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

Data in Brief (Apr 2020)

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.

Journal volume & issue: Vol. 29

Abstract

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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

Published in Data in Brief

ISSN: 2352-3409 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Science: Science (General)
Website: http://www.journals.elsevier.com/data-in-brief/

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