Adaptive functional connectivity network learning and application in brain disorders identification

SUN Lei; ZHANG Yining; XUE Yanfang; QIAO Lishan; ZHANG Limei

doi:10.3969/j.issn.0253-2778.2020.08.009

JUSTC > 2020 > 50(8): 1102. > DOI: 10.3969/j.issn.0253-2778.2020.08.009

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Adaptive functional connectivity network learning and application in brain disorders identification

College of Mathematical Science, Liaocheng University, Liaocheng 273100, China

Cite this: JUSTC, 2020, 50(8): 1102-1109

https://doi.org/10.3969/j.issn.0253-2778.2020.08.009

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Received Date: June 19, 2020
Revised Date: July 06, 2020
Accepted Date: July 06, 2020
Published Date: August 30, 2020

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Abstract

Abstract

In recent years, functional connectivity networks (FCN) based on functional magnetic resonance imaging (fMRI) have provided an important tool for the early intervention of brain disorders, such as Alzheimer's disorder (AD) and Autism spectrum disorder (ASD). However, the obtained data are inevitably introduced into structural noises due to participants’ breath, heartbeats and head motions during the scan, which often brings great challenges to the final construction of FCNs. Although conventional data preprocessing methods have been utilized to improve the quality of the data, they still operate in the original data space and separate the data denoising from the FCNs estimation, and thus breaking the internal connection between two steps. Researches shows that data in a certain transform domain may be low-noisy and more informative. Inspired by the transform domain, we propose an adaptive brain network learning model in the light of the transform domain (TD-FCN), which not only improves the quality of the observed data, but also learns the adaptive brain graph in a single framework simultaneously. To verify the effectiveness of the proposed method, we conduct experiments on two public datasets (i.e., ADNI and ABIDE) to identify the patients with mild cognitive impairments (MCIs) and ASDs from health controls (HCs). Experimental results demonstrate that the proposed approach yields statistically significant improvement in multiple performance metrics over traditional methods.

Abstract

In recent years, functional connectivity networks (FCN) based on functional magnetic resonance imaging (fMRI) have provided an important tool for the early intervention of brain disorders, such as Alzheimer's disorder (AD) and Autism spectrum disorder (ASD). However, the obtained data are inevitably introduced into structural noises due to participants’ breath, heartbeats and head motions during the scan, which often brings great challenges to the final construction of FCNs. Although conventional data preprocessing methods have been utilized to improve the quality of the data, they still operate in the original data space and separate the data denoising from the FCNs estimation, and thus breaking the internal connection between two steps. Researches shows that data in a certain transform domain may be low-noisy and more informative. Inspired by the transform domain, we propose an adaptive brain network learning model in the light of the transform domain (TD-FCN), which not only improves the quality of the observed data, but also learns the adaptive brain graph in a single framework simultaneously. To verify the effectiveness of the proposed method, we conduct experiments on two public datasets (i.e., ADNI and ABIDE) to identify the patients with mild cognitive impairments (MCIs) and ASDs from health controls (HCs). Experimental results demonstrate that the proposed approach yields statistically significant improvement in multiple performance metrics over traditional methods.

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References (38)

References

[1]	ACHARD S, BULLMORE E. Efficiency and cost of economical brain functional networks[J]. PLOS Computational Biology, 2007, 3(2):17.
[2]	AHONEN T, HADID A, PIETIKAINEN M. Face description with local binary patterns: Application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28: 2037-2041.
[3]	BEALL E B, LOWE M J. SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction[J]. Neuroimage, 2014, 101: 21-34.
[4]	BECKMANN C F. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data[J]. Neuroimage, 2015, 112: 267-277.
[5]	BISWAL B, YETKIN F Z, HAUGHTON V M, et al. Functional connectivity in the motor cortex of resting human brain using echo‐planar mri[J]. Magnetic Resonance in Medicine, 2005, 34: 537-541.
[6]	BISWAL B B, YETKIN F Z, HAUGHTON V M, et al. Functional connectivity in the motor cortex of resting human brain using echo‐planar mri[J]. Magnetic Resonance in Medicine, 1995, 34: 537-541.
[7]	DASH M, LIU H. Feature selection for classification[J]. Intelligent Data Analysis, 1997:131-156.
[8]	FRISTON K J, WILLIAMS S C R, HOWARD R, et al. Movement‐related effects in fMRI time-series[J]. Magnetic Resonance in Medicine, 1996, 35: 346-355.
[9]	GRUNDMAN M, PETERSEN R C, FERRIS S H, et al. Mild cognitive impairment can be distinguished from alzheimer disease and normal aging for clinical trials[J]. JAMA Neurology, 2004, 61: 59-66.
[10]	GUYON I, ELISSEEFF A. An introduction to variable and feature selection[J]. Journal of Machine Learning Research, 2003, 3: 1157-1182.
[11]	HOTELLING H. Analysis of a complex of statistical variables into principal components[J]. Journal of Educational Psychology, 1933, 24: 498-520.
[12]	WINGATE M, KIRBY R S, PETTYGROVE S, et al. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010[J]. MMWR Survllance summaries, 2014, 63: 1-21.
[13]	JI S, YE J. An accelerated gradient method for trace norm minimization[C]// Proceedings of the 26th annual international conference on machine learning. ACM, 2009:457-464.
[14]	LEE H, LEE D S, KANG H, et al. Sparse brain network recovery under compressed sensing[J]. IEEE Transactions on Medical Imaging, 2011, 30: 1154-1165.
[15]	LI W, WANG Z, ZHANG L, et al. Remodeling pearson's correlation for functional brain network estimation and autism spectrum disorder identification[J]. Frontiers in Neuroinformatics, 2017, 11: 55.
[16]	LI W, ZHANG L, QIAO L, et al. Toward a better estimation of functional brain network for mild cognitive impairment identification: A transfer learning view[J]. IEEE Journal of Biomedical and Health Informatics, 2019: 1-1.
[17]	LIN. LIBSVM: A library for support vector machines[J]. Acm Transactions on Intelligent Systems & Technology, 2011, 2: 1-27.
[18]	LU J, LI Y, WANG L, et al. A new method to remove the Gaussian noise from image in wavelet domain [C]// Nonlinear Signal & Image Processing, Nsip Abstracts IEEE-EURASIP. IEEE, 2005.
[19]	MARRELEC G, KRAINIK A, DUFFAU H, et al. Partial correlation for functional brain interactivity investigation in functional MRI[J]. Neuroimage, 2006, 32: 228-237.
[20]	MILHAM M P. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics[J]. Neuroimage, 2013, 76: 183-201.
[21]	MORADI E, PEPE A, GASER C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. Neuroimage, 2015, 104: 398-412.
[22]	OGAWA S, LEE T M, KAY A R, et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation[J]. Proceedings of the National Academy of Sciences of the United States of America, 1990, 87(24): 9868-9872.
[23]	POWER J D, BARNES K A, SNYDER A Z, et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion[J]. Neuroimage, 2012, 59: 2142-2154.
[24]	PRUIM R H R, MENNES M, VAN R D, et al. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data[J]. Neuroimage, 2015, 112: 267-277.
[25]	QIAO L, ZHANG H, KIM M, et al. Estimating functional brain networks by incorporating a modularity prior[J]. Neuroimage, 2016, 141: 399-407.
[26]	QIAO L, ZHANG L, CHEN S, et al. Data-driven graph construction and graph learning: A review[J]. Neurocomputing, 2018, 312: 336-351.
[27]	RASHID B, CALHOUN V. Towards a brain‐based predictome of mental illness[J]. Human Brain Mapping, 2020.
[28]	SMITH S M, MILLER K L, SALIMI-KHORSHIDI G, et al. Network modelling methods for FMRI[J]. Neuroimage, 2011, 54(2): 875-891.
[29]	TZOURIO-MAZOYER N, LANDEAU B, PAPATHANASSIOU D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. Neuroimage, 2002, 15: 273-289.
[30]	WANG H, YAN S, XU D, et al. Trace ratio vs. ratio trace for dimensionality reduction[C]// Computer Vision and Pattern Recognition. IEEE, 2007:1-8.
[31]	WANG Z, LIANG P, JIA X, et al. Baseline and longitudinal patterns of hippocampal connectivity in mild cognitive impairment: Evidence from resting state fMRI[J]. Journal of the Neurological Sciences, 2011, 309: 79-85.
[32]	WEE C Y, YAP P T, SHEN D. Diagnosis of autism spectrum disorders using temporally distinct resting-state functional connectivity networks[J]. Cns Neuroscience & Therapeutics, 2016, 22: 212-219.
[33]	WEE C Y, YAP P T, ZHANG D, et al. Identification of MCI individuals using structural and functional connectivity networks[J]. Neuroimage, 2012, 59: 2045-2056.
[34]	WEISSENBACHER A, KASESS C, GERSTL F, et al. Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies[J]. Neuroimage, 2009, 47: 1408-1416.
[35]	YAN C G, WANG X D, ZUO X N, et al. DPABI: Data processing & analysis for (resting-state) brain imaging[J]. Neuroinformatics, 2016, 14: 339-351.
[36]	ZHANG L, CHEN S, QIAO L. Graph optimization for dimensionality reduction with sparsity constraints[J]. Pattern Recognition, 2012, 45: 1205-1210.
[37]	ZHOU Y, ZHANG L, TENG S, et al. Improving sparsity and modularity of high-order functional connectivity networks for MCI and ASD identification[J]. Frontiers in Neuroscience, 2018: 12.
[38]	梁夏，王金辉，贺勇. 人脑连接组研究：脑结构网络和脑功能网络[J]. 科学通报，2010,55:1563-1583.

