Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification
Year: 2017
Type: article
Abstract: Functional brain network (FBN) has been becoming an increasingly important measurement for exploring the cerebral working mechanism and mining informative biomarkers for assisting diagnosis of some neurodegenerative disorders. Despite its potential performance in discovering the valuable patterns hidden in the brains, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., BOLD signal series). In practice, a preprocessing pipeline is usually employed for improving the data quality prior to the FBN estimation; but, even so, some data points in the time series are still not clean enough, possibly including original artifacts (e.g., micro head motion), non-resting functional disturbing (e.g., mind-wandering), and new “noises” caused by the preprocessing pipeline per se. Therefore, not all data points in the time series can contribute to the subsequent FBN estimation. To address this issue, in this paper, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for scrubbing the data and estimating FBN simultaneously in a single framework. As a result, we can obtain more accurate FBNs with the self-scrubbing data. To illustrate the effectiveness of the proposed method, we conduct experiments on two publicly available datasets to identify mild cognitive impairment (MCI) patients from normal control (NC) subjects based on the estimated FBNs. Experimental results show that the proposed FBN modelling method can achieve higher classification accuracy, significantly outperforming the baseline methods.
Source: Frontiers in Neuroinformatics
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funders: Foundation for the National Institutes of Health, National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Chongqing Municipal Education Commission, National Institutes of Health, Chongqing Graduate Student Research Innovation Project, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering
Awards: G1499720639, G1600120695, G1610907132, G1858496802, G1936500827, G2561755286, G3139536118, G3662821113, G4305135499, G456455038, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, G4646680210, G4657494644, G4844396119, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks, Longitudinal Mapping of Human Brain Development in the First Years of Life, G5262501365, Fast, Robust Analysis of Large Population Data, G635188979, 4D Cortical Surface Analysis Tools for Study of Early Brain Development in Typical and High-risk Infants, Imaging Genomics Based Brain Disease Prediction, G6748413423, 复杂噪声场景下的新型非局部图像滤波器设, G7112823909, G7144738877, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G7609625574, G8255494865
OpenAlex ID: W2753113451