Paper Info
Title
Reconstructing Cortical Intrinsic Connectivity Networks Using A Regression Method Combining Eeg Data From Sensor And Source Levels
Abstract
Intrinsic connectivity networks (ICNs) have been widely studied using functional magnetic resonance imaging (fMRI) data and electrophysiological data (e.g., electroencephalography (EEG) or magnetoencephalography (MEG)). Two major methods, i.e., seed-based correlation analysis (SBCA) and independent component analysis (ICA), are widely used to extract ICNs. Among them, ICA usually involves a dual regression analysis in order to obtain final spatial definitions of ICNs. Recently, we proposed a framework that includes cortical source imaging, source-level ICA, and statistical correlation analysis, to extract cortical ICNs from resting-state EEG data. In the present study, we proposed an alternative framework that uses sensor-level ICA and regression analysis instead of source-level ICA and correlation analysis, considering the well-studied characteristics of sensor-level ICs in differentiating neural activities from artifacts and the benefit of regression in accommodating multivariate analysis over correlation. In the present study, we mainly investigated the performance of the proposed procedure in extracting cortical ICNs. Meanwhile, we also investigated different variants of the regressors sampled at different frequencies to formulate the regression model. The results demonstrated that cortical ICNs corresponding to major ICNs identified in literature could be obtained by the proposed framework. In general, spatial patterns of cortical ICNs obtained via both correlation and regression analyses show statistically significant similarity. However, the cortical ICNs reconstructed using the regression analysis exhibit more focal and more superficial spatial patterns, in general, that the cortical ICNs from the correlation analysis. The different variants of regressors at the same sampling frequency do not produce obvious impacts on spatial patterns of cortical ICNs, while the different sampling frequencies show large effects on extracted spatial patterns of cortical ICNs. In summary, it is suggested that the proposed framework with the regression analysis is promising in reconstructing cortical ICNs from EEG, while the sampling frequency used in the formulation process of regressors may have large impacts on reconstructed cortical ICN patterns.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857687
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Functional magnetic resonance imaging,Regression,Pattern recognition,Computer science,Regression analysis,Correlation,Artificial intelligence,Independent component analysis,Sampling (statistics),Magnetoencephalography,Electroencephalography
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Guofa Shou103.72
Lei Ding214226.77