TY - GEN
T1 - Functional brain mapping with locally smoothed regression
AU - Feng, Lu
AU - Liu, Jiangang
AU - Li, Ling
AU - Wei, Wenjuan
AU - Tian, Jie
AU - Lee, Kang
PY - 2013
Y1 - 2013
N2 - High-resolution functional magnetic resonance imaging (hi-res fMRI) methodology offers an opportunity for neuroscientists to gain insight about brain activities at a finer scale, and is thus becoming increasingly common. Traditional voxel-wise general linear model (GLM) is not suitable for hi-res functional brain mapping because local averaging may lose valuable fine-grained information boasted by hi-res fMRI. The searchlight approach may be more suited for this situation, but it can be improved to integrate multi-voxel information more completely and effectively. We propose a locally smoothed regression (LSR) to find the spatial organizations of neural activities, especially for hi-res data. LSR is a flexible model whereby the traditional voxel-wise regression can be seen as a special case of LSR. Further, LSR can be integrated into Mahalanobis-distance-based searchlight framework. This new approach promises to provide improved and reliable activation mapping as illustrated here by applying it to analyze a real set of data using hi-res fMRI imaging.
AB - High-resolution functional magnetic resonance imaging (hi-res fMRI) methodology offers an opportunity for neuroscientists to gain insight about brain activities at a finer scale, and is thus becoming increasingly common. Traditional voxel-wise general linear model (GLM) is not suitable for hi-res functional brain mapping because local averaging may lose valuable fine-grained information boasted by hi-res fMRI. The searchlight approach may be more suited for this situation, but it can be improved to integrate multi-voxel information more completely and effectively. We propose a locally smoothed regression (LSR) to find the spatial organizations of neural activities, especially for hi-res data. LSR is a flexible model whereby the traditional voxel-wise regression can be seen as a special case of LSR. Further, LSR can be integrated into Mahalanobis-distance-based searchlight framework. This new approach promises to provide improved and reliable activation mapping as illustrated here by applying it to analyze a real set of data using hi-res fMRI imaging.
KW - Brain activation localization
KW - High-resolution functional magnetic resonance imaging
KW - Multivariate pattern analysis
KW - Searchlight approach
UR - https://www.scopus.com/pages/publications/84881634402
U2 - 10.1109/ISBI.2013.6556820
DO - 10.1109/ISBI.2013.6556820
M3 - 会议稿件
AN - SCOPUS:84881634402
SN - 9781467364546
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1504
EP - 1507
BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Y2 - 7 April 2013 through 11 April 2013
ER -