# Resurrecting Brinley Plots for a Novel Use: Meta-Analyses of Functional Brain Imaging Data in Older Adults

Authors: Ann M. Peiffer; Joseph A. Maldjian; Paul J. Laurienti
Journal: International Journal of Biomedical Imaging
 (2008)

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## Abstract

By plotting response times of young and older adults across a variety of tasks, Brinley spurred investigation and debate into the theory of general cognitive slowing. Though controversial, Brinley plots can assess between-task differences, the impact of increasing task demand, and the relationship between responses in two groups of subjects. Since a relationship exists between response times and the  blood-oxygen level dependent (BOLD) signal of functional MRI (fMRI), Brinley's plotting method could be applied as a meta-analysis tool in fMRI studies of aging. Here, fledgling “Peiffer plots” are discussed for their potential impact on understanding general cognitive brain activity in aging. Preliminary results suggest that general cognitive slowing may be localized at the sensorimotor transformation in the precentral gyrus. Although this meta-analysis method is naturally used with imaging studies of aging, theoretically it may be applied to other study pairs (e.g., schizophrenic versus normal) or imaging datasets (e.g., PET).


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## Body

## 1. Introduction

In the mid 1960s, Brinley
presented a novel plotting
method to consider the
relationship between response times in young and older
adults across a variety
of tasks with varying levels of cognitive difficulty
[1]. The average response times
for both the young and older groups of adults on each task were
placed on a
scatter-plot, and a regression line was then fitted to the
data using the
operation of y=mx+b.
In doing so, a relationship was
realized between the
behavior in young adults and its ability to predict behavior
on the same task
in older adults. The power of this method of data analysis is
that it can be
used across task types to pool data from multiple studies.
Here we harvest the
powerful aspects of the Brinley analysis method and apply
them to functional
magnetic resonance imaging (fMRI) data. Meta-analyses of
fMRI data are
typically hindered by differences in the types of tasks
used across studies.
The use of the Brinley method allows for a
meta-analysis of fMRI data that
actually takes advantage of multiple cognitive tasks.
However, the remaining
challenge is to carefully interpret the findings.The history of Brinley plots is rife with
debate and discussion on what the plot is measuring
and what it means. Feelings in opposing camps can
even be so strong as to
elicit T-shirts emblazoned with the No symbol over
the word Brinley. Later
researchers inferred that aspects of Brinley plots
could provide information on
general cognitive functioning in older adults since
Brinley's data contained
both task switching and nonswitching data subsets
[2–7]. For example, if the slope
for the fitted line of a group of tasks is 1, then there is
equal change between tasks for younger and older adults.
However, when the slope of the
fitted line deviates and is greater than 1, there is
an increased slowing in
older adults associated with more cognitively demanding tasks
(i.e., a general cognitive slowing deficit is evident in
older adults regardless of task [e.g.,
[2]]). Other researchers have
disagreed with this Brinley plot interpretation and suggest
that the plot
reflects a difference in response variability
between the age groups rather
than processing speed, per se
[8].
Finally, current work in how
aging affects processing speed questions the degree
to which general cognitive
slowing can be summarized with one linear
function across all types of tasks
[5, 9, 10].In addition to describing the behavioral appearance
of cognitive slowing, research
has pursued localizing the phenomenon within the
aging brain. Generalized
cognitive slowing has been thought to
result from pervasive slowing of all
cognitive brain functions in older adults.
With this in mind, some researchers
have proposed that the locus of general
cognitive slowing occurs where sensory
impulses transfer to a common site of motor
generation for the response and
that this sensorimotor dysregulation intensifies
with more difficult tasks [11–13]. Yordanova and colleagues
found evidence to support this hypothesis using event-related
potentials, which
are able to evaluate the timing and strength
of cognitive processing in
response to external stimuli
[11]. However, when considering
task-specific slowing impact, other researchers point to
additional slowing in
specific cognitive areas such as working memory,
visual search and mental
rotation [10, 14],
which may occur in addition to
or instead of the sensorimotor slowing.After reviewing the body of research literature
on general cognitive slowing in
aging, we adopted the early Brinley method
for plotting reaction times and
applied it to the  blood-oxygen level dependent (BOLD)
signal from multiple fMRI
studies. In doing so, an attempt is made to localize
brain areas responsible
for the deviant slope in the response time Brinley plot.
Unlike reaction times,
the BOLD  signal has a
legitimate negative value
(i.e., deactivations) that occurs when contrasting activity
during two different events. For example,
certain brain areas are more active during
baseline than during any particular task.
These areas prominently include
posterior cingulate cortex and inferior parietal lobe
areas and are known as
the default network [15].
In applying Brinley's
plotting method on the BOLD signal, we are able to construct
novel Peiffer
plots, a meta-analysis of fMRI data that is not limited to
site locations of
activation maxima and thus not skewed to evaluate only areas
identified as
statistically different within a study
[see discussion in [16]].
Typical meta-analyses of
fMRI data use location-centered approaches
where the focus of peak activity is
evaluated [16–20]. This can underestimate
between-task differences because subthreshold
activity differences are
overlooked [see discussion in [16]].
Further, differences in task
parameters and paradigm domain limit the tasks
compared in many fMRI
meta-analyses to a single type of task
(e.g., Stroop interference task
[16, 21]).
With the novel method
proposed here, a plot can be made across a
variety of fMRI studies to evaluate
two different population groups to identify areas
showing between-task
differences that may not necessarily be identified as
deviant within an
individual study comparison. Lastly, in using this method to
compare young and
older adults, we may be able to show localization of the areas
that may in fact
identify differences in age-related information
processing that characterize
general cognitive slowing.


