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Biomedical subjects

Olaf Sporns

Publications and source records attributed to Olaf Sporns.

6 recordsLinked to original sources

PubMed2004

Motifs in brain networks.

Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.

Algorithms↗

PubMed2004

Organization, development and function of complex brain networks.

Recent research has revealed general principles in the structural and functional organization of complex networks which are shared by various natural, social and technological systems. This review examines these principles as applied to the organization, development and function of complex brain networks. Specifically, we examine the structural properties of large-scale anatomical and functional brain networks and discuss how they might arise in the course of network growth and rewiring. Moreover, we examine the relationship between the structural substrate of neuroanatomy and more dynamic functional and effective connectivity patterns that underlie human cognition. We suggest that network analysis offers new fundamental insights into global and integrative aspects of brain function, including the origin of flexible and coherent cognitive states within the neural architecture.

Brain↗

PubMed2004

The small world of the cerebral cortex.

While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize a wide variety of cortical connectivity data sets using a specific set of graph theory methods. We measure global aspects of cortical graphs including the abundance of small structural motifs such as cycles, the degree of local clustering of connections and the average path length. We examine large-scale cortical connection matrices obtained from neuroanatomical data bases, as well as probabilistic connection matrices at the level of small cortical neuronal populations linked by intra-areal and inter-areal connections. All cortical connection matrices examined in this study exhibit "small-world" attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements. We discuss the significance of these universal organizational features of cortex in light of functional brain anatomy. Supplementary materials are at www.indiana.edu/~cortex/lab.htm.

Animals↗

PubMed2003

Measuring information integration.

BACKGROUND: To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system. RESULTS: The capacity to integrate information, or Phi, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Phi is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration). CONCLUSION: Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.

Brain↗

PubMed2003

EEG synchronization to modulated auditory tones in schizophrenia, schizoaffective disorder, and schizotypal personality disorder.

OBJECTIVE: The authors tested whether neural synchronization deficits were present in subjects with schizophrenia and schizotypal personality disorder. METHOD: Amplitude-modulated tones were used to evaluate auditory steady-state evoked potential entrainment in a combined group of 21 subjects with schizophrenia or schizoaffective disorder, 11 subjects with schizotypal personality disorder, and 22 nonpsychiatric comparison subjects. RESULTS: The schizophrenia or schizoaffective disorder group exhibited decreased power compared to the schizotypal personality disorder and nonpsychiatric comparison groups. There were no differences between groups in N100 amplitude. CONCLUSIONS: Subjects with schizophrenia but not subjects with schizotypal personality disorder have deficits in steady-state responses to periodic stimuli, despite an intact response to sensory-evoked potentials (N100). These deficits reflect aberrant neural synchronization or resolution and may contribute to disturbed perceptual and cognitive integration in schizophrenia.

Acoustic Stimulation↗

PubMedDate not supplied

Neuromodulation and plasticity in an autonomous robot.

In this paper we implement a computational model of a neuromodulatory system in an autonomous robot. The output of the neuromodulatory system acts as a value signal, modulating widely distributed synaptic changes. The model is based on anatomical and physiological properties of midbrain diffuse ascending systems, in particular parts of the dopamine and noradrenaline systems. During reward conditioning, the model learns to generate tonic and phasic signals that represent predictions and prediction errors, including precisely timed negative signals if expected rewards are omitted or delayed. We test the robot's learning and behavior in different environmental contexts and observe changes in the development of the neuromodulatory system that depend upon environmental factors. Simulation of a computational model incorporating both reward-related and aversive stimuli leads to the emergence of conditioned reward and aversive behaviors. These studies represent a step towards investigating computational aspects of neuromodulatory systems in autonomous robots.

Models, Neurological↗