Autism spectrum disorder diagnosis using fractal and non-fractal-based functional connectivity analysis and machine learning methods

Rakshe, Chetan; Kunneth, Suja; Sundaram, Soumya; Murugappan, Murugappan; Agastinose Ronickom, Jac Fredo

doi:10.1007/s00521-024-09770-3

Neural Comput & Applic

2024

DOI: 10.1007/s00521-024-09770-3

|View full text |Cite|

Autism spectrum disorder diagnosis using fractal and non-fractal-based functional connectivity analysis and machine learning methods

Chetan Rakshe,

Suja Kunneth,

Soumya Sundaram

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Preprint1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Advancing ASD diagnostic classification using time-frequency spectrograms of fMRI BOLD signals and machine learning

Tikaram,

Raj,

Ratnaik

et al. 2024

Preprint

View full text Add to dashboard Cite

In this study, our goal was to develop a diagnostic framework for autism spectrum disorder (ASD) by analyzing time-frequency spectrograms generated from BOLD signals in functional magnetic resonance imaging (fMRI) data. We used fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) database and performed brain parcellation with Gordon’s, Harvard-Oxford, and Diedrichsen atlases. Time-frequency spectrograms were generated from the average time series of each region of interest (ROI) using methods like short-time Fourier transform, continuous wavelet transform, Mel frequency cepstrum (MFC), and smoothed pseudo Wigner-Ville distribution. From these spectrograms, we extracted various features, including the grey-level co-occurrence matrix, grey-level run-length matrix, fractal dimension texture analysis, Zernike moments, Hu moments, and first-order statistics. To evaluate the diagnostic model, we applied machine learning classifiers, including logistic regression, support vector machine (SVM), extreme gradient boosting, and random forest, alongside recursive feature elimination with 5-fold cross-validation (RFECV) and hyperparameter tuning. The SVM classifier using MFC spectrograms and RFECV yielded the highest performance, achieving an overall accuracy of 95.71%, sensitivity of 100%, specificity of 91.42%, F1-score of 95.76%, and area under the curve (AUC) of 95.71% with the top 36 features for the fronto-parietal task control network. In contrast, utilizing all 85 features for the somatosensory motor hand network resulted in an accuracy of 80.38%, sensitivity of 77.77%, specificity of 82.85%, F1-score of 80.27%, and AUC of 80.31%. These findings underscore the model's potential in the precise classification of ASD, offering valuable implications for early diagnosis and intervention.

show abstract

Advancing ASD diagnostic classification using time-frequency spectrograms of fMRI BOLD signals and machine learning

Tikaram,

Raj,

Ratnaik

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Autism spectrum disorder diagnosis using fractal and non-fractal-based functional connectivity analysis and machine learning methods

Cited by 1 publication

References 82 publications

Advancing ASD diagnostic classification using time-frequency spectrograms of fMRI BOLD signals and machine learning

Advancing ASD diagnostic classification using time-frequency spectrograms of fMRI BOLD signals and machine learning

Contact Info

Product

Resources

About