ModelArray: An R package for statistical analysis of fixel-wise data

• Published in: NeuroImage (Orlando, Fla.) Vol. 271; p. 120037
• Main Authors: Zhao, Chenying, Tapera, Tinashe M., Bagautdinova, Joëlle, Bourque, Josiane, Covitz, Sydney, Gur, Raquel E., Gur, Ruben C., Larsen, Bart, Mehta, Kahini, Meisler, Steven L., Murtha, Kristin, Muschelli, John, Roalf, David R., Sydnor, Valerie J., Valcarcel, Alessandra M., Shinohara, Russell T., Cieslak, Matthew, Satterthwaite, Theodore D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2023; Elsevier Limited; Elsevier
• Subjects: Big data; Development; Diffusion Magnetic Resonance Imaging - methods; Efficiency; Fixel-based analysis; Humans; Hypotheses; Hypothesis testing; Life span; Mathematical models; Models, Statistical; MRI; Research Design; Software; Statistical analysis; Substantia alba; White Matter; Development; Software; Big data; Statistical analysis; MRI; Fixel-based analysis
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120037

•ModelArray is an R package for statistical analysis of fixel-wise data.•ModelArray supports linear and nonlinear modeling and is extensible to more models.•ModelArray is scalable for large-scale datasets.•ModelArray facilitates easy statistical analysis of large-scale fixel-wise data. Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data. [Display omitted]