MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data
Pradyumna Lanka,
D. Rangaprakash,
Sai Sheshan Roy Gotoor,
Michael N. Dretsch,
Jeffrey S. Katz,
Thomas S. Denney, Jr.,
Gopikrishna Deshpande
Affiliations
Pradyumna Lanka
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychological Sciences, University of California Merced, Merced, CA, USA
D. Rangaprakash
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Division of Health Science and Technology, Massachusetts Institute of Technology, Boston, MA, USA
Sai Sheshan Roy Gotoor
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
Michael N. Dretsch
U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA; US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, USA; Department of Psychology, Auburn University, Auburn, AL, USA
Jeffrey S. Katz
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA
Thomas S. Denney, Jr.
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA
Gopikrishna Deshpande
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA; Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India; School of Psychology, Capital Normal University, Beijing, China; Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China; Corresponding author. AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, 36849, USA.
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been extensively used for diagnostic classification because it does not require task compliance and is easier to pool data from multiple imaging sites, thereby increasing the sample size. A MATLAB-based toolbox called Machine Learning in NeuroImaging (MALINI) for feature extraction and disease classification is presented. The MALINI toolbox extracts functional and effective connectivity features from preprocessed rs-fMRI data and performs classification between healthy and disease groups using any of 18 popular and widely used machine learning algorithms that are based on diverse principles. A consensus classifier combining the power of multiple classifiers is also presented. The utility of the toolbox is illustrated by accompanying data consisting of resting-state functional connectivity features from healthy controls and subjects with various brain-based disorders: autism spectrum disorder from autism brain imaging data exchange (ABIDE), Alzheimer's disease and mild cognitive impairment from Alzheimer's disease neuroimaging initiative (ADNI), attention deficit hyperactivity disorder from ADHD-200, and post-traumatic stress disorder and post-concussion syndrome acquired in-house. Results of classification performed on the above datasets can be obtained from the main article titled “Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets” [1]. The data was divided into homogeneous and heterogeneous splits, such that 80% could be used for training, model building and cross-validation, while the remaining 20% of the data could be used as a hold-out independent test data for replication of the classification performance, to ensure the robustness of the classifiers to population variance in image acquisition site and age of the sample. Keywords: Resting-state functional MRI, Supervised machine learning, Diagnostic classification, Functional connectivity, Autism, ADHD, Alzheimer's disease, PTSD
