Open research software infrastructure in Neuro-Medicine

<p>A virtual talk given at the ZI Mannheim Open Science Symposium on open source software tools in the INM-7 of the research center Jülich, and plans for a recent collaborative project combing them.</p><p> </p><p>Abstract:</p><p>The Institute of Neuroscience and Medicine: Brain and Behavior (INM-7) at the research center Jülich combines clinical science with open source software development and open science. This talk will highlight central open source software projects of our institute, and their application in larger projects. As one of its developers, I will introduce the data management software DataLad (<a href="http://www.datalad.org">www.datalad.org</a>) and some of its applications - from data management in consortia to reproducible and privacy-aware data analysis at scale. In addition, I will outline a recently established collaborative platform for digital medicine in North Rhine Westphalia, the ABCD-JU project. Building on our institute's open source projects for data management, mobile health applications and machine-learning (<a href="https://juaml.github.io/julearn/main/index.html">juaml.github.io/julearn</a>), it aims to establish an integrated, user-friendly, and FAIR infrastructure for digital biomarker collection, storage, and exchange for clinical scientists.</p><p>Beyond an overview of our tools and projects, this talk aims to spark discussions around synergies and interoperability with projects at the ZI.</p&gt

Loneliness and Health: The Moderating Effect of Cross-cultural Individualism/Collectivism

Objectives: The adverse health effects of loneliness are well documented, but less is known about cultural moderators of this relationship. Contributing to the literature, we examined whether cross-cultural differences in individualism moderate the effect of loneliness on health. Methods: We used population-based longitudinal data of 14 countries (N = 40,797), as provided by the Survey of Health, Ageing, and Retirement in Europe data. Multilevel regression analyses were employed. Moderating effects were analyzed for multiple health outcomes: activities of daily living, instrumental activities of daily living, grip strength, life satisfaction, depression, memory performance, verbal fluency, and numeracy. Results: Cultural individualism significantly moderated the effect of loneliness on health regarding most health outcomes. In general, the effect of loneliness on health became stronger in less individualistic/more collectivistic countries. Discussion: Cultural individualism proved to be one important moderator of the loneliness–health relationship. As previous studies mostly used samples from highly individualistic countries, the current literature might severely underestimate the global public health burden of loneliness

multimatch-gaze: The MultiMatch algorithm for gaze path comparison in Python

Multimatch-gaze is a Python package for computing the similarity of eye-movement sequences, so called scan paths. Scan paths are the trace of eye-movements in space and time, usually captured with eye tracking devices. Scan path similarity is a measure that is used in a variety of disciplines ranging from cognitive psychology, medicine, and marketing to human-machine interfaces. In addition to quantifying position and order of a series of eye-movements, comparing their temporo-spatial sequence adds an insightful dimension to the traditional analysis of eye tracking data. It reveals commonalities and differences of viewing behavior within and between observers, and is used to study how people explore visual information. For example, scan path comparisons are used to study analogy-making (French, Glady, & Thibaut, 2017), visual exploration and imagery (Johansson, Holsanova, & Holmqvist, 2006), habituation in repetitive visual search (Burmester & Mast, 2010), or spatial attention allocation in dynamic scenes (Mital, Smith, Hill, & Henderson, 2011). The method is applied within individuals as a measure of change (Burmester & Mast, 2010), or across samples to study group differences (French et al., 2017).Therefore, in recent years, interest in the study of eye movement sequences has sparked the development of novel methodologies and algorithms to perform scan path comparisons. However, many of the contemporary scan path comparison algorithms are implemented inclosed-source, non-free software such as Matlab.Multimatch-gaze is a Python-based reimplementation of the MultiMatch toolbox for scanpath comparison, originally developed by Jarodzka, Holmqvist, & Nyström (2010) and implemented by Dewhurst et al. (2012) in Matlab. This algorithm represents scan paths asgeometrical vectors in a two-dimensional space: Any scan path is built up of a coordinate vector sequence in which the start and end position of vectors represent fixations, and the vectors represent saccades. Two such vector sequences are, after optional simplification based on angular relations and amplitudes of saccades, compared on the five dimensions “vector shape”, “vector length (amplitude)”, “vector position”, “vector direction”, and “fixation duration” for a multidimensional similarity evaluation.This reimplementation in Python aims at providing an accessible, documented, and tested open source alternative to the existing MultiMatch toolbox. The algorithm is an established tool for scan path comparison (N. C. Anderson, Anderson, Kingstone, & Bischof, 2015),and improved availability aids adoption in a broader research community. multimatch-gaze is available from its Github repository and as the Python package multimatch-gaze via pip install multimatch-gaze. The module contains the same functionality as the original Matlab toolbox, that is, scan path comparison with optional simplification according to userdefined thresholds, and it provides this functionality via a command line interface or a PythonAPI. Data for scan path comparison can be supplied as nx3 fixation vectors with columns corresponding to x-coordinates, y-coordinates, and duration of the fixation in seconds (as for the original Matlab toolbox). Alternatively, multimatch-gaze can natively read in event detection output produced by REMoDNaV (Dar, Wagner, & Hanke, 2019), a velocity-based eye movement classification algorithm written in Python. For REMoDNaV-based input, users can additionally specify whether smooth pursuit events in the data should be kept in the scan path or discarded

A pragmatic approach to reusable research outputs

Science is an incremental process that produces and builds on more than journal articles¹. Code, data, results, or tools of previous finished or unfinished projects (research outputs) fuel new undertakings.Reusing research objects allows for reproduction, verification, and extending existing work, evidence synthesis, and minimizing duplicate efforts². The more reusable outputs are, at any stage of a project, the better. The FAIR principles³ center around richly curated metadata to reach maximal reusability. In practice, creating fully FAIR resources is difficult in systems that yet lack or fail to incentivize the necessary standards and procedures. But reusability should be improved nevertheless.We highlight four widely accessible strategies that can elevate reusability as a byproduct of pragmatic research data management, even when compliance to FAIR is not yet possible. ¹ Mons, B. (2018). Data Stewardship for Open Science: Implementing FAIR Principles. CRC Press. ISBN 9780815348184² Thanos, C. (2017). Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies. doi.org/10.3390/publications5010002³ Wilkinson, M. D. et al., (2016). The FAIR Guiding Principles for scientific data management and stewardship. doi.org/10.1038/sdata.2016.1

REMoDNaV: robust eye-movement classification for dynamic stimulation

Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm-built on an existing velocity-based approach-that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources