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

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

Research output: Contribution to journalArticle

75 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)342-356
Number of pages15
JournalBrain Imaging and Behavior
Volume10
Issue number2
DOIs
Publication statusPublished - 2016 Jun 1

Keywords

  • Mild Cognitive Impairment (MCI)
  • Resting-state functional MRI (R-fMRI)
  • Sliding window correlation
  • Sparse temporal networks
  • Temporal dynamics
  • Temporal smoothness

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Cognitive Neuroscience
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

Fingerprint Dive into the research topics of 'Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification'. Together they form a unique fingerprint.

  • Cite this