34 lines
2.4 KiB
Markdown
34 lines
2.4 KiB
Markdown
---
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title: Study highlight - Age- and sex-related changes in motor functions
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topics:
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- datamanagement
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contributors:
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- peter-weiss-blankenhorn
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software:
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- datalad
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sites:
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- ucologne
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- fzj
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---
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{{< lead >}}
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This study provides an example of data analysis enabled by using DataLad to create a database of motor parameters in healthy individuals and patients with neurological and psychiatric disorders.
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{{< /lead >}}
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In a study published in [*Frontiers in Aging Neuroscience*](https://www.frontiersin.org/journals/aging-neuroscience), researchers from ABCD-J sites, [University of Cologne]({{< relref path="../../sites/ucologne" lang="en" >}}) and [Research Centre Jülich]({{< relref path="../../sites/fzj" lang="en" >}}) showcase the potential of reproducible neuroscience through the use of a [DataLad]({{< relref path="../../software/datalad" lang="en" >}})-based database of motor parameters.
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The data were collected by conducting comprehensive motor assessments among 444 non-left-handed adults without history of neurological, psychiatric, or orthopedic diseases.
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The assessments included five standardized tests to evaluate basic (grip strength, finger-tapping frequency) and complex (Action Research Arm Test, Jebsen-Taylor Hand Function Test, Purdue Pegboard Test) motor functions.
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The study revealed reduced motor function across the lifespan, with significant sex-specific differences.
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Namely, men demonstrated higher grip strengths and finger-tapping frequencies, while women outperformed men in the Purdue Pegboard Test.
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Motor performance could predict the age of the participants, and Principal Component Analysis (PCA) unveiled three robust motor components: dexterity, force, and speed.
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These results not only provide a comprehensive assessment of age- and sex-dependent motor functions in healthy individuals, but also illustrate the type of insight that becomes possible with reusable, well-structured datasets.
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By leveraging DataLad to build the database behind these data, this work lays the groundwork for a scalable resource that can support future motor function research.
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For example, the data and results presented in this study could inform a future study to classify depressive patients using motor parameters.
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Source: [Wunderle et al., 2024; https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2024.1368052/](https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2024.1368052/)
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