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Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification

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Abstract

A brain network can be constructed from various imaging modalities such as magnetic resonance imaging (MRI), representing the functional or structural connectivity between brain regions. The challenge of brain network analysis is efficient dimensionality reduction while retaining feature interpretability. We propose a new method to extract features from graph-structured data based on maximum mutual information (MMI-GSD). First, we develop a novel equation for the feature extraction from GSD and evaluate the interpretability of the features. We establish a framework to optimize the extracted features using the MMI. We conduct experiments on synthetic networks to validate the effectiveness of the proposed MMI-GSD. Next, we conduct experiments on 119 cognitively normal (CN), 105 mild cognitive impairment (MCI), and 36 Alzheimer’s disease (AD) individuals from the Alzheimer’s Disease Neuroimaging Initiative. The classification performance of the proposed method is significantly better than using traditional network metrics and existing feature extraction methods. In the clinical interpretation, we discover discriminative brain regions showing significant differences between the MCI and AD groups and identify significant abnormal connections concentrated in the left hemisphere.

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References

  1. Hebert LE, Weuve J, Scherr PA, Evans DA (2013) Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurol. 80(19):1778–1783. https://doi.org/10.1212/WNL.0b013e31828726f5

    Google Scholar 

  2. Alzheimer’s Association (2020) 2020 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia 16(3):391–460. https://doi.org/10.1002/alz.12068

    Google Scholar 

  3. U.S. Department of Health and Human Services Centers for Disease Control and Prevention & National Center for Health Statistics (2020) CDC WONDER online database: About Underlying Cause of Death, 1999-2018. https://wonder.cdc.gov/ucd-icd10.html

  4. Zhang Q et al (2018) Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer’s disease. Acta Neuropathologica Communications 6:19. https://doi.org/10.1186/s40478-018-0524-2

    Google Scholar 

  5. Dai Z et al (2015) Identifying and mapping connectivity patterns of brain network hubs in alzheimer’s disease. Cereb Cortex 25:3723–3742. https://doi.org/10.1093/cercor/bhu246

    Google Scholar 

  6. Coninck JCP, et al. (2020) Network properties of healthy and Alzheimer brains. Physica A Stat Mechan Appl 547:124475. https://doi.org/10.1016/j.physa.2020.124475

    MathSciNet  MATH  Google Scholar 

  7. Bi X, Zhao X, Huang H, Chen D, Ma Y (2020) Functional brain network classification for alzheimer’s disease detection with deep features and extreme learning machine. Cogn Comput 12:513–527. https://doi.org/10.1007/s12559-019-09688-2

    Google Scholar 

  8. Mheich A, Wendling F, Hassan M (2020) Brain network similarity: methods and applications. Netw Neurosci 4:507–527. https://doi.org/10.1162/netn_a_00133

    Google Scholar 

  9. Huang B et al (2021) Deep learning network for medical volume data segmentation based on multi axial plane fusion. Comput Methods Prog Biomed 212:106480. https://doi.org/10.1016/j.cmpb.2021.106480

    Google Scholar 

  10. Yamanakkanavar N, Choi JY, Lee B (2020) MRI segmentation and classification of human brain using deep learning for diagnosis of alzheimer’s disease: a survey. Sensors 20:3243. https://doi.org/10.3390/s20113243

    Google Scholar 

  11. Huo Y et al (2019) 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage 194:105–119. https://doi.org/10.1016/j.neuroimage.2019.03.041

    Google Scholar 

  12. Li Y, Li H, Fan Y (2021) ACENet: Anatomical context- encoding network for neuroanatomy segmentation. Med Image Anal 70:101991. https://doi.org/10.1016/j.media.2021.101991

    Google Scholar 

  13. Magnin B et al (2009) Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51:73–83. https://doi.org/10.1007/s00234-008-0463-x

    Google Scholar 

  14. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A (2015) Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin Neurophysiol 126:2132–2141. https://doi.org/10.1016/j.clinph.2015.02.060

    Google Scholar 

  15. Wee C-Y et al (2011) Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage 54:1812–1822. https://doi.org/10.1016/j.neuroimage.2010.10.026

