BOLD Correlates of Trial-by-Trial Reaction Time
Variability in Gray and White Matter: A Multi-Study fMRI
Tal Yarkoni1*, Deanna M. Barch1,2, Jeremy R. Gray3, Thomas E. Conturo2, Todd S. Braver1,2
1Department of Psychology, Washington University, Saint Louis, Missouri, United States of America, 2Department of Radiology, Washington University School of
Medicine, Saint Louis, Missouri, United States of America, 3Department of Psychology, Yale University, New Haven, Connecticut, United States of America
Background: Reaction time (RT) is one of the most widely used measures of performance in experimental psychology, yet
relatively few fMRI studies have included trial-by-trial differences in RT as a predictor variable in their analyses. Using a multi-
study approach, we investigated whether there are brain regions that show a general relationship between trial-by-trial RT
variability and activation across a range of cognitive tasks.
Methodology/Principal Findings: The relation between trial-by-trial differences in RT and brain activation was modeled in
five different fMRI datasets spanning a range of experimental tasks and stimulus modalities. Three main findings were
identified. First, in a widely distributed set of gray and white matter regions, activation was delayed on trials with long RTs
relative to short RTs, suggesting delayed initiation of underlying physiological processes. Second, in lateral and medial
frontal regions, activation showed a ‘‘time-on-task’’ effect, increasing linearly as a function of RT. Finally, RT variability
reliably modulated the BOLD signal not only in gray matter but also in diffuse regions of white matter.
Conclusions/Significance: The results highlight the importance of modeling trial-by-trial RT in fMRI analyses and raise the
possibility that RT variability may provide a powerful probe for investigating the previously elusive white matter BOLD signal.
Citation: Yarkoni T, Barch DM, Gray JR, Conturo TE, Braver TS (2009) BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-
Study fMRI Analysis. PLoS ONE 4(1): e4257. doi:10.1371/journal.pone.0004257
Editor: Bernhard Baune, James Cook University, Australia
Received June 25, 2008; Accepted December 9, 2008; Published January 23, 2009
Copyright: ? 2009 Yarkoni 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 work was funded by NIH grants MH066088, MH066031,MH71616, and NS39538. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Reaction time (RT) is one of the most widely used measures of
performance in experimental psychology. Many influential exper-
imental paradigms (e.g., the Stroop task) employ RT as their
primary dependent variable, and countless others measure RT in
order to ensure that differences in response accuracy are not
confounded with strategic shifts in response speed (the ‘‘speed-
accuracy tradeoff’’). Surprisingly, however, the analysis of RT has
received limited attention in the functional neuroimaging literature
(e.g., [1–4]). Although an ever-growing number of studies include
RT as a trial-by-trial regressor in their analyses [e.g., 1,5,6–9], such
studies still represent a small fraction of the literature as a whole (for
a quantitative review, see ). Moreover, the RT regressor is
typically not the regressor of interest in such cases, but is included to
ensure that activity differences between experimental conditions are
not confounded by corresponding differences in RT. Finally, even
in studies for which BOLD signal correlates of RT variability have
been a focus of interest [e.g., 1,4,10], analyses have been conducted
within relatively narrow, task-specific contexts.To our knowledge,
no study has investigated the association between RT and brain
activation across multiple experimental paradigms in order to
identify potential task-general relations.
There are several reasons to predict the existence of task-
independent relations between activation and RT. First, many
cognitive processes are expected to be time-locked to participants’
overt responses (e.g., initiation of the motor response, processing of
tactile or visual feedback, etc.). Consequently, the temporal onset
of the hemodynamic response (HDR) should vary as a function of
RT in sensorimotor brain regions; on trials when participants
respond more slowly, activation should initiate later than on trials
when participants respond quickly (Figure 1A). This prediction has
been confirmed in a number of previous fMRI studies [2,11] and
serves as an important validation tool in the present context,
because if a basic relation between RT and delayed HDR onset
cannot be replicated across multiple studies, other kinds of
relations are unlikely to be uncovered.
A second reason to predict a broad RT-brain activation
relationship follows from the empirical observation that the
BOLD signal measured by fMRI sums approximately linearly as
a function of stimulation duration and intensity at short intervals
. If trial-by-trial differences in RT are viewed as naturally-
occurring analogues of experimentally-manipulated differences in
stimulus parameters, variation in either the amplitude or the
duration of neurocognitive processes might be expected to reliably
modulate RT. In cases where short-RT and long-RT trials are
PLoS ONE | www.plosone.org 1January 2009 | Volume 4 | Issue 1 | e4257
differentiated only by the duration over which some neurocognitive
process unfolds, with no difference in amplitude, linear summation
predicts that the BOLD response should attain a larger amplitude
for trials with longer RTs (Figure 1B). For example, if one
supposes that participants generally sustain attention to an on-
screen stimulus until a relevant response is made, activation in
brain regions that support attention (e.g., lateral frontal cortex and
posterior parietal cortex [13,14]) should increase approximately
linearly with RT, other things being equal [cf. 8]. A number of
previous fMRI studies have observed such positive relations in
isolated paradigms [3,5,8,15–17].
Third, one might predict that trial-by-trial differences in RT
would be associated with changes in the amplitude or intensity of
some cognitive processes rather than—or in addition to—their
duration. For example, it is intuitive to think that natural
fluctuations in cognitive effort or preparation level should produce
trial-by-trial differences in RT. Other things being equal (i.e.,
assuming all trials have equal difficulty), as the cognitive resources
Figure 1. Hypothetical effects of changes in RT-related physiological processes on the BOLD response. (A) changes in onset. (B)
changes in duration. (C) changes in amplitude.
Neural Correlates of RT
PLoS ONE | www.plosone.org2January 2009 | Volume 4 | Issue 1 | e4257
allocated to a trial increase, the amplitude of activation in brain
regions that support those resources should increase while RTs
should decrease (Figure 1C). Thus, this view predicts that there
should be a negative correlation between RT and the BOLD
response in regions associated with deployment of task-related
resources. Such a relation is particularly likely to emerge early on
or even prior to each trial, when there is an opportunity to exercise
preparatory processing [1,18]. Limited evidence for such a
relationship has been observed in a number of studies [1,19–21];
for example, Weissman and colleagues recently found that
decreased activation in frontoparietal regions just prior to trial
onset was associated with longer RTs, a result they attributed to
momentary lapses of attention . However, the generalizability
of such findings has not yet been systematically investigated.
These three possibilities (a temporal shift, a positive correlation
with duration, and a negative correlation with amplitude) are not
exhaustive, nor are they mutually exclusive. To the contrary, it is
likely that RTs on most trials reflects a mix of influences, resulting
in complex response shapes. Figure 1D illustrates the hypothetical
response for two trials that differ in their onset, amplitude, and
duration. The presence of multiple influences could potentially
make RT-related activation difficult to detect if general trade-offs
exist (e.g., if an increase in amplitude is precisely offset by a
decrease in duration, it would be difficult to detect a difference in
the resulting HDRs at short durations; Figure 1D). On the other
hand, some types of influences might be stronger than others,
resulting in easily-detectable RT effects across a variety of task
paradigms. Ultimately, the question is an empirical one.
To test for the presence of systematic relations between RT and
brain activation, the present study sought to assess the relationship
between trial-by-trial variation in RT and brain activation at a
relatively broad, task-independent level. Data from five different
fMRI experiments were reanalyzed, with datasets chosen to span a
range of experimental tasks (working memory, episodic memory,
decision-making, and affective rating tasks), fMRI designs (event-
related and mixed blocked/event-related), and stimulus modalities
(words, affective pictures, faces, and numbers). We searched for
regions that showed a consistent relationship across studies
between BOLD activation and RT. Results provided strong
evidence for two of three patterns predicted above. Specifically, we
identified (a) temporal shifts in the onset of the BOLD response on
trials with longer RTs throughout much of the brain, and (b)
positive correlations between RT and activation in a number of
frontal, parietal, and thalamic regions. Surprisingly, in addition to
these expected RT effects in gray matter regions, we identified
remarkably consistent relations between trial-by-trial changes in
RT and activation strength in white matter regions, providing the
most convincing evidence to date that it is possible to detect
BOLD signal in white matter.
