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

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

Research output: Chapter in Book/Report/Conference proceedingChapter

50 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages115-123
Number of pages9
Volume14
EditionPt 3
StatePublished - 2011

Fingerprint

Cognition
Alzheimer Disease
Joints
Biomarkers
Neuroimaging
Databases
Brain

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A., Shen, L., & ADNI (2011). Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 14, pp. 115-123)

Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. / Wang, Hua; Nie, Feiping; Huang, Heng; Risacher, Shannon; Saykin, Andrew; Shen, Li; ADNI.

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

Research output: Chapter in Book/Report/Conference proceedingChapter

Wang, H, Nie, F, Huang, H, Risacher, S, Saykin, A, Shen, L & ADNI 2011, Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 14, pp. 115-123.
Wang H, Nie F, Huang H, Risacher S, Saykin A, Shen L et al. Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 14. 2011. p. 115-123
Wang, Hua ; Nie, Feiping ; Huang, Heng ; Risacher, Shannon ; Saykin, Andrew ; Shen, Li ; ADNI. / Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 14 Pt 3. ed. 2011. pp. 115-123
@inbook{e400ce891dec44b1805cc69477ac64bf,
title = "Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression.",
abstract = "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.",
author = "Hua Wang and Feiping Nie and Heng Huang and Shannon Risacher and Andrew Saykin and Li Shen and ADNI",
year = "2011",
language = "English (US)",
volume = "14",
pages = "115--123",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 3",

}

TY - CHAP

T1 - Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression.

AU - Wang, Hua

AU - Nie, Feiping

AU - Huang, Heng

AU - Risacher, Shannon

AU - Saykin, Andrew

AU - Shen, Li

AU - ADNI,

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=82255164574&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=82255164574&partnerID=8YFLogxK

M3 - Chapter

C2 - 22003691

AN - SCOPUS:82255164574

VL - 14

SP - 115

EP - 123

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -