Download Schizophrenia is a multi-faceted disorder with highly complex p

Project Description
I. Introduction
Our goal in this collaborative research is to integrate combined behavioral and brain imaging studies with
sophisticated data evaluation techniques and computational models of associative learning in healthy control
and schizophrenic patients to explain reduced performance of the latter ones. Our working hypothesis is that
schizphrenia is a ''disconnection syndrome" (Friston and Firth 1995, Friston 2002), and we give quantitative
estimation for the partial impairment of functional losses.
General plan
Schizophrenia is a multi-faceted disorder with highly complex pathophysiology (ref). The illness is proposed to
be a disconnection syndrome (ref) mediated by altered synaptic plasticity (ref). fMRI studies of pairedassociate learning are well suited to studying mechanisms of cortico-cortical connectivity (ref) as well as
contributions of synaptic plasticity (ref). Therefore these studies as applied to schizophrenia may elucidate
fundamental neurobiological alterations in the illness. However, computational models are essential to
integrate date obtained by different techniques and different levels of organization. These models help to
understand not only the mechanisms tof normal, but also pathological behavior.
Therefore, we propose a conceptual and translational framework that integrates in vivo fMRI studies of pairedassociate learning and memory, with the computational modeling of network interactions between implicated
regions identified with fMRI (ref). The modeling will progress along two converging tracks. In the first
approach, a neural-network model will be developed on the known functional biology of paired-associate
learning and memory (ref). Network interactions will be assessed to explain experimentally achieved normal
behavioral performance. The lesioning of critical network constituents, and the exploration of parameter
spaces of the model will be used to assess its predictive power in simulating schizophrenia-like behavior (ref).
Finally, the use of theoretically motivated dynamic causal models will be employed to explain BOLD activity
based on network interactions (ref).
Twenty five early course and stable schizophrenia patients (18<age<30 yrs) and twenty five healthy controls
with no family history of psychosis (to the 2nd degree) will be recruited to participate in an object-location
paired-associate memory task (ref). The goal of the task is to learn associations between nine unique objects
and nine locations in a spatial grid. The task requires the maintenance of spatial (dorsal/parietal cortex) and
object information (ventral/inferior temporal cortex) in working memory (prefrontal cortex), with subsequent
binding in the hippocampus (ref). fMRI studies suggest that increased effective connectivity between
hippocampal and neo-cortical regions underlies successful memory consolidation (ref). The complexity of this
paradigm renders it ideal to the study of dysfunction in schizophrenia and the modeling of inter-cortical
interactions.
II. Specific aims
Aim 1a: To measure learning curves from behavioral data to estimate learning dynamics in healthy and
schizophrenic individuals.
Aim 1b: Using BOLD fMRI to assess learning-related dynamics in key structures including the hippocampus,
prefrontal, parietal and inferior temporal cortices in terms of behavior-related signal change.
Aim 2: To develop a biologically plausible functional neural model of interconnected cortical regions that
describes behavioral performance of control and schizophrenia patients. The model will implement neural
relevant principles including ventral/object and dorsal/spatial stream separation (ref), and gamma rhythm
synchronization to implement binding between object and spatial inputs (ref).
Aim 3: To develop a dynamical casual model (ref) to solve the "inverse problem", that is to estimate effective
connectivity parameters that describe patterns of BOLD activity across regions of interest. This model
framework will be deployed to assess which connections are impaired during schizophrenia, and the measure
of functional reduction of information flow associated with the illness.
This general framework may be extended in the future to understanding the pharmacologic bases of the
illness, as learning dynamics may be related to glutamatergic and dopaminergic function (ref), both of which
are impaired in schizophrenia (ref). Computational neuropharmacology is a new way of offering therapeutic
strategies (ref: aradi-erdi).
The global burden of schizophrenia is immense and pharmaco- and cognitive-therapies will be better served by
more detailed understanding of illness pathophysiology. Our proposal attempts to achieve this understanding
through the integration of experimental in vivo imaging with diverse methods of computational neuroscience.
III. Background and Significance
Schizophrenia is one the most debilitating mental illnesses in the world. Global prevalence rates are estimated
at between 1-2% and the illness has profound personal costs for patients and their families, and widespread
societal costs . Despite the illness being widely accepted as biological following decades of biological
research, series challenges toward the understanding of schizophrenia remain. Experimental studies have
proliferated the literature in several in vivo imaging modalities including functional and structural MRI (ref) and
MR spectroscopy (ref). In conjunction with post-mortem studies of brain tissue (ref), these studies have
provided innumerable examples of specific and general deficits in function and structure in the living and
deceased schizophrenia brain. Yet very significant shortcomings in understanding remain. Among the
foremost is the near total absence of the application of formal computational models of brain function that can
provide theoretically motivated frameworks from which to interpret biological data. Notwithstanding a handful
of efforts (ref), such an absence is glaring because the diverse biological findings are rarely reconciled within a
formal framework that can be provided by such models.
