Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Analysis of WLAN’s Receiving Signal Strength Indication for Indoor Positioning Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Multilayer Sparse Representation of Dynamic Brain Functional Network Based on Hypergraph Theory for ADHD Classification

Authors : Yuduo Zhang, Zhichao Lian, Chanying Huang

Published in: Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Nowadays, studies on the brain show that the resting brain is still dynamic, and the dynamics of brain functional connectivity remains to be proven, which is very important for the research and diagnosis of mental disorders. In this paper, we apply the Bayesian Connection Change Point Model (BCCPM) to perform dynamic testing on the brain. A sparse model is used to construct a hypergraph to represent the brain function connectivity network, and then the dictionary obtained by sparse learning is used to further extract the features of brain function network. The experimental results on ADHD data show that the accuracy of the proposed method has been improved. Meanwhile, we find that there are obvious differences in the sparse features values of the brain functional networks between patients and normal controls. In addition, the comparison between the proposed method with/without the BCCPM demonstrated the importance of dynamic detection further.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Car Sales Prediction Using Gated Recurrent Units Neural Networks with Reinforcement Learning
next chapter Stress Wave Tomography of Wood Internal Defects Based on Deep Learning and Contour Constraint Under Sparse Sampling
Literature
1.
Song, M., Du, H., Wu, N., et al.: Impaired resting-state functional integrations within default mode network of generalized tonic-clonic seizures epilepsy. PLoS ONE 6(2), 1729 (2011)
2.
Rashid, B., Arbabshirani, M.R., Damaraju, E., et al.: Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity. In: IEEE, International Symposium on Biomedical Imaging, pp. 645–657. IEEE (2015)
3.
Wang, X., Ren, Y., Yang, Y., et al.: A weighted discriminative dictionary learning method for depression disorder classification using fMRI data. In: IEEE International Conferences on Big Data and Cloud Computing, pp. 618–623. IEEE (2016)
4.
Golbabaei, S., Vahid, A., Hatami, J., et al.: Classification of Alzheimer’s disease and mild cognitive impairment: machine learning applied to rs-fMRI brain graphs. In: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME). IEEE (2016)
5.
Cicek, G., Akan, A., Orman, Z.: Classification of ADHD by using textural analysis of MR images. In: Medical Technologies National Congress, pp. 1–4 (2017)
6.
Biswal, B., Yetkin, F.Z., Haughton, V.M., et al.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995)CrossRef
7.
Raichle, M.E., Mintun, M.A.: Brain work and brain imaging. Annu. Rev. Neurosci. 29, 449–476 (2006)CrossRef
8.
Allen, E.A., Damaraju, E., Plis, S.M., et al.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663–676 (2014)CrossRef
9.
Zhang, X., Li, X., Jin, C., et al.: Identifying and characterizing resting state networks in temporally dynamic functional connectomes. Brain Topogr. 27(6), 747–765 (2014)CrossRef
10.
Lian, Z., Li, X., Xing, J., et al.: Exploring functional brain dynamics via a Bayesian connectivity change point model. In: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. IEEE (2014)
11.
Li, Y., Lian, Z., Li, M., et al.: ELM-based classification of ADHD patients using a novel local feature extraction method. In: IEEE International Conference on Bioinformatics & Biomedicine. IEEE (2017)
12.
Jie, B., Wee, C.Y., Shen, D., et al.: Hyper-connectivity of functional networks for brain disease diagnosis. Med. Image Anal. 32, 84–100 (2016). S1361841516300044CrossRef
13.
Ke, M., Duan, X., Zhang, F., Yang, X.: Complex brain network of patients with epilepsy using cascading failure analysis of fMRI data. In: 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, pp. 1–6 (2015)
14.
Bahramf, M., Hossein-Zadeh, G.: Functional parcellations affect the network measures in graph analysis of resting-state fMRI. In: 2014 21th Iranian Conference on Biomedical Engineering (ICBME), Tehran, pp. 263–268 (2014)
15.
Hiroyasu, T., Kohri, Y., Hiwa, S.: Sparse feature selection method by Pareto-front exploration—Extraction of functional brain network and ROI for fMRI data. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, pp. 1–8 (2017)
16.
Guo, H., Li, Y., Xu, Y., et al.: Resting-state brain functional hyper-network construction based on elastic net and group lasso methods. Front. Neuroinform. 12, 25 (2018)CrossRef
17.
Zhang, X., Guo, L., Li, X., et al.: Characterization of task-free and task-performance brain states via functional connectome patterns. Med. Image Anal. 17(8), 1106–1122 (2013)CrossRef
18.
Liu, J., Ji, S., Ye, J.: SLEP: sparse learning with efficient projections. Comput. Sci. Eng. 61 (2011)
Metadata
Title
A Multilayer Sparse Representation of Dynamic Brain Functional Network Based on Hypergraph Theory for ADHD Classification
Authors
Yuduo Zhang
Zhichao Lian
Chanying Huang
Copyright Year
2019
Book
Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Print ISBN: 978-3-030-36203-4

Electronic ISBN: 978-3-030-36204-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-36204-1_27

Premium Partner

    Image Credits