their association with lateralization measures of structural connectivity of the arcuate fasciculus (Catani et al., 2007).

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Test for the assessment of the narrative speech (TENS): This test was administered to assess micro-

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and macro- linguistic skills of the subjects. Each participant sat in front of a computer screen and four different colored visual stimuli were presented. Two of these stimuli were single-picture scenes, and the other two were cartoon stories with six pictures, each presented on the same page

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(see (Marini and Urgesi, 2012) for details). For each visual stimulus subjects were asked to tell a story describing the picture, which was tape-recorded and subsequently transcribed verbatim. On

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these narrative samples, a multi-level procedure for discourse analysis was conducted to obtain measures of productivity, lexical and grammatical processing, narrative organization and informativeness (Marini and Carlomagno, 2004; Marini and Urgesi, 2012). Two specific microlinguistic measures were selected for the present study, namely Speech Rate (in terms of words per minute) and Percentage of Lexical Informativeness obtained by dividing the amount of Lexical Information Units (i.e. words that were phonologically well-formed and appropriate from a grammatical and pragmatic point of view) by the total amount of words uttered and multiplying this value by 100. We focused on these two measures as accumulating evidence suggests that they reflect left-lateralized language functions. Indeed, the former is usually deficitarian after lefthemispheric lesions leading to both non-fluent and fluent aphasia (e.g., (Andreetta and Marini, 2015; Marini et al., 2007)), whereas the latter was found to be reduced after inhibition of the left 8

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance (but not right) inferior frontal gyrus in healthy individuals using TMS (Marini and Urgesi, 2012). The measures obtained with the TAP and the Nine Hole Peg Test were not the focus of the present

Tasks and stimuli for the study of task-evoked BOLD activity.

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paper and will not be discussed further.

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Subjects performed six tasks in the scanner, one block of three language tasks in Italian (word

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generation, verb generation, and sentence comprehension), one block of a motor task (fingertapping, FTT) (Witt et al., 2008), one block of the Landmark task (LT) (Cai et al., 2013), and five

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blocks of the Posner task (PT) (Posner, 1980) (Figure 1A). Blocks of the Posner task were

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intermixed with the other tasks. Since the present paper focused on the language tasks, the analysis of the other tasks will not be discussed. Before the actual experiment, subjects were trained in each task inside a mock scanner, to ensure that they understood the instructions. Visual stimuli were

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presented by using E-Prime, Version 1.1 (Psychology Software Tools, Pittsburgh, Pennsylvania). In the MR scanner, images were projected onto a screen positioned at the back of the scanner via a

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LCD projector and viewed through a mirror attached over the head coil. Behavioral responses were collected using a Cedrus RB-830 USB Response Pad. Word generation task (WGT). WGTs have been used in numerous studies as they produce strong lateralization of brain activation (Brannen et al., 2001; Cuenod et al., 1995; Hertz-Pannier et al., 1997; Pujol et al., 1999). In the present study subjects performed a covert orthographically-cued WGT (Figure 1B). The paradigm consisted of six 14.5 s blocks of task performance interspersed with 14.5 s of rest periods. During the task period, a red letter was presented centrally on a black background for 1 s, followed by a white fixation cross during which subjects were instructed to covertly generate as many words as possible that started with that letter. After 13.5 s, a red cross presented for 1 s and followed by a white fixation cross marked the beginning of a rest period.

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Verb generation task (VGT). The VGT is commonly used to assess language lateralization in the clinical practice (Deblaere et al., 2002; Lurito and Dzemidzic, 2001; Schlosser et al., 2002) and it is

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the most common paradigm for the quantitative assessment of hemispheric lateralization with fMRI (Liegeois et al., 2002; Rowan et al., 2004). We used a covert orthographically-cued block-design

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task (Figure 1B), consisting of five 16 s task blocks (verb generation) and five 16 s sensory control

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blocks intermixed with eleven fixation blocks of 14 s. During the active period a noun was presented centrally on a black background for 1 s, followed by a 1 s white fixation cross during

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which subjects were instructed to think of pronouncing one associated verb. Eight nouns were

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presented on each active block. Stimuli were selected from the list of words used by Crescentini et al. (Crescentini et al., 2008). During the control period, a consonant letter string was presented for 1 s, followed by a 1 s white fixation cross, during which subjects were instructed to maintain central

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fixation. Eight consonant letter strings, balanced for length with those of the active blocks, were presented in each control block.

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Sentence comprehension task (SCT). This task is known to provide the identification of the brain regions that subserve verbal comprehension (Binder et al., 1997; Bookheimer, 2002; Demonet et al., 1992; Ferstl et al., 2008; Price, 2000, 2010). The present SCT consisted of six task blocks (sentence comprehension) and six control blocks of 15 s each, presented alternatively and intermixed with fixation blocks of 14.5 duration (Figure 1B). During task blocks a phrase of five words was presented centrally (one word at the time, 0.5 s each) in white letters on a black background, describing a scene taken from the Italian-adapted version of the Aachener Aphasie Test (AAT; (Huber et al., 1983; Luzzatti et al., 1996). Two b/w images from the AAT were then presented for 2.5 s, one above and one below a white central fixation cross. Subjects were instructed to select the image corresponding to the meaning of the previous sentence using the middle (upper image) or the index (lower image) finger of both hands simultaneously. The decision to use both hands was made 10

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance to avoid coarse asymmetric activations due to the use of the dominant hand alone. The procedure of the control blocks was similar to that of the task block, although a sequence of five letter strings

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(shuffled letter strings from the words of the active condition, balanced for length) was presented and subjects had to select the image that represented the girl/woman. This condition was aimed at

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controlling for BOLD activity associated with sensory stimulation and decision-making.

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MRI methods

Imaging data were acquired using a Philips Achieva 3T scanner. Preprocessing and data analysis

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were conducted using FSL (FMRIB’s Software Library v.5.0.8, http://www.fmrib.ox.ac.uk/fsl/)

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(Smith et al., 2004) and MedINRIA (ASCLEPIOS Research Team, Sophia Antipolis Cedex, France, v.1.9.0, http://www-sop.inria.fr/asclepios). Data were visualized using FSLview and Caret (v 5.64, http://brainvis.wustl.edu/wiki/index.php/Caret:About). Anatomical localizations were

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established according to the Harvard-Oxford cortical and subcortical structural atlases included in the FSL distribution (http://www.fmrib.ox.ac.uk/fsl/data/atlas descriptions.html). Statistical

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analyses were performed using SPSS statistics software (version 22.0). Task-evoked fMRI activity.

Acquisition. Functional T2*-weighted images were collected using a gradient-echo EPI sequence to measure the BOLD contrast over the whole brain (TR = 1710 ms, TE = 30 ms, 34 slices acquired in ascending interleaved order, voxel size = 3.59x3.59x3.59 mm, 64x64 matrix, flip angle = 70°). The fMRI runs of the WGT, VGT and SCT included 118, 190 and 226 volumes, respectively. Structural images were collected using a sagittal T1-fast field echo (T1-FFE) sequence to improve registration of BOLD images (TR = 8.13 ms, TE = 3.72 ms, voxelsize = 1x1x1 mm, 240x240 matrix, flip angle = 8°, 160 slices). Preprocessing. Single-subject pre-processing was carried out using FEAT (FMRI Expert Analysis Tool), Version 6.00 part of FSL. Pre-statistical processing consisted of motion correction using 11

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance MCFLIRT (Jenkinson et al., 2002), brain extraction using BET (Smith, 2002), slice timing correction and spatial smoothing using a Gaussian kernel of full-width at half-maximum (FWHM)

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of 8 mm. Since both VGT and SCT included a control condition, for these paradigms data were high-pass filtered using a cut-off set at 128 seconds (approximately the double of a complete single

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cycle length for these paradigms). A cut-off value of 58 seconds was instead used for the WGT,

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which did not include a control condition. Registration to high resolution structural and/or standard space images was carried out using FLIRT (Jenkinson et al., 2002; Jenkinson and Smith, 2001). EPI

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volumes were registered to the individual’s structural scan using FLIRT_BBR (Boundary-Based

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Registration) tool (Greve and Fischl, 2009). Registration from high resolution structural to standard Montreal Neurological Institute (MNI) space was then further refined using FNIRT nonlinear registration (Andersson et al., 2010).

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Activation maps - main effect of language tasks. Whole-brain voxelwise regression analyses were performed using FSL’s FEAT. Time-series statistical analysis was carried out using FILM with

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local autocorrelation correction (Woolrich et al., 2001). For each task, first-level single-subject general linear model (GLM) analysis was performed using a separate explanatory variable (EV) for each condition (task condition for the WGT, task > control conditions for VGT and SCT). Each block was modelled with a boxcar function convolved with a double-gamma hemodynamic response function (HRF), adding temporal filtering and a temporal derivative. Contrast images of interest (task> rest for WGT, task > control for VGT and SCT, and reverse contrasts) were produced from these individual analyses. Higher level analyses were carried out using the FLAME (FMRIB's Local Analysis of Mixed Effects) mixed-effects model, stage 1 (Beckmann et al., 2003; Woolrich, 2008; Woolrich et al., 2004). Group-level statistical parametric maps were formed through one-sample t-tests for each language task. The significance level was set at a family-wise error (FWE) corrected threshold of Z>3.1 and a cluster-based threshold of p<0.05 (Worsley, 2001). 12

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Voxelwise assessment of HLL (Statistical Lateralization Maps). To obtain a direct statistical comparison of fMRI activation in homotopic voxels between hemispheres, we generated individual

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subjects voxelwise maps of the laterality difference, i.e. statistical lateralization maps (SLMs) (Baciu et al., 2005; Josse et al., 2008; Liegeois et al., 2002; Seghier et al., 2011). Preprocessing was

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performed as described above, except that registration from individual structural space to standard

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space was accomplished using a custom-made symmetrical T1 template, to minimize morphological asymmetries that could prevent left and right voxels from being homotopic (Rowan et al., 2004;

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Salmond et al., 2000). This was obtained from our sample of 20 subjects after spatial normalization

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to the canonical FSL’s MNI_152_T1_2mm template, by averaging the original “unflipped” normalized structural images with “flipped” images, i.e. the same image rotated 180° on the y-axis (anterior-posterior) (Berlingeri et al., 2013). This method compromises the exact mapping of MNI

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coordinates but allows to compare activation between similar brain structures from each cerebral hemisphere in a less anatomically biased way (Stevens et al., 2005).

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The direct statistical comparison of the magnitude of task-induced activation in homotopic voxels of the two hemispheres was obtained in the following way: contrast unflipped images from the firstlevel analysis (task>rest for WGT, task>control for VGT and SCT), were rotated 180° about the yaxis to obtained flipped images. Individual language SLMs were then obtained using a paired t-test between unflipped and flipped images in which suprathreshold voxels represent voxels in which task-induced activation was significantly greater in one hemisphere compared to the other. Higher level group analyses were then carried out on these SLMs using ordinary least squares (OLS) simple mixed effects FSL’s FEAT tool. One sample t-tests over these voxel based SLMs were used to reveal the voxels with the most consistently lateralized activation for each of the three language tasks. Thus, each subject contributed with a single SLM to the higher-level group analyses. Paired ttests were used to assess significant differences between pairs of SLMs from different tasks. The 13

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance significance level was set at a family-wise error (FWE) corrected threshold of Z>3.1 and a clusterbased threshold of p<0.05. To visualize our data on surface, we warped the Caret volume

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template

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MeanBuckner12_FLIRT

(http://brainvis.wustl.edu/help/pals_volume_normalization/) and then applied our warp to the

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inflated population average landmark surface (PALS, (Van Essen, 2005)) FLIRT coords.

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Selection of Regions of Interest (ROIs) for the assessment of regional HLL. To avoid potential bias in ROI selection caused by thresholding, left-to-right hemisphere flipping

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and presence of right-hemisphere deactivation (e.g. (Jansen et al., 2006; Seghier, 2008)), measures

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of regional asymmetry of task-evoked activity were obtained independently from task execution (see (Briganti et al., 2012)). ROIs were created using 6-mm-radius spheres centered on reference coordinates (left Broca: x = -51, y = 20, z = 3; left Wernicke: x = -59, y = -35, z = -1; left

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Geschwind: x = -57, y = -37, z = 23) (Seghier et al., 2004; Vigneau et al., 2006). Since the coordinates from the literature were in Talairach space, they were converted in MNI space using the

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web application from BioImage Suite (http://sprout022.sprout.yale.edu/mni2tal/mni2tal.html). Firstlevel subject-specific SLMs were then masked with the defined ROIs to extract regional values of HLL (SLM scores).

To test the robustness of the results, regional LIs corresponding to the three perisylvian regions were also obtained in each task using a set of ROIs derived from the analysis of the resting state functional connectivity (see supplementary material). The rs-FC dataset was used for the following reasons: (i) rs-FC ROIs are specific for the present group of subjects and derived from a data-driven approach, (ii) this procedure allows a direct comparison of task-evoked and rs-FC measures in the same perisylvian regions, (iii) unlike the SLM method (Stevens et al., 2005), this procedure does not compromises the exact mapping of MNI coordinates. Resting state functional connectivity (rs-FC MRI). 14

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Acquisition. For each participant, three resting state runs were collected, with the same acquisition parameters used for task-evoked activity. Each scan consisted of 170 functional volumes. During

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these scans, subjects were instructed to relax and remain as still as possible while maintaining fixation on a central white cross over a black background.

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Preprocessing. Single-subject pre-processing was similar to that of the task-evoked fMRI data, with

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the exception of high-pass filtering cut-off, set at 150 seconds (0.007 Hz) (Filippini et al., 2009). Registration from individual structural space to standard space was accomplished using FSL's linear

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and nonlinear registration tools as for the task-evoked activity fMRI scans.

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Independent Component Analysis (ICA): identification of the Language Network (LN). To identify the LN, a data driven rs-FC analysis was carried out using the independent component analysis (ICA) tool MELODIC (Multivariate Exploratory Linear Optimized Decomposition into

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Independent Components) (Beckmann et al., 2005), version 3.14 part of FSL v. 5.0.8 (FMRIB’s Software Library). Preprocessed functional data were temporally concatenated in a single 4D data

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set to carry out group-wise ICA. FMRI data were then projected into a 32-dimensional subspace using probabilistic Principal Component Analysis where the number of dimensions was estimated using the Laplace approximation to the Bayesian evidence of the model order (Beckmann and Smith, 2004; Minka, 2000). RSNs of interest covered the entire brain and were selected by visual inspection against sets of previously defined maps (Beckmann et al., 2005; Smith et al., 2009). To identify the LN, we selected the IC showing the highest spatial correlation coefficient (fslcc tool in FSL) with the three maps of task-evoked activity. Selection of ROIs for seed-based connectivity analysis. A seed-based connectivity analysis was performed using three perisylvian ROIs (corresponding to Broca’s, Wernicke’s and Geschwind’s territories) (Catani et al., 2005) and their homologues in the right hemisphere, defined using 6-mmradius spheres centered on the peaks of the LN obtained with the ICA analysis (left Broca: x = -50, 15

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance y = 20, z = -4; right Broca: x = 48, y = 24, z = -2; left Wernicke: x = -52, y = -36, z = 0; right Wernicke: x = 50, y = -36, z = 0; left Geschwind: x = -58, y = -48, z = 28). Since no right

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homologue of the left Geschwind territory could be identified in the ICA LN, the right hemisphere seed ROI was generated by flipping the left hemisphere ROI around the y axis.

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Nuisance signal regression. To control for the effects of physiological processes and motion, we

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modelled the signals from white matter (WM), cerebrospinal fluid (CSF) of the lateral ventricles and six motion parameters as nuisance regressors (output of MCFLIRT) (Kelly et al., 2009). WM

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and CSF masks were obtained using FMRIB’s Automated Segmentation Tool (FAST) (Zhang et al.,

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2001) on the T1-weighted images. Partial volume maps were thresholded at 0.99, binarized and registered to functional space. These WM registered images were then further thresholded at intensity of 0.95 to further reduce the effects of partial voluming (Kucyi et al., 2012), while CSF

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images were masked with FSL’s Ventricle Mask and then thresholded at 0.55. Fslmeants was then used to extract mean time-series from the obtained WM and ventricular masks. While the use of

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whole brain signal regression is currently debated (Keller et al., 2013; Murphy et al., 2009; Scholvinck et al., 2010) the presence of hemispheric asymmetries in the topography of the whole brain signal have been recently demonstrated (McAvoy et al., 2015). Therefore, to exclude the possibility to remove a source of asymmetry from our data, we did not perform global brain signal regression. Finally, in order to account for large and small motion effects, we also included the matrix

generated

by

fsl_motion_outliers

script

(FMRIB's

Software

Library,

www.fmrib.ox.ac.uk/fsl) as an additional confound. This nuisance signal regression step produced a 4D residuals volume for each participant, which was spatially normalized by applying the previously computed transformation to MNI152 standard space (Kelly et al., 2009). Time series extraction, correlational analyses and HLL assessment. All ROIs of interest were merged into a single 4D file and multiple linear regression analysis was performed using 16

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance dual_regression FSL’s tool. The dual_regression stage 1 then provided individual region-specific mean time courses. For each participant, Pearson's correlation coefficient (z-transformed to obtain

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an approximately normal distribution of the FC values) was used to estimate Interhemispheric-FC between homologue ROI pairs and intra-hemispheric-FC between ROI pairs within the same

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hemisphere. Lateralization indices (LIs) were then calculated on the z-transformed correlation

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coefficients of intra-hemispheric-FC, according to the following formula: LI = (L-R) / (|L|+|R|). In this case a modified expression of the classic formula LI = (L-R) / (L+R) was employed to avoid

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misinterpretation of functional LIs due to the presence of negative values (Jansen et al., 2006;

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Seghier, 2008). Positive values indicate a leftward lateralization. One-sample t tests were used to assess the asymmetry across subjects of the intra-hemispheric LIs. Anatomical connectivity (DT-MRI).