What might be a meaningful computational framework to apply to the understanding some aspects of
schizophrenia pathology?. We propose the following translational and integrative approach.
1. The use of fMRI studies as the tool of choice to study in vivo function. As an imaging technique, fMRI is
noteworthy for its ability to measure blood-flow related surrogates of neuronal activity (ref), thereby
providing the most current in vivo measures of function and dysfunction available (ref).
2. The use of functional paradigms that have at least two attributes. That they involve functions that may be
attributable to pharmacologic systems that
are of relevance to schizophrenia and that
they target specific cortical systems and
engage inter-regional connectivity that is of
relevance to schizophrenia.
3. The use of network models of inter-regional
interaction that provide detailed
specifications of how task-behavior arises in
normal and pathological conditions.
4. The use of theoretical models of network
behavior that will explain the experimental
fMRI data in both normal and pathological
samples.
Figure 1. A schematic overview of the proposed collaborative studies
involving the use of models of cortical function to describe normal and
pathological cortical function during learning dynamics.
This collaborative approach is essential to integrate findings across several domains of basic research, and
translate them into informing clinical and pharmacologic practice .
Figure 1 provides a schematic overview of this collaborative proposal emphasizing the central role of
computational models in integrating the results of animal and human studies and synthesizing the relevance of
this work for schizophrenia.
What is an appropriate choice of fMRI paradigms to use to study schizophrenia and what is the motivation for
using them? Associative learning and learning dynamics are relatively well understood from animal studies
(ref), in vivo fMRI studies (ref) and computational neuroscience (ref). Animal studies suggest that learning
dynamics are related to mechanisms of synaptic plasticity of hippocampal neurons (ref), where changes in the
strength of neuronal connections may mediate the strength of encoded memories. Altered synaptic plasticity is
considered central to the disconnection hypothesis of schizophrenia (ref). Further, detailed computational
models of hippocampal function based on animal work, have provided important insights into the function of
the medial temporal lobe and its role in integrating inputs from unimodal cortical regions such as the superior
parietal and inferior temporal cortices (ref). Thus associative learning is a fertile domain in which to develop
models of normal and pathological brain behavior and to conduct in vivo fMRI studies of normal and
pathological brain function.
III.1. The functional network for associative memory with a focus on the hippocampus and its subregions.
Associative learning relies on the consolidation and retrieval of associations between diverse memoranda,
sensory inputs and streams of neural activity , particularly by hippocampal and medial temporal lobe neurons .
This detection and consolidation of correlated spatio-temporal patterns of neuronal activity was proposed in
classic neuroscience as a centerpiece of learning and memory (Hebb, 1949). The idea is that coincidence
detection between two contemporaneously active synapses results in a consolidation of linkage between
these cells thereby forming the building blocks for the localization of memories. Indeed this basic idea has
been expanded upon in subsequent iterations of theories of neural encoding including theories of long term
potentiation , neuronal population selection , and coherence to name a select few. In neuroimaging, the
collection of large scale in vivo and surface imaging modalities has allowed the estimation of coherence or
connectivity using a variety of statistical and clustering techniques including coherence analysis , principal
components analyses and analyses of functional and effective connectivity between brain regions.
The weight of experimental evidence clearly indicates that the medial temporal lobe, including the
hippocampus and its sub-units such as the cornu ammonis (CA), the dentate gyrus (DG) and the subiculum,
and adjacent structures such as the entorhinal cortex are central to the formation of long term memories.
These regions occupy a unique anatomical place within the realm of cortical and sub-cortical connections
receiving inputs from the sensory areas in unimodal association cortex and from heteromodal areas such as
the dorsal and ventral prefrontal cortex via the entorhinal cortex. This region is therefore uniquely positioned in
a “hierarchy of associativity” to integrate multi-modal inputs from unimodal areas before redistribution of
potentiated associations into the neo-cortex . This general framework provides a good explanation for the
patterns of anterograde amnesia in classic neuropsychological studies of patients with hippocampal lesions in
which the retention of memories before the lesion is preserved but the formation of new long term memories is
impaired. It also is consistent with experimental work in animals: Lesions that are applied to the hippocampus
early during learning devastate trace conditioning preventing eventual consolidation of traces in long term
memory . When the function of the medial temporal lobe is impaired during learning of associations, memorial
representations that rely on this hippocampal activity are either not formed or are formed to inadequate
strength . Thus, memory is inadequately established and is unavailable at the fidelity needed when recall is
required. Therefore in a disorder such as schizophrenia, impaired hippocampal activity during critical periods
of learning and memory may form a central basis of impaired memory formation.
