Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso

Liu, Xiaoli; Cao, Peng; Gonçalves, André R.; Zhao, Dazhe; Banerjee, Arindam

doi:10.1145/3230668

Title: Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso

Journal Article · 17 October 2018 · ACM Transactions on Knowledge Discovery from Data

DOI: https://doi.org/10.1145/3230668 · OSTI ID:1557047

Liu, Xiaoli ^[1]; Cao, Peng ^[2]; Gonçalves, André R. ^[3]; Zhao, Dazhe ^[2]; Banerjee, Arindam ^[4]

Northeastern Univ., Shenyang (China); Univ. of Minnesota, Minneapolis, MN (United States)
Northeastern Univ., Shenyang (China)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Univ. of Minnesota, Minneapolis, MN (United States)

Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is furthermore an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. Here, we introduce the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the presented model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Natural Science Foundation of China (NSF)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1557047

Report Number(s):: LLNL-JRNL--753218; 939429

Journal Information:: ACM Transactions on Knowledge Discovery from Data, Journal Name: ACM Transactions on Knowledge Discovery from Data Journal Issue: 6 Vol. 12; ISSN 1556-4681

Publisher:: Association for Computing MachineryCopyright Statement

Country of Publication:: United States

Language:: English

References (62)

Applied Regression Analysis Draper, Norman R.; Smith, Harry https://doi.org/10.1002/9781118625590	book	January 1998
Entorhinal cortex pathology in Alzheimer's disease van Hoesen, Gary W.; Hyman, Bradley T.; Damasio, Antonio R. Hippocampus, Vol. 1, Issue 1 https://doi.org/10.1002/hipo.450010102	journal	January 1991
On areas of transition between entorhinal allocortex and temporal isocortex in the human brain. Normal morphology and lamina-specific pathology in Alzheimer's disease Braak, H.; Braak, E. Acta Neuropathologica, Vol. 68, Issue 4 https://doi.org/10.1007/BF00690836	journal	January 1985
Atrophy in the parahippocampal gyrus as an early biomarker of Alzheimer’s disease Echávarri, C.; Aalten, P.; Uylings, H. B. M. Brain Structure and Function, Vol. 215, Issue 3-4 https://doi.org/10.1007/s00429-010-0283-8	journal	October 2010
The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent Chen, Caihua; He, Bingsheng; Ye, Yinyu Mathematical Programming, Vol. 155, Issue 1-2 https://doi.org/10.1007/s10107-014-0826-5	journal	October 2014
On the linear convergence of the alternating direction method of multipliers Hong, Mingyi; Luo, Zhi-Quan Mathematical Programming, Vol. 162, Issue 1-2 https://doi.org/10.1007/s10107-016-1034-2	journal	July 2016
Parallel Multi-Block ADMM with o(1 / k) Convergence Deng, Wei; Lai, Ming-Jun; Peng, Zhimin Journal of Scientific Computing, Vol. 71, Issue 2 https://doi.org/10.1007/s10915-016-0318-2	journal	November 2016
Convex multi-task feature learning Argyriou, Andreas; Evgeniou, Theodoros; Pontil, Massimiliano Machine Learning, Vol. 73, Issue 3 https://doi.org/10.1007/s10994-007-5040-8	journal	January 2008
RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning Liu, Jinxin; Wang, Chengdi; Guo, Jixiang International Journal of Computer Assisted Radiology and Surgery, Vol. 16, Issue 6 https://doi.org/10.1007/s11548-021-02360-x	journal	April 2021
Laplacian matrices of graphs: a survey Merris, Russell Linear Algebra and its Applications, Vol. 197-198 https://doi.org/10.1016/0024-3795(94)90486-3	journal	January 1994
Test-retest reliable coefficients and 5-year change scores for the MMSE and 3MS Tombaugh, T. Archives of Clinical Neuropsychology, Vol. 20, Issue 4 https://doi.org/10.1016/j.acn.2004.11.004	journal	June 2005
Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment Xu, Lele; Wu, Xia; Chen, Kewei Computer Methods and Programs in Biomedicine, Vol. 122, Issue 2 https://doi.org/10.1016/j.cmpb.2015.08.004	journal	November 2015
Classification of CT brain images based on deep learning networks Gao, Xiaohong W.; Hui, Rui; Tian, Zengmin Computer Methods and Programs in Biomedicine, Vol. 138 https://doi.org/10.1016/j.cmpb.2016.10.007	journal	January 2017
The Alzheimer's Disease Neuroimaging Initiative: Progress report and future plans Weiner, Michael W.; Aisen, Paul S.; Jack, Clifford R. Alzheimer's & Dementia, Vol. 6, Issue 3 https://doi.org/10.1016/j.jalz.2010.03.007	journal	May 2010
Disease progression model for cognitive deterioration from Alzheimer's Disease Neuroimaging Initiative database Ito, Kaori; Corrigan, Brian; Zhao, Qinying Alzheimer's & Dementia, Vol. 7, Issue 2 https://doi.org/10.1016/j.jalz.2010.03.018	journal	September 2010
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials Weiner, Michael W.; Veitch, Dallas P.; Aisen, Paul S. Alzheimer's & Dementia, Vol. 13, Issue 4 https://doi.org/10.1016/j.jalz.2016.11.007	journal	April 2017
Subregions of the inferior parietal lobule are affected in the progression to Alzheimer's disease Greene, Sarah J.; Killiany, Ronald J. Neurobiology of Aging, Vol. 31, Issue 8 https://doi.org/10.1016/j.neurobiolaging.2010.04.026	journal	August 2010
Empowering imaging biomarkers of Alzheimer's disease Gutman, Boris A.; Wang, Yalin; Yanovsky, Igor Neurobiology of Aging, Vol. 36 https://doi.org/10.1016/j.neurobiolaging.2014.05.038	journal	January 2015
Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm Yan, Jingwen; Li, Taiyong; Wang, Hua Neurobiology of Aging, Vol. 36 https://doi.org/10.1016/j.neurobiolaging.2014.07.045	journal	January 2015
Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest Huang, Lei; Jin, Yan; Gao, Yaozong Neurobiology of Aging, Vol. 46 https://doi.org/10.1016/j.neurobiolaging.2016.07.005	journal	October 2016
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI☆ Misra, C.; Fan, Y.; Davatzikos, C. NeuroImage, Vol. 44, Issue 4 https://doi.org/10.1016/j.neuroimage.2008.10.031	journal	February 2009
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease Stonnington, Cynthia M.; Chu, Carlton; Klöppel, Stefan NeuroImage, Vol. 51, Issue 4 https://doi.org/10.1016/j.neuroimage.2010.03.051	journal	July 2010
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease Zhang, Daoqiang; Shen, Dinggang NeuroImage, Vol. 59, Issue 2 https://doi.org/10.1016/j.neuroimage.2011.09.069	journal	January 2012
Modeling disease progression via multi-task learning Zhou, Jiayu; Liu, Jun; Narayan, Vaibhav A. NeuroImage, Vol. 78 https://doi.org/10.1016/j.neuroimage.2013.03.073	journal	September 2013
Locally linear embedding (LLE) for MRI based Alzheimer's disease classification Liu, Xin; Tosun, Duygu; Weiner, Michael W. NeuroImage, Vol. 83 https://doi.org/10.1016/j.neuroimage.2013.06.033	journal	December 2013
Multitask Learning Caruana, Rich Machine Learning, Vol. 28, Issue 1, p. 41-75 https://doi.org/10.1023/A:1007379606734	journal	January 1997
Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer's disease Khan, Usman A.; Liu, Li; Provenzano, Frank A. Nature Neuroscience, Vol. 17, Issue 2 https://doi.org/10.1038/nn.3606	journal	December 2013
The clinical use of structural MRI in Alzheimer disease Frisoni, Giovanni B.; Fox, Nick C.; Jack, Clifford R. Nature Reviews Neurology, Vol. 6, Issue 2 https://doi.org/10.1038/nrneurol.2009.215	journal	February 2010
Super-resolution photoacoustic and ultrasound imaging with sparse arrays Vilov, Sergey; Arnal, Bastien; Hojman, Eliel Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-61083-2	journal	March 2020
Spectral Graph Theory Chung, Fan R. K. https://doi.org/10.1090/cbms/092	book	January 1997
