Functional Connectivity Network Properties Research Articles

Altered small-world and disrupted topological properties of functional connectivity networks in patients with nonarteritic anterior ischemic optic neuropathy

BackgroundNonarteritic anterior ischemic optic neuropathy (NAION) is a disease of the optic nerve, but its effect on brain network topology is still unclear.This study aimed to investigate brain network alterations in NAION patients and to explore their relationship with functional impairment. MethodsResting-state functional MRI data were collected from 23 NAION patients and 23 matched healthy control subjects.We used graph theory analysis to investigate the global and nodal network topological properties,and network-based statistical (NBS) methods were used to explore intergroup differences in functional connectivity (FC) strength. ResultsCompared to the control group, NAION patients had lower global efficiency, normalized clustering coefficient and small-world values and higher characteristic path length (P < 0.05). In the hub distributions of functional networks, the NAION group had one hub region disappearing and four hub regions appearing in nodal degree centrality (Dc), and two hubs disappearing and one hub region appearing in nodal betweenness centrality (Bc). The NAION group also had enhanced brain FC primarily associated with the frontal, prefrontal, parietal lobes and cerebellum. Furthermore, the right temporal pole, superior temporal gyrus (r = –0.424), the right inferior temporal gyrus (r = –0.414), the right cerebellar lobule Ⅵ (r = 0.450), and the left cerebellar lobule crus Ⅰ (r = 0.584) were significantly correlated with clinical severity. ConclusionNAION patients show disruption and redistribution of FC in specific regions of the brain network, which may be associated with visual impairment.

Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children.

Tourette syndrome (TS) is a childhood-onset neurological disorder. To date, accurate TS diagnosis remains challenging due to its varied clinical expressions and dependency on qualitative description of symptoms. Therefore, identifying accurate and objective neuroimaging biomarkers may help improve early TS diagnosis. As resting-state functional MRI (rs-fMRI) has been demonstrated as a promising neuroimaging tool for TS diagnosis, previous rs-fMRI studies on TS revealed functional connectivity (FC) changes in a few local brain networks or circuits. However, no study explored the disrupted topological organization of whole-brain FC networks in TS children. Meanwhile, very few studies have examined brain functional networks using machine-learning methods for diagnostics. In this study, we construct individual whole-brain, ROI-level FC networks for 29 drug-naive TS children and 37 healthy children. Then, we use graph theory analysis to investigate the topological disruptions between groups. The identified disrupted regions in FC networks not only involved the sensorimotor association regions but also the visual, default-mode and language areas, all highly related to TS. Furthermore, we propose a novel classification framework based on similarity network fusion (SNF) algorithm, to both diagnose an individual subject and explore the discriminative power of FC network topological properties in distinguishing between TS children and controls. We achieved a high accuracy of 88.79%, and the involved discriminative regions for classification were also highly related to TS. Together, both the disrupted topological properties between groups and the discriminative topological features for classification may be considered as comprehensive and helpful neuroimaging biomarkers for assisting the clinical TS diagnosis.

Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods.

An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.