III.2. The “hierarchy of associativity”
Why are the hippocampus and its sub-units crucial to memory formation? In Figure 2 we provide a schematic
model for object-location association, and of the unique pattern of uni- and poly-modal inputs to the
hippocampus, where these inputs are subsequently bound into a-modal associations. Network locations are
depicted on a statistical
Figure 2. Information flow during object- map of fMRI-measured
location associative memory (based on brain activity in a single
Lavenex & Amaral, 2000). The cortical subject during memory
pathway is overlaid on a medial slice consolidation in the
depicting brain activity (p<.001) during object-location association
memory consolidation during object- task. As seen, location
location associative learning (see and object information are
Preliminary data for further details). relayed from dorsal
Regions labeled are: V1: Primary Visual (Superior Parietal) and
Cortex; IT: Inferior Temporal Cortex; SP: ventral (Inferior Temporal)
Superior
Parietal
Cortex;
Hipp: inputs respectively to the
Hippocampus;
PFC:
Dorso-lateral hippocampus. The CA
prefrontal cortex.
and DG sub-regions in the
hippocampus process
information via uni- or bi-directional connections from the entorhinal cortex. CA regions (primarily CA3) and
DG form a mutual excitatory network for encoding amodal information (associations). Entorhinal connectivity
with prefrontal cortex provides access to intermediately encoded associations (short time scales) and for the
eventual disbursal of memories into the neo- cortex (longer time scales). Through this funneling of information,
the degree of abstraction of the information increases through this pathway before assuming its most abstract
form in the encoding within the CA and DG units within the hippocampus . In the context of schizophrenia, it is
plausible that abnormal prefrontal-hippocampal and glutamate-dopamine interactions lead to associative
memory deficits.
As the figure indicates, there are multiple neural regions that can targeted with fMRI to identify impaired
function in a network disorder such as schizophrenia.
III.3. fMRI/BOLD studies of paired-associate learning and memory.
Several in vivo fMRI studies of pair-associate memory and learning have identified correlates of the BOLD
(Blood Oxygen Level Difference) response with learning. Using an object-location paired-associated learning
task, Buchel and colleagues demonstrated plasticity (i.e., reduction of the BOLD response) in heteromodal
cortical regions such as the inferior temporal and superior parietal associated with increased learning, and
increased effective connectivity between these regions, the hippocampus and the prefrontal cortex with
learning. These studies and others have emphasized the crucial role played by the hippocampus in the
formation and consolidation of new memories that may then be distributed over time in neo-cortical systems .
These studies suggest that paradigms of associative memory and learning are ideal to probe the functioning
of, and the interactions between the hippocampus and neo-cortical regions such as the prefrontal cortex. As
suggested, the convergence of the pharmacologic and fMRI findings is of direct relevance to schizophrenia:
(a) deficits of the glutamatergic and dopaminergic systems are considered as being central to the
pathophysiology of schizophrenia , and
(b) impairments in the structure and function of both the medial temporal lobe and the prefrontal cortex are
widely associated with the illness.
Studies of associative learning in schizophrenia that quantify changes in the BOLD response in the
hippocampus and the prefrontal cortex are directly related to the pathology of brain regions . By identifying
pharmacologically relevant biomarkers of function, such studies may eventually aid in the process of drug
discovery in schizophrenia .
III.4. Hippocampal and prefrontal deficits in schizophrenia: Pharmacology and Imaging. Several lines of
work have suggested pharmacologic and imaging deficits in the hippocampus and prefrontal cortex in
schizophrenia. The list is too exhaustive to review. For example, hippocampal volume deficits that have been
documented in at-risk, prodromal and chronic schizophrenia by us and others though not in bipolar patients .
Neurochemical imaging studies also provide robust evidence of hippocampal deficits. Reduced expression of
the subunits for the three ionotropic receptors (NMDA, AMPA, and kainate) has been documented in the
hippocampus in schizophrenia , providing evidence of glutamatergic-related impairment in this critical memoryrelated structure. This effect may be modulated by the expression of vulnerability genes including DISC1 and
GRM3 that have been associated with susceptibility for schizophrenia . The mechanisms that relate reduced
NMDAR sensitivity to psychosis are obscure, but such putative reductions may have cascading effects
including tonic reduction in glutamatergic transmission , and its ultimate expression in selective behavioral
deficits on fronto-temporal lobe tasks or excitatory glutamatergic neurotoxicity . Neurochemical studies of the
hippocampus and other structures in schizophrenia are consistent with this idea. In vivo spectroscopy
suggests systematic patterns of pathology in the hippocampus, including reduced N-acetyl-aspartate (NAA; an
intra-neuronal marker of integrity).
