Evidence from Intrinsic Activity That Asymmetry of the Human Brain Is Controlled by Multiple Factors

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Evidence from intrinsic activity that asymmetry of the human brain is controlled by multiple factors

Hesheng Liua, Steven M. Stufﬂebeama,b, Jorge Sepulcrea,c,d, Trey Heddena,c, and Randy L. Bucknera,c,d,e,1 aAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129; bHarvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA 02139; cHarvard University Department of Psychology, Center for Brain Science, Cambridge, MA 02138; dHoward Hughes Medical Institute, Cambridge, MA 02138; and eDepartment of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129

Edited by Marcus E. Raichle, Washington University School of Medicine, St. Louis, MO, and approved October 12, 2009 (received for review July 18, 2009) Cerebral lateralization is a fundamental property of the human Here we show strong evidence that multiple factors associate brain and a marker of successful development. Here we provide with asymmetry of distinct brain systems and provide a method evidence that multiple mechanisms control asymmetry for distinct to measure the degree of lateralization of each of these factors brain systems. Using intrinsic activity to measure asymmetry in 300 in individual subjects. We first developed an approach to quan- adults, we mapped the most strongly lateralized brain regions. tify functional laterality based on intrinsic activity fluctuations Both men and women showed strong asymmetries with a signif- using fMRI (23, 24). Factor analysis was then performed to icant, but small, group difference. Factor analysis on the asymmet- explore whether all lateralized brain systems arise from a ric regions revealed 4 separate factors that each accounted for common factor or through multiple, distinct factors. Clear significant variation across subjects. The factors were associated evidence was obtained that showed separate factors influence with brain systems involved in vision, internal thought (the default the lateralization of distinct brain systems. network), attention, and language. An independent sample of right- and left-handed individuals showed that hand dominance Results affects brain asymmetry but differentially across the 4 factors We found that the brain’s intrinsic activity at rest is sufficient to supporting their independence. These findings show the feasibility measure functional asymmetry. Our analyses began by selecting 400 of measuring brain asymmetry using intrinsic activity fluctuations equally spaced spherical seed regions (7 mm in radius, 200 in each NEUROSCIENCE and suggest that multiple genetic or environmental mechanisms hemisphere) covering the entire cerebral cortex but excluding the control cerebral lateralization. white matter and cerebellum (Fig. S1). For each pair of seed regions within one hemisphere, the homologous regions in the opposite fMRI ͉ functional connectivity ͉ laterality hemisphere were identified and used to derive a laterality index based on the relative functional correlation strengths among the 4 fundamental property of brain organization is the presence regions (see Fig. 1A, Fig. S2, and Materials and Methods). We refer of structural and functional asymmetries between the hemi- to this measure as the intrinsic laterality index (iLI). iLI estimates A ϫ spheres (1–4). Lateralization of function is thought to contribute for all 200 199 possible seed pairs were computed in an to the evolution of human language and reasoning by providing exploratory sample of 100 subjects (50 men, 50 women). Those Ͼ Ͻ an axis for specialization of cortical systems. Estimates based on regions revealing the highest level of asymmetry (iLI 0.3oriLI Ϫ sodium amobarbital injection (5), task-based functional MRI 0.3) were combined into one metric for the most left-lateralized (fMRI) (6), and perfusion (7) suggest considerable variability regions (37 regions) and another for the most right-lateralized among people in the degree of brain asymmetry with language regions (47 regions; see Fig. 1B and Materials and Methods). The predominantly left-lateralized in most healthy right-handed most left-lateralized regions included certain traditional language adults; 2%–8% show reversed right-lateralized dominance (7). regions as well as distributed regions along the cortical midline. The Left-handed individuals show a shift with a greater percentage most strongly right-lateralized regions were localized in the visual demonstrating a dominance reversal (5). At the population level, cortex, the occipital-parietal junction, the angular gyrus, and the atypical lateralization is present in neuropsychiatric disorders, insula (Fig. 1B). including autism (8, 9) and schizophrenia (10, 11), presumably as To explore the distribution of laterality across people in an a reflection of aberrant development (12). unbiased manner, the regions identified in the initial sample of Suggesting that lateralization is partly controlled by genetic 100 subjects were examined in an independent sample of 200 factors, prominent structural asymmetries are present at birth (13, subjects (Fig. 1 C and D). Dominance for both left- and 14) and show high heritability estimates in twin studies (15). Most right-lateralized regions was found to be on a continuum rather leading theories of lateralization emphasize a single factor that than a dichotomy (7). A significant correlation was also found controls brain lateralization, often with the additional assumption between the iLI and a language laterality index determined using that the factor also gives rise to hand dominance. For example, the language task performance (see SI Text, Fig. S3, and Fig. S4). prominent ‘‘right-shift theory’’ of cerebral dominance by Annett Sex was found to be associated with functional asymmetry, (16) suggests the existence of a single gene with 2 alleles, one of though the effect was small. Previous imaging studies have which influences the distribution of asymmetries. Though recog- nizing that multiple factors contribute to the development of Author contributions: H.L. and R.L.B. designed research; H.L. performed research; S.M.S., lateralization, the influential Geschwind-Galaburda hypothesis J.S., and T.H. contributed new reagents/analytic tools; H.L. and R.L.B. analyzed data; and proposed that brain asymmetries are dependent upon circulating H.L. and R.L.B. wrote the paper. testosterone levels in the intrauterine environment (3, 17). Debate The authors declare no conflict of interest. persists as to whether sex is a factor contributing to cerebral This article is a PNAS Direct Submission. lateralization (18). Men with lateralized brain lesions are more Freely available online through the PNAS open access option. prone to language impairment than women (19), but later studies 1To whom correspondence should be addressed at: Harvard University, 52 Oxford Street, have qualified this observation (20). Similarly, imaging studies have Room 280, Cambridge, MA 02138. E-mail: [email protected]. observed sex differences in language lateralization, but these effects This article contains supporting information online at www.pnas.org/cgi/content/full/ have not been uniformly observed (21, 22). 0908073106/DCSupplemental. www.pnas.org͞cgi͞doi͞10.1073͞pnas.0908073106 PNAS Early Edition ͉ 1of5 A B Left Seed Right Seed

