Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder
With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients...
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Online Access: | https://www.mdpi.com/1424-8220/20/21/6001 |
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doaj-55fc8a9bef0d4266963e1e1ce43f8d832020-11-25T04:08:57ZengMDPI AGSensors1424-82202020-10-01206001600110.3390/s20216001Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum DisorderZarina Rakhimberdina0Xin Liu1and Tsuyoshi Murata2Department of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, JapanAIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo Institute of Technology, Tokyo 152-8550, JapanDepartment of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, JapanWith the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.https://www.mdpi.com/1424-8220/20/21/6001brain functional connectivitygraph neural networkgraph signal processing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zarina Rakhimberdina Xin Liu and Tsuyoshi Murata |
spellingShingle |
Zarina Rakhimberdina Xin Liu and Tsuyoshi Murata Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder Sensors brain functional connectivity graph neural network graph signal processing |
author_facet |
Zarina Rakhimberdina Xin Liu and Tsuyoshi Murata |
author_sort |
Zarina Rakhimberdina |
title |
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder |
title_short |
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder |
title_full |
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder |
title_fullStr |
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder |
title_full_unstemmed |
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder |
title_sort |
population graph-based multi-model ensemble method for diagnosing autism spectrum disorder |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-10-01 |
description |
With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset. |
topic |
brain functional connectivity graph neural network graph signal processing |
url |
https://www.mdpi.com/1424-8220/20/21/6001 |
work_keys_str_mv |
AT zarinarakhimberdina populationgraphbasedmultimodelensemblemethodfordiagnosingautismspectrumdisorder AT xinliu populationgraphbasedmultimodelensemblemethodfordiagnosingautismspectrumdisorder AT andtsuyoshimurata populationgraphbasedmultimodelensemblemethodfordiagnosingautismspectrumdisorder |
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1724423962265387008 |