## 2. Methods

BOLD data and
behavioral response times were obtained from 4 simple
detection tasks (3 visual
and 1 auditory). In order to plot a BOLD signal value
for young and older
adults in each study, original fMRI data was needed.
The fMRI Data Center (http://www.fmridc.org) contributed
a complete
dataset from Buckner and colleagues
(Accession no. 2-2000-1118W) for 2 points in
the analysis (young = 14; older adults = 14)
[22]. These BOLD signal
measurements related to responses associated with the
presentation of a single
or double flashing checkerboard. The other 2 points were
from studies performed
in our laboratory (young = 20; older adults = 20)
[23]. These BOLD signal
measurements were related to block activity during an
auditory task where
subjects needed to respond when they heard a target tone or
in a visual task
where they responded to the blurring of a flashing checkerboard.
For all points
in the meta-analysis, all fMRI comparisons were between
task and baseline
(i.e., fixation cross) and were preprocessed with global
signal correction. Further,
during the preprocessing of the data,
it is spatially normalized to MNI
template space. Normalized task specific
“con” images reflecting the
task-related BOLD activity change from baseline
were computed with SPM99 for
all individuals in each dataset. These individual
“con” images were then
averaged within age group for each study resulting in a
total of 8 average BOLD
activity maps (2 age groups over 4 tasks).
This process emulated the
construction of a traditional Brinley plot which averaged
the response times
for each task within each age group.
Within the BOLD average signal maps, each
voxel contains a value representing the age
group's average BOLD activity for
that task at that standardized MNI x,y,z
coordinate.Using the 4 average young maps as observedx-values and
the corresponding 4 average older adult maps as observed
y-values, a linear
regression analysis (y=mx+b) was calculated
within each voxel that contained
at least 3 x,y data points (see Figure
1 for
a representative voxel).
Individual 3D maps were computed that contained
voxels with individual
regression parameters of interest (e.g., slope, b-intercept,
R-square, predicted y-value, etc.).
Since the null hypothesis (H0) in question was whether or
not there was equivalent change between young and older
adults across tasks,
the slope value for H0 was 1. To evaluate
H0 : m=1, the
absolute residual values were calculated at each data point in
SPM2 (α=|y−y^|wherey^=mx+bandβ=|x−x^|wherex^=(y−b)/m).
The resulting 8 residual maps (2
age groups over 4 tasks) were then statistically compared
using a t-test in
SPM. When the n is large enough, theoretically,
it would be more statistically
correct to analyze the difference between these residuals with
a paired t-test.
If the null hypothesis was true,
then the absolute residual values would be
equal and not statistically different from each other
(α=β).
If the slope was not equal to 1, then the
voxel's t-test would be significant
(α≠β).
Multiple comparisons were controlled for by using FWE of
P < 0.05 and an
extent threshold of at least 3 consecutive voxels.
The sign (+ or −) and value of
the slope could then be assessed to determine how
the two groups deviated in
their BOLD signals across the tasks plotted in the analysis
(e.g., one group activates an area more across tasks than the other group).Figure 1
Representative voxel illustrating testing of the
null hypothesis (H0 : m=1).
To assess whether the null hypothesis is true in any given voxel,
a t-test
comparison of the αandβ residual
values was used. When α≠β,
then the line was not significantly close to one and
the voxel was considered
to have a significant deviant slope. If the T
value survived the correction for
multiple comparison (3 contiguous voxels where FWE P < .05),
the respective
brain area was considered to be a loci for
general cognitive slowing. A second
analysis is needed to identify areas where
voxels with slopes of −1 were
eliminated in the residual analysis.As graciously pointed out by an anonymous reviewer,
slopes of −1 should not be considered as
part of the null hypothesis, since the direction of
activity across tasks for
the age groups would actually be opposite
(i.e., young adults activating across
tasks while older adults deactivate).
Therefore, it is also important to
investigate areas where the slope is significantly negative,
since the above
“residual” analysis would not just eliminate slopes
of +1 but also slopes which
were not significantly different from −1.
An example of this can be seen in the
plotted data of Figure1
in which this particular voxel would not be significant
in the above “residual” analysis, but still
represents an interesting result. Significant
voxels with a slope of −1 can be identified using the
P-value
of the regression
used to fit the Peiffer plot
(i.e., P < .05 for the slope to be different from
zero). Three contiguous voxels with a significant regression
P-value
and a negative slope will be
considered a cluster of interest as well.To evaluate a significant voxel's b-intercept,
the 95% confidence interval was assessed. If this interval
contained zero, the voxel's b-intercept was considered
not to deviate from
zero. Again, b-intercept clusters were considered
significant if they contained
at lest 3 contiguous voxels with significantly nonzero
b-intercepts.