Materials and Methods
We reanalyzed data from 5 previous experiments. Detailed
methods for most studies have been previously reported, and we
therefore summarize only key aspects of each study’s methodology
here (Table 1). All experiments were approved by the Washington
University in St. Louis institutional review board.
Samples 1 and 2 were drawn from two large studies (n=102 and
50, respectively) of healthy young adults who performed a 3-back
working memory task involving face and word stimuli during
scanning at 1.5 or 3 Tesla. A mixed blocked/event-related design
 was used in both studies. Detailed methods have been
previously reported [10,23,24]. Sample 3 (n=26) was drawn from a
decision-making study involving a sample of healthy young adults.
Participants selected cards from one of two decks and were
rewarded with variable point rewards exchanged for money at the
end of the experiment [25,26]. A mixed blocked/event-related
design was used to analyze the data. Sample 4 was drawn from a
larger experimental dataset investigating emotional processing in
schizophrenia. For present purposes, only data from healthy
control participants (n=35) were analyzed. Participants rated the
valence and arousal of affective stimuli (pictures, words, and faces)
during scanning. A rapid event-related design was used to analyze
the data. Sample 5 was drawn from a larger experimental dataset
investigating cognitive function in schizophrenia [27,28]. Only
data from healthy control participants (n=39) were used.
Participants were scanned while they performed several different
memory encoding and working memory tasks involving word and
face stimuli. Data was analyzed with a rapid event-related design.
In total, the 5 samples comprised a sample size of n=252.
Responses in all samples were made manually by pressing a
button, and RT was defined as the total time elapsed (in
milliseconds) between the onset of a stimulus and registration of
the participant’s manual response.
All analyses were conducted using a general linear model
approach [GLM; ]. To identify the neural correlates of trial-
by-trial differences in RT, we computed a new general linear
model for each subject in each dataset, adding parametric
regressors coding for RT on each trial. In principle, a parametric
RT effect can be modeled in the GLM using a number of
approaches. The most common approach is to use a variable
impulse model, which models RT differences by varying the
amplitude of the RT regressor across trials while holding its
duration constant (for review, see ). However, Grinband and
colleagues recently demonstrated that this approach incurs
considerable power loss when the underlying signal varies only
Table 1. Key characteristics of the five datasets.
Sample PopulationnTask(s)Stimuli ScannerfMRI design Mean RT (ms)
1 Young adults503-back WMW, F1.5TMixed994
2 Young adults102 3-back WMW, F3T Mixed 1066
4Young adults35 emotion ratingsP, W, F3TEvent-related1274
5Young adults 39memoryW, F1.5TEvent-related 974
Neural Correlates of RT
PLoS ONE | www.plosone.org3January 2009 | Volume 4 | Issue 1 | e4257
in duration and not in amplitude . Grinband et al advocated the
use of a variable epoch model that models RT by varying the
duration and not the amplitude of the RT regressor, on the
assumption that this approach more closely reflects the dynamics
of underlying physiological processes. However, as noted above, it
is theoretically possible for differences in RT to reflect differences
in both the amplitude and the duration of underlying neurocog-
nitive processes (e.g., when an increase in effort leads to a
reduction in processing time; Figure 1D). To avoid making any
assumptions about the shape of the RT-related response, we used
an empirical estimation approach. In each data set, a Finite
Impulse Response (FIR) basis set was used to estimate the
influence of RT variability independently at 7 discrete time points
following stimulus onset. This approach allowed us to accurately
characterize the shape of the RT-related response at the cost of a
slight reduction in detection power (due to consumption of
additional degrees of freedom).
To increase power to detect RT-related activation, we
employed the simplest design matrix possible in each sample.
Thus, we collapsed across all non-essential experimental variables
in each case, and estimated the influence of RT across all available
trials. For example, the data for Samples 1 and 2 have previously
been modeled using separate effects for different stimulus types,
trial types, and/or mood induction conditions [10,23,30]. In the
present analyses, we collapsed across such variables and modeled
them all as a single effect coding for the difference between
experimental trials and the fixation baseline. However, to ensure
that putative RT effects could not be accounted for by other
intercorrelated experimental variables (e.g., response accuracy), a
subsequent validation analysis that included a broad range of
experimental covariates was conducted in the largest sample
(Sample 2). Additionally, because three of the samples used mixed
blocked/event-related designs (samples 1–3), which require
separate estimation of blocked and event-related effects ,
samples 1–3 retained separate effects for these two different
estimates (in addition to RT).
For each subject within each dataset, RT values were
standardized across trials prior to GLM estimation (i.e., each
RT value was demeaned and divided by the standard deviation).
The resulting standardized RTs were modeled independently at
each of the 7 time points post-stimulus onset. No transformation
was applied to the RT values before or after standardization.
Thus, estimates of RT-related activation reflected a linear effect of
RT. Volumes lacking an associated RT value (i.e., those occurring
within baseline periods, or on trials in which subjects failed to
respond) were assigned the mean standardized value of 0. This
procedure ensured that response-less volumes would be assigned
no weight in the regression and therefore would not influence the
resulting first level (i.e., within-subject) estimates.
For each dataset, subjects’ data were smoothed with a 3 mm
FWHM smoothing kernel prior to GLM estimation. Regions that
showed a significant relation with trial-by-trial RT variability were
then identified by performing a whole-brain mixed-effects
repeated measures ANOVA, with time (i.e., the 7 FIR regressors)
as a fixed variable and subject as a random variable. The resulting
F-test map representing the main effect of time was corrected for
non-sphericity (i.e., autocorrelation in each participant’s time
series) and transformed to a z-map in order to weight samples of
different sizes equally. Each map was statistically corrected for
multiple comparisons using a voxel-wise (intensity) threshold of
p,.001 and a cluster-wise (extent) threshold of 8 voxels. To assess
the cross-experiment consistency of RT effects, we employed a
conjunction analysis ; only clusters that showed a significant
RT effect in all five samples were considered significant. Note that
this approach is extremely conservative, because any region that
failed to show an RT effect in at least one sample would be
excluded from further consideration even if strong effects were
observed in all other samples. Because the resulting map contained
several very large clusters (.10,000 mm3) that each contained
multiple anatomical structures, an automated peak-search algo-
rithm was used to delineate boundaries of smaller ROIs by
defining spherical ROIs around all peaks and repeatedly
consolidating peaks within 20 mm of each other .
Although an ANOVA approach provides a powerful omnibus
test for detecting RT-related brain activation that varies over time,
a significant result implies only that there is some difference in
activation over time, and provides no insight into the specific
nature of that effect. We therefore conducted post-hoc analyses in
order to characterize the pattern of activation present in the
regions identified by the ANOVA analysis. Two linear contrasts
designed to detect patterns of a priori interest were applied. First,
to identify regions that showed a linear increase or decrease in
response amplitude as a function of RT (Figure 1B–C), the
coefficients of the 7 FIR regressors were weighted to fit a gamma
hemodynamic response function (HRF ). Second, to identify
regions in which underlying RT-related processes varied only in
temporal onset and not in magnitude or duration (Figure 1A), the
coefficients of the 7 FIR regressors were fit to a temporal derivative
of a canonical HRF. The temporal derivative is formally
equivalent to the difference between two identical HRFs staggered
in time; thus, this contrast was optimized to detect regions in
which activation ‘‘shifted’’ as a function of RT but did not
otherwise change. Each of the two contrasts was applied at a
regional level by testing the average of all voxels within each ROI
identified by the ANOVA.
