77,757 research outputs found
Structural patterns in complex networks through spectral analysis
The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network
Decoupling of brain function from structure reveals regional behavioral specialization in humans
The brain is an assembly of neuronal populations interconnected by structural
pathways. Brain activity is expressed on and constrained by this substrate.
Therefore, statistical dependencies between functional signals in directly
connected areas can be expected higher. However, the degree to which brain
function is bound by the underlying wiring diagram remains a complex question
that has been only partially answered. Here, we introduce the
structural-decoupling index to quantify the coupling strength between structure
and function, and we reveal a macroscale gradient from brain regions more
strongly coupled, to regions more strongly decoupled, than expected by
realistic surrogate data. This gradient spans behavioral domains from
lower-level sensory function to high-level cognitive ones and shows for the
first time that the strength of structure-function coupling is spatially
varying in line with evidence derived from other modalities, such as functional
connectivity, gene expression, microstructural properties and temporal
hierarchy
EEG sleep stages identification based on weighted undirected complex networks
Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.
Methods
each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks.
Results
In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by NaĂŻve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals.
Conclusions
An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard
- …