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;
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
bHarvard-Massachusetts
Edited by Marcus E. Raichle, Washington University School of Medicine, St. Louis, MO, and approved October 12, 2009 (received for review July 18, 2009)
fMRI 兩 functional connectivity 兩 laterality
A
fundamental property of brain organization is the presence
of structural and functional asymmetries between the hemispheres (1–4). Lateralization of function is thought to contribute
to the evolution of human language and reasoning by providing
an axis for specialization of cortical systems. Estimates based on
sodium amobarbital injection (5), task-based functional MRI
(fMRI) (6), and perfusion (7) suggest considerable variability
among people in the degree of brain asymmetry with language
predominantly left-lateralized in most healthy right-handed
adults; 2%–8% show reversed right-lateralized dominance (7).
Left-handed individuals show a shift with a greater percentage
demonstrating a dominance reversal (5). At the population level,
atypical lateralization is present in neuropsychiatric disorders,
including autism (8, 9) and schizophrenia (10, 11), presumably as
a reflection of aberrant development (12).
Suggesting that lateralization is partly controlled by genetic
factors, prominent structural asymmetries are present at birth (13,
14) and show high heritability estimates in twin studies (15). Most
leading theories of lateralization emphasize a single factor that
controls brain lateralization, often with the additional assumption
that the factor also gives rise to hand dominance. For example, the
prominent ‘‘right-shift theory’’ of cerebral dominance by Annett
(16) suggests the existence of a single gene with 2 alleles, one of
which influences the distribution of asymmetries. Though recognizing that multiple factors contribute to the development of
lateralization, the influential Geschwind-Galaburda hypothesis
proposed that brain asymmetries are dependent upon circulating
testosterone levels in the intrauterine environment (3, 17). Debate
persists as to whether sex is a factor contributing to cerebral
lateralization (18). Men with lateralized brain lesions are more
prone to language impairment than women (19), but later studies
have qualified this observation (20). Similarly, imaging studies have
observed sex differences in language lateralization, but these effects
have not been uniformly observed (21, 22).
www.pnas.org兾cgi兾doi兾10.1073兾pnas.0908073106
Here we show strong evidence that multiple factors associate
with asymmetry of distinct brain systems and provide a method
to measure the degree of lateralization of each of these factors
in individual subjects. We first developed an approach to quantify functional laterality based on intrinsic activity fluctuations
using fMRI (23, 24). Factor analysis was then performed to
explore whether all lateralized brain systems arise from a
common factor or through multiple, distinct factors. Clear
evidence was obtained that showed separate factors influence
the lateralization of distinct brain systems.
Results
We found that the brain’s intrinsic activity at rest is sufficient to
measure functional asymmetry. Our analyses began by selecting 400
equally spaced spherical seed regions (7 mm in radius, 200 in each
hemisphere) covering the entire cerebral cortex but excluding the
white matter and cerebellum (Fig. S1). For each pair of seed regions
within one hemisphere, the homologous regions in the opposite
hemisphere were identified and used to derive a laterality index
based on the relative functional correlation strengths among the 4
regions (see Fig. 1A, Fig. S2, and Materials and Methods). We refer
to this measure as the intrinsic laterality index (iLI). iLI estimates
for all 200 ⫻ 199 possible seed pairs were computed in an
exploratory sample of 100 subjects (50 men, 50 women). Those
regions revealing the highest level of asymmetry (iLI ⬎ 0.3 or iLI ⬍
⫺0.3) were combined into one metric for the most left-lateralized
regions (37 regions) and another for the most right-lateralized
regions (47 regions; see Fig. 1B and Materials and Methods). The
most left-lateralized regions included certain traditional language
regions as well as distributed regions along the cortical midline. The
most strongly right-lateralized regions were localized in the visual
cortex, the occipital-parietal junction, the angular gyrus, and the
insula (Fig. 1B).
