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Reproducibility is crucial for scientific progress, yet a clear research data analysis workflow is challenging to implement and maintain. As a result, a record of computational steps performed on the data to arrive at the key research findings is often missing. We developed Scikick, a tool that eases the configuration, execution, and presentation of scientific computational analyses. Scikick allows for workflow configurations with notebooks as the units of execution, defines a standard structure for the project, automatically tracks the defined interdependencies between the data analysis steps, and implements methods to compile all research results into a cohesive final report. Utilities provided by Scikick help turn the complicated management of transparent data analysis workflows into a standardized and feasible practice. Scikick version 0.2.1 code and documentation is available as supplementary material. The Scikick software is available on GitHub (https://github.com/matthewcarlucci/scikick) and is distributed with PyPi (https://pypi.org/project/scikick/) under a GPL-3 license. [ABSTRACT FROM AUTHOR]

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy‐to‐use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test–retest data from three public data sets to determine within‐version reliability and between‐version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37–0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter‐version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version‐related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/. [ABSTRACT FROM AUTHOR]

In order to study the application of the VR sensor image combined with sports games in the treatment of autistic children, this study mainly takes qualitative research as the main research method and uses physical intervention based on sensory integration training to study autistic children aged 6-9. Three children, A, B, and C, are mainly selected in order to promote the improvement of children's physical function and motor skills and further have a positive impact on behavior and psychology. By analyzing the effects measured before and after the experiment, this paper explores the impact of sports intervention based on sensory integration training on the motor ability of autistic children over the age of 6, as well as its impact on their sensory integration function, daily behavior, and psychological activities, so as to enrich the empirical research on the content, principles, implementation methods, and specific implementation process of sensory integration training, to provide reference and enlightenment for schools, institutions, and children's parents engaged in educational intervention for autistic children. [ABSTRACT FROM AUTHOR]

Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing. [ABSTRACT FROM AUTHOR]