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Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

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Abstract

In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI sub-series. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients.

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Notes

  1. http://www.adni-info.org

  2. http://www.fil.ion.ucl.ac.uk/spm/software/spm8/

  3. http://www.adcs.org/studies/imagineadni.aspx

  4. http://adcs.org/studies/ImagineADNI2.aspx

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Acknowledgments

This work was supported in part by NIH grants AG041721, AG042599, EB008374, EB009634, and MH100217. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. 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 Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.

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Correspondence to Dinggang Shen.

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ADNI Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.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. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf.

Appendix A: The Alzheimer’s disease neuroimaging initiative (ADNI)

Appendix A: The Alzheimer’s disease neuroimaging initiative (ADNI)

The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million as a landmark study that gathered and analyzed thousands of brain scans, genetic profiles and biomarkers in blood and cerebrospinal fluid (CSF). Although the original goal was to define biomarkers for use in clinical trials to determine the best way to measure treatment effects of AD, the goal has been expanded to find more sensitive and accurate methods to detect AD at a pre-dementia stage and mark its progress through biomarkers. ADNI 1 involves scientists at 59 research centers, 54 in USA and 5 in Canada. The Principal Investigator of this initiative is Dr. Michael W. Weiner, MD, VA Medical Center and University of California, San Francisco. Originally 800 participants were enrolled, which comprised of 200 with AD patients, 400 with MCI and 200 with normal cognition. In 2010, the ADNI study moved into the ADNI GOFootnote 3 phase to focus on participants who exhibit the earliest signs of memory loss in MCI. While the ADNI GO project work continues, the overall ADNI effort is rapidly moving into a third phase, i.e., ADNI 2.Footnote 4 ADNI 2 will build upon the successes of earlier ADNI phases to identify the earliest signs of AD. Researchers are eager to determine when damage to the brain begins. The ADNI 2 phase of the study includes a large number of new volunteers in the earliest stages of cognitive impairment.

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Wee, CY., Yang, S., Yap, PT. et al. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior 10, 342–356 (2016). https://doi.org/10.1007/s11682-015-9408-2

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