    Google Scholar 

  16. Prasad G, Joshi SH, Nir TM, Toga AW, Thompson PM (2015) Brain connectivity and novel network measures for Alzheimer’s disease classification. Neurobiol Aging 36:S121–S131. https://doi.org/10.1016/j.neurobiolaging.2014.04.037

    Google Scholar 

  17. Hu C, He S, Wang Y (2021) A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Appl Intell 51:2609–2621. https://doi.org/10.1007/s10489-020-02011-9

    Google Scholar 

  18. Li Z, Fan J, Ren Y, Tang L (2020) A novel feature extraction approach based on neighborhood rough set and PCA for migraine rs-fMRI. J Intell Fuzz Syst 38:5731–5741. https://doi.org/10.3233/JIFS-179661

    Google Scholar 

  19. Bilgen I, Guvercin G, Rekik I (2020) Machine learning methods for brain network classification: Application to autism diagnosis using cortical morphological networks. J Neurosci Methods 343:108799. https://doi.org/10.1016/j.jneumeth.2020.108799

    Google Scholar 

  20. Salvatore C et al (2015) Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Frontiers in Neuroscience 9. https://doi.org/10.3389/fnins.2015.00307

  21. Cover TM (1999) Elements of information theory. Wiley, New Jersey

    Google Scholar 

  22. Marinoni A, Gamba P (2017) Unsupervised data driven feature extraction by means of mutual information maximization. IEEE Trans Comput Imaging 3:243–253. https://doi.org/10.1109/TCI.2017.2669731

    MathSciNet  Google Scholar 

  23. Özdenizci O, Erdoğmuş D (2020) Information theoretic feature transformation learning for brain interfaces. IEEE Trans Biomed Eng 67:69–78. https://doi.org/10.1109/TBME.2019.2908099

    Google Scholar 

  24. Hu C, Wang Y, Gu J (2020) Cross-domain intelligent fault classification of bearings based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks. Knowl-Based Syst 209:106214. https://doi.org/10.1016/j.knosys.2020.106214

    Google Scholar 

  25. Liu M et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. NeuroImage 208:116459. https://doi.org/10.1016/j.neuroimage.2019.116459

    Google Scholar 

  26. Mehmood A, Maqsood M, Bashir M, Shuyuan Y (2020) A deep siamese convolution neural network for multi-Class classification of alzheimer disease. Brain Sci 10:84. https://doi.org/10.3390/brainsci10020084

    Google Scholar 

  27. Janghel RR, Rathore YK (2021) Deep convolution neural network based system for early diagnosis of alzheimer’s disease. IRBM 42:258–267. https://doi.org/10.1016/j.irbm.2020.06.006

    Google Scholar 

  28. Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and Locally connected networks on graphs. International Conference on Learning Representations (ICLR2014). Banff, Canada

    Google Scholar 

  29. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems, vol 29. Curran Associates Inc, pp 3844–3852

  30. Song X, Elazab A, Zhang Y (2020) Classification of mild cognitive impairment based on a combined high-Order network and graph convolutional network. IEEE Access 8:42816–42827. https://doi.org/10.1109/ACCESS.2020.2974997

    Google Scholar 

  31. Parisot S et al (2018) Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease. Med Image Anal 48:117–130. https://doi.org/10.1016/j.media.2018.06.001

    Google Scholar 

  32. Liu J et al (2021) MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning. Clust Comput 24:103–113. https://doi.org/10.1007/s10586-020-03199-8

    Google Scholar 

  33. Jack CR et al (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685–691. https://doi.org/10.1002/jmri.21049

    Google Scholar 

  34. Weiner MW et al (2017) The alzheimer’s disease neuroimaging initiative 3: Continued innovation for clinical trial improvement. Alzheimer’s & Dementia 13:561–571. https://doi.org/10.1016/j.jalz.2016.10.006

    Google Scholar 

  35. Cui Z, Zhong S, Xu P, Gong G, He Y (2013) PANDA: A pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience 7. https://doi.org/10.3389/fnhum.2013.00042

  36. Tzourio-Mazoyer N et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-Subject brain. NeuroImage 15:273–289. https://doi.org/10.1006/nimg.2001.0978

    Google Scholar 

  37. Dl C, Tm PNP, Ac E (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192–205

    Google Scholar 

  38. Dahl J, Vandenberghe L, Roychowdhury V (2008) Covariance selection for nonchordal graphs via chordal embedding. Optim Methods Softw 23:501–520. https://doi.org/10.1080/10556780802102693

    MathSciNet  MATH  Google Scholar 

  39. Dempster AP (1972) Covariance selection. Biometrics 28:157–175. https://doi.org/10.2307/2528966

    MathSciNet  Google Scholar 

  40. Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer Series in Statistics. Springer, New York

    MATH  Google Scholar 

  41. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory 39–43

  42. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059– 1069

    Google Scholar 

  43. Yang J et al (2020) Transfer learning from grid-structured data to graph-structured data: Application to diagnosis of depression, Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, 1373–1378 (Research Publishing, Singapore. Venice, Italy

  44. Bakkour A, Morris JC, Wolk DA, Dickerson BC (2013) The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: Specificity and differential relationships with cognition. NeuroImage 76:332–344. https://doi.org/10.1016/j.neuroimage.2013.02.059

    Google Scholar 

  45. Fjell AM, et al. (2009) High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex 19:2001–2012. https://doi.org/10.1093/cercor/bhn232

    Google Scholar 

  46. Cajanus A et al (2019) The Association Between Distinct Frontal Brain Volumes and Behavioral Symptoms in Mild Cognitive Impairment, Alzheimer’s Disease, and Frontotemporal Dementia. Frontiers in Neurology 10. https://doi.org/10.3389/fneur.2019.01059

  47. Zhang T et al (2019) Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI. Frontiers in Psychiatry 10. https://doi.org/10.3389/fpsyt.2019.00572

  48. Yang H et al (2019) Study of brain morphology change in Alzheimer’s disease and amnestic mild cognitive impairment compared with normal controls. Gen Psychiatr 32:e100005. https://doi.org/10.1136/gpsych-2018-100005

    Google Scholar 

  49. Persson K et al (2018) Finding of increased caudate nucleus in patients with Alzheimer’s disease. Acta Neurol Scand 137:224–232. https://doi.org/10.1111/ane.12800

    Google Scholar 

  50. Hamasaki H et al (2019) Tauopathy in basal ganglia involvement is exacerbated in a subset of patients with Alzheimer’s disease: The Hisayama study. Alzheimer’s & Dementia: Diagnosis. Assessment & Disease Monitoring 11:415–423. https://doi.org/10.1016/j.dadm.2019.04.008

    Google Scholar 

  51. Berron D, van Westen D, Ossenkoppele R, Strandberg O, Hansson O (2020) Medial temporal lobe connectivity and its associations with cognition in early Alzheimer’s disease. Brain 143:1233–1248. https://doi.org/10.1093/brain/awaa068

    Google Scholar 

  52. Sun Y et al (2019) Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer’s Disease Based on the Brain Structural Connectome. Frontiers in Neurology 9. https://doi.org/10.3389/fneur.2018.01178

  53. Penniello M.-J. et al (1995) A PET study of the functional neuroanatomy of writing impairment in Alzheimer’s disease The role of the left supramarginal and left angular gyri. Brain 118:697–706. https://doi.org/10.1093/brain/118.3.697

    Google Scholar 

  54. Binder JR, Medler DA, Westbury CF, Liebenthal E, Buchanan L (2006) Tuning of the human left fusiform gyrus to sublexical orthographic structure. NeuroImage 33:739–748. https://doi.org/10.1016/j.neuroimage.2006.06.053

    Google Scholar 

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Acknowledgements

This work was supported by Beijing Advanced Innovation Center for Big Data-based Precision Medicine. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Jiawei Yang & Shaoping Wang

  2. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China

    Shaoping Wang

  3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University and ASU-Mayo Center for Innovative Imaging, Tempe, AZ, 85287, USA

    Teresa Wu

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  1. Jiawei Yang
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Correspondence to Shaoping Wang.

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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Yang, J., Wang, S. & Wu, T. Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification. Appl Intell 53, 1870–1886 (2023). https://doi.org/10.1007/s10489-022-03528-x

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