Diagnosis of Brain Diseases via Multi-Scale Time-Series Model.

Junhai Xu,Fei Guo,Jijun Tang,Zehua Zhang,Quan Zou

doi:10.3389/fnins.2019.00197

Abstract

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks.

Highlights

The functional Magnetic Resonance Imaging technique provides an opportunity to quantify functional integration via measuring the correlation between intrinsic Blood-OxygenLevel-Dependent (BOLD) signal fluctuations of distributed brain regions at rest
Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset
To overcome the shortcoming of traditional Pearson Correlation Coefficient (PCC) methods, we propose a novel framework for feature extraction of brain functional connection

Summary

Introduction

The functional Magnetic Resonance Imaging (fMRI) technique provides an opportunity to quantify functional integration via measuring the correlation between intrinsic Blood-OxygenLevel-Dependent (BOLD) signal fluctuations of distributed brain regions at rest. Functional connectivity (FC), defined as the temporal correlation of BOLD signals in different brain regions, can exhibit how structurally segregated and functionally specialized brain regions interact with each other. The functional connection corresponds to magnitudes of temporal correlations in activity and occurs between some pairs of anatomically unconnected regions, which may reflect linear or nonlinear interactions, as well as interactions within different time scales (Zhou et al, 2009). As a brain network analysis approach, the graph theory offers two important advantages (Tijms et al, 2013). One is that it provides quantitative measurement, which can preserve the connectivity information in the network and reflect the segregated and integrated nature of local brain activity. The other is that, it provides a general framework for comparing heterogeneous graphs constructed by different types of data, such as anatomical and functional data

Methods

Results

Conclusion