A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure

Shijie Zhao; Junwei Han; Jinglei Lv; Xi Jiang; Xintao Hu; Shu Zhang; Mary Ellen Lynch; Claire Coles; Lei Guo; Xiaoping Hu; Tianming Liu

doi:10.1007/978-3-319-46720-7_4

A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure

Shijie Zhao, Junwei Han, Jinglei Lv, Xi Jiang, Xintao Hu, Shu Zhang, Mary Ellen Lynch, Claire Coles, Lei Guo, Xiaoping Hu, Tianming Liu

School of Automation

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

1 Scopus citations

Abstract

In clinical neuroscience,task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally,most studies adopt the general linear model (GLM) to detect task-evoked activations. However,GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast,sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However,this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper,we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE),we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
Publisher	Springer Verlag
Pages	28-36
Number of pages	9
ISBN (Print)	9783319467191
DOIs	https://doi.org/10.1007/978-3-319-46720-7_4
State	Published - 2016
Event	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 21 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9900 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	21/10/16 → 21/10/16

Keywords

Dictionary learning
PAE
Sparse coding
Task-based fMRI

Access to Document

10.1007/978-3-319-46720-7_4

Cite this

Zhao, S., Han, J., Lv, J., Jiang, X., Hu, X., Zhang, S., Lynch, M. E., Coles, C., Guo, L., Hu, X., & Liu, T. (2016). A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, W. Wells, & G. Unal (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 28-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_4

Zhao, Shijie ; Han, Junwei ; Lv, Jinglei et al. / A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal. Springer Verlag, 2016. pp. 28-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b731d7aabc324c0ca5707d4c6bd79a76,

title = "A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure",

abstract = "In clinical neuroscience,task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally,most studies adopt the general linear model (GLM) to detect task-evoked activations. However,GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast,sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However,this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper,we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE),we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE.",

keywords = "Dictionary learning, PAE, Sparse coding, Task-based fMRI",

author = "Shijie Zhao and Junwei Han and Jinglei Lv and Xi Jiang and Xintao Hu and Shu Zhang and Lynch, {Mary Ellen} and Claire Coles and Lei Guo and Xiaoping Hu and Tianming Liu",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46720-7_4",

language = "英语",

isbn = "9783319467191",

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

publisher = "Springer Verlag",

pages = "28--36",

editor = "Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells and Gozde Unal",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",

}

Zhao, S, Han, J, Lv, J, Jiang, X, Hu, X, Zhang, S, Lynch, ME, Coles, C, Guo, L, Hu, X & Liu, T 2016, A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure. in S Ourselin, L Joskowicz, MR Sabuncu, W Wells & G Unal (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 28-36, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 21/10/16. https://doi.org/10.1007/978-3-319-46720-7_4

A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure. / Zhao, Shijie; Han, Junwei; Lv, Jinglei et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal. Springer Verlag, 2016. p. 28-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

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

TY - GEN

T1 - A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure

AU - Zhao, Shijie

AU - Han, Junwei

AU - Lv, Jinglei

AU - Jiang, Xi

AU - Hu, Xintao

AU - Zhang, Shu

AU - Lynch, Mary Ellen

AU - Coles, Claire

AU - Guo, Lei

AU - Hu, Xiaoping

AU - Liu, Tianming

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - In clinical neuroscience,task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally,most studies adopt the general linear model (GLM) to detect task-evoked activations. However,GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast,sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However,this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper,we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE),we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE.

AB - In clinical neuroscience,task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally,most studies adopt the general linear model (GLM) to detect task-evoked activations. However,GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast,sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However,this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper,we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE),we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE.

KW - Dictionary learning

KW - PAE

KW - Sparse coding

KW - Task-based fMRI

UR - http://www.scopus.com/inward/record.url?scp=84996567025&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46720-7_4

DO - 10.1007/978-3-319-46720-7_4

M3 - 会议稿件

AN - SCOPUS:84996567025

SN - 9783319467191

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 28

EP - 36

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

A2 - Ourselin, Sebastian

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Wells, William

A2 - Unal, Gozde

PB - Springer Verlag

T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

Y2 - 21 October 2016 through 21 October 2016

ER -

Zhao S, Han J, Lv J, Jiang X, Hu X, Zhang S et al. A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure. In Ourselin S, Joskowicz L, Sabuncu MR, Wells W, Unal G, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 28-36. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46720-7_4

A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this