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

Abstract

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.