LL RR

Left Target Right Target

C D 16 RIGHT LEFT 16 RIGHT LEFT 14 14 12 12 10 10 8 8 6 6 Count (percent) Count (percent) 4 4 2 2 0 0 0.8 0.4 0 0.4 0.8 0.8 0.4 0 0.4 0.8 Laterality Index Laterality Index

Fig. 1. Intrinsic activity identifies lateralized brain regions. (A) Functional laterality was computed using intrinsic (spontaneous) activity by examining relative correlation strengths between seed and target regions in the 2 hemispheres. LL is the strength of correlation between the left hemisphere target region and the left hemisphere seed; LR represents the strength of correlation between the left seed and the right target; and RR and RL represent the contralateral homologues. The intrinsic laterality index (iLI), defined in Eq. 1 (see Materials and Methods), represents the relative correlation strength difference between the left and right hemispheres. (B) Using resting-state fMRI data from 100 right-handed subjects, the iLI of 39,800 pairwise correlations were computed and ranked. The 37 most left-lateralized regions (iLI Ͼ 0.3, top row) and 47 most right-lateralized regions (iLI ϽϪ0.3, bottom row) are projected onto a surface representation of the brain. (C) The laterality distribution for left-lateralized regions is displayed for an independent sample of 200 right-handed subjects and fit by a Gaussian. The iLI was defined as the mean laterality index of the 37 left-lateralized regions shown in (B). Positive iLI reflects left dominance. (D) The laterality distribution based on the 47 right-lateralized regions is displayed. reported that language is more strongly lateralized in men than 100 subjects consisting of 50 men and 50 women. The factors that women, though some other studies have not detected sex dif- emerged contained almost identical cortical topographies to the ferences (21, 22). Here we asked if cerebral lateralization differs first sample. The factors were also found to yield stable within- between right-handed men and women using intrinsic laterality. subject estimates over multiple scanning sessions. A control We calculated iLI for all 300 subjects as described above and group (n ϭ 22) was imaged on a second occasion within 3 months compared the distributions for the men (n ϭ 131) and women (n ϭ 169) (Fig. 2). Significant sex differences were found for both left-lateralized and right-lateralized systems, with stronger laterality in men than women [left, P Ͻ 0.01 (Fig. 2); right, P Ͻ 12 Male 0.05]. Though statistically reliable, the effects were small, with Female both sexes showing strong functional asymmetry. Intrinsic laterality provides a means to ask directly how 8 cerebral lateralization is organized by examining variance across subjects and asking whether the laterality of all systems track together as a single factor or whether multiple factors emerge 4

