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Resting-state functional magnetic resonance imaging (rs-fMRI) is an efficient tool to measure brain connectivity and it can reveal patterns that distinguish autism spectrum disorder (ASD) from normal controls (NC). It is established that the fractal nature of neuroimaging signals will affect the estimation of brain&#x2019;s functional connectivity. Therefore, the ordinary correlation of rs-fMRI may not provide the original neuronal activity of the brain. In this work, the non-oscillatory brain connectivity method is proposed to distinguish subtypes of ASD from NC. The three subtypes of ASD namely autistic disorder (ATD), Asperger&#x2019;s disorder (APD), and Pervasive developmental disorder-not other specified (PDD) are classified from NC by extracting the non-oscillatory connectivity from the BOLD rs-fMRI signal. A number of significant connections are extracted by utilizing the <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-value analysis and these significant connections are fed to machine learning (ML) classifiers for classification of ASD subtypes against normal control. The performance for binary classification is recorded at accuracy of 98.6&#x0025;, 97.2&#x0025;, 97.2&#x0025;, respectively, for ATD vs. NC, APD vs. NC and PDD vs. NC. Whereas, for multiclass (ATD, APD, PDD and NC), the best accuracy is 88.9&#x0025;. Both binary and multiclass classification outperformed the conventional Pearson correlation-based connectivity and benchmark approaches in terms of accuracy, sensitivity, specificity. This work demonstrates the great potential of non-oscillatory connectivity approaches, not only for autism diagnosis but also for other neurological disorders.

Non-Oscillatory Connectivity Approach for Classification of Autism Spectrum Disorder Subtypes Using Resting-State fMRI