The broad, long-term objective of the proposed research is to develop, analyze and evaluate computer models of small cortical strokes and post- stroke recovery. Work completed during the previous project period has shown that sensory maps in neural models of neocortex will reorganize in response to simulated focal lesions. Such map reorganization resembles that seen experimentally in important ways, and has led to testable predictions about the mechanisms of post-stoke recovery.
The specific aims of the research planned for the next project period are to extend the scope of this work to encompass primary motor cortex in a multiregional cortical model, and to incorporate biochemical and metabolic features important in ischemic stroke. The approach taken is to develop and study two separate computational models, and then to combine them, as follows. First, a motor control model that directs positioning of a simulated arm in three dimensional space will be developed. This model, incorporating both primary motor cortex (area 4) and proprioceptive cortex (area 3a), will permit investigation of the hypothesis that feature maps resembling those observed in physiological experiments emerge during learning due to synaptic modifications in a closed-loop network. The motor control model will then be used to study motor map reorganization following acute focal lesions. Second, an enhanced cortex model incorporating biochemical and metabolic features not usually found in neural models will be implemented and studied independently to assess the effects of these factors in perilesion cortex. Once the effects of lesions on these two models are understood in isolation, their features will be combined in a single unified model so that interactions between these features following simulated lesions can be analyzed. By systematically varying model parameters and the size and location of lesions, specific hypotheses will be tested about model features that 1) produce a perilesion zone of relative neural inactivity (""""""""ischemic penumbra""""""""), 2) cause or prevent recruitment of this zone into the permanent infarct, 3) lead to evolving stroke, and 4) lead to motor map reorganization during the post-stroke period. This research will thus complement traditional clinical and animal model approaches to studying cerebrovascular disease by contributing to our understanding of the dynamics of neocortical information processing generally, and of the pathophysiology of ischemic stroke more specifically.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG7-SSS-3 (13))
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University of Maryland College Park
Biostatistics & Other Math Sci
Schools of Engineering
College Park
United States
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Shevtsova, N; Reggia, J A (2000) Interhemispheric effects of simulated lesions in a neural model of letter identification. Brain Cogn 44:577-603
Ruppin, E; Revett, K; Ofer, E et al. (1999) Penumbral tissue damage following acute stroke: a computational investigation. Prog Brain Res 121:243-60
Ruppin, E; Ofer, E; Reggia, J A et al. (1999) Pathogenic mechanisms in ischemic damage: a computational study. Comput Biol Med 29:39-59
Revett, K; Ruppin, E; Goodall, S et al. (1998) Spreading depression in focal ischemia: a computational study. J Cereb Blood Flow Metab 18:998-1007
Goodall, S; Reggia, J A; Chen, Y et al. (1997) A computational model of acute focal cortical lesions. Stroke 28:101-9
Reggia, J A; Ruppin, E; Berndt, R S (1997) Computer modeling: a new approach to the investigation of disease. MD Comput 14:160, 162, 164 passim
Chen, Y; Reggia, J A (1996) Alignment of coexisting cortical maps in a motor control model. Neural Comput 8:731-55
Reggia, J A; Montgomery, D (1996) A computational model of visual hallucinations in migraine. Comput Biol Med 26:133-41
Grundstrom, E L; Reggia, J A (1996) Learning activation rules rather than connection weights. Int J Neural Syst 7:129-47
Ruppin, E; Reggia, J A (1995) Patterns of functional damage in neural network models of associative memory. Neural Comput 7:1105-27

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