Tü Lay Adali1, Vince D Calhoun2. 1. Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland. 2. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.
Abstract
PURPOSE OF REVIEW: Machine learning solutions are being increasingly used in the analysis of neuroimaging (NI) data, and as a result, there is an increase in the emphasis of the reproducibility and replicability of these data-driven solutions. Although this is a very positive trend, related terminology is often not properly defined, and more importantly, (computational) reproducibility that refers to obtaining consistent results using the same data and the same code is often disregarded. RECENT FINDINGS: We review the findings of a recent paper on the topic along with other relevant literature, and present two examples that demonstrate the importance of accounting for reproducibility in widely used software for NI data. SUMMARY: We note that reproducibility should be a first step in all NI data analyses including those focusing on replicability, and introduce available solutions for assessing reproducibility. We add the cautionary remark that when not taken into account, lack of reproducibility can significantly bias all subsequent analysis stages.
PURPOSE OF REVIEW: Machine learning solutions are being increasingly used in the analysis of neuroimaging (NI) data, and as a result, there is an increase in the emphasis of the reproducibility and replicability of these data-driven solutions. Although this is a very positive trend, related terminology is often not properly defined, and more importantly, (computational) reproducibility that refers to obtaining consistent results using the same data and the same code is often disregarded. RECENT FINDINGS: We review the findings of a recent paper on the topic along with other relevant literature, and present two examples that demonstrate the importance of accounting for reproducibility in widely used software for NI data. SUMMARY: We note that reproducibility should be a first step in all NI data analyses including those focusing on replicability, and introduce available solutions for assessing reproducibility. We add the cautionary remark that when not taken into account, lack of reproducibility can significantly bias all subsequent analysis stages.
Authors: Radek Mareček; Martin Lamoš; René Labounek; Marek Bartoň; Tomáš Slavíček; Michal Mikl; Ivan Rektor; Milan Brázdil Journal: Neural Comput Date: 2017-01-17 Impact factor: 2.026
Authors: Sai Ma; Nicolle M Correa; Xi-Lin Li; Tom Eichele; Vince D Calhoun; Tülay Adalı Journal: IEEE Trans Biomed Eng Date: 2011-09-06 Impact factor: 4.538
Authors: Alex H Williams; Tony Hyun Kim; Forea Wang; Saurabh Vyas; Stephen I Ryu; Krishna V Shenoy; Mark Schnitzer; Tamara G Kolda; Surya Ganguli Journal: Neuron Date: 2018-06-07 Impact factor: 17.173