TY - JOUR
T1 - Differential network knockoff filter with application to brain connectivity analysis
AU - Ji, Jiadong
AU - Hou, Zhendong
AU - He, Yong
AU - Liu, Lei
AU - Xue, Fuzhong
AU - Chen, Hao
AU - Yuan, Zhongshang
N1 - Publisher Copyright:
© 2024 John Wiley & Sons Ltd.
PY - 2024/9/10
Y1 - 2024/9/10
N2 - The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.
AB - The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.
KW - brain functional connectivity
KW - differential network analysis
KW - FDR control
KW - knockoff filter
KW - matrix-variate data
KW - neurodegenerative disease
UR - http://www.scopus.com/inward/record.url?scp=85196705656&partnerID=8YFLogxK
U2 - 10.1002/sim.10155
DO - 10.1002/sim.10155
M3 - Article
C2 - 38922944
AN - SCOPUS:85196705656
SN - 0277-6715
VL - 43
SP - 3830
EP - 3861
JO - Statistics in medicine
JF - Statistics in medicine
IS - 20
ER -