Welcome to the Stark Memory Research Group!
PI: Craig
E. Stark, Ph.D., Director of the Center for the
Neurobiology of Learning and Memory
Research in the Stark Memory Research
Group is concerned with the mechanisms that underlie
memory. The central question guiding research in the lab is
how is it that we learn and remember information such that
our past experiences influence our behavior? Using the
techniques of functional magnetic resonance imaging (fMRI),
traditional experimental psychology, neuropsychological
studies of amnesic patients, and connectionist modeling,
research in the Stark lab is focused on how the neural
systems supporting these two types of memory operate and
interact.
Pattern
Separation and Pattern Completion
In recent
years, we have been interested in the concepts of pattern
separation and pattern completion as underlying processes
supported differentially within substructures of the
hippocampus using the high-resolution fMRI
techniques my lab has developed. Following our paper that
came out in Science
(Bakker et
al.,2008, Science),
we have completed behavioral and neuroimaging studies that
test the hypothesis that a subregion of the hippocampus
(the dentate gyrus) is performing pattern separation (a
computation that leads it its role in episodic memory). Our
previous work was able to show that activity in the dentate
gyrus and the adjacent CA3 field of the hippocampus was
sensitive to small changes in the input. Here, “lure
items” that are similar to previously-seen items have
activity consistent with the regions treating these as
novel items (having “pattern separated” this
similar input). In contrast, other regions in the
hippocampus and adjacent MTL cortices have activity for
these items that is more consistent with treating them as
if they were repetitions of the previously seen item
(referred to as “pattern completion”). While
influential, a limitation of this work was that we only had
three distinct levels of “mnemonic similarity”:
new items, previously-seen items, and items similar to
previously-seen items. Therefore, we could not measure the
activity transfer function of the separate subregions with
much resolution (how activity in each region responds to
change in the input – a key prediction of the model
we are working with).
To address this limitation, we
developed a large set of stimuli with known levels of
mnemonic similarity. By getting a strong handle on how
readily participants (~150 tested) would confuse a similar
lure item for a true repetition of an item, we have a
manipulation much like one performed in a number of recent
electrophysiological studies in rodents that can give us
the x-axis in our investigations of the transfer functions.
We have used this task now in both behavioral and
neuroimaging studies using both young and aged (see below)
volunteers to test our model’s predictions. In the
most direct application (Lacy et al.,2011,
Learning
and Memory), the stimuli were used as a
direct extension of the Bakker et al., (2008) paper.
Consistent with our predictions (derived from computational
models and both unit-recording and immediate early gene
work in the rodent), we observed a very sharp transfer
function in the CA3/dentate subregion (indicative of
pattern separation) and a gradual (roughly linear) transfer
function in the CA1 subregion. We hope this will help cast
many of the current theories of human hippocampal function
in a new light and allow us to make stronger connections
between the human and animal literature. In addition to
this task, we have developed a spatial pattern separation
task to test the model’s predictions that is derived
from another area of the rodent literature (Stark et al.,
2010, Learning &
Memory).
Aging and Early
Dementia
A second theme has been our
rapid expansion into the study of aging and dementia. This
is a very exciting area for us as a number of the theories
and techniques we have been developing for our studies of
pattern separation have clear applications in research on
aging. We are attempting to unite our work on pattern
separation with a rodent model of aging that highlights
structural changes in the hippocampal network associated
with healthy aging. In particular, the model highlights the
age-related degradation of the perforant path – the
input to the dentate gyrus and CA3. Thus, studying healthy
aging provides us with a way of examining the effect of
removing these structures (or at least impairing their
function). This model allows us to test the hypothesis that
these regions are performing pattern separation and it
gives us a new perspective on age-related memory loss in
humans – one that focuses on the anatomy and the
computations performed by subregions rather than on more
descriptive-level analyses.
We have now tested these predictions in several ways. Using
the mnemonic-similarity task described above, we observed
age-related behavioral changes in participants’
ability to accurately identify lure items as being similar
to (but not the same as) previously-seen items (Yassa et
al., 2010, Hippocampus).
This effect is behaviorally amplified in Mild Cognitive
Impairment (MCI; Yassa et al., 2010, NeuroImage).
The behavioral effect was paralleled by (and correlated
with) activity changes in the CA3/DG consistent with an
age-related change in the structures responsible for
pattern separation. As noted above, we have developed a
related task that assesses spatial pattern separation
abilities to test these predictions as well. In a
behavioral study (Stark et al., 2010, Learning &
Memory),
we observed a similar age-related shift away from pattern
separation and towards pattern completion
– again consistent with the model. Finally,
using new diffusion tensor imaging (DTI) scanning and
analysis techniques we have developed (see below), we have
been able to visualize and quantify the integrity of the
perforant path in humans (Yassa et al., 2010,
PNAS).
We have not only observed the predicted age-related loss in
the perforant path, but have been able to correlate this
with standardized neuropsychological test scores on a task
that taxes pattern separation.
Interactions Across
Memory Systems
A third theme we have been
developing in the lab concerns the relationship between
learning in the medial temporal lobe and learning in the
striatum. Much of our work has involved tasks that can (and
are) learned by both systems and our focus has previously
been on the medial temporal lobe. Now, we are extending our
scope to the striatum and to examining how these systems
may interact (how reward-prediction errors in areas such as
the nucleus accumbens may be interacting with the MTL). The
literature has made much of the fact that these two memory
system can
be
independent. These
studies have moved our field forward considerably and
provided some of the critical evidence to show that there
are many different forms of memory and many different brain
systems participating in memory. Memory is not one thing.
That said, it is not clear that they should be viewed as
entirely independent in normal operation.
We have recently (Mattfeld & Stark, 2010,
Cerebral
Cortex)
used fMRI to examine changes in activity in both systems
over the course of arbitrary paired-associate learning
(using a task my lab previously developed specifically to
link fMRI with non-human primate neurophysiology; Law et
al, 2005, Journal of
Neuroscience). We observed multiple
learning-related signals in the striatum (e.g., activity
tracking probability correct versus the rate of learning)
and were able to localize them with greater precision than
was previously possible. Similar signals were also observed
in the MTL. We also observed “functional
connectivity” between these regions (correlation in
the activity). What was most interesting, however, was not
the presence or absence of these signals or the simple
correlations in activity, but how the correlations changed
over the course of learning. This form of analysis had
never been applied to these issues but was able to show
clear dissociations across striatal regions. The ventral
striatum was robustly coupled with the MTL, but only during
the peak periods of learning. The associative striatum
showed the converse. Together, these are consistent with
the hypothesis that the nucleus accumbens (ventral
striatum) has a functional relationship with the MTL but
that there is a functional dissociation between the caudate
and the MTL.
Neuroimaging
Methods
Fitting in with all three of the
research areas mentioned so far has been our work in fMRI
methods. In particular, we have recently finished an
extensive analysis and quantification of the ability of
over a dozen techniques to co-register MRI scans of
structures in the MTL across individuals (Yassa &
Stark, 2009, NeuroImage).
The results of this study show conclusively that the
approach we have been developing over the last several
years (“ROI-AL”) is significantly more accurate
than anything else currently in use. A chief problem with
our approach is that in the past it took several hours to
align each participant and it could only be run on the
computers at the Center for Imaging Sciences at JHU. Thus,
while we were able to use the technique, few others could,
severely limiting its application. In our recent paper, we
present a new implementation of the approach that takes
only minutes to run and we have made the software available
for use on Windows, Linux and Mac OS X platforms.
In addition to working on alignment methods (which are
critical if one wishes to make claims about subregions of
structures like the hippocampus – already not
large), my lab has been active in pushing MRI’s
resolution. Using techniques we developed (Kirwan et al.,
2007, Human Brain
Mapping)
at JHU and brought to UCI, we collect functional imaging
from the hippocampus at one of if not the highest
resolutions of any lab in the world. We developed this as
this resolution was needed to address our scientific
questions about the role of subregions of the hippocampus
(and now striatum).
Diffusion Tensor Imaging (DTI) has been a popular scanning
technique as it provides a non-invasive method for mapping
white matter tracts in the brain. We have not used it
though, as its resolution (2-3 mm) has been insufficient to
resolve the fibers that would inform our work. Recently,
however, the need to resolve these fibers grew as we wanted
to be able to assess changes in the perforant path
associated with age. As it is only a few millimeters wide
and as it is buried inside other white matter tracts, we
needed to develop numerous tools to measure and quantify
it. In collaboration with Dr. Tugan Muftuler here at UCI,
we were able to cut the in-plane resolution down to 0.66
mm. I then devised a technique that used anatomical
constraints (derived from tract-tracing studies in the
monkey) to identify and measure the integrity of the
perforant path from this high-resolution DTI data. Our
paper (Yassa et al., 2010, PNAS)
shows the predicted age-related change in the perforant
path (described above) and details the new contribution
made to DTI data analysis.
Last Edited January 2011