Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis

PH-BiLSTM

Procedure Code Introduction

1. Detect the topology of efficient connectivity networks via group-constrained structure detection algorithm;

2. Apply an adaptive Kalman Filter algorithm to recursively estimate the efficient connectivity strength;

3. Split the dataset into train data and test data, extract topological features from adaptive FC networks with the optimal parameters, train a PH-BiLSTM model and test it with the test data.

Run Matlab & Python

1. Implement GroupLasso algorithm

   ' matlab/GroupLasso.m '

2. Implement adaptive dFC via Kalman filter algorithm by RARX matlab toolbox

   ' matlab/rarx_kf.m '

   ' matlab/get_kalmanDim.m % convert and squeeze matrix '

3. Implement PH-BiLSTM by Tensorflow and Keras deep learning model

   'phbilstm.py '

Comparision Experiment

1. Sliding Window Connectivity (SWC) algorithm

   ' matlab/loworder_net_built.m '

Data

• dataset from ADNI rs-fMRI including NC/MCI classification.
• for the laboratory resources, the dataset is private.

Publications

The architecture implemented in this repository is described in detail in a preprint at researchgate. If you use this architecture in your research work please cite the paper, with the following bibtex:

@inproceedings{jiang2019adaptive,
  title={Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis},
  author={Jiang, Yiqiao and Huang, Huifang and Liu, Jingyu and Wee, Chong-Yaw and Li, Yang},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={507--515},
  year={2019},
  organization={Springer}
}