[tt] Distributed neural system for general intelligence revealed by lesion mapping

[Eugen Leitl]

http://www.pnas.org/content/107/10/4705.full

Distributed neural system for general intelligence revealed by lesion mapping

J. Gläschera,b,1, D. Rudraufc,d, R. Colome, L. K. Paula, D. Tranelc, H.
Damasiof, and R. Adolphsa,g

+ Author Affiliations

aDivision of Humanities and Social Sciences, California Institute of
Technology, Pasadena, CA 91125;

bNeuroimage Nord, Department of Systems Neuroscience, University Medical
Center Hamburg-Eppendorf, 20246 Hamburg, Germany;

cDepartment of Neurology and

dLaboratory of Brain Imaging and Cognitive Neuroscience, University of Iowa,
Iowa City, IA 52242;

e Universidad Autonoma de Madrid, 28049 Madrid, Spain;

fDornsife Cognitive Neuroscience Imaging Center and Brain and Creativity
Institute, University of Southern California, Los Angeles, CA 90089; and

gDivision of Biology, California Institute of Technology, Pasadena, CA 91125
Edited by Edward E. Smith, Columbia University, New York, NY, and approved
January 25, 2010 (received for review September 10, 2009)

 
Next Section

Abstract

General intelligence (g) captures the performance variance shared across
cognitive tasks and correlates with real-world success. Yet it remains
debated whether g reflects the combined performance of brain systems involved
in these tasks or draws on specialized systems mediating their interactions.
Here we investigated the neural substrates of g in 241 patients with focal
brain damage using voxel-based lesion–symptom mapping. A hierarchical factor
analysis across multiple cognitive tasks was used to derive a robust measure
of g. Statistically significant associations were found between g and damage
to a remarkably circumscribed albeit distributed network in frontal and
parietal cortex, critically including white matter association tracts and
frontopolar cortex. We suggest that general intelligence draws on connections
between regions that integrate verbal, visuospatial, working memory, and
executive processes.

lesion patients voxel-based lesion–symptom mapping Wechsler Adult
Intelligence Scale white matter

Individual performances across a wide range of cognitive tasks are
correlated: those people who perform well on some tasks tend to perform well
across most tasks; those people who perform poorly on some tasks tend to
perform poorly across most tasks. This effect is captured by the construct of
general intelligence (or g), conceptualized by Spearman in 1904 (1) as that
psychometric aspect of cognition whose variance is shared maximally across a
wide variety of more specialized tests tapping verbal skills, spatial
reasoning, memory, and other cognitive domains. There is strong evidence that
Spearman's g is not merely a statistical abstraction but a distinct and
pervasive cognitive ability. It comes into play in particular during
demanding, effortful, nonautomated cognitive tasks requiring working memory
capacity (2, 3). It is highly heritable (estimates are approximately 0.8) (4,
5), and it is a common source of interindividual differences in all cognitive
tasks (6). Furthermore, g is the psychological trait with the largest number
of social and real-life correlates (e.g., income level and other measures of
success) (7). Not surprisingly, efforts to understand its neurobiological
substrate have been high on the list of priorities in fields ranging from
biology to psychology and sociology. Here we address the question of whether
g draws upon specific brain regions, as opposed to being correlated with
global brain properties (such as total brain volume). Identifying such brain
regions would help shed light on how g contributes to information processing
and open the door to further exploration of its biological underpinnings,
such as its emergence through evolution and development, and its alteration
through psychiatric or neurological disease.

In the past 20 years a number of functional (3, 8, 9) and structural imaging
studies (10 –13), predominantly in healthy individuals and sometimes in
combination with studies of heritability (5, 14), have investigated the
neural signatures of g. Its neurobiological substrates have been variably
linked to prefrontal cortex and the role of this brain region in cognitive
control and flexibility (8), or instead to more distributed cortical regions
(15). The latter account argues that g should involve interregional
communication among many brain regions and therefore critically rely on the
white matter connections between them, whereas the former account argues for
a distinct region or network of regions implementing g. It thus remains
debated whether g should be thought of as a single ability upon which other
cognitive processes might draw, or whether it itself is constituted by the
multiple cognitive processes from which it is psychometrically derived.

