Structural Determinants of Functional Brain Dynamics

Sporns, Olaf; Tononi, Giulio

doi:10.1007/978-3-540-71512-2_4

Cited by 25 publications

(15 citation statements)

References 133 publications

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“…Multiple navigational strategies, including both path integration and landmark-based taxon, praxic, or route navigation methods, require the integration of sensory input and motor output with spatial maps of the external environment observed in subcortical structures (Andersen and Mountcastle 1983;O'Keefe and Nadel 1978;Redish 1999). Associative cortical regions, such as the retrosplenial cortex (RSC) and posterior parietal cortex (PPC), serve as multisensory processing hubs potentially capable of combining sensory and motor information encoded in egocentric coordinates with allocentric spatial information for effective navigation (Byrne et al 2007;Oess et al 2017;Sporns and Tononi 2007). Accordingly, lesion or inactivation of either PPC or RSC results in a variety of spatial navigation impairments (Cooper et al 2001;DiMattia and Kesner 1988;Hindley et al 2014;Hoh et al 2003;Kolb and Walkey 1987;Pothuizen et al 2009;Save and Poucet 2000;Takahashi et al 1997;Vann et al 2003;Vann and Aggleton 2005).…”

Section: Spatial Navigation In Posterior Parietal and Retrosplenial Amentioning

confidence: 99%

Neural mechanisms of navigation involving interactions of cortical and subcortical structures

Hinman

Dannenberg

Alexander

et al. 2018

Journal of Neurophysiology

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Animals must perform spatial navigation for a range of different behaviors, including selection of trajectories toward goal locations and foraging for food sources. To serve this function, a number of different brain regions play a role in coding different dimensions of sensory input important for spatial behavior, including the entorhinal cortex, the retrosplenial cortex, the hippocampus, and the medial septum. This article will review data concerning the coding of the spatial aspects of animal behavior, including location of the animal within an environment, the speed of movement, the trajectory of movement, the direction of the head in the environment, and the position of barriers and objects both relative to the animal's head direction (egocentric) and relative to the layout of the environment (allocentric). The mechanisms for coding these important spatial representations are not yet fully understood but could involve mechanisms including integration of self-motion information or coding of location based on the angle of sensory features in the environment. We will review available data and theories about the mechanisms for coding of spatial representations. The computation of different aspects of spatial representation from available sensory input requires complex cortical processing mechanisms for transformation from egocentric to allocentric coordinates that will only be understood through a combination of neurophysiological studies and computational modeling.

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Section: Spatial Navigation In Posterior Parietal and Retrosplenial Amentioning

confidence: 99%

Neural mechanisms of navigation involving interactions of cortical and subcortical structures

Hinman

Dannenberg

Alexander

et al. 2018

Journal of Neurophysiology

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“…The anatomical connectivity of the cerebral cortex, in particular, appears to have evolved to support contextual processing. The cortex consists of a large number of areas profusely interconnected in a complex topological structure, which places strong constraints on its functional dynamics (Sporns et al, 2000;Bressler & Tognoli 2006;Sporns & Tononi 2007). In sensory systems, local cortical networks are interconnected by feedforward, feedback, and lateral connections (Felleman & Van Essen 1991), all of which may serve to provide neural context for the processing that occurs in a given local network.…”

Section: Anatomical and Physiological Foundations Of Neural Contextmentioning

confidence: 99%

The Role of Neural Context in Large-Scale Neurocognitive Network Operations

Bressler

McIntosh

2007

Understanding Complex Systems

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Brain connectivity has been a central factor in the development of theories about the mind-brain link. In its simplest form, brain connectivity analysis has revealed serial processing systems, wherein specific neural elements (neurons, neuronal assemblies, neuronal populations) cooperate to express a circumscribed function that is realized as information passes through the system in a feedforward manner. Consideration of parallel architectures provides a more complex view of system processing by revealing that each brain region may impact many other regions through direct and indirect routes, including areas from which it receives its input. Regardless of the specific architecture, the notion that cognition results from the operations of large-scale neural networks has been present in various forms throughout the history of neuroscience (Finger, 1994; Bressler 1995Bressler , 2002. For a large part of that history, it was difficult to verify this notion because most available methods only allowed investigation of small parts of the nervous system in isolation. Ideally, simultaneous measures from many parts of the brain must be analyzed to understand the operations of large-scale networks that underlie cognition. In the past few decades, advances in functional neuroimaging, including Positron Emission Tomography (PET) functional Magnetic Resonance Imaging (fMRI), and EEG/MEG-based source localization, have allowed simultaneous distributed measures of brain function to be related to cognition.This chapter examines the role of a critical aspect of brain function, which we call neural context, in the selective functioning of interacting neural systems in cognition. We define neural context as the local processing environment of a given neural element that is created by modulatory influences from other neural elements. Neural context allows the response properties of one element in a network to be profoundly affected by the status of other neural elements in that network. As a result of neural context, the relevance of a given neural element for cognitive function typically depends on the status of other interacting elements (McIntosh 1999;Bressler 2003a). By this definition, the processing performed by a given brain area may be modulated by a potentially

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“…Nevertheless, the material presented in this chapter argues that complex network approaches (Strogatz, 2001;Amaral and Ottino, 2004;Boccaletti et al, 2006) may provide a fundamental basis for our understanding of brain function (see also Sporns and Tononi, 2007). In recent years network science approaches have offered significant new insights into how the structure of the brain shapes its dynamics and how the elements of a neural network can make different contributions to brain function on the basis of how they are interconnected.…”

Section: Introductionmentioning

confidence: 95%

The Human Brain Network

Sporns

2009

Handbook on Biological Networks

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This article summarizes recent empirical findings and modeling approaches aimed at unraveling the structure of the human brain network. The physical structure of the human brain is still only partially mapped and how structural brain connectivity gives rise to functional brain dynamics is only incompletely understood. Initial mapping studies indicate that the human brain forms a smallworld architecture that is structurally organized into modules interlinked by hub regions. This connectivity structure shapes endogenous dynamics as well as responses of the brain to external perturbations. Thus, the topology of the human brain network may be an important ingredient in brain function. We will briefly discuss how complex brain networks may shape human cognition in the healthy and the diseased brain. IntroductionModern neuroscience allows the simultaneous recording and analysis of large numbers of neurons, and even of neural activity across the entire human brainyet our understanding of how the brain functions as an integrated system organized into a complex biological network is still in its infancy. Nevertheless, the material presented in this chapter argues that complex network approaches (Strogatz, 2001;Amaral and Ottino, 2004;Boccaletti et al., 2006) may provide a fundamental basis for our understanding of brain function (see also Sporns and Tononi, 2007). In recent years network science approaches have offered significant new insights into how the structure of the brain shapes its dynamics and how the elements of a neural network can make different contributions to brain function on the basis of how they are interconnected. The dichotomy between brain structure and dynamics presents unique challenges to network approaches. A comprehensive analysis of brain networks needs to address the central issue of how functional brain networks (represented as dynamic linkages between parts of the brain that are transient in nature) can Handbook on Biological Networks Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/28/15. For personal use only.

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Structural Determinants of Functional Brain Dynamics

Cited by 25 publications

References 133 publications

Neural mechanisms of navigation involving interactions of cortical and subcortical structures

Neural mechanisms of navigation involving interactions of cortical and subcortical structures

The Role of Neural Context in Large-Scale Neurocognitive Network Operations

The Human Brain Network

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