Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data

Rui Liu; Zhi An Huang; Min Jiang; Kay Chen Tan

doi:10.1109/IAI50351.2020.9262176

Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data

Rui Liu
, Zhi An Huang
, Min Jiang
, Kay Chen Tan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

19 Citations (Scopus)

Abstract

Attention deficit hyperactivity disorder (ADHD) is a widespread mental disorder among young children. Due to the complex pathological mechanisms and clinical symptoms, the diagnosis of ADHD is still challenging. In this paper, we propose a novel multi-network of long short term memory (multi-LSTM) for the identification of ADHD. The Gaussian mixture model (GMM) is introduced to cluster different regions of interests (ROIs) for feature selection. Then, the data augmentation and phenotypic information are used to further improve the classification performance. The simulation experiment demonstrates that the proposed model outperforms the state-of-the-art methods based on the multi-site ADHD-200 global competition dataset. It is anticipated that the proposed ROI-based clustering method and multi-LSTM model can provide valuable insights into the auxiliary diagnosis of ADHD from the rs-fMRI signal.

Original language	English
Title of host publication	2nd International Conference on Industrial Artificial Intelligence, IAI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
ISBN (Electronic)	9781728182162
DOIs	https://doi.org/10.1109/IAI50351.2020.9262176
Publication status	Published - 23 Oct 2020
Externally published	Yes
Event	2nd International Conference on Industrial Artificial Intelligence, IAI 2020 - Shenyang, China Duration: 23 Oct 2020 → 25 Oct 2020

Publication series

Name	2nd International Conference on Industrial Artificial Intelligence, IAI 2020

Conference

Conference	2nd International Conference on Industrial Artificial Intelligence, IAI 2020
Country/Territory	China
City	Shenyang
Period	23/10/20 → 25/10/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Attention deficit hyperactivity disorder (ADHD)
functional magnetic resonance imaging (fMRI)
Long short-term memory (LSTM)
regions of interests (ROIs)

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality

More information

10.1109/IAI50351.2020.9262176

Cite this

Liu, R., Huang, Z. A., Jiang, M., & Tan, K. C. (2020). Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data. In 2nd International Conference on Industrial Artificial Intelligence, IAI 2020 (pp. 1-6). Article 9262176 (2nd International Conference on Industrial Artificial Intelligence, IAI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAI50351.2020.9262176

Liu, Rui ; Huang, Zhi An ; Jiang, Min et al. / Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data. 2nd International Conference on Industrial Artificial Intelligence, IAI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1-6 (2nd International Conference on Industrial Artificial Intelligence, IAI 2020).

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title = "Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data",

abstract = "Attention deficit hyperactivity disorder (ADHD) is a widespread mental disorder among young children. Due to the complex pathological mechanisms and clinical symptoms, the diagnosis of ADHD is still challenging. In this paper, we propose a novel multi-network of long short term memory (multi-LSTM) for the identification of ADHD. The Gaussian mixture model (GMM) is introduced to cluster different regions of interests (ROIs) for feature selection. Then, the data augmentation and phenotypic information are used to further improve the classification performance. The simulation experiment demonstrates that the proposed model outperforms the state-of-the-art methods based on the multi-site ADHD-200 global competition dataset. It is anticipated that the proposed ROI-based clustering method and multi-LSTM model can provide valuable insights into the auxiliary diagnosis of ADHD from the rs-fMRI signal. ",

keywords = "Attention deficit hyperactivity disorder (ADHD), functional magnetic resonance imaging (fMRI), Long short-term memory (LSTM), regions of interests (ROIs)",

author = "Rui Liu and Huang, \{Zhi An\} and Min Jiang and Tan, \{Kay Chen\}",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61876162 and Grant 61673328, in part by the Shenzhen Scientific Research and Development Funding Program under Grant JCYJ20180307123637294, and in part by the Research Grants Council of the Hong Kong SAR under Grant CityU11202418 and Grant CityU11209219. (Corresponding authors: Zhi-An Huang; Min Jiang.) Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Industrial Artificial Intelligence, IAI 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

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month = oct,

day = "23",

doi = "10.1109/IAI50351.2020.9262176",

language = "English",

series = "2nd International Conference on Industrial Artificial Intelligence, IAI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Liu, R, Huang, ZA, Jiang, M & Tan, KC 2020, Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data. in 2nd International Conference on Industrial Artificial Intelligence, IAI 2020., 9262176, 2nd International Conference on Industrial Artificial Intelligence, IAI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2nd International Conference on Industrial Artificial Intelligence, IAI 2020, Shenyang, China, 23/10/20. https://doi.org/10.1109/IAI50351.2020.9262176

Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data. / Liu, Rui; Huang, Zhi An; Jiang, Min et al.
2nd International Conference on Industrial Artificial Intelligence, IAI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1-6 9262176 (2nd International Conference on Industrial Artificial Intelligence, IAI 2020).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Huang, Zhi An

AU - Jiang, Min

AU - Tan, Kay Chen

N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61876162 and Grant 61673328, in part by the Shenzhen Scientific Research and Development Funding Program under Grant JCYJ20180307123637294, and in part by the Research Grants Council of the Hong Kong SAR under Grant CityU11202418 and Grant CityU11209219. (Corresponding authors: Zhi-An Huang; Min Jiang.) Publisher Copyright: © 2020 IEEE.

PY - 2020/10/23

Y1 - 2020/10/23

N2 - Attention deficit hyperactivity disorder (ADHD) is a widespread mental disorder among young children. Due to the complex pathological mechanisms and clinical symptoms, the diagnosis of ADHD is still challenging. In this paper, we propose a novel multi-network of long short term memory (multi-LSTM) for the identification of ADHD. The Gaussian mixture model (GMM) is introduced to cluster different regions of interests (ROIs) for feature selection. Then, the data augmentation and phenotypic information are used to further improve the classification performance. The simulation experiment demonstrates that the proposed model outperforms the state-of-the-art methods based on the multi-site ADHD-200 global competition dataset. It is anticipated that the proposed ROI-based clustering method and multi-LSTM model can provide valuable insights into the auxiliary diagnosis of ADHD from the rs-fMRI signal.

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DO - 10.1109/IAI50351.2020.9262176

M3 - Conference article published in proceeding or book

AN - SCOPUS:85098492315

T3 - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020

SP - 1

EP - 6

BT - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Liu R, Huang ZA, Jiang M, Tan KC. Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data. In 2nd International Conference on Industrial Artificial Intelligence, IAI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1-6. 9262176. (2nd International Conference on Industrial Artificial Intelligence, IAI 2020). doi: 10.1109/IAI50351.2020.9262176

Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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