Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction

@article{Rahim2016TransmodalLO,
  title={Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction},
  author={Mehdi Rahim and Bertrand Thirion and Claude Comtat and Ga{\"e}l Varoquaux},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2016},
  volume={10},
  pages={1204-1213}
}
Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomark-er of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference… CONTINUE READING

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