Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain 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 early versus late MCI.

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

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

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

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

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

[6]  Paul M. Thompson,et al.  Disrupted Brain Networks in the Aging HIV+ Population , 2012, Brain Connect..

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

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

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

[10]  Jesse A. Brown,et al.  Abnormal Brain Network Organization in Body Dysmorphic Disorder , 2013, Neuropsychopharmacology.

[11]  Paul M. Thompson,et al.  A Framework for Quantifying Node-Level Community Structure Group Differences in Brain Connectivity Networks , 2012, MICCAI.

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

[13]  Paul M. Thompson,et al.  Heritability of brain network topology in 853 twins and siblings , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[14]  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.

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

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

[17]  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.

[18]  J. Morris,et al.  Clinical core of the Alzheimer's disease neuroimaging initiative: Progress and plans , 2010, Alzheimer's & Dementia.

[19]  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.

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

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

[22]  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.

[23]  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.

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

[25]  Liang Zhan,et al.  Impaired Inter-Hemispheric Integration in Bipolar Disorder Revealed with Brain Network Analyses , 2013, Biological Psychiatry.