Independent Component Analysis of the Effect of L-dopa
on fMRI of Language Processing
Namhee Kim1, Prem K. Goel1, Madalina E. Tivarus2, Ashleigh Hillier3, David Q. Beversdorf4,5*
1Department of Statistics, The Ohio State University, Columbus, Ohio, United States of America, 2Department of Imaging Science and the Rochester Center for Brain
Imaging, University of Rochester, Rochester, New York, United States of America, 3Department of Psychology, University of Massachusetts-Lowell, Lowell, Massachusetts,
United States of America, 4Departments of Radiology, Neurology and Psychology, University of Missouri, Columbia, Missouri, United States of America, 5The Thompson
Center, University of Missouri, Columbia, Missouri, United States of America
L-dopa, which is a precursor for dopamine, acts to amplify strong signals, and dampen weak signals as suggested by
previous studies. The effect of L-dopa has been demonstrated in language studies, suggesting restriction of the semantic
network. In this study, we aimed to examine the effect of L-dopa on language processing with fMRI using Independent
Component Analysis (ICA). Two types of language tasks (phonological and semantic categorization tasks) were tested under
two drug conditions (placebo and L-dopa) in 16 healthy subjects. Probabilistic ICA (PICA), part of FSL, was implemented to
generate Independent Components (IC) for each subject for the four conditions and the ICs were classified into task-
relevant source groups by a correlation threshold criterion. Our key findings include: (i) The highly task-relevant brain
regions including the Left Inferior Frontal Gyrus (LIFG), Left Fusiform Gyrus (LFUS), Left Parietal lobe (LPAR) and Superior
Temporal Gyrus (STG) were activated with both L-dopa and placebo for both tasks, and (ii) as compared to placebo, L-dopa
was associated with increased activity in posterior regions, including the superior temporal area (BA 22), and decreased
activity in the thalamus (pulvinar) and inferior frontal gyrus (BA 11/47) for both tasks. These results raise the possibility that
L-dopa may exert an indirect effect on posterior regions mediated by the thalamus (pulvinar).
Citation: Kim N, Goel PK, Tivarus ME, Hillier A, Beversdorf DQ (2010) Independent Component Analysis of the Effect of L-dopa on fMRI of Language
Processing. PLoS ONE 5(8): e11933. doi:10.1371/journal.pone.0011933
Editor: Pedro Antonio Valdes-Sosa, Cuban Neuroscience Center, Cuba
Received April 8, 2010; Accepted May 26, 2010; Published August 17, 2010
Copyright: ? 2010 Kim et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research is funded by a Pilot Grant from the National Alliance for Autism Research (now Autism Speaks, www.autismspeaks.org), and by grants
from National Institute of Neurological Disorders and Stroke (NINDS) (K23 NS43222) (www.nih.gov), the Ohio State University (OSU) Research Investment Fund,
the OSU Department of Statistics Release Time Fund, the Wright Center for Innovation, and the University of Missouri Department of Radiology Research
Investment Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: This study was funded in part by the National Alliance for Autism Research (now Autism Speaks), which is a privately funded research
foundation. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors.
* E-mail: firstname.lastname@example.org
Cognitive tasks such as those involving language require the
integration of a wide range of brain regions. Previous research
[1–4] reveals that anterior Left Inferior Prefrontal Cortex (LIPC)
(BA 45/47/10), posterior LIPC (BA 44/45/46) and posterior Left
Middle Temporal Gyrus (LMTG) (BA21), bilateral fusiform gyrus,
bilateral cerebellum, left dorsal caudate and ventral anterior
thalamus are involved in a variety of language tasks. More
specifically, the role of the left inferior prefrontal cortex appears to
be implicated in the selection of competing words in semantic
language tasks . Both the posterior and anterior LIPC areas (BA
45/47) are typically activated during semantic tasks whereas the
posterior LIPC (BA 44/45) area is preferentially activated in tasks
which involve attending to phonology, involving decisions
regarding auditory syllables or rhymes [1,2]. However, such a
spatial division in the LIPC for semantics and phonology was not
significant when the two tasks are directly contrasted . The left
Middle Temporal Gyrus (MTG) (BA21) is known to be involved in
auditory information processing. Similar temporal activation on
fMRI is found for both semantic and phonological tasks [3,4]. It is
believed that the visually presented verbal stimuli invoke the sound
of the word automatically via network activation, manifested by
left MTG activation. Moreover, bilateral fusiform gyrus (BA37),
bilateral cerebellum, left dorsal caudate and ventral anterior
thalamus were found to be activated in both semantic and
phonological tasks .
