Fusion of fMRI and non-imaging data for ADHD classification

• Published in: Computerized medical imaging and graphics Vol. 65; pp. 115 - 128
• Main Authors: Riaz, Atif, Asad, Muhammad, Alonso, Eduardo, Slabaugh, Greg
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2018; Elsevier Science Ltd
• Subjects: ADHD; Affinity propagation; Algorithms; Attention Deficit Disorder with Hyperactivity - classification; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention deficit hyperactivity disorder; Brain; Brain mapping; Brain Mapping - methods; Children; Classification; Clustering; Computational neuroscience; Datasets as Topic; Density clustering; Disorders; Elastic net; Functional magnetic resonance imaging; Humans; Indexing; Learning algorithms; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neuroimaging; Neurology; NMR; Non-imaging data; Nuclear magnetic resonance; Performance enhancement; Support Vector Machine; Support vector machines; ADHD; Density clustering; Non-imaging data; Elastic net; Affinity propagation
• ISSN: 0895-6111; 1879-0771; 1879-0771
• DOI: 10.1016/j.compmedimag.2017.10.002

•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. 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.