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References

[1]	ACHARD S, BULLMORE E. Efficiency and cost of economical brain functional networks[J]. PLOS Computational Biology, 2007, 3(2):17.
[2]	AHONEN T, HADID A, PIETIKAINEN M. Face description with local binary patterns: Application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28: 2037-2041.
[3]	BEALL E B, LOWE M J. SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction[J]. Neuroimage, 2014, 101: 21-34.
[4]	BECKMANN C F. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data[J]. Neuroimage, 2015, 112: 267-277.
[5]	BISWAL B, YETKIN F Z, HAUGHTON V M, et al. Functional connectivity in the motor cortex of resting human brain using echo‐planar mri[J]. Magnetic Resonance in Medicine, 2005, 34: 537-541.
[6]	BISWAL B B, YETKIN F Z, HAUGHTON V M, et al. Functional connectivity in the motor cortex of resting human brain using echo‐planar mri[J]. Magnetic Resonance in Medicine, 1995, 34: 537-541.
[7]	DASH M, LIU H. Feature selection for classification[J]. Intelligent Data Analysis, 1997:131-156.
[8]	FRISTON K J, WILLIAMS S C R, HOWARD R, et al. Movement‐related effects in fMRI time-series[J]. Magnetic Resonance in Medicine, 1996, 35: 346-355.
[9]	GRUNDMAN M, PETERSEN R C, FERRIS S H, et al. Mild cognitive impairment can be distinguished from alzheimer disease and normal aging for clinical trials[J]. JAMA Neurology, 2004, 61: 59-66.
[10]	GUYON I, ELISSEEFF A. An introduction to variable and feature selection[J]. Journal of Machine Learning Research, 2003, 3: 1157-1182.
[11]	HOTELLING H. Analysis of a complex of statistical variables into principal components[J]. Journal of Educational Psychology, 1933, 24: 498-520.
[12]	WINGATE M, KIRBY R S, PETTYGROVE S, et al. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010[J]. MMWR Survllance summaries, 2014, 63: 1-21.
[13]	JI S, YE J. An accelerated gradient method for trace norm minimization[C]// Proceedings of the 26th annual international conference on machine learning. ACM, 2009:457-464.
[14]	LEE H, LEE D S, KANG H, et al. Sparse brain network recovery under compressed sensing[J]. IEEE Transactions on Medical Imaging, 2011, 30: 1154-1165.
[15]	LI W, WANG Z, ZHANG L, et al. Remodeling pearson's correlation for functional brain network estimation and autism spectrum disorder identification[J]. Frontiers in Neuroinformatics, 2017, 11: 55.
[16]	LI W, ZHANG L, QIAO L, et al. Toward a better estimation of functional brain network for mild cognitive impairment identification: A transfer learning view[J]. IEEE Journal of Biomedical and Health Informatics, 2019: 1-1.
[17]	LIN. LIBSVM: A library for support vector machines[J]. Acm Transactions on Intelligent Systems & Technology, 2011, 2: 1-27.
[18]	LU J, LI Y, WANG L, et al. A new method to remove the Gaussian noise from image in wavelet domain [C]// Nonlinear Signal & Image Processing, Nsip Abstracts IEEE-EURASIP. IEEE, 2005.