Finally, in addition to testing for specific response shapes, we
conducted a more liberal exploratory analysis intended to identify
any brain regions that showed RT-related activation in all five
samples at any point in the activation time course. This analysis
could potentially detect effects that were temporally consistent but
not strong enough to attain significance in a full repeated-measures
analysis in all five samples. At each acquisition timepoint (i.e., for
each of the 7 FIR regressors), we identified voxels in which BOLD
response magnitude was systematically correlated with trial-by-
trial RT differences (p,.05 uncorrected, one-tailed) in the same
direction in all five samples (i.e., positively correlated in all samples
or negatively correlated in all samples). Note that although the
HRF was modeled over seven acquisition volumes in all five
samples, the length of each TR (or MRI repetition time) differed
across studies, ranging from 2.36 s (Samples 1 and 2) to 3 s
(Sample 4). This difference made the analysis more conservative,
because it forced voxels to display consistent RT-related activation
over a longer duration of time in order to be identified (e.g., an
effect of RT at the fourth TR in all samples corresponded to a
temporal window between 7–12 seconds post trial onset). We
deemed this approach preferable to the less conservative and less
computationally efficient method of sub-sampling TRs or
interpolating time courses across multiple samples on a voxel-wise
Consistent RT-related activation in gray and white matter
An initial whole-brain repeated-measures ANOVA identified all
regions in which trial-by-trial RT variability correlated with
BOLD signal changes in all five datasets. The resulting set of
regions included large bilateral foci in medial frontal cortex,
Neural Correlates of RT
PLoS ONE | www.plosone.org4 January 2009 | Volume 4 | Issue 1 | e4257
frontal operculum, lateral PFC, anterior PFC, visual cortex,
medial cerebellum, and thalamus, as well as lateralized and/or
more circumscribed foci in the precuneus, posterior cingulate
cortex, and inferior parietal cortex (Figure 2; Table 2). Unexpect-
edly, in addition to these activations in cortical and subcortical
gray matter regions, a number of activations were found in regions
located within white matter. Specifically, RT-related activation
was identified in the right lateral genu of the corpus callosum and
in parts of the posterior corona radiata bilaterally (Table 2). The
latter finding was surprising given that the BOLD signal in white
matter is widely assumed to be considerably weaker in white
matter than gray matter, presumably due to the lower metabolic
activity of white matter [e.g., see 34]. To address potential sources
of artifact that might have generated spurious RT-related signals
in gray and/or white matter, we conducted several validation
analyses that are reported later in this section.
To characterize the pattern of RT-related activation within the
regions identified by the whole-brain ANOVA, we employed two
approaches. First, we visually inspected the empirically estimated
time course of RT-related activation in each ANOVA ROI. RT-
related timecourses for each of the five samples are presented for
several representative gray matter (Figure 3) and white matter
(Figure 4) regions. Figure 5A displays the mean timecourse
averaged over all samples for each of the 33 ANOVA ROIs. The
time courses illustrate three important points. First, RT-related
activation showed a marked degree of spatiotemporal consistency.
The shape of the response generally differed to a greater extent
across brain regions within a single sample than across samples
within a single region (Figures 3–4). Thus, regional differences in
the shape of the HDR appear to manifest reliably not only in
standard experimental contrasts [35,36] but also with respect to
functional differences in RT. Second, virtually all gray matter
regions showed both (a) an initial ‘‘dip’’ in the RT-related time
course approximately 2s post-onset, and (b) uniformly greater
activation for longer RTs thereafter (Figure 5A). This pattern is
consistent with the presence of both a temporal shift in the
response (i.e., later initiation of the response for longer RTs) and a
time-on-task effect (i.e., greater summation of the BOLD response
on long-RT trials due to increased processing duration). Finally,
strikingly different response shapes were observed in gray and
white matter regions, with the latter exhibiting a smaller amplitude
and a substantial delay in time-to-peak (approximately 10–12 s
versus 7–10 s) relative to the former (Figure 5A).
Second, for each ANOVA ROI, we applied linear contrasts
designed to identify activation that showed either (a) a temporal
shift in the hemodynamic response without a corresponding
change in magnitude (shift contrast); or (b) a linear increase or
decrease in the magnitude of activation as a function of RT
(amplitude contrast). Figure 6 (A, B) displays the results of these two
contrasts for each sample in each ROI. Each colored circle
represents the z-score obtained in a different sample. The black
squares represent the fixed-effects sum of all five z-scores (i.e., the
sum of all z-scores divided by the square root of the number of
studies ). The Figure supports several conclusions. First,
Figure 2. Cortical regions that showed significant RT-related activation in all five samples. Clockwise from top left: ,left lateral, right
lateral, left medial, and right medial views of the cortical surface.
Neural Correlates of RT
PLoS ONE | www.plosone.org5January 2009 | Volume 4 | Issue 1 | e4257
consistent with the above qualitative interpretation of Figure 5A,
all 33 ROIs showed a positive temporal shift (i.e., a later peak for
longer RTs; all ps,.05), consistent with the notion that a general
delay in the initiation of task-related processing is one contributor
to longer RTs. Shift effects were particularly robust in visual,
cerebellar, and parietal regions that are presumably involved in
processing sensory feedback related to the motor response.
Second, highly significant positive correlations between RT and
activation were found predominantly in frontal regions, though
several regions in parietal cortex and the thalamus also showed a
positive correlation (p,.05). Negative correlations between RT
and activation were found only in the 6 white matter ROIs.
However, as noted above, the HDR in white matter ROIs
appeared to evolve much more slowly than the model HDR.
Thus, the apparent presence of negative correlations with RT may
reflect a failure of the model-based amplitude contrast to
accurately characterize the shape of the white matter response.
Visually, time courses of RT-related activation in white matter
ROIs appeared in large part to reflect delayed onset for longer
RTs rather than a change in amplitude (Figures 4,5). The
Region IDDescription Hem.BAxyz mm3
1Medial frontal cortexM 6/8/32112 48 14634
2 Medial frontal gyrusM6/24
3Anterior insulaL 13
232 195 4509
4Anterior PFCL 10
2512 32 891
6Ventrolateral PFC/anterior insulaR45/44/13 4122311178
7Dorsolateral PFCR9/46 4412 326561
8Anterior PFCR 1029 49 181215
Parietal/posterior cingulate regions
9 Posterior cingulateM 310
10 Posterior cingulateM 293
276 36 891
275 33 270
13Inferior parietal lobuleL 40
17Postcentral gyrusR 40 56
224 22 324
18Middle temporal gyrusR 22 51
19 CuneusM 183
21Fusiform gyrusL 19
22Lingual gyrusL 18/17
24Culmen (cerebellum)R 27
White matter regions
222 19 19216
31White matterR 192610486
32White matterR 25
33White matterR 20
Neural Correlates of RT
PLoS ONE | www.plosone.org6 January 2009 | Volume 4 | Issue 1 | e4257
divergence between model-based and inspection-based interpre-
tations of RT-related activations underscores the value of
empirically estimating RT-related time courses rather than using
a strictly model-based approach.
Relation between RT-related and task-related activation
Because RT-related changes in activation were statistically
orthogonal to more general differences in task-related activation
(i.e., the contrast between task performance and a fixation
Figure 3. Time courses of RT-related activation in representative gray matter ROIs. Each line represents activation in a different sample.
Left time course column: RT-related activation; right time course column: general task-related activation (i.e., task vs. baseline). Region labels (R14, R1,
R7, R12) refer to region IDs in Table 2. Error bars reflect 95% confidence intervals.
Neural Correlates of RT
PLoS ONE | www.plosone.org7January 2009 | Volume 4 | Issue 1 | e4257
baseline), we next investigated the relation between these two types
of effects. For each ROI that showed an effect of RT, we estimated
and plotted the corresponding task-related responses (Figures 3–5)
and applied the same linear contrasts testing for shift versus
amplitude differences (Figure 6C,D). Task-related responses
differed qualitatively from RT-related responses in both gray
and white matter ROIs. In gray matter ROIs, task-related changes
in the amplitude of activation were generally stronger than
Figure 4. Time courses of RT-related activation in representative white matter ROIs.
