Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA скачать в хорошем качестве

Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA

Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning Xin Yang , Ning Zhang and Donglin Wang , Middle Tennessee State University, USA, St. Ambrose University, USA Abstract: The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically developing (TD) participants using the functional connectivity calculated from the derived functional networks. In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. We downloaded a total of 120 training samples, including 58 ASD and 62 TD participants, which were obtained from the public repository: Autism Brain Imaging Data Exchange I (ABIDE I). Our methodology and results have five main parts. First, we utilize a group ICA model to extract functional networks from the ASD group and rank the top 20 regions of interest (ROIs). Keywords: Functional connectivity, rs-fMRI, autism spectrum disorder (ASD), group ICA, Dictionary Learning. Abstract URL: https://aircconline.com/csit/abstract... Full-Text URL: https://aircconline.com/csit/papers/v... Volume URL: http://airccse.org/csit/V11N07.html #Functionalconnectivity #rsfMRI #autismspectrumdisorder(ASD) #groupICA #DictionaryLearning #machinelearning #deeplearning