A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks
This article proposes a model to diagnose autism patients using graphical neural networks. A graphical neural network relates the subjects (nodes) using the features (edges). In our model, radiomic features obtained from sMRI are used as edges, and spatial-temporal data obtained through rs-fMRI are...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/6/1143 |
| _version_ | 1797612405257142272 |
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| author | Kiruthigha Manikantan Suresh Jaganathan |
| author_facet | Kiruthigha Manikantan Suresh Jaganathan |
| author_sort | Kiruthigha Manikantan |
| collection | DOAJ |
| description | This article proposes a model to diagnose autism patients using graphical neural networks. A graphical neural network relates the subjects (nodes) using the features (edges). In our model, radiomic features obtained from sMRI are used as edges, and spatial-temporal data obtained through rs-fMRI are used as nodes. The similarity between first-order and texture features from the sMRI data of subjects are derived using radiomics to construct the edges of a graph. The features from brain summaries are assembled and learned using 3DCNN to represent the features of each node of the graph. Using the structural similarities of the brain rather than phenotypic data or graph kernel functions provides better accuracy. The proposed model was applied to a standard dataset, ABIDE, and it was shown that the classification results improved with the use of both spatial (sMRI) and statistical measures (brain summaries of rs-fMRI) instead of using only medical images. |
| first_indexed | 2024-03-11T06:40:48Z |
| format | Article |
| id | doaj.art-5ce12b3c13e646e0b81c2eb8b02c1d93 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T06:40:48Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-5ce12b3c13e646e0b81c2eb8b02c1d932023-11-17T10:34:57ZengMDPI AGDiagnostics2075-44182023-03-01136114310.3390/diagnostics13061143A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural NetworksKiruthigha Manikantan0Suresh Jaganathan1Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, IndiaDepartment of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, IndiaThis article proposes a model to diagnose autism patients using graphical neural networks. A graphical neural network relates the subjects (nodes) using the features (edges). In our model, radiomic features obtained from sMRI are used as edges, and spatial-temporal data obtained through rs-fMRI are used as nodes. The similarity between first-order and texture features from the sMRI data of subjects are derived using radiomics to construct the edges of a graph. The features from brain summaries are assembled and learned using 3DCNN to represent the features of each node of the graph. Using the structural similarities of the brain rather than phenotypic data or graph kernel functions provides better accuracy. The proposed model was applied to a standard dataset, ABIDE, and it was shown that the classification results improved with the use of both spatial (sMRI) and statistical measures (brain summaries of rs-fMRI) instead of using only medical images.https://www.mdpi.com/2075-4418/13/6/1143autism spectrum disorderdeep learninggraph convolution networkssMRIrs-fMRI |
| spellingShingle | Kiruthigha Manikantan Suresh Jaganathan A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks Diagnostics autism spectrum disorder deep learning graph convolution networks sMRI rs-fMRI |
| title | A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks |
| title_full | A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks |
| title_fullStr | A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks |
| title_full_unstemmed | A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks |
| title_short | A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks |
| title_sort | model for diagnosing autism patients using spatial and statistical measures using rs fmri and smri by adopting graphical neural networks |
| topic | autism spectrum disorder deep learning graph convolution networks sMRI rs-fMRI |
| url | https://www.mdpi.com/2075-4418/13/6/1143 |
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