Convolutional Neural Network for the Classification of Independent Components of rs-fMRI

Keywords: Independent Component Analysis, Principal Component Analysis, Convolutional Neural Network, denoising in fMRI, resting-state

Abstract

Resting state functional magnetic resonance imaging (rs-fMRI) is one of the most relevant techniques in brain exploration. However, it is susceptible to many external factors that can occlude the signal of interest. In this order of ideas, rs-fMRI images have been studied adopting different approaches, with a particular interest in artifact removal techniques through Independent Component Analysis (ICA). Such an approach is a powerful tool for blind source separation, where elements associated with noise can be eliminated. Nevertheless, such removal is subject to the identification or classification of the components provided by the ICA. In that sense, this study focuses on finding an alternative strategy to classify the independent components. The problem was addressed in two stages. In the first one, the components (3D volumes) were reduced to images by Principal Component Analysis (PCA) and by obtaining the median planes. The methods achieved a reduction of up to two orders of magnitude in the weight of the data size, and they were shown to preserve the spatial characteristics of the independent components. In the second stage, the reductions were used to train six models of convolutional neural networks. The networks analyzed in this study reached accuracies around 98 % in classification, one of them even up to 98.82 %, which reflects the high discrimination capacity of convolutional neural networks.

Author Biographies

Leonel Mera-Jiménez*, Universidad de Antioquia, Colombia

Medellín-Colombia, leonel.mera@udea.edu.co

John F. Ochoa-Gómez, Universidad de Antioquia, Colombia

Medellín-Colombia, john.ochoa@udea.edu.co

References

G. A. Ascoli; M. Halavi, “Neuroinformatics,” Encyclopedia of Neuroscience. pp. 477–484, 2009. https://doi.org/10.1016/B978-008045046-9.00872-X

P. M. Rossini et al., “Methods for analysis of brain connectivity: An IFCN-sponsored review,” Clinical Neurophysiology, vol. 130, no. 10, pp. 1833–1858, Oct. 2019. https://doi.org/10.1016/j.clinph.2019.06.006

L. A. Muñoz-Bedoya; L. E. Mendoza; J. Velandia-Villamizar, “Segmentation of Magnetic Resonance Imaging MRI using LS-SVM and Wavelet Multiresolution Analysis,” TecnoLógicas, edición especial, pp. 681-693, Oct. 2013. https://doi.org/10.22430/22565337.381

C. Guarnizo-Lemus, “Análisis de reducción de ruido en señales EEG orientado al reconocimiento de patrones,” TecnoLógicas, no. 21, pp. 67-80, Dec. 2008. https://doi.org/10.22430/22565337.248

J. L. Armony; D. Trejo Martínez; D. Hernández, “Resonancia Magnética Funcional (RMf): principios y aplicaciones en Neuropsicología y Neurociencias Cognitivas,” Rev. Neuropsicol. Latinoam., vol. 4, no. 2, pp. 36–50, Apr. 2012. https://www.neuropsicolatina.org/index.php/Neuropsicologia_Latinoamericana/article/view/103

M. E. Raichle, “The Brain’s Default Mode Network,” Annu. Rev. Neurosci., vol. 38, pp. 433–447, May. 2015. https://doi.org/10.1146/annurev-neuro-071013-014030

W. Qian et al., “Delusions in Alzheimer Disease are Associated with Decreased Default Mode Network Functional Connectivity,” Am. J. Geriatr. Psychiatry, vol. 27, no. 10, pp. 1060–1068, Oct. 2019. https://doi.org/10.1016/j.jagp.2019.03.020

R. Franciotti et al., “Somatic symptoms disorders in Parkinson’s disease are related to default mode and salience network dysfunction,” NeuroImage Clin., vol. 23, Apr. 2019. https://doi.org/10.1016/j.nicl.2019.101932

S. Lang; N. Duncan; G. Northoff, “Resting-state functional magnetic resonance imaging: Review of neurosurgical applications,” Neurosurgery, vol. 74, no. 5. pp. 453–464, Jan. 2014. https://doi.org/10.1227/NEU.0000000000000307

G. D. Pearlson, “Applications of Resting State Functional MR Imaging to Neuropsychiatric Diseases,” Neuroimaging Clin N. Am., vol. 27, no. 4, pp. 709–723, Nov. 2017. https://doi.org/10.1016/j.nic.2017.06.005

