Temporally dynamic resting-state functional connectivity networks for early MCI identification

Chong Yaw Wee, Sen Yang, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Resting-state functional Magnetic Resonance Imaging (R-fMRI) scan provides a rich characterization of the dynamic changes or temporal variabilities caused by neural interactions that may happen within the scan duration. Multiple functional connectivity networks can be estimated from R-fMRI time series to effectively capture subtle yet short neural connectivity changes induced by disease pathologies. To effectively extract the temporally dynamic information, we utilize a sliding window approach to generate multiple shorter, yet overlapping sub-series from a full R-fMRI time series. Whole-brain sliding window correlations are computed based on these sub-series to generate a series of temporal networks, characterize the neural interactions between brain regions at different time scales. Individual estimation of these temporal networks overlooks the intrinsic temporal smoothness between successive overlapping R-fMRI sub-series. To handle this problem, we suggest to jointly estimate temporal networks by maximizing a penalized log likelihood via a fused lasso regularization: 1) l1-norm penalty ensures a sparse solution; 2) fused regularization preserves the temporal smoothness while allows correlation variability. The estimated temporal networks were applied for early Mild Cognitive Impairment (eMCI) identification, and our results demonstrate the importance of including temporally dynamic R-fMRI scan information for accurate diagnosis of eMCI.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PublisherSpringer Verlag
Pages139-146
Number of pages8
ISBN (Print)9783319022666
DOIs
Publication statusPublished - 2013
Event4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8184 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wee, C. Y., Yang, S., Yap, P. T., & Shen, D. (2013). Temporally dynamic resting-state functional connectivity networks for early MCI identification. In Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings (pp. 139-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8184 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-02267-3_18