Ingalhalikar, M and Shinde, S and Karmarkar, A and Rajan, A and Rangaprakash, D and Deshpande, G (2021) Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. In: IEEE Transactions on Biomedical Engineering, 68 (12). pp. 3628-3637.
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Abstract
Objective: The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized. Methods: We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data. Results: Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism. Conclusion: Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. Significance: ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry. © 1964-2012 IEEE.
| Item Type: | Journal Article |
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| Publication: | IEEE Transactions on Biomedical Engineering |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to IEEE Computer Society. |
| Keywords: | Brain mapping; Decision support systems; Diseases; Electronic data interchange; Machine learning; Neural networks; Sampling, Acquisition parameters; Classification accuracy; Clinical decision support systems; Curse of dimensionality; Data harmonization; Functional connectivity; Multiple Classification; Verbal communications, Classification (of information), Article; artificial neural network; autism; autism brain imaging data exchange; autoencoder; Bayesian network; brain region; classification algorithm; clinical decision support system; combat technique; communication disorder; controlled study; data analysis; diagnostic accuracy; diagnostic test accuracy study; functional connectivity; functional magnetic resonance imaging; hemodynamic parameters; human; machine learning; neuroimaging; random forest; sample size; verbal communication; autism; Bayes theorem; brain; diagnostic imaging; nuclear magnetic resonance imaging, Autism Spectrum Disorder; Autistic Disorder; Bayes Theorem; Brain; Humans; Magnetic Resonance Imaging |
| Department/Centre: | Autonomous Societies / Centres > Centre for Brain Research |
| Date Deposited: | 20 Feb 2023 11:11 |
| Last Modified: | 20 Feb 2023 11:11 |
| URI: | https://eprints.iisc.ac.in/id/eprint/80419 |
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