Fusion of fMRI and non-imaging data for ADHD classification. (April 2018)

• Record Type: Journal Article
• Authors: Riaz, Atif; Asad, Muhammad; Alonso, Eduardo; Slabaugh, Greg
• Is Part Of: Computerized medical imaging and graphics. Volume 65(2018)
• Page Start: 115
• Page End: 128
• Publication Date: 2018-04
• Subjects: ADHD -- Density clustering -- Affinity propagation -- Elastic net -- Non-imaging data; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2017.10.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A novel method to initialize the Affinity Propagation clustering algorithm. The importance of non-imaging data for classification of ADHD is evaluated. The Frontal and Parietal lobes have the largest number of connectivity alterations. Abstract: Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behavior analysis. This paper addresses the problem of classification of ADHD based on resting state fMRI and proposes a machine learning framework with integration of non-imaging data with imaging data to investigate functional connectivity alterations between ADHD and control subjects (not diagnosed with ADHD). Our aim is to apply computational techniques to (1) automatically classify a subject as ADHD or control, (2) identify differences in functional connectivity of these two groups and (3) evaluate the importance of fusing non-imaging with imaging data for classification. In the first stage of our framework, we determine the functional connectivity of brain regions by grouping brain activity using clustering algorithms. Next, we employ Elastic Net based feature selection to select the most discriminant features from the dense functional brain network and integrate non-imaging data. Finally, a Support Vector Machine classifier is trained to classify ADHD subjects vs. control. The proposed framework was evaluated on a public ADHD-200 dataset, and our results suggest that fusion of non-imaging data improves the performance of the framework. Classification results outperform the state-of-the-art on some subsets of the data.