To explore the distribution of laterality across people in an
unbiased manner, the regions identified in the initial sample of
100 subjects were examined in an independent sample of 200
subjects (Fig. 1 C and D). Dominance for both left- and
right-lateralized regions was found to be on a continuum rather
than a dichotomy (7). A significant correlation was also found
between the iLI and a language laterality index determined using
language task performance (see SI Text, Fig. S3, and Fig. S4).
Sex was found to be associated with functional asymmetry,
though the effect was small. Previous imaging studies have
Author contributions: H.L. and R.L.B. designed research; H.L. performed research; S.M.S.,
J.S., and T.H. contributed new reagents/analytic tools; H.L. and R.L.B. analyzed data; and
H.L. and R.L.B. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1To whom correspondence should be addressed at: Harvard University, 52 Oxford Street,
Room 280, Cambridge, MA 02138. E-mail: rbuckner@wjh.harvard.edu.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0908073106/DCSupplemental.
PNAS Early Edition 兩 1 of 5
NEUROSCIENCE
Cerebral lateralization is a fundamental property of the human
brain and a marker of successful development. Here we provide
evidence that multiple mechanisms control asymmetry for distinct
brain systems. Using intrinsic activity to measure asymmetry in 300
adults, we mapped the most strongly lateralized brain regions.
Both men and women showed strong asymmetries with a significant, but small, group difference. Factor analysis on the asymmetric regions revealed 4 separate factors that each accounted for
significant variation across subjects. The factors were associated
with brain systems involved in vision, internal thought (the default
network), attention, and language. An independent sample of
right- and left-handed individuals showed that hand dominance
affects brain asymmetry but differentially across the 4 factors
supporting their independence. These findings show the feasibility
of measuring brain asymmetry using intrinsic activity fluctuations
and suggest that multiple genetic or environmental mechanisms
control cerebral lateralization.
A
Left Seed
Right Seed
B
LR
RR
LL
RL
Left Target
Right Target
C
D
16 RIGHT
LEFT
16 RIGHT
12
Count (percent)
14
12
Count (percent)
14
10
10
8
6
4
8
6
4
2
2
0
LEFT
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 ⬎ 0.3, top row) and 47 most right-lateralized regions (iLI ⬍ ⫺0.3, 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.
2 of 5 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0908073106
100 subjects consisting of 50 men and 50 women. The factors that
emerged contained almost identical cortical topographies to the
first sample. The factors were also found to yield stable withinsubject estimates over multiple scanning sessions. A control
group (n ⫽ 22) was imaged on a second occasion within 3 months
12
Count (percent)
reported that language is more strongly lateralized in men than
women, though some other studies have not detected sex differences (21, 22). Here we asked if cerebral lateralization differs
between right-handed men and women using intrinsic laterality.
We calculated iLI for all 300 subjects as described above and
compared the distributions for the men (n ⫽ 131) and women
(n ⫽ 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 ⬍ 0.01 (Fig. 2); right, P ⬍
0.05]. Though statistically reliable, the effects were small, with
both sexes showing strong functional asymmetry.
Intrinsic laterality provides a means to ask directly how
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
(3). We found evidence that multiple factors control functional
lateralization. To perform this analysis, the 84 regions from Fig.
1 were subjected to a factor analysis in the initial sample of 100
subjects (see Materials and Methods). The number of extracted
factors was determined by principal component with the criterion that eigenvalues equal or exceed 1. The cortical topographies of the 3 major factors that each explained ⬎5% of the
variance and a 4th factor linked to frontal and temporal regions
associated with language (which explained 4.3% of the variance)
are illustrated in Fig. 3.
To determine whether these 4 factors were reliable, we
replicated the data-driven analysis in an independent sample of
Male
Female
8
4
0
-0.2
0
0.2
0.4
0.6
0.8
Laterality Index
Fig. 2. Sex differences are present but small. Sex differences of the laterality
index distribution for left-lateralized regions (blue regions in Fig. 1) are
shown. The distribution for left-lateralized regions is displayed split by men
(blue bars) and women (red bars). Overlap is shown in dark blue. The distributions are fit by Gaussian curves revealing that women show more symmetric
functional organization than men (Kolmogorov-Smirnov test, P ⬍ 0.01). A
similar effect is present for the right-lateralized regions.
Liu et al.
Sample 1 (N=100)
Sample 2 (N=100)
11.8%
11.1%
Factor 2
10.5%
8.7%
Factor 3
6.7%
5.6%
Factor 4
4.3%
3.5%
NEUROSCIENCE
Factor 1
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 ⬎5% 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)
for the 4 factors were 0.79, 0.55, 0.58, and 0.59.
The distributed anatomy of each factor roughly corresponded to
a well-studied brain system, although more detailed analysis of the
factors in relation to task-based studies will be required to verify
correspondence. The first factor included regions within the visual
system. The second largest factor was associated with a network
linked to internal thought often referred to as the ‘‘default network’’
(25, 26). The third factor was a right-lateralized network including
the angular gyrus and the insula that has previously been associated
with an attentional system important for detecting unattended
events (27). The last left-lateralized factor included frontal and
temporal regions associated with language—in particular, controlled semantic processing.
Expanding the analysis to include factors that capture smaller
proportions of the variance showed that 69% and 71% of the
variance can be explained by 20 factors in the first and second
datasets, respectively. The 4 factors illustrated in Fig. 3 capture
significant, reproducible contributions to functional asymmetry,
so we conservatively conclude that there are at least 4 major
factors that determine cerebral lateralization in the human brain.
As a final analysis, we explored whether the 4 factors could be
dissociated. For this analysis, the factors were measured in an
independent sample that included handedness as a factor (38
left-handed and 38 right-handed age- and sex-match individuals). A significant interaction of hand dominance and the
lateralization factors was observed (P ⬍ 0.005; Fig. 4A). Factor
3, linked to attention, showed markedly stronger asymmetry in
the right-handed individuals (P ⬍ 0.001). Factor 2 also showed
a trend for an effect of handedness (P ⫽ 0.07). However, the
factor 2 effect was carried by 2 left-handed individuals with
Liu et al.
anomalous dominance (Fig. 4B). These 2 left-handed individuals
may reflect examples of ‘‘cerebral situs invertus’’ (3) as they had
right-dominant factor 2 estimates greater than any right-handed
subject in the initial 300-person sample. The factor 3 effect was
not carried by a few individuals but rather reflected a shift in the
distribution of laterality scores (Fig. 4C). Fig. S5 displays the
laterality distributions for all 4 factors.
Discussion
Understanding the genetic and developmental basis of brain
asymmetry will illuminate cerebral specialization and shed light
on neuropsychiatric, neurologic, and other developmental disorders that alter brain laterality (8–12, 28, 29). Genetic models
have been proposed to account for cerebral dominance (15, 16),
and anatomical asymmetries are likely influenced by genetic
factors (30). However, so far no gene or pathway has been
identified as a determinant of lateralization, although there are
a number of candidates (12, 28, 31).
The present findings indicate that brain asymmetries unlikely
arise from a single mechanism, but rather that multiple separate
factors contribute to the development of cerebral lateralization.
A single gene, as proposed by Annett (16), cannot explain the
multiple effects observed but might explain a subset of brain
asymmetries such as that observed for the factor linked to
handedness (factor 3). Similarly, a single environmental factor
associated with hormonal levels (3, 17) cannot account for the
effects, especially considering the modest contribution of sex,
although intrauterine testosterone levels could still play an
important role. It is presently unclear whether, or to what degree,
local anatomical asymmetries contribute to the present findings.
PNAS Early Edition 兩 3 of 5
A 0.3
Materials and Methods
Left-handed
Right-handed
Participants. Three hundred healthy right-handed adults participated for payment in the main MRI study (131 men, age 22.3 ⫾ 3.2) and 38 right- and 38
left-handed adults participated in the second study that examined effects of
handedness (17 men in each group, age 20.8 ⫾ 1.7). Handedness was assessed by
the Edinburgh handedness inventory (32). All participants performed between 1
and 4 rest runs to estimate intrinsic functional lateralization. Thirty-five participants additionally performed 3 runs of a language task. All participants were
native English speakers and had normal or corrected-to-normal vision. Participants were screened to exclude individuals with a history of neurologic or
psychiatric conditions as well as those using psychoactive medications.
Mean Laterality Index
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
Count (percent)
B
25
***
Factor 1 Factor 2 Factor 3 Factor 4
Factor 2
20
15
10
5
0
-0.3
-0.1
0.1
0.3
0.5
Laterality Index
Count (percent)
C
20
Factor 3
15
10
5
0
-0.6
-0.4
-0.2
0
0.2
0.4
Laterality Index
Fig. 4. Handedness differentially affects asymmetry across distinct brain
systems. Mean laterality estimates for each of the 4 factors in Fig. 3 are plotted
for independent data samples of right-handed (n ⫽ 38) and left-handed (n ⫽
38) individuals. (A) A significant interaction between hand dominance and
factor is observed (P ⬍ 0.005) with an effect of handedness observed for factor
3 (***, P ⬍ 0.001). Bars represent standard error of the mean. (B) The laterality
index distribution for factor 2 is plotted split by handedness. Though the
distributions overlap, there are 2 left-handed individuals with complete reversal of asymmetry. (C) The distributions for factor 3 show a marked effect of
handedness with right-handed individuals demonstrating greater asymmetry.
Our findings suggest that models of brain asymmetry should
seek to identify multiple, distinct factors that lead to individual
differences in cortical lateralization perhaps through their influence on early developmental events that promote cortical
specialization. Providing insight into mechanisms that may give
rise to functional lateralization, molecular studies of transcription have revealed an array of asymmetric gene expression
patterns in multiple forebrain structures during early human
fetal development (12, 28).
The present observations also show the feasibility and efficiency of estimating functional brain asymmetries using intrinsic
activity fluctuations. Rest-state laterality indices were computed
within individual subjects from rapidly acquired MRI data (⬇10
min). Within-subject estimates showed moderate test-retest reliability. Thus, the present methods provide an approach to
explore determinants of lateralization in large human samples,
such as required for genetic explorations, as well as in patients
where determination of functional dominance is clinically relevant (e.g., neurosurgical planning).
4 of 5 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0908073106
MRI Acquisition Procedures. Scanning was performed on 3 Tesla TimTrio
systems (Siemens) using the 12-channel phased-array head coil supplied by the
vendor. Structural images were acquired using a sagittal MP-RAGE threedimensional T1-weighted sequence (TR ⫽ 2,530 ms, TE ⫽ 3.44 ms, FA ⫽ 7o, 1.0
mm isotropic voxels; FOV 256 ⫻ 256).
For the main group of 300 subjects, the fixation (rest) runs were 390 s (156
time points) or 370 s (148 time points) in duration, and a black visual crosshair
(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
was provided. Subjects were also instructed to minimize head movement.
Images were acquired using the gradient-echo echo-planar pulse sequence
(TR ⫽ 2,500 ms, TE ⫽ 30 ms, flip angle ⫽ 90o, 3-mm isotropic voxels). For the
76 subjects that contributed to the analysis of the effect of handedness, 2 rest
runs were acquired (each 372 s, 120 TP, TR ⫽ 3,000 ms). For these runs, subjects
rested with their eyes open.
Thirty-five subjects within the main subject cohort also performed 3 runs of
a language task involving semantic classification of words previously used to
determine language dominance (6, 33). Images were acquired using an echo
planar imaging (EPI) gradient-echo sequence sensitive to blood oxygenation
level-dependent (BOLD) contrast (TR ⫽ 2,000 ms, TE ⫽ 30 ms, FA ⫽ 90o, slice
number ⫽ 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
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
visual stimuli were generated on an Apple PowerBook G4 computer (Apple,
Inc. ) using Matlab (Mathworks, Inc.) and the Psychophysics Toolbox extensions (34). Stimuli were projected onto a screen positioned at the head of the
magnet bore. Participants were asked to indicate if the word was concrete or
abstract. They were instructed to respond quickly and accurately, and indicate
their response by key press (left-hand key press for abstract words, right-hand
key press for concrete words).
MRI Preprocessing. Both the resting-state data and the task data were preprocessed using the following steps: (1) slice timing correction (SPM2, Wellcome Department of Cognitive Neurology, London), (2) rigid body correction
for head motion, (3) normalization for global mean signal intensity across
runs, and (4) transformation of the data into a standard atlas space. The
second step provided a record of head position that was later used as a
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
Oxford, Oxford, U.K.). The atlas representative template conformed to the
Montreal Neurological Institute (MNI) atlas (MNI152). Motion correction and
atlas transformation were combined into a single step to yield a motioncorrected time series resampled to 2 mm isotropic voxels.
The resting-state data were analyzed using region-based correlation analysis,
often referred to as functional connectivity MRI (fcMRI) analysis. The present
methods extend from Biswal et al. (23) and are described in detail in Vincent et al.
(35) and Buckner et al. (36). Task data were analyzed using the general linear
model as implemented in SPM2 (Wellcome Department of Cognitive Neurology,
London). Regressors of no interest included motion correction parameters and
low-frequency drift. The task blocks used a gamma function convolved with a
boxcar function to model the hemodynamic response function.
The Quantitative Intrinsic Laterality Index (iLI). The present exploration required the development of a quantitative measure of a region’s intrinsic
laterality. Functional mapping studies based on intrinsic activity have observed lateralization within the attention system in normal subjects (37) and
the memory system in patients (38). Building from these earlier observations,
we developed a generic approach to explore lateralization across all regions
of cortex simultaneously. To compute an intrinsic laterality index (iLI), the
relative strengths of correlations between seed and target region pairs were
Liu et al.
Laterality Index ⫽
(LL ⫺ RL) ⫺ (RR ⫺ LR)
兩LL兩 ⫹ 兩LR兩 ⫹ 兩RR兩 ⫹ 兩RL兩
[1]
For each one of these pairwise correlations, we averaged the corresponding iLI values across the exploratory dataset of 100 individuals. The resulting
39,800 mean iLIs were then sorted to determine those regions showing the
strongest levels of lateralization. The most left-lateralized correlations (iLI ⬎
0.3, 37 regions) and most right-lateralized correlations (iLI ⬍ ⫺0.3, 47 regions)
were combined together into a single iLI metric (Fig. 1B). The threshold is
somewhat arbitrary but was selected to reduce the number of regions to a
number appropriate to factor analysis. A level of 0.3 ensured no ⬎100 regions
would be selected for further factor analysis. Alternative threshold values do
not change the results for these strongly lateralized regions but may lead to
differences for weakly lateralized regions. The iLIs values for the left- and
right-lateralized regions were then calculated on an independent sample of
200 subjects to derive an unbiased estimate of the distribution of lateralization (Fig. 2). Note also that the data sample used to derive regions for analysis
included an equal number of men and women, allowing unbiased analysis of
sex differences.
Factor Analysis. Principal axis factoring was used for the factor analysis (39). The
iLIs of the 84 regions were used as the observed variables. The number of
extracted factors was determined by principal component with the criterion that
eigenvalues equal or exceed 1. The resulting factor loading values were rotated
using normalized varimax rotation. Factor analysis was performed on the first
sample of 100 subjects and repeated on an independent sample of 100 subjects.
The factor loading values for the top 3 factors are shown in Fig. S6. The results on
2 independent data samples show similar clusters of regions corresponding to
each factor, indicating that these factors are highly reproducible across data
samples. Local anatomic asymmetries may contribute to the factors.
Note that LL–RL corresponds to the left target region on the difference map
(see the temporal region in Fig. S2 as an example), and RR–LR corresponds to
the right target region on the difference map. When the denominator fell
below 0.2, iLI was set to zero. iLI was computed for all 200 seed regions in each
hemisphere against the 199 possible target regions, yielding 39,800 pairwise
correlations for each subject.
