Prediction of Cannabis Addictive Patients with Graph Neural Networks

Shulin Wen; Shihao Yang; Xinglong Ju; Ting Liao; Feng Liu

doi:10.1007/978-3-031-43075-6_26

Prediction of Cannabis Addictive Patients with Graph Neural Networks

Shulin Wen
, Shihao Yang
, Xinglong Ju
, Ting Liao
, Feng Liu

Department of Systems Engineering

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

3 Scopus citations

Abstract

Neurological research is closely intertwined with public health issues, and artificial intelligence (AI) holds substantial potential in this domain. This study aims to investigate the enhancement of brain imaging classification performance in diverse populations using Graph Neural Networks (GNN) and its variants. Brain activity data are sourced from public neuroimaging databases, including functional Magnetic Resonance Imaging (fMRI) data of cannabis addicts and a healthy control group. Our results show that, compared to the healthy control group, cannabis addicts exhibit significant alterations in functional connectivity in certain brain regions. With the application of AI tools, we can distinguish the two groups based on brain imaging. We observed a significant improvement in brain imaging classification performance, and this model has achieved an accuracy rate of approximately 80%. These AI tools’ robust generalizability and vast developmental potential were also highlighted. These findings not only provided a novel perspective on the role of AI in brain imaging studies but also suggested potential new strategies for addressing public health issues.

Original language	English
Title of host publication	Brain Informatics - 16th International Conference, BI 2023, Proceedings
Editors	Feng Liu, Hongjun Wang, Yu Zhang, Hongzhi Kuai, Emily P. Stephen
Pages	297-307
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-031-43075-6_26
State	Published - 2023
Event	16th International Conference on Brain Informatics, BI 2023 - Hoboken, United States Duration: 1 Aug 2023 → 3 Aug 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13974 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Brain Informatics, BI 2023
Country/Territory	United States
City	Hoboken
Period	1/08/23 → 3/08/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Deep Learning
Graph Neural Network
functional Magnetic Resonance Imaging (fMRI)

Access to Document

10.1007/978-3-031-43075-6_26

Cite this

Wen, S., Yang, S., Ju, X., Liao, T., & Liu, F. (2023). Prediction of Cannabis Addictive Patients with Graph Neural Networks. In F. Liu, H. Wang, Y. Zhang, H. Kuai, & E. P. Stephen (Eds.), Brain Informatics - 16th International Conference, BI 2023, Proceedings (pp. 297-307). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13974 LNAI). https://doi.org/10.1007/978-3-031-43075-6_26

Wen, Shulin ; Yang, Shihao ; Ju, Xinglong et al. / Prediction of Cannabis Addictive Patients with Graph Neural Networks. Brain Informatics - 16th International Conference, BI 2023, Proceedings. editor / Feng Liu ; Hongjun Wang ; Yu Zhang ; Hongzhi Kuai ; Emily P. Stephen. 2023. pp. 297-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f9d6b77bbad54fe6a62997e93f9f6f64,

title = "Prediction of Cannabis Addictive Patients with Graph Neural Networks",

abstract = "Neurological research is closely intertwined with public health issues, and artificial intelligence (AI) holds substantial potential in this domain. This study aims to investigate the enhancement of brain imaging classification performance in diverse populations using Graph Neural Networks (GNN) and its variants. Brain activity data are sourced from public neuroimaging databases, including functional Magnetic Resonance Imaging (fMRI) data of cannabis addicts and a healthy control group. Our results show that, compared to the healthy control group, cannabis addicts exhibit significant alterations in functional connectivity in certain brain regions. With the application of AI tools, we can distinguish the two groups based on brain imaging. We observed a significant improvement in brain imaging classification performance, and this model has achieved an accuracy rate of approximately 80\%. These AI tools{\textquoteright} robust generalizability and vast developmental potential were also highlighted. These findings not only provided a novel perspective on the role of AI in brain imaging studies but also suggested potential new strategies for addressing public health issues.",

keywords = "Deep Learning, Graph Neural Network, functional Magnetic Resonance Imaging (fMRI)",

author = "Shulin Wen and Shihao Yang and Xinglong Ju and Ting Liao and Feng Liu",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 16th International Conference on Brain Informatics, BI 2023 ; Conference date: 01-08-2023 Through 03-08-2023",

year = "2023",

doi = "10.1007/978-3-031-43075-6\_26",

language = "English",

isbn = "9783031430749",

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

pages = "297--307",

editor = "Feng Liu and Hongjun Wang and Yu Zhang and Hongzhi Kuai and Stephen, \{Emily P.\}",

booktitle = "Brain Informatics - 16th International Conference, BI 2023, Proceedings",