[19]	MARRELEC G, KRAINIK A, DUFFAU H, et al. Partial correlation for functional brain interactivity investigation in functional MRI[J]. Neuroimage, 2006, 32: 228-237.
[20]	MILHAM M P. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics[J]. Neuroimage, 2013, 76: 183-201.
[21]	MORADI E, PEPE A, GASER C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. Neuroimage, 2015, 104: 398-412.
[22]	OGAWA S, LEE T M, KAY A R, et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation[J]. Proceedings of the National Academy of Sciences of the United States of America, 1990, 87(24): 9868-9872.
[23]	POWER J D, BARNES K A, SNYDER A Z, et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion[J]. Neuroimage, 2012, 59: 2142-2154.
[24]	PRUIM R H R, MENNES M, VAN R D, et al. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data[J]. Neuroimage, 2015, 112: 267-277.
[25]	QIAO L, ZHANG H, KIM M, et al. Estimating functional brain networks by incorporating a modularity prior[J]. Neuroimage, 2016, 141: 399-407.
[26]	QIAO L, ZHANG L, CHEN S, et al. Data-driven graph construction and graph learning: A review[J]. Neurocomputing, 2018, 312: 336-351.
[27]	RASHID B, CALHOUN V. Towards a brain‐based predictome of mental illness[J]. Human Brain Mapping, 2020.
[28]	SMITH S M, MILLER K L, SALIMI-KHORSHIDI G, et al. Network modelling methods for FMRI[J]. Neuroimage, 2011, 54(2): 875-891.
[29]	TZOURIO-MAZOYER N, LANDEAU B, PAPATHANASSIOU D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. Neuroimage, 2002, 15: 273-289.
[30]	WANG H, YAN S, XU D, et al. Trace ratio vs. ratio trace for dimensionality reduction[C]// Computer Vision and Pattern Recognition. IEEE, 2007:1-8.
[31]	WANG Z, LIANG P, JIA X, et al. Baseline and longitudinal patterns of hippocampal connectivity in mild cognitive impairment: Evidence from resting state fMRI[J]. Journal of the Neurological Sciences, 2011, 309: 79-85.
[32]	WEE C Y, YAP P T, SHEN D. Diagnosis of autism spectrum disorders using temporally distinct resting-state functional connectivity networks[J]. Cns Neuroscience & Therapeutics, 2016, 22: 212-219.
[33]	WEE C Y, YAP P T, ZHANG D, et al. Identification of MCI individuals using structural and functional connectivity networks[J]. Neuroimage, 2012, 59: 2045-2056.
[34]	WEISSENBACHER A, KASESS C, GERSTL F, et al. Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies[J]. Neuroimage, 2009, 47: 1408-1416.
[35]	YAN C G, WANG X D, ZUO X N, et al. DPABI: Data processing & analysis for (resting-state) brain imaging[J]. Neuroinformatics, 2016, 14: 339-351.
[36]	ZHANG L, CHEN S, QIAO L. Graph optimization for dimensionality reduction with sparsity constraints[J]. Pattern Recognition, 2012, 45: 1205-1210.
[37]	ZHOU Y, ZHANG L, TENG S, et al. Improving sparsity and modularity of high-order functional connectivity networks for MCI and ASD identification[J]. Frontiers in Neuroscience, 2018: 12.
[38]	梁夏，王金辉，贺勇. 人脑连接组研究：脑结构网络和脑功能网络[J]. 科学通报，2010,55:1563-1583.

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