Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification

Abstract The classification of mild cognitive impairment (MCI), which is a early stage of Alzheimer’s disease and is associated with brain structural and functional changes, is still a challenging task. Recent studies have shown great promise for improving the performance of MCI classification by combining multiple structural and functional features, such as grey matter volume and clustering coefficient. However, extracting which features and how to combine multiple features to improve the performance of MCI classification have always been challenging problems. To address these problems, in this study we propose a new method to enhance the feature representation of multi-modal MRI data by combining multi-view information to improve the performance of MCI classification. Firstly, we extract two structural features (including grey matter volume and cortical thickness) and two functional features (including clustering coefficient and shortest path length) of each cortical brain region based on automated anatomical labeling (AAL) atlas from both T1w MRI and rs-fMRI data of each subject. Then, in order to obtain features that are more helpful in distinguishing MCI subjects, an improved multi-task feature selection method, namely MTFS-gLASSO-TTR, is proposed. Finally, a multi-kernel learning algorithm is adopted to combine multiple features to perform the MCI classification task. Our proposed MCI classification method is evaluated on 315 subjects (including 105 LMCI subjects, 105 EMCI subjects and 105 NCs) with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed method achieves an accuracy of 88.5% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.897 for LMCI/NC classification, an accuracy of 82.7% and an AUC of 0.832 for EMCI/NC classification, and an accuracy of 79.6% and an AUC of 0.803 for LMCI/EMCI classification, respectively. In addition, by comparison, the accuracy and AUC values of our proposed method are better than those of some existing state-of-the-art methods in MCI classification. Overall, our proposed MCI classification method is effective and promising for automatic diagnosis of MCI in clinical practice.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  Daoqiang Zhang,et al.  Identification of MCI individuals using structural and functional connectivity networks , 2012, NeuroImage.

[3]  K. Kaski,et al.  Intensity and coherence of motifs in weighted complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Yi Pan,et al.  Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[6]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[7]  Samuel Kadoury,et al.  Sub-cortical shape morphology and voxel-based features for Alzheimer's disease classification , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[8]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[9]  Dinggang Shen,et al.  Novel Effective Connectivity Network Inference for MCI Identification , 2017, MLMI@MICCAI.

[10]  Ying Yu,et al.  Automatic ICD code assignment of Chinese clinical notes based on multilayer attention BiRNN , 2019, J. Biomed. Informatics.

[11]  Yi Pan,et al.  Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier , 2019, Neurocomputing.

[12]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[13]  Moein Khajehnejad,et al.  Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning , 2017, Brain sciences.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Jianxin Wang,et al.  High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[16]  Jianxin Wang,et al.  Efficient multi-kernel DCNN with pixel dropout for stroke MRI segmentation , 2019, Neurocomputing.

[17]  Yves Grandvalet,et al.  More efficiency in multiple kernel learning , 2007, ICML '07.

[18]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[19]  Xiaofeng Zhu,et al.  Deep convolutional neural network for accurate segmentation and quantification of white matter hyperintensities , 2020, Neurocomputing.

[20]  Vesna Jelic,et al.  A critical discussion of the role of neuroimaging in mild cognitive impairment * , 2003, Acta neurologica Scandinavica. Supplementum.

[21]  Jie Tian,et al.  FMRI connectivity analysis of acupuncture effects on the whole brain network in mild cognitive impairment patients. , 2012, Magnetic resonance imaging.

[22]  A. Fagan,et al.  Functional connectivity and graph theory in preclinical Alzheimer's disease , 2014, Neurobiology of Aging.

[23]  Michael Weiner,et al.  and the Alzheimer’s Disease Neuroimaging Initiative* , 2007 .

[24]  Z. Yao,et al.  Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer's Disease. , 2015, Journal of Alzheimer's disease : JAD.

[25]  Bin Hu,et al.  Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression , 2015, IEEE Transactions on NanoBioscience.

[26]  Zidong Wang,et al.  Image-Based Quantitative Analysis of Gold Immunochromatographic Strip via Cellular Neural Network Approach , 2014, IEEE Transactions on Medical Imaging.

[27]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[28]  Kee-Eung Kim,et al.  An Improved Particle Filter With a Novel Hybrid Proposal Distribution for Quantitative Analysis of Gold Immunochromatographic Strips , 2019, IEEE Transactions on Nanotechnology.

[29]  Nick C. Fox,et al.  Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease , 2004, NeuroImage.

[30]  Bin Hu,et al.  Alzheimer’s Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features , 2017, IEEE Transactions on NanoBioscience.

[31]  Gang Chen,et al.  Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. , 2011, Radiology.

[32]  Jin Liu,et al.  Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks , 2020, Frontiers in Bioengineering and Biotechnology.

[33]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[34]  Yang Li,et al.  Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification , 2018, Front. Neuroinform..

[35]  Zidong Wang,et al.  A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease , 2018, Neurocomputing.

[36]  Yi Pan,et al.  Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey , 2017, Complex..

[37]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[38]  Alejandro F Frangi,et al.  Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments , 2017, Alzheimer disease and associated disorders.

[39]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[40]  Ghassem Tofighi,et al.  Deep Learning-based Pipeline to Recognize Alzheimer’s Disease using fMRI Data , 2016, bioRxiv.

[41]  Yong He,et al.  Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer's Disease , 2013, Biological Psychiatry.

[42]  J. Weuve,et al.  2016 Alzheimer's disease facts and figures , 2016 .

[43]  Zein Al-Atrache,et al.  CHLAMYDIA PNEUMONIAE-INFECTED ASTROCYTES ALTER THEIR EXPRESSION OF ADAM10, BACE1, AND PRESENILIN-1 PROTEASES , 2016, Alzheimer's & Dementia.

[44]  C. Jack,et al.  Risk of dementia in MCI , 2009, Neurology.

[45]  Jyoti Islam,et al.  Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks , 2018, Brain Informatics.

[46]  Yi Pan,et al.  Improving Alzheimer's Disease Classification by Combining Multiple Measures , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[47]  Yi Pan,et al.  MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning , 2017, Multimedia Tools and Applications.

[48]  Michael W. Weiner,et al.  Worldwide Alzheimer’s Disease Neuroimaging Initiative , 2012, Alzheimer's & Dementia.

[49]  Dinggang Shen,et al.  Multi‐task diagnosis for autism spectrum disorders using multi‐modality features: A multi‐center study , 2017, Human brain mapping.

[50]  Yuan Zhou,et al.  Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease , 2010, PLoS Comput. Biol..

[51]  Yi Pan,et al.  Classification of Schizophrenia Based on Individual Hierarchical Brain Networks Constructed From Structural MRI Images , 2017, IEEE Transactions on NanoBioscience.

[52]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[53]  Dinggang Shen,et al.  Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease , 2018, Medical Image Anal..

[54]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .