Advertisement

Deep Learning via Fused Bidirectional Attention Stacked Long Short-Term Memory for Obsessive-Compulsive Disorder Diagnosis and Risk Screening

  • Chiyu Feng
  • Lili Jin
  • Chuangyong Xu
  • Peng Yang
  • Tianfu Wang
  • Baiying LeiEmail author
  • Ziwen PengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11843)

Abstract

The compulsive urges to perform stereotyped behaviors are typical symptoms of obsessive-compulsive disorder (OCD). OCD has certain hereditary tendencies and the direct OCD relatives (i.e., sibling (Sib)) have 50% of the same genes as patients. Sib has a higher probability of suffering from the same disease. Resting-state functional magnetic resonance imaging (R-fMRI) has made great progress by diagnosing OCD and identifying its high-risk population. Accordingly, we design a new deep learning framework for OCD diagnosis via R-fMRI data. Specifically, the fused bidirectional attention stacking long short-term memory (FBAS-LSTM) is exploited. First, we obtain two independent time series from the original R-fMRI by frame separation, which can reduce the length of R-fMRI sequence and alleviate the training difficulty. Second, we apply two independent BAS-LSTM learning on the hidden spatial information to obtain preliminary classification results. Lastly, the final diagnosis results are obtained by voting from the two diagnostic results. We validate our method on our in-house dataset including 62 OCD, 53 siblings (Sib) and 65 healthy controls (HC). Our method achieves average accuracies of 71.66% for differentiating OCD vs. Sib vs. HC, and outperforms the related algorithms.

Keywords

Obsessive-compulsive disorder diagnosis Risk screening Attention LSTM Fusion 

References

  1. 1.
    Paula, B., et al.: Imbalance in habitual versus goal directed neural systems during symptom provocation in obsessive-compulsive disorder. Brain 138, 798–811 (2015)CrossRefGoogle Scholar
  2. 2.
    International, O.C.D.F.G.C., et al.: Revealing the complex genetic architecture of obsessive–compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2017)Google Scholar
  3. 3.
    Howard, J., Serrano, W.C.: Anxiety, depression, and OCD: understanding common psychiatric conditions in the dermatological patient. In: França, K., Jafferany, M. (eds.) Stress and Skin Disorders, pp. 19–37. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-46352-0_3CrossRefGoogle Scholar
  4. 4.
    Tadayonnejad, R., et al.: Pregenual anterior cingulate dysfunction associated with depression in OCD: an integrated multimodal fMRI/1 H MRS study. Neuropsychopharmacology 43, 1146–1155 (2018)CrossRefGoogle Scholar
  5. 5.
    Lenhard, F., et al.: Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: a machine learning approach. Int. J. Methods Psychiatr. Res. 27, e1576 (2017)CrossRefGoogle Scholar
  6. 6.
    Wang, H., et al.: Recognizing brain states using deep sparse recurrent neural network. IEEE Trans. Med. Imaging 38(4), 1058–1068 (2018)CrossRefGoogle Scholar
  7. 7.
    Yan, W., et al.: Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis. Med. Image Comput. Comput. Assist. Intervention 2018, 249–257 (2018)Google Scholar
  8. 8.
    Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S.: Identifying autism from resting-state fMRI using long short-term memory networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 362–370. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67389-9_42CrossRefGoogle Scholar
  9. 9.
    Yang, Z., et al.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)Google Scholar
  10. 10.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  11. 11.
    Graves, A.: Generating sequences with recurrent neural networks. Computer Science (2013)Google Scholar
  12. 12.
    Xing, X., et al.: Diagnosis of OCD using functional connectome and Riemann kernel PCA. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 109502C. International Society for Optics and Photonics (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical EngineeringHealth Science Center, Shenzhen UniversityShenzhenChina
  2. 2.College of Psychology and SociologyShenzhen UniversityShenzhenChina
  3. 3.Department of Child PsychiatryShenzhen Kangning Hospital, Shenzhen University School of MedicineShenzhenChina

Personalised recommendations