Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging

作者：

Highlights：

• A novel modelling time series approach, which is applied on rs-fMRI brain imaging, is presented.

• The proposed approach restores informative features related to neuro-psychiatric disease, such as Autism Spectrum Disorder, as exemplified by statistically robust gains in classification metrics when compared to other feature extraction methods.

• The proposed approach decreases the amount of data needed to store patient imaging data history.

• The proposed analysis method is validated on a world-wide multi-site database (ABIDE) in which different methods of imaging acquisition were used.

摘要

•A novel modelling time series approach, which is applied on rs-fMRI brain imaging, is presented.•The proposed approach restores informative features related to neuro-psychiatric disease, such as Autism Spectrum Disorder, as exemplified by statistically robust gains in classification metrics when compared to other feature extraction methods.•The proposed approach decreases the amount of data needed to store patient imaging data history.•The proposed analysis method is validated on a world-wide multi-site database (ABIDE) in which different methods of imaging acquisition were used.

论文关键词：Graph signal processing,Machine learning,Resting-state analysis,Neuroimaging,Classification

论文评审过程：Received 28 September 2019, Revised 2 March 2020, Accepted 2 May 2020, Available online 21 May 2020, Version of Record 5 June 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.101870