Here we investigated the neural substrates for g using nonparametric (16)
voxel-based lesion–symptom mapping (VLSM) (17) in a large sample of 241
lesion patients (see Table S1 for demographic data) who had been tested on
the Wechsler Adult Intelligence Scale (WAIS) (18) (see Table S2 for sample
sizes and mean standardized scores on all WAIS subtests). VLSM compares, for
every voxel, scores from patients with a lesion at that voxel contrasted
against those without a lesion at that voxel. Unlike functional neuroimaging
studies, which typically rely on the metabolic demands of gray matter and
provide a purely correlational association between brain regions and
cognitive processes (19), lesion mapping methods can identify regions,
including white matter tracts, playing a causal role in a particular
cognitive domain by mapping where damage can interfere with performance (20).
We used hierarchical factor analysis across multiple cognitive tasks (10, 21)
to derive a robust measure of g and found statistically significant
associations between g and damage to a remarkably circumscribed albeit
distributed set of regions in left frontal and right parietal cortex, as well
as white matter association tracts connecting these sectors. These findings
suggest that g reflects the ability to effectively integrate verbal,
visuospatial, working memory, and executive processes via a circumscribed set
of cortical connections.

Previous Section Next Section Results

Extracting g with Hierarchical Factor Analysis. Spearman's g is often
measured using problem-solving tasks like Raven's Advanced Progressive
Matrices (RAPM) (3, 9, 22) that require relational integration across
different stimulus dimensions (23). However, using a single task runs counter
to the cross-task variance concept of g (1). A procedure more in keeping with
the original psychometric construct involves extracting g from a battery of
cognitive tests using hierarchical factor analysis (Schmid-Leiman
transformation, SLT) (10, 21), in which the loadings of the primary variables
on a second-order g factor take precedence over the loadings on the
first-order factors. Using this approach (see Materials and Methods for
details), we extracted g and three first-order factors (verbal abilities,
visuospatial abilities, and working memory) from nine WAIS subtests chosen
for their unequivocal expression of a well-known factor structure (Fig. 1).
The SLT revealed that all verbal subtests (loadings: 0.57–0.66) as well as
the Arithmetic (0.67) and Block Design (0.57) subtests exhibit high loadings
on g, consistent with previous accounts (10). A direct comparison of the
first-order loading matrix before and after SLT (Fig. 1 A and B) further
showed that the factor structure is clearly preserved in our lesion patients,
even after accounting for g. This confirmed that g absorbed the shared
variance among the subtests without perturbing the psychometric architecture
of the three domain-specific factors, yielding an accurate measure of g that
abstracts from any specific cognitive ability.

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Fig. 1. Extracting Spearman's g using hierarchical factor analysis. (A)
Loading matrix for nine WAIS subtests onto three first-order factors [denoted
“verbal,” “spatial,” and “working memory” (WM)] extracted from a
promax-rotated common factor analysis using principal axis fskills are
vulnerable to damage in much larger areas of the right hemisphere than those
involved in g (Fig. S3). However, working memory and verbal skills overlapped
more substantially with the left hemispheric correlate of g, most notably for
the Arithmetic (0.42) and Similarities (0.39) subtests. These particular
subtests rely heavily on the capacity for complex reasoning and integration
of various forms of knowledge and cognitive processes, in addition to basic
verbal and working memory skills. As such, they recruit skills from a
distributed area of cortex and depend on cortical connections (26). Likewise,
Similarities requires integration of various forms of knowledge and cognitive
processes to generate abstract conceptualizations (26). Thus it seems that
the neural substrate of g involves long-range connections that are broadly
distributed throughout the cortex and yet a more circumscribed neural
substrate than that of the underlying select skills (Fig. S3).

Is there a neural region whose damage uniquely impacts g but not any of the
nine WAIS subtests from which g is psychometrically derived? We addressed
this question by examining the nonoverlap between a disjunction (logical
“OR”) of all WAIS subtests and the lesion pattern found for g (Fig. 3). This
analysis revealed a single region in the left frontal pole [Brodmann Area
(BA) 10] that showed a significant effect unique to g.


