Unsupervised learning of the brain connectivity dynamic using residual D-net
This work addresses the challenge of efficient unsupervised learning for brain dynamics in medical imaging, with potential applications in diagnosing cognitive impairments, though it is incremental in its method adaptation.
The paper tackled the problem of learning brain connectivity dynamics from limited rs-fMRI data using a residual D-net architecture, achieving significantly higher classification accuracy in differentiating early and late stage MCI from healthy controls compared to prior methods.
In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using a deep learning architecture named residual D-net. As it is often the case in medical research, in contrast to typical deep learning tasks, the size of the resting-state functional Magnetic Resonance Image (rs-fMRI) datasets for training is limited. Thus, the available data should be very efficiently used to learn the complex patterns underneath the brain connectivity dynamics. To address this issue, we use residual connections to alleviate the training complexity through recurrent multi-scale representation. We conduct two classification tasks to differentiate early and late stage Mild Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The experiments verify that our proposed residual D-net indeed learns the brain connectivity dynamics, leading to significantly higher classification accuracy compared to previously published techniques.