These findings may collectively reflect an impairment in N-methyl-D-aspartate (NMDA) glutamatergic
neurotransmission, that may involve the dysregulated function or the physical loss of NMDA synapses in the
hippocampal and prefrontal regions and that may thus be central and proximate to the pathophysiology of the
illness . The functional relevance of these deficits as markers for evaluating treatment efficacy have been
probed using animal models with tasks that include pre-pulse inhibition . However such tasks are more
circumscribed in their cortical demands and unlike associative learning, do not necessarily depend on
widespread interactions between cortical regions. Driving widespread interactions between cortical regions is
central to understanding whether schizophrenia is associated with NMDA-mediated synaptic dysplasticity that
may impact inter-regional connectivity .
IV. Preliminary Studies
Data from preliminary studies demonstrate the following: a) feasibility to conduct complex fMRI paradigms in a
4T environment; b) using a complex tasks such as associative learning to assess trends in the data that
indicate altered memory related dynamics in BOLD in schizophrenia patients and d) emerging modeling data
documenting relationships between parameterized synaptic firing rates and behavioral performance.
The fMRI paradigm is depicted in Figure 4. During learning
subjects alternated
between blocks of
Figure 5. Learning dynamics in
consolidation,
controls and patients over time are
rest/rehearsal and
plotted. Note the shift in linear to
retrieval. During
asymptotic performance regimes in
consolidation, nine
both groups (arrows) and poorer
equi-familiar objects poorer rates of memory consolidation
over time compared to control
with monosyllabic
object names were subjects. Error bars in the graph are ±
sem.
presented in
Figure 4: The fMRI paradigm alternated sequential random
between blocks of encoding, rest/rehearsal order (3s/object) in
(“+”) and retrieval (each 27s).
grid locations for
naming ( “bed” and
“book” are depicted
in Figure 4). Following a brief rest/rehearsal interval, memory for
object-location associations was tested by cuing grid locations for
retrieving objects associated with them (3s/cue). Object names were
ENC
RET
ENC
RET
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monosyllabic to minimize head motion. Eight blocks (each cycling between encoding, rest and retrieval) were
employed.
Healthy Controls (n=11; mean age=22 yrs, sd=5; 5 females) and stable early course schizophrenia patients
(n=11; mean age=26 yrs; sd=5; 3 females) gave informed consent. Groups did not differ in terms of age
(p>.10). Patients were diagnosed using DSM-IV, SCID and consensus diagnosis. All were on a regimen of
atypical antipsychotics (Risperidone, Olanzapine or Aripiprazole).
IV.1. Behavioral data: Reduced learning rates and capacity in schizophrenia. Behavioral data were
analyzed to: a) approximate learning functions for each group and b) assess differences between groups in
learning potential and rate. Means of achieved performance are plotted separately for schizophrenia (green
squares) and HC (blue circles; the color scheme is retained for the remaining graphs) in Figure 5 (Behavioral
Data were lost for three subjects on account of experimenter error). Patients learn at a slower rate than
controls, but show monotonic increases in performance, suggesting (sub-optimal) engagement of memory
consolidation systems. Furthermore, controls transition from linear to asymptotic performance about halfway
into the task (see top arrow indicating the point that characterizes shifts between “Early” and “Late”
performance). This memory dynamic is altered in patients, as evidence by the rightward time shift in
transitioning from linear to asymptotic learning. This distinction between “Early” and “Late” memory is retained
as a central independent variable in subsequent analyses of BOLD data as we attempt to understand altered
physiological dynamics of learning and memory in schizophrenia. As seen in Figure 6a, monotonic increases
in performance from “Early” to “Late” performance are observed in both groups with patients showing impaired
consolidation during both phase, F1,16=10.33, p<.01. Analyses of learning rates (K) indicated lower rates of
memory consolidation in patients, F1,17=15.96, p<.01 (Fig 6b), consistent with animal studies of hippocampal
impairment and relation to leaning rate . These data provide support for Hypothesis 1 indicating behavioral
and rate differences in learning in schizophrenia.
IV.2a. fMRI data: Impaired memory dynamics in the hippocampus and its sub-regions and the
prefrontal cortex: A priori region of interest
analyses. Our preliminary fMRI analyses is based on
(a)
hypotheses driven analyses of BOLD time series data
extracted from regions of interest defined in
stereotactic space. Image data were unsmoothed to
minimize post-processing artifacts and to ensure
separation of signal within hippocampal sub-regions
that are proximally located in space . All analyses
were conducted over extracted image data to assess
the neural substrates of learning dynamics.
