3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI
Attention deficit hyperactivity disorder (ADHD) is one of the most common mental-health disorders. As a neurodevelopment disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored as biomarkers in ADHD. Amo...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2017
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| Online Access: | https://hdl.handle.net/10356/86887 http://hdl.handle.net/10220/44209 |
| _version_ | 1811683043651354624 |
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| author | Zou, Liang Zheng, Jiannan Miao, Chunyan Mckeown, Martin J. Wang, Z. Jane |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zou, Liang Zheng, Jiannan Miao, Chunyan Mckeown, Martin J. Wang, Z. Jane |
| author_sort | Zou, Liang |
| collection | NTU |
| description | Attention deficit hyperactivity disorder (ADHD) is one of the most common mental-health disorders. As a neurodevelopment disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored as biomarkers in ADHD. Among various machine learning methods, deep learning has demonstrated excellent performance on many imaging tasks. With the availability of publically-available, large neuroimaging data sets for training purposes, deep learning-based automatic diagnosis of psychiatric disorders can become feasible. In this paper, we develop a deep learning-based ADHD classification method via 3-D convolutional neural networks (CNNs) applied to MRI scans. Since deep neural networks may utilize millions of parameters, even the large number of MRI samples in pooled data sets is still relatively limited if one is to learn discriminative features from the raw data. Instead, here we propose to first extract meaningful 3-D low-level features from functional MRI (fMRI) and structural MRI (sMRI) data. Furthermore, inspired by radiologists’ typical approach for examining brain images, we design a 3-D CNN model to investigate the local spatial patterns of MRI features. Finally, we discover that brain functional and structural information are complementary, and design a multi-modality CNN architecture to combine fMRI and sMRI features. Evaluations on the hold-out testing data of the ADHD-200 global competition shows that the proposed multi-modality 3-D CNN approach achieves the state-of-the-art accuracy of 69.15% and outperforms reported classifiers in the literature, even with fewer training samples. We suggest that multi-modality classification will be a promising direction to find potential neuroimaging biomarkers of neurodevelopment disorders. |
| first_indexed | 2024-10-01T04:06:28Z |
| format | Journal Article |
| id | ntu-10356/86887 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:28Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/868872020-03-07T11:48:58Z 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI Zou, Liang Zheng, Jiannan Miao, Chunyan Mckeown, Martin J. Wang, Z. Jane School of Computer Science and Engineering Attention Deficit Hyperactive Disorder 3D CNN Attention deficit hyperactivity disorder (ADHD) is one of the most common mental-health disorders. As a neurodevelopment disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored as biomarkers in ADHD. Among various machine learning methods, deep learning has demonstrated excellent performance on many imaging tasks. With the availability of publically-available, large neuroimaging data sets for training purposes, deep learning-based automatic diagnosis of psychiatric disorders can become feasible. In this paper, we develop a deep learning-based ADHD classification method via 3-D convolutional neural networks (CNNs) applied to MRI scans. Since deep neural networks may utilize millions of parameters, even the large number of MRI samples in pooled data sets is still relatively limited if one is to learn discriminative features from the raw data. Instead, here we propose to first extract meaningful 3-D low-level features from functional MRI (fMRI) and structural MRI (sMRI) data. Furthermore, inspired by radiologists’ typical approach for examining brain images, we design a 3-D CNN model to investigate the local spatial patterns of MRI features. Finally, we discover that brain functional and structural information are complementary, and design a multi-modality CNN architecture to combine fMRI and sMRI features. Evaluations on the hold-out testing data of the ADHD-200 global competition shows that the proposed multi-modality 3-D CNN approach achieves the state-of-the-art accuracy of 69.15% and outperforms reported classifiers in the literature, even with fewer training samples. We suggest that multi-modality classification will be a promising direction to find potential neuroimaging biomarkers of neurodevelopment disorders. Published version 2017-12-28T04:48:33Z 2019-12-06T16:30:58Z 2017-12-28T04:48:33Z 2019-12-06T16:30:58Z 2017 Journal Article Zou, L., Zheng, J., Miao, C., Mckeown, M. J., & Wang, Z. J. (2017). 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI. IEEE Access, 5, 23626-23636. https://hdl.handle.net/10356/86887 http://hdl.handle.net/10220/44209 10.1109/ACCESS.2017.2762703 en IEEE Access © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 11 p. application/pdf |
| spellingShingle | Attention Deficit Hyperactive Disorder 3D CNN Zou, Liang Zheng, Jiannan Miao, Chunyan Mckeown, Martin J. Wang, Z. Jane 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI |
| title | 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI |
| title_full | 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI |
| title_fullStr | 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI |
| title_full_unstemmed | 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI |
| title_short | 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI |
| title_sort | 3d cnn based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural mri |
| topic | Attention Deficit Hyperactive Disorder 3D CNN |
| url | https://hdl.handle.net/10356/86887 http://hdl.handle.net/10220/44209 |
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