Synchronization likelihood with explicit time-frequency priors

Montez, Teresa; Linkenkaer‐Hansen, Klaus; Dijk, Bob W. van; Stam, Cornelis J.

doi:10.1016/j.neuroimage.2006.06.066

Cited by 168 publications

(164 citation statements)

References 33 publications

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“…In the present study the following parameters were used: P ref was set at 0.01, for the state space embedding a time lag of 10 samples, an embedding dimension of 10 and a Theiler correction of 0.1 [Montez et al, 2006;Ponten et al, 2007].…”

Section: Resultsmentioning

confidence: 99%

“…We computed the SL using the DIGEEGXP software developed by one of the authors (Cornelis J. Stam). The parameter settings for this computation were as described by Montez et al [2006]. A brief description is given in the Appendix A.…”

Section: Methodsmentioning

confidence: 99%

“…The SL is a measure of statistical independencies between a time series and one or more other time series within a dynamical system sensitive to linear and nonlinear independencies. It has been used in normal and pathological studies and it takes in account the linear as well as the nonlinear properties of the EEG signals [Montez et al, 2006;Ponten et al, 2007;Stam, 2005;. Using a zero phase pass-band filer, we defined the EEG bands, using the classic EEG bands as employed in many EEG studies.…”

Section: Methodsmentioning

confidence: 99%

“…Since the structure of the graph is generally biased by the number of existing edges, statistical measures should be calculated on graphs of equal degree K. Therefore, threshold T was chosen such for each analyzed epoch that all the produced graphs had a fixed mean degree (K 5 5), which is the average number of edges per node of the graph (see and Appendix B). The choice for K 5 5 was identical to a previous study on similar experiment, in which C and L were calculated as a function of K and subsequent t-tests between the two groups revealed significant differences of L in a neighborhood around K 5 5 [Micheloyannis et al, 2006;Montez et al, 2006]. Then, we computed their characteristic parameters C (clustering coefficient) and L (path length) as well as the other graph theoretical parameters (for explanations, see Appendix B).…”

Section: Methodsmentioning

confidence: 99%

“…A threshold was applied on the synchronization matrix to derive unweighted (binary) graphs [see also Montez et al, 2006;Stam and Reijneveld, 2007;. According to this, elements of the synchronization matrix exceeding a certain threshold T designate an existing connection between the corresponding pair of nodes (i.e.…”

Section: Appendix B Explanation Of the Graph Parameters Calculationmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Appendix B Explanation Of the Graph Parameters Calculationmentioning

confidence: 99%

See 3 more Smart Citations

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

Micheloyannis

Vourkas

Tsirka

et al. 2007

Human Brain Mapping

134

View full text Add to dashboard Cite

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Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats

Liu

Ren

et al. 2022

Anim Models and Exp Med

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Background: Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies. When they are exposed to innate threats, visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray (PAG) to induce defensive behaviors. However, how the dorsal PAG (dPAG) encodes the two defensive behaviors is unclear.Methods: Multi-array electrodes were implanted in the dPAG nuclei of C57BL/6 mice.Two kinds of visual stimuli (looming and sweeping) were used to induce defensive behaviors in mice. Neural signals under different defense behaviors were recorded, and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations. Finally, synchronization of neural activity during the defense process was analyzed.

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Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity

Smit

Stam

Posthuma

et al. 2007

Human Brain Mapping

252

176

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Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic features of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and average path length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a smallworld organization are viable markers of genetic differences in brain organization.

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Synchronization likelihood with explicit time-frequency priors

Cited by 168 publications

References 33 publications

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

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

Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats

Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity

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