(b)
Figure 9. (a) BOLD in the DG (outlined on adjacent
coronal slice) during memory consolidation periods
(encoding)
is
plotted
relative
to
adjacent
Rest/Rehearsal periods) for controls (blue) and
patients (green) during early and late intervals. (b)
Data as in (a) plotted for the CA. Error bars are ±
sem across images.
To assess memory-related dynamics of BOLD in the
dentate gyrus and the cornu ammonis, individual
effects of interest maps constructed for each subject
(pFWE<.05) identifying maximally task driven voxels
were overlaid on maximum probability maps of the CA
and the DG. For individual images, activity was
expressed as percent signal change (psc) relative to
average activation in the adjacent rest/rehearsal
intervals. Thus positive values indicate more
consolidation-related activity, whereas negative values
indicate more rehearsal related activity Individual
images (psc values 2.5 sd from the mean were
excluded from the analyses) were initially submitted to
analyses of variance with group (control vs. patient), phase of consolidation (early vs. late) and hemisphere
(left vs. right) as factors. Strong right hemispheric lateralization was detected in both regions (p<10-6) with no
hemisphere x group interaction (p>.20), consistent with several other studies that document preferential
processing of pair-associate learning in the right medial temporal cortex . and subsequent analyses focus on
the right hemisphere. In the DG (Fig 9a), significant main effects of group and time were observed, (F1,2446 ≥
15.27, p<10-4) indicating significant time-related changes in each group and significant hypo activity in
schizophrenia patients. By comparison, in the CA (Fig 9b), a significant interaction, F1,2565=4.2, p<.04,
suggested lower activity during both stages of learning in patients, with time-dependent changes in response
amplitude in controls. Pair wise contrasts, revealed significantly greater activity early in learning in controls
(t1291=2.19, p<.03) but not in patients (p>.20) suggesting group-related specificity in dynamic changes in activity
in this region. These results are notable for their convergence with the hypothesized model of intrahippocampal dysfunction outline in Figure 2. They suggest altered learning related dynamics in the DG and
the CA in schizophrenia, with decreased activity in the “upstream” DG, leading to consequent hypofunction
and dysplasticity in the CA. The absence of early CA activity may be critical to altered memory dynamics in
schizophrenia, an effect that may be similar to hippocampal lesions during early paired-associate conditioning
which result in profound deficits in memory acquisition, compared to lesions that are performed later in
conditioning .
IV.2.b. Memory dynamics in the lateral prefrontal cortex (lateral Brodmann Areas 9 & 46).
A core feature of hippocampal-prefrontal interactions is that these interactions are driven in part by impaired
memory consolidation in the early stage in critical hippocampal regions such as the DG and the CA. A result of
impaired memory consolidation mechanisms in the medial temporal lobe , is to “raise the cost” of consolidation
to other neo-cortical systems such as the prefrontal cortex. In the context of a network disorder such as
schizophrenia, this would magnify the hypothesized inefficiency in prefrontal function that has been observed
in fMRI studies and in animal models of glutamate function in the prefrontal cortex . A straightforward
prediction is that demand for neurophysiological resources in the prefrontal cortex would be high during linear
regimes in memory consolidation (i.e. during early
phases) when demand for control-related activity is
high . In contrast, this demand would be low
under conditions of efficient recall (i.e. during late
phases) when well-consolidated associations are
processed efficiently. Figure 12 depicts the
bilateral response of the dorso-lateral prefrontal
cortical ribbon (Brodmann areas 9 & 46) outlined
on a single slice in Figure 12 during memory
consolidation epochs (relatively to activity in
adjacent rest/rehearsal intervals). Two effects are
observed: First there is a significant bilateral
interaction between group and learning phase,
F1,3343=24.89, p<.001. Percent signal change in
controls decreases over time (related to increase
efficiency of processing and increased memory
consolidation). The opposite effect is observed in
Figure 12.
Bilateral dorsolateral prefrontal responses
decrease with learning in controls but increase in patients, patients. These data provide support for
reflecting abnormal hippocampal-prefrontal interactions. Hypothesis 2a and indicate altered memory
dynamics in hippocampal sub-regions and the
Error bars are ± sem over images.
prefrontal cortex in schizophrenia. They also
indicate that in studying the dynamic characteristic of memory, fMRI can assess more than merely “hypo” or
“hyper”activity in schizophrenia. Rather fMRI reflects an intricate balance across network constituents and
provides many potential biomarkers of network dysfunction that may be suitable targets for assess therapeutic
efficacy. Below we further probe the network characteristics of this dysfunction with functional connectivity
analyses.
IV.2.c. Hippocampal – Cortical disconnectivity during memory consolidation: Altered network
dynamics in schizophrenia.
Analyses of activity in upstream “sensory” areas including bilateral superior parietal cortex suggested reduced
function during encoding F1,5189=11.93, p<.001 (Fig 13). These results imply that network dysfunction may
underlie learning deficits in schizophrenia. The idea of NMDA-mediated reductions in synaptic plasticity and
therefore cortico-cortical connectivity has been mooted before but has not been directly supported in dynamic
tasks of memory consolidation that specifically rely on NMDA receptor function . To that end we employed the
Psycho-physiological interaction (PPI) tool in SPM2 to assess changes in the modulation of cortical activity by
a seed region chosen from the right CA region. Contrast maps (Encoding > Rest/Rehearsal) were created for
each subject and thresholded at pFWE<.05 within a right CA mask. For each subject, the first eigenvariate
drawn from a 2mm radius ROI centered on the maximum intensity peak within the CA was convolved with the
Enc > Rest/Rehearsal contrast in a first level effects of interest analysis, capturing modulation of activity across
the cortex by activity within the seed voxel . Individual effects of interest maps were submitted to second level
analyses to assess group-wise differences in connectivity in HR compared to HC. Contrast analyses were
restricted to a priori masks of lateral Brodmann areas 9 & 46 and the hippocampus . Significantly reduced
connectivity during encoding, (Contrast structure: SCZ(Enc>Rest) < HC(Enc > Rest)) was observed in dorso-lateral
prefrontal cortex and across the ipsi- and contra-lateral medial temporal lobe. Clusters of significance are
rendered in sagittal and axial views in Fig 14. Significance peaks are depicted (cross-hairs) for BA 9, t19=3.79,
pFDR<.05, x=20, y=37, z=35 (Fig 14a) and in an axial view of the medial temporal lobe, t19=4.65, pFDR<.02,
x=26, y=-12, z=-10 (Fig 14b) respectively
(a) Figure 14. (a)
L
R
Sagittal view of
significance peak
in Brodmann
area 9 (crosshairs) of reduced
CA-frontal
connectivity in
schizophrenia.
(b) Axial view of
the medial
temporal lobe
showing
bilaterally
reduced intrahippocampal
connectivity in
schizophrenia.
These data provide support for the idea of a network dysfunction in
schizophrenia that underlies altered learning dynamics . Region
specific alterations in BOLD were documented with varying patterns
of learning related change. These range from reduced activity
associated with learning in controls but an absence of learning
dependent signal change in patients in the CA sub-region of the
hippocampus, altered retrieval dynamics in the subiculum and
increased activity in lateral prefrontal cortex with learning.
Further to assess changes in functional connectivity as a function of
time, a linear time domain regressor was convolved with the task
(Encoding) and the hemodynamic response function. The resultant
model captures time related dynamics during encoding in the form of
an ascending response function. Then, for each subject, the
maximum activation peak (Enc > Rest) was identified (pFWE<.05). The
first eigenvariate drawn from a 2mm radius ROI centered on this
intensity peak was then convolved with regressor, ((Time*Enc) > Rest). The contrast map of the resultant
(b)
interaction term captures ascending modulation of activity across the cortex by activity within the seed CA
region , and is correspondingly a measure of increased connectivity with the seed time series over time.
Contrast maps for 11 healthy controls (HC) and 11 patients (SCZ) were submitted to a 2nd level analysis to
assess reductions in time-related functional connectivity (FC) in SCZ compared to HC. This analysis
represents:
Figure x. Results of time-related functional
connectivity analysis in HC and SCZ
indicating, FC(HC(Time*Enc) > Rest)> FC(SCZ
(Time*Enc) > Rest). Insets identify significant
clusters in the prefrontal, parietal and medial
temporal lobe regions. The results reflect
time-related modulation of cortical activity by
activity in the seed CA region and indicated
that time related increases in connectivity
between CA and prefrontal and CA and
parietal regions is reduced in SCZ.
FC(HC(Time
FC(SCZ
(Time*Enc) > Rest)
Key regions of
significance from
the resulting FC
difference map are
rendered on a
*Enc) > Rest)>
sagittal view in Figure x. As can be seen, outside of the medial temporal lobe, two principal regions of
significance were observed. In the dorsolateral prefrontal cortex, significantly reduced time-related
connectivity was observed, t20=4.63, p<.005 (peak: x=28, y=27, z=37, talairach). In the superior parietal
cortex, significantly reduced time-related connectivity was observed, t20=3.46, p<.005 (peak: x=28, y=-59,
z=55, talairach). Clusters of significance in the prefrontal cortex, the superior parietal cortex and the medial
temporal lobe are denoted in the insets.IV.3. Computational models
IV.3.1. Neural network modeling of associative learning TO BE WRITTEN!!
