A Computational Model of Acute Focal Cortical Lesions

Goodall, Sharon; Reggia, James A.; Chen, Yinong; Ruppin, Eytan; Whitney, Carol

doi:10.1161/01.str.28.1.101

Cited by 41 publications

(33 citation statements)

References 22 publications

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“…With regard to stroke, this observation, implicit in the general schema and made explicit by Reggia and colleagues (34,37,38), means that recovery from stroke will necessarily involve two phases. Immediately after the stroke occurs, the normal dynamic of the network is suddenly disrupted.…”

Section: Dynamics Of the Cortical Mapmentioning

confidence: 99%

■ REVIEW : Computer Models of Stroke Recovery: Implications for Neurorehabilitation

Lytton

Stark²,

Yamasaki³

et al. 1999

Neuroscientist

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The persistence of cortical plasticity in the adult can help explain functional recovery after stroke. Computer modeling tools developed to explain the process of early development of sensory systems can be extended to help us relate cortical plasticity to both behavior and to underlying molecular and cellular mechanisms. Computer modeling results suggest a two-phase recovery process, involving immediate alterations in activity patterns caused by the loss of the infarcted neurons ("dynamic plasticity"), followed by true plastic changes as the new activity alters synaptic weights between neurons. Recognition of these two phases suggests that timing of physiotherapy and pharmacotherapy may play an important role in their efficacy.Causal links are nonintuitive in complex systems. In the realm of weather, a rogue current in the mid-Pacific can throw off your vacation plans on the Atlantic seaboard. In the brain, a drug or other intervention can have farreaching and unexpected effects. The computer has proven a valuable tool for permitting predictions to be made in complex systems. This is done by making connections between different levels of organization ( 1 ). In the nervous system, these levels of organization can be identified anatomically and physiologically (Box 1) (2, 3). A major goal of computational neuroscience is to reach across these levels-for example, to explain and predict molecular effects at the network level or network effects at the behavioral level. Creation of these conceptual links is important in neurological disease because pharmacotherapeutic interventions are aimed at the molecular level, whereas clinical outcomes are sought at the functional, behavioral levels.In the early 1950s, computer-science pioneer A.M. Turing developed a series of equations that help explain how the leopard got its spots (4). He noted that a chemical process marked by a difference in reaction rates with diffusing reactants could produce a variety of striped and spotted designs that could be responsible for a wide variety of biological patterns (5). The processes of evoked activity and activity-dependent plasticity present in the central nervous system can be viewed as &dquo;reactions&dquo; that occur at different rates, resulting in the development of patterns such as ocular dominance and orientation columns (6). Given mathematical methods that can explain the development of patterns, it was natural to apply these methods to the problem of recreation of appropriate or inappropriate brain patterns after the disruption produced by a stroke. Plasticity in the Adult BrainThe pioneering studies of Hubel and Wiesel introduced the concept of a critical period in brain development. They found that appropriate neural connections from the eye either formed during this period or did not form at all, leaving the animal visually impaired. Extrapolation from this result suggested the hypothesis that neural organization of primary cortex was largely fixed at some definable developmental stage, restricting adult plasticity to remote...

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Section: Dynamics Of the Cortical Mapmentioning

confidence: 99%

■ REVIEW : Computer Models of Stroke Recovery: Implications for Neurorehabilitation

Lytton

Stark²,

Yamasaki³

et al. 1999

Neuroscientist

View full text Add to dashboard Cite

show abstract

“…Although increasingly more detailed descriptions of the kinematic and dynamic features of arm movement after stroke are becoming available (Beer, Dewald, & Rymer, 2000;Levin, 1996;Reinkensmeyer, McKenna, Cole, Kahn, & Kamper, 2002), little is known about the neuronal mechanisms by which these features arise, and few neural models have been proposed to explain these features (Goodall, Reggia, Chen, Ruppin, & Whitney, 1997). The purpose of this study was to test whether a population vector model of the control of hand movement, damaged to simulate the consequences of stroke, could account for directional reaching errors commonly observed after stroke.…”

Section: Introductionmentioning

confidence: 99%

Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability

et al. 2003

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The directional control of reaching after stroke was simulated by including cell death and firing-rate noise in a population vector model of movement control. In this model, cortical activity was assumed to cause the hand to move in the direction of a population vector, defined by a summation of responses from neurons with cosine directional tuning. Two types of directional error were analyzed: the between-target variability, defined as the standard deviation of the directional error across a wide range of target directions, and the within-target variability, defined as the standard deviation of the directional error for many reaches to a single target. Both between- and within-target variability increased with increasing cell death. The increase in between-target variability arose because cell death caused a nonuniform distribution of preferred directions. The increase in within-target variability arose because the magnitude of the population vector decreased more quickly than its standard deviation for increasing cell death, provided appropriate levels of firing-rate noise were present. Comparisons to reaching data from 29 stroke subjects revealed similar increases in between- and within-target variability as clinical impairment severity increased. Relationships between simulated cell death and impairment severity were derived using the between- and within-target variability results. For both relationships, impairment severity increased similarly with decreasing percentage of surviving cells, consistent with results from previous imaging studies. These results demonstrate that a population vector model of movement control that incorporates cosine tuning, linear summation of unitary responses, firing-rate noise, and random cell death can account for some features of impaired arm movement after stroke.

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“…There is a past work on modeling self-organizing motor maps coupled with simulated robotic arms (Chen 1997;Goodall et al 1997). Our cortical model is partially based on their work.…”

Section: Cpg-musculo-skeletal System As Embodied Coupled Chaos Systemmentioning

confidence: 99%

“…The model of S1 and M1 is based on Chen's work (Chen 1997;Goodall et al 1997). It consists of self-organizing maps with continuous dynamics, a variant of Kohonen's model (Kohonen 1997).…”

Section: Somatosensory and Motor Area Modelsmentioning

confidence: 99%

Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal-musculo-skeletal model

2006

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Early human motor development has the nature of spontaneous exploration and boot-strap learning, leading to open-ended acquisition of versatile flexible motor skills. Since dexterous motor skills often exploit body-environment dynamics, we formulate the developmental principle as the spontaneous exploration of consistent dynamical patterns of the neural-body-environment system. We propose that partially ordered dynamical patterns emergent from chaotic oscillators coupled through embodiment serve as the core driving mechanism of such exploration. A model of neuro-musculo-skeletal system is constructed capturing essential features of biological systems. It consists of a skeleton, muscles, spindles, tendon organs, spinal circuits, medullar circuits (CPGs), and a basic cortical model. Through a series of experiments with a minimally simple body model, it is shown that the model has the capability of generating partially ordered behavior, a mixture of chaotic exploration and ordered entrained patterns. Models of self-organizing cortical areas for primary somatosensory and motor areas are introduced. They participate in the explorative learning by simultaneously learning and controlling the movement patterns. A scaled up version of the model, a human infant model, is constructed and put through preliminary experiments. Some meaningful motor behavior emerged including rolling over and crawling-like motion. The results show the possibility that a rich variety of meaningful behavior can be discovered and acquired by the neural-body dynamics without pre-defined coordinated control circuits.

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A Computational Model of Acute Focal Cortical Lesions

Abstract: 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.

Cited by 41 publications

References 22 publications

■ REVIEW : Computer Models of Stroke Recovery: Implications for Neurorehabilitation

■ REVIEW : Computer Models of Stroke Recovery: Implications for Neurorehabilitation

Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability

Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal-musculo-skeletal model

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