Figure 5. Mean RT and task-related time courses in all ANOVA ROIs. Each time course represents the time course of RT-related activation (A)
or task-related activation (B) in a single region, averaged over all five samples. Blue: gray matter ROIs; green: white matter ROIs.
Neural Correlates of RT
PLoS ONE | www.plosone.org8January 2009 | Volume 4 | Issue 1 | e4257
corresponding RT-related effects. That is, z-scores for the
amplitude contrast were consistently larger for task-related effects
than for RT-related effects, despite the fact that it was the RT
effect that was used to define the ROIs in the first place (compare
z-scores in Figure 6, panels A vs. C). In contrast, in white matter
ROIs, a striking discrepancy was observed between RT-related
and task-related responses. Task-related responses were much less
reliable than RT-related responses, showing little consistency
across studies and generally failing to resemble a canonical HRF
(Figures 4–5). This divergence is consistent with previous failures
to detect a reliable BOLD signal in white matter using
conventional subtractive contrasts, and suggests that it is
specifically the RT-related modulation of the BOLD signal in
white matter that is strong enough to be reliably detected.
To complement the whole-brain ANOVA, which identified
regions that showed a highly significant effect of RT across the
entire time course (i.e., a main effect of time), we conducted a
more liberal exploratory analysis intended to identify any brain
regions that showed consistent RT-related activation across studies
at specific points in the activation time course. A separate search
was conducted at each acquisition volume for regions that
correlated with RT in the same direction in all samples. Results
were broadly consistent with the ANOVA results (Figure 7). At TR
1 (0–3 seconds post-onset), no region correlated either positively or
negatively with RT. At TR 2 (2.36–6 seconds post-onset), no
positive correlations with RT were found, but negative correla-
tions with RT were observed in somatosensory cortex, mid-
cingulate gyrus, thalamus and cerebellar cortical regions. At TR 3
(4.72–9 seconds), positive correlations with RT were found in
medial frontal cortex, lateral prefrontal cortex, and frontal
operculum. Negative correlations were found diffusely in white
matter. Thereafter, at TRs 4–6 (7.08–18 seconds post-stimulus
onset), correlations with RT were exclusively positive, and were
observed throughout much of the cortex, basal ganglia, thalamus,
and cerebellum. No correlations with RT were detected at TR 7.
Figure 6. Statistical fit of RT-related and task-related activation to a priori linear contrasts. Top (panels A–B): RT-related activation.
Bottom (C–D): task-related activation. Contrast weights for the amplitude contrast (panels A and C) and temporal shift contrast (panels B and D) are
displayed on the left. The statistical significance (z-score) of the resulting test is displayed on the right for each of the 5 samples in each of the 33
ANOVA ROIs. Each color represents a different sample; black squares represent the fixed-effects z-score sum of all studies (see text). Region numbers
correspond to IDs in Table 2 and Figures 3–5. Dashed lines represent a p,.05 cut-off (|z|=1.96).
Neural Correlates of RT
PLoS ONE | www.plosone.org9January 2009 | Volume 4 | Issue 1 | e4257
The presence of consistent correlations between activation and
RT across fMRI datasets involving different samples, scanners,
experimental tasks, and analysis streams suggested that the
association was unlikely to depend on task- or study-specific factors,
e.g., stimulus modality or length of response window. However, RT
could still be confounded with other task-general experimental
factors such as response accuracy or trial difficulty, or with
systematic artifact sources such as head movement. To assess the
impact of such factors, we conducted a series of validation analyses
in the largest sample (sample 2, n=102). First, we created new
GLMs that included additional regressors for several experimental
covariates (for full details of the experimental design, see ).
These included (a) response accuracy (error vs. correct), (b) stimulus
type (words vs. faces), (c) emotion condition (approach, neutral, and
withdrawal), and (d) 3-back trial type (lure, target, and novel). The
effect of RT remained highly significant in all 33 ROIs (all Fs.6.4,
ps,.0001). Note that this analysis is highly conservative, as it
removes any variance shared between RT and other variables (e.g.,
accuracy), regardless of which variable has causal primacy.
Second, we recomputed the above model with RT estimated
separately for each of the three types of 3-back trial types (lure,
Figure 7. Timepoint-specific negative and positive correlations with RT. The white digit in each panel indicates the timepoint (i.e., the
acquisition volume relative to trial onset) at which the correlation with RT occurred. Blue: negative correlations with RT in all five samples; red:
positive correlations with RT in all five samples.
Neural Correlates of RT
PLoS ONE | www.plosone.org 10January 2009 | Volume 4 | Issue 1 | e4257
target, and novel) in order to determine whether the relation
between RT and brain activation held not only at the overall task
level but also for different experimental conditions associated with
different cognitive demands . This analysis was even more
conservative than the previous analysis, because each of the
covariates (stimulus type, emotion condition, response accuracy,
etc.) was also estimated separately for each trial type in order to
ensure consistent treatment of RT, effective tripling the degrees of
freedom consumed. Nonetheless, despite the substantial reduction
in power, the RT effect remained significant in all 33 ROIs for
target trials (p,.05), in 32/33 ROIs for novel trials (p,.05), and in
23/33 ROIs for lure trials (p,.05; note that the reduction in
number of significant effects for lure trials likely reflected
decreased estimation power, because lure trials comprised only
16% of all trials). Critically, in regions that showed a significant
RT effect for all 3 trial types, time courses were virtually
indistinguishable in shape (e.g., Figure 8). Thus, the relation
between RT and brain activation held not only at an overall task
level but also for specific experimental conditions.
Third, we assessed the impact of head movement on estimates
of RT-related activation. Although a six-parameter affine
transform was used to correct for movement during preprocessing,
it was conceivable that a residual influence might bias the GLM
estimates if movement happened to be correlated with trial-by-trial
variation in RT. This concern was particularly applicable to the
observed white matter effects, because the intensity of the BOLD
signal in white matter was weaker than in gray matter, and thus
potentially more susceptible to systematic noise. To control for
movement, we computed two separate sets of GLMs, each of
which added several movement regressors to the existing set in
each study. One set coded for directional movements using 12
separate regressors. Six regressors coded for absolute shift in head
position relative to the start of the first run, and six regressors
coded for volume-by-volume differences in movement. Of each set
of six, three regressors coded for translation in the x, y, and z
planes and three regressors coded for rotation in the same planes.
The second set of GLMs coded for absolute rather than directional
movement, and included two different regressors, one reflecting
total translational movement and one reflecting total rotational
movement (each computed as the square root of the sum of
squares of x, y, and z movements in each volume). The RT
estimate was not affected in either analysis. Effects remained
significant across all ROIs in both models (p,.05 in one ROI; all
Fs.6, ps,.0001 in all other ROIs).
Fourth, we constructed a GLM that controlled for the serial
position of each trial within the overall scan sequence (i.e., trial
number). This analysis controlled for potential confounding
influences of practice or fatigue effects. We reasoned that if RT
varied systematically as a function of task experience (e.g.,
decreasing over time as responses became more automated, or
increasing over time because of greater fatigue), and if for some
reason there was a systematic change in BOLD signal in gray or
white matter over the course of the experiment, one might expect
a spurious correlation between activation and RT to emerge (note
that this effect would have to be independent of scanner drift,
which was modeled using nuisance regressors in all GLMs).
Figure 8. RT-related activation in somatosensory cortex estimated separately by trial type in Sample 2. Each colored line represents the
time course of RT-related activation estimated for a different trial type, after controlling for a range of experimental covariates (see text). The black
line represents the original estimate (cf. Figure 4A) when collapsing across all trial types. Error bars indicate 95% C.I.
