Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework

Computational reproducibility of "Goal relevance and goal conduciveness appraisals lead to differential autonomic reactivity in emotional responding to performance feedback" (Kreibig, Gendolla, & Scherer, 2012): A guide and new evidence

The emerging field of the psychophysiology of motivation bears many new findings, but little replication. Using my own data (Kreibig, Gendolla, & Scherer, 2012), I test the reproducibility of this specific study, provide the necessary materials to make the study reproducible, and instantiate proper reproducibility practices that other researchers can use as a road map toward the same goal. In addition, based on re-analyses of the original data, I report new evidence for the motivational effects of emotional responding to performance feedback. Specifically, greater appraisal of goal relevance amplifies the emotional response to events appraised as conducive (i.e., effort mobilization), but not to those appraised as obstructive to a person's goals (i.e., effort withdrawal). I conclude by providing a ten-step road map of best practices to facilitate computational reproducibility for future studies.

Provision of an integrated data analysis platform for computational neuroscience experiments

Purpose

– The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

Design/methodology/approach

– The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

Findings

– The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

Originality/value

– The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

Parallel workflows for data-driven structural equation modeling in functional neuroimaging

We present a computational framework suitable for a data-driven approach to structural equation modeling (SEM) and describe several workflows for modeling functional magnetic resonance imaging (fMRI) data within this framework. The Computational Neuroscience Applications Research Infrastructure (CNARI) employs a high-level scripting language called Swift, which is capable of spawning hundreds of thousands of simultaneous R processes (R Development Core Team, 2008), consisting of self-contained SEMs, on a high performance computing system (HPC). These self-contained R processing jobs are data objects generated by OpenMx, a plug-in for R, which can generate a single model object containing the matrices and algebraic information necessary to estimate parameters of the model. With such an infrastructure in place a structural modeler may begin to investigate exhaustive searches of the model space. Specific applications of the infrastructure, statistics related to model fit, and limitations are discussed in relation to exhaustive SEM. In particular, we discuss how workflow management techniques can help to solve large computational problems in neuroimaging.