Skip to main content
SpringerLink
Log in

Identification and classification of voxels of human brain for rewardless-related decision making using ANN technique

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Functional magnetic resonance imaging (fMRI) data analysis is developing rapidly in a fast emerging society, because of temporal and spatial resolution and the inoffensive feature of their acquisition in human brains. Spatial resolution governs how “sharp” the image is in appearance, whereas temporal resolution denotes the precision of a measurement with respect to time. The goal of fMRI technique is to identify the activation pattern and functional connectivity in the brain regions. In our study, artificial neural network technique was used for the identification and classification of decision-making voxels of fMRI data based on Brodmann areas 10 and 47 from the prefrontal cortex of human brain. The total number of voxels of Brodmann areas was 159, and we determined that some particular voxels played dominant role for decision-making process while performing a visual task. We also analyzed true positive and false positive classification rates between two decisions in the context of a well-known receiver operating characteristic curve (ROC).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Norman KA, Polyn SM (2006) Beyond mind-reading; multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 10(9):424–430

    Article  Google Scholar 

  2. Onut IV, Ghorbani AA (2004) Classifying cognitive states from fMRI data using neural networks. In: IEEE international joint conference on neural networks, Budapest, Hungary

  3. Ye Z, Kutas M (2012) Rearranging the world: neural network supporting the processing of temporal connectives. NeuroImage 59(4):3662–3667

    Article  Google Scholar 

  4. Boehm O, Hardoon DR (2011) Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. Int J Mach Learn Cybernet 2(3):125–134

    Article  Google Scholar 

  5. Niculescu RS, Mitchell TM (2006) Automated fMRI feature abstraction using neural network clustering techniques. In: NIPS 06 workshops, Whistler, Canada

  6. Daria R, Isabelle R, Giovanna Z (2006) Language: normal and pathological development. John Libbey Eurotext, Montrouge

    Google Scholar 

  7. Alabi M, Issa S, Afolayan R (2013) An application of artificial intelligent neural network and discriminant analyses on credit scoring. Math Theory Model 3(11):20–28

    Google Scholar 

  8. Peterson C, Rognvaldsson T (1991) An introduction to artificial neural networks. In: 14th CERN School of computing, Sweden

  9. Ahmad F, Ullah G (2012) A neighborhood method for statistical analysis of fMRI data. Open J Biophys 2(1):15–22

    Article  Google Scholar 

  10. Franck R, Schmied A (2004) Predicting currency crisis contagion from East Asia to Russia and Brazil: an artificial neural network approach. AMCB working paper 2, Bar-Ilan University

  11. Gunther F, Fritsch S (2010) Neuralnet: training of neural networks. R J 2(1):30–38

    Google Scholar 

  12. Sarle WS (1994) Neural networks and statistical models. In: 19th annual SAS users group international conference, USA

  13. Ma N, Zhai Y, Li WF, Li CH, Wang SS, Zhou L (2013) Neural network algorithm based method for stock price trend prediction. J Appl Sci 13(22):5384–5390

    Article  Google Scholar 

  14. Zhang G, Eddy PB, Hu YM (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35–62

    Article  Google Scholar 

  15. Ahmad Z, Saleem A (2014) Impact of governance on human development. Pak J Commer Soc Sci 8(3):612–628

    MathSciNet  Google Scholar 

  16. Laurence JG (1994) Brodmann’s localisation in the cerebral cortex. Smith-Gordon Company, Barnet, pp 105–125

    Google Scholar 

  17. Belilovsky A, Gkirtzou K, Misyrlis M, Konova AB, Honorio J, Klein NA, Goldstein RZ, Samaras D, Blaschko MB (2015) Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm. Comput Med Imaging Graph 46(1):40–46

    Article  Google Scholar 

  18. Floren A, Naylor B, Miikkulaninen R, Ress D (2015) Accurately decoding visual information from fMRI data obtained in a realistic virtual environment. Front Hum Neurosci 9(38):1–13

    Google Scholar 

  19. Ahmad F, Lee N, Kim E, Kim SH, Park HW (2013) A shrinkage method for causal network detection of brain regions. Int J Imaging Syst Technol 23(2):969–981

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, University of Gujrat, Gujrat, Pakistan

    Fayyaz Ahmad & Waqar Mahmood Dar

  2. Department of Mathematics, University of Gujrat, Gujrat, Pakistan

    Iftikhar Ahmad

Authors
  1. Fayyaz Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iftikhar Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Waqar Mahmood Dar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fayyaz Ahmad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, F., Ahmad, I. & Dar, W.M. Identification and classification of voxels of human brain for rewardless-related decision making using ANN technique. Neural Comput & Applic 28 (Suppl 1), 1035–1041 (2017). https://doi.org/10.1007/s00521-016-2413-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2413-6

Keywords

Search

Navigation