DOI: 10.1126/science.1247003
, 424 (2014);344Science
Daniel Baldauf and Robert Desimone
Neural Mechanisms of Object-Based Attention
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Acknowledgments: We thank C. Perry and H. Swanson for technical
assistance, the entire Deisseroth laboratory for helpful discussions,
and T. Jardetzky for use of a Biotek Synergy4 plate reader. K.D.
is supported by the National Institute of Mental Health, the Simons
Foundation Autism Research Initiative, the National Institute on
Drug Abuse, the Defense Advanced Research Projects Agency,
the Gatsby Charitable Foundation, and the Wiegers Family Fund.
A.B. received support from the German Academic Exchange Service
(DAAD), and S.Y.L. received support from the Fidelity Foundation.
Optogenetic tools and methods reported in this paper are
distributed and supported freely (www.optogenetics.org).
Supplementary Materials
www.sciencemag.org/content/344/6182/420/suppl/DC1
Materials and Methods
Figs. S1 to S3
18 February 2014; accepted 19 March 2014
10.1126/science.1252367
Neural Mechanisms of
Object-Based Attention
Daniel Baldauf* and Robert Desimone
How we attend to objects and their features that cannot be separated by location is not understood.
We presented two temporally and spatially overlapping streams of objects, faces versus houses, and used
magnetoencephalography and functional magnetic resonance imaging to separate neuronal responses
to attended and unattended objects. Attention to faces versus houses enhanced the sensory responses
in the fusiform face area (FFA) and parahippocampal place area (PPA), respectively. The increases in
sensory responses were accompanied by induced gamma synchrony between the inferior frontal junction,
IFJ, and either FFA or PPA, depending on which object was attended. The IFJ appeared to be the driver
of the synchrony, as gamma phases were advanced by 20 ms in IFJ compared to FFA or PPA. Thus, the
IFJ may direct the flow of visual processing during object-based attention, at least in part through
coupled oscillations with specialized areas such as FFA and PPA.
W
hen covertly attending to a location in
theperiphery,visualprocessingisbiased
toward the attended location, and the
sources of top-down signals include the frontal
eye fields (FEF) (1, 2) and parietal cortex (PC).
FEF may modulate visual processing through a
combination of firing rates and gamma frequency
synchrony with visual cortex (2). For nonspatial
attention, the mechanisms of top-down attention
are much less clear. When people attend to a feature,
such as a particular color (3–5), or to one of
several objects at the same location (6–8), activity
in the extrastriate areas representing properties of
the attended object is enhanced. But where do the
attentional biases (9) come from, and how do they
enhance object processing when the distractors are
not spatially separate?
We combined magnetoencephalography
(MEG), supplemented by functional magnetic
resonance imaging (fMRI) and diffusion tensor
imaging to optimize both spatial and temporal
resolution. In the MEG experiment, two spatially
overlapping streams of objects (faces and houses)
were tagged at different presentation frequencies
(1.5 and 2.0 Hz) (Fig. 1, A and B) (5, 10–12). The
stimuli went in and out of “phase coherence,” so
that they were modulated in visibility over time
but did not change in luminance or flash on and
off. When subjects were cued to attend to one of
the streams and to detect occasional targets within
the cued stream, frequency analyses allowed
identifying brain regions that followed the stimulus
oscillations.
Using MEG data only (13), the strongest activity
evoked by the face tag was in the right fusiform
gyrus, whereas the activity evoked by
the house tag was more medially in the inferiortemporal
cortex (IT) (Fig. 1C; figs. S1 and 2 for
individual subjects and alternative source reconstruction
approaches). These areas were roughly
consistent with the locations of fusiform face area
(FFA) and parahippocampal place area (PPA)
determined previously in fMRI (14–16). To increase
the accuracy of localization in each subject,
we added high-resolution fMRI localizers
for FFA and PPA (Fig. 2, B and D, and fig. S3A),
which were focused at the expected spots (Fig. 2F).