Functional connectivity of the fusiform gyrus during a face-matching task in subjects with mild cognitive impairment Bokde, A. L. W.; Lopez-Bayo, P.; Meindl, T. Brain, Vol. 129, Issue 5 https://doi.org/10.1093/brain/awl051	journal	March 2006
Vision in Alzheimer's Disease Cronin-Golomb, A. The Gerontologist, Vol. 35, Issue 3 https://doi.org/10.1093/geront/35.3.370	journal	June 1995
3D Mapping of Mini-mental State Examination Performance in Clinical and Preclinical Alzheimer Disease Apostolova, Liana G.; Lu, Po H.; Rogers, Steve Alzheimer Disease & Associated Disorders, Vol. 20, Issue 4 https://doi.org/10.1097/01.wad.0000213857.89613.10	journal	January 2006
Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification Zhu, Xiaofeng; Suk, Heung-Il; Lee, Seong-Whan IEEE Transactions on Biomedical Engineering, Vol. 63, Issue 3 https://doi.org/10.1109/TBME.2015.2466616	journal	March 2016
Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning Wan, Jing; Zhang, Zhilin; Rao, Bhaskar D. IEEE Transactions on Medical Imaging, Vol. 33, Issue 7 https://doi.org/10.1109/TMI.2014.2314712	journal	July 2014
Efficient Methods for Overlapping Group Lasso IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 9 https://doi.org/10.1109/TPAMI.2013.17	journal	September 2013
Sparsity and smoothness via the fused lasso Tibshirani, Robert; Saunders, Michael; Rosset, Saharon Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 67, Issue 1 https://doi.org/10.1111/j.1467-9868.2005.00490.x	journal	February 2005
Stability selection: Stability Selection Meinshausen, Nicolai; Bühlmann, Peter Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 72, Issue 4 https://doi.org/10.1111/j.1467-9868.2010.00740.x	journal	July 2010
Group sparse optimization by alternating direction method Deng, Wei; Yin, Wotao; Zhang, Yin SPIE Optical Engineering + Applications, SPIE Proceedings https://doi.org/10.1117/12.2024410	conference	September 2013
Alzheimer's disease Burns, A.; Iliffe, S. BMJ, Vol. 338, Issue feb05 1 https://doi.org/10.1136/bmj.b158	journal	February 2009
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems Beck, Amir; Teboulle, Marc SIAM Journal on Imaging Sciences, Vol. 2, Issue 1 https://doi.org/10.1137/080716542	journal	January 2009
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction Wang, Yilun; Yang, Junfeng; Yin, Wotao SIAM Journal on Imaging Sciences, Vol. 1, Issue 3 https://doi.org/10.1137/080724265	journal	January 2008
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing Yang, Junfeng; Zhang, Yin SIAM Journal on Scientific Computing, Vol. 33, Issue 1 https://doi.org/10.1137/090777761	journal	January 2011
Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming He, Bingsheng; Tao, Min; Yuan, Xiaoming SIAM Journal on Optimization, Vol. 22, Issue 2 https://doi.org/10.1137/110822347	journal	January 2012
Heterogeneous data fusion for alzheimer's disease study Ye, Jieping; Alexander, Gene; Reiman, Eric Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08 https://doi.org/10.1145/1401890.1402012	conference	January 2008
Efficient Euclidean projections in linear time Liu, Jun; Ye, Jieping Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09 https://doi.org/10.1145/1553374.1553459	conference	January 2009
An efficient algorithm for a class of fused lasso problems Liu, Jun; Yuan, Lei; Ye, Jieping Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10 https://doi.org/10.1145/1835804.1835847	conference	January 2010
A multi-task learning formulation for predicting disease progression Zhou, Jiayu; Yuan, Lei; Liu, Jun Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11 https://doi.org/10.1145/2020408.2020549	conference	January 2011
Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data Ye, Jieping; Farnum, Michael; Yang, Eric BMC Neurology, Vol. 12, Issue 1 https://doi.org/10.1186/1471-2377-12-46	journal	June 2012