The preliminary model is designed to simulate the behavioral associative learning task and final model output
are learning curves that depict output over each iteration of recall. As will be evident, the model incorporates
the separation between encoding/consolidation and cued recall while also retaining biological plausible
relationships between model architecture and neural systems, as well as known learning parameters in the
brain.
Encoding/consolidation
Recall
Simulation results
IV.3.2. Neural model of interacting regions for normal and impaired learning
A computational model has already been constructed to analyse the mechanism underlying behavioural
differences between schizophrenic and control subjects (Figure 1). The model has two parts: A simple visual
system, and a more detailed model of the hippocampal formation.
We did not intended to model the visual signal processing system in its details, because these mainly sensory
areas are not affected by the illness. However, we implemented a
feed-forward network to analyse the retinal image, and to create the
representation of the object (the model of the area IT in the ventral
stream), and its location (as the area SP in the dorsal stream). The
proposed role of the hippocampus is to bind these two representation
together (Milner, 1997) so that when cued by the location, the correct
object could be recalled.
The highly precessed sensory input enters the hippocampus through
the mossy fiber pathway, originating in the entorhinal cortex which is
not explicitly modelled here. The EC itself has reciprocal connections
with both the hippocampus and various neocortical, including visual
areas and considered as a relay for information coming from
multimodal association areas.. Mossy fibers terminate on the dentate
granule cells and hippocampal pyramidal neurons. Two regions of
the hippocampal formation was modeled: the dentate gyrus and the
CA3 region. We used firing rate models, where the activation of each
Fig. C3.1. The structure of the model. Each
unit was calculated by the linear sum of its input. Synaptic
layer is labelled according to the modelled
connections in the IT, DG and CA3 was modified by simple Hebbian
area in the brain. The horizontal red line
plasticity. We used large number of neurons in the simulations
separates
the visual (above) and the
(typically ~500 in one layer) in order to be able to implement
associative memory (below) systems. Arrows
distributed encoding in a realistic range of sparsity (0.1 in the
represent synaptic connections, red colour
hippocampus).
indicates the modifiable ones. The entorhinal
Our hippocampal model was built according to the following key
cortex (light grey) is not explicitly modelled.
assumptions (Rolls and Treves, 1998):
1. The DG performs pattern separation by competitive learning: it
removes redundancies from the input and produce a sparse representation for learning in the CA3 region.
This process can be considered as a translation from the neocortical to the hippocampal language.
2. The granule cells in the DG innervates CA3 pyramidal cells with particularly large and efficient synapses
(the mossy fiber pathway) that makes postsynaptic neurons fire. Hebbian plasticity between active CA3
neurons and the perforant path axons associates the activity pattern in the CA3 to its incoming input
(hetero-associative plasticity). After the encoding, the same CA3 assembly can be activated by the
presentation of the partial or noisy version of the original input (e.g., only the object or the location).
3. Next, connections between CA3 cells and IT cells are modified, to translate back the hippocampal to the
neocortical code.
4. Finally, objects are stored in a long term memory system in the inferio-temporal cortex forming an attractor
network. During recall, the activity of this subsystem converges to one of the stored items (objects).
The performance of the hippocampal model on the associative learning task is shown on Figure 2. We note,
that this is not the ideal performance of the model: The capacity of the system with 500 units and 0.1 sparsity is
around a few hundreds of associations. However, with random initial synaptic weights and small learning rate,
it requires some repetitions to learn new associations appropriately.
As an other bottle-neck of the system is the domain of attraction of the attractor network in the IT. If the
attraction basin is smaller, than the recall cue should be more precise. Our results with the hippocampal model
indicate, that the poorer performance of schizophrenic patients on the associative memory task is mainly due
to the shallower attractor basin and not necessary to a lower learning rate in the hippocampus.
Fig. C3.2. The performance of the model on the associative memory task. Left: Behavioral data from control subjects are
shown in red, and fitted model results with black circles. Right: the same for schizophrenic patients in green, and the fitted
mode with open circles. Error bars show the standard deviation.
IV.4. Analysis of fMRI data
Our preliminary data analysis focused on finding regions, where the activity is correlated with block of learning
or recall.
