Interhemispheric effects on map organization following simulated cortical lesions

During recent years there has been increasing use of neural models to investigate the implications of hypotheses about brain and cognitive disorders. Here we systematically study the effects of sudden simulated lesions on cortical maps in a neural model consisting of left and right hemispheric regions connected by a corpus callosum. The model identifies conditions under which damage to one hemispheric region leads to reorganization of the contralateral, intact hemispheric region. The intact hemisphere's participation in the recovery process is found to be a function of pre-existing map lateralization/symmetry and lesion size, indicating that interpretation of future experimental work should take these factors into account.

A neural network model of lateralization during letter identification

The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.

Pathogenic mechanisms in ischemic damage: a computational study

The pathogenesis of penumbral tissue infarction during acute ischemic stroke is controversial. This peri-infarct tissue may subsequently die, or survive and recuperate, and its preservation has been a prime goal of recent therapeutic trials in acute stroke. Two major hypotheses currently under consideration are that penumbral tissue is recruited into an infarct by cortical spreading depression (CSD) waves, or by a non-wave self-propagating process such as glutamate excitotoxicity (GE). Careful experimental attempts to discriminate between these two hypotheses have so far been quite ambiguous. Using a computational metabolic model of acute focal stroke we show here that the spatial patterns of tissue damage arising from artificially induced foci of infarction having specific geometric shapes are inherently different. This is due to the distinct propagation characteristics underlying self-regenerating waves and non-wave diffusional processes. The experimental testing of these predicted spatial patterns of damage may help determine the relative contributions of the two pathological mechanisms hypothesized for ischemic tissue damage.

Spreading depression in focal ischemia: a computational study

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.

Computational studies of lateralization of phoneme sequence generation

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.

Self-replicating structures: evolution, emergence and computation

Since von Neumann's seminal work around 1950, computer scientists and others have studied the algorithms needed to support self-replicating systems. Much of this work has focused on abstract logical machines (automata) embedded in two-dimensional cellular spaces. This research was motivated by a desire to understand the basic information-processing principles underlying self-replication, the potential long-term applications of programmable self-replicating machines, and the possibility of gaining insight into biological replication and the origins of life. We view past research as taking three main directions: early complex universal computer-constructors modeled after Turing machines, qualitatively simpler self-replicating loops, and efforts to view self-replication as an emergent phenomenon. We discuss our recent studies in the latter category showing that self-replicating structures can emerge from nonreplicating components, and that genetic algorithms can be applied to program automatically simple but arbitrary structures to replicate. We also describe recent work in which self-replicating structures are successfully programmed to do useful problem solving as they replicate. We conclude by identifying some implications and important research directions for the future.

Metrics for cortical map organization and lateralization

Cerebral lateralization refers to the poorly understood fact that some functions are better controlled by one side of the brain than the other (e.g. handedness, language). Of particular concern here are the asymmetries apparent in cortical topographic maps that can be demonstrated electrophysiologically in mirror-image locations of the cerebral cortex. In spite of great interest in issues surrounding cerebral lateralization, methods for measuring the degree of organization and asymmetry in cortical maps are currently quite limited. In this paper, several measures are developed and used to assess the degree of organization, lateralization, and mirror symmetry in topographic map formation. These measures correct for large constant displacements as well as curving of maps. The behavior of the measures is tested on several topographic maps obtained by self-organization of an initially random artificial neural network model of a bihemispheric brain, and the results are compared with subjective assessments made by humans.

Computational models for the formation of protocell structures

There have been various attempts to simulate the self-assembly process of lipid aggregates by computers. However, due to the computationally complex nature of the problem, previous simulations were often conducted with unrealistic simplifications of the molecules' morphology, intermolecular interactions, and the environment in which the lipid molecules interact. In this article, we present a new computational model in which each lipid is simulated by a more realistic amphiphilic particle consisting of a hydrophilic head and a long hydrophobic tail. The intermolecular interactions are approximated by a set of simple forces reflecting physical and chemical properties of lipids, for example, hydrophobicity and electrostatic forces, which are believed to be crucial for the formation of various aggregates. With a set of carefully selected parameters, this model is able to simulate successfully the formation of micelles in an aqueous environment and reversed micelle structures in an oil solvent from an initially randomly distributed set of lipidlike particles. This model can be used to study, at the microscopic level, the self-assembly of different protocell structures in the evolutionary process and the impact of environmental conditions on the formation of these structures. It may be further generalized to simulate the formation of other, more complex structures of amphiphilic molecules such as monolayer and bilayer aggregates.

A computational model of acute focal cortical lesions

BACKGROUND AND PURPOSE

Determining how cerebral cortex adapts to sudden focal damage is important for gaining a better understanding of stroke. In this study we used a computational model to examine the hypothesis that cortical map reorganization after a simulated infarct is critically dependent on perilesion excitability and to identify factors that influence the extent of poststroke reorganization.

METHODS

A previously reported artificial neural network model of primary sensorimotor cortex, controlling a simulated arm, was subjected to acute focal damage. The perilesion excitability and cortical map reorganization were measured over time and compared.

RESULTS

Simulated lesions to cortical regions with increased perilesion excitability were associated with a remapping of the lesioned area into the immediate perilesion cortex, where responsiveness increased with time. In contrast, when lesions caused a perilesion zone of decreased activity to appear, this zone enlarged and intensified with time, with loss of the perilesion map. Increasing the assumed extent of intracortical connections produced a wider perilesion zone of inactivity. These effects were independent of lesion size.

CONCLUSIONS

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.

Frequency-spatial transformation: a proposal for parsimonious intra-cortical communication

This work examines a neural network model of a cortical module, where neurons are organized on a 2-dimensional sheet and are connected with higher probability to their spatial neighbors. Motivated by recent findings that cortical neurons have a resonant peak in their impedance magnitude function, we present a frequency-spatial transformation scheme that is schematically described as follows: An external input signal, applied to a small input subset of the neurons, spreads along the network. Due to a stochastic component in the dynamics of the neurons, the frequency of the spreading signal decreases as it propagates through the network. Depending on the input signal frequency, different neural assemblies will hence fire at their specific resonance frequency. We show analytically that the resulting frequency-spatial transformation is well-formed; an injective, fixed, mapping is obtained. Extensive numerical simulations demonstrate that a homogeneous, well-formed transformation may also be obtained in neural networks with cortical-like "Mexican-hat" connectivity. We hypothesize that a frequency-spatial transformation may serve as a basis for parsimonious cortical communication.

Feature discovery and classification of Doppler umbilical artery blood flow velocity waveforms

Doppler umbilical artery blood flow velocity waveform measurements are used in perinatal surveillance for the evaluation of fetal condition. There is an ongoing debate on the predictive value of Doppler measurements concerning the critical effect of the selection of parameters for the interpretation of Doppler waveforms. In this paper, we describe how neural network methods can be used both to discover relevant classification features and subsequently to classify Doppler umbilical artery blood flow velocity waveforms. Results obtained from 199 normal and high risk patients' umbilical artery waveforms highlighted a classification concordance varying from 90 to 98% accuracy.

Alignment of coexisting cortical maps in a motor control model

How do multiple feature maps that coexist in the same region of cerebral cortex align with each other? We hypothesize that such alignment is governed by temporal correlations: features in one map that are temporally correlated with those in another come to occupy the same spatial locations in cortex over time. To examine the feasibility of this hypothesis and to establish some of its detailed implications, we studied a multilayered, closed-loop computational model of primary sensorimotor cortex. A simulated arm moving in three dimensions formed the external environment for the model cortical regions. Coexisting proprioceptive and motor maps formed and generally aligned in a fashion consistent with the temporal correlation hypothesis. For example, in simulated proprioceptive sensory cortex the map of elements responding strongly to stretch of a particular muscle matched the map of tension sensitivity in antagonist muscles. In simulated primary motor cortex the map of elements responding strongly to increased tension in specific muscles matched the map of output elements for the same muscles. These computational results suggest specific experimental measurements that can support or refute the temporal correlation hypothesis for map alignments.

Learning activation rules rather than connection weights

In the construction of neural networks involving associative recall, information is sometimes best encoded with a local representation. Moreover, a priori knowledge can lead to a natural selection of connection weights for these networks. With predetermined and fixed weights, standard learning algorithms that work by altering connection strengths are unable to train such networks. To address this problem, this paper derives a supervised learning rule based on gradient descent, where connection weights are fixed and a network is trained by changing the activation rule. It incorporates both traditional and competitive activation mechanisms, the latter being an efficient method for instilling competition in a network. The learning rule has been implemented, and the results from several test networks demonstrate that it works effectively.

A computational model of visual hallucinations in migraine

Cortical spreading depression is a wave of electrical and biochemical changes that spreads across the cerebral cortex. It has been hypothesized to be an important underlying cause of the visual disturbances occurring during the migraine aura, but this is difficult to test in animals or humans. We created a computational model of cortical spreading depression and found that during the wave of biochemical changes the spatial pattern of neural activity broke up into irregular patterns of lines and small patches of highly activated elements. The corresponding visual disturbances that would be produced by these patterns of neural activity resemble the hallucinations reported during the migraine aura, providing strong support for the cortical spreading depression hypothesis of migraine. The model also makes the testable prediction that these hallucinations move at an exponentially increasing speed across the visual field.

Pathogenesis of schizophrenic delusions and hallucinations: a neural model

We implement and study a computational model of Stevens' theory of the pathogenesis of schizophrenia. This theory hypothesizes that the onset of schizophrenia is associated with reactive synaptic regeneration in brain regions that receive degenerating temporal lobe projections. Concentrating on one such area, the frontal cortex, we model a frontal module as an associative memory neural network whose input synapses represent incoming temporal projections. Modeling Stevens' hypothesized pathological synaptic changes in this framework results in adverse side effects similar to hallucinations and delusions seen in schizophrenia: spontaneous, stimulus-independent retrieval of stored memories focused on just a few of the stored patterns. These could account for the delusions and hallucinations that occur in schizophrenia without any apparent external trigger and for their tendency to concentrate on a few central cognitive and perceptual themes. The model explains why the positive symptoms of schizophrenia tend to wane as the disease progresses, why delayed therapeutic intervention leads to a much slower response, and why delusions and hallucinations may persist for a long time when they do occur.

Patterns of functional damage in neural network models of associative memory

Current understanding of the effects of damage on neural networks is rudimentary, even though such understanding could lead to important insights concerning neurological and psychiatric disorders. Motivated by this consideration, we present a simple analytical framework for estimating the functional damage resulting from focal structural lesions to a neural network model. The effects of focal lesions of varying area, shape, and number on the retrieval capacities of a spatially organized associative memory are quantified, leading to specific scaling laws that may be further examined experimentally. It is predicted that multiple focal lesions will impair performance more than a single lesion of the same size, that slit like lesions are more damaging than rounder lesions, and that the same fraction of damage (relative to the total network size) will result in significantly less performance decrease in larger networks. Our study is clinically motivated by the observation that in multi-infarct dementia, the size of metabolically impaired tissue correlates with the level of cognitive impairment more than the size of structural damage. Our results account for the detrimental effect of the number of infarcts rather than their overall size or structural damage, and for the "multiplicative" interaction between Alzheimer's disease and multi-infarct dementia.

A neural model of memory impairment in diffuse cerebral atrophy

BACKGROUND

Computer-supported neural network models have been subjected to diffuse, progressive deletion of synapses/neurons, to show that modelling cerebral neuropathological changes can predict the pattern of memory degradation in diffuse degenerative processes such as Alzheimer's disease. However, it has been suggested that neural models cannot account for more detailed aspects of memory impairment, such as the relative sparing of remote versus recent memories.

METHOD

The latter claim is examined from a computational perspective, using a neural associative memory model.

RESULTS

The neural network model not only demonstrates progressive memory deterioration as diffuse network damage occurs, but also exhibits differential sparing of remote versus recent memories.

CONCLUSIONS

Our results show that neural models can account for a large variety of experimental phenomena characterising memory degradation in Alzheimer's patients. Specific testable predictions are generated concerning the relation between the neuraonatomical findings and the clinical manifestations of Alzheimer's disease.

High-specificity neurological localization using a connectionist model

Most previous connectionist models for diagnosis have been developed using error backpropagation. While these systems function reasonably well, they have been limited by their need for a large database of test cases, to situations where a single disorder is present, and by the large number of connections required between fully-connected sets of processing units. Here we describe a recently developed connectionist model that overcomes these limitations. This approach can reuse existing causal knowledge bases, works well in situations where multiple disorders can occur simultaneously, and does not require fully-connected sets of processing units. We demonstrate that the accuracy of this model is comparable to that of more conventional AI programs using the same knowledge base in determining precisely the site of brain damage in a group of 50 stroke patients. These results support the conclusion that connectionist models can effectively use pre-existing causal knowledge bases from AI systems, and that they can function accurately when handling actual clinical problems.

A neural model of cortical map reorganization following a focal lesion

Neural models based on fairly simple assumptions have been able to account for topographic map formation in sensory cortex and the map reorganization that occurs following repetitive stimulation and deafferentation. The spontaneous reorganization that follows an acute focal cortical lesion, however, has not been modeled successfully. We have developed a computational model of cortex based on the hypothesis that cortical activation is distributed competitively. This model exhibited spontaneous reorganization following a focal cortical lesion and makes a testable prediction about the time course of that reorganization. We describe our model and the hypotheses upon which it is based, and examine some of the factors which influence post-lesion reorganization. We also demonstrate that the extent of post-lesion reorganization can be greatly improved through selective repetitive stimulation, suggesting a clinical rehabilitation technique that can be tried in an experimental setting for patients suffering sensory loss due to focal brain damage.

Functional pronunciation units in English words

Two distinct factors limit the orthographic regularity of English words: (a) Most characters can correspond to several different sounds and (b) characters can either stand alone or be combined in various ways for pronunciation as a single phoneme. This study addresses the second of these issues through the analysis of a large corpus of English words. Data are presented describing the frequency that each character (or character cluster) functioned in the corpus as a correspondent of a single phoneme rather than being combined with other characters (or decomposed). Examples are provided regarding potential applications of these data in the construction of stimulus materials for cognitive studies, in neuropsychological investigations of dyslexia, and in computational models of word naming.