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Fig. 3. Overlap (yellow) of g (green) and a disjunction (logical “OR”) of
nine WAIS subtests (red) thresholded at 5% FDR. A region in the left frontal
pole (white circles) is unique to g and not captured by any other subtest.
Immediately adjacent (left lateral orbitofrontal cortex and underlying white
matter) lies the significant lesion–deficit effect for the Information
subtest from the WAIS, which partially overlaps with the unique frontal polar
region for g (two left-most circles).  Previous Section Next Section
Discussion

In this study we investigated the specific neural correlaf g reported here is
remarkably circumscribed, concentrated in the core of white matter, and
essentially always comprises a narrow subset of the regions associated with
performance on individual WAIS subtests. The largest overlap between WAIS
subtests and g was found for Arithmetic, Similarities, Information, and Digit
Span; the former two tests also exhibited the greatest conjunction with g.
These subtests assess verbal knowledge about the world, verbal reasoning, and
abstraction, as well as working memory capacity, and are associated with the
left inferior frontal gyrus, the superior longitudinal/arcuate fascicule, and
to some degree with parietal cortex (27) (Fig. S3). This suggests that g
draws on the combination of conceptual knowledge and working memory, and that
the communication between areas associated with these capacities is of
crucial importance (2). Such an interpretation is consistent with the
Parieto-Frontal Integration Theory (P-FIT) (15), which postulates roles for
cortical regions in the prefrontal (BA 6, 9–10, 45–47), parietal (BA 7,
39–40), occipital (BA 18–19), and temporal association cortex (BA 21, 37).
Our results emphasize the important role of white matter tracts in binding
the proposed regions together into a unified system subserving g, in line
with a recent study relating white matter integrity to intellectual
performance (28): the study reported significant correlations between
integrity of the superior fronto-occipital fasciculus and full-scale
intelligence quotient (IQ) (a measure related, but not identical, to g).

Working memory, which seems to be left lateralized when tested in the verbal
domain (29), is considered a key cognitive ability strongly related to g (2,
3, 30). The white matter tracts identified in our analysis connect
ventrolateral prefrontal cortex (VLPFC) and DLPFC with the inferior parietal
cortex and terminate in the superior parietal lobule. In general, VLPFC is
associated with processing intentions and switches between cognitive sets,
which—in the context of working memory—could correspond to stimulus–response
mappings underlying succes in the manipulation of items in working memory
(31). Finally, the left posterior parietal cortex has been associated with
the storage of verbal material in working memory (32).

Although our findings generally support the P-FIT model, they also suggest
that a sector of prefrontal cortex may play a unique role in g (Fig. 3).
Interestingly, this region (left lateral aspect of BA 10) has been also
associated with increased blood oxygen level–dependent (BOLD) activity during
a variety of higher-order cognitive processes (23), including retrieval of
abstract semantic knowledge, as is required for the Similarities subtest
(33), as well as difficult problem-solving tasks akin to the RAPM (34), a
test commonly used to measure g (3, 9, 15, 22). Our finding of a unique
neural correlate of g in BA 10 argues for another, distinct aspect
complementing the distributed processing discussed above: the need for
hierarchical processing control. BA 10 has a documented role in cognitive
control and subgoal processing (35 –38) and may thus be involved in the
allocation of the working memory resources necessary for successful
performance on specific cognitive tasks. Taken together, our findings argue
that g may critically rely on efficient interregional communication
subserving processes for configuring and holding items in working memory,
along with a hierarchical component for flexible control in the frontopolar
cortex. Such an interpretation would be consistent, respectively, with the
fluid information processing nature of g as well as its effortful “executive”
aspect (8, 30).

It could be argued that the high g loadings of the verbal scales are driving
the predominant left lateralization that we observed. However, if g was
dominated by purely verbal performance (as captured in the “verbal” factor in
our factor analysis; Fig. 1A), then g should have picked up more shared
variance from these scales, leading to a disproportionate reduction of
first-order “verbal” loadings after the SLT (Fig. 1B). This was, however, not
generally the case. The labulary, Arithmetic, and Digit Span (Fig. S4), the
latter two originally belonging to the working memory factor, which
reinforces our suggestion that working memory and the frontoparietal regions
it involves contributes largely to g (2, 3, 26, 27).

An interesting question pertains to the stability of g as a psychological
trait across the lifespan. The age de-differentiation hypothesis predicts an
increasing contribution of g to cognitive performances during the later
stages of life (39). This would predict that g explains a greater amount of
task variance in older subjects. We tested this hypothesis in our sample by
dividing the subject sample into young and old subjects (median-split) but
found no support for the hypothesis (percentage explained variance: young
subjects, 36.0%; old subjects, 30.6%). This lack of support for the age
de-differentiation hypothesis is consistent with earlier work (40, 41).

In conclusion, we show that g draws on a distributed but circumscribed set of
cortical regions and their white matter connections. These comprise regions
related to working memory capacity, verbal and visuospatial processing,
subserved by a frontoparietal system, along with an executive component
subserved by left frontopolar cortex. Two closing caveats should be noted.
First, given that lesions influencing g were found in both hemispheres, we
would expect commissural tracts to contribute significantly. However, these
were underrepresented in our lesion sample and thus may not have been
detected. Second, Spearman's g disregards theories of multiple intelligences
(42) and does not incorporate specific emotional abilities (43). Therefore we
may have isolated an anatomical network important for processing external
stimuli, which might operate in parallel with others that are more critical
for stimulus reward processing and interoception.

Previous Section Next Section Materials and Methods

Subjects and Neuropsychological Data. The WAIS-R and/or WAIS-III was
administered to 241 neurologic patients who were being evaluated in
connection with their enrollment in the Iowa Cognitive Neuroscience
Patientpproximately 2 decades. When only WAIS-R data were available, the
subtest scores were converted to WAIS-III equivalents according to the
standardized scores reported in the WAIS-III manual. Under the auspices of
the Registry, the patients had been extensively characterized in terms of
their neuropsychologic (44) and neuroanatomic status (45). Demographic data
are given in Table S1. Where multiple datasets were available, we chose
neuropsychological and neuroanatomical datasets that were as contemporaneous
as possible. All patients had single, focal, stable, chronic lesions of the
brain, and the Registry excludes patients with progressive disease or
psychiatric illness. All subjects had given written informed consent to
participate in these research studies.

Neuroanatomical Data. All neuroanatomical data were mapped using MAP-3, as
described previously (45, 46). Briefly, the visible lesion in each subject's
MRI or CT scan was manually traced, slice by slice, onto corresponding
regions of a single, normal reference brain (template brain) that has been
used in all prior studies with this method. All of the lesions were traced by
a single expert (H.D.) who has demonstrated high reliability (47). This
manual tracing was only done when confidence could be achieved for matching
corresponding slices between the lesion brain and the reference brain. Thus,
lesions were only mapped, if (i) they were clearly distinguishable from the
(possibly dilated) ventricular system, (ii) there were no coexisting signs of
cortical atrophy, and (iii) the MRI or CT scan showed no imaging artifact.
Because the neuroanatomical data were manually traced to a stereotaxic
template, no automated spatial normalization was required. The lesion maps
for each subject were resampled to an isotropic voxel size of 1 mm3,
spatially smoothed with a 4-mm full-width-at-half-maximum (FWHM) Gaussian
kernel, binarized at a threshold of 0.2, and finally converted to the NiFTI
file format.

Hierarchical Factor Analysis. We computed g loadings from 9 WAIS subtests and
individuayis (48), which has been recommended as the preferential method for
extracting a g factor (49). In the original data matrix Z of 241 patients
missing data were replaced by the mean. We then analyzed the data correlation
matrix R in a common factor analysis and extracted three promax-rotated
principal factors, resulting in the first-order loading matrix P1 shown in
Fig. 1A. The factor correlation matrix F of this first-order factor analysis
was analyzed in a second-order common factor analysis extracting a single
factor (g) resulting in a second-order loading matrix P2.  In the framework
of the Schmid-Leiman factor transformation, loadings for the primary
variables (WAIS subtests) onto the second-order factor (g) take precedence
over loadings onto the first-order factors (verbal, spatial, and working
memory), which are reduced to part correlations (50) and thus differ from the
loadings of the initial factor analyses above (Fig. 1B). The residualized
first-order loading matrix is computed by postmultiplying the original
first-order loading matrix P1 with the second-order uniqueness U2: P1SL = P ×
U2 (Fig. 1B). Similarly, the g loadings of the primary variables (WAIS
subtests) were determined by multiplication of both loading matrices: P2SL =
P1 × P2 (Fig. 1B).