Figure C4.1. shows the active regions during encoding (black) and recall (red) in healthy control (open
symbols) and schizophrenic subjects (filled symbols). The row data was de-trended and normalized, and
correlated with a simple signal that is one during the block learning and zero otherwise. Similar analysis was
performed with the recall signal. The data presented here are averaged over subjects. The results show high
activation in the visual areas V1 and SP both during learning and recall, but only during encoding in the inferior
temporal cortex, the part of the ventral stream engaged in object recognition. Interestingly, the the
hippocampus was mostly active during encoding, whereas the prefrontal regions (PFC, Fro) during the recall of
information. Differences between healthy controls and schizophrenic patients are remarkable in the
hippocampus and the prefrontal regions.
V. Detailed plan
V.1 Behavioral experiments and fMRI measurements
V.2. Data analysis techniques HOW DETAILED
THIS PART SHOULD BE????
Figure C4.1. Correlations with blocks. Vertical axis:
different regions: Occ: occipital, SP: superior
parietal, IT inferio-temporal, Hpc Hippocampal, PFC
prefrontal, Fro: Frontal, CNG: Cingulum. Data from
left and right hemispheres are plotted separately.
Based on the available data on the activity of five interconnected regions (superior parietal cortex, inferiotemporal cortex, prefrontal cortex, primary visual cortex and the hippocampus) a computational model will
be established based on the interaction of the regions to understand the generation of normal and
pathological temporal patterns. A dynamical casual model (Stephan KE et al: Dynamic causal models of
neural system dynamics: current state and future extensions, J. Biosci. 31(4),October 2006,) should be
established and use it to solve the "inverse problem", i.e. to estimate the effective connectivity parameters.
This model framework would give answer for the question whether which connections are impaired during
schizophrenia, and what is the measure of functional reduction of the information flow?
DCM.fig
V.3 Computational modeling
Our present research aims to study the cooperation between the prefrontal working memory and the
hippocampal associative memory systems in both normal and pathological subjects In the hippocampus ,
memories are stored by the synaptic connection between pyramidal neurons. The modification of these
synapses is a relatively slow process, and requires multiple presentation of the same pattern, but the capacity
of the hippocampal system is high. The prefrontal working memory system stores memories in the persistent
activity of cell-assemblies. The storage is fast, single presentation of a specific pattern leads to the formation of
the memory but this system has a very limited capacity.
VSTM can be readily distinguished from verbal short term memory. Brain damage can lead to a disruption of
verbal short term memory without a disruption of VSTM and vice versa In addition, it is possible to fill up verbal
short term memory with one task without impacting VSTM for another task and vice versa. VSTM can also be
subdivided into spatial and object subsystems. Our preliminary hippocampal model will be completed by
working memory modules.
We will adapt the model of Lisman and his colleagues (Lisman and Idiart, 1995, Jensen and Lisman 1996),
which is capable of storing multiple items in an oscillatory network. A memory is encoded by a subset of
principal neurons that fire synchronously in a particular gamma subcycle. Firing is maintained by a membrane
process intrinsic to each cell (Figure D3.1.).
Fig.D3.1. Schematic illustration of the involvement of working memory systems in making short term
associations. The different modules stores different features of the same input by the persistent
activation of neural assemblies. Oscillations allow the network to store multiple items at the same
time. However, the capacity of the system is limited, which results in storage failures, or in the
overwriting of previously stored items. The binding between these features can be realized by
synchronization.
We propose, that there are two similar working memory modules involved in the short term storage of locations
and object, respectively. These different features are connected by the gamma frequency synchronization
among cortical regions, similarly to the mechanism proposed for sensory binding (Gray, 1999; Singer, 1999).
The involvement of the prefrontal regions in associative learning has dual function:
1. The WM system teaches the hippocampal associative memory system by the repeated presentation of the
information.
The WM memory buffer itself can store the memories through the delay period (independently from the
hippocampus), and it can increase the performance by recalling elements not being succesfully stored in the
hippocampus (yet).VI . Intellectaul Merit, Broader Impact, Integration of Research and Education
IM
BI
IRE
The funding of this proposal would give the possibility for the PI to involve more stundents into the research.
Kalamazo College just opened a Neuroscience concentration with a significant component in computational
neuroscience. The grant help to bring to campus gradate students and post-graduate fellows, and provide an
advanced research atmosphere for undergraduate students, too. Students participating in the project are
supposed to be well-prepared to enter Graduate School.
General Plan of Work
Phase I
Dates
Kalamazoo Team
August 2008 –
August 2009
Develop computational
models of associative
learning
Wayne State Memphis team
Team
August 2008December 2009
Phase II
August 2009August 2010
Phase III
August 2010June 2012
Report writing
Synergistic
activity:
Report writing