Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI
Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationsh...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/4/1031 |
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| author | Sambath Kumar Sethuraman Nandhini Malaiyappan Rajakumar Ramalingam Shakila Basheer Mamoon Rashid Nazir Ahmad |
| author_facet | Sambath Kumar Sethuraman Nandhini Malaiyappan Rajakumar Ramalingam Shakila Basheer Mamoon Rashid Nazir Ahmad |
| author_sort | Sambath Kumar Sethuraman |
| collection | DOAJ |
| description | Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI time-series data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction. |
| first_indexed | 2024-03-11T08:53:56Z |
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| language | English |
| last_indexed | 2024-03-11T08:53:56Z |
| publishDate | 2023-02-01 |
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| series | Electronics |
| spelling | doaj.art-7c588bd5ca7c43c69e87a283254b65372023-11-16T20:13:53ZengMDPI AGElectronics2079-92922023-02-01124103110.3390/electronics12041031Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRISambath Kumar Sethuraman0Nandhini Malaiyappan1Rajakumar Ramalingam2Shakila Basheer3Mamoon Rashid4Nazir Ahmad5Department of Computer Science, Lovely Professional University, Phagwara 140011, IndiaDepartment of Computer Science, Pondicherry University, Pondicherry 605014, IndiaDepartment of Computer Science and Technology, Madanapalle Institute of Technology & Science, Madanapalle 517325, IndiaDepartment of Information Systems, College of computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, IndiaDepartment of Information System, College of Applied Sciences, King Khalid University, P.O. Box 61913, Muhayel 63317, Saudi ArabiaResting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI time-series data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction.https://www.mdpi.com/2079-9292/12/4/1031rs-fMRIclassificationshigh-order neuro-dynamic functional networkdeep learningAlzheimer’s disease |
| spellingShingle | Sambath Kumar Sethuraman Nandhini Malaiyappan Rajakumar Ramalingam Shakila Basheer Mamoon Rashid Nazir Ahmad Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI Electronics rs-fMRI classifications high-order neuro-dynamic functional network deep learning Alzheimer’s disease |
| title | Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI |
| title_full | Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI |
| title_fullStr | Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI |
| title_full_unstemmed | Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI |
| title_short | Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI |
| title_sort | predicting alzheimer s disease using deep neuro functional networks with resting state fmri |
| topic | rs-fMRI classifications high-order neuro-dynamic functional network deep learning Alzheimer’s disease |
| url | https://www.mdpi.com/2079-9292/12/4/1031 |
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