Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression

@article{Zhang2015RestingStateWF,
  title={Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression},
  author={Xiaowei Zhang and B. J. Hu and Xu Ma and Linxin Xu},
  journal={IEEE Transactions on NanoBioscience},
  year={2015},
  volume={14},
  pages={237-247}
}
Mild cognitive impairment (MCI) has been considered as a transition phase to Alzheimer's disease (AD), and the diagnosis of MCI may help patients to carry out appropriate treatments to delay or even prevent AD. Recent advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been widely used to get more comprehensive understanding of neurological disorders at a whole-brain connectivity level. However, how to explore effective brain… CONTINUE READING