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Acquisition. Diffusion imaging data were acquired using a spin echo EPI sequence (EPI factor 59, TR = 8555.25 ms, TE = 102.98 ms, voxel size = 2x2x2 mm, 112x112 matrix, flip angle= 90°, 60

s/mm2.

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slices). Diffusion weighting was isotropically distributed along 60 directions, b = 0 and of 1500

Preprocessing. Data were treated with different tools from FDT (FMRIB diffusion toolbox, part of FSL). Images were first corrected for eddy current distortion and head motion using a 12 parameter affine registration to the first no-diffusion weighted volume of each subject, and the gradient directions were rotated accordingly (Leemans and Jones, 2009). Brain volumes were skull-stripped using BET (Smith, 2002). Spatial normalization and realignment into standard MNI space by both linear and nonlinear registration were performed on the DTI data. Transformation matrices, and their inverses, were derived from diffusion to structural space and from structural to standard space. Relevant matrices were concatenated to produce transformation matrices between diffusion and standard space (Andersson et al., 2010; Jenkinson et al., 2002; Jenkinson and Smith, 2001). 17

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Tractography Algorithm. Whole-brain tractography was performed using DTI Track module, part of the open-source software MedINRIA (ASCLEPIOS Research Team, Sophia Antipolis Cedex,

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France, v.1.9.0, http://www-sop.inria.fr/asclepios). The fiber tractographic algorithm implemented in MedINRIA is a modified tensor deflection algorithm that uses a log-Euclidean framework for

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diffusion-tensor estimation (Fillard et al., 2007; Toussaint et al., 2007). Tractography parameters

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were set as the following: FA threshold, less than 0.2; minimal length of fibers, 10 mm; smoothing deactivated during fiber tracking process.

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Reconstruction of perisylvian white matter pathways. Following the procedure described in Catani

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et al. (Catani et al., 2005), a virtual in vivo dissection of the three segments (anterior, direct, and posterior) of the arcuate fasciculus (AF) was performed by using a two-ROI approach. Three separate ROIs of interest were manually defined in MNI standard space for both hemispheres

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according to those reported by Catani et al. (Catani et al., 2007; Catani et al., 2005). The ROIs representing Broca’s territory included the inferior frontal cortex and part of the middle frontal

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gyrus, the ROIs of Wernicke’s territory included superior temporal cortex and posterior middle temporal gyrus and the ROIs of Geschwind’s territory were located in the inferior parietal cortex. There were no significant differences across subjects (p>0.05) between the number of voxels in the left and right Broca's, Wernicke's and Geschwind's ROIs. At the termination of tracking, the number of streamlines of the reconstructed pathways as well as measurements of Fractional Anisotropy (FA) were collected for each segment of each subject. FA is commonly DTI-derived measure used to quantify the degree of anisotropic diffusion within the single voxel; higher FA values are thought to reflect better WM integrity as a result of greater intravoxel coherence of fiber orientation, axon density and diameter and/or myelination (Beaulieu et al., 1996; Caminiti et al., 2013; Sen and Basser, 2005).

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Assessment of structural HLL. For each of the three reconstructed segment, a LI was calculated for the number of streamlines (N.streamlines) and for the FA values according to the following

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formula: i.e (left N.streamlines - right N.streamlines) / (left N.streamlines+ right N.streamlines). Positive values of the LIs indicate a leftward asymmetry. One-sample t tests were used to assess the

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asymmetry across subjects of the lateralization indices of FA and N.streamlines relative to each

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arcuate’s segment. Comparisons of regional indices of HLL across techniques

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Non-parametric Spearman’s correlation analyses were used to investigate the presence of

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significant correlations between LIs obtained with measures of DTI (FA and N.streamlines of each segment of the AF), task-evoked activity (regional LIs corresponding to the three perisylvian regions for each tasks) and rs-FC (intra-hemispheric LIs and inter-hemispheric connectivity

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between perisylvian regions).

Principal component analyses (PCA) and brain-behavioral correlations. A two-step data reduction

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approach was used to assess the correlation between the obtained lateralization measures and behavioral performance. First, a within-technique PCA with Varimax rotation was perfomed on the obtained lateralization measures from each MRI technique (regional LI of each perisylvian territory in each task for task-evoked activity; LIs of intra-hemispheric connectivity and measures of interhemispheric connectivity of each perisylvian territory for rs-FC; LIs for FA and N.streamlines of each segment of the arcuate fasciculus for DTI). Second, an across-technique PCA was performed on the selected components to obtain higher-order macro-factors (Turken and Dronkers, 2011). A within-domain PCA was also performed on the behavioral measures from the neuropsychological assessment (Full Scale Total IQ, Verbal IQ, Performance IQ, vocabulary and block design subtests for the WAIS-R; immediate and delayed total words for the LSRUW; Speech Rate and ratio of Lexical Informativenes for the TENS). Following a previous study (Corbetta et al., 2015) we 19

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance selected those components that satisfied two criteria: eingenvalues >1 and percentage of explained variance >10%. Finally, Spearman's rho was used to test the presence of a significant correlation

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between the MRI macro-factors and the behavioral components. For those behavioral factors that showed a significant correlation with HLL macro-factors, a correlation coefficient was calculated

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between each pair of variables loading the factors. Hypothesis-driven correlation analyses were also

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performed to investigate the presence of a previously reported relationship between the direct segment of the AF and measures of verbal recall (Catani et al., 2007). Therefore, a correlation

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analysis was performed between structural LIs of the three segments of the AF and measures of

Statistical analyses: significance criteria.

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immediate and delayed total words from the LSRUW Test.

The results of all the correlation analyses between lateralization measures from different MRI

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techniques were corrected for multiple comparison with an FDR (Benjamini and Hochberg, 1995) procedure, applied separately for each pair of MRI technique. Correlations were considered

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significant if the corrected p-value was smaller than 0.05. Correlation associated to an uncorrected p-value smaller than 0.01 were reported for completeness but were not further discussed. The same FDR correction was also used for the correlation analysis between MRI and behavioral macrofactors. The only exception to this criterion concerned the hypothesis-driven analysis to test a significant relationship between the direct segment of the AF and verbal recall scores (Catani et al., 2007). In this case, an uncorrected p-value smaller than 0.05 was considered as significant.

Results. Lateralization of evoked activity across multiple language tasks. Figure 2A shows the voxelwise maps of BOLD activity separately for the three language tasks (one-sample t-tests, p<0.05, FWE corrected). At a qualitative level, the topography of task-evoked 20

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance activity suggests the presence of a clear left dominant pattern for word (Figure 2A, top) and verb (Figure 2A, middle) generation tasks consistent with previous reports (Jansen et al., 2006; Rowan et

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al., 2004; Vigneau et al., 2006). Task-evoked activity during the WGT was mainly confined to the left prefrontal cortex (Broca’s territory) and right cerebellum, whereas bilateral activity was

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observed only in the anterior insula (Table 1). A similar topography was observed for the VGT

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(task > control) (see Figure 2A, middle), although significant BOLD response was detected also in the left temporal lobe (Wernicke’s territory) (Table 1). In contrast, the sentence comprehension task

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(task > control, Figure 2A, bottom) evoked a widespread pattern of activity with the inclusion of

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several regions of the right hemisphere. Notably, robust activity was observed both in the left STG/MTG (corresponding to the Wernicke’s territory) and in the SMG/AG (corresponding to the Geschwind’s territory) (Table 1).