Dopaminergic neurons are present mainly in the ventral
tegmental area (VTA) of the midbrain, substantia nigra, and the
subthalamic nucleus. Dopaminergic projections arise from the
VTA and then project diffusely across the frontal cortex [6,7].
Dopamine has a major role in regulating motor function. The loss
of dopamine neurons in the substantia nigra results in Parkinson
disease, with a loss in the ability to initiate controlled movements.
From a cognitive standpoint, the dopaminergic system has effects
on cognitive flexibility of the set shifting type [8–10], reversal
learning , spatial planning [12,13], and working memory
[14,15], all of which are tasks highly dependent on frontal lobe
function. Dopamine also appears to modulate a signal-to-noise
ratio, strengthening strong signals and dampening weak signals
. According to this model, decreased dopaminergic activation
of cortical areas leads to a decrease in the functional focus of
cortical neuronal network activity, whereas increased dopaminer-
gic activation leads a high signal-to-noise ratio. However, it should
be noted that the effects of dopaminergic treatment, as evidenced
by studies on cognition in Parkinson’s disease as well as studies in
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animal models, are characterized by an inverted ‘‘U’’-shaped
response curve, such that increasing doses of dopaminergic
agonists can improve or deteriorate performance on executive
function-related tasks .
The administration of L-dihydroxypheylalanine (L-dopa), the
precursor of the dopamine, has been found to cause restriction of
the semantic network in a semantic priming task . Subjects
were presented with a series of word pairs in which the first word
was paired with a closely related word, a distantly related word, a
non-related word, or a non-word. Subjects then performed a
lexical decision task in which they were asked to decide whether
the second series of letters was a word or a non-word. In the
placebo condition, subjects recognized the closely and distantly
related words more quickly than non-related words. L-dopa
treatment resulted in significantly quicker recognition of closely
related words than non-related words, but the recognition
latencies of distantly related words and non-related words were
not significantly different, providing evidence of the restriction of
the semantic network.
Dopaminergic modulation of the semantic network using
functional Magnetic Resonance Image (fMRI) was examined by
Tivarus et al. . The analysis revealed a change in functional
connectivity (FC) in one pair of brain regions, left fusiform gyrus
(LFUS) and left parietal lobe (LPAR) with L-dopa. However, as
dopamine projects predominantly to the frontal lobe [6,7], it was
unclear why only these more posterior regions were affected.
Although, as these posterior brain regions (LFUS and LPAR) are
important for word recognition, such an effect on FC might
otherwise be expected due to the aforementioned effects of L-dopa
on priming .
We wished to determine whether other brain regions,
undetected in previous work using general linear model (GLM)
, might act as an intermediary between the frontal targets of
dopaminergic projections and the posterior brain regions which
demonstrate the FC effect of L-dopa. Therefore, in this study, we
use the Independent Component Analysis (ICA), a nonparametric
analysis tool for independent source separation, on fMRI data to
examine the differences in language processing between L-dopa
and placebo groups. The underlying experimental paradigm is not
designed for detection of behavioral effects, as it is a simple task
where the performance is at ceiling . Rather, the purpose is to
examine whether ICA reveals novel information regarding how
frontal dopaminergic projections may affect the more posterior
language areas as compared to placebo.
No side effects were observed with drug administration in this
study . There are four experimental conditions derived from
the combination of two drug types (L-dopa and placebo) and two
task types (phonological and semantic). The BOLD signals for
each subject were analyzed using ICA and post-processed, which
led to an individual summary map (see Materials and Methods
for details). This collection of summary maps was used as input to
the flexible factorial model of SPM5. The group activation maps
produced by SPM(t) for the phonological and the semantic
tasks are presented in Figure 1a and 1b respectively, in which
activation with placebo and L-dopa is indicated by different
colors. Since our primary focus was to examine the difference
in activation between drugs (L-dopa, placebo), the two drug
conditions (L-dopa, placebo) were tested under each task
condition. The results of this analysis are shown in Figure 2
and 3. McDermott et al.  and Tivarus et al.  used GLM to
test the difference between activations under the two task
conditions with placebo. We repeat this comparison in order to
compare the results based on our method with those based on
Group activation maps
Regions activated by both tasks during both drug conditions are
bilateral inferior frontal cortex (BA 44/45/47) which extends
through premotor and motor areas (BA 4/6), bilateral cerebellum,
bilateral occipital cortex (BA 17/18/19), bilateral fusiform gyrus
(BA37), bilateral posterior superior and middle temporal gyrus (BA
21/22), thalamus, bilateral superior and middle frontal gyrus (BA
9/46) and left parietal lobe (bilateral for phonological task) (BA40).
