Characterization of anatomical and functional connectivity in the brain: A complex networks perspective

Stam, Cornelis J.

doi:10.1016/j.ijpsycho.2010.06.024

Cited by 154 publications

(115 citation statements)

References 85 publications

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“…Another important issue in network analysis is the temporal evolution of network organization. The studies of Lee et al have shown that the MST can be used for this type of analysis (Lee et al, 2006;2010). Perhaps the most interesting challenge of all is to find a good generative model of complex brain networks.…”

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

“…Both structural and functional networks have been studied in a range of organisms, from C. Elegans to macaque, cat and human, during development and in health and disease Sporns, 2009, 2012;Stam, 2010;Stam and van Straaten, 2012;van den Heuvel and Hulshoff Pol, 2010;van Straaten and Stam, 2012). Several important conclusions that have emerged from this rapidly growing field are the following.…”

Section: Introduction: Modern Brain Network Analysismentioning

confidence: 99%

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The trees and the forest: Characterization of complex brain networks with minimum spanning trees

Stam

Tewarie

Dellen

et al. 2014

International Journal of Psychophysiology

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316

409

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In recent years there has been a shift in focus from the study of local, mostly task-related activation to the exploration of the organization and functioning of large-scale structural and functional complex brain networks. Progress in the interdisciplinary field of modern network science has introduced many new concepts, analytical tools and models which allow a systematic interpretation of multivariate data obtained from structural and functional MRI, EEG and MEG. However, progress in this field has been hampered by the absence of a simple, unbiased method to represent the essential features of brain networks, and to compare these across different conditions, behavioural states and neuropsychiatric/neurological diseases. One promising solution to this problem is to represent brain networks by a minimum spanning tree (MST), a unique acyclic subgraph that connects all nodes and maximizes a property of interest such as synchronization between brain areas. We explain how the global and local properties of an MST can be characterized. We then review early and more recent applications of the MST to EEG and MEG in epilepsy, development, schizophrenia, brain tumours, multiple sclerosis and Parkinson's disease, and show how MST characterization performs compared to more conventional graph analysis. Finally, we illustrate how MST characterization allows representation of observed brain networks in a space of all possible tree configurations and discuss how this may simplify the construction of simple generative models of normal and abnormal brain network organization.

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Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Section: Introduction: Modern Brain Network Analysismentioning

confidence: 99%

The trees and the forest: Characterization of complex brain networks with minimum spanning trees

Stam

Tewarie

Dellen

et al. 2014

International Journal of Psychophysiology

Self Cite

316

409

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show abstract

“…Topological filtering relies on graph-based analysis (Bullmore and Sporns, 2009;Bassett et al, 2009;He and Evans, 2010;Stam, 2010;Dimitriadis et al, 2010aDimitriadis et al, ,b,2012aDimitriadis et al, ,b,c,2013a, which is used to capture the structure of the neural Global cost efficiency is defined as the global efficiency GE at a given cost C minus the cost (GE-C), which typically has a positive maximum value at some cost , for an economical small-world network. Importantly, this metric of network topology is independent of arbitrary, investigator-specified thresholds.…”

Section: Significant Linksmentioning

confidence: 99%

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Antonakakis

Dimitriadis

Zervakis

et al. 2016

International Journal of Psychophysiology

122

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Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients.Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

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“…Such studies have implicated PV inhibitory neurons in gain control (Atallah et al, Neuron 73:159-170, 2012;Lee et al, Nature 488:379-383, 2012;Nienborg et al, J Neurosci 33:11145-11154, 2013), SST interneurons in the suppression of lateral and feedback (top-down) interactions (Adesnik and Scanziani, Nature 464:1155-1160, 2010Nienborg et al, J Neurosci 33:11145-11154, 2013), and VIP interneurons in the dynamic regulation of SST cells under the control of brain state-dependent neuromodulators (Kawaguchi, J Neurophysiol 78:1743-1747Alitto and Dan, Front Syst Neurosci 6:79, 2012;Lee et al, Nat Neurosci 16:1662-1670Pi et al, Nature 503:521-524, 2013;Polack et al, Nat Neurosci 16:1331-1339Fu et al, Cell 156:1139-1152Stryker, Cold Spring Harbor Symp Quant Biol 79:1-9, 2014;Zhang et al, Science 345:660-665, 2014).…”

Section: Inhibitory Cell Types Circuits and Receptive Fields In Mousmentioning

confidence: 99%

“…Once brain network data have been rendered in matrix form, they are amenable to an extremely wide range of statistical and modeling tools coming from network science, especially the mathematical framework of graph theory (Bullmore and Sporns 2009). A comprehensive overview of the application and interpretation of graph-theoretical approaches to brain networks is beyond the scope of this chapter [for reviews, see Rubinov and Sporns (2010), Stam (2010), and Lohmann et al (2013)]. Briefly, descriptive measures of brain network connectivity fall into at least three different categories, reporting on different aspects of network organization.…”

Section: Brain Network and Graph Theorymentioning

confidence: 99%

Micro-, Meso- and Macro-Connectomics of the Brain

Kennedy¹,

Essen²,

Christen³

2016

Research and Perspectives in Neurosciences

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Characterization of anatomical and functional connectivity in the brain: A complex networks perspective

Cited by 154 publications

References 85 publications

The trees and the forest: Characterization of complex brain networks with minimum spanning trees

The trees and the forest: Characterization of complex brain networks with minimum spanning trees

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Micro-, Meso- and Macro-Connectomics of the Brain

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