Inferring functional network-based signatures via structurally-weighted LASSO model
Year: 2013
Type: conference-paper
Abstract: Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature.
Authors: Dajiang Zhu, Dinggang Shen, Tianming Liu
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Infant Brain Measurement and Super-Resolution Atlas Construction, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, Fast, Robust Analysis of Large Population Data, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI
OpenAlex ID: W2040811113