Andrew J. Saykin

School of Medicine, Radiology & Imaging Sciences

Scopus Publication Detail

The publication detail shows the title, authors (with indicators showing other profiled authors), information on the publishing organization, abstract and a link to the article in Scopus. This abstract is what is used to create the fingerprint of the publication.

Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression.

Hua Wang; Feiping Nie; Heng Huang; Shannon Risacher; Andrew J Saykin; L. Shen; ADNI (Profiled Authors: Andrew J. Saykin; Li Shen)

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2011;14(Pt 3):115-123.

Traditional neuroimaging studies in Alzheimer's disease (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.

PMID: 22003691    

Scientific Context

This section shows information related to the publication - computed using the fingerprint of the publication - including related publications, related experts with fingerprints representing significant amounts of overlap between their fingerprint and this publication. The red dots indicate whether those experts or terms appear within the publication, thereby showing potential and actual connections.