## 3. Results

The traditional Brinley plot using average response times
across the 4 tasks
included in the fMRI meta-analysis showed the established
differences between
young and older adults
(see Figure2).
The slope of 1.4 supports general
cognitive slowing within the dataset even though
relatively simple response
time tasks were used, and although uninterpretable
for response time data, the
negative b-intercept is also typical. With a slope
greater than one, older
adults had greater differences between
tasks in response time than younger
adults.Figure 2
Traditional
Brinley plot of response times for the 4
tasks used in the fMRI meta-analysis.
If young and older adults showed equivalent
between-task change in the speed of
responses across these studies, the slope of
the fitted line would be 1 (dotted
blue line); however, results indicated
that some general cognitive slowing is
evident within the datasets since the
slope of the fitted line was 1.4 (solid
green line).For the fMRI meta-analysis, five distinct clusters
survived the stringent correction applied
for multiple comparisons. The location of these clusters
is summarized visually
in Figure3 and details
are given in Table 1 .
Clusters of interest to competing
theories of general cognitive slowing were
found within the left pre- and
postcentral gyrus areas as well as within the right
medial frontal gyrus. All
clusters identified in the analysis had, on average,
a slope that was
significantly less than one yet significantly greater
than negative one (see Figure 4).
This slope indicates that between these 4 tasks, younger
adults had greater BOLD signal change than older adults
in these brain areas. Notably,
there is a lack of difference in between-task BOLD activity
within primary
sensory areas such as vision between older and younger adults,
even though several studies have reported older adults
having less activity than younger
adults in sensory areas
[24–26].Table 1
SPM volume summary
Cluster numberTalairach daemon labelCluster sizeFWE-correctedP-valueTx,y,z (mm)1Left postcentral gyrus7.00043.33−44, −24, 552Right thalamus4.00041.358, −20, 203Left precentral gyrus6.00037.85−20, −20, 754Subgyral right MFG3.00127.4828, 8, 455Subgyral/left insula5.00322.91−36, 16, 15Figure 3
Contiguous
clusters that survived FWE correction.
These clusters had slopes that were
not significantly equal to one (color-coded for clarity),
and theoretically,
they localized areas of differences in
between-task BOLD signal change for
older and younger adults.Figure 4
Average
slope values for clusters with slopes significantly
different from one. A
total of 5 clusters (color-coded for clarity)
survived and had average slope
values less than 1 and greater than −1.
These slope values are the result of
greater between-task BOLD signal change in the
cohort of young adults than
older adults.When the Peiffer plot was explored for 3 contiguous
voxels with significantly negative slopes less than zero,
19 total clusters were identified. Nine of these
clusters (47%) were located within the right
middle and superior frontal gyri and included a total of
68 voxels  (see Figure5).
Across these clusters older adults showed BOLD deactivation on
tasks when younger adults tended to slightly activate and
older adults had BOLD activation
when younger adults were deactivating on a task
(average slope −1.89 +/− 0.21).
These areas appear to be activating in opposition between
the age groups and
are contiguous to the right middle frontal gyrus area
(cluster no. 4) identified
in the “residual” analysis.
Other clusters, showing similar activity
differences were seen within left medial frontal gyrus
(2 clusters; 6 voxels);
left inferior parietal lobule (3 clusters; 10 voxels);
cingulate gyrus (2 clusters; 7 voxels);
and single clusters within the basal ganglia (5 voxels),
midbrain (3 voxels), and the left posterior
lobe of the cerebellum (7 voxels).
It is important to note, however, that these findings,
