Localizing Sources of Brain Disease Progression with Network Diffusion Model

Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. In this paper, we propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy. To reliably estimate the atrophy sources, we impose sparse regularization on the source distribution and solve the inverse problem with an efficient gradient descent method. We localize the possible origins of Alzheimer's disease (AD) based on a large set of repeated magnetic resonance imaging (MRI) scans in Alzheimer's Disease Neuroimaging Initiative database. The distribution of the sources averaged over the sample population is evaluated. We find that the dementia sources have different concentrations in the brain lobes for AD patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Moreover, we demonstrate that we can effectively predict changes of brain atrophy patterns with the proposed model. Our work could help understand the dynamics and origin of dementia, as well as monitor the progression of the diseases in an early stage.

[1]  Michael Weiner,et al.  Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease. , 2015, Cell reports.

[2]  Shannon L. Risacher,et al.  High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction , 2012, NIPS.

[3]  Mathias Jucker,et al.  Self-propagation of pathogenic protein aggregates in neurodegenerative diseases , 2013, Nature.

[4]  Paul M. Thompson,et al.  Inferring Sources of Dementia Progression with Network Diffusion Model , 2014, MLMI.

[5]  Dietmar R. Thal,et al.  Stages of the Pathologic Process in Alzheimer Disease: Age Categories From 1 to 100 Years , 2011, Journal of neuropathology and experimental neurology.

[6]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[7]  Daniel L. Rubin,et al.  Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease , 2008, PLoS Comput. Biol..

[8]  B. Dawant,et al.  Characterizing changes in MR images with color-coded Jacobians. , 2004, Magnetic resonance imaging.

[9]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[10]  Ruth A. Carper,et al.  Autism and Abnormal Development of Brain Connectivity , 2004, The Journal of Neuroscience.

[11]  Charles Nicholson,et al.  Diffusion and related transport mechanisms in brain tissue , 2001 .

[12]  Jinbo Bi,et al.  Quadratic optimization to identify highly heritable quantitative traits from complex phenotypic features , 2013, KDD.

[13]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[14]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[15]  Lars-Olof Wahlund,et al.  Cingulate cortex hypoperfusion predicts Alzheimer's disease in mild cognitive impairment , 2002, BMC neurology.

[16]  E. Bullmore,et al.  Human brain networks in health and disease , 2009, Current opinion in neurology.

[17]  Daniel C. Alexander,et al.  Probabilistic Event Cascades for Alzheimer's disease , 2012, NIPS.

[18]  M. Weiner,et al.  The dynamics of cortical and hippocampal atrophy in Alzheimer disease. , 2011, Archives of neurology.

[19]  M. Stone.  An Asymptotic Equivalence of Choice of Model by Cross‐Validation and Akaike's Criterion , 1977 .

[20]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[21]  J. Morris,et al.  Profound Loss of Layer II Entorhinal Cortex Neurons Occurs in Very Mild Alzheimer’s Disease , 1996, The Journal of Neuroscience.

[22]  Alan C. Evans,et al.  Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders , 2014, PLoS Comput. Biol..

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

[24]  Clifford R. Jack,et al.  Understanding atrophy trajectories in alzheimer's disease using association rules on MRI images , 2011, KDD.

[25]  H. Braak,et al.  Alzheimer's Disease: Striatal Amyloid Deposits and Neurofibrillary Changes , 1990, Journal of neuropathology and experimental neurology.

[26]  M. Weiner,et al.  A Network Diffusion Model of Disease Progression in Dementia , 2012, Neuron.

[27]  Mark W. Schmidt,et al.  Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches , 2007, ECML.

[28]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[29]  H. Voss,et al.  Network diffusion accurately models the relationship between structural and functional brain connectivity networks , 2014, NeuroImage.

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

[31]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[32]  Martin Vetterli,et al.  Sensor networks for diffusion fields: Detection of sources in space and time , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[33]  Naruhiko Sahara,et al.  Propagation of Tau Pathology in a Model of Early Alzheimer's Disease , 2012, Neuron.

[34]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

[35]  Markus Tolnay,et al.  The propagation of prion-like protein inclusions in neurodegenerative diseases , 2010, Trends in Neurosciences.

[36]  Jiayu Zhou,et al.  Modeling disease progression via fused sparse group lasso , 2012, KDD.

[37]  Christos Davatzikos,et al.  Dynamic Bayesian network modeling for longitudinal brain morphometry , 2012, NeuroImage.

[38]  Kiralee M. Hayashi,et al.  Dynamics of Gray Matter Loss in Alzheimer's Disease , 2003, The Journal of Neuroscience.

[39]  G. Wenk,et al.  Neuropathologic changes in Alzheimer's disease. , 2003, The Journal of clinical psychiatry.

[40]  Norbert Schuff,et al.  Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: An ADNI study of 515 subjects , 2009, NeuroImage.

[41]  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.

[42]  B. Miller,et al.  Neurodegenerative Diseases Target Large-Scale Human Brain Networks , 2009, Neuron.

[43]  Michael R. Lyu,et al.  Mining social networks using heat diffusion processes for marketing candidates selection , 2008, CIKM '08.

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

[45]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[46]  K. Jellinger,et al.  Mild Cognitive Impairment. Aging to Alzheimer's disease , 2003 .

[47]  Moo K. Chung,et al.  Sparse Brain Network Recovery Under Compressed Sensing , 2011, IEEE Transactions on Medical Imaging.

[48]  Quanzheng Li,et al.  Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification , 2013, IPMI.

[49]  Wuqiong Luo,et al.  Identifying Infection Sources and Regions in Large Networks , 2012, IEEE Transactions on Signal Processing.