Predicting progression of Alzheimer's disease Doody, Rachelle S.; Pavlik, Valory; Massman, Paul Alzheimer's Research & Therapy, Vol. 2, Issue 1 https://doi.org/10.1186/alzrt25	journal	January 2010
Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease Devanand, D. P.; Pradhaban, G.; Liu, X. Neurology, Vol. 68, Issue 11 https://doi.org/10.1212/01.wnl.0000256697.20968.d7	journal	March 2007
MRI and CSF biomarkers in normal, MCI, and AD subjects: Predicting future clinical change Vemuri, P.; Wiste, H. J.; Weigand, S. D. Neurology, Vol. 73, Issue 4 https://doi.org/10.1212/WNL.0b013e3181af79fb	journal	July 2009
Alpha-Band Hypersynchronization in Progressive Mild Cognitive Impairment: A Magnetoencephalography Study Lopez, M. E.; Bruna, R.; Aurtenetxe, S. Journal of Neuroscience, Vol. 34, Issue 44 https://doi.org/10.1523/JNEUROSCI.0964-14.2014	journal	October 2014
Optimization with Sparsity-Inducing Penalties Bach, Francis Foundations and Trends® in Machine Learning, Vol. 4, Issue 1 https://doi.org/10.1561/2200000015	journal	January 2011
Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification Xiaofeng, Zhu,; Seong-Whan, Lee,; Heung-Il, Suk, The University of North Carolina at Chapel Hill University Libraries https://doi.org/10.17615/3hk3-3450	text	January 2016
Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest Yan, Jin,; Yaozong, Gao,; Dinggang, Shen, The University of North Carolina at Chapel Hill University Libraries https://doi.org/10.17615/8y3z-bn29	text	January 2016
Convex Multi-Task Feature Learning Argyriou, Andreas; Evgeniou, Theodoros; Pontil, Massimo SSRN Electronic Journal https://doi.org/10.2139/ssrn.1031158	journal	January 2007
Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort Risacher, Shannon; Saykin, Andrew; Wes, John Current Alzheimer Research, Vol. 6, Issue 4 https://doi.org/10.2174/156720509788929273	journal	August 2009
Alterations in Cortical Thickness and White Matter Integrity in Mild Cognitive Impairment Measured by Whole-Brain Cortical Thickness Mapping and Diffusion Tensor Imaging Wang, L.; Goldstein, F. C.; Veledar, E. American Journal of Neuroradiology, Vol. 30, Issue 5 https://doi.org/10.3174/ajnr.A1484	journal	March 2009
Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers Xu, Lele; Wu, Xia; Li, Rui Journal of Alzheimer's Disease, Vol. 51, Issue 4 https://doi.org/10.3233/JAD-151010	journal	April 2016
Parallel Multi-Block ADMM with o(1/k) Convergence Deng, Wei; Lai, Ming-Jun; Peng, Zhimin arXiv https://doi.org/10.48550/arxiv.1312.3040	preprint	January 2013
Alzheimer'S Disease Edwards, A. T.; Latham, O. Medical Journal of Australia, Vol. 1, Issue 12 https://doi.org/10.5694/j.1326-5377.1943.tb44303.x	journal	March 1943

Cited By (1)

Group Guided Fused Laplacian Sparse Group Lasso for Modeling Alzheimer’s Disease Progression Liu, Xiaoli; Wang, Jianzhong; Ren, Fulong Computational and Mathematical Methods in Medicine, Vol. 2020 https://doi.org/10.1155/2020/4036560	journal	February 2020

Similar Records

MicroRNAs as diagnostic and therapeutic tools for Alzheimer’s disease: advances and limitations

Journal Article · 2019 · Neural Regeneration Research · OSTI ID:1628699

Martinez, Bridget; Peplow, PhilipV

The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement

Journal Article · 2016 · Alzheimer's & Dementia · OSTI ID:1393230

Weiner, Michael W.; Veitch, Dallas P.; Aisen, Paul S.; +13 more

The biological substrates of Alzheimer's disease

Book · 1986 · OSTI ID:6837006

Scheibel, A B; Wechsler, A F; Brazier, M A.B.

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING
ADMM
Alzheimer’s Disease
Disease Progression
Graph Laplacian
Multi-task Learning

Title: Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso

Citation Formats

References (62)

Cited By (1)

Similar Records

Related Subjects