Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression
By: Researcher II
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| Authors | Hua Wang, Feiping Nie, Heng Huang, Shannon Risacher, Andrew J Saykin, Li Shen, ADNI |
| Journal/Conference Name | The 14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) |
| Paper Category | Computer Science |
| Paper Abstract | 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. |
| Date of publication | 2011 |
| Code Programming Language | MATLAB |
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