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FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data

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Abstract

Functional Magnetic Resonance Imaging (fMRI) is the popular technique where it is possible to capture neural activity in brain regions when subjected to different stimuli. However, due to fMRI datasets' high dimensional and sparse nature, the best features' selection plays an essential role in providing the best classification accuracy in fMRI models. This paper selects the stable feature set from the fMRI dataset using hybrid Fast Fourier Transform with Particle Swarm Optimization and Genetic Algorithm (FFTPSOGA). Fast Fourier Transform (FFT) is used on the extracted features by PSO-GA to convert the magnitude of features into phase values for better performance. Next, the machine learning algorithms of GaussianNB, Support Vector Machine (SVM), and XGboost has been trained based on these extracted features of six subjects of the dataset. The experimental analysis reveals that the proposed algorithm resulted in optimum features that helped extract informative Regions of Interest (ROI) with better classification accuracy. Our implemented algorithm FFTPSOGA extracted the best voxels in six subjects of the dataset by selecting minimum ROIs with a model classification accuracy of 0.98, 0.95, 0.95, 0.95, 0.97, and 0.96 for the SVM classifier. Comparison of the proposed scheme with state-of-the-art techniques show that our algorithm resulted in best voxels and outperformed work in [1, 9, 25] by achieving higher accuracy of 98% and low computational costs with only 127 number of features. Due to its better performance, we believe that it can be used for the pattern identification of brain responses in multi-subject fMRI data.

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Data availability

The data used for carrying out experiments in this research contains multi-subject fMRI data of 6 persons and is available online at http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/.

References

  1. Albalawi F, Alshehri S, Chahid A, Laleg-Kirati T (2020) M “voxel weight matrix-based feature extraction for biomedical applications”. IEEE Access 8:121451–121459

    Article  Google Scholar 

  2. Anter AM, Wei Y, Su J, Yuan Y, Lei B, Duan G, Fu Z (2019) A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI. Inf Sci 503:670–687

    Article  MathSciNet  Google Scholar 

  3. Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Inform Syst 13(3):329–351

    Article  Google Scholar 

  4. Bhatti UA, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W, Mehmood A “Local Similarity-Based Spatial–Spectral Fusion Hyperspectral Image Classification with Deep CNN and Gabor Filtering”. IEEE Trans Geosci Remote Sens, vol. 60, pp. 1-15, 2021.

  5. Chen JE, Glover GH (2015) Functional magnetic resonance imaging methods. Neuropsychol Rev 25(3):289–313

    Article  Google Scholar 

  6. Cohen JD, Daw N, Engelhardt B, Hasson U, Li K, Niv Y, Norman KA et al (2017) Computational approaches to fMRI analysis. Nat Neurosci 20(3):304–313

    Article  Google Scholar 

  7. Eklund A, Andersson M, Knutsson H (2012) fMRI analysis on the GPU—possibilities and challenges. Comput Methods Prog Biomed 105(2):145–161

    Article  Google Scholar 

  8. Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD (2011) Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32(12):2075–2095

    Article  Google Scholar 

  9. Fan M, Chou CA (2016) Exploring stability-based voxel selection methods in mvpa using cognitive neuroimaging data: a comprehensive study. Brain Inform 3(3):193–203

    Article  Google Scholar 

  10. Fang Y, Liu J, Li J, Cheng J, Hu J, Yi D, Bhatti UA (2022) Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT. Multimed Tools Appl 81(12):16863–16879

    Article  Google Scholar 

  11. Ghamisi P, Benediktsson JA (2014) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett 12(2):309–313

    Article  Google Scholar 

  12. Jin B, Strasburger A, Laken SJ, Kozel FA, Johnson KA, George MS, Lu X (2009) Feature selection for fMRI-based deception detection. BMC Bioinform 10(9):1–7 BioMed Central

    Google Scholar 

  13. Kassraian-Fard P, Matthis C, Balsters JH, Maathuis MH, Wenderoth N (2016) Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example. Front Psychiatry no. 7:177

    Google Scholar 

  14. Kauttonen J, Hlushchuk Y, Tikka P (2015) Optimizing methods for linking cinematic features to fMRI data. Neuroimage 110:136–148

    Article  Google Scholar 

  15. Korhonen O, Saarimäki H, Glerean E, Sams M, Saramäki J (2017) Consistency of regions of interest as nodes of fMRI functional brain networks. Network Neurosci 1(3):254–274

    Article  Google Scholar 

  16. Lahiri R, Rakshit P, Konar A (2017) Evolutionary perspective for optimal selection of EEG electrodes and features. Biomed Signal Process Control 36:113–137

    Article  Google Scholar 

  17. Liu J, Ji J, Jia X, Zhang A (2019) Learning brain effective connectivity network structure using ant colony optimization combining with voxel activation information. IEEE J Biomed Health Inform 24(7):2028–2040

    Google Scholar 

  18. Ma X, Chou CA, Sayama H, Chaovalitwongse WA (2016) Brain response pattern identification of fMRI data using a particle swarm optimization-based approach. Brain Inform 3(3):181–192

    Article  Google Scholar 

  19. Metawa N, Hassan MK, Elhoseny M (2017) Genetic algorithm-based model for optimizing bank lending decisions. Expert Syst Appl 80:75–82

