id: 05978916
dt: a
an: 05978916
au: Wang, Hua; Nie, Feiping; Huang, Heng; Risacher, Shannon; Saykin, Andrew J.;
    Shen, Li; ADNI
ti: Identifying AD-sensitive and cognition-relevant imaging biomarkers via
    joint classification and regression.
so: Fichtinger, Gabor (ed.) et al., Medical image computing and
    computer-assisted intervention ‒ MICCAI 2011. 14th international
    conference, Toronto, Canada, September 18‒22, 2011. Proceedings, Part
    III. Berlin: Springer (ISBN 978-3-642-23625-9/pbk). Lecture Notes in
    Computer Science 6893, 115-123 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-23626-6_15
ab: Summary: Traditional neuroimaging studies in (AD) typically employ
    independent and pairwise analyses between multimodal data, which treat
    imaging biomarkers, cognitive measures, and disease status as isolated
    units. To enhance mechanistic understanding of AD, in this paper, we
    conduct a new study for identifying imaging biomarkers that are
    associated with both cognitive measures and AD. To achieve this goal,
    we propose a new sparse joint classification and regression method. The
    imaging biomarkers identified by our method are AD-sensitive and
    cognition-relevant and can help reveal complex relationships among
    brain structure, cognition and disease status. Using the imaging and
    cognition data from Alzheimer’s Disease Neuroimaging Initiative ,
    database, the effectiveness of the proposed method is demonstrated by
    clearly improved performance on predicting both cognitive scores and
    disease status.
rv: