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      Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power

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          Abstract

          One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA.

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          Most cited references24

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          Region of interest analysis for fMRI.

          A common approach to the analysis of fMRI data involves the extraction of signal from specified regions of interest (or ROI's). Three approaches to ROI analysis are described, and the strengths and assumptions of each method are outlined.
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            One-dimensional statistical parametric mapping in Python.

            Statistical parametric mapping (SPM) is a topological methodology for detecting field changes in smooth n-dimensional continua. Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. The current paper accompanies release of 'SPM1D', a free and open-source Python package for conducting SPM analyses on a set of registered 1D curves. Three example applications are presented: (i) kinematics, (ii) ground reaction forces and (iii) contact pressure distribution in probabilistic finite element modelling. In addition to offering a high-level interface to a variety of common statistical tests like t tests, regression and ANOVA, SPM1D also emphasises fundamental concepts of SPM theory through stand-alone example scripts. Source code and documentation are available at: www.tpataky.net/spm1d/.
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              Divide and conquer: a defense of functional localizers.

              Numerous functionally distinct regions of cortex (e.g., V1, MT, the fusiform face area) can be easily identified in any normal human subject in just a few minutes of fMRI scanning. However, the locations of these regions vary across subjects. Investigations of these regions have therefore often used a functional region of interest (fROI) approach in which the region is first identified functionally in each subject individually, before subsequent scans in the same subjects test specific hypotheses concerning that region. This fROI method, which resembled long-established practice in visual neurophysiology, has methodological, statistical, and theoretical advantages over standard alternatives (such as whole-brain analyses of group data): (i) because functional properties are more consistently and robustly associated with fROIs than with locations in stereotaxic space, functional hypotheses concerning fROIs are often the most straightforward to frame, motivate, and test, (ii) because hypotheses are tested in only a handful of fROIs (instead of in tens of thousands of voxels), advance specification of fROIs provides a massive increase in statistical power over whole-brain analyses, and (iii) some fROIs may serve as candidate distinct components of the mind/brain worth investigation as such. Of course fROIs can be productively used in conjunction with other complementary methods. Here, we explain the motivation for and advantages of the fROI approach, and we rebut the criticism of this method offered by Friston et al. (Friston, K., Rotshtein, P., Geng, J., Sterzer, P., Henson, R., in press. A critique of functional localizers. NeuroImage).
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                2 November 2016
                2016
                : 4
                : e2652
                Affiliations
                [1 ]Institute for Fiber Engineering, Department of Bioengineering, Shinshu University , Ueda, Nagano, Japan
                [2 ]Research Institute for Sport and Exercise Sciences, Liverpool John Moores University , Liverpool, United Kingdom
                [3 ]Department of Rehabilitation Sciences, Katholieke Universiteit Leuven , Belgium
                Article
                2652
                10.7717/peerj.2652
                5101620
                27833816
                c4a619ca-b157-44f5-bdb8-62a3d061f82f
                ©2016 Pataky et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 13 July 2016
                : 4 October 2016
                Funding
                Funded by: Japan Society for the Promotion of Science
                Award ID: 15H05360
                This work was supported by Wakate A Grant 15H05360 from the Japan Society for the Promotion of Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Animal Behavior
                Bioengineering
                Kinesiology
                Statistics

                time series analysis,kinematics,constrained hypotheses,statistical parametric mapping,dynamics,random field theory,hypothesis testing,biomechanics,human movement

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