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

Hua Wang; Feiping Nie; Heng Huang; Shannon Risacher; Andrew J. Saykin; Li Shen

doi:10.1007/978-3-642-23626-6_15

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

Hua Wang, Feiping Nie, Heng Huang, Shannon Risacher, Andrew J. Saykin, Li Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

61 Scopus citations

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.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Pages	115-123
Number of pages	9
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-23626-6_15
State	Published - 2011
Externally published	Yes
Event	14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada Duration: 18 Sep 2011 → 22 Sep 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6893 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Country/Territory	Canada
City	Toronto, ON
Period	18/09/11 → 22/09/11

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10.1007/978-3-642-23626-6_15

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Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A. J., & Shen, L. (2011). Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings (PART 3 ed., pp. 115-123). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-23626-6_15

Wang, Hua ; Nie, Feiping ; Huang, Heng et al. / Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3. ed. 2011. pp. 115-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression",

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.",

author = "Hua Wang and Feiping Nie and Heng Huang and Shannon Risacher and Saykin, {Andrew J.} and Li Shen",

year = "2011",

doi = "10.1007/978-3-642-23626-6_15",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings",

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Wang, H, Nie, F, Huang, H, Risacher, S, Saykin, AJ & Shen, L 2011, Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6893 LNCS, pp. 115-123, 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011, Toronto, ON, Canada, 18/09/11. https://doi.org/10.1007/978-3-642-23626-6_15

Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. / Wang, Hua; Nie, Feiping; Huang, Heng et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3. ed. 2011. p. 115-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Wang H, Nie F, Huang H, Risacher S, Saykin AJ, Shen L. Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3 ed. 2011. p. 115-123. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-23626-6_15

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

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