unlike those from the “residual”
analysis above, have not been stringently controlled for
multiple comparisons
aside from retaining the requirement for 3 contiguous voxels.Figure 5
Contiguous
clusters in right frontal cortex where the slope is −1.
Several clusters
were identified in the secondary
analysis to assess for areas where activity
was opposite in younger and older adults.
These areas within right frontal
cortex tended to be active across tasks
in younger adults and deactivated
across task in older adults. Further,
these areas correspond to regions
involved in attention and task decisions,
which have also been implicated in
general cognitive slowing theories.To assess whether these slope findings were dependent
on age and not an epiphenomenon of the
datasets, a randomization of the age groups was
performed within each dataset.
Individuals were randomized in two groups so that the average
age of both
groups was roughly equal (∼51 years of age).
When the Peiffer plot was
constructed for these new groups, no significant clusters were
identified where
the null hypothesis (H0 : m=1)
was false. Additonally, no significant
areas were identified where the slope was −1.
These findings thus support the
claim that the results of the original plot
were not due to the dataset
composition (i.e., scanner, site, or paradigm)
but were dependent on separating
the study populations by age.Assessment of
the b-intercept indicated that the lack of significant
slopes within the
primary sensory areas may be due to a baseline shift in activity
between the
age groups (see Figure6).
For example, within visual cortex, several areas were
identified that had negative b-intercepts which
indicated that across the tasks
older adults tended to start from a lower BOLD
activity level than young adults
(if x=0, then
y= a negative BOLD signal).
This result is a continual within-task
difference, which is also seen in the published literature
[23–27]; however, since this reduced
BOLD signal in older adults is constant across several tasks,
it does not have
a slope which deviates significantly from 1.
In addition, an area within right
motor cortex shows a positive b-intercept and thus greater
activity in older
adults relative to younger adults.
As graciously pointed out by an anonymous
reviewer, this result is consistent with the model hypothesis
of hemispheric asymmetry
in older adults (HAROLD).
The HAROLD model states that there is reduced
lateralization of brain activity in older adults
relative to younger adults,
which results from changes in neural architecture
and not cognitive strategy
[28, 29]. Due to the small number of
data points used to construct these plots,
the area included within the 95% confidence
interval of the b-intercept is relatively large.
Therefore, these early
findings may underestimate the amount of brain activity
which could be
described as being affected by an age-related
DC-shift and is thus an
age-related BOLD signal difference that is independent of task.Figure 6
B-intercept
map for Peiffer plot atx=10 mm.
Cool-colored
voxels show negative
b-intercept values where older adults have
lower activity than younger adults.
Note the large cluster within the occipital area.
Warm-colored voxels indicate
positive b-intercepts where older adults show
greater activity than younger
adults. Interestingly, several contiguous voxels
were identified within right
motor cortex and indicated increased
bilateral activity in older relative to
younger adults across the 4 detection tasks.Finally, an epiphenomenon of the method was
revealed when evaluating the goodness of fit,
as measured by the R-squared value.
Areas where the slope was similar to one
showed very high R-squared values (>0.8)
suggesting a high predictability
for older adults' BOLD signal in several
brain regions; however, within the
clusters identified as significantly deviant
from one, R-squared values were
lower and ranged between 0.1–0.46.


## 4. Discussion

Here we report the preliminary use of a
novel meta-analysis technique in studies on
aging, which localizes one factor of general
cognitive slowing to the
sensorimotor transfer. These findings lend support to
existing data from
event-related potential work indicating that
slowing occurs predominately
during the time for generation of the response in older
adults and not when evaluating
incoming sensory material
[11, 13]. Deviant slopes could be
found in between-task BOLD activity values where younger
adults have greater
BOLD activity change than older adults in the
sensorimotor transfer area in the
left hemisphere. Additionally, right frontal
areas were identified with slopes
near −1, indicating that older and younger
adults were activating these
attentional areas in opposite directions across the tasks.
Not surprisingly,
more attentionally demanding tasks have shown
that older adults have
differential patterns of activation within the
frontal cortex in response to the
task when compared to younger participants
[30, 31].An important caveat exists, however,
since datasets within this analysis were obtained from
relatively noncognitively demanding tasks.
In other words, more brain areas
may be involved as loci of general cognitive
slowing and would emerge as more
cognitively demanding datasets become available for assessment.
Comparing our
current datasets to the existing literature on Brinley plots of
reaction times,
simple discrimination tasks show the least amount of response
time slowing;
therefore, these BOLD signal findings presumably will only
become stronger with
the addition of datasets containing more cognitively demanding
conditions
(e.g., working memory). In addition, with more tasks requiring
greater attentional demand, the negative slope found within right
frontal cortex may
steepen and be found within the residual analysis which
can control for the
multiple comparisons inherent within imaging data.
Power analyses of these
preliminary results suggest that roughly 9 datasets are needed to
perform a meta-analysis with a paired t-test to achieve a
power-level of 80%.Interestingly, all
significant clusters identified as deviant within the
meta-analysis were
separate from peak activity differences reported in any of the
individual tasks
used within the datasets.
If traditional meta-analysis techniques were used
[16–20], none of these areas would
have been found. Utilizing this novel meta-analysis technique,
it is possible
to assess between-task differences in BOLD activity between
groups regardless
of paradigm design, task parameters, and location of the scan.
While global
signal correction was used to normalize
the datasets within this study, the
assessment method could also be performed using
average group z-maps that would
allow datasets from multiple fMRI processing software
packages to be analyzed
collectively. Additionally, this method may yield
interesting findings in a
variety of study groups where a clear “normal”
group can be identified and used
on the x-axis
(e.g., schizophrenics versus normals;
dyslexics versus normals; AD versus
normal older adults). It is important to keep in mind that
the use of this
method is to determine between-task differences among
two populations and not
to differentiate the two groups within any one paradigm
of the analysis. Thus,
differences in whether or not an area is
identified as deviant come from how
the BOLD signal responds across a wide array of tasks.An existing disadvantage of this meta-analysis technique
is that it requires access to raw
fMRI data to obtain subjects' normalized contrast
weighted BOLD activity maps
(task - baseline) from multiple tasks. With the
continued increase of complete data sets maintained in
accessible repositories
like the fMRI Data Center, this should
hopefully become less burdensome in the near future.Overall as a meta-analysis method in the fMRI field,
this plotting addresses several
limitations of existing analysis methods.
Specifically, it allows the
assessment of between-task differences regardless of a
task's paradigm domain
or baseline condition. Further, it can identify
areas of subthreshold effects
in addition to the suprathreshold
within-task differences that are identified
by performing a meta-analysis on voxel quadrants
identified in individual
studies as the local maxima. Lastly, this method
provides imaging researchers
the ability to localize between-task differences
in BOLD signal and apply that
knowledge to existing behavioral evidence not only in aging but
in other complex conditions
(e.g., dyslexia, schizophrenia, Alzheimer's disease, etc.)
as well.



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*Source: 167078-2007-10-03.xml*