}

Wen, S, Yang, S, Ju, X, Liao, T & Liu, F 2023, Prediction of Cannabis Addictive Patients with Graph Neural Networks. in F Liu, H Wang, Y Zhang, H Kuai & EP Stephen (eds), Brain Informatics - 16th International Conference, BI 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13974 LNAI, pp. 297-307, 16th International Conference on Brain Informatics, BI 2023, Hoboken, United States, 1/08/23. https://doi.org/10.1007/978-3-031-43075-6_26

Prediction of Cannabis Addictive Patients with Graph Neural Networks. / Wen, Shulin; Yang, Shihao; Ju, Xinglong et al.
Brain Informatics - 16th International Conference, BI 2023, Proceedings. ed. / Feng Liu; Hongjun Wang; Yu Zhang; Hongzhi Kuai; Emily P. Stephen. 2023. p. 297-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13974 LNAI).

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

TY - GEN

T1 - Prediction of Cannabis Addictive Patients with Graph Neural Networks

AU - Wen, Shulin

AU - Yang, Shihao

AU - Ju, Xinglong

AU - Liao, Ting

AU - Liu, Feng

PY - 2023

Y1 - 2023

N2 - Neurological research is closely intertwined with public health issues, and artificial intelligence (AI) holds substantial potential in this domain. This study aims to investigate the enhancement of brain imaging classification performance in diverse populations using Graph Neural Networks (GNN) and its variants. Brain activity data are sourced from public neuroimaging databases, including functional Magnetic Resonance Imaging (fMRI) data of cannabis addicts and a healthy control group. Our results show that, compared to the healthy control group, cannabis addicts exhibit significant alterations in functional connectivity in certain brain regions. With the application of AI tools, we can distinguish the two groups based on brain imaging. We observed a significant improvement in brain imaging classification performance, and this model has achieved an accuracy rate of approximately 80%. These AI tools’ robust generalizability and vast developmental potential were also highlighted. These findings not only provided a novel perspective on the role of AI in brain imaging studies but also suggested potential new strategies for addressing public health issues.

AB - Neurological research is closely intertwined with public health issues, and artificial intelligence (AI) holds substantial potential in this domain. This study aims to investigate the enhancement of brain imaging classification performance in diverse populations using Graph Neural Networks (GNN) and its variants. Brain activity data are sourced from public neuroimaging databases, including functional Magnetic Resonance Imaging (fMRI) data of cannabis addicts and a healthy control group. Our results show that, compared to the healthy control group, cannabis addicts exhibit significant alterations in functional connectivity in certain brain regions. With the application of AI tools, we can distinguish the two groups based on brain imaging. We observed a significant improvement in brain imaging classification performance, and this model has achieved an accuracy rate of approximately 80%. These AI tools’ robust generalizability and vast developmental potential were also highlighted. These findings not only provided a novel perspective on the role of AI in brain imaging studies but also suggested potential new strategies for addressing public health issues.

KW - Deep Learning

KW - Graph Neural Network

KW - functional Magnetic Resonance Imaging (fMRI)

UR - https://www.scopus.com/pages/publications/85172422027

UR - https://www.scopus.com/pages/publications/85172422027#tab=citedBy

U2 - 10.1007/978-3-031-43075-6_26

DO - 10.1007/978-3-031-43075-6_26

M3 - Conference contribution

AN - SCOPUS:85172422027

SN - 9783031430749

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

SP - 297

EP - 307

BT - Brain Informatics - 16th International Conference, BI 2023, Proceedings

A2 - Liu, Feng

A2 - Wang, Hongjun

A2 - Zhang, Yu

A2 - Kuai, Hongzhi

A2 - Stephen, Emily P.

T2 - 16th International Conference on Brain Informatics, BI 2023

Y2 - 1 August 2023 through 3 August 2023

ER -

Wen S, Yang S, Ju X, Liao T , Liu F. Prediction of Cannabis Addictive Patients with Graph Neural Networks. In Liu F, Wang H, Zhang Y, Kuai H, Stephen EP, editors, Brain Informatics - 16th International Conference, BI 2023, Proceedings. 2023. p. 297-307. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43075-6_26