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Next, we quantified the presence of a significant hemispheric lateralization by examining the statistical lateralization maps for each tasks. One-sample t-tests performed on SLMs of each task

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revealed the presence of a left-hemisphere lateralization largely confined to the perisylvian network (Figure 2B), along with a consistent right lateralization in the cerebellum (p<0.05, FWE corrected). For the WGT (Figure 2B, top), a widespread left-lateralization was observed in the prefrontal cortex, including the IFG, pars opercularis and triangularis, but also the MFG and SFG (Table 2). Additional left-lateralized activity was observed in regions of the temporo-parietal cortex. A similar topography was observed for the VGT (Figure 2B, middle), although, qualitatively, this task produced a wider left-lateralization in posterior nodes, including the SMG (Table 2). Interestingly, while the result of the original unflipped contrast images of the comprehension task (Figure 2B, bottom) suggested the presence of bilateral activity, a clear left-lateralization of the perysilvian network was observed in the corresponding SLM (Figure 2B, bottom and Table 2). Therefore, a consistent, statistically significant left-lateralization of the language network was observed across 21

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance tasks. However, important task-related differences emerged in the degree of HLL when statistical comparisons (paired t-tests) were performed between pairs of SLMs from different tasks (Figure

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2C). Compared to the SCT, both production tasks showed a stronger anterior left-lateralization including the Broca’s territory (Table 3). Moreover, both VGT and SCT revealed higher leftward

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lateralization in posterior regions compared to the WGT. Finally, the SCT showed no regions with

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stronger left-lateralization respect to the VGT. Individual values of HLL for each task in each perisylvian region are reported in supplementary Table 1.

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Identification of the Language Network and assessment of hemispheric asymmetry of rs-FC.

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The independent component analysis (ICA) yielded 32 independent components representing group-averaged networks of brain regions with BOLD fMRI signals that were temporally correlated. Components corresponding to classical RSNs were selected upon visual inspection

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against sets of previously reported maps in the resting-fMRI literature (Damoiseaux et al., 2006; Smith et al., 2009). A spatial cross-correlation analysis revealed that all the three task-evoked

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activity language maps presented the highest spatial correlation coefficients [WGT (r = 0.34), VGT (r = 0.45), SCT (r = 0.41)] with a specific IC, whose spatial extent largely resembles those of previous reports (De Luca et al., 2006; Tie et al., 2014) (Figure 3A). This network included the main nodes of the perisylvian network, such as the left IFG (pars triangularis and opercularis), the posterior portion of the STG/MTG and the SMG (Table 4). Although the topography of the LN appears left lateralized, the LN also included regions of the right hemisphere, e.g. IFG, opercular and lateral temporal regions. We then tested the presence of a leftward lateralization of the intrahemispheric connectivity of the identified network, since an eventual asymmetry of the original IC map does not necessarily reflect the presence of stronger connectivity between nodes of one hemisphere compared to connectivity between their homologues. Figure 3B illustrates the bilateral ROIs corresponding to the three perisylvian nodes that were defined on the original LN map (see 22

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance methods) and used in the regional analysis. One sample t-tests revealed a significant leftward asymmetry of the intra-hemispheric rs-FC between Wernicke and Geschwind territories [mean LI =

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0.28 ± 0.45; t(19) = 2.81, p = 0.01] (Figure 3C), while no statistically significant differences were found between left and right intra-hemispheric rs-FC of Broca-Wernicke [mean LI = 0.12 ± 0.31;

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t(19) = 1.74, p = 0.1] and Broca-Geschwind territories [mean LI = 0.14 ± 0.32; t(19) = 1.94, p =

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0.07], although the latter presented a trend toward a significant left-lateralization (individual Interhemispheric functional connectivity measures and Intra-hemispheric rs-FC LIs are reported in

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supplementary Table 2). These results suggest that, overall, the intra-hemispheric rs-FC between

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key nodes of the perisylvian network does not present a robust leftward asymmetry, and that a mild leftward asymmetry specifically involves the connectivity of the Geschwind’s territory. Asymmetries of perisylvian white matter pathways.

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Next, we quantified the lateralization of each of the three branches of the arcuate fasciculus connecting the nodes of the perisylvian network in our group of subjects. Figure 4A shows the

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reconstruction of the three segments of the AF from a representative subject according to the method developed by Catani and collaborators (Catani et al., 2005). As expected on the basis of previous reports (Budisavljevic et al., 2015; Catani et al., 2007; Lopez-Barroso et al., 2013), we observed a mean leftward asymmetry of the direct segment connecting Broca with Wernicke territories across subjects [number of streamlines, mean LI=0.33 ± 0.42; t(19) = 3.5, p = 0.002, Figure 4B]. Interestingly, the degree of lateralization of the direct segment varied considerably across subjects, ranging from an extreme left-lateralization (LI = 0.99) to a mild right-lateralization (LI = - 0.38). Three representative subjects with left-, no- and right-lateralization of the direct segment are shown in Figure 4C, while individual LIs of the micro and macro-structural properties of the three segment of the arcuate fasciculus are reported in supplementary Table 3). Notably, a significant leftward distribution was also found for the posterior segment of the arcuate fasciculus 23

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance [number of streamlines, mean LI = 0.25 ± 0.38; t(19) = 2.9, p = 0.009] connecting posterior language territories (Figure 4B). Finally, a significant rightward distribution was observed for the

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anterior segment [number of streamlines, mean LI = -0.15 ± 0.28; t(19) = -2.44, p = 0.024], consistent with previous reports (Budisavljevic et al., 2015; Lopez-Barroso et al., 2013) (Figure

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4B). There were no significant differences of the FA LIs of the anterior [mean LI = -0.005 ± 0.03;

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t(19) = -0.66, p = 0.52], direct [mean LI = 0 ± 0.03; t(19) = -0.08, p = 0.94] and posterior [mean LI = -0,004 ± 0.03; t(19) = -0.73, p = 0.48] segments of the AF, suggesting that the structural

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asymmetries observed in our sample reflected a macro- rather than a micro-structural difference

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across hemispheres.

Relationship between measures of asymmetry from different MRI techniques. The correlation analysis between the HLL measures of task-evoked activity (SLM scores of the

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three perisylvian regions for each tasks) and the degree of asymmetry of intra-hemispheric rs-FC between pairs of perisylvian regions (shown in Figure 3B) indicated the absence of a significant

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relationship after correction for multiple comparisons. Also, no significant correlation was found between asymmetry of task-evoked activity and measures of inter-hemispheric connectivity of the three perisylvian regions. Therefore, our data do not suggest a positive relationship between taskevoked activity and functional connectivity lateralization measures of the key nodes of the perisylvian network. No significant correlation was also observed between structural LIs (FA and N.streamlines of each segment of the AF) and asymmetry of task-evoked activity after correction for multiple comparisons. The same results were obtained when using regional task-evoked LIs obtained using a different set of ROIs derived from the rs-FC database (see supplementary material). However, two uncorrected negative correlations between the regional lateralization measure of the Wernicke’s region during VGT and the LI of FA of the direct (r = -.57, p=0.009) and posterior (r = -.60, p=0.005) segments of the arcuate were observed. Next, we tested whether 24

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance the lateralization index of the three AF segments were related to the degree of intra-hemispheric asymmetry of the rs-FC between pairs of perisylvian regions (shown in Figure 3B).

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Consistent with a previous report (Lopez-Barroso et al., 2013), no significant correlation was found between the lateralization of the three branches of the AF, both in terms of number of streamlines

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and FA, and the lateralization indices of intra-hemispheric functional connectivity (all p>0.05 after

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FDR correction). However, we also tested if the structural asymmetry was related to the amount of inter-hemispheric connectivity rather than the degree of asymmetry of intra-hemispheric

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connectivity. Interestingly, a robust negative correlation was observed between the FA lateralization

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index of the direct segment and measures of inter-hemispheric connectivity of both Broca’s (r = .64, p < 0.002) and Geschwind’s (r = -.64, p < 0.002) territories (correlations were significant at p < 0.05 after FDR correction). This means that subjects with greater structural asymmetry showed

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lower levels of inter-hemispheric communication. No significant correlation was observed with the other two branches, nor with the LI of the number of streamlines (all p>0.05).

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Principal Component Analyses of MRI and neuropsychological measures. In order to identify multimodal components of hemispheric asymmetry for the assessment of brainbehavioral correlations, we first performed a data reduction analysis for both the MRI and the neuropsychological measures. Multimodal components of hemispheric asymmetry were obtained using a two-step PCA approach.

The PCA on the measures of task-evoked activity identified three main factors with eigenvalues > 1 and explained variance > 10%, accounting for 71% of the total variance. The first factor explained 29% of the variance and loaded with SLM scores of Broca’s territory for WGT, VGT and SCT (loading = 0.809; 0.861; 0.757, respectively) and the SLM score of Wernicke’s territory for SCT (loading = 0.494). The second factor explained 25% of the variance and loaded with SLM scores of Geschwind’s territory for WGT, VGT and SCT (loading = 0.766; 0.854; 0.610, respectively). The 25

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance third factor explained 17% of the variance, loading with SLM scores of Wernicke’s territory for WGT and VGT (loading = 0.761; 0.755, respectively). Thus, the PCA analysis did not separate

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measures according to the task, but rather according to the perisylvian node, and further indicated a similar pattern of activity between the two production tasks in the Wernicke’s territory.

