Improvement of Classification Performance in High-Dimension Low-Sample-Size Modeling by Sparse Functional Connectivity States in Subjects with Attention Deficit-Hyperactivity Disorder and Healthy Controls

Background: The precise identification of attention deficit-hyperactivity disorder (ADHD) is one of the challenging clinical processes. Disorganizations in functional neural networks revealed via functional magnetic resonance imaging have recently been contributing. Machine learning approaches, particularly classification methods, have commonly been employed as a framework for diverse data analysis, indicating promising medical diagnosis results. However, as the neuroimaging data are high-dimensional with a low sample size (the current dataset), this study aimed to evaluate the classification performance of the models by considering the specific contribution of the sparsity of data matrices. Methods: This cross-sectional study analyzed the preprocessed data from the 2011 ADHD-200 Global Competition. A total of 768 and 171 data items were considered training and test, respectively. The diagnosis status was used as a response variable. Age, gender, hand dominance, and activity relationship between 116 brain regions derived from inverse covariance matrix and inverse sparse covariance matrix were used as predictive variables. Accordingly, this study compared the performance of three models, namely support vector machine (SVM), distance-weighted discrimination (DWD), and data maximum dispersion classifier (DMDC) for ADHD Results: The highest value for the total accuracy was reported for the SVM model on the sparse covariance matrix. Moreover, the highest values for the balanced classification rate (BCR) (59%) and sensitivity (64%) were reported for DMDC on the sparse covariance matrix. The best level of specificity (99%) was obtained from DWD using the sparse covariance matrix. The highest levels of the values (i.e., total accuracy and BCR) were achieved through the model fitting on the sparse matrices. Among the six models, the DMDC model on sparse covariance matrix was the most optimal algorithm due to the superiority of the two indices (i.e., accuracy: 60% and BCR: 60%) and the favorable balance between sensitivity and specificity values. Conclusions: Among the current studied three models, DMDC performance, applying the sparse data, remarkably improved the results of classification processes. Based on the present findings, the neuronal connectivity among subcortical structures comprising parts of the basal ganglia and cerebellum provides a distinction between ADHD subjects and healthy controls.