Brain Functional Connections: Graph Theory and Complex Systems Quantifying Topologies of The Network Approach Essay

Several studies have revealed that cerebral ischemia could lead to reversible disruption of functional connections both locally and remote to the lesion. Brain functional connections has been described using graph theory, an approach which depicts important properties of complex systems quantifying topologies of network (Boccaletti and Pecora, 2006). Brain functional activity requires a balance between local specialization and global integration. This balance is properly quantified by clustering coefficient, an index of segregation, and path lengthcoefficient, an index of integration (Bassett and Bullmore, 2006). A connectivity pattern characterized by high C and short L, known as a small world network model (Watts and Strogatz, 1998), reflects the need of the brain networks to satisfy the competitive demands of local and global processing. Recently, some studies have demonstrated that stroke presents functional balance disruption with respect to control subjects (Yin, et al., 2014).
Here, we analyzed an EEG Holter recording in a patient during a stroke attach respect to the some patient before the event.
Methods
Electrophysiological recordings and pre-processing
EEG was recording trough an EEG Holter machine in a XX years men from 8 electrodes (Fp1, Fp2, O1, O2, T3, C3, C4, T4). After more than 14 hours of recording, patient presented a stroke attack while resting on bed. We derived form the Holter data three 40 minutes-periods of interest Baseline (12 hours before the event), Stroke (first part of the event), Stroke II (second part).
Data were analyzed with Matlab software and using scripts based on EEGLAB. The EEG recordings were band-pass filtered from 0.2 to 47 Hz using a finite impulse response (FIR) filter, and the sampling rate frequency was set up at 256 Hz. Imported data were fragmented in 2 s duration epochs and detection and rejection of artifacts were completed also through independent component analysis (ICA) using EEGLAB.
Graph theory
Functional connectivity
Functional connectivity analysis was obtained by spectral coherence algorithm of the coupling between two (EEG) signals at any given frequency. It was computed by magnitude squared coherence (mscohere) with an homemade software developed under Matlab. The frequency bands of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz).
Graph analysis
A network is a mathematical representation of a real-world complex system and is defined by a collection of nodes (vertices) and links (edges) between pairs of nodes. Nodes in large-scale brain networks usually represent brain regions, while links represent anatomical, functional, or effective connections.
Weighted and undirected networks were built. The vertices of the network are electrodes' contacts, the edges are weighted by mscohere value. The software instrument used for the graph analysis was the Brain Connectivity Toolbox (BCT, http//www.brain-connectivity-toolbox.net/), adapted by own Matlab scripts.
Weighted Clustering Cw and Weighted Characteristic Path length Lw were used to compute the small world coefficient Sw, defined as the ratio between normalized Cw and Lw. Sw is used to describe the balance between local connectedness and global integration of a network.