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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 ENC 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