(3). We found evidence that multiple factors control functional Count (percent) lateralization. To perform this analysis, the 84 regions from Fig. 1 were subjected to a factor analysis in the initial sample of 100 0 subjects (see Materials and Methods). The number of extracted -0.2 0 0.2 0.4 0.6 0.8 factors was determined by principal component with the crite- Laterality Index rion that eigenvalues equal or exceed 1. The cortical topographies of the 3 major factors that each explained Ͼ5% of the Fig. 2. Sex differences are present but small. Sex differences of the laterality variance and a 4th factor linked to frontal and temporal regions index distribution for left-lateralized regions (blue regions in Fig. 1) are shown. The distribution for left-lateralized regions is displayed split by men associated with language (which explained 4.3% of the variance) (blue bars) and women (red bars). Overlap is shown in dark blue. The distri- are illustrated in Fig. 3. butions are ﬁt by Gaussian curves revealing that women show more symmetric To determine whether these 4 factors were reliable, we functional organization than men (Kolmogorov-Smirnov test, P Ͻ 0.01). A replicated the data-driven analysis in an independent sample of similar effect is present for the right-lateralized regions.

2of5 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0908073106 Liu et al. Sample 1 (N=100) Sample 2 (N=100)

Factor 1 11.8% 11.1%

Factor 2 10.5% 8.7%

Factor 3 6.7% 5.6%

Factor 4 4.3% 3.5% NEUROSCIENCE 0.7 0 1.0

Fig. 3. Cerebral lateralization is controlled by multiple, distinct mechanisms. Factor analysis derived from the lateralized regions of Fig. 1 reveals 4 factors that are replicable across independent data samples (color intensity represents loading value of the factor, blue and yellow color schemes reﬂect the different hemispheres). Factors tended to center around individual regions and involve their correlated partner regions. Each sample consists of 50 men and 50 women. The top 3 factors each account for Ͼ5% of the between-subject variance (explained variance is shown next to each plot). A factor that included putative language regions (ranked 5th) is also shown. Results were replicated in terms of the cortical topography, ranking, and explained variance associated with each factor. The presence of distinct factors suggests that cerebral lateralization in humans arises from multiple genetic or environmental mechanisms. of the initial session. Between-session correlations (Pearson’s r) anomalous dominance (Fig. 4B). These 2 left-handed individuals for the 4 factors were 0.79, 0.55, 0.58, and 0.59. may reflect examples of ‘‘cerebral situs invertus’’ (3) as they had The distributed anatomy of each factor roughly corresponded to right-dominant factor 2 estimates greater than any right-handed a well-studied brain system, although more detailed analysis of the subject in the initial 300-person sample. The factor 3 effect was factors in relation to task-based studies will be required to verify not carried by a few individuals but rather reflected a shift in the correspondence. The first factor included regions within the visual distribution of laterality scores (Fig. 4C). Fig. S5 displays the system. The second largest factor was associated with a network laterality distributions for all 4 factors. linked to internal thought often referred to as the ‘‘default network’’ (25, 26). The third factor was a right-lateralized network including Discussion the angular gyrus and the insula that has previously been associated Understanding the genetic and developmental basis of brain with an attentional system important for detecting unattended asymmetry will illuminate cerebral specialization and shed light events (27). The last left-lateralized factor included frontal and on neuropsychiatric, neurologic, and other developmental dis- temporal regions associated with language—in particular, con- orders that alter brain laterality (8–12, 28, 29). Genetic models trolled semantic processing. have been proposed to account for cerebral dominance (15, 16), Expanding the analysis to include factors that capture smaller and anatomical asymmetries are likely influenced by genetic proportions of the variance showed that 69% and 71% of the factors (30). However, so far no gene or pathway has been variance can be explained by 20 factors in the first and second identified as a determinant of lateralization, although there are datasets, respectively. The 4 factors illustrated in Fig. 3 capture significant, reproducible contributions to functional asymmetry, a number of candidates (12, 28, 31). so we conservatively conclude that there are at least 4 major The present findings indicate that brain asymmetries unlikely factors that determine cerebral lateralization in the human brain. arise from a single mechanism, but rather that multiple separate As a final analysis, we explored whether the 4 factors could be factors contribute to the development of cerebral lateralization. dissociated. For this analysis, the factors were measured in an A single gene, as proposed by Annett (16), cannot explain the independent sample that included handedness as a factor (38 multiple effects observed but might explain a subset of brain left-handed and 38 right-handed age- and sex-match individu- asymmetries such as that observed for the factor linked to als). A significant interaction of hand dominance and the handedness (factor 3). Similarly, a single environmental factor lateralization factors was observed (P Ͻ 0.005; Fig. 4A). Factor associated with hormonal levels (3, 17) cannot account for the 3, linked to attention, showed markedly stronger asymmetry in effects, especially considering the modest contribution of sex, the right-handed individuals (P Ͻ 0.001). Factor 2 also showed although intrauterine testosterone levels could still play an a trend for an effect of handedness (P ϭ 0.07). However, the important role. It is presently unclear whether, or to what degree, factor 2 effect was carried by 2 left-handed individuals with local anatomical asymmetries contribute to the present findings.