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For the measures of rs-FC, two main factors accounted for 66% of the total variance. The first

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factor explained 42% of the variance and loaded with all three inter-hemispheric-FC measures between homologue ROI pairs of Broca’s, Wernicke’s and Geschwind’s territories (loading =

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0.891; 0.786; 0.766, respectively). The second factor accounted for 24% of variance and loaded

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with LIs of intra-hemispheric rs-FC of all three non-homotopic pairs, Broca-Wernicke, WernickeGeschwind, Broca-Geschwind (loading = 0.721; 0.720; 0.563, respectively). Therefore, the analysis indicated a clear separation of the intra- and inter-hemispheric functional connectivity measure of

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lateralization.

For the DTI measures, the PCA revealed the presence of two main factors, accounting for 60% of

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the total variance. The first factor explained 42% of the variance and mainly reflected the contribution of the indirect segments of the arcuate fasciculus. This factor loaded with the LIs of FA and N.streamlines of the anterior (loading = 0.78; -0.58, respectively) and posterior segments of the arcuate (loading = -0.75; 0.73, respectively). The second factor explained 18% of the variance and loaded with LI of FA of the direct segment of the arcuate (loading = 0.87), thus reflecting a separate contribution of the direct segment. The LI of N.streamlines of the direct segment showed an intermediate loading with each factor (0.48; 0.43, respectively). Next, a higher-order across-technique PCA was conducted on the factors that resulted from the within-domain analyses (Figure 5, bottom). The analysis identified three main macro-factors, accounting for 64% of the total variance. The first factor, named DTI/FC/TASK, explained 26% of variance and loaded with the LI of the direct segment (FA) of the arcuate fasciculus, the rs-FC inter26

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance hemispheric lateralization measures of all the homologue ROI pairs and the SLM scores of Wernicke’s territory for WGT and VGT (loading = -0.779; 0.845; 0.554, respectively). The second

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factor, named FC/TASK, explained 21% of variance and loaded with LIs of intra-hemispheric rsFC of all three non-homotopic pairs and with SLM scores of Broca’s territory for WGT, VGT and

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SCT and the SLM score of Wernicke’s territory for SCT (loading = -0.701; 0.818, respectively).

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The third factor, named DTI/TASK, accounted for the 17% of the variance, loading with the LIs of the indirect segments of the arcuate (FA and N.streamlines), the LI (N.streamlines) of the direct

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segment and task-evoked activity SLM scores of Geschwind’s territory for WGT, VGT and

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SCT(loading = 0.812; -0.796, respectively). Thus, only the first macro-factor included HLL measures obtained from all the MRI techniques. Noteworthy, when performing the PCA with regional task-evoked LIs obtained using a different set of ROIs (derived from the analysis of the

material).

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resting state functional connectivity) the same three macro-factors emerged (see supplementary

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The PCA performed on the neuropsychological measures identified three main factors accounting for 86% of the total variance (Figure 5, left). The first factor, named vINT for verbal intelligence, explained 51% of the variance and loaded with the Total IQ, Verbal IQ and vocabulary subtest of the WAIS-R (loading = 0.92; 0.87; 0.86, respectively). The second factor, named vMEM for verbal memory and conceptual ability, explained 18% of the variance and loaded with immediate and delayed verbal recall scores of the LSRUW and the Lexical Informativeness of the TENS (loading = 0.94; 0.95; 0.64, respectively). The third factor, named EXE for executive functions, accounted for 17% of the variance and loaded with Performance IQ and block design subtest of the WAIS-R and the with the Speech Rate of the TENS (loading = 0.76; 0.86; -0.75, respectively). Relationship between hemispheric asymmetry and language abilities.

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Correlation analyses were performed between the three macro-factors that emerged from the higherorder PCA and the three main factors from the behavioral PCA (Figure 5, center). A robust positive

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correlation (r = .70, p=0.001) was found between the macro-factor DTI/FC/TASK and the behavioral factor vINT (correlation was significant at p < 0.005 after FDR correction, highlighted in

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red in Figure 5). Thus, according to the signs of the loading factors, higher language performance

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was related to more symmetrical structural organization of the direct segment, stronger interhemispheric functional connectivity and higher lateralization of the Wernicke’s territory during

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both production tasks. Noteworthy, no correlation between each pair of variables forming the

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behavioral factor and the MRI macro-factor exceeded the value of r = .5, supporting the hypothesis that a multimodal index combining information from multiple techniques better captures a relationship with language performance. Importantly, no significant correlation was found between

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the same macro-factor and the EXE behavioral factor that included the control behavioral measures, indicating a specificity for language abilities. Furthermore, no significant correlation was found

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between the other two macro-factors and any behavioral factors, suggesting the presence of a specific brain-behavioral relationship only with the more comprehensive macro-factor of HLL. Importantly, the same correlation between the macro-factor DTI/FC/TASK and the behavioral factor vINT emerged when using task-evoked regional LIs obtained with a different set of ROIs (see supplementary material).

Despite the strong correlation with the vINT factor, the DTI/FC/TASK macro-factor did not show a significant correlation with the vMEM factor, in apparent contrast with the previously reported relationship between the asymmetry in the number of streamlines of the direct segment and measures of verbal recall (Catani et al., 2007). A direct replication of the analysis conducted by Catani and colleagues, however, revealed a significant negative correlation between the LI of the direct segment (N.streamlines) and the immediate verbal recall score from the LSRUW (r = -.48, p 28

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance < 0.03), thus confirming the existence of a specific positive relationship between the symmetric

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pattern of anatomical connectivity and the ability to remember words using semantic associations.

Discussion.

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In the last two decades, multiple imaging techniques and analytic methods have been proposed to

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assess the hemispheric lateralization for language functions. To date, the agreement across techniques (e.g., (Doucet et al., 2015; Powell et al., 2006; Propper et al., 2010; Vernooij et al.,

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2007)) and the behavioral significance of the HLL (e.g., (Catani et al., 2007; Everts et al., 2009;

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Gotts et al., 2013; van Ettinger-Veenstra et al., 2010)) are still a matter of debate. The aim of the present work was to obtain both functional (task-evoked activity fMRI and rs-FC) and structural (DTI) measures of HLL in the same group of healthy, right-handed subjects and assess their

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relationship by using comparable statistical procedures. We also aimed to identify macro-factors of lateralization by combining information from multiple techniques and examine their relationship

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with language ability. The results support the presence of hemispheric lateralization across MRI techniques but do not suggest a direct relationship between different lateralization measures. A strong negative relationship was observed between measures of functional (inter-hemispheric) and anatomical (lateralization of the direct AF segment) connectivity, which might reflect the role of anatomical lateralization in shaping inter-hemispheric communication. Instead, no significant relationship was found between HLL measures of task-evoked activity and either anatomical or functional connectivity after correction for multiple comparisons, supporting the lack of a strong direct relationship between task-related and task-free HLL measures. Finally, only the macro-factor of HLL that combined information from all the MRI techniques exhibited a robust relationship with language abilities. Specifically, better performance was related to more symmetrical structural organization and stronger inter-hemispheric communication at rest, but also to higher left29

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance lateralized activity during production tasks. This finding supports the relevance of a combined measure of HLL in the assessment of brain-behavioral relationships, and further supports the

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different contribution of task-free and task-related HLL measures for language performance.

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Multimodal assessment of HLL.

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To our knowledge, no direct comparison has ever been performed between Statistical Lateralization Maps from different language tasks. The quantitative assessment of task-evoked HLL using SLMs

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showed that leftward lateralization was consistently observed in key nodes of the perisylvian

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network, also when the original activation map showed either no activity in some of the nodes (i.e. WGT), or presence of widespread right-hemispheric activity (i.e. SCT). The consistent performance of the SLM method over tasks tapping on multiple language abilities confirms its sensitivity in the

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assessment of lateralization and its specificity for the identification of language-related areas, and supports its use as a valid alternative to invasive methods for the lateralization of cognitive

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functions (Liegeois et al., 2002; Rowan et al., 2004). In addition, the lateralization performance of the verb generation task, which shows higher left-lateralization in anterior and posterior nodes of the perisylvian network compared to the SCT and the WGT, respectively, provides quantitative evidence supporting the advantage of this paradigm for a complete assessment of the HLL. The rs-FC analysis identified a previously reported left-lateralized IC (De Luca et al., 2006; Tie et al., 2014) that showed the highest topographical agreement with maps of task-evoked activity. Notably, this network was located more ventrally compared to another fronto-parietal network that frequently fractionates into a left- and a right-hemisphere component in ICA (Smith et al., 2009) and is more generally associated with cognitive control (Dosenbach et al., 2008; Vincent et al., 2008). Interestingly, the regional analysis revealed a significant leftward lateralization of the intrahemispheric functional connectivity only between Wernicke’s and Geschwind’s territories, 30

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance suggesting a less diffused pattern of leftward asymmetry compared to the strong and spatially extended pattern observed in the analysis of task-evoked activity.