To identify other areas important for nonspatial
attention, we contrasted the brain state
when attending to one of the two superimposed
object classes with a similarly demanding state
that did not require attending to either object
class. The attention-related fMRI localizers revealed
consistent activation in the inferior frontal
junction (IFJ) at the intersection of the inferiorfrontal
and precentral sulcus (17–19) (Fig. 2, A,
C, and E), with weaker and less-consistent signals
in posterior-parietal and in inferior-temporal
cortex (fig. S3C). A control experiment confirmed
that IFJ's activation was indeed related to nonspatial
attention, rather than simply memory
(fig. S4).
Each subject's individual fMRI localizers
were then used as regions of interest (ROIs) to
guide the analysis of the MEG signals (see supplementary
material for a description of the coregistration
of fMRI and MEG). The modulation
of sensory responses by attention in the taggingfrequency
range is shown in Fig. 2G (fig. S5B for
individual subjects). FFA and PPA had the strongest
responses, with FFA more responsive to the
attended face tag (t test, P < 0.001) and PPA more
responsive to the attended house tag (t test, P <
0.01). Thus, object-specific attention modulates
the sensory responses in FFA and PPA. Weaker
sensory responses were found in region V1.
Although weaker in amplitude, sensory responses
were also found in IFJ, and the attention
effects were much stronger—there was a tagging
frequency response only to the attended object
(both t test, P < 0.001). Control regions in the
FEF (localized in separate fMRI runs, fig. S3D),
PC (localized in the attention-related fMRI experiment
in some participants) and the frontal pole
(anatomically defined) showed only minor and
less consistent responses. The general pattern of
McGovern Institute for Brain Research, Massachusetts
Institute of Technology, Cambridge, 02139 MA, USA.
*Corresponding author. E-mail: baldauf@mit.edu
25 APRIL 2014 VOL 344 SCIENCE www.sciencemag.org424
REPORTS
results did not depend on the specific tagging frequency
assignment to faces or houses (fig. S5).
In temporal cortex, both MEG and fMRI results
showed a moderate tendency of lateralization: FFA
totherightandPPAtothelefthemisphere(Fig.2H).
The attentional effects in IFJ were slightly lateralized
to the right.
We used Fourier transformations to extract the
phase relation between the frequency-tag response
and the stimulus on the screen, i.e., the latency of the
sensory responses (Fig. 2I). The phase lag of IFJ (corresponding
to 208 ms) was shifted by about 25 ms
in comparison to FFA and PPA (188 and 171 ms)
(Fig. 2J and fig. S5) (20), which likely accounts for
transmissiontime andsynapticdelaysbetweenareas.
Frequency [Hz] Lateralization
FFA/PPA phase lag [ms]
0
1
2
V1
0
1
2
IFJ
0
2
FFA
0
2
PPA
attend Face
attend House
2
2
100 200
100
200
FFAPPA IFJV1
200
250
150
100
FFA
PPA
A
B
C
D
E
F
G H
I J
Fig. 2. Measures of attention localization. (A) Average of the fMRI localizers for
attention-related and (B) object-related ROIs (blue: FFA, red: PPA, P < 0.001, FW errorcorrected).
(C and D) All subjects' individual ROIs superimposed on a standardized brain
surface (red: middle frontal gyrus; blue: inferior frontal gyrus; dashed line: BA44) and (E) on
an inflated brain. (F) Comparison of the average MEG-based (filled) and fMRI-based (outlines)
localization of face- (blue) and house-related (red) activity. (G) Spectral power in the participants'
individual ROIs when attending houses (1.5 Hz, red) or faces (2.0 Hz, blue).
(H) Lateralization of the attentional effects. (I) Phase lags of the neural activity to the
physical stimuli on screen. (J) Systematic phase advancement from FFA/PPA to IFJ.
CA B
attend FACE (2.0Hz)
attend HOUSE (1.5Hz)
2.0
1.0
5.0
H
H
H
F
F
F
F
1cm
Power
(2.0Hz)
Power
(1.5Hz)
Face
F
F F
F
F
F
F
Fig. 1. Stimuliandattention.(A) Stimuli used in the MEG experiment (see online
methods). (B) Sequence of stimuli consisting of an overlay of two streams of objects
(faces and houses), fading in and out of a phase-scrambled noise mask at different
frequencies (1.5 and 2.0 Hz). Subjects had to attend the cued stream and report
occasional 1-back repeats. (C) Fourier-transform of the minimum norm estimate
when attending to faces (2.0 Hz) or houses (1.5 Hz, P < 0.05, FDR-corrected).
www.sciencemag.org SCIENCE VOL 344 25 APRIL 2014 425
REPORTS
To test for functional interactions among the
areas, we analyzed coherence between the frontal
and temporal ROIs (Fig. 3C) across a wide frequency
spectrum, including frequency bands that
were not time-locked to the stimuli (see timefrequency
power spectra and an analysis of frequency
nesting in fig. S6). The baseline-corrected
coherence between IFJ-FFA (top) and IFJ-PPA
(bottom) in the tagging-frequency range is shown
in Fig. 3A. When attending into an area's preferred
stimulus domain, that area became functionally
connected with IFJ at the respective tagging frequency
(both t test, P < 0.001), as responses in
both areas were phase-locked to the attended stimulus
but with different phase lags.
Coherence at frequencies higher than the
tagging frequency was dominated by shared
background coherence, as typical in MEG. To
reduce the influence of background coherence,
we analyzed patterns of domain-specific coherence
by computing an attention index, the
AIC = (attend preferred – attend unpreferred)/
(attend preferred + attend unpreferred)
which directly contrasts both attentional conditions
and, therefore, is more sensitive to subtle
attentional effects on coherence (Fig. 3B). When
attending to faces (top, blue) coherence between
IFJ and FFA increased not only at the tagging
frequency (2.0 Hz) but also in a high-frequency
band (70 to 100 Hz, both t test, P < 0.05). Similarly,
when attending to the house stimuli (red),
IFJ and PPA exhibited increased coherence,
both at the tagging frequency (1.5 Hz) and in
a high-frequency band (60 to 90 Hz, both t test,
P < 0.01). In this high-frequency gamma range,
the individual subjects varied considerably in their
respective peak modulation frequency. As a
check for whether the coherence in the gamma
range resulted from common stimulus-locked
onsets, we reran the analysis in a control data
set, with shuffled trial order within each ROI
(fig. S7C), which completely eliminated gamma
coherence. Attentional modulations of coherence
between IT and PC were weaker and nonsignificant
(fig. S7D).
To test the directionality of the gamma-band
coherence between IFJ and FFA/PPA, we analyzed
the instantaneous phase lags between the
two areas. Because portions of the signal in both
sites are shared background coherence (due to
electromagnetic field spread) or random noise, we
first baseline-corrected the phase lag distributions
to dissociate shared background coherence (which
is simultaneous) and noise (which is uniformly
distributed) from phase coherence that results from
axonally transmitted synchronization (see supplementary
methods and figs. S8 and S9). We then
compared the residual phase lag distribution across
a range of frequency bands around the subject's
frequency of maximal coherence (peak T10 Hz).
In most subjects (9 out of 12), the baseline-corrected
phase lags systematically increased as a function
of frequency, consistent with IFJ leading FFA/PPA
with a constant time lag of about 20 ms (SE = 6 ms)
(Fig. 3D and figs. S10 and S11). The three other
subjects seemed to have stronger bottom-up or
balanced coherence (see supplementary materials).
To determine whether IFJ is anatomically connected
with FFA or PPA, we computed maps of
probabilistic connectivities (21) to the seed regions
in FFA and PPA. When normalizing to the site of
maximal activity within frontal cortex, both FFAand
PPA-connectomes revealed areas around IFJ
to have the highest connection probabilities (see
Fig. 3E and fig. S12).
