Combination of grey matter and white matter features for early prediction of posttraumatic stress disorder

Si Wang, Hao Hu, Shanshan Su, Luyan Liu, Zhen Wang, Qian Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder. In previous researches, there are few studies about structural and functional alterations of the whole brain simultaneously about PTSD prediction. Early alterations could provide evidence of early diagnosis and treatment. Early diagnosis of PTSD plays an important role during the treatment. In this work, we extract discriminant features from multi-modal images and implement classification-based prediction for PTSD onset. Specifically, discriminant features are a collection of measures derived from grey matter (GM) and white matter (WM). We choose cortical thickness of GM and three descriptions of WM connection which are fiber count, fractional anisotropy (FA), and mean diffusivity (MD). After applying automated anatomical labeling (AAL) to parcellate the whole brain into 90 regions-of-interest (ROIs), the descriptions can be quantified. Then, a weighted clustering coefficient of every ROI connected with the remaining ROIs is extracted as feature. GM features and WM features are combined and selected automatically, which are later utilized by support vector machine (SVM) for early identification of the patients. The classification accuracy is around 79.86 % as the area of receiver operating characteristic (ROC) curve is 0.816 evaluated via dual leave-one-out cross-validation.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings
PublisherSpringer Verlag
Pages560-567
Number of pages8
Volume9937 LNCS
ISBN (Print)9783319462561
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 - Yangzhou, China
Duration: 2016 Oct 122016 Oct 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9937 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
CountryChina
CityYangzhou
Period16/10/1216/10/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wang, S., Hu, H., Su, S., Liu, L., Wang, Z., Wang, Q., & Shen, D. (2016). Combination of grey matter and white matter features for early prediction of posttraumatic stress disorder. In Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings (Vol. 9937 LNCS, pp. 560-567). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9937 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46257-8_60