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As expected on the basis of previous reports that investigated the asymmetry of the three segments of the arcuate fasciculus in right-handers samples (Budisavljevic et al., 2015; Catani et al., 2007;

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Lopez-Barroso et al., 2013; Thiebaut de Schotten et al., 2011b), the present structural analysis

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indicated a leftward macro-structural asymmetry of the direct segment connecting Broca’s and Wernicke’s territories. However, we observed greater inter-subject variability compared to previous

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studies, with less subjects showing extreme left-lateralization (Budisavljevic et al., 2015; Catani et

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al., 2007; Lopez-Barroso et al., 2013). While we cannot exclude that the difference reflects the specific mother tongue of the two samples, the presence of such variability in the distribution of the LIs across subjects suggests the need to conduct large-sample studies to generalize the results to the

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whole population. At the macro-structural level, a significant leftward distribution was also found for the posterior segment of the AF connecting posterior language territories, while a significant

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rightward distribution was observed for the anterior segment. This latter finding is consistent with previous reports (Budisavljevic et al., 2015; Lopez-Barroso et al., 2013; Thiebaut de Schotten et al., 2011b) and with the idea that the anterior segment of the AF corresponds to the third branch of the Superior Longitudinal Fasciculus (SLFIII) (Schmahmann and Pandya, 2006; Thiebaut de Schotten et al., 2005; Urbanski et al., 2011), known to be right-lateralized in right-handed healthy subjects (Thiebaut de Schotten et al., 2011a). Instead, we did not find significant asymmetries in any of the three segments at the micro-structural level. Compared to macro-structural asymmetries, there is no general consensus about the presence of a lateralization of micro-structural properties, such as the fractional anisotropy, in these pathways (Catani et al., 2007; Lopez-Barroso et al., 2013; Thiebaut de Schotten et al., 2011b). Moreover, due to our relatively small sample size, we did not investigate potential sex differences in the degree of lateralization across MRI techniques. This issue has been 31

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance addresses in previous studies that showed higher leftward lateralization of the direct segment in men with respect to women (Catani et al., 2007; Thiebaut de Schotten et al., 2011b) and indicated an

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interaction between sex and handedness in the variations of the arcuate fasciculus asymmetry (Hagmann et al., 2006). Future multimodal studies with larger samples should consider the potential

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role of sex-related differences in HLL.

Relationship across techniques.

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Overall, our data do not support the presence of a direct pattern of inter-dependency between

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different MRI techniques for HLL assessment, but rather suggests the presence of a more complex relationship. First, no correlation was found between functional indices. Although several studies have proposed that rs-FC recapitulates the pattern of task-evoked activity, conceptualizing resting

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state networks as priors for task networks (e.g. (Smith et al., 2009)), the relevance of this relationship for hemispheric asymmetry has been only recently addressed (Doucet et al., 2015;

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Joliot et al., 2016). Specifically, the study by Joliot and colleagues (2016) suggests the presence of a positive relationship between asymmetry of intra-hemispheric rs-FC and language hemispheric dominance in a large sample of subjects. The lack of a significant relationship in our study might reflect either the lower sample size or methodological differences concerning, for example, ROIs definition and assessment of lateralization. However, we agree with previous advices for caution in interpreting hemispheric asymmetry of rs-FC, because spontaneous and task-evoked activity may represent independent properties of functional networks (Buckner et al., 2013; Hermundstad et al., 2013; Wang et al., 2014). Second, we did not find evidence for a strong direct relationship between structural and regional task-evoked SLM scores, a result that supports the idea, expressed in previous studies (Lopez-Barroso et al., 2013; Vernooij et al., 2007), that a direct relationship between the two measures does not have to be necessarily assumed, at least in right handed 32

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance subjects. To date, a significant relationship has only been reported under specific conditions, i.e. left-handers (Propper et al., 2010) and in monozygotic twin pairs (Haberling et al., 2013), although

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this last result does not come from a test of linear relationship. Finally, also the correlation analysis between the LIs of the two task-free methods yielded no

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significant results. Instead, we found a strong, negative relationship between the LI of the direct AF

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segment and inter-hemispheric measures of functional connectivity of Broca’s and Geschwind’s territories. It is possible that the leftward structural asymmetry of the direct segment might have

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determined a down-regulation of the level of inter-hemispheric connectivity, especially of those

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regions showing stronger left-lateralized pattern of connectivity at rest (i.e. left Broca vs. Wernicke, (Zhu et al., 2014)). We further speculate that the presence of greater inter-hemispheric connectivity might in turn allow a stronger inter-hemispheric inhibition, and thus the development of an

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asymmetry of task-evoked activity. Although a relationship between inter-hemispheric connectivity and task-evoked activity was not observed, their inclusion in the same behaviorally-relevant macro-

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factor (see below) fits with this interpretation. An interesting paper by Tzourio-Mazoyer et al. (2016) reported a negative correlation between language lateralization during sentence production and the strength of resting-state synchronizations across homotopic areas, considered as an index of Corpus Callosum (CC) function. This result led the authors to suggest that the intrinsic connectivity, mediated through CC, mainly reflects the strength of excitatory activity. However, both inhibitory and excitatory models of CC function have been proposed, with larger and smaller diameter fibers considered as excitatory and inhibitory, respectively (see (van der Knaap and van der Ham, 2011) for a review). While the majority of the interhemispheric connections between language areas might be excitatory, as suggested by Tzourio-Mazoyer and colleagues, the relationship might be reversed when considering those regions that are connected by thinner axons,

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ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance as, for example, those originating from temporo-parietal regions (Aboitiz et al., 1992; Hagmann et

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al., 2006)).

Relevance of HLL for language performance.

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The second aim of the present study was to assess the behavioral significance of the HLL obtained

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with different techniques. Instead of testing the relationship between each HLL index and measures of language performance, we first conducted a principal component analysis with the twofold

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purpose of summarizing the data and combining information into multimodal indices of HLL. We

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hypothesized that such an approach would better capture complex patterns of brain organization and facilitate the detection of a significant relationship between HLL and language abilities. The analysis identified three main macro-factors, one that combined information from the three

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techniques, the other two combining measures of task-evoked activity with either structural or functional connectivity. Importantly, only the first macro-factor showed a strong relationship with

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the behavioral factor representing general verbal intelligence (including verbal IQ and the vocabulary subtest of the WAIS-R). Specifically, better general language performance was associated with lesser anatomical asymmetry of the direct segment, higher inter-hemispheric FC but also higher regional lateralization of Wernicke’s territory during language production tasks. The robustness of the present findings is supported by a control analysis that used an alternative set of ROIs derived from the rs-FC database, which provided almost identical results. In addition, the brain-behavior correlation was stronger than every single possible correlation between pairs of behavioral and MRI variables, indicating the higher sensitivity of the multimodal approach. This finding has several implications. First, it supports the hypothesis that an extreme lateralization of the direct perisylvian pathway is not advantageous for language abilities (Catani et al., 2007). This argument is further bolstered by the positive relationship between language performance and 34

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance inter-hemispheric resting state connectivity, suggesting the importance of the proper equilibrium between excitation and inhibition across hemispheres for optimal function (Bloom and Hynd, 2005;

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Manson et al., 2008). However, performance appears to be positively associated with task-evoked lateralization of Wernicke’s region. While the involvement of this region is consistent with the

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reported prevalence of cross-hemispheric inhibition of temporal, but not frontal, regions (Josse et

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al., 2008; Josse and Tzourio-Mazoyer, 2004), the results clearly indicate the different contribution of the two functional measures of HLL for behavioral performance. In this respect, hemispheric

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asymmetry seems to confer behavioral advantage only during task-evoked activity, consistent with

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a mechanism of on-line reorganization of the language network architecture based on task demands (DeSalvo et al., 2014).

