Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data

Yudan Ren; Xintao Hu; Jinglei Lv; Lei Quo; Junwei Han; Tianming Liu

doi:10.1109/ISBI.2016.7493500

Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data

Yudan Ren
, Xintao Hu
, Jinglei Lv
, Lei Quo
, Junwei Han
, Tianming Liu

School of Automation

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

7 Scopus citations

Abstract

Exploring abnormal functional connectivities in neurodevelopmental disorders have received great attention in recent years. However, identifying functionally homogeneous brain regions as nodes in functional brain connectivity analysis is still challenging. In this paper, we adopt an effective data-driven framework of sparse representation to identify brain network nodes inspired by the nature of sparse population coding of the human brain. Using the default mode network (DMN) in patients with Autism as a test-bed, we evaluate sparse coding method and compared it with widely used group-wise independent components analysis (group-ICA). The experimental results demonstrate that the network nodes identified by sparse representation are more functionally homogeneous, which may explain the superiority of sparse representation in differentiating Autism and healthy controls by brain connectivity biomarkers.

Original language	English
Title of host publication	2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	1278-1281
Number of pages	4
ISBN (Electronic)	9781479923502
DOIs	https://doi.org/10.1109/ISBI.2016.7493500
State	Published - 15 Jun 2016
Event	13th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic Duration: 13 Apr 2016 → 16 Apr 2016

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2016-June
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	13th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/Territory	Czech Republic
City	Prague
Period	13/04/16 → 16/04/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

autism
default mode network
functional connectivity
resting-state fMRI
sparse coding

Access to Document

10.1109/ISBI.2016.7493500

Cite this

Ren, Y., Hu, X., Lv, J., Quo, L., Han, J., & Liu, T. (2016). Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (pp. 1278-1281). Article 7493500 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June). IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493500

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title = "Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data",

abstract = "Exploring abnormal functional connectivities in neurodevelopmental disorders have received great attention in recent years. However, identifying functionally homogeneous brain regions as nodes in functional brain connectivity analysis is still challenging. In this paper, we adopt an effective data-driven framework of sparse representation to identify brain network nodes inspired by the nature of sparse population coding of the human brain. Using the default mode network (DMN) in patients with Autism as a test-bed, we evaluate sparse coding method and compared it with widely used group-wise independent components analysis (group-ICA). The experimental results demonstrate that the network nodes identified by sparse representation are more functionally homogeneous, which may explain the superiority of sparse representation in differentiating Autism and healthy controls by brain connectivity biomarkers.",

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Ren, Y, Hu, X, Lv, J, Quo, L , Han, J & Liu, T 2016, Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data. in 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings., 7493500, Proceedings - International Symposium on Biomedical Imaging, vol. 2016-June, IEEE Computer Society, pp. 1278-1281, 13th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, Czech Republic, 13/04/16. https://doi.org/10.1109/ISBI.2016.7493500

Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data. / Ren, Yudan; Hu, Xintao; Lv, Jinglei et al.
2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society, 2016. p. 1278-1281 7493500 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June).

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

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Ren Y, Hu X, Lv J, Quo L , Han J, Liu T. Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society. 2016. p. 1278-1281. 7493500. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2016.7493500

Identifying autism biomarkers in default mode network using sparse representation of resting-state fMRI data

Abstract

Publication series

Conference

UN SDGs

Keywords

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