ACKNOWLEDGMENTS. We thank Abraham Snyder, Jessica Andrews-Hanna,
Itamar Kahn, Ting Ren, and Tanveer Talukdar for assistance with functional
connectivity; Marisa Hollinshead and Betsy Hemphill for data collection; Timothy
O’Keefe and Gabriele Fariello for neuroinformatics; and the Harvard Center for
Brain Science and the Athinoula A. Martinos Center for Biomedical Imaging for
imaging support. This work was supported by National Institutes of Health Grants
R01AG021910, R01AG034556, P41RR14074, and K08MH067966; the Simons
Foundation; and the Howard Hughes Medical Institute.
1. Geschwind N, Levitsky W (1968) Human brain: Left-right asymmetries in temporal
speech region. Science 161(3837):186 –187.
2. Milner B (1971) Interhemispheric differences in the localization of psychological
processes in man. Br Med Bull 27(3):272–277.
3. Geschwind N, Galaburda AM (1985) Cerebral lateralization. Biological mechanisms,
associations, and pathology: I. A hypothesis and a program for research. Arch Neurol
42(7):428 – 459.
4. Toga AW, Thompson PM (2003) Mapping brain asymmetry. Nat Rev Neurosci 4(1):37– 48.
5. Rasmussen T, Milner B (1977) The role of early left-brain injury in determining lateralization of cerebral speech functions. Ann N Y Acad Sci 299:355–369.
6. Desmond JE, et al. (1995) Functional MRI measurement of language lateralization in
Wada-tested patients. Brain 118:1411–1419.
7. Knecht S, et al. (2000) Language lateralization in healthy right-handers. Brain 123:74 –
81.
8. Herbert MR, et al. (2002) Abnormal asymmetry in language association cortex in
autism. Ann Neurol 52(5):588 –596.
9. Kleinhans NM, Müller RA, Cohen DN, Courchesne E (2008) Atypical functional lateralization of language in autism spectrum disorders. Brain Res 1221:115–125.
10. Crow TJ, et al. (1989) Schizophrenia as an anomaly of development of cerebral
asymmetry: A postmortem study and a proposal concerning the genetic basis of the
disease. Arch Gen Psychiatry 46(12):1145–1150.
11. Sommer IEC, Ramsey NF, Kahn RS (2001) Language lateralization in schizophrenia, an
fMRI study. Schizophr Res 52:57– 67.
12. Sun T, et al. (2005) Early asymmetry of gene transcription in embryonic human left and
right cerebral cortex. Science 308(5729):1794 –1798.
13. Witelson SF, Pallie W (1973) Left hemisphere specialization for language in the
newborn: Neuroanatomical evidence of asymmetry. Brain 96(3):641– 646.
14. Gilmore JH, et al. (2004) 3 Tesla magnetic resonance imaging of the brain in newborns.
Psychiatry Res 132(1):81– 85.
15. Geschwind DH, Miller BL, DeCarli C, Carmelli D (2002) Heritability of lobar brain
volumes in twins supports genetic models of cerebral laterality and handedness. Proc
Natl Acad Sci USA 99(5):3176 –3181.
16. Annett M (1964) A model of the inheritance of handedness and cerebral dominance.
Nature 204:59 – 60.
17. Geschwind N, Galaburda AM (1985) Cerebral lateralization. Biological mechanisms,
associations, and pathology: III. A hypothesis and a program for research. Arch Neurol
42(7):634 – 654.
18. Kimura D (2000) Sex and Cognition (MIT Press, Cambridge, MA).
19. McGlone J (1980) Sex differences in human brain asymmetry: A critical survey. Behav
Brain Sci 3:215–227.
20. Damasio H, Tranel D, Spradling J, Alliger R (1989) Aphasia in men and women. From
Reading to Neurons, Galaburda AM, ed (MIT Press, Cambridge, MA), pp 307–325.
21. Shaywitz BA, et al. (1995) Sex differences in the functional organization of the brain
for language. Nature 373(6515):607– 609.
22. Sommer IEC, Aleman A, Bouma A, Kahn RS (2004) Do women really have more bilateral
language representation than men? A meta-analysis of functional imaging studies.
Brain 127(8):1845–1852.
23. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the
motor cortex of resting human brain using echo-planar MRI. Magn Reson Med
34(4):537–541.
24. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with
functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700 –711.
25. Gusnard DA, Raichle ME (2001) Searching for a baseline: Functional imaging and the
resting human brain. Nat Rev Neurosci 2(10):685– 694.
26. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network:
Anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38.
27. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention
in the brain. Nat Rev Neurosci 3(3):201–215.
28. Sun T, Walsh CA (2006) Molecular approaches to brain asymmetry and handedness. Nat
Rev Neurosci 7(8):655– 662.
29. Escalante-Mead PR, Minshew NJ, Sweeney JA (2003) Abnormal brain lateralization in
high-functioning autism. J Autism Dev Disord 33(5):539 –543.
30. Thompson PM, et al. (2001) Genetic influences on brain structure. Nat Neurosci
4(12):1253–1258.
31. Lai CSL, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP (2001) A forkhead-domain
gene is mutated in a severe speech and language disorder. Nature 413(6855):519 –523.
32. Oldfield RC (1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia 9(1):97–113.
33. Demb JB, et al. (1995) Semantic encoding and retrieval in the left inferior prefrontal
cortex: A functional MRI study of task difficulty and process specificity. J Neurosci
15(9):5870 –5878.
34. Brainard DH (1997) The Psychophysics Toolbox. Spat Vis 10(4):433– 436.
35. Vincent JL, et al. (2006) Coherent spontaneous activity identifies a hippocampalparietal memory network. J Neurophysiol 96(6):3517–3531.
36. Buckner RL, et al. (2009) Cortical hubs revealed by intrinsic functional connectivity:
Mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci
29(6):1860 –1873.
37. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME (2006) Spontaneous neuronal
activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci
USA 103(26):10046 –10051.
38. Wang Z, Mechanic-Hamilton D, Pluta J, Glynn S, Detre JA (2009) Function lateralization
via measuring coherence laterality. Neuroimage 47(1):281–288.
39. Harman HH (1976) Modern Factor Analysis (Univ of Chicago Press, Chicago).
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NEUROSCIENCE
measured between the hemispheres (see Fig. 1). Two hundred spherical
regions were defined in each hemisphere, covering the gray matter of the
cerebral cortex (Fig. S1). All possible estimates were computed (e.g., 200 ⫻ 199
pairwise combinations). From these, the regions with the strongest lateralization were identified (iLI ⬎ 0.3 or ⬍ ⫺0.3).
For illustration purposes, an example of asymmetric correlations is shown
in Fig. S2. Functional correlation maps are displayed for a seed region in the
left hemisphere and its homologous region in the right hemisphere (see top
2 rows in Fig. S2; circles in the frontal region indicate the seed regions). The
map associated with the right hemisphere seed is subtracted from the map
associated with the left hemisphere seed to derive a difference map (third row
in Fig. S2). In this example, the left temporal region (marked by the circles in
the third row), among other regions, is more strongly correlated with the left
frontal seed region than the mirroring correlations in the right hemisphere.
This asymmetry is easily appreciated in the difference map; the laterality index
in Eq. 1 below quantifies this asymmetry.