Liu et al. PNAS Early Edition ͉ 3of5 Materials and Methods A 0.3 Left-handed Participants. Three hundred healthy right-handed adults participated for pay- Right-handed ment in the main MRI study (131 men, age 22.3 Ϯ 3.2) and 38 right- and 38 0.2 left-handed adults participated in the second study that examined effects of handedness (17 men in each group, age 20.8 Ϯ 1.7). Handedness was assessed by 0.1 the Edinburgh handedness inventory (32). All participants performed between 1 and 4 rest runs to estimate intrinsic functional lateralization. Thirty-five partici- 0 pants additionally performed 3 runs of a language task. All participants were native English speakers and had normal or corrected-to-normal vision. Partici- -0.1 pants were screened to exclude individuals with a history of neurologic or psychiatric conditions as well as those using psychoactive medications. -0.2 Mean Laterality Index MRI Acquisition Procedures. Scanning was performed on 3 Tesla TimTrio -0.3 systems (Siemens) using the 12-channel phased-array head coil supplied by the *** vendor. Structural images were acquired using a sagittal MP-RAGE three- -0.4 dimensional T1-weighted sequence (TR ϭ 2,530 ms, TE ϭ 3.44 ms, FA ϭ 7o, 1.0 Factor 1 Factor 2 Factor 3 Factor 4 mm isotropic voxels; FOV 256 ϫ 256). B 25 For the main group of 300 subjects, the fixation (rest) runs were 390 s (156 Factor 2 time points) or 370 s (148 time points) in duration, and a black visual crosshair 20 (plus sign) was centered on a white screen during the entire run. Subjects were instructed to stay awake and look at the crosshair; no other task instruction 15 was provided. Subjects were also instructed to minimize head movement. Images were acquired using the gradient-echo echo-planar pulse sequence 10 (TR ϭ 2,500 ms, TE ϭ 30 ms, flip angle ϭ 90o, 3-mm isotropic voxels). For the 76 subjects that contributed to the analysis of the effect of handedness, 2 rest