    Article  Google Scholar 

  20. Michel V, Damon C, Thirion B (2008) Mutual information-based feature selection enhances fMRI brain activity classification. In: In 2008 5th IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 592–595

    Google Scholar 

  21. Mirzaei S, Soltanian-Zadeh H (2019) Overlapping brain community detection using Bayesian tensor decomposition. J Neurosci Methods 318:47–55

    Article  Google Scholar 

  22. Ota K, Oishi N, Ito K, Fukuyama H (2015) Sead-J study group, & Alzheimer's disease neuroimaging Initiative. “Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease”. J Neurosci Methods 256:168–183

    Article  Google Scholar 

  23. Paul S, Das S (2019) Simultaneous feature selection and weighting–an evolutionary multi-objective optimization approach. Pattern Recogn Lett 65:51–59

    Article  Google Scholar 

  24. Poldrack RA (2012) The future of fMRI in cognitive neuroscience. Neuroimage 62(2):1216–1220

    Article  Google Scholar 

  25. Ramakrishna JS, Ramasangu H Classification of cognitive state using clustering based maximum margin feature selection framework”, In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1092-1096. IEEE

  26. Rashid M, Singh H, Goyal V (2020) The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—a systematic review. Expert Syst 37(6):e12644

    Article  Google Scholar 

  27. Satoru HIWA, Kohri Y, Hachisuka K, Hiroyasu T (2016) Region-of-interest extraction of fMRI data using genetic algorithms. In: In 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 1–7

    Google Scholar 

  28. Sengupta S, Basak S, Peters RA (2019) Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach Learn Knowl Extrac 1(1):157–191

    Article  Google Scholar 

  29. Serra A, Galdi P, Tagliaferri R (2018) Machine learning for bioinformatics and neuroimaging. Wiley Interdisciplinary Rev: Data Mining Knowl Discov 8(5):e1248

    Google Scholar 

  30. Shahamat H, Pouyan AA (2015) Feature selection using genetic algorithm for classification of schizophrenia using fMRI data. J AI Data Mining 3(1):30–37

    Google Scholar 

  31. Shi Y, Zeng W, Wang N, Zhao L (2018) A new constrained spatiotemporal ICA method based on multi-objective optimization for fMRI data analysis. IEEE Trans Neural Syst Rehab Eng 26(9):1690–1699

    Article  Google Scholar 

  32. Sidhu G (2019) Locally linear embedding and fMRI feature selection in psychiatric classification. IEEE J Trans Eng Health Med 7:1–11

    Article  Google Scholar 

  33. Smith SM, Hyvärinen A, Varoquaux G, Miller KL, Beckmann CF (2014) Group-PCA for very large fMRI datasets. Neuroimage 101:738–749

    Article  Google Scholar 

  34. Song Y, Wang F, Chen X (2019) An improved genetic algorithm for numerical function optimization. Appl Intell 49(5):1880–1902

    Article  Google Scholar 

  35. Sumanaweera T, Liu D (2005) Medical image reconstruction with the FFT. GPU Gems 2:765–784

    Google Scholar 

  36. Tian D, Shi Z (2018) MPSO: modified particle swarm optimization and its applications. Swarm Evol Comput 41:49–68

    Article  Google Scholar 

  37. Tom Mitchell WW: Starplus fmri data. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/

  38. Wang Y, Ji J, Liang P (2016) Feature selection of fMRI data based on normalized mutual information and fisher discriminant ratio. J X-ray Sci Technol 24(3):467–475

    Google Scholar 

  39. Xu W, Li Q, Liu X, Zhen Z, Wu X (2020) Comparison of feature selection methods based on discrimination and reliability for fMRI decoding analysis. J Neurosci Methods no. 335:108567

    Article  Google Scholar 

  40. Yang Z, Zhuang X, Sreenivasan K, Mishra V, Cordes D, Initiative A's DN (2020) Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network. NeuroImage no. 223:117340

    Article  Google Scholar 

  41. Young KS, Maj A, van der Velden MG, Craske KJ, Pallesen LF, Roepstorff A, Parsons CE (2018) The impact of mindfulness-based interventions on brain activity: a systematic review of functional magnetic resonance imaging studies. Neurosci Biobehav Rev 84:424–433

    Article  Google Scholar 

  42. Zeng C, Liu J, Li J, Cheng J, Zhou J, Nawaz SA, Bhatti UA (2022) Multi-watermarking algorithm for medical image based on KAZE-DCT. J Ambient Intell Human Comput:1–9

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Punjabi University, Patiala, India

    Mamoon Rashid

  2. Department of Computer Science, Mata Gujri College, Sahib, Fatehgarh, India

    Harjeet Singh

  3. Department of Computer Science, Punjabi University, Patiala, India

    Vishal Goyal

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  1. Mamoon Rashid
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  2. Harjeet Singh
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  3. Vishal Goyal
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Correspondence to Mamoon Rashid.

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Rashid, M., Singh, H. & Goyal, V. FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data. Multimed Tools Appl 82, 45433–45452 (2023). https://doi.org/10.1007/s11042-023-15471-1

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  • DOI: https://doi.org/10.1007/s11042-023-15471-1

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