The neural mechanism that enables attention
to an object or feature seems intuitively more
complex than spatial attention, which may only
require a spatial-biasing signal that targets a relevant
location. Yet the present study reveals some
striking parallels in neural mechanisms: Prefrontal
cortex seems to be a common source of top-down
biasing signals, with FEF supplying signals for
spatial attention and IFJ supplying signals for
object or feature attention. With spatial attention,
cells in FEF and visual cortex begin to oscillate
together in the gamma frequency range, with FEF
the “driver” in these oscillations (2). Here, we
find that IJF—although it has delayed sensory
responses—is also the “driver” in coupled gamma
oscillations with FFA/PPA. In primates, coherent
gamma oscillations in FEF are phase-shifted
by about 10 ms compared with oscillations in
area V4, which has been argued to account for
the axonal conductance time and synaptic delays
between the two areas (2). With the phase shift,
spikes of FEF cells presumably affect cells in V4
at a time of maximum depolarization, which increases
their impact. Here, a phase shift of 25 ms
may allow for longer transmission times from IFJ
to FFA and PPA in humans. Thus, spikes originating
from IFJ may arrive in FFA and PPA respectively,
and vice versa, at a time of maximum
depolarization in the receiving area, magnifying
their impact. The directing of IFJ signals to the
FFA versus PPA may not be inherently more
BA C D
π
2π
φ∆
E
1
0
Fig. 3. Coherence measures of attention. (A) Cross-area coherence spectra.
(B) Attention indices, converted into changes of coherence. When attending to
the preferred stimulus (faces for FFA, houses for PPA), coherence between IFJ
and the respective temporal area increased at the respective tagging frequency
and in a high-frequency band (70 to 100 Hz). Dots represent subjects' peaks
of attentional modulations. (C) Schematic of the fronto-temporal connectivity.
(D) Directionality measure of gamma phase-lags between IFJ and FFA/PPA in
polar (right) and Cartesian (left) coordinates. In 9 of 12 subjects the phase-lag
of FFA/PPA to IFJ increased linearly with increasing frequencies around the
subject's peak of gamma coherence, consistent with IFJ cycles leading over
FFA/PPA cycles. (E) Parcellation-based probability maps of frontal connectivity
to FFA/PPA.
25 APRIL 2014 VOL 344 SCIENCE www.sciencemag.org426
REPORTS
complex than shifting FEF signals between different
locations in the visual field.
IFJ may include areas that function as general
executive modules (22, 23). Also, IFJ is close to
areas Ba45 and Ba46, homologs of which have
been described in nonhuman primate recordings
to encode information about object-categories in
delayed match-to-sample tasks (23, 24). Indeed,
the “attentional template” that specifies the relevant
location or object in spatial or feature attention
is hardly distinguishable from working
memory for these qualities (9), which is known to
involve prefrontal cortex (24). Coupled interactions
between prefrontal areas and visual areas
(25–31) could underlie many cognitive phenomena
in vision, with shared neural mechanisms
but variations in the site of origin and the site of
termination.
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Acknowledgments: We thank J. Liang, D. Pantazis,
M. Hämäläinen, D. Dilks, D. Osher, Y. Zhang, C. Triantafyllou,
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Supplementary Materials
www.sciencemag.org/content/344/6182/424/suppl/DC1
Materials and Methods
Supplementary Text
Figs. S1 to S13
References (32–78)
8 October 2013; accepted 1 April 2014
Published online 10 April 2014;
10.1126/science.1247003
A Chloroplast Retrograde Signal
Regulates Nuclear Alternative Splicing
Ezequiel Petrillo,1
* Micaela A. Godoy Herz,1
Armin Fuchs,2
Dominik Reifer,2
John Fuller,3
Marcelo J. Yanovsky,4
Craig Simpson,3
John W. S. Brown,3,5
Andrea Barta,2
Maria Kalyna,2
† Alberto R. Kornblihtt1
‡
Light is a source of energy and also a regulator of plant physiological adaptations. We show here
that light/dark conditions affect alternative splicing of a subset of Arabidopsis genes preferentially
encoding proteins involved in RNA processing. The effect requires functional chloroplasts and is
also observed in roots when the communication with the photosynthetic tissues is not interrupted,
suggesting that a signaling molecule travels through the plant. Using photosynthetic electron
transfer inhibitors with different mechanisms of action, we deduce that the reduced pool of
plastoquinones initiates a chloroplast retrograde signaling that regulates nuclear alternative
splicing and is necessary for proper plant responses to varying light conditions.
