osl-dynamics: a toolbox for modelling fast dynamic brain activity
Neural activity contains rich spatio-temporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of a tens of milliseconds. While these processes can be accessed through bra...
| Main Authors: | , , , , , , |
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| Format: | Internet publication |
| Language: | English |
| Published: |
bioRxiv
2023
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| _version_ | 1826312120351850496 |
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| author | Gohil, C Huang, R Roberts, E van Es, MWJ Quinn, AJ Vidaurre, D Woolrich, MW |
| author_facet | Gohil, C Huang, R Roberts, E van Es, MWJ Quinn, AJ Vidaurre, D Woolrich, MW |
| author_sort | Gohil, C |
| collection | OXFORD |
| description | Neural activity contains rich spatio-temporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of a tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modelling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events is often a priori unknown. Here we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behaviour and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modelling of fast dynamic processes. |
| first_indexed | 2024-03-07T08:22:51Z |
| format | Internet publication |
| id | oxford-uuid:324c7720-8097-469a-84ec-95ec1bdd7208 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:22:51Z |
| publishDate | 2023 |
| publisher | bioRxiv |
| record_format | dspace |
| spelling | oxford-uuid:324c7720-8097-469a-84ec-95ec1bdd72082024-02-07T16:39:56Zosl-dynamics: a toolbox for modelling fast dynamic brain activityInternet publicationhttp://purl.org/coar/resource_type/c_7ad9uuid:324c7720-8097-469a-84ec-95ec1bdd7208EnglishSymplectic ElementsbioRxiv2023Gohil, CHuang, RRoberts, Evan Es, MWJQuinn, AJVidaurre, DWoolrich, MWNeural activity contains rich spatio-temporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of a tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modelling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events is often a priori unknown. Here we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behaviour and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modelling of fast dynamic processes. |
| spellingShingle | Gohil, C Huang, R Roberts, E van Es, MWJ Quinn, AJ Vidaurre, D Woolrich, MW osl-dynamics: a toolbox for modelling fast dynamic brain activity |
| title | osl-dynamics: a toolbox for modelling fast dynamic brain activity |
| title_full | osl-dynamics: a toolbox for modelling fast dynamic brain activity |
| title_fullStr | osl-dynamics: a toolbox for modelling fast dynamic brain activity |
| title_full_unstemmed | osl-dynamics: a toolbox for modelling fast dynamic brain activity |
| title_short | osl-dynamics: a toolbox for modelling fast dynamic brain activity |
| title_sort | osl dynamics a toolbox for modelling fast dynamic brain activity |
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