Count (percent) 5 runs were acquired (each 372 s, 120 TP, TR ϭ 3,000 ms). For these runs, subjects 0 rested with their eyes open. -0.3 -0.1 0.1 0.3 0.5 Thirty-five subjects within the main subject cohort also performed 3 runs of Laterality Index a language task involving semantic classification of words previously used to determine language dominance (6, 33). Images were acquired using an echo C 20 Factor 3 planar imaging (EPI) gradient-echo sequence sensitive to blood oxygenation level-dependent (BOLD) contrast (TR ϭ 2,000 ms, TE ϭ 30 ms, FA ϭ 90o, slice 15 number ϭ 33, 3-mm isotropic voxels). Each run consisted of three 36-s block of the task, four 28-s blocks of fixation. During the task blocks, 12 words (6 10 concrete and 6 abstract words in random order) were presented for 2 s each with a 1-s interstimulus interval. In total, 108 stimuli were presented. The 5 Count (percent) visual stimuli were generated on an Apple PowerBook G4 computer (Apple, Inc. ) using Matlab (Mathworks, Inc.) and the Psychophysics Toolbox exten- 0 sions (34). Stimuli were projected onto a screen positioned at the head of the -0.6 -0.4 -0.2 0 0.2 0.4 magnet bore. Participants were asked to indicate if the word was concrete or Laterality Index abstract. They were instructed to respond quickly and accurately, and indicate Fig. 4. Handedness differentially affects asymmetry across distinct brain their response by key press (left-hand key press for abstract words, right-hand systems. Mean laterality estimates for each of the 4 factors in Fig. 3 are plotted key press for concrete words). for independent data samples of right-handed (n ϭ 38) and left-handed (n ϭ 38) individuals. (A) A significant interaction between hand dominance and MRI Preprocessing. Both the resting-state data and the task data were pre- factor is observed (P Ͻ 0.005) with an effect of handedness observed for factor processed using the following steps: (1) slice timing correction (SPM2, Well- 3(***, P Ͻ 0.001). Bars represent standard error of the mean. (B) The laterality come Department of Cognitive Neurology, London), (2) rigid body correction index distribution for factor 2 is plotted split by handedness. Though the for head motion, (3) normalization for global mean signal intensity across distributions overlap, there are 2 left-handed individuals with complete re- runs, and (4) transformation of the data into a standard atlas space. The versal of asymmetry. (C) The distributions for factor 3 show a marked effect of second step provided a record of head position that was later used as a handedness with right-handed individuals demonstrating greater asymmetry. nuisance regressor for correlation analysis. Atlas registration was achieved by computing affine transforms connecting the first image volume of the first functional run with the T2*-weighted functional image target (RIB, Univ of Our findings suggest that models of brain asymmetry should Oxford, Oxford, U.K.). The atlas representative template conformed to the seek to identify multiple, distinct factors that lead to individual Montreal Neurological Institute (MNI) atlas (MNI152). Motion correction and differences in cortical lateralization perhaps through their in- atlas transformation were combined into a single step to yield a motion- fluence on early developmental events that promote cortical corrected time series resampled to 2 mm isotropic voxels. The resting-state data were analyzed using region-based correlation analysis, specialization. Providing insight into mechanisms that may give often referred to as functional connectivity MRI (fcMRI) analysis. The present rise to functional lateralization, molecular studies of transcrip- methods extend from Biswal et al. (23) and are described in detail in Vincent et al. tion have revealed an array of asymmetric gene expression (35) and Buckner et al. (36). Task data were analyzed using the general linear patterns in multiple forebrain structures during early human model as implemented in SPM2 (Wellcome Department of Cognitive Neurology, fetal development (12, 28). London). Regressors of no interest included motion correction parameters and The present observations also show the feasibility and effi- low-frequency drift. The task blocks used a gamma function convolved with a ciency of estimating functional brain asymmetries using intrinsic boxcar function to model the hemodynamic response function. activity fluctuations. Rest-state laterality indices were computed within individual subjects from rapidly acquired MRI data (Ϸ10 The Quantitative Intrinsic Laterality Index (iLI). The present exploration re- min). Within-subject estimates showed moderate test-retest required the development of a quantitative measure of a region’s intrinsic laterality. Functional mapping studies based on intrinsic activity have ob- liability. Thus, the present methods provide an approach to served lateralization within the attention system in normal subjects (37) and explore determinants of lateralization in large human samples, the memory system in patients (38). Building from these earlier observations, such as required for genetic explorations, as well as in patients we developed a generic approach to explore lateralization across all regions where determination of functional dominance is clinically rele- of cortex simultaneously. To compute an intrinsic laterality index (iLI), the vant (e.g., neurosurgical planning). relative strengths of correlations between seed and target region pairs were

4of5 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0908073106 Liu et al. measured between the hemispheres (see Fig. 1). Two hundred spherical For each one of these pairwise correlations, we averaged the correspond- regions were defined in each hemisphere, covering the gray matter of the ing iLI values across the exploratory dataset of 100 individuals. The resulting cerebral cortex (Fig. S1). All possible estimates were computed (e.g., 200 ϫ 199 39,800 mean iLIs were then sorted to determine those regions showing the pairwise combinations). From these, the regions with the strongest lateral- strongest levels of lateralization. The most left-lateralized correlations (iLI Ͼ ization were identified (iLI Ͼ 0.3 or ϽϪ0.3). 0.3, 37 regions) and most right-lateralized correlations (iLI ϽϪ0.3, 47 regions) For illustration purposes, an example of asymmetric correlations is shown were combined together into a single iLI metric (Fig. 1B). The threshold is in Fig. S2. Functional correlation maps are displayed for a seed region in the somewhat arbitrary but was selected to reduce the number of regions to a Ͼ left hemisphere and its homologous region in the right hemisphere (see top number appropriate to factor analysis. A level of 0.3 ensured no 100 regions 2 rows in Fig. S2; circles in the frontal region indicate the seed regions). The would be selected for further factor analysis. Alternative threshold values do map associated with the right hemisphere seed is subtracted from the map not change the results for these strongly lateralized regions but may lead to associated with the left hemisphere seed to derive a difference map (third row differences for weakly lateralized regions. The iLIs values for the left- and right-lateralized regions were then calculated on an independent sample of in Fig. S2). In this example, the left temporal region (marked by the circles in 200 subjects to derive an unbiased estimate of the distribution of lateraliza- the third row), among other regions, is more strongly correlated with the left tion (Fig. 2). Note also that the data sample used to derive regions for analysis frontal seed region than the mirroring correlations in the right hemisphere. included an equal number of men and women, allowing unbiased analysis of This asymmetry is easily appreciated in the difference map; the laterality index sex differences. in Eq. 1 below quantifies this asymmetry. Specifically, for each seed region, the iLI was defined based on the relative Factor Analysis. Principal axis factoring was used for the factor analysis (39). The correlation differences between the seed and target regions across the 2 hemi- iLIs of the 84 regions were used as the observed variables. The number of spheres. Fig. 1A illustrates the correlation values used in this calculation, where LL extracted factors was determined by principal component with the criterion that is the strength of the correlation between the left hemisphere seed and the left eigenvalues equal or exceed 1. The resulting factor loading values were rotated hemisphere target regions; LR represents the strength of the correlation between using normalized varimax rotation. Factor analysis was performed on the first the left hemisphere seed and right hemisphere target regions; and RR and RL sample of 100 subjects and repeated on an independent sample of 100 subjects. represent the contralateral homologues. From these 4 seed-target correlations, The factor loading values for the top 3 factors are shown in Fig. S6. The results on iLI is then calculated according to the following equation: 2 independent data samples show similar clusters of regions corresponding to Ϫ Ϫ Ϫ each factor, indicating that these factors are highly reproducible across data (LL RL) (RR LR) samples. Local anatomic asymmetries may contribute to the factors. Laterality Index ϭ [1] ͉LL͉ ϩ ͉LR͉ ϩ ͉RR͉ ϩ ͉RL͉ ACKNOWLEDGMENTS. We thank Abraham Snyder, Jessica Andrews-Hanna, Note that LL–RL corresponds to the left target region on the difference map Itamar Kahn, Ting Ren, and Tanveer Talukdar for assistance with functional connectivity; Marisa Hollinshead and Betsy Hemphill for data collection; Timothy (see the temporal region in Fig. S2 as an example), and RR–LR corresponds to NEUROSCIENCE O’Keefe and Gabriele Fariello for neuroinformatics; and the Harvard Center for the right target region on the difference map. When the denominator fell Brain Science and the Athinoula A. Martinos Center for Biomedical Imaging for below 0.2, iLI was set to zero. iLI was computed for all 200 seed regions in each imaging support. This work was supported by National Institutes of Health Grants hemisphere against the 199 possible target regions, yielding 39,800 pairwise R01AG021910, R01AG034556, P41RR14074, and K08MH067966; the Simons correlations for each subject. Foundation; and the Howard Hughes Medical Institute.

