Fusion of fMRI and non-imaging data for ADHD classification

•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...

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Veröffentlicht in:Computerized medical imaging and graphics Jg. 65; S. 115 - 128
Hauptverfasser: Riaz, Atif, Asad, Muhammad, Alonso, Eduardo, Slabaugh, Greg
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.04.2018
Elsevier Science Ltd
Schlagworte:
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
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Zusammenfassung:•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.
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ISSN:0895-6111
1879-0771
1879-0771
DOI:10.1016/j.compmedimag.2017.10.002