Neural Correlates of RT
PLoS ONE | www.plosone.org11January 2009 | Volume 4 | Issue 1 | e4257
However, no such effect was observed. When controlling for trial
number, the RT effect remained highly significant in all ROIs (all
Finally, we systematically inspected the preprocessing stream
used in all five samples in order to identify any potential steps that
might introduce systematic artifact correlated with trial-by-trial RT
differences. No obvious candidate emerged. The most obvious
candidate step would be a correction for global intensity differences,
which has previously been shown to induce spurious white matter
deactivations  (i.e., if the average intensity of the entire volume
changed as a function of RT due to changes in gray matter,
normalizing all volumes to have the same mean could potentially
induce a spurious shift in white matter signal). However, such a
processing step was not used in any of the samples.
The primary finding of the present study was the identification
of gray and white matter brain regions in which activation
correlated systematically with trial-by-trial differences in RT
across a broad range of experimental tasks. Strong evidence was
found for both temporal shifts in RT-related activation, presum-
ably reflecting delayed onset of cognitive processing, and uniform
positive correlations between RT and activation in frontal regions,
likely reflecting a ‘‘time-on-task’’ effect of sustained attention.
Additionally, strong evidence emerged for a reliable effect of RT
on BOLD signal in white matter. We now turn to discuss the
theoretical and methodological implications of these findings.
Time-on-task versus temporal shift effects of RT
Virtually all RT-related activations identified in the present
study could be characterized as either an amplitude increase (i.e.,
systematically greater activation for long RTs than short RTs) or a
temporal shift (i.e., delayed onset of the HDR for long RTs relative
to short RTs with little or no change in shape). These two patterns
showed a moderate degree of spatial segregation, with amplitude
effects restricted primarily to frontoparietal and thalamic regions,
whereas temporal shift effects were ubiquitous throughout the
brain but were strongest in somatomotor, visual, cerebellar, and
posterior midline cortical regions. This anatomical dissociation is
consistent with a division of labor between brain regions that
support cognitive processes that occur prior to the motor response
and brain regions that support response-locked processes such as
motor execution, tactile feedback processing, and processing of
visual display changes.
The fact that positive correlations between RT and BOLD
amplitude were found primarily in frontal regions is consistent with
the conventional wisdom that MFC and lateral PFC regions are
central components of a cognitive control network broadly
implicated in supporting effortful, goal-directed activity [40,41].
Of particular relevance is a recent multi-study analysis by
Dosenbach and colleagues in which the authors identified highly
consistent sustained task-related activations in MFC and frontal
operculum regions that overlapped closely with those identified in
the present study . Dosenbach and colleagues suggested that
these regions are necessary for the implementation and mainte-
nance of a goal-directed task set. While they focused on temporally
extended activation that persisted throughout entire task blocks, the
present findings point to a direct analog at much shorter intervals.
Given that participants are usually free to relax their attention and
‘‘mind wander’’ for the remainder of a trial once they have
responded to the stimulus, neural activity in frontal regions
necessary for sustaining goal-directed attention should persist for
the duration over which attention is maintained [cf. 43,44]—a
duration closely indexed by RT. Because the BOLD response sums
approximately linearly overt short intervals , trials with long
RTs should then produce proportionally larger activations in the
same frontal regions.
It is important to note that the presence of robust time-on-task
effects does not conclusively rule out the possibility that there are
other relatively broad relations between brain activation and RT
variability. At very short intervals (e.g.,,2 seconds), changes in the
duration versus amplitude of physiological processes are likely to
exert similar effects on the BOLD response (e.g., compare panels B
20% increase in frontal activation results in a 20% reduction in RT,
other things being equal), it may be difficult if not impossible to
detectusing the presentapproach. Thus,the present findings should
not be taken to imply that increases in preparatory processing or
mental alertness (which presumably would be associated with
increased frontal activation [20,45]) have no effect on RT.
Considerable evidence demonstrates the existence of such effects;
for example, increased ACC and DLPFC activation predicts faster
and more accurate responses during upcoming trials [18–21,46].
What the present results do suggest is simply that the influence of
task-general preparatory or alertness-related processes on RT is
relatively negligible in comparison to the dominant time-on-task
effect. This conclusion is entirely compatible with reports of larger
preparation-related decreases in RT in studies that involve specific
kinds of experimental conditions (e.g., the presence of cue
information), or with the general notion that variability in mental
preparedness (e.g., the occurrence of attentional lapses prior to trial
onset) has an influence on RT [1,8]).
Interestingly, the present findings do provide some evidence for
a weak effect of cognitive preparation or alertness on RT. Virtually
all RT-related ROIs showed a small negative correlation with RT
very early in the activation time course (Figure 5). Moreover, the
early decreases contrasted sharply with task-related responses in
the same regions, which were strictly positive-going in most cases.
Weissman and colleagues  recently suggested that these early
negative correlations with RT are functionally coupled to the later
positive correlations. Specifically, they argued that deactivations in
regions associated with attentional control reflect lapses of
attention, and that the late positive increases reflect a subsequent
attempt to compensate for such lapses by reasserting additional
control. However, the present results argue against such an
interpretation, because (a) in frontal regions associated with
cognitive control, the late positive correlations with RT were
substantially larger than the early negative correlations, and (b) an
early dip in activation was observed in virtually all regions,
including sensorimotor regions that are unlikely to play a role in
asserting control. A more plausible interpretation is that the two
phenomena are largely independent. That is, lapses of attention
contribute to the ubiquitous temporal shifts we observed (i.e., task-
related processing initiates slightly later in virtually all brain
regions immediately following a lapse), whereas frontal regions
play a preferential role in sustaining controlled processing for the
duration of a trial until a response is made.
Methodological implications of a time-on-task effect
The present findings have clear and important methodological
implications for the inclusion (or lack thereof) of RT as a covariate
in functional neuroimaging studies. It is both common sense and
an axiom of experimental psychology that RT and accuracy are
inversely related under most circumstances—that is, the longer a
person takes to respond, the more likely their response is to be
accurate, assuming that experimental conditions are held constant.
Neural Correlates of RT
PLoS ONE | www.plosone.org12 January 2009 | Volume 4 | Issue 1 | e4257
In behavioral studies that use response accuracy as the primary
dependent variable, it is standard practice to explicitly rule out the
possibility of a speed-accuracy tradeoff, e.g., by statistically
covarying out RT or demonstrating that there are no meaningful
differences in RT between conditions. This concern is equally
applicable to neuroimaging studies, where differences in activation
between two conditions could theoretically be confounded with
differences in both RT and response accuracy.
Surprisingly, while many fMRI researchers routinely take pains
to eliminate response accuracy differences as a potential confound
(e.g., by only analyzing trials with correct responses), relatively few
studies have systematically controlled for trial-by-trial RT differ-
ences [e.g., 5,7,47]; a recent survey of 170 fMRI studies found that
only 9% had explicitly modeled RT . The present results suggest
that this omission may not be benign. The strength of the RT effects
we observed in frontal regions suggests that RT variability may
explain a considerable amount of variance in frontal activation in
most tasks. If two experimental conditions differ substantially in
mean RT,acorrespondingdifferenceinfrontal activation islikelyto
be observed irrespective of any other differences in task structure. Moreover,
given that the present study focused only on RT-related activation
that was relatively independent of task-specific demands, one might
expect similar, but more task-specific, time-on-task effects to be
present in other brain regions.
At present, there is no easy way to determine the extent to
which quantitative differences in trial-by-trial RT variability might
account for fMRI effects previously attributed to qualitative
differences between experimental conditions. Relatively few
studies have directly contrasted effects with and without RT
covariates, and these studies have reported mixed results. In some
cases, controlling for RT produces no discernible impact on
experimental effects of interest [e.g., 24,47,48,49]. In other cases,
some effects of interest may be eliminated or even reversed when
RT is explicitly modeled [e.g., 7,50]. It is important to note that
the widespread practice of including the temporal derivatives of
modeled responses in GLM analyses in order to account for
temporal differences in HDR onset will have virtually no influence
on estimates of RT-related activation in regions that show a time-
on-task effect. Including temporal derivatives in the GLMs used in
the present study would likely have reduced or eliminated the
temporal shift effects identified in somatomotor, visual, and
cerebellar regions; however, regions that show relatively uniform
positive activations as a function of RT (e.g., MFC and lateral
PFC) would be largely unaffected, because activation in the latter
regions appears to increase at virtually all timepoints. To account
for such effects, trial-by-trial differences in RT should be explicitly
modeled within the GLM—either by empirically estimating the
RT-related response, as in the present study, or by using an
alternative approach such as a variable impulse or variable epoch
model (for discussion, see ).