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Liu et al. PNAS Early Edition ͉ 5of5 Supporting Information

Liu et al. 10.1073/pnas.0908073106 SI Text relatively small number of the most lateralized regions were Measuring Language Laterality Based on Task-Based Imaging. To identified, sufficiently constrained to allow for a well-powered validate the intrinsic laterality index we compared it with a factor analysis. Here we define a language laterality index to be laterality index determined based on actual language task per- comparable to the task-based estimates. We used the mask formance. The task-based language laterality index was derived derived from the task activation map and selected all left- lateralized regions within the mask. Note that no information on the group activation map (Fig. S3). The regions showing about between-subject variability in the task-based or intrinsic left-lateralized activation in the group image were used as a mask laterality estimates is used to define the language mask. The for each individual subject but excluded the visual cortex. We Ͼ task-based and intrinsic language laterality indices showed sig- then summed the number of significant voxels (z 1.25) within nificant correlation between subjects, suggesting language iLI the mask of each hemisphere and then computed a laterality successfully captures between-subject variation in language lat- index defined as eralization (r ϭ 0.48, P Ͻ 0.005; Fig. S4B). As a further validation check, we also computed the language iLI on 17 patients with L Ϫ R Ͼ Laterality Index ϭ [S1] epilepsy; 16 patients showed left-lateralized language (iLI 0) L ϩ R and 1 patient showed right-lateralized language (iLI ϭϪ0.05). The patient revealing right-lateralized language iLI was re- where L is the sum voxel count in left hemisphere mask and R cruited to perform the language task in MRI. Results confirmed is the sum voxel count in the right hemisphere mask. atypical language lateralization (Fig. S4C). The other 16 patients Within the mask, the seed pairs were analyzed, yielding 322 all showed left-lateralized language in the task-based measure. left-lateralized regions that were combined into a single measure These findings indicate that intrinsic laterality can measure of language system iLI (Fig. S4A). In the previous analyses language lateralization and may provide an efficient method for described in the main text, we set a threshold of 0.3 to ensure a presurgical planning.

Liu et al. www.pnas.org/cgi/content/short/0908073106 1of7 Fig. S1. Seed regions used for laterality analysis cover the cerebral cortex. Four hundred equally spaced spherical regions (7-mm radius) were selected as the seed regions to study intrinsic laterality (200 seed regions per hemisphere). The seed regions were spaced to cover the entire cerebral cortex mantle excluding the cerebellum. The placements of the seed regions are displayed on transverse sections on top of the mean structural image from the 100 included subjects contributing to Fig. 1.

Liu et al. www.pnas.org/cgi/content/short/0908073106 2of7 Fig. S2. Procedure to identify lateralized correlations. An example pair of correlation maps for right- and left-lateralized seed regions illustrates how lateralization is determined. The functional correlation maps for a mirrored pair of right and left seed regions in frontal cortex. The first row shows the functional correlation map with a left-hemisphere seed region. The seed region is indicated by a circle. The second row shows the correlation map based on the right-hemisphere seed. The third row is the difference between the first 2 rows; the asymmetric correlations can be easily identified. The temporal regions (indicated by the circles) are strongly correlated to the left hemisphere seed region but have weaker correlations to the right hemisphere seed region. The present approach quantifies this asymmetry and was conducted for all possible seed and target region combinations to determine those seed regions with the most asymmetric functional correlations. Local anatomic asymmetries may contribute to the observed functional correlations.

