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Jahani A, Jahani I, Khadem A, Braden BB, Delrobaei M, MacIntosh BJ. Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder. Sci Rep 2024; 14:20120. [PMID: 39209988 PMCID: PMC11362281 DOI: 10.1038/s41598-024-71174-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.
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Affiliation(s)
- Ali Jahani
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Iman Jahani
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - B Blair Braden
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Mehdi Delrobaei
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
| | - Bradley J MacIntosh
- Hurvitz Brain Sciences, Sandra Black Centre for Brain Resilience and Recovery, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
- Computational Radiology and Artificial Intelligence Unit, Departments of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
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Shao L, Fu C, Chen X. A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder. BMC Bioinformatics 2023; 24:363. [PMID: 37759189 PMCID: PMC10536734 DOI: 10.1186/s12859-023-05495-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/21/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models to graph structured data, have shown great advantages. However, in graph neural methods, because the graphs constructed are homogeneous, the phenotype information of the subjects cannot be fully utilized. This affects the improvement of the classification performance. METHODS To fully utilize the phenotype information, this paper proposes a heterogeneous graph convolutional attention network (HCAN) model to classify ASD. By combining an attention mechanism and a heterogeneous graph convolutional network, important aggregated features can be extracted in the HCAN. The model consists of a multilayer HCAN feature extractor and a multilayer perceptron (MLP) classifier. First, a heterogeneous population graph was constructed based on the fMRI and phenotypic data. Then, a multilayer HCAN is used to mine graph-based features from the heterogeneous graph. Finally, the extracted features are fed into an MLP for the final classification. RESULTS The proposed method is assessed on the autism brain imaging data exchange (ABIDE) repository. In total, 871 subjects in the ABIDE I dataset are used for the classification task. The best classification accuracy of 82.9% is achieved. Compared to the other methods using exactly the same subjects in the literature, the proposed method achieves superior performance to the best reported result. CONCLUSIONS The proposed method can effectively integrate heterogeneous graph convolutional networks with a semantic attention mechanism so that the phenotype features of the subjects can be fully utilized. Moreover, it shows great potential in the diagnosis of brain functional disorders with fMRI data.
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Affiliation(s)
- Lizhen Shao
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, Beijing, 100083, China.
- Lancaster University, Lancaster, LA1 4YX, UK.
| | - Cong Fu
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, Beijing, 100083, China
- Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China
| | - Xunying Chen
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, Beijing, 100083, China
- Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China
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Saleh AI, Rabie AH. A new Autism Spectrum Disorder Discovery (ASDD) strategy using data mining techniques based on blood tests. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abbas SQ, Chi L, Chen YPP. DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder. Artif Intell Med 2023; 136:102475. [PMID: 36710063 DOI: 10.1016/j.artmed.2022.102475] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 10/24/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
The growing prevalence of neurological disorders, e.g., Autism Spectrum Disorder (ASD), demands robust computer-aided diagnosis (CAD) due to the diverse symptoms which require early intervention, particularly in young children. The absence of a benchmark neuroimaging diagnostics paves the way to study transitions in the brain's anatomical structure and neurological patterns associated with ASD. The existing CADs take advantage of the large-scale baseline dataset from the Autism Brain Imaging Data Exchange (ABIDE) repository to improve diagnostic performance, but the involvement of multisite data also amplifies the variabilities and heterogeneities that hinder satisfactory results. To resolve this problem, we propose a Deep Multimodal Neuroimaging Framework (DeepMNF) that employs Functional Magnetic Resonance Imaging (fMRI) and Structural Magnetic Resonance Imaging (sMRI) to integrate cross-modality spatiotemporal information by exploiting 2-dimensional time-series data along with 3-dimensional images. The purpose is to fuse complementary information that increases group differences and homogeneities. To the best of our knowledge, our DeepMNF achieves superior validation performance than the best reported result on the ABIDE-1 repository involving datasets from all available screening sites. In this work, we also demonstrate the performance of the studied modalities in a single model as well as their possible combinations to develop the multimodal framework.
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Affiliation(s)
- S Qasim Abbas
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia.
| | - Lianhua Chi
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia.
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia.
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Cîrstian R, Pilmeyer J, Bernas A, Jansen JFA, Breeuwer M, Aldenkamp AP, Zinger S. Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI. J Neuroimaging 2023; 33:404-414. [PMID: 36710075 DOI: 10.1111/jon.13085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND AND PURPOSE The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.
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Affiliation(s)
- Ramona Cîrstian
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Antoine Bernas
- Department of Biophysics, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
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Selcuk Nogay H, Adeli H. Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Chen LZ, Holmes AJ, Zuo XN, Dong Q. Neuroimaging brain growth charts: A road to mental health. PSYCHORADIOLOGY 2021; 1:272-286. [PMID: 35028568 PMCID: PMC8739332 DOI: 10.1093/psyrad/kkab022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/03/2021] [Accepted: 12/17/2021] [Indexed: 12/30/2022]
Abstract
Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.
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Affiliation(s)
- Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511, USA
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- National Basic Science Data Center, Beijing 100190, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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