Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder

Abstract

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about the variations of each ROI during the time. Thereafter, time-frequency domains are partitioned into sub-matrices and then, their fuzzy entropies are calculated as features. Next, discriminative features are chosen by using the two-sample Kolmogorov–Smirnov (K–S) test. Finally, the data are classified by the leave-one-out cross-validation (LOOCV) method using the support vector machine (SVM) classifier. To see the effectiveness of the proposed method, the experiments are performed on the ADHD-200 database. We consider different scenarios including classification of TDCs and ADHDs as well as classification of ADHD subtypes. We also assess the performance by considering the age and sex as phenotypic information. The proposed method gives good results in the classification procedure and identifying the connection paths between ROIs. The results indicate that the proposed method can distinguish ADHD disorder in a more accurate manner in comparison with other methods. The connectivity paths show that there is a reduction in the input of cerebellar regions and the left mid orbitofrontal cortex in ADHDs compared to TDCs.