Diagnosis of Autism Spectrum Disorders Based on fMRI
Pages 259 - 264
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and repetitive behaviors, with its exact etiology remaining unclear. Early and accurate diagnosis is critical for effective interventions. This paper introduces a diagnostic method based on functional magnetic resonance imaging (fMRI) data, aiming to extract more effective features and understand the relationships between them. This study utilizes a multi-site fMRI dataset provided by the Autism Brain Imaging Data Exchange (ABIDE) to objectively identify functional connectivity patterns in ASD participants. A deep learning model combining autoencoders and Transformers is proposed to learn complex patterns within the brain’s functional connectivity matrix and capture interactions between different brain regions. The study evaluated 883 subjects from the ABIDE cohort and, by comparing its performance with that of the ASD-DiagNet[12] model on the same dataset, validated the efficacy of the proposed method. Experimental results demonstrate that the method achieved high classification accuracy in identifying ASD, laying a foundation for future research and suggesting potential applications in clinical diagnosis of ASD.
References
[1]
American Psychiatric Association et al. 2000. Diagnostic and statistical manual of mental disorders. Text revision (2000).
[2]
Abdelbasset Brahim and Nicolas Farrugia. 2020. Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging. Artificial Intelligence in Medicine 106 (2020), 101870.
[3]
N Chaitra, PA Vijaya, and Gopikrishna Deshpande. 2020. Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework. Biomedical Signal Processing and Control 62 (2020), 102099.
[4]
Heng Chen, Xujun Duan, Feng Liu, Fengmei Lu, Xujing Ma, Youxue Zhang, Lucina Q Uddin, and Huafu Chen. 2016. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—a multi-center study. Progress in Neuro-Psychopharmacology and Biological Psychiatry 64 (2016), 1–9.
[5]
Zikuan Chen and Vince Calhoun. 2011. A computational multiresolution BOLD fMRI model. IEEE transactions on biomedical engineering 58, 10 (2011), 2995–2999.
[6]
Cameron Craddock, Yassine Benhajali, Carlton Chu, Francois Chouinard, Alan Evans, András Jakab, Budhachandra Singh Khundrakpam, John David Lewis, Qingyang Li, Michael Milham, et al. 2013. The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics 7, 27 (2013), 5.
[7]
R Cameron Craddock, G Andrew James, Paul E Holtzheimer III, Xiaoping P Hu, and Helen S Mayberg. 2012. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human brain mapping 33, 8 (2012), 1914–1928.
[8]
Xin Deng, Jiahao Zhang, Rui Liu, and Ke Liu. 2022. Classifying ASD based on time-series fMRI using spatial–temporal transformer. Computers in biology and medicine 151 (2022), 106320.
[9]
Adriana Di Martino, Chao-Gan Yan, Qingyang Li, Erin Denio, Francisco X Castellanos, Kaat Alaerts, Jeffrey S Anderson, Michal Assaf, Susan Y Bookheimer, Mirella Dapretto, et al. 2014. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry 19, 6 (2014), 659–667.
[10]
Serge O Dumoulin, Alessio Fracasso, Wietske van der Zwaag, Jeroen CW Siero, and Natalia Petridou. 2018. Ultra-high field MRI: Advancing systems neuroscience towards mesoscopic human brain function. Neuroimage 168 (2018), 345–357.
[11]
Nicha C Dvornek, Xiaoxiao Li, Juntang Zhuang, and James S Duncan. 2019. Jointly discriminative and generative recurrent neural networks for learning from fMRI. In Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10. Springer, 382–390.
[12]
Taban Eslami, Vahid Mirjalili, Alvis Fong, Angela R Laird, and Fahad Saeed. 2019. ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Frontiers in neuroinformatics 13 (2019), 70.
[13]
Yu Fu, Jun Song, Junru Guo, Yanzhao Fu, and Yu Cai. 2024. Prediction and Analysis of Sea Surface Temperature Based on LSTM-Transformer Model. Regional Studies in Marine Science (2024), 103726.
[14]
Afrooz Jahedi, Chanond A Nasamran, Brian Faires, Juanjuan Fan, and Ralph-Axel Müller. 2017. Distributed intrinsic functional connectivity patterns predict diagnostic status in large autism cohort. Brain connectivity 7, 8 (2017), 515–525.