Specifically, for each seed region, the iLI was defined based on the relative
correlation differences between the seed and target regions across the 2 hemispheres. Fig. 1 A illustrates the correlation values used in this calculation, where LL
is the strength of the correlation between the left hemisphere seed and the left
hemisphere target regions; LR represents the strength of the correlation between
the left hemisphere seed and right hemisphere target regions; and RR and RL
represent the contralateral homologues. From these 4 seed-target correlations,
iLI is then calculated according to the following equation:
Supporting Information
Liu et al. 10.1073/pnas.0908073106
SI Text
Measuring Language Laterality Based on Task-Based Imaging. To
validate the intrinsic laterality index we compared it with a
laterality index determined based on actual language task performance. The task-based language laterality index was derived
on the group activation map (Fig. S3). The regions showing
left-lateralized activation in the group image were used as a mask
for each individual subject but excluded the visual cortex. We
then summed the number of significant voxels (z ⬎ 1.25) within
the mask of each hemisphere and then computed a laterality
index defined as
Laterality Index ⫽
L⫺R
L⫹R
[S1]
where L is the sum voxel count in left hemisphere mask and R
is the sum voxel count in the right hemisphere mask.
Within the mask, the seed pairs were analyzed, yielding 322
left-lateralized regions that were combined into a single measure
of language system iLI (Fig. S4A). In the previous analyses
described in the main text, we set a threshold of 0.3 to ensure a
Liu et al. www.pnas.org/cgi/content/short/0908073106
relatively small number of the most lateralized regions were
identified, sufficiently constrained to allow for a well-powered
factor analysis. Here we define a language laterality index to be
comparable to the task-based estimates. We used the mask
derived from the task activation map and selected all leftlateralized regions within the mask. Note that no information
about between-subject variability in the task-based or intrinsic
laterality estimates is used to define the language mask. The
task-based and intrinsic language laterality indices showed significant correlation between subjects, suggesting language iLI
successfully captures between-subject variation in language lateralization (r ⫽ 0.48, P ⬍ 0.005; Fig. S4B). As a further validation
check, we also computed the language iLI on 17 patients with
epilepsy; 16 patients showed left-lateralized language (iLI ⬎ 0)
and 1 patient showed right-lateralized language (iLI ⫽ ⫺0.05).
The patient revealing right-lateralized language iLI was recruited to perform the language task in MRI. Results confirmed
atypical language lateralization (Fig. S4C). The other 16 patients
all showed left-lateralized language in the task-based measure.
These findings indicate that intrinsic laterality can measure
language lateralization and may provide an efficient method for
presurgical planning.
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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
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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
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Fig. S3. fMRI activation during actual language tasks. A cortical surface projection shows the group results (n ⫽ 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.
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A
0.9
B
LI Task
0.6
C
0.3
0
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15
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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 ⫽ 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 ⫽ 0.48, P ⬍ 0.005). (C) In an independent cohort of patients, an
atypical subject was found to have a right-lateralized language laterality index of ⫺0.05. 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
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25
Factor 1
15
Count (percent)
Count (percent)
20
10
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20
15
10
5
0
0
-0.5
-0.3
-0.1
0.1
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-0.3
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Laterality Index
20
Factor 3
15
Count (percent)
Count (percent)
20
10
5
0
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-0.4
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0
Laterality Index
0.1
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Laterality Index
0.2
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15
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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 ⫽ left-handed, red ⫽ 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.
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Sample 1 (N=100)
0
5
2830 3431
22 7 3
14191 4
8
18
21
17
15
16
58
56
5544
41
64
39
84
69
66
Sample 2 (N=100)
1
58
39 44
4155
69
64
74
45
47 5375
73 72
50
77
38
24
62
10 60
32
42
63
52
12
656159
79
37
23
83
49
35
9
36
46 43
67
68
2640
2511
48 80 81
70
20
2 71 57
76
33 29
54 51
27
78
13
6
56
84
Factor 3
Factor 3
1
66
18
4
21
34
30
1
19
22 31 7
3
14
0
67
49
82 7372
50 74
47
754577
53
6861
959
43 46
70
37
12
32
63
79
57
52
238342
25
71
28
4065
60
2935
48
11
24
10
38
2
62
20
36
78
8 26
80
54
33
81
51
27
16
76
13
6
15 17
5
82
−1
1
−1
1
1
1
0
0
F
−1
−1
r1
to
ac
2
2
to
ac
r1
or
or
0
ct
Fa
ct
Fa
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−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.
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