Given that the interpretation of many results might change
considerably depending on whether effects are independent of RT
or not, there is a clear incentive for researchers to include RT as a
covariate in analyses. A particularly informative approach might be
to analyze one’s data both with and without RT in the model,
enabling more precise inferences about whether the neurocognitive
processes recruited by different experimental conditions vary
by demonstrating that differences in frontoparietal activation are
fully explained by RT differences, and are purely quantitative in
nature; for example, one might hypothesize that increasing the load
in a Sternberg working memory task  from 3 to 4 items should
produce a strictly quantitative change in brain activation and RT,
and that no difference in the former should remain after controlling
for the latter. In contrast, other hypotheses might require a
demonstration that activation differences remain significant even
after controlling for RT. For example, one would expect activation
differences for word naming versus non-word naming to remain
significant even after controlling for RT, reflecting the fact that
word naming can recruit pathways that non-word naming cannot
. In general, there is no reason, save perhaps expediency, not to
include RT as a covariate in parallel fMRI analyses, while the
potential benefits are considerable.
Reliable effects of RT on BOLD signal in white matter
A surprising finding of the present study was the presence of a
consistent association between trial-by-trial RT variability and
BOLD signal in white matter regions. The precise nature of this
association is somewhat unclear due to the atypical shape of the
hemodynamic response in white matter (Figure 5)—the white
matter response appears to have the same fundamental charac-
teristics as the gray matter response, but evolves much more
slowly. A parsimonious interpretation of the present findings is that
RT effects in white matter regions reflect temporal shifts similar to
those observed in gray matter regions such as somatosensory
cortex that are simply ‘‘stretched’’ in time. That is, on trials with
long RTs, the BOLD response in white matter is delayed relative
to trials with short RTs, presumably because processes supported
by white matter (e.g., conduction of action potentials along
corticospinal pathways) initiate later in time. However, an
alternative possibility is that the very late increase in RT-related
activation observed in white matter reflects an ‘‘overshoot’’ phase
of a negative-going impulse. On this view, increases in white
matter activation might be systematically associated with shorter
RTs because they serve some functional purpose, e.g., facilitating
more rapid communication between different gray matter regions
on trials with short RTs.
Interpretative issues aside, the identification of a reliable BOLD
signal in white matter has potentially important implications for
fMRImethodologyand ourunderstandingof the BOLD signal. It is
widely assumed in the functional neuroimaging community that it is
difficult if not impossibleto reliably detectBOLDresponses in white
matter because metabolic rates, vascular density, and cerebral
perfusion are much lower in white matter than in gray matter
[34,52]. Logothetis  captured this sentiment in a recent review
of mechanisms underlying the BOLD signal, noting that ‘‘activation
of the white matter has been rarely reported in the neuroimaging
BOLD signal in white matter altogether’’ (p. 755). While there is no
doubt that the present findings are unexpected, there are several
reasons to believe that the observed white matter activations
veridically reflect underlying physiological processes.
First, it is worth noting that the widespread assumption that
BOLD signal is undetectable in white matter is based largely on
negative evidence—that is, a failure to observe significant white
matter activations. There is no positive evidence to suggest that
such activations are impossible in principle. To the contrary, there
are good reasons to predict the presence of BOLD signal in white
matter. The BOLD signal reflects a complex interplay between
changes in cerebral blood flow (CBF), cerebral blood volume
(CBV), and oxidative metabolism [53–55]. Such factors might be
expected to operate in white matter as well as gray matter, because
(a) the balance between oxidative metabolism and blood flow is
similar in white and gray matter (as evidenced by similar oxygen
extraction fractions in white matter [56,57]), and (b) CBF and
CBV are only 2–3 times lower in white matter than in gray matter
[57–60]. Thus, in principle, white matter BOLD signal should be
detectable given a sufficiently large sample size, sensitive
Neural Correlates of RT
PLoS ONE | www.plosone.org13 January 2009 | Volume 4 | Issue 1 | e4257
acquisition techniques, and a sufficiently sensitive analytic probe.
Moreover, recent discoveries that some types of glial cells
participate in glutamatergic signaling [61,62] and can even
generate action potentials  provide potential theoretical bases
for the presence of functional relationships between cognitive
processes and BOLD signal in white matter.
Second, from a statistical standpoint, the probability of jointly
observing consistent white matter activations in all five samples is
infinitesimally small (p,.0015). Moreover, as illustrated in Figure 4,
different samples produced extremely similar RT-related time
courses in virtually all regions, despite the fact that the model-free
ANOVA procedure used to identifyROIs imposedno constrainton
the shape of activation in each case. Thus, while it is conceivable
that white matter RT effects might reflect an unidentified
confounding variable, they cannot be rejected as false positives.
Third, and related to the above concern about potential
confounds, consistent white matter effects were observed in
samples obtained using different fMRI scanners, experimental
tasks, and analytic designs. Thus, any potential source of artifact
would have to be extremely general. The most obvious candidate,
namely, head movement, had no discernible influence on the RT
effect when explicitly modeled in the GLM. Similarly, controlling
for a variety of experimental factors (e.g., response accuracy, trial
number, etc.) or modeling the RT effect separately for different
types of trials did not affect the results.
Fourth, it is important to note that white matter effects were
specific to the trial-by-trial RT effect in the present datasets. We
found no consistent white matter activation across studies when
contrasting task-related activation with baseline. Thus, the present
results are entirely compatible with previous failures to detect a white
matter BOLD signal. A plausible explanation for the fact that the
RT-related signal appears to be much more reliable than the task-
related signal is that the production of an overt motor response may
require generation of highly synchronized and relatively strong
impulses in corticospinal motor axons that are conveniently time-
locked to the onset of the motor response. In contrast, when
activation during two experimental conditions is contrasted subtrac-
tively(e.g.,task vs. baseline),the BOLD signal in white matter is likely
to reflectthe noisy summation of many different impulses that vary in
between different cortical and subcortical regions is liable to occur
during both task periods and fixation baseline), making significant
differences much more difficult to detect.
Fifth, although reports of BOLD signal in white matter are rare,
several studies have in fact observed such effects using experimental
approaches broadly consistent with the present focus on RT
variability. Two recent studies that used visual-manual RT tasks to
investigate the neural correlates of interhemispheric transfer found
greater activation in the corpus callosum on trials that required
interhemispheric transfer of information than on trials that did not
[64,65]. Strikingly, both studies reported white matter activation in
a region of the right genu of the corpus callosum (peak coordinates:
14, 28, 16 and 10, 26, 24, respectively) that overlapped closely with
an ROI identified in all five samples in the present study (center-of-
mass coordinates: 20, 26, 9).
Finally, several diffusion tensor imaging (DTI) studies have
found correlations between individual differences in mean RTs
and white matter integrity [66–69]. These correlations are
universally negative, i.e., individuals with greater white matter
integrity have shorter RTs across a range of different cognitive
tasks. Although the individual differences results of DTI studies are
not directly comparable with the within-subjects (i.e., trial-by-trial)
BOLD effects identified in the present study, the DTI results
nevertheless provide a conceptual corroboration of the present
results inasmuch as they suggest that variability in white matter
structure has functional implications for RT variability. Future
studies could combine DTI and BOLD data to directly test for a
relationship between the two measures. For example, one might
predict that individuals with greater structural integrity in white
matter tracts should have a larger dynamic range of activation,
and might therefore show greater modulation of white matter
BOLD as a function of trial-by-trial RT differences. In sum, while
we remain open to the possibility that the white matter activations
reported here will prove to be artifactual, we believe there are
sufficient methodological and theoretical grounds to warrant
The present results provide strong support for the existence of
task-independent relationships between trial-by-trial differences in
RT and gray and white matter activation. The presence of robust
time-on-task effects in frontoparietal brain regions underscores the
importance of explicitly modeling RT in fMRI analyses, whether as
a covariate of no interest or as a variable of interest in its own right.