The results are presented in Figure 1a and 1b, where activated
regions with placebo and L-dopa are indicated in blue and yellow,
Differentially activated regions between drug conditions
during the phonological task
The regions preferentially activated with L-dopa were left
cerebellum, bilateral occipital cortex, bilateral posterior superior
temporal gyrus (BA 22), bilateral fusiform gyrus (BA 37), thalamus
(mediodorsal), posterior cingulate (BA 31), and bilateral inferior
parietal lobe (BA 40) near the supramarginal gyrus whereas the
regions preferentially activated with placebo were bilateral
superior, middle and inferior frontal gyrus (BA 9/10/11/47),
anterior cingulate (BA 24/32), thalamus (pulvinar), and bilateral
inferior parietal gyrus (BA 40) near the supramarginal gyrus which
are located superior to the region activated with L-dopa. The
results are presented in Figure 2a and 2b.
Differentially activated regions between drug conditions
during the semantic task
The regions preferentially activated with L-dopa were right
cerebellum (medial), thalamus (mediodorsal), left insula, cuneus
(BA 18), inferior parietal lobe (BA 40) and posterior cingulate (BA
31) whereas the regions preferentially activated with placebo were
left middle and superior frontal gyrus extending to the medial
frontal lobe (BA 46/9), bilateral inferior frontal gyrus (BA 11/47),
anterior cingulate gyrus (BA 24/32), thalamus (pulvinar), and
bilateral inferior parietal lobe (BA 40) near the supramarginal
gyrus. The results are presented in Figure 3a and 3b.
Functional connectivity of thalamic regions of interest
Since dopamine projects heavily to frontal areas, but posterior
areas were revealed to be affected by L-dopa in this and Tivarus
et al.  studies, and thalamic areas were also affected by L-dopa
in this study, we wished to examine post hoc the functional
connectivity of the thalamic areas with frontal and posterior areas
to examine support for the hypothesis that effects on posterior
regions might be mediated by indirect effects from frontal
dopaminergic projections by actions on the thalamus.
The pulvinar and the medial dorsal thalamus were revealed to
have significant time series correlation, as our measure of
connectivity, with BA 9, 11, and 47 among frontal regions and
BA 20, 21, 22, 37 and 40 among posterior regions. Among these
ROI pairs, significant increases in connectivity were observed with
L-dopa for the pulvinar-BA11 and mediodorsal thalamus-BA11
ROI pairs, particularly for the semantic task. Increases with
L-dopa were also observed for posterior connections as well for the
semantic task, as observed for pulvinar-BA37, and mediodorsal
thalamus-BA20. Increases in connectivity with L-dopa are also
observed in some thalamic connections for the phonological task
as well (mediodorsal thalamus-BA9, mediodorsal thalamus-BA21,
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with a decrease found between both thalamic regions and BA20
with L-dopa). The correlations are presented in Table 1.
Differentially activated regions between tasks with
The regions preferentially activated with the sematic task were
left inferior frontal gyrus (BA 11/44/45/47), middle/medial
frontal gyrus (BA 8/9/10), right inferior frontal gyrus (BA 44/
45), left posterior superior/middle temporal gyrus (BA 21/22),
bilateral superior temporal gyrus (BA38), bilateral cerebellum, left
fusiform gyrus (BA37), posterior cingulate gyrus (BA23), supra-
marginal gyrus (BA40), and caudate head. These regions include
the regions found by McDermott et al.  and Tivarus et al. 
in the same condition.
The regions preferentially activated with the phonological task
were left precentral gyrus (BA6), posterior inferior and anterior
superior insula, bilateral inferior parietal lobe (BA40), left middle
occipital gyrus (BA19), and anterior cingulate gyrus (BA32).
Except for the anterior cingulate gyrus (BA 32), these results are
also similar to previous studies by McDermott et al.  and
Tivarus et al. .
