Dynamic network centrality summarizes learning in the human brain

Mantzaris, Alexander V.; Bassett, Danielle S.; Wymbs, Nicholas F.; Estrada, Ernesto; Porter, Mason A.; Mucha, Peter J.; Grafton, Scott T.; Higham, Desmond J.

doi:10.1093/comnet/cnt001

Cited by 68 publications

(70 citation statements)

References 33 publications

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“…Our results suggest that the extent and anatomical specificity of interhemispheric coordination is time-dependent, highlighting the usefulness of dynamic network methods to explore brain function (41,42,65). As shown in Fig.…”

Section: Discussionsupporting

confidence: 56%

“…Examination of MEG data using these techniques has demonstrated that functional brain network organization is a biologically meaningful phenotype being altered in disease states (25) in ways that can be linked to behavioral task performance (60)(61)(62). Moreover, network organization demonstrates temporal variability that can be induced by cognitive remediation strategies (63), task practice (64), learning (41,53,65), or task difficulty (66).…”

Section: Discussionmentioning

confidence: 99%

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Dynamic network structure of interhemispheric coordination

Doron

Bassett

Gazzaniga

2012

Proc. Natl. Acad. Sci. U.S.A.

137

146

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Fifty years ago Gazzaniga and coworkers published a seminal article that discussed the separate roles of the cerebral hemispheres in humans. Today, the study of interhemispheric communication is facilitated by a battery of novel data analysis techniques drawn from across disciplinary boundaries, including dynamic systems theory and network theory. These techniques enable the characterization of dynamic changes in the brain's functional connectivity, thereby providing an unprecedented means of decoding interhemispheric communication. Here, we illustrate the use of these techniques to examine interhemispheric coordination in healthy human participants performing a split visual field experiment in which they process lexical stimuli. We find that interhemispheric coordination is greater when lexical information is introduced to the right hemisphere and must subsequently be transferred to the left hemisphere for language processing than when it is directly introduced to the language-dominant (left) hemisphere. Further, we find that putative functional modules defined by coherent interhemispheric coordination come online in a transient manner, highlighting the underlying dynamic nature of brain communication. Our work illustrates that recently developed dynamic, network-based analysis techniques can provide novel and previously unapproachable insights into the role of interhemispheric coordination in cognition.T he field of split-brain research began several decades ago with the use of a drastic surgical solution for intractable epilepsy (1-3): the severing of the corpus callosum. This procedure significantly decreased the connectivity between the hemispheres, thereby irrevocably altering interhemispheric communication. Although a few interhemispheric pathways remained through subcortical structures and the anterior commissure, examination of callosal function using split visual field experiments clearly demonstrated its role in interhemispheric communication (2). The callosal projection plays a particularly crucial role in a variety of sensory-motor integrative functions of the two hemispheres (4), shows changes with age, and demonstrates the potential for dynamic changes with training (5). In fact, experiments in patients with severance of the callosum revealed that it subserves a large range of behaviors and cognitive functions that underlie the varied workings of the mind.Interhemispheric communication has been studied using two basic approaches. The first uses behavioral experiments to examine cognitive phenotypes, and the second uses neuroscientific experiments to examine brain phenotypes. Split-brain research initially focused on the former: Patients demonstrated striking behavioral phenotypes that provided clues regarding the underlying mechanics of brain communication. For example, although most perceptual processing appears to be isolated in each hemisphere following surgery, some attentional and emotional mechanisms initiated in one hemisphere can still be communicated to the other, cortically disconnected,...

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Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Dynamic network structure of interhemispheric coordination

Doron

Bassett

Gazzaniga

2012

Proc. Natl. Acad. Sci. U.S.A.

137

146

View full text Add to dashboard Cite

show abstract

“…Secondly, temporal stationarity assumptions are often violated in real-world applications. It is therefore of critical importance to divide the observed time series data into stationary segments [77], allowing for the inference of causal networks that are time-dependent [45]. Finally, information causality suggests physical causality, but they are not necessarily equivalent [33,53].…”

Section: Discussionmentioning

confidence: 99%

Causal Network Inference by Optimal Causation Entropy

Sun

Taylor²,

Bollt

2015

SIAM J. Appl. Dyn. Syst.

189

193

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Abstract. The broad abundance of time series data, which is in sharp contrast to limited knowledge of the underlying network dynamic processes that produce such observations, calls for a rigorous and efficient method of causal network inference. Here we develop mathematical theory of causation entropy, an information-theoretic statistic designed for model-free causality inference. For stationary Markov processes, we prove that for a given node in the network, its causal parents forms the minimal set of nodes that maximizes causation entropy, a result we refer to as the optimal causation entropy principle. Furthermore, this principle guides us to develop computational and data efficient algorithms for causal network inference based on a two-step discovery and removal algorithm for time series data for a network-couple dynamical system. Validation in terms of analytical and numerical results for Gaussian processes on large random networks highlight that inference by our algorithm outperforms previous leading methods including conditioned Granger causality and transfer entropy. Interestingly, our numerical results suggest that the number of samples required for accurate inference depends strongly on network characteristics such as the density of links and information diffusion rate and not necessarily on the number of nodes.

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“…Efforts in nonlinear dynamics define basins of attraction and perturbations driving a system between basins [14, 15]. Efforts in network science define graph metrics and report statistical correlations with observed functions such as attention [16] and learning [17, 18]. Neither approach offers comprehensive analytical solutions explaining mechanisms of control.…”

mentioning

confidence: 99%

Role of graph architecture in controlling dynamical networks with applications to neural systems

Kim¹,

Soffer²,

Kahn³

et al. 2017

Nature Phys

126

166

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Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain’s control performance by removing single edges in the network. Generally, our results ground the expectation of a control system’s behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

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Dynamic network centrality summarizes learning in the human brain

Cited by 68 publications

References 33 publications

Dynamic network structure of interhemispheric coordination

Dynamic network structure of interhemispheric coordination

Causal Network Inference by Optimal Causation Entropy

Role of graph architecture in controlling dynamical networks with applications to neural systems

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