Paper Info
Title
A Multi-stage Sparse Coding Framework to Explore the Effects of Prenatal Alcohol Exposure.
Abstract
In clinical neuroscience, task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally, most studies adopt the general linear model (GLM) to detect task-evoked activations. However, GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast, sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However, this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper, we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE), we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE.
Year
Venue
Field
2016
MICCAI
Brain network,Dictionary learning,Pattern recognition,Healthy control,Neural coding,Computer science,General linear model,Sparse approximation,Artificial intelligence,Heterogeneous network,Prenatal alcohol exposure
DocType
Citations 
PageRank 
Conference
1
0.35
References 
Authors
5
11
Name
Order
Citations
PageRank
Shijie Zhao16610.85
Junwei Han23501194.57
Jinglei Lv320526.70
Xi Jiang431137.88
Xintao Hu511813.53
Shu Zhang612613.64
Mary Ellen Lynch710.35
Claire Coles810.35
Lei Guo91661142.63
Xiaoping Hu10353.30
Tianming Liu111033112.95