RAVE: comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data

Magnotti, John F.; Wang, Zhengjia; Beauchamp, Michael S.

doi:10.1101/2020.06.02.129676

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…These tools allow scientists to inspect their own data for quality control, and allow data reusers to quickly understand the contents of a published NWB file. In addition to these general-purpose tools, many application-specific tools, e.g., RAVE 32 , ecogVIS 33 , Brainstorm 34 , Neo 35 and others are already supporting or are in the process of developing support for analysis of NWB files.…”

Section: Resultsmentioning

confidence: 99%

The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel

Tritt

et al. 2021

Preprint

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The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

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Section: Resultsmentioning

confidence: 99%

The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel

Tritt

et al. 2021

Preprint

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“…Users may also connect existing cloud storage accounts with DABI should they prefer cloud-based storage. DABI links with several analytics packages including R Analysis and Visualization of iEEG (RAVE) 26 , an open-source software for iEEG analysis funded by the BRAIN Initiative and Jupyter Notebook 25 , a web-based interface that supports sharable coding and visualization.…”

Section: Methods: Dabi Architecturementioning

confidence: 99%

Data Archive for the BRAIN Initiative (DABI)

et al. 2023

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Data sharing is becoming ubiquitous and can be advantageous for most biomedical research. However, some data are inherently more amenable to sharing than others. For example, human intracranial neurophysiology recordings and associated multimodal data have unique