Finally, the present brain-behavioral relationship selectively involved measures of verbal

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intelligence, as no link was found with other behavioral factors associated with either executive functions or verbal memory. The absence of a relationship between the asymmetry of anatomical

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connectivity and verbal recall was somewhat unexpected, given previous evidence (Catani et al., 2007). Indeed, a moderate yet significant relationship was found in our sample when replicating the analytic strategy of the aforementioned study. Such moderate correlation coefficient might depend on the current sample size, which was dictated by our major interest in collecting multiple MRI and behavioral measures in the same group of subjects. However, the strong brain-behavioral correlation presented above suggests that the study sample was not underpowered. Thus, while our multimodal approach emphasized a general, strong brain-behavioral relationship, further research is needed to better address the link between HLL, verbal memory and micro-/macro-linguistic skills. To further extend our findings to the whole population, it would be also important to include lefthanded subjects or participants with atypical language dominance. A recent task-evoked fMRI study conducted by Mellet et al. (2014) on a large sample of left and right-handed subjects reported 35

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance that, compared to both typical and strongly-atypical subjects, those ambilateral for language production (i.e. those subjects with no dominant hemisphere for language according to a

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hemispheric functional LI) had lower performances in verbal but also non-verbal domains. This result partially fits with those of our multimodal approach, since we reported a behavioral

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advantage (at least for verbal abilities) for those subjects showing higher task-evoked leftward

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lateralization of Wernicke’s region during WGT and VGT. However, our study also evidenced that strong structural/functional asymmetry at rest does not seem to be advantageous for linguistic

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performance, suggesting the existence of a more complex pattern of brain organization for language

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functions as well as a different contributions of task-free and task-related HLL measures for language performance. In conclusion, to the best of our knowledge this is the first study aimed at evaluating the concordance between structural and functional HLL measures and assessing the

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relationship between multimodal indices of language lateralization and language performance. The behavioral relevance of our results, and its confirmation in larger-scale studies, could have

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significant implication for understanding recovery and planning treatment of patients suffering from language disorders.

Acknowledgements

We thank Riccardo Navarra for providing the DWI sequence, Cristiano Crescentini for providing the lists of words used in the Verb Generation Task and Sara Spadone for technical support in visualizing symmetric data on Caret.

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verb generation task (VGT) active and control task blocks were intermixed with rest periods. During the active period a noun was presented on a black background and was followed by a white

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fixation cross, during which subjects were instructed to think of pronouncing one associated verb. Stimuli were selected from the list of words used by Crescentini et al. (Crescentini et al., 2008). During the control period, a consonant letter string was followed by a white fixation cross and subjects were instructed to just maintain central fixation. In the sentence comprehension task (SCT), active and control task blocks were alternated. During active periods, a phrase of five words was presented centrally (one word at the time) in white letters on a black background, describing a scene taken from the AAT (Huber et al., 1983; Luzzatti et al., 1996). Then, two b/w images from the AAT were presented above and below a white central fixation cross and subjects had to select the image corresponding to the meaning of the previous sentence using the middle (upper image) or the index (lower image) finger of both hands simultaneously. During the control block, a sequence of five letter strings was presented and subjects had to select the image representing the girl/woman. 44

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Verb Generation Task (VGT) and Sentence Comprehension Task (SCT). Voxelwise maps of the main effect of the three tasks are superimposed over the lateral representation of both hemisphere of

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the population average landmark surface (PALS) Atlas (Caret 5.64 Software (Van Essen, 2005)).

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B. Direct statistical comparison of fMRI activation in homotopic voxels between hemispheres (Statistical Lateralization Maps, SLMs) (p<0.05 FWE corrected). To visualize SLMs on surface, we

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the cerebellum (not shown). The only cortical rightward lateralization was observed for the SCT in the postcentral gyrus (see Table 2).

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C. Brain regions of the SLMs showing task-related differences in the degree of HLL. Both production tasks showed stronger left-lateralization of Broca’s territory compared to the SCT. VGT and SCT showed instead higher leftward lateralization in posterior nodes of the perisylvian network compared to the WGT.

Figure 3. A. The Language Network (LN) as identified by means of independent component analysis (ICA) of rs-FC data. B. Regions of Interest (ROIs) used to obtain Intra-hemispheric Lateralization Indices (LIs) and Inter-hemispheric measures of rs-FC superimposed on the flat PALS representation of the left hemisphere (B = Broca’s territory; W = Wernicke’s territory; G = Geschwind’s territory). 45

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B. Lateralization Indices (LIs) showing the individual asymmetry of the three segments of the AF

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(colors are in agreement with panel A). The bars underlined in gray indicate the values from the three subjects represented in panel C. For each segment of the AF, group mean LIs were also

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reported. A statistically significant rightward asymmetry was found for the number of streamlines of the anterior segment, while leftward asymmetries were found for the number of streamlines of the direct and posterior segments. *p < 0.05 and **p < 0.01. C. Example of the variation of the degree of asymmetry of the direct segment (red) across subjects. Representative subjects with symmetrical (s01, top), extreme left-lateralized (s10, middle) and mild right-lateralized (s14, bottom) direct segments are presented.

Figure 5. Schematic representation of the results of the Principal Component Analyses (PCA) performed on neuropsychological measures and MRI HLL data, and scatterplots showing the relationship between factors. Behavioral Factors of verbal intelligence (vINT), verbal memory (vMEM) and executive 46

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on the horizontal axis (LI FA ant/dir/pos = laterality indices of fractional anisotropy for the anterior, direct and posterior segments of the arcuate fasciculus; LI N.Stream. ant/dir/pos = laterality indices

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(top/left scatterplot delimited in red).

Supplementary Figure 1. Results of the Principal Component Analyses (PCA) performed on

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neuropsychological measures and MRI HLL data, and scatterplots showing the relationship between factors. Behavioral Factors of verbal intelligence (vINT), verbal memory (vMEM) and executive functions (EXE) are shown on the vertical axis. The final higher-order macro-factors of HLL (DTI/FC/TASK, FC/TASK, DTI/TASK) coming from the two-step PCA on MRI data are presented on the horizontal axis (LI FA ant/dir/pos = laterality indices of fractional anisotropy for the anterior, direct and posterior segments of the arcuate fasciculus; LI N.Stream. ant/dir/pos = laterality indices of number of streamlines for the anterior, direct and posterior segments of the arcuate; B/W/G Intra/Inter FC = Intra-hemispheric lateralization indices and Inter-hemispheric measures for the three perisylvian territories; LI B/W/G WGT/VGT/SCT = lateralization indices of each perisylvian region for each task obtained using ROIs derived from the rs-FC analysis). The scatterplots represent the intersection between the factor on the row and the macro-factor on the column. 47

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Table 1. Brain regions significantly activated in the comparison of the main task effect during WGT, VGT and SCT for the conditions of interest (task condition for the WGT, task > control

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conditions for VGT and SCT). Results are FWE corrected at Z > 3.1, cluster-based threshold of P < 0.05. Peak MNI coordinates (mm) within clusters were identified using a minimum peak-distance

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Brain Region

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Paracinguate Gyrus Inferior Frontal Gyrus, pars opercularis Inferior Frontal Gyrus, pars triangularis Insular Cortex Precentral Gyrus Putamen Superior Frontal Gyrus Thalamus Insular Cortex Cerebellum (VI) Paracingulate Gyrus

7.01 6.90 6.49 6.16 5.69 5.21 4.89 3.68 6.27 6.22 3.45

-6 -44 -46 -26 -50 -18 -20 -12 38 26 14

14 12 32 24 -10 6 -6 -14 20 -62 24

48 22 12 2 50 10 56 6 2 -26 28

Middle Frontal Gyrus Paracingulate Gyrus Inferior Frontal Gyrus, pars triangularis Inferior Frontal Gyrus, pars opercularis Superior Frontal Gyrus Thalamus Middle Temporal Gyrus, temp-occipit. part Putamen Frontal Pole Cerebellum (Crus I) Insular Cortex Cerebellum (Crus II) Paracingulate Gyrus

6.78 6.23 6.14 5.73 4.98 4.57 4.46 4.39 3.69 5.65 5.51 5.34 4.83

-44 -8 -46 -42 -14 -18 -52 -16 -32 40 36 12 10

2 16 28 12 8 -6 -46 10 44 -62 20 -74 20

50 48 4 24 70 14 2 0 14 -28 -2 -34 40

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Verb Generation Task (VGT) Task > Control condition Left hemisphere

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cerebellar atlas.

Sentence Comprehension Task (SCT) Task > Control condition

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Inferior Frontal Gyrus, pars triangularis Superior Temporal Gyrus, posterior division Superior Temporal Gyrus, anterior division Precentral Gyrus Frontal Orbital Cortex Cerebellum (Crus I) Angular Gyrus Temporal Pole Inferior Temporal Gyrus, temp-occipit. part Superior Frontal Gyrus Occipital Pole Lateral Occipital Cortex, superior division Lateral Occipital Cortex, inferior division Thalamus Middle Frontal Gyrus Precuneus Cortex Cerebellum (VI) Cerebellum (I-IV) Amygdala Supramarginal Gyrus, posterior division Cerebellum (VI) Frontal Orbital Cortex Angular Gyrus Lateral Occipital Cortex, superior division Temporal Pole Temporal Occipital Fusiform Cortex Occipital Pole Inferior Frontal Gyrus, pars opercularis Caudate Superior Parietal Lobule Superior Temporal Gyrus, posterior division Precentral Gyrus Superior Frontal Gyrus Middle Temporal Gyrus Paracingulate Gyrus Middle Frontal Gyrus Inferior Temporal Gyrus, temp-occipit. part

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-48 -54 -54 -36 -38 -8 -56 -50 -42 -6 -12 -30 -44 -6 -30 -10 -28 -4 -20 -46 14 34 46 44 48 42 16 52 12 24 50 38 8 50 4 34 60

26 -30 -10 8 28 -78 -54 14 -50 10 -92 -76 -68 -10 18 -50 -60 -52 -6 -48 -70 26 -46 -70 18 -50 -90 22 6 -52 -28 6 30 -12 44 2 -62

18 0 -8 26 -6 -28 20 -16 -14 64 0 28 6 6 58 44 -34 -18 -16 44 -28 -6 16 16 -22 -18 0 20 4 46 0 32 54 -16 26 56 -14

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Table 2. Brain regions of the Statistical Lateralization Maps (SLMs) significantly lateralized in each task (WGT, VGT, SCT) for each condition of interest (task condition for the WGT, task > control conditions

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for VGT and SCT). Results are FWE corrected at Z > 3.1, cluster-based threshold of P < 0.05. Peak MNI

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Precentral Gyrus Thalamus Inferior Frontal Gyrus, pars triangularis Superior Parietal Lobule Supramarginal Gyrus, posterior division Left Putamen Superior Frontal Gyrus Lateral Occipital Cortex. superior division Inferior Temporal Gyrus, temp-occipit. part Inferior Frontal Gyrus, pars opercularis Paracingulate Gyrus Supplementary Motor Cortex Frontal Orbital Cortex Cingulate Gyrus. posterior division Frontal Pole Occipital Pole Hippocampus Intracalcarine Cortex Amygdala Parietal Operculum Cortex Cerebellum (Vermis) Cerebellum (Crus II) Cerebellum (Crus I)

6.66 6.08 5.68 5.25 5.17 5.01 4.79 4.75 4.72 4.7 4.45 4.25 4.02 4.34 4.28 4.19 3.80 3.56 3.55 3.33 5.18 4.11 3.94

-42 -4 -44 -28 -54 -22 -28 -26 -44 -50 -2 -4 -26 -2 -4 -2 -32 -8 -26 -44 2 20 36

2 -8 28 -48 -48 2 -2 -66 -48 10 16 0 20 -52 64 -94 -22 -74 0 -36 -72 -82 -72

36 12 16 36 10 0 66 48 -12 4 50 72 -30 14 34 -6 -12 12 -22 30 -22 -36 -24

Inferior Frontal Gyrus, pars opercularis Angular Gyrus Middle Temporal Gyrus, posterior division Inferior Frontal Gyrus, pars triangularis Precentral Gyrus Precuneous Cortex

6.59 6.24 6.20 5.84 5.81 5.54

-52 -36 -58 -48 -42 -2

12 -54 -42 30 4 -54

20 42 2 2 38 12

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-32 -8 -2 -48 -44 -42 -20 -58 -18 -18 -6 -54 14 36 6

12 46 -26 -60 -40 28 24 -4 16 -76 0 -2 -70 -74 -54

64 42 14 22 -14 -20 48 -10 -2 60 56 -40 -28 -26 -38

5.91 5.56 5.07 4.94 4.76 4.67 4.6 4.31 3.71 3.52 3.21 4.28 4.28 3.96

-64 -58 -48 -50 -52 -50 -2 -28 -42 -56 -28 34 30 40

-24 -44 24 14 4 0 12 14 -52 -44 -4 -82 -50 -24

-4 12 -2 28 -18 50 70 66 30 46 54 -34 -30 52

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5.46 5.25 5.19 4.98 4.86 4.76 4.76 4.54 4.52 4.40 3.80 3.46 6.52 6.02 5.36

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Superior Temporal Gyrus, posterior division Supramarginal Gyrus, posterior division Frontal Operculum Cortex Inferior Frontal Gyrus, pars opercularis Temporal Pole Precentral Gyrus Superior Frontal Gyrus Middle Frontal Gyrus Angular Gyrus Supramarginal Gyrus, posterior division Middle Frontal Gyrus Cerebellum (Crus I) Cerebellum (VI) Postcentral Gyrus

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Middle Frontal Gyrus Frontal Pole Thalamus Angular Gyrus Inferior Temporal Gyrus, posterior division Frontal Orbital Cortex Superior Frontal Gyrus Superior Temporal Gyrus, anterior division Putamen Lateral Occipital Cortex, superior division Supplementary Motor Cortex Inferior Temporal Gyrus, anterior division Cerebellum (VI) Cerebellum (Crus I) Cerebellum (IX)

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the degree of HLL. Results are FWE corrected at Z > 3.1, cluster-based threshold of P < 0.05. Peak

PT

coordinates were identified using a minimum peak-distance between local maxima of 20mm.

Z score

x

y

z

Left hemisphere VGT > WGT Left hemisphere

Precentral Gyrus

4.95

-56

-6

40

4.57 4.55 4.49 4.27 3.66 3.65

-8 -54 -54 -62 -48 -46

46 -2 -46 -34 42 -54

40 -32 42 -2 0 14

5.10 5.02 4.83 4.62 4.60 4.40 3.43

-50 -32 -8 -52 -4 -40 -26

-10 -50 -16 10 6 36 -68

48 44 4 26 52 20 36

Angular Gyrus Superior Temporal Gyrus, anterior division Middle Temporal Gyrus, temp-occipit. part Superior Temporal Gyrus, posterior division

5.20 4.59 4.29 3.88

-60 -56 -48 -64

-54 4 -38 -18

32 -16 2 -4

Superior Parietal Lobule Inferior Frontal Gyrus, pars triangularis Paracingulate Gyrus Temporal Pole Middle Frontal Gyrus Frontal Pole Inferior Frontal Gyrus, pars opercularis Middle Temporal Gyrus, temp-occipit. part Superior Frontal Gyrus Lateral Occipital Cortex, superior division

5.45 5.12 4.83 4.79 4.66 4.65 4.52 4.42 4.18 3.74

-32 -50 -8 -42 -50 -26 -54 -50 -22 -26

-52 32 18 20 26 52 14 -48 6 -74

40 8 42 -24 28 8 -2 -6 58 56

RI

Brain Region

SC

WGT > VGT

MA

NU

Superior Frontal Gyrus Middle Temporal Gyrus, anterior division Angular Gyrus Middle Temporal Gyrus, posterior division Frontal Pole Middle Temporal Gyrus, temp-occipit. part

Precentral Gyrus Superior Parietal Lobule Thalamus Inferior Frontal Gyrus, pars opercularis Supplementary Motor Cortex Middle Frontal Gyrus

AC CE P

SCT > WGT Left hemisphere

TE

D

WGT > SCT Left hemisphere

VGT > SCT Left hemisphere

Peak voxel (MNI coordinates)

57

ACCEPTED MANUSCRIPT Multimodal hemispheric lateralization and language performance Table 4. Peak MNI coordinates of the brain regions of the Language Network (LN), derived from group independent components analysis (ICA) of 20 healthy subjects (LN map was thresholded at Z > 3.1). Peak

Inferior Frontal Gyrus, pars triangularis Superior Temporal Gyrus, posterior division Superior Temporal Gyrus, posterior division Inferior Temporal Gyrus, anterior division Supramarginal Gyrus, posterior division Superior Frontal Gyrus Paracingulate Gyrus Superior Frontal Gyrus Frontal Pole Middle Frontal Gyrus Frontal Operculum Cortex Middle Temporal Gyrus, posterior division Temporal Pole

Z score

x

y

z

20.7 10.8 7.91 6.86 6.78 10.3 4.65 3.58 5.66 6.28 9.11 6.23 4.42

-50 -52 -50 -46 -58 -6 -6 -8 -24 -44 48 50 50

20 -36 -16 -2 -48 12 20 36 50 2 24 -36 14

-4 0 -10 -38 28 62 36 50 26 52 -2 0 -24

Peak voxel (MNI coordinates)

AC CE P

TE

D

Right hemisphere

MA

NU

SC

Left hemisphere

Brain Region

RI

Language Network (LN)

PT

coordinates were identified using a minimum peak-distance between local maxima of 20mm.

58