Dynamic functional connectivity

Ioannides, Andreas A.

doi:10.1016/j.conb.2007.03.008

Cited by 123 publications

(91 citation statements)

References 59 publications

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“…Here, we describe three important measures and refer the interested reader to more comprehensive material (Sporns 2011;Stam and van Straaten 2012;Ioannides 2007;Bullmore and Basset 2011).…”

Section: Graph Theorymentioning

confidence: 99%

Studying Dynamic Neural Interactions with MEG

Schoffelen

Groß

2014

Magnetoencephalography

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Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) is suited to capture these interactions because it provides whole head measurements of brain activity with temporal resolution in the millisecond range. Many different measures of connectivity exist and in order to take the connectivity analysis results at face value one should be aware of the strengths and weaknesses of these measures. Next to this, an important challenge in MEG connectivity analysis lies in the fact that more than one sensor picks up the activity of any underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. As a consequence, neuronal interactions should be ideally studied on the level of the reconstructed sources. MEG is well suited for this purpose, since its signal properties and high spatial sampling allows for relatively accurate unmixing of the sensor recordings. This chapter provides some necessary background on connectivity analysis in general, and proceeds by describing the challenges that are associated with the analysis of MEG-based connectivity at the sensor level. Source level approaches are described and some recent advances with respect to MEG-based connectivity during the resting state and graph theoretic approaches are described.

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“…Here, we describe three important measures and refer the interested reader to more comprehensive material (Sporns 2011;Stam and van Straaten 2012;Ioannides 2007;Bullmore and Basset 2011).…”

Section: Graph Theorymentioning

confidence: 99%

Studying Dynamic Neural Interactions with MEG

Schoffelen

Groß

2014

Magnetoencephalography

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“…The topology of networks allows efficient dynamic interactions between spatially distinct areas through oscillatory electromagnetic activities sustaining networks' functionality. In this field, during the last decade, MR imaging has dramatically modified our understanding of brain organization at the macroscopic scale by studying the structural and functional connectivity of large-scale networks in vivo non-invasively [1][2][3][4][5]. Indeed, anatomical connectivity study has highly benefited from the development of diffusion MR and tractography (able to define and quantify anatomical links between remote brain areas through white matter bundles) ( [6] for review) and, to a lesser extent up to now, from the introduction of the cortical thickness correlation technique (a new method that indirectly measure structural links by calculating the correlation of the thickness of anatomically connected cortical areas [7]).…”

Section: Introductionmentioning

confidence: 99%

Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

Guye

Bettus

Bartolomei

et al. 2010

Magn Reson Mater Phy

256

170

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Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits "small-world" properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer's disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.

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“…Connectivity at rest and during cognitive tasks has been studied using several methods [Ioannides, 2007;Sporns et al, 2000;. Recently, tools from graph theory have been used in several studies to investigate the general properties of the spatial structure of anatomical as well as functional networks as studied with EEG, MEG and fMRI [Achard et al, 2006;Basset and Bullmore, 2006;He et al, 2007;Humphries, 2006;Stam and Reijneveld, 2007;.…”

Section: Introductionmentioning

confidence: 99%

The influence of ageing on complex brain networks: A graph theoretical analysis

Micheloyannis

Vourkas

Tsirka

et al. 2007

Human Brain Mapping

134

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Objective: To determine the functional connectivity of different EEG bands at the ''baseline'' situation (rest) and during mathematical thinking in children and young adults to study the maturation effect on brain networks at rest and during a cognitive task. Methods: Twenty children (8-12 years) and twenty students (21-26 years) were studied. The synchronization likelihood was used to evaluate the interregional synchronization of different EEG frequency bands in children and adults, at rest and during math. Then, graphs were constructed and characterized in terms of local structure (clustering coefficient) and overall integration (path length) and the ''optimal'' organization of the connectivity i.e., the small world network (SWN). Results: The main findings were: (i) Enhanced synchronization for theta band during math more prominent in adults. (ii) Decrease of the optimal SWN organization of the alpha2 band during math. (iii) The beta and especially gamma bands showed lower synchronization and signs of lower SWN organization in both situations in adults. Conclusion: There are interesting findings related to the two age groups and the two situations. The theta band showed higher synchronization during math in adults as a result of higher capacity of the working memory in this age group. The alpha2 band showed some SWN disorganization during math, a process analog to the known desynchronization. In adults, a dramatic reduction of the connections in gray matter occurs. Although this maturation process is probably related to higher efficiency, reduced connectivity is expressed by lower synchronization and lower mean values of the graph parameters in adults.

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Dynamic functional connectivity

Cited by 123 publications

References 59 publications

Studying Dynamic Neural Interactions with MEG

Studying Dynamic Neural Interactions with MEG

Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

The influence of ageing on complex brain networks: A graph theoretical analysis

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