In this study, a method for summarizing task-related ICs within
subject for each task and drug condition is proposed. Selecting
the independent component that contributes the most to a voxel,
a higher sensitivity for the detection of differential activation
seems to have been achieved. It should be noted that significant
differences in activation maps between L-dopa and placebo were
not found in GLM analysis . The activated regions for both
tasks include the anterior and posterior inferior prefrontal regions
(BA 9/11/44/45/46/47) involved in the selection of competing
words and the decision regarding auditory syllables or rhymes,
fusiform gyrus (BA 37) involved in visual word form processing,
the inferior parietal lobe and the superior and middle temporal
gyrus (BA 21/22/40) involved in retrieval of word meaning
[4,19–22] or sound [4,22] as expected with these language tasks.
The cerebellum involved in motor control and language
processing , motor (BA 6) and premotor (BA 4) involved in
the motor task of button pressing, and occipital cortex (BA 17/
18/19) involved in the visual stimuli processing were also
activated for both tasks as expected. These activation maps are
similar to the expected activation maps given the stimulus and
Figure 1. Group activation map. (a) The activation map with the phonological task was obtained by FDR of 5% and spatial extent significance
level of 5% which corresponds to 40 voxels. Drug conditions are indicated by yellow (L-dopa) and blue (placebo). From top, three-dimensional
representations of bilateral fusiform gyrus (BA37), bilateral inferior frontal gyrus (BA44/45), bilateral posterior superior temporal gyrus (BA22), bilateral
inferior parietal lobe (BA40) and bilateral occipital gyrus (BA19) are presented; (b) The activation map with the semantic task was obtained by FDR of
5% and spatial extent significance level of 5% which corresponds to 40 voxels. Drug conditions are indicated by yellow (L-dopa) and blue (placebo).
From top, three-dimensional representations of bilateral fusiform gyrus (BA37), bilateral inferior frontal gyrus (BA44/45), bilateral posterior superior
temporal gyrus (BA22), left inferior parietal lobe (BA40) and bilateral occipital gyrus (BA19) are presented.
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response and the expected language activation for the phonolog-
ical and the semantic tasks as per McDermott et al.  and
Tivarus et al. .
In the comparison between drug conditions, effects of L-dopa
on frontal regions may be expected due to the high degree of
posterior superior temporal gyrus (BA 22) was activated more
with L-dopa than with placebo, as were the posterior cingulate
(BA 31), bilateral fusiform gyrus (BA 37), and left inferior
parietal lobe (BA 40). Many of these posterior regions of the
brain are involved in retrieval of stored information: word
meaning (posterior region of BA 21/22) [4,19–22] or sound (BA
7/40) [4,22]. Further, the posterior fusiform gyrus (BA 37) is
involved with visual word form recognition . Anatomically,
the inferior parietal lobe (BA 40) lies in the region bounded
ventrally by the superior and middle temporal gyrus (BA 21/22)
and is a part of Wernicke’s area, a region important in the
speech comprehension. Therefore, since these posterior regions
preferentially activated with L-dopa in our study, they seem to
have importance in the effect of L-dopa on language processing.
This suggests the possibility that L-dopa may affect a semantic
network search to retrieve stored information through effects on
these posterior areas of the brain. The increased activation in
the frontal lobes.However,
the left fusiform gyrus (BA 37) and left inferior parietal lobe (BA
40) with L-dopa may also relate to the findings by Tivarus et al.
 in which the functional connectivity between left fusiform
gyrus (BA 37) and left inferior parietal lobe (BA 40) was
increased with L-dopa. This may suggest greater integration
between these two regions and less outside integration of other
inputs with drug, as might be expected with restricted access to
the semantic network with L-dopa. In our study, the greater
activation in fusiform gyrus and left inferior parietal lobe with L-
dopa in the phonological task may relate to the spread of the
activation from the visual word form processing area to the
regions involved in the retrieving of the word’s sound. These
posterior effects, though, seem surprising given the distribution
of dopamine projections which arise from ventral tegmental
areas and spread primarily to the frontal cortex and much less to
posterior regions [6,7].
In the thalamus, anterior and dorsal medial regions showed
greater activation with L-dopa whereas the pulvinar showed
greater activation with placebo. The pulvinar region of the
thalamus is known to project to posterior parietal lobe and inferior
temporal gyrus [25–27] as well as the frontal cortex [26–28], and
receive projections from the frontal and parietal cortices ,
whereas the dorsal medial thalamus projects to dorsal and medial
Figure 2. Differentially activated regions by drug conditions in the phonological task. (a) The regions showing greater activation with L-
dopa were obtained by voxel level significance of 5% (uncorrected) and spatial extent significance level of 5% (uncorrected) which corresponds to 70
voxels. From top, three-dimensional representations of mediodorsal thalamus, bilateral fusiform gyrus (BA 37), left posterior temporal gyrus (BA 22),
inferior parietal lobe near supramarginal gyrus (BA 40), and posterior cingulate gyrus (BA 31) are presented. (b) The regions showing greater
activation with placebo were obtained by voxel level significance of 5% (uncorrected) and spatial extent significance level of 5% (uncorrected) which
corresponds to 70 voxels. From top, three-dimensional representations of bilateral inferior frontal regions (BA 11/47), medial frontal gyrus (BA 9),
superior frontal gyrus (BA 10), pulvinar, and anterior cingulate gyrus (BA 24) are presented.
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prefrontal cortex [25,30], and is known to be important for
memory retrieval. Therefore, these posterior effects of L-dopa on
activation detected in our study, as well as the functional
connectivity effects previously reported on posterior areas ,
may be mediated indirectly by effects on pulvinar, where the
decreased activity of the inhibitory GABAergic neurons in the
pulvinar in the L-dopa condition might result in increased activity
of its projections to the posterior parietal and inferior temporal
areas. This is supported by the high degree of functional
connectivity between these thalamic regions and the proposed
associated frontal and posterior regions in this study in our post-
hoc analysis, and the increased frontal-thalamic connectivity for
many of these cortical areas with L-dopa during the semantic task,
and for some regions the phonological task as well. The pulvinar is
involved in visual processing and appears to participate in the
cortical alarm system for subliminal fear . However, the
pulvinar has also been proposed to be involved with interacting
with frontal and parietal areas during ambiguity resolution in
language  and visual attention , which appears to be
consistent with this hypothesis of the pulvinar mediating the
frontal L-dopa effects on posterior regions. However, further
research will be necessary to explore this possibility, as our data
cannot address issues such as the pharmacology of these potential
One drawback of the GLM analysis in fMRI reported
previously , is that it assumes a linear association between
each hemodynamic response function (HRF) and the expected
task related HRF, but with noisy signal, it may not be able to
detect significant differences. ICA, an independent source
separation tool, is able to select only task relevant independent
sources by using the proposed correlation threshold, which is
expected to reduce noise and enhance sensitivity, yielding novel
significant findings described herein. The second drawback of the
earlier GLM analysis seems to be that it cannot represent the
association between two lagged-time courses due to delayed
response, since delayed response implies lack of linear association
between two time courses. Furthermore, an activation map
containing only the voxels that cross a strict voxelwise threshold
based on temporal consistency with the task related HRF, may not
be able to uncover the interconnected voxels with delayed and
bidirectional relationships. Since ICA extracts spatial patterns of
interest identified by applying a correlation threshold for each IC,
it may possibly avoid rigid voxelwise thresholding and bring
functionally connected voxels into the activation map. In this
study, we aimed to use this technique to understand how these
frontal dopaminergic projections may affect the more posterior
language areas. As a partial answer, we found that this effect may
be mediated indirectly by effects on the thalamus.
Figure 3. Differentially activated regions by drug conditions in the semantic task. (a) The regions showing greater activation with L-dopa
were obtained by voxel level significance of 5% (uncorrected) and spatial extent significance level of 5% (uncorrected) which corresponds to 70
voxels. From top, three-dimensional representations of mediodorsal thalamus, left insula, inferior parietal lobe (BA 40), cuneus (BA 18), and posterior
cingulate gyrus (BA 31) are presented. (b) The regions showing greater activation with placebo were obtained by voxel level significance of 5%
(uncorrected) and spatial extent significance level of 5% (uncorrected) which corresponds to 70 voxels. From top, three-dimensional representations
of inferior frontal regions (BA 11/47), medial frontal gyrus (BA 9), inferior parietal lobe (BA 40), pulvinar, and anterior cingulate gyrus (BA 24) are
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Materials and Methods
fMRI data for each subject and for each session were collected
with a 1.5 T General Electric (Milwaukee, WI) Signa scanner with
a quadrature head coil. The BOLD contrast functional data were
collected using a gradient echo EPI pulse sequence (TR=3s;
TE=40ms; a=90; FOV=240mm; matrix 64664, 28 axial slices
for whole brain coverage; 5mm slice thickness).