J. D. Kropotov, “Functional Magnetic Resonance Imaging,” in Functional Neuromarkers for Psychiatry applications for diagnosis and treatment, Elsevier inc., 2016, pp. 17–25. https://doi.org/10.1016/B978-0-12-410513-3.00003-6

J. Mohan; V. Krishnaveni; Y. Guo, “A survey on the magnetic resonance image denoising methods,” Biomed. Signal Process, vol. 9, no. 1, pp. 56–69, Jan. 2014. https://doi.org/10.1016/j.bspc.2013.10.007

L. L. Wald, “Ultimate MRI,” J. Magn. Reson., vol. 306, pp. 139–144, Sep. 2019. https://doi.org/10.1016/j.jmr.2019.07.016

D. S. Margulies et al., “Resting developments: A review of fMRI post-processing methodologies for spontaneous brain activity,” Magn. Reson. Mater. Physics, Biol. Med., vol. 23, no. 5–6, pp. 289–307, Oct. 2010. https://doi.org/10.1007/s10334-010-0228-5

M. A. Lindquist, “The Statistical Analysis of fMRI Data,” Statistical Science., vol. 23, no. 4, pp. 439–464, 2008. http://dx.doi.org/10.1214/09-STS282

K. Chen; A. Azeez; D. Y. Chen; B. B. Biswal, “Resting-state Functional Connectivity: Signal Origins and Analytic Methos,” Neuroimag Clin N. Am, vol. 30, no. 1, pp. 15–23, Feb. 2020. https://doi.org/10.1016/j.nic.2019.09.012

F. Gregory Ashby, Statistical Analysis of fMRI Data, Second. MIT press. 2019. https://doi.org/10.7551/mitpress/11557.001.0001

M. M. Monti, “Statistical analysis of fMRI time-series: a critical review of the GLM approach,” in Front. Hum. Neurosci, vol. 5, no. 28, pp. 147–154. Mar. 2011. https://doi.org/10.3389/fnhum.2011.00028

M. Khosla; K. Jamison; G. H. Ngo; A. Kuceyeski; M. R. Sabuncu, “Machine learning in resting-state fMRI analysis,” Magnetic Resonance Imaging, vol. 64, pp. 101–121, Dec. 2019. https://doi.org/10.1016/j.mri.2019.05.031

C. F. Beckmann, M. DeLuca, J. T. Devlin; S. M. Smith, “Investigations into resting-state connectivity using independent component analysis,” Philos. Trans. R. Soc. B Biol. Sci., vol. 360, no. 1457, pp. 1001–1013, May. 2005. https://doi.org/10.1098/rstb.2005.1634

R. H. R. Pruim; M. Mennes; D. Van Rooij; A. Llera; J. K. Buitelaar; C. F. Beckmann, “ICA-AROMA : A robust ICA-based strategy for removing motion artifacts from fMRI data,” Neuroimage, vol. 112, pp. 267–277, May. 2015. https://doi.org/10.1016/j.neuroimage.2015.02.064

L. Griffanti et al., “Hand classification of fMRI ICA noise components,” Neuroimage, vol. 154, pp. 188–205, Jul. 2017. https://doi.org/10.1016/j.neuroimage.2016.12.036

L. Griffanti et al., “ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging,” Neuroimage, vol. 95, pp. 232–247, Jul. 2014. https://doi.org/10.1016/j.neuroimage.2014.03.034

G. Salimi-Khorshidi; G. Douaud; C. F. Beckmann; M. F. Glasser; L. Griffanti; S. M. Smith, “Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers,” Neuroimage, vol. 90, pp. 449–468, Apr. 2014, https://doi.org/10.1016/j.neuroimage.2013.11.046

D. Ravi et al., “Deep Learning for Health Informatics,” IEEE J. Biomed. Heal. Informatics, vol. 21, no. 1, pp. 4–21, Jan. 2017. https://doi.org/10.1109/JBHI.2016.2636665

J. A. Peña-Torres; R. E. Gutiérrez; V. A. Bucheli; F. A. González, “Cómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicas,” TecnoLógicas, vol. 22, Edición especial, pp. 49–62, Dic. 2019. http://dx.doi.org/10.22430/22565337.1483

W. Liu; Z. Wang; X. Liu; N. Zeng; Y. Liu; F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, no. 19, pp. 11–26, Apr. 2017. https://doi.org/10.1016/j.neucom.2016.12.038

K. He; X. Zhang; S. Ren; J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, Santiago de chile, 2015. pp. 1026–1034. https://doi.org/10.1109/ICCV.2015.123

