A Novel Synchronization-Based Approach for Functional Connectivity Analysis

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 taskrelated 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.

Effects of HD‐tDCS on Resting‐State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

“…Accidental damage or degeneration of this area due to a brain disease, such as AD or frontotemporal dementia, causes various problems in daily life in our rapidly aging society. Several studies have used functional connectivity as a marker for detecting reduced cognitive performance, as it is directly related to cognitive performance [14,[78][79][80][81][82]. Therefore, fNIRS imaging is an important tool for the early detection of brain diseases, and combining it with tDCS is ideal due to the optical nature of fNIRS, which is not affected by the electric field during stimulation.…”

High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

“…Rosales et al [48] proposed a new implementation of the Synchronization Likelihood algorithm, which improves significantly its computational and memory performance. Lombardi et al [49] described a new synchronization-based metric which is more sensitive to nonlinear coupling phenomena between time series and more robust with respect to noise. In this work we study the brain network behavior for 10 healthy subjects (CNT), 21 MCI, and 9 AD patients.…”

Parcellation of the human amygdala using recurrence quantification analysis

“…RQA can be performed with short time series and allows us to assess multidimensional relations in signal features reflecting different aspects of recurrence. This method was successfully used for detecting task-related activity and for estimating functional connectivity patterns on the basis of fMRI data (Bianciardi, et al, 2008;Lombardi et al, 2017).…”