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

doi:10.1016/j.dib.2020.105213

MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data Brief. 2020 Jan 31:29:105213. doi: 10.1016/j.dib.2020.105213. eCollection 2020 Apr.

Authors

Pradyumna Lanka^{1

2}, D Rangaprakash^{1

3

4}, Sai Sheshan Roy Gotoor¹, Michael N Dretsch^{5

6

7}, Jeffrey S Katz^{1

7

8

9}, Thomas S Denney Jr^{1

7

8

9}, Gopikrishna Deshpande^{1

7

8

10

9

11

12

13}

Affiliations

¹ AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
² Department of Psychological Sciences, University of California Merced, Merced, CA, USA.
³ Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁴ Division of Health Science and Technology, Massachusetts Institute of Technology, Boston, MA, USA.
⁵ U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.
⁶ US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, USA.
⁷ Department of Psychology, Auburn University, Auburn, AL, USA.
⁸ Alabama Advanced Imaging Consortium, Birmingham, AL, USA.
⁹ Center for Neuroscience, Auburn University, Auburn, AL, USA.
¹⁰ Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.
¹¹ Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India.
¹² School of Psychology, Capital Normal University, Beijing, China.
¹³ Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China.

Abstract

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: ADHD; Alzheimer's disease; Autism; Diagnostic classification; Functional connectivity; PTSD; Resting-state functional MRI; Supervised machine learning.