Following ref. 51, we computed factor scores using regression of the data
correlation matrix R onto the first-order structure matrix S = P1 × F.
Parameter estimates B were determined by B = R−1 × S. These parameter
estimates were rescaled using a diagonal matrix D = sqrt[diag(BT×S)]−1. The
resulting weighting matrix W = B × D was used to project the original data
onto the residualized first-order factor space: FS = Z × W, where FS are the
factor scores. An analogous procedure was used to determine g factor scores.

Extraction g from the WAIS and Comparison of Different Factor Solutions. We
selected nine WAIS subtests for extracting g. Three subtests (Matrix
Reasoning, Letter–Number Sequencing, and Symbol Search) were undersampled (n
< 100) compared with the rest of thet administered to all of our patients. We
therefore excluded these from our final set. Table S2 lists the means, SDs,
and sample sizes of all WAIS subtests.  The WAIS exhibits a robust
first-order factor structure, which is used to derive index scores for three
factors: (i) verbal (Vocabulary, Similarities, Information, and
Comprehension), (ii) spatial (Picture Completion, Block Design, and Picture
Arrangement), and (iii) working memory (Digit Span and Arithmetic) (10). This
factor structure was used to extract g loadings and factor scores. Here,
however, to demonstrate the robustness of our extraction of g we report the
factor solutions for 10 and 13 WAIS subtests for comparison with the solution
for 9 subtests used as the main analysis (Table S3, Table S4, and Table S5).
The g factor scores derived from either solution were highly correlated:
r(WAIS13, WAIS9) = 0.97, r(WAIS13, WAIS10) = 0.98, r(WAIS10, WAIS9) = 0.99.

Lesion Analysis of g Factor Scores. The g factor scores were used for
nonparametric (16) VLSM (17) as implemented in “Nonparametric Mapping,” which
is part of the MRIcron software package
(http://www.sph.sc.edu/comd/rorden/mricron/). This mass-univariate analysis
compares the g factor scores between patients with and without a lesion at
each and every voxel in the brain. It uses the Brunner-Munzel test, a
nonparametric variant of the two-sample t test that allows for
heteroscedasticity of the variances in both groups (52). We used a threshold
of 5% FDR (53) to control for multiple comparisons and an extent threshold of
50 voxels per cluster. Maps of statistical power (54) were computed to verify
that we had sufficient coverage to detect a significant lesion–deficit
relationship in almost the entire brain (Fig. S2). These power calculations
(16) use the nonparametric Wilcoxon-Mann-Whitney probability to estimate a
power threshold. For instance, had our sample size been only 10 patients of
whom (at a particular voxel) only 3 had a lesion, then the most extreme
ranking would be W = 6 (sum of the rank 1, 2, anue of 2.13. Therefore, if our
statistical threshold corresponding to a 5% FDR threshold had been Z = 2.56,
we would not expect to detect this voxel not matter how large the effect size
actually is.  Previous Section Next Section Acknowledgments

We thank Prof. M. Spezio for fruitful discussions of methodological issues;
and Prof. M. Cassell (University of Iowa) for neuranatomical advice regarding
white matter tracts. This work was supported by Akademie der Naturforscher
Leopoldina Grant 9901/8-140 (to J.G.) and by National Institutes of Health
Grants P01NS19632 (to D.T., H.D., and R.A.), R01DA022549 (to D.T.), and
R01MH080721 (to R.A.), a grant by the Simons Foundation to R.A., as well as
by the Tamagawa University global Centers of Excellence Program of the
Japanese Ministry of Education, Culture, Sports, and Technology. R.C. was
funded by Grant SEJ-2006-07890 from the Ministry of Education and Culture,
Spain, and Grant PR2008-0038 from the Ministry of Science and Innovation,
Spain.

Previous Section Next Section Footnotes

1To whom correspondence should be addressed. E-mail:
glascher at hss.caltech.edu.  Author contributions: J.G. and R.A. designed
research; D.T. and H.D. performed research; H.D. contributed new
reagents/analytic tools; J.G., D.R., and R.C. analyzed data; and J.G., D.R.,
R.C., L.K.P., D.T., H.D., and R.A. wrote the paper.  The authors declare no
conflict of interest.  This article is a PNAS Direct Submission.  This
article contains supporting information online at
www.pnas.org/cgi/content/full/0910397107/DCSupplemental.  Freely available
online through the PNAS open access option.  Previous Section
 
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