Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

[1]  Michael Weiner,et al.  Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: An MRI study of 676 AD, MCI, and normal subjects , 2008, NeuroImage.

[2]  I. Veer,et al.  Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: an MRI study , 2008, Brain : a journal of neurology.

[3]  G Gerig,et al.  Network inefficiencies in autism spectrum disorder at 24 months , 2014, Translational Psychiatry.

[4]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..

[5]  Paul M. Thompson,et al.  Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[6]  Paul M. Thompson,et al.  White matter integrity in traumatic brain injury: Effects of permissible fiber turning angle , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[7]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[8]  A. Dale,et al.  CSF Biomarkers in Prediction of Cerebral and Clinical Change in Mild Cognitive Impairment and Alzheimer's Disease , 2010, The Journal of Neuroscience.

[9]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Christophe Lenglet,et al.  Sex differences in the human connectome: 4-Tesla high angular resolution diffusion imaging (HARDI) tractography in 234 young adult twins , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  H. Benali,et al.  Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI , 2009, Neuroradiology.

[12]  R. Tibshirani,et al.  Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[13]  Stephen Todd,et al.  Survival in dementia and predictors of mortality: a review , 2013, International journal of geriatric psychiatry.

[14]  E. Bullmore,et al.  Disrupted Axonal Fiber Connectivity in Schizophrenia , 2011, Biological Psychiatry.

[15]  Paul M. Thompson,et al.  Heritability of White Matter Fiber Tract Shapes: A HARDI Study of 198 Twins , 2011, MBIA.

[16]  A. Toga,et al.  Mapping the human connectome. , 2012, Neurosurgery.

[17]  Peter Bühlmann.  Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani): Comments on the presentation , 2011 .

[18]  C. Jack,et al.  Angular versus spatial resolution trade‐offs for diffusion imaging under time constraints , 2013, Human brain mapping.

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

[20]  Geoffrey J M Parker,et al.  A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements , 2003, Journal of magnetic resonance imaging : JMRI.

[21]  Michael Weiner,et al.  Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials , 2013, NeuroImage.

[22]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[23]  Shuiwang Ji,et al.  SLEP: Sparse Learning with Efficient Projections , 2011 .

[24]  R. Petersen,et al.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects , 2009, Annals of neurology.

[25]  Paul M. Thompson,et al.  Diffusion tensor imaging in seven minutes: Determining trade-offs between spatial and directional resolution , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[26]  Paul M. Thompson,et al.  Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics , 2014, NeuroImage.

[27]  Paul M. Thompson,et al.  White matter disruption in moderate/severe pediatric traumatic brain injury: Advanced tract-based analyses , 2015, NeuroImage: Clinical.

[28]  D. Selkoe.  Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[29]  Michael W. Weiner,et al.  Empowering imaging biomarkers of Alzheimer's disease , 2015, Neurobiology of Aging.

[30]  D. Louis Collins,et al.  Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls , 2011, NeuroImage.

[31]  Essa Yacoub,et al.  Rich Club Analysis of Structural Brain Connectivity at 7 Tesla Versus 3 Tesla , 2013, CDMRI/MMBC@MICCAI.

[32]  Paul M. Thompson,et al.  Multiple Stages Classification of Alzheimer’s Disease Based on Structural Brain Networks Using Generalized Low Rank Approximations (GLRAM) , 2014, MICCAI 2014.

[33]  Paul M. Thompson,et al.  Voxelwise Spectral Diffusional Connectivity and Its Applications to Alzheimer's Disease and Intelligence Prediction , 2013, MICCAI.

[34]  Paul M. Thompson,et al.  Automated multi-atlas labeling of the fornix and its integrity in alzheimer's disease , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[35]  A. Convit,et al.  Hippocampal formation glucose metabolism and volume losses in MCI and AD , 2001, Neurobiology of Aging.

[36]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[37]  Paul M. Thompson,et al.  Labeling white matter tracts in hardi by fusing multiple tract atlases with applications to genetics , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[38]  et al.,et al.  Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline , 2008, NeuroImage.

[39]  Essa Yacoub,et al.  A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography , 2011, Medical Image Anal..

[40]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[41]  A. Alexander,et al.  White matter tractography using diffusion tensor deflection , 2003, Human brain mapping.

[42]  Paul M. Thompson,et al.  Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[43]  Essa Yacoub,et al.  Magnetic Resonance Field Strength Effects on Diffusion Measures and Brain Connectivity Networks , 2013, Brain Connect..

[44]  Paul M. Thompson,et al.  Understanding scanner upgrade effects on brain integrity & connectivity measures , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[45]  A. Burns.  Alzheimer's disease: on the verges of treatment and prevention , 2009, The Lancet Neurology.

[46]  W. M. van der Flier,et al.  CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. , 2009, JAMA.

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

[48]  C. Jack,et al.  Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. , 2004, Archives of neurology.

[49]  Norbert Schuff,et al.  3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry , 2008, NeuroImage.

[50]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[51]  Paul M. Thompson,et al.  Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases , 2012, MBIA.

[52]  Cassandra D. Leonardo,et al.  Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease , 2015, Front. Aging Neurosci..

[53]  Norbert Schuff,et al.  Mapping Alzheimer's Disease Progression in 1309 Mri Scans: Power Estimates for Different Inter-scan Intervals ☆ ⁎ and the Alzheimer's Disease Neuroimaging Initiative , 2022 .

[54]  Q. Mcnemar.  Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[55]  R. Petersen,et al.  Mild cognitive impairment , 2006, The Lancet.

[56]  J. Price,et al.  Mild cognitive impairment represents early-stage Alzheimer disease. , 2001, Archives of neurology.

[57]  I. Jolliffe.  Principal Component Analysis , 2002 .

[58]  Marie Chupin,et al.  Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging , 2009, NeuroImage.

[59]  R. Verleger,et al.  Principal component analysis of event-related potentials: a note on misallocation of variance. , 1986, Electroencephalography and clinical neurophysiology.

[60]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[61]  Paul M. Thompson,et al.  How do spatial and angular resolution affect brain connectivity maps from diffusion MRI? , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[62]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[63]  E. Tangalos,et al.  Mild Cognitive Impairment Clinical Characterization and Outcome , 1999 .

[64]  W. M. van der Flier,et al.  Longitudinal changes of CSF biomarkers in memory clinic patients , 2007, Neurology.

[65]  P. Hof,et al.  Does Alzheimer's disease begin in the brainstem? , 2009, Neuropathology and applied neurobiology.

[66]  C. Jack,et al.  Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging☆ , 2013, NeuroImage: Clinical.