Abstract
Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks.
| Original language | English (US) |
|---|---|
| Title of host publication | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2740-2743 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538636466 |
| DOIs | |
| State | Published - Oct 26 2018 |
| Event | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States Duration: Jul 18 2018 → Jul 21 2018 |
Publication series
| Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
|---|---|
| Volume | 2018-July |
| ISSN (Print) | 1557-170X |
Other
| Other | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 7/18/18 → 7/21/18 |
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PubMed: MeSH publication types
- Journal Article
Cite this
Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier. / Chu, Shu Hsien; Lenglet, Christophe; Schreiner, Mindy Westlund; Klimes-Dougan, Bonnie; Cullen, Kathryn R; Parhi, Keshab K.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2740-2743 8512852 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier
AU - Chu, Shu Hsien
AU - Lenglet, Christophe
AU - Schreiner, Mindy Westlund
AU - Klimes-Dougan, Bonnie
AU - Cullen, Kathryn R
AU - Parhi, Keshab K
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks.
AB - Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks.
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UR - http://www.scopus.com/inward/citedby.url?scp=85056643489&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512852
DO - 10.1109/EMBC.2018.8512852
M3 - Conference contribution
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2740
EP - 2743
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -