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Complexity
Volume 2017 (2017), Article ID 7190758, 12 pages
https://doi.org/10.1155/2017/7190758
Research Article

A Novel Synchronization-Based Approach for Functional Connectivity Analysis

1Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Via E. Orabona 4, 70125 Bari, Italy
2Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via E. Orabona 4, 70125 Bari, Italy
3Dipartimento Interateneo di Fisica “M. Merlin”, Universitá degli Studi di Bari “A. Moro”, Via E. Orabona 4, 70125 Bari, Italy
4Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Universitá degli Studi di Bari “A. Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
5Azienda Ospedaliero-Universitaria Consorziale Policlinico, 70124 Bari, Italy
6IRCCS “Casa Sollievo della Sofferenza”, 71013 San Giovanni Rotondo, Italy

Correspondence should be addressed to Sabina Tangaro; ti.nfni.ab@oragnat.ainos

Received 24 May 2017; Revised 4 September 2017; Accepted 4 October 2017; Published 30 October 2017

Academic Editor: Angelo Bifone

Copyright © 2017 Angela Lombardi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Complex network analysis has become a gold standard to investigate functional connectivity in the human brain. Popular approaches for quantifying functional coupling between fMRI time series are linear zero-lag correlation methods; however, they might reveal only partial aspects of the functional links between brain areas. In this work, we propose a novel approach for assessing functional coupling between fMRI time series and constructing functional brain networks. A phase space framework is used to map couples of signals exploiting their cross recurrence plots (CRPs) to compare the trajectories of the interacting systems. A synchronization metric is extracted from the CRP to assess the coupling behavior of the time series. Since the functional communities of a healthy population are expected to be highly consistent for the same task, we defined functional networks of task-related fMRI data of a cohort of healthy subjects and applied a modularity algorithm in order to determine the community structures of the networks. The within-group similarity of communities is evaluated to verify whether such new metric is robust enough against noise. The synchronization metric is also compared with Pearson’s correlation coefficient and the detected communities seem to better reflect the functional brain organization during the specific task.