Liu et al. www.pnas.org/cgi/content/short/0908073106 3of7 0 5.0 -logP

Fig. S3. fMRI activation during actual language tasks. A cortical surface projection shows the group results (n ϭ 35) of the semantic classification task on words on the left- and right-lateral surfaces. The language task blocks were contrasted with baseline fixation to identify regions significantly increasing activation during the language task. Note the strong asymmetric response in particular for prefrontal cortex along the inferior frontal gyrus. This activation map was used as a mask for the analysis reported in Fig. S4.

Liu et al. www.pnas.org/cgi/content/short/0908073106 4of7 A B 0.9

0.6

C LI Task 0.3

0 0 15 -logP -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 LI Rest

Fig. S4. Intrinsic laterality within the language system correlates with task-based estimates of language laterality. (A) Seed pairs falling within the mask deﬁned by the group results (n ϭ 35) of task-based fMRI are displayed. Seed regions included those along the lateral surface and midline. (B) The intrinsic language laterality index shows a signiﬁcant correlation with the task-based language laterality index (r ϭ 0.48, P Ͻ 0.005). (C) In an independent cohort of patients, an atypical subject was found to have a right-lateralized language laterality index of Ϫ0.05. Consistent with atypical dominance, the language task elicited strong activation in right prefrontal and temporal language areas.

Liu et al. www.pnas.org/cgi/content/short/0908073106 5of7 20 Factor 125 Factor 2

20 15 15 10 10 5 5 Count (percent) Count (percent)

0 0 -0.5 -0.3 -0.1 0.1 0.3 -0.3 -0.1 0.1 0.3 0.5 Laterality Index Laterality Index

20 Factor 320 Factor 4

15 15

10 10

5 5 Count (percent) Count (percent)

0 0 -0.6 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 0.6 Laterality Index Laterality Index

Fig. S5. Four factors are differentially associated with hand dominance. The distributions of the laterality indices for the 4 factors are displayed split by hand dominance (blue ϭ left-handed, red ϭ right-handed). Two left-handed individuals (indicated by arrows) display factor 2 estimates that are greater than any right-handed subjects. Factor 3 showed a signiﬁcant effect of hand dominance. The factor 3 effect was not carried by a few individuals but rather reﬂected a shift in the distribution of laterality scores. Control analyses revealed the effect was not due to thresholding the iLI to zero when correlation strength was weak (see text). However, local anatomic asymmetries cannot be ruled out as an explanation.

Liu et al. www.pnas.org/cgi/content/short/0908073106 6of7 Sample 1 (N=100) Sample 2 (N=100) 1 1 58 56 55 58 41 44 64 39 44 39 4155 84 64 69 69 56 84 66 74 5 4553 66 30 34 47 18 82 73 28 31 75 4 72 22 73 72 21 34 5074 0 7 3 38 0 30 49 47 19 50 19 1 67 75 14 14 24 62 77 22 31 4577 8 3210 60 7 3 61

18 Factor 3 Factor 3 63 6142 14 68 53 1265 5952 9 4346 21 37 79 5 3759577032126379 3635 238346 949 28 2523 427152 406867 43 82 293524 102 83113860406548 251126 48 20 62 80 81 36 8 78 20 29 2 71 5770 76 2680 54 33 51 33 54 27 81 51 27 16 17 78 6 13 76 6 13 16 15 17 15 −1 −1 1 1

1 1 Factor 2 Factor 2 0 0 0 0 Factor 1 Factor 1 −1 −1 −1 −1

Fig. S6. Loading values for the top 3 factors that resulted from the factor analysis. Loading values for the 84 variables are plotted in the space spanned by the 3 largest factors. The variables are labeled by numbers 1–84 corresponding to the included regions. The results on 2 independent data samples show similar clusters of regions corresponding to each factor, indicating these factors are highly reproducible.

Liu et al. www.pnas.org/cgi/content/short/0908073106 7of7