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Solana-Lavalle G, Cusimano MD, Steeves T, Rosas-Romero R, Tyrrell PN. Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging. Tomography 2024; 10:894-911. [PMID: 38921945 PMCID: PMC11209036 DOI: 10.3390/tomography10060068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/23/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024] Open
Abstract
In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.
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Affiliation(s)
- Gabriel Solana-Lavalle
- Department of Computing, Electronics, and Mechatronics, Universidad de las Américas Puebla Santa Catarina Mártir, San Andrés Cholula, Puebla 78210, Mexico; (G.S.-L.); (R.R.-R.)
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Michael D. Cusimano
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A1, Canada;
- Division of Neurosurgery, Unity Health Toronto, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
| | - Thomas Steeves
- Division of Neurology, Unity Health Toronto, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada;
| | - Roberto Rosas-Romero
- Department of Computing, Electronics, and Mechatronics, Universidad de las Américas Puebla Santa Catarina Mártir, San Andrés Cholula, Puebla 78210, Mexico; (G.S.-L.); (R.R.-R.)
| | - Pascal N. Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A1, Canada;
- Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 1A1, Canada
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Valizadeh A, Moassefi M, Nakhostin-Ansari A, Heidari Some'eh S, Hosseini-Asl H, Saghab Torbati M, Aghajani R, Maleki Ghorbani Z, Menbari-Oskouie I, Aghajani F, Mirzamohamadi A, Ghafouri M, Faghani S, Memari AH. Automated diagnosis of autism with artificial intelligence: State of the art. Rev Neurosci 2024; 35:141-163. [PMID: 37678819 DOI: 10.1515/revneuro-2023-0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/28/2023] [Indexed: 09/09/2023]
Abstract
Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.
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Affiliation(s)
- Amir Valizadeh
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Mana Moassefi
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Amin Nakhostin-Ansari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Soheil Heidari Some'eh
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Hossein Hosseini-Asl
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | | | - Reyhaneh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Zahra Maleki Ghorbani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Iman Menbari-Oskouie
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Faezeh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Research Development Center, Arash Women's Hospital, Tehran University of Medical Sciences, PO: 14695542, Tehran, Iran
| | - Alireza Mirzamohamadi
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Mohammad Ghafouri
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Shahriar Faghani
- Shariati Hospital, Department of Radiology, Tehran University of Medical Sciences, PO: 1411713135, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, PO: 1416634793, Tehran, Iran
| | - Amir Hossein Memari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
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3
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Wang H, Zhu R, Tian S, Shao J, Dai Z, Xue L, Sun Y, Chen Z, Yao Z, Lu Q. Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI. Cogn Neurodyn 2023; 17:1609-1619. [PMID: 37974586 PMCID: PMC10640554 DOI: 10.1007/s11571-022-09907-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 07/19/2022] [Accepted: 10/28/2022] [Indexed: 12/04/2022] Open
Abstract
The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09907-x.
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Affiliation(s)
- Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhilu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Zhijian Yao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093 China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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Ahmed ZAT, Aldhyani THH, Jadhav ME, Alzahrani MY, Alzahrani ME, Althobaiti MM, Alassery F, Alshaflut A, Alzahrani NM, Al-madani AM. Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3941049. [PMID: 35419082 PMCID: PMC9001065 DOI: 10.1155/2022/3941049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 12/04/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.
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Affiliation(s)
- Zeyad A. T. Ahmed
- Department of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, India
| | - Theyazn H. H. Aldhyani
- Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
| | - Mukti E. Jadhav
- Shri Shivaji Science & Arts College, Chikhli Dist. Buldana, India
| | - Mohammed Y. Alzahrani
- Department of Computer Sciences and Information Technology, Albaha University, Albaha, P.O. Box 1988, Saudi Arabia
| | - Mohammad Eid Alzahrani
- Department of Engineering and Computer Science, Al Baha University, Albaha, P.O. Box 1988, Saudi Arabia
| | - Maha M. Althobaiti
- Department of Computer Science, College of Computing and Information technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia
| | - Fawaz Alassery
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahmed Alshaflut
- College of Computer Science and Information Technology, Albaha University, Albaha, P.O. Box 1988, Saudi Arabia
| | - Nouf Matar Alzahrani
- College of Computer Science and Information Technology, Albaha University, Albaha, P.O. Box 1988, Saudi Arabia
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Chaddad A, Li J, Lu Q, Li Y, Okuwobi IP, Tanougast C, Desrosiers C, Niazi T. Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review. Diagnostics (Basel) 2021; 11:2032. [PMID: 34829379 PMCID: PMC8618159 DOI: 10.3390/diagnostics11112032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/31/2021] [Accepted: 10/31/2021] [Indexed: 11/16/2022] Open
Abstract
Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada;
| | - Jiali Li
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Qizong Lu
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Yujie Li
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Idowu Paul Okuwobi
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Camel Tanougast
- Laboratoire de Conception, Optimisation et Modélisation des Systèmes, University of Lorraine, 57070 Metz, France;
| | - Christian Desrosiers
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada;
| | - Tamim Niazi
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada;
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Shi C, Xin X, Zhang J. Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data. Brain Sci 2021; 11:603. [PMID: 34066816 PMCID: PMC8150603 DOI: 10.3390/brainsci11050603] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/29/2021] [Accepted: 05/04/2021] [Indexed: 11/29/2022] Open
Abstract
Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.
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Affiliation(s)
- Chunlei Shi
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China; (C.S.); (X.X.)
| | - Xianwei Xin
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China; (C.S.); (X.X.)
| | - Jiacai Zhang
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China; (C.S.); (X.X.)
- Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing 100875, China
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8
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A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11083636] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.
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