Although the association between white matter activation and trial-
by-trial differences was not predicted a priori, and its precise nature
remains unclear, the current study provides the strongest evidence
to date that BOLD signal can be reliably detected in white matter.
Future investigations could potentially use trial-by-trial changes in
RT to probe the integrity of white matter function as well as the
physiological basis of the BOLD signal.
The authors wish to acknowledge the help of Avi Snyder, who provided
valuable discussion and suggested one of the validation analyses, and Jack
Grinband, who provided comments on the manuscript.
Conceived and designed the experiments: TY TEC TB. Performed the
experiments: TY. Analyzed the data: TY. Contributed reagents/materials/
analysis tools: TY DMB JG. Wrote the paper: TY TEC TB.
1. Weissman DH, Roberts KC, Visscher KM, Woldorff MG (2006) The neural
bases of momentary lapses in attention. Nature Neuroscience 9: 971–978.
2. Bellgowan PSF, Saad ZS, Bandettini PA (2003) Understanding neural system
dynamics through task modulation and measurement of functional MRI
amplitude, latency, and width. Proceedings of the National Academy of
Sciences 100: 1415–1419.
3. Grinband J, Wager TD, Lindquist M, Ferrera VP, Hirsch J (2008) Detection of
time-varying signals in event-related fMRI designs. Neuroimage.
4. Connolly JD, Goodale MA, Goltz HC, Munoz DP (2005) fMRI Activation in
the Human Frontal Eye Field Is Correlated With Saccadic Reaction Time.
Journal of Neurophysiology 94: 605–611.
5. Binder JR, Medler DA, Desai R, Conant LL, Liebenthal E (2005) Some
neurophysiological constraints on models of word naming. Neuroimage 27:
6. Dux PE, Ivanoff J, Asplund CL, Marois R (2006) Isolation of a Central
Bottleneck of Information Processing with Time-Resolved fMRI. Neuron 52:
7. Christoff K, Prabhakaran V, Dorfman J, Zhao Z, Kroger JK, et al. (2001)
Rostrolateral prefrontal cortex involvement in relational integration during
reasoning. Neuroimage 14: 1136–1149.
8. Hahn B, Ross TJ, Stein EA (2007) Cingulate Activation Increases Dynamically
with Response Speed under Stimulus Unpredictability. Cerebral Cortex 17:
9. Braver TS, Reynolds JR, Donaldson DI (2003) Neural mechanisms of transient
and sustained cognitive control during task switching. Neuron 39: 713–726.
10. Schaefer A, Braver TS, Reynolds JR, Burgess GC, Yarkoni T, et al. (2006)
Individual differences in amygdala activity predict response speed during
working memory. Journal of Neuroscience 26: 10120–10128.
Neural Correlates of RT
PLoS ONE | www.plosone.org 14January 2009 | Volume 4 | Issue 1 | e4257
11. Menon RS, Luknowsky DC, Gati JS (1998) Mental chronometry using latency-
resolved functional MRI. National Acad Sciences. pp 10902–10907.
12. Dale AM, Buckner RL (1997) Selective averaging of rapidly presented individual
trials using fMRI. Human Brain Mapping 5: 329–340.
13. Hopfinger JB, Buonocore MH, Mangun GR (2000) The neural mechanisms of
top-down attentional control. Nature Neuroscience 3: 284–291.
14. Corbetta M, Akbudak E, Conturo TE, Snyder AZ, Ollinger JM, et al. (1998) A
common network of functional areas for attention and eye movements. Neuron
15. Desai R, Conant LL, Waldron E, Binder JR (2006) fMRI of Past Tense
Processing: The Effects of Phonological Complexity and Task Difficulty. Journal
of Cognitive Neuroscience 18: 278–297.
16. Fleck MS, Daselaar SM, Dobbins IG, Cabeza R (2006) Role of Prefrontal and
Anterior Cingulate Regions in Decision-Making Processes Shared by Memory
and Nonmemory Tasks. Cerebral Cortex 16: 1623.
17. Sapir A, d’Avossa G, McAvoy M, Shulman GL, Corbetta M (2005) Brain signals
for spatial attention predict performance in a motion discrimination task.
Proceedings of the National Academy of Sciences 102: 17810–17815.
18. Hester RL, Murphy K, Foxe JJ, Foxe DM, Javitt DC, et al. (2004) Predicting
success: patterns of cortical activation and deactivation prior to response
inhibition. J Cogn Neurosci 16: 776–785.
19. Garavan H, Ross TJ, Murphy K, Roche RA, Stein EA (2002) Dissociable
executive functions in the dynamic control of behavior: inhibition, error
detection, and correction. Neuroimage 17: 1820–1829.
20. Kerns JG, Cohen JD, MacDonald AW, Cho RY, Stenger VA, et al. (2004)
Anterior Cingulate Conflict Monitoring and Adjustments in Control. American
Association for the Advancement of Science. pp 1023–1026.
21. Kerns JG (2006) Anterior cingulate and prefrontal cortex activity in an FMRI
study of trial-to-trial adjustments on the Simon task. NeuroImage 33: 399–405.
22. Visscher KM, Miezin FM, Kelly JE, Buckner RL, Donaldson DI, et al. (2003)
Mixed blocked/event-related designs separate transient and sustained activity in
fMRI. Neuroimage 19: 1694–1708.
23. Gray JR, Chabris CF, Braver TS (2003) Neural mechanisms of general fluid
intelligence. Nature Neuroscience 6: 316–322.
24. Yarkoni T, Gray JR, Braver TS (submitted) Medial posterior parietal cortex
activation predicts working memory performance within and across subjects.
25. Yarkoni T, Gray JR, Chrastil ER, Barch DM, Green L, et al. (2005) Sustained
neural activity associated with cognitive control during temporally extended
decision making. Cognitive Brain Research 23: 71–84.
26. Yarkoni T, Braver TS, Gray JR, Green L (2005) Prefrontal brain activity
predicts temporally extended decision-making behavior. Journal of the
Experimental Analysis of Behavior 84: 537–554.
27. Bonner-Jackson A, Haut K, Csernansky JG, Barch DM (2005) The Influence of
Encoding Strategy on Episodic Memory and Cortical Activity in Schizophrenia.
Biological Psychiatry 58: 47–55.
28. Barch DM, Csernansky JG (2007) Abnormal Parietal Cortex Activation During
Working Memory in Schizophrenia: Verbal Phonological Coding Disturbances
Versus Domain-General Executive Dysfunction. American Journal of Psychiatry
29. Friston KJ, Holmes AP, Worsley KJ, Poline JB, Frith CD, et al. (1995) Statistical
parametric maps in functional imaging: a general linear approach. Human Brain
Mapping 2: 189–210.
30. Gray JR, Burgess GC, Schaefer A, Yarkoni T, Larsen RJ, et al. (2005) Affective
personality differences in neural processing efficiency confirmed using fMRI.
Cognitive, Affective, & Behavioral Neuroscience 5: 182–190.
31. Nichols T, Brett M, Andersson J, Wager T, Poline JB (2005) Valid conjunction
inference with the minimum statistic. Neuroimage 25: 653–660.
32. Kerr DL, Gusnard DA, Snyder AZ, Raichle ME (2004) Effect of practice on
reading performance and brain function. Neuroreport 15: 607–610.
33. Boynton GM, Engel SA, Glover GH, Heeger DJ (1996) Linear Systems Analysis
of Functional Magnetic Resonance Imaging in Human V1. Journal of
Neuroscience 16: 4207.
34. Powers WJ, Grubb R, Darriet D, Raichle ME (1985) Cerebral blood flow and
cerebral metabolic rate of oxygen requirements for cerebral function and
variability in humans. Journal of cerebral blood flow and metabolism 5:
35. Buckner RL, Koutstaal W, Schacter DL, Dale AM, Rotte M, et al. (1998)
Functional–Anatomic Study of Episodic Retrieval II. Selective Averaging of
Event-Related fMRI Trials to Test the Retrieval Success Hypothesis. Neuro-
image 7: 163–175.
36. Miezin FM, Maccotta L, Ollinger JM, Petersen SE, Buckner RL (2000)
Characterizing the Hemodynamic Response: Effects of Presentation Rate,
Sampling Procedure, and the Possibility of Ordering Brain Activity Based on
Relative Timing. Neuroimage 11: 735–759.
37. Rosenthal R (1978) Combining results of independent studies. Psychological
Bulletin 85: 185–193.
38. Kane MJ, Conway AR, Miura TK, Colflesh GJ (2007) Working memory,
attention control, and the n-back task: A question of construct validity. Journal of
Experimental Psychology: Learning, Memory, and Cognition 33: 615–622.
39. Desjardins AE, Kiehl KA, Liddle PF (2001) Removal of confounding effects of
global signal in functional MRI analyses. Neuroimage 13: 751–758.
40. Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function.
Annu Rev Neurosci 24: 167–202.
41. Duncan J, Owen AM (2000) Common regions of the human frontal lobe
recruited by diverse cognitive demands. Trends in Neurosciences 23: 475–483.
42. Dosenbach NU, Visscher KM, Palmer ED, Miezin FM, Wenger KK, et al.
(2006) A core system for the implementation of task sets. Neuron 50: 799–812.
43. Funahashi S, Bruce CJ, Goldman-Rakic PS (1989) Mnemonic coding of visual
space in the monkey’s dorsolateral prefrontal cortex. Journal of Neurophysiology
44. Fuster JM, Alexander GE (1971) Neuron Activity Related to Short-Term
Memory. Science 173: 652.
45. Luks TL, Simpson GV, Feiwell RJ, Miller WL (2002) Evidence for Anterior
Cingulate Cortex Involvement in Monitoring Preparatory Attentional Set.
Neuroimage 17: 792–802.
46. Stern ER, Wager TD, Egner T, Hirsch J, Mangels JA (2007) Preparatory neural
activity predicts performance on a conflict task. Brain Research 1176: 92–102.
47. Dobbins IG, Han S (2006) Cue-versus Probe-dependent Prefrontal Cortex
Activity during Contextual Remembering. Journal of Cognitive Neuroscience
48. Simons JS, Scho ¨lvinck ML, Gilbert SJ, Frith CD, Burgess PW (2006) Differential
components of prospective memory? Evidence from fMRI. Neuropsychologia
49. Gilbert SJ, Frith CD, Burgess PW (2005) Involvement of rostral prefrontal cortex
in selection between stimulus-oriented and stimulus-independent thought.
European Journal of Neuroscience 21: 1423–1431.
50. Epstein RA, Parker WE, Feiler AM (2007) Where Am I Now? Distinct Roles for
Parahippocampal and Retrosplenial Cortices in Place Recognition. Journal of
Neuroscience 27: 6141.
51. Sternberg S (1966) High-Speed Scanning in Human Memory. ? 1966 by the
American Association for the Advancement of Science. pp 652–654.
52. Cavaglia M, Dombrowski SM, Drazba J, Vasanji A, Bokesch PM, et al. (2001)
Regional variation in brain capillary density and vascular response to ischemia.
Brain Research 910: 81–93.
53. Logothetis NK, Wandell BA (2004) Interpreting the BOLD signal. Annu Rev
Physiol 66: 735–769.
54. Davis TL, Kwong KK, Weisskoff RM, Rosen BR (1998) Calibrated functional
MRI: Mapping the dynamics of oxidative metabolism. Proceedings of the
National Academy of Sciences 95: 1834.
55. Mandeville JB, Marota JJA, Ayata C, Moskowitz MA, Weisskoff RM, et al.
(1999) MRI Measurement of the Temporal Evolution of Relative CMRO 2
During Rat Forepaw Stimulation. Magnetic Resonance in Medicine 42:
56. Carpenter DA (1990) Borderzone hemodynamics in cerebrovascular disease.
Neurology 40: 1587–1592.
57. Leenders KL, Perani D, Lammertsma AA, Heather JD, Buckingham P, et al.
(1990) Cerebral blood flow, blood volume and oxygen utilization. Normal values
and effect of age. Brain 113: 27–47.
58. Østergaard L, Johannsen P, Høst-Poulsen P, Vestergaard-Poulsen P, Asboe H,
et al. (1998) Cerebral Blood Flow Measurements by Magnetic Resonance
Imaging Bolus Tracking: Comparison With [15 O] H 2 O Positron Emission
Tomography in Humans. Journal of Cerebral Blood Flow & Metabolism 18:
59. Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, et al.
(1996) High resolution measurement of cerebral blood flow using intravascular
tracer bolus passages. Part II: Experimental comparison and preliminary results.
Magn Reson Med 36: 726–736.
60. Rempp KA, Brix G, Wenz F, Becker CR, Guckel F, et al. (1994) Quantification
of regional cerebral blood flow and volume with dynamic susceptibility contrast-
enhanced MR imaging. Radiology 193: 637–641.
61. Ziskin JL, Nishiyama A, Rubio M, Fukaya M, Bergles DE (2007) Vesicular
release of glutamate from unmyelinated axons in white matter. Nature
Neuroscience 10: 321–330.
62. Kukley M, Capetillo-Zarate E, Dietrich D (2007) Vesicular glutamate release
from axons in white matter. Nature Neuroscience 10: 311–320.
63. Ka ´rado ´ttir R, Hamilton NB, Bakiri Y, Attwell D (2008) Spiking and nonspiking
classes of oligodendrocyte precursor glia in CNS white matter. Nat Neurosci.
64. Tettamanti M, Paulesu E, Scifo P, Maravita A, Fazio F, et al. (2002)
Interhemispheric Transmission of Visuomotor Information in Humans: fMRI
Evidence. Am Physiological Soc. pp 1051–1058.
65. Weber B, Treyer V, Oberholzer N, Jaermann T, Boesiger P, et al. (2005)
Attention and Interhemispheric Transfer: A Behavioral and fMRI Study.
Journal of Cognitive Neuroscience 17: 113–123.
66. Madden DJ, Whiting WL, Huettel SA (2004) Diffusion tensor imaging of adult
age differences in cerebral white matter: relation to response time. Neuroimage
67. Baird AA, Colvin MK, VanHorn JD, Inati S, Gazzaniga MS (2005) Functional
Connectivity: Integrating Behavioral, Diffusion Tensor Imaging, and Functional
Magnetic Resonance Imaging Data Sets. Journal of Cognitive Neuroscience 17:
68. Schulte T, Sullivan EV, Mu ¨ller-Oehring EM, Adalsteinsson E, Pfefferbaum A
(2005) Corpus Callosal Microstructural Integrity Influences Interhemispheric
Processing: A Diffusion Tensor Imaging Study. Cerebral Cortex 15: 1384–1392.
69. Tuch DS, Salat DH, Wisco JJ, Zaleta AK, Hevelone ND, et al. (2005) Choice
reaction time performance correlates with diffusion anisotropy in white matter
pathways supporting visuospatial attention. Proceedings of the National
Academy of Sciences 102: 12212–12217.
Neural Correlates of RT
PLoS ONE | www.plosone.org15January 2009 | Volume 4 | Issue 1 | e4257