The two types of language tasks used in this experiment, semantic
and phonological, were derived from McDermott et al. . Tivarus
et al.  used these tasks under two separate drug conditions, L-
dopa and placebo. The fMRI data for the present study was
obtained from the experiment by Tivarus et al. . Sixteen right
handed, native English speaking subjects (eight male and eight
female, mean age 28.3 years with range of 21–49) without a history
of psychiatric or neurological disease or learning disabilities (such as
dyslexia) participated in this study. All subjects had normal or
corrected-to-normal vision, and abstained from caffeine prior to the
study to avoid hemodynamic effects from this agent. All subjects
gave written consent in accordance with the Institutional Review
Board of The Ohio State University, who specifically approved this
study. The sample size in the proposed within-subjects analysis were
expected to yield a significant L-dopa effectas suggested by previous
research which yielded significant behavioral results  as well as
significant imaging results , for L-dopa as compared to placebo
with this sample size. The dose of L-dopa administered orally was
100mg, with 25mg carbidopa to block systemic effects, and testing
was performed 90 minutes after administration to allow a peak level
to be reached, as described in the previous research . Drug was
administered in a placebo-controlled, double blinded manner. Each
participant received both drugs (L-dopa and placebo), one at each
session. The two sessions were separated by at least one week. The
order of drug administration was counterbalanced. During eachtest
session, representing one drug condition, each participant per-
formed two scanning runs. One of the runs consisted of alternating
blocks of the semantic task (24 sec) and rest (30 sec), and the other
run consisted of alternating blocks of the phonological task (24 sec)
and rest (30 sec), for a total of 4 min and 6 seconds for each run.
Each task block (24 sec) consisted of 15 words. In the semantic
condition, 10 of the words in the list were related by meaning and 5
were unrelated to a cue word, while in the phonological condition
10 of the words rhymed and 5 did not rhyme with the cue word.
The block design was as followed: a cue word was presented in
capital letters for 3 seconds followed by the list of 15 words. Each
word in the list was presented for approximately 1100ms, with a
300ms inter-stimulus interval (a blank screen), for a total of for
1.4 seconds for each word. Participants were instructed to attend to
the meaning or sound of the lists presented. They responded by
pressing one of two buttons YES or NO to indicate whether the
word in the list was related to the cue by meaning or sound.
Schematic diagrams of the two task conditions are described in
detail in Figure S1 in the supporting information section.
BOLD signal is an indirect measure of neuronal activity and is
thus affected by many other sources, including head movements
during the scanning process, heart beat and respiratory related
physiological changes in addition to the assigned cognitive task.
Such sources contribute to the level of BOLD signal intensity in
the functional MR image. A BOLD signal can be assumed to be a
linear mixture of the sources influencing BOLD intensity level.
Such sources may not be explained easily with parametric
functions because of the presence of complex regional interactions
within the brain and variations in activation across subjects.
Independent Component Analysis has been used in Blind Source
Separation (BSS), which does not need any parametric assumption
but depends only on data for estimation of the independent
sources. This analysis has been compared to a cocktail party
problem, where a listener must separate the independent voices
chattering at a cocktail party. ICA has been used in several fMRI
studies [34–39]. ICA analysis denotes the observed BOLD signals
for a subject during a given task and drug condition as a matrix X
with T (the number of image acquisitions) rows and N (the number
of voxels) columns. With linear ICA, X is modeled as a linear
Table 1. Correlations between thalamic and cortical regions.
SemanticTask Condition PhonologicalTask Condition
BA Placebo L-dopaDifference PlaceboL-dopa Difference
Pulvinar9 0.57***0.55***0.02 0.43*** 0.46***
11 0.11 0.21*
20.10* 0.25** 0.30**
47 0.40*** 0.39***0.010.26** 0.33***
20.04 0.42*** 0.34***0.08*
21 0.34***0.34***0.00 0.41***0.46***
11 0.26** 0.39***
*** p-value (uncorrected) v 0.001, ** p-value v 0.01, * p-value v 0.05.
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mixture of independent sources, i.e., X~ASzE, where A is a
mixing matrix with T rows and Q (the number of sources)
columns; S is a source matrix with Q rows and N columns; and E
is a matrix of random errors. ICA searches for linear projections
providing maximum independence of estimated sources without
relying on any parametric assumptions, whereas GLM searches for
the best linear projection based on the experimental design.
