2000 Special IssueConnectivity and complexity: the relationship between neuroanatomy and brain dynamics
Introduction
Humans experience the world as composed of coherent objects and events, arranged in an ever-changing multi-modal mental scene (Edelman & Tononi, 2000). The coherency of perceptual and cognitive states is achieved rapidly and effortlessly, and it is of critical importance for the ability to execute behavior and motor action that depends on multiple sources of information. How is this remarkable capacity to integrate information rooted in the anatomy and physiology of the nervous system? Integration must occur across vast numbers of specialized neuronal groups and segregated areas of the brain, involving changing subsets of components depending upon the functional state of the organism, its current sensory inputs, and motor behavior. Functional integration accompanying cognitive or behavioral states is associated with patterns of functional connectivity, expressed as deviations from statistical independence (e.g. temporal correlations) across distributed neuronal groups and areas. There is an increasing amount of empirical evidence for the importance of functional connectivity in perception and cognition (reviewed below). Over recent years, neurophysiological and neuroimaging experiments as well as detailed computer simulations of neuronal networks have contributed to our understanding of the neural mechanisms generating functional connectivity.
Several multivariate statistical methods such as multidimensional scaling, path analysis or cluster analysis (Büchel and Friston, 1997, Friston et al., 1996, McIntosh et al., 1994) can be used to characterize patterns of functional connectivity, but none of these methods directly addresses the issue of integrating neural activity across segregated groups and areas. Because we consider functional integration to be central for an understanding of perceptual and cognitive states, we have pursued an approach based on concepts of statistical information theory to measuring integration among elements of a neural system. This approach led to the development of a series of quantitative measures all aiming at how well a given functional architecture balances the dual requirements of functional segregation and integration (Tononi, Edelman, & Sporns, 1998). One measure, called complexity (Tononi, Sporns, & Edelman, 1994), captures deviations from statistical independence (e.g. temporal correlations) across all hierarchical levels within a system and quantifies to what extent specialized local units are globally integrated, participating in coherent states. Another measure, called functional cluster index (Tononi, McIntosh, Russell, & Edelman, 1998), can be used to identify specific subsets of units or areas that interact more strongly with each other than with the rest of the neural system, thus forming a functional cluster. These measures can be applied to multidimensional data sets from computer simulations as well as from neurophysiology or neuroimaging.
It is obvious that functional integration must occur within a structural substrate, defined by the anatomy of the underlying neuronal network. Some computational approaches such as covariance structural equation modeling (e.g. McIntosh et al., 1994, Taylor et al., 2000) aim at inferring causal relations between brain areas from their pattern of covariance, by extracting networks of effective connectivity (Friston, 1994) from functional data sets. We have asked a different, but related, question. In computer simulations, it is easy to observe that different kinds of anatomical structures give rise to different patterns of functional connectivity. This basic observation raises the question of whether distinct neuroanatomical motifs are consistently associated with particular classes of functional connectivity, e.g. those having high levels of complexity. In other words, can high levels of complexity (indicating that specialized information is integrated across a distributed network) be achieved in many qualitatively different ways that are structurally unrelated, or are there unique categories of connectivity or anatomical motifs that favor the emergence of complexity? What selectional processes can drive the emergence of high complexity as networks interact with an environment through inputs and outputs? Answers to these questions may shed light not only on how different neuroanatomical patterns give rise to complex dynamical states, but may also point to factors that have driven the selection of particular anatomies in the course of development and evolution.
Section snippets
Functional segregation and integration in the brain
Virtually every part of the cerebral cortex has been found to be parcellated into anatomically and functionally distinct areas (Mountcastle, 1998), including the auditory somatosensory and motor cortex, as well as higher cortical regions. Functional segregation has been especially well studied in the visual cortex, which is comprised of numerous anatomically and physiologically distinct areas, each specialized to deal with a particular aspect of the visual scene (Felleman and Van Essen, 1991,
Computational models of linking and binding
As soon as experimental evidence on synchronicity in the visual cortex became available, detailed computer simulations were conducted (Sporns, Gally, Reeke, & Edelman, 1989) showing that dynamic reentrant interactions between functionally specialized groups of neurons can lead to patterns of short-term correlations. (For a recent review of other computational models of feature linking and binding see Von der Malsburg, 1999.) Individual groups of neurons, composed of sparsely interconnected sets
Characterizing patterns of functional connectivity
The experimental and computational studies briefly reviewed here are consistent with the view that the cortex is composed of functionally specialized local populations of neurons that are interacting dynamically along reentrant anatomical loops and pathways, both within and between segregated areas. Fundamentally similar dynamic processes operate at multiple levels of scale and across widely separated regions of the brain. The large-scale patterns of temporal correlations generated by the
Theoretical neuroanatomy
Complexity is a descriptor of dynamics, or, more precisely, of the overall pattern of statistical deviations (e.g. temporal correlations) generated by a system's activity. Clearly, a system's dynamics must strongly depend on the underlying structure of the network. In the case of the brain, this structure is equivalent to its neuroanatomy. Given that some of our earlier studies suggested that brain-like patterns of interconnectivity result in generally highly complex dynamics, we asked if there
Discussion
The theoretical studies reviewed in this paper have focused on how the integration of information across multiple segregated areas of the brain is accomplished, both in terms of neural dynamics and underlying neuroanatomy. We have investigated the interplay between functional segregation and integration using computational models of neuronal networks as well as concepts from information theory to construct a series of measures that can be applied to patterns of functional connectivity. If such
Acknowledgements
The work reviewed in this paper was carried out as part of the theoretical neurobiology program at The Neurosciences Institute, which is supported by Neurosciences Research Foundation. The Foundation receives major support for this work from the W.M. Keck Foundation.
References (71)
Neural component placement
Trends in Neurosciences
(1995)The temporal correlation hypothesis of visual feature integration: still alive and well
Neuron
(1999)- et al.
Complexity and adaptation
Physica
(1986) - et al.
Integrator or coincidence detector? The role of the cortical neuron revisited
Trends in Neurosciences
(1996) The binding problem
Current Opinions in Neurobiology
(1996)- et al.
Complexity and coherency: integrating information in the brain
Trends in Cognitive Sciences
(1998) - et al.
Functional clustering: identifying strongly interactive brain regions in neuroimaging data
Neuroimage
(1998) The what and why of binding: the modeler's perspective
Neuron
(1999)- et al.
Visual features that vary together over time group together over space
Nature Neuroscience
(1998) - et al.
Spatial and temporal coherence in perceptual binding
Proceedings of the National Academy of Sciences USA
(1997)
Episodic multiregional cortical coherence at multiple frequencies during visual task performance
Nature
Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modeling and fMRI
Cerebral Cortex
Information coding in the cortex by independent or coordinated populations
Proceedings of the National Academy of Sciences USA
Primary cortical representation of sounds by the coordination of action-potential timing
Nature
Integrative visuomotor behavior is associated with interregionally coherent oscillations in the human brain
Journal of Neurophysiology
Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive (AMVAR) modeling: data preprocessing, model variation, and variability assessment
Biological Cybernetics
Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements
Journal of Neurophysiology
Coherent oscillations: a mechanism for feature linking in the visual cortex?
Biological Cybernetics
The problem of molecular recognition by a selective system
Group selection and phasic re-entrant signalling: a theory of higher brain function
Neural Darwinism
A universe of consciousness: how matter becomes imagination
Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex
Science
Distributed hierarchical processing in the primate cerebral cortex
Cerebral Cortex
Cultural evolution of civilizations
Annual Review of Ecology and Systematics
Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry
Proceedings of the National Academy of Sciences USA
Functional and effective connectivity in neuroimaging: a synthesis
Human Brain Mapping
Functional topography: multidimensional scaling and functional connectivity in the brain
Cerebral Cortex
Temporal coding as a means of information transfer in the primate visual system
Critical Reviews in the Neurosciences
Columnar specificity of intrinsic horizontal and cortico-cortical connections in cat visual cortex
Journal of Neuroscience
Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties
Nature
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Proceedings of the National Academy of Sciences USA
Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat
Philosophical Transactions of the Royal Society London B
Synaptic physiology of horizontal connections in the cat visual cortex
Journal of Neuroscience
The organization of vision
Cited by (416)
Resting state EEG complexity as a predictor of cognitive performance
2023, Physica A: Statistical Mechanics and its ApplicationsCognitive workload classification: Towards generalization through innovative pipeline interface using HMM
2022, Biomedical Signal Processing and ControlThe Early Motor Repertoire in Preterm Infancy and Cognition in Young Adulthood: Preliminary Findings
2023, Journal of the International Neuropsychological Society