Abstract
ADHD is the most commonly diagnosed psychiatric disorder in children and, although its diagnosis is done in a subjective way, it can be characterized by abnormality work of specific brain regions. Datasets obtained by rs-fMRI cooperate to the large amount of brain information, but they lead to the curse-of-dimensionality problem. This paper aims to compare dimensionality reduction methods belonging to feature selection task using reliable features for multiple datasets obtained by rs-fMRI in ADHD prediction. Experiments showed that features evaluated in multiple datasets were able to improve the correct labeling rate, including the 87% obtained by MRMD that overcomes the higher accuracy in rs-fMRI ADHD prediction. They also eliminated the curse-of-dimensionality problem and identified relevant brain regions related to this disorder.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Banaschewski, T., Zuddas, A., Asherson, P., et al.: ADHD and Hyperkinetic Disorder, 2nd edn. Oxford University Press, USA (2015)
Lim, L., Marquand, A., Cubillo, A., et al.: Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PLoS ONE 8(5), e63660 (2013). doi:10.1371/journal.pone.0063660
Mwangi, B., Tian, T., Soares, T.: A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2), 229–244 (2014). doi:10.1007/s12021-013-9204-3
Zhu, C., Zang, Y., Cao, Q., et al.: Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage 40(1), 110–120 (2008). doi:10.1016/j.neuroimage.2007.11.029
Wolfers, T., Buitelaar, J., Beckmann, C., et al.: From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci. Biobehav. Rev. 57, 328–349 (2015). doi:10.1016/j.neubiorev.2015.08.001
Wang, X., Jiao, Y., Lu, Z.: Discriminative analysis of resting-state brain functional connectivity patterns of attention-deficit hyperactivity disorder using kernel principal component analysis. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1938–1941 (2011). doi:10.1109/FSKD.2011.6019911
Liang, S., Hsieh, T., Chen, P., et al.: Differentiation between resting-state fMRI data from ADHD and normal subjects: based on functional connectivity and machine learning. In: IEEE International Conference on Fuzzy Theory and it’s Applications (iFUZZY), pp. 294–298 (2012). doi:10.1109/iFUZZY.2012.6409719
Sato, J., Hoexter, M., Fujita, A., et al.: Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Front. Syst. Neurosci. 6, 68 (2012). doi:10.3389/fnsys.2012.00068
Lazar, C., Taminau, J., Meganck, S., et al.: A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 9(4), 1106–1119 (2012). doi:10.1109/TCBB.2012.33
Zou, Q., Zeng, L., Ji, R.: A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173, 346–354 (2016). doi:10.1016/j.neucom.2014.12.123
Acknowledgement
The authors of this paper would like to acknowledge CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for the financial support of this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Garcia, R., Paraiso, E.C., Nievola, J.C. (2017). Comparative Study of Dimensionality Reduction Methods Using Reliable Features for Multiple Datasets Obtained by rs-fMRI in ADHD Prediction. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-57351-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57350-2
Online ISBN: 978-3-319-57351-9
eBook Packages: Computer ScienceComputer Science (R0)