Frequently used ICA algorithms to solve the aforementioned
linear equation are the Infomax algorithm by Bell and Sejnowski
 and the Fast ICA algorithm by Hyva ¨rinen . The Infomax
algorithm maximizes the information transferred from inputs
through a non-linear neural network transfer function whereas the
Fast ICA algorithm maximizes independence among estimated
ICs, which is achieved via maximization of negentropy, a measure
of deviation from Gaussian distributions.
In this study, the Probabilistic Independent Component
Analysis (PICA) algorithm proposed by Beckmann and Smith
 was employed, which estimates sources by maximizing non-
Gaussianity in terms of negentropy. In this estimation, spatial
distribution of each source is assumed to have zero mean and unit
variance. Since PICA assumes the presence of true signal as well as
noise in the data, it is able to prevent over-fitting to noisy data.
The number of sources for the decomposition for each subject was
estimated using Laplace approximation to the posterior distribu-
tion of the model order and its mode . The algorithm was
implemented by utilizing Multivariate Exploratory Linear Opti-
mized Decomposition into Independent Components (MELOD-
IC), part of FSL, and the number of sources was generated for
each subject in each experimental condition.
Since ICA is blind to any parametric assumption with regard to
expected spatial and/or temporal patterns, it generates a number of
independent sources irrespective of whether the estimated sources
have any meaningful interpretation. Therefore, ICA requires a post-
processing step to choose task-related ICs, which has been
implemented by establishing thresholds on correlation between each
(HRF),as explained in Text S1 in the supporting information section.
Furthermore, as mentioned by Calhoun et al. , single-subject
ICAs cannot be directly used to make a group inference. Various
approaches for making an inference on a group of subjects with ICA
have been proposed [36,42,43]. Schmithorst and Holland 
described the methods of subject-wise concatenation, row-wise concatenation,
and across-subject averaging. The subject-wise concatenation method
 involves three steps, namely: (1) Principal Component Analysis
(PCA) for each subject for dimension reduction, (2) Combine the
Principal Components (PC) from each subject by concatenation for
across-subject analysis using PCA, (3) ICA on the PCs from Step 2.
On the other hand, the row-wise concatenation method  simply
uses row-wise concatenated BOLD data (a kTxN matrix) in the ICA
on the combined BOLD data across subjects. Finally, the across-
subject averaging method  simply uses BOLD data averaged
across subjects for the ICA. Based on a simulation, Schmithorst and
Holland  concluded that subject-wise concatenation has better
accuracy of estimated sources than the other two methods, when the
number of subjects with simulated common components is relatively
small and subject specific unique sources are present. It should be
noted that these methods first obtain group level ICs based on
combined data from all subjects, and then find subject-wise IC maps
from the group level ICs. Thus the individual IC maps are based on
all subjects. However, because of subject-to-subject variability in
cognitive tasks, it may be better to estimate sources for each subject
independently, irrespective of other subjects. Our approach for
estimation of a unified task-related activation map based on one or
more task-related ICs for a subject in a fixed task and drug condition,
called the individual summary map, is described below. This
collection of summary maps is then used as input to the SPM 5 for
between- subject analysis.
In linear ICA, BOLD response at location v in the brain for a
subject is written as a linear combination of Q-independent sources
s(v)~½s1(v),s2(v),...,sQ(v)?Twith time-varying mixing rates of
each source. Calhoun et al.  interpret sj(v) as the contribution
(or weight) of the j-th source at location v. Out of the Q sources, we
select only task-positive ICs based on thresholds for correlation
between the time course of each source and the task-related HRF,
which are established in post-IC processing. The ICs with time
courses which showed a correlation beyond the threshold were
selected as task-related. The correlation threshold was obtained by
applying multiplicity correction with an independence assumption.
Since the number Q of ICs generated by PICA differs for each
subject for each condition, the correlation threshold applied for
choosing task-related ICsdiffersforeach subject,foreachcondition.
(See Text S1 in the supporting information section for the details of
the calculation). Furthermore, the number of ICs that met the
correlationthresholdcriteriaforeachsubject,foreach taskand drug
each subject, for each task and drug condition is presented in
Table 2. For example, for Subject 8, with placebo during the
phonological task, three task-related ICs (IC #2, #7, and #18)
were selected, and are displayed in the first three images of Figure 4.
Whenever a subject showed multiple task-related ICs for a
condition, another step was applied to summarize these sources in
order to obtain the individual summary map. We compare absolute
values of the contributions, j~1,...,Q?of task-related ICs at voxel
v and assign the source value corresponding to the absolute
maximum contribution to each subject’s summary map. This rule
can be described as follows.
Table 2. Number of task-related ICs of subjects by task and
SubjectPhonological taskSemantic task
Since subject 7 in the semantic task with L-dopa has no ICs above the
correlation threshold, an IC showing the greatest correlation to the task HRF
was selected for the further analysis.
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The fourth image of Figure 4 displays the summary map of
Subject 8 for the phonological task with placebo. The above step
provides each subject’s summary map for each task and drug
condition. For across-subjects analysis, Calhoun et al.  tested
for voxel-wise significance on reconstructed individual ICs from an
IC of Step 3 described above via one-sample t-test. In our study,
Figure 4. Task-related ICs and the summary map. Task-related ICs and the summary map at axial planes z=225,210, 6, 21, 37, 53 (mm) for
Subject 8 with placebo during the phonological task. The first three images in each row are the task-related IC maps whereas the fourth image is the
summary map made by the proposed method. Three IC maps are displayed in the order of the 2th, 7th and 18th ICs.
ICA of L-dopa and Language
PLoS ONE | www.plosone.org8 August 2010 | Volume 5 | Issue 8 | e11933
we employed the same strategy to quantify statistical significance
of the collection of individual summary maps.
The collection of individual summary maps, for each task and
drug condition were then analyzed, voxel-by-voxel, via the flexible
factorial model in SPM5 (http://www.fil.ion.ucl.ac.uk/spm) run in
Matlab. The design matrix for the factorial model, as well as
justification of the assumptions for the voxel-wise significance test
are given in Text S2 in the supporting information section.
ICA. The preprocessing steps applied for this study were motion
correction, spatial normalization to standard template and spatial
smoothing as implemented in SPM5. The images were first
corrected for head motion during the scan. The registered images
were then normalized into the standard space provided by
Montreal Neurological Institute (MNI) and smoothed using the
Gaussian kernel with Full-Width Half-Maximum (FWHM) of
summary maps for the conditions of task and drug for all
subjects were then analyzed by 2 (tasks)62 (drugs) flexible factorial
model. The activation in each condition was determined by a
False Discovery Rate (FDR) of 5% and by a spatial extent
threshold of 5% (uncorrected). For the comparison between L-
dopa and placebo for each task, and the comparison between the
semantic task and the phonological task for placebo condition, the
activation was determined by a voxel level threshold of 5%
significance (uncorrected) and by a spatial extent threshold of 5%
Preprocessing preceded the described
two tasks. The experimental design and an example of a list of
words for each task are illustrated in Figure S1. Two separate
block design experiments for two task conditions were employed at
each drug session. In the list of words for the phonological task,
‘‘bottle’’, ‘‘car’’, ‘‘picture’’, ‘‘pool’’, and ‘‘horse’’ are not related
with the cue word ‘‘deer’’ by rhyme or sound. In the list of words
for the semantic task, ‘‘wheel’’, ‘‘mouse’’, ‘‘book’’, ‘‘draw’’, and
‘‘chair’’ are not related with the cue word ‘‘cold’’ by meaning.
Found at: doi:10.1371/journal.pone.0011933.s001 (0.01 MB TIF)
Design of the experiment and lists of words for the
Found at: doi:10.1371/journal.pone.0011933.s002 (0.05 MB
Correlation threshold calculation.
Found at: doi:10.1371/journal.pone.0011933.s003 (0.11 MB
Assumptions for the test statistics in the fexible factorial
We thank Professor P. Schmalbrock who helped us with technical advice
on acquiring fMRI data during the entire experiment.
Conceived and designed the experiments: MET AH DQB. Performed the
experiments: MET AH DQB. Analyzed the data: NK PG MET DQB.
Contributed reagents/materials/analysis tools: NK PG. Wrote the paper:
NK PG DQB.
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