These simulation results suggest that functional cortical reorganization after an ischemic stroke is a two-phase process in which perilesion excitability plays a critical role.

When a cerebral infarction occurs, surrounding the core of dying tissue there usually is an ischemic penumbra of nonfunctional but still viable tissue. One current but controversial hypothesis is that this penumbra tissue often eventually dies because of the metabolic stress imposed by multiple cortical spreading depression (CSD) waves, that is, by ischemic depolarizations. We describe here a computational model of CSD developed to study the implications of this hypothesis. After simulated infarction, the model displays the linear relation between final infarct size and the number of CSD waves traversing the penumbra that has been reported experimentally, although damage with each individual wave progresses nonlinearly with time. It successfully reproduces the experimental dependency of final infarct size on midpenumbra cerebral blood flow and potassium reuptake rates, and predicts a critical penumbra blood flow rate beyond which damage does not occur. The model reproduces the dependency of CSD wave propagation on N-methyl-D-aspartate activation. It also makes testable predictions about the number, velocity, and duration of ischemic CSD waves and predicts a positive correlation between the duration of elevated potassium in the infarct core and the number of CSD waves. These findings support the hypothesis that CSD waves play an important causal role in the death of ischemic penumbra tissue.

The mechanisms underlying cerebral lateralization of language are poorly understood. Asymmetries in the size of hemispheric regions and other factors have been suggested as possible underlying causal factors, and the corpus callosum (interhemispheric connections) has also been postulated to play a role. To examine these issues, we created a neural model consisting of paired cerebral hemispheric regions interacting via the corpus callosum. The model was trained to generate the correct sequence of phonemes for 50 monosyllabic words (simulated reading aloud) under a variety of assumptions about hemispheric asymmetries and callosal effects. After training, the ability of the full model and each hemisphere acting alone to perform this task was measured. Lateralization occurred readily toward the side having larger size, higher excitability, or higher-learning-rate parameter. Lateralization appeared most readily and intensely with strongly inhibitory callosal connections, supporting past arguments that the effective functionality of the corpus callosum is inhibitory. Many of the results are interpretable as the outcome of a "race to learn" between the model's two hemispheric regions, leading to the concept that asymmetric hemispheric plasticity is a critical common causative factor in lateralization. To our knowledge, this is the first computational model to demonstrate spontaneous lateralization of function, and it suggests that such models can be useful for understanding the mechanisms of cerebral lateralization.

Abductive diagnostic problem-solving systems use causal relations to infer plausible diagnostic hypotheses. An important but controversial issue for such models is what characteristics should define the most plausible hypotheses. While there are theoretical predictions relevant to this issue; there are almost no empirical data on which to base rational decisions. Accordingly, this study examines four different criteria of hypothesis plausibility in diagnosing the site of brain damage in 100 medical patients. The criteria examined are (1) naive minimal cardinality, (2) irredundancy, (3) most probable (Bayesian), and (4) minimal cardinality when adjacency relations are taken into account. Model performance when these different hypothesis plausibility criteria are used confirms the previously predicted inadequacy of minimal cardinality. It also indicates that irredundancy ('minimality'), the criterion most widely used in current AI models, is not useful in this setting because of the large number of alternative, implausible hypotheses it produces. The most interesting result is that a modified minimal cardinality criterion produces the best hypotheses when measured as the ratio of agreements with human experts per hypothesis generated. In addition, comparing the results of this study to two previous rule-based systems for a similar application indicates that abductive diagnostic systems can be very powerful as application programs. These results, useful in themselves, underscore the need for more systematic empirical studies of abductive problem-solving models.