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Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features

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Abstract

In this study, we analyzed the performance of the morphological features (MF) and morphological distance-related features (MDRF) in the classification of autism spectrum disorder (ASD) and typical development (TD). Initially, we preprocessed the structural magnetic resonance images (sMRI) of ASD and TD from seven sites publicly available in the autism brain imaging data exchange (ABIDE-I and ABIDE-II) database using a standard pipeline. Further, sMRI images were parcellated into different regions using the Destrieux atlas. Moreover, MF (surface area) and MDRF were calculated from each region. We tested the performance of the MF and MDRF on each site by feeding them to classifiers such as random forest (RF), support vector machines (SVM), and multi-layer perceptron (MLP). Our results suggested that the MDRF could classify the ASD and TD better than the MF. Furthermore, RF gave a single-site average classification accuracy of 91.78% and 95.27% using MF and MDRF, respectively. We achieved the average classification accuracy of 69.08% and 82.91% between the sites using MF and MDRF, respectively. Our results suggested that the frontal lobe and right hemisphere contributed more MDRF to the machine learning model. Furthermore, in the USM site, many features were found to be from the frontal lobe (15 distance features) and frontal-parietal (11 distance features) lobe. The results suggest that the MDRF can be a valuable feature metric for classifying ASD-like neurodevelopmental disorders.

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Data availability

The data used for this study was obtained from the publicly available ABIDE database https://fcon_1000.projects.nitrc.org/indi/abide/.

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Acknowledgements

This work was supported by the Param Shivay supercomputing center of the Indian Institute of Technology (BHU), Varanasi.

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Authors and Affiliations

  1. School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India

    Gokul Manoj, Vaibhavi Gupta, Shaik Gadda Abdul Aleem, Dhanvi Vedantham & Jac Fredo Agastinose Ronickom

  2. Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Aditi Bhattacharya

  3. Department of Electrical and Electronics Engineering, BITS Pilani, K.K Birla Goa Campus, Sancoale, India

    Amalin Prince A

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Contributions

Conceptualization: Amalin Prince A, Jac Fredo Agastinose Ronickom; Methodology: Gokul Manoj, Vaibhavi Gupta, Aditi Bhattacharya, Amalin Prince A, Jac Fredo Agastinose Ronickom; Formal analysis and investigation: Gokul Manoj, Vaibhavi Gupta Aditi Bhattacharya, Shaik Gadda Abdul Aleem, Dhanvi Vedantham, Amalin Prince A, Jac Fredo Agastinose Ronickom; Software: Gokul Manoj, Vaibhavi Gupta Aditi Bhattacharya, Shaik Gadda Abdul Aleem, Dhanvi Vedantham; Data Evaluation: Shaik Gadda Abdul Aleem, Dhanvi Vedantham; Writing—original draft preparation: Gokul Manoj, Vaibhavi Gupta Aditi Bhattacharya, Amalin Prince A, Jac Fredo Agastinose Ronickom.; Writing—review and editing: Gokul Manoj, Vaibhavi Gupta Aditi Bhattacharya, Amalin Prince A, Jac Fredo Agastinose Ronickom.; Visualization: Gokul Manoj, Vaibhavi Gupta Aditi Bhattacharya, Jac Fredo Agastinose Ronickom; Supervision: Amalin Prince A, Jac Fredo Agastinose Ronickom; Project administration: Amalin Prince A, Jac Fredo Agastinose Ronickom.

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Correspondence to Gokul Manoj.

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Manoj, G., Gupta, V., Bhattacharya, A. et al. Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18817-5

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