L
ight regulates about 20% of the transcriptome
in Arabidopsis thaliana and rice
(1, 2). Alternative splicing has been shown
to modulate gene expression during plant development
and in response to environmental cues
(3). We observed that the alternative splicing
of At-RS31 (Fig. 1A), encoding a Ser-Arg–rich
splicing factor (4), changed in different light regimes,
which led us to investigate how light regulates
alternative splicing in plants.
Seedlings were grown for a week in constant
white light to minimize interference from the circadian
clock and then transferred to light or dark
conditions for different times (see the supplementary
materials). We observed a two- and fourfold
increase in the splicing index (SI)—defined
as the abundance of the longest splicing isoform
relative to the levels of all possible isoforms—of
At-RS31[mRNA3/(mRNA1+mRNA2+mRNA3)]
after 24 and 48 hours in the dark, respectively
(Fig. 1B). This effect was rapidly reversed when
seedlings were placed back in light, with total
recovery of the original SI in about 3 hours (Fig.
1C), indicating that the kinetics of the splicing
response is slower from light to dark than from
dark to light.
The light effect is gene specific (fig. S1) and
is also observed in diurnal cycles under short-day
conditions (Fig. 1D and fig. S2). Furthermore,
three circadian clock mutants behaved like the
wild type (WT) in the response of At-RS31 alternative
splicing to light/dark (fig. S3). Changes
in At-RS31 splicing are proportional to light intensity
both under constant light and in short-day–
grown seedlings (fig. S4).
Both red (660 nm) and blue (470 nm) lights
produced similar results as white light (Fig. 1E).
Moreover, At-RS31 alternative splicing responses
to light/dark are not affected in phytochrome and
cryptochrome signaling mutants (5, 6), ruling out
photosensory pathways in this light regulation
(Fig. 1F and figs. S5 and S6).
Light-triggered changes in At-RS31 mRNA patterns
are not due to differential mRNA degradation.
First, the light effect is not observed in the presence
of the transcription inhibitor actinomycin D
(Fig. 1G). Second, the effects are still observed in
upf mutants, defective in the nonsense-mediated
mRNA decay (NMD) pathway (7) (Fig. 1H and
fig. S7). Third, overexpression of the constitutive
splicing factor U2AF65
(8) in Arabidopsis protoplasts
mimics the effects of light on At-RS31 alternative
splicing (Fig. 1I).
mRNA1 is the only isoform encoding a fulllength
At-RS31 protein (9). mRNA3 and mRNA2
are almost fully retained in the nucleus (fig. S8).
mRNA1 levels decrease considerably in dark without
significant changes in the total amount of
At-RS31 transcripts (Fig. 2A and fig. S9), which
suggests that alternative splicing is instrumental
1
Laboratorio de Fisiología y Biología Molecular, Departamento
de Fisiología, Biología Molecular y Celular, IFIBYNE-CONICET,
Facultad de Ciencias Exactas y Naturales, Universidad de
Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA
Buenos Aires, Argentina. 2
Max F. Perutz Laboratories, Medical
University of Vienna, A-1030 Vienna, Austria. 3
Cell and Molecular
Sciences, The James Hutton Institute, Invergowrie, Dundee,
Scotland. 4
Fundación Instituto Leloir, IIBBA-CONICET, C1405BWE
Buenos Aires, Argentina. 5
Division of Plant Sciences, University of
Dundee at The James Hutton Institute, Invergowrie, Dundee,
Scotland.
*Present address: Max F. Perutz Laboratories, Medical University
of Vienna, A-1030 Vienna, Austria.
†Present address: Department of Applied Genetics and Cell
Biology, BOKU, University of Natural Resources and Life
Sciences, Muthgasse 18, A-1190 Vienna, Austria.
‡Corresponding author. E-mail: ark@fbmc.fcen.uba.ar
www.sciencemag.org SCIENCE VOL 344 25 APRIL 2014 427
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