Modified sparse functional principal component analysis for fMRI data process

Abstract

Sparse and functional principal component analysis is a technique to extract sparse and smooth principal components from a matrix. In this paper, we propose a modified sparse and functional principal component analysis model for feature extraction. We measure the tuning parameters by their robustness against random perturbation, and select the tuning parameters by derivative-free optimization. We test our algorithm on the ADNI dataset to distinguish between the patients with Alzheimer's disease and the control group. By applying proper classification methods for sparse features, we get better result than classic singular value decomposition, support vector machine and logistic regression.

Keywords:

• Sparse
• functional
• principal component analysis
• fMRI

Acknowledgments

Prof. Bin Dong from Beijing International Center of Mathematical Research made great contribution to the idea of this paper. Sheng Hu from Peking University also offered help in the process of our work. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Zhengyang Fang  http://orcid.org/0000-0003-1065-9258

Additional information

Funding

This work was supported by National Natural Science Foundation of China. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Notes on contributors

Zhengyang Fang

Zhengyang Fang is pursuing master's degrees in statistics at the University of Chicago. He received his Bachelor of Science in Mathematics from Peking University.

J. Y. Han

J. Y. Han is former graduate student in the department of biostatistics at University of Washington.

N. Simon

N. Simon is assistant professor in Department of Biostatistics at University of Washington.

X. H. Zhou

Dr X. H. Zhou is Boya Chair Professor in Beijing International Center for Mathematical Research and Chair of the Department of Biostatistics at Peking University. Previously he was Professor in the Department of Biostatistics at University of Washington.