[15]
Junzhong Ji, Xinying Xing, Yao Yao, Junwei Li, and Xiaodan Zhang. 2021. Convolutional kernels with an element-wise weighting mechanism for identifying abnormal brain connectivity patterns. Pattern Recognition 109 (2021), 107570.
[16]
Amirali Kazeminejad and Roberto C Sotero. 2020. The importance of anti-correlations in graph theory based classification of autism spectrum disorder. Frontiers in neuroscience 14 (2020), 676.
[17]
Jiachen Li, Dan Jin, Ang Li, Bing Liu, Chengyuan Song, Pan Wang, Dawei Wang, Kaibin Xu, Hongwei Yang, Hongxiang Yao, et al. 2019. ASAF: altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI. Science Bulletin 64, 14 (2019), 998–1010.
[18]
Wenhui Li, Shiyuan Wang, and Guangyuan Liu. 2022. Transformer-based model for fMRI data: ABIDE results. In 2022 7th International Conference on Computer and Communication Systems (ICCCS). IEEE, 162–167.
[19]
Ruth CM Philip, Maria R Dauvermann, Heather C Whalley, Katie Baynham, Stephen M Lawrie, and Andrew C Stanfield. 2012. A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neuroscience & Biobehavioral Reviews 36, 2 (2012), 901–942.
[20]
Mark Plitt, Kelly Anne Barnes, and Alex Martin. 2015. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical 7 (2015), 359–366.
[21]
Jonathan D Power, Alexander L Cohen, Steven M Nelson, Gagan S Wig, Kelly Anne Barnes, Jessica A Church, Alecia C Vogel, Timothy O Laumann, Fran M Miezin, Bradley L Schlaggar, et al. 2011. Functional network organization of the human brain. Neuron 72, 4 (2011), 665–678.
[22]
Archit Rathore, Sourabh Palande, Jeffrey S Anderson, Brandon A Zielinski, P Thomas Fletcher, and Bei Wang. 2019. Autism classification using topological features and deep learning: A cautionary tale. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22. Springer, 736–744.
[23]
Lizhen Shao, Yang You, Haipeng Du, and Dongmei Fu. 2020. Classification of ADHD with fMRI data and multi-objective optimization. Computer Methods and Programs in Biomedicine 196 (2020), 105676.
[24]
Jia-Wei Sun, Rui Fan, Qing Wang, Qian-Qian Wang, Xi-Ze Jia, and Hui-Bin Ma. 2021. Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification. Brain Research 1757 (2021), 147299.
[25]
Lei Sun, Yanfang Xue, Yining Zhang, Lishan Qiao, Limei Zhang, and Mingxia Liu. 2021. Estimating sparse functional connectivity networks via hyperparameter-free learning model. Artificial Intelligence in Medicine 111 (2021), 102004.
[26]
Lucina Q Uddin, AM Clare Kelly, Bharat B Biswal, F Xavier Castellanos, and Michael P Milham. 2009. Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Human brain mapping 30, 2 (2009), 625–637.
[27]
Victor M Vergara and Vince Calhoun. 2016. Randomness in resting state functional connectivity matrices. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 5563–5566.
[28]
Chong-Yaw Wee, Pew-Thian Yap, Daoqiang Zhang, Lihong Wang, and Dinggang Shen. 2014. Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Structure and Function 219 (2014), 641–656.
[29]
Qiuhong Wei, Huiling Cao, Yuan Shi, Ximing Xu, and Tingyu Li. 2023. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. Journal of biomedical informatics 137 (2023), 104254.
[30]
Feng Zhao, Zhiyuan Chen, Islem Rekik, Seong-Whan Lee, and Dinggang Shen. 2020. Diagnosis of autism spectrum disorder using central-moment features from low-and high-order dynamic resting-state functional connectivity networks. Frontiers in neuroscience 14 (2020), 258.
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- Diagnosis of Autism Spectrum Disorders Based on fMRI
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Published In
October 2024
448 pages
ISBN:9798400717482
DOI:10.1145/3704323
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Published: 07 January 2025
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ICCPR 2024
ICCPR 2024: 2024 13th International Conference on Computing and Pattern Recognition
October 25 - 27, 2024
Tianjin, China
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