Z. Mao et al., “Spatio-temporal deep learning method for ADHD fMRI classification,” Inf. Sci., vol. 499, pp. 1–11, Oct. 2019. https://doi.org/10.1016/j.ins.2019.05.043

A. Riaz; M. Asad; E. Alonso; G. Slabaugh, “DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI,” J. Neurosci. Methods, vol. 335, p. 108506, Apr. 2020. https://doi.org/10.1016/j.jneumeth.2019.108506

M. P. Hosseini; T. X. Tran; D. Pompili; K. Elisevich; H. Soltanian-Zadeh, “Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing,” Artif. Intell. Med., vol. 104, Apr. 2020. https://doi.org/10.1016/j.artmed.2020.101813

A. S. Lundervold; A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Zeitschrift fßr Medizinische Phys., vol. 29, no. 2, pp. 102–127, May. 2019. https://doi.org/10.1016/j.zemedi.2018.11.002

M. Mostapha; M. Styner, “Role of deep learning in infant brain MRI analysis,” Magnetic Resonance Imaging, vol. 64, pp. 171–189, Dec. 2019. https://doi.org/10.1016/j.mri.2019.06.009

Y. Guo; Y. Liu, A. Oerlemans; S. Lao; S. Wu; M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, Apr. 2016. https://doi.org/10.1016/j.neucom.2015.09.116

W. Hernandez; A. Mendez, “Application of Principal Component Analysis to Image Compression,” in Statistics - Growing Data Sets and Growing Demand for Statistics, Türkmen Gö., 2018. http://dx.doi.org/10.5772/intechopen.75007

J. Teuwen; N. Moriakova, “Convolutional neural networks,” in Handbook of Medical Image Computing and Computer Assisted Intervention, Academic P., Ed. 2020, pp. 481–501. https://doi.org/10.1016/B978-0-12-816176-0.00025-9

M. F. Glasser et al., “The minimal preprocessing pipelines for the Human Connectome Project,” Neuroimage, vol. 80, pp. 105–124, Oct. 2013. https://doi.org/10.1016/j.neuroimage.2013.04.127

T. H. C. Projet, “HCP Young Adult - Connectome – Publications an overview,” 2009. https://www.humanconnectome.org/study/hcp-young-adult

Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX “Whitehall Imaging Oxford”. https://www.psych.ox.ac.uk/research/neurobiology-of-ageing/research-projects-1/whitehall-oxford

“FMRIB Software Library v6.0,” Created by the Analysis Group, FMRIB, Oxford, UK. 2020. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki

M. Jekinson; C. F. Beckmann; T. E. J. Behrens; M. W. Woolrich; S. M. Smith, “FSL,” Neuroimage, vol. 62, no. 2, pp. 782–790, Aug. 2012. https://doi.org/10.1016/j.neuroimage.2011.09.015

The Analysis Group FMRIB, “MELODIC.”, version 3.04, 2019. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC

G. Salimi-khorshidi et al., “Fix Hand-Training Datasets.” https://www.fmrib.ox.ac.uk/datasets/FIX-training

S. M. Anwar; M. Majid; A. Qayyum; M. Awais; M. Alnowami; M. K. Khan, “Medical Image Analysis using Convolutional Neural Networks: A Review,” Journal of Medical Systems, vol. 42, no. 11, pp. 1–13, 2018. https://doi.org/10.1007/s10916-018-1088-1

Y. LeCun; L. Bottou; Y. Bengio; P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2323, Nov. 1998. https://doi.org/10.1109/5.726791

N. J. Tustison; B. B. Avants; J. C. Gee, “Learning image-based spatial transformations via convolutional neural networks: A review,” Magn. Reson. Imaging, vol. 64, pp. 142–153, Dec. 2019. https://doi.org/10.1016/j.mri.2019.05.037

S. Vieira; W. H. Lopez Pinaya; A. Mechelli, Main concepts in machine learning.en Machine Learnin. Methods and Applications to Brain Disorders. Elsevier Inc., 2020. https://doi.org/10.1016/B978-0-12-815739-8.00002-X

Y. Lecun; Y. Bengio; G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015.

https://doi.org/10.1038/nature14539

How to Cite
Mera-Jiménez, L., & Ochoa-Gómez, J. F. (2021). Convolutional Neural Network for the Classification of Independent Components of rs-fMRI. TecnoLógicas, 24(50), e1626. https://doi.org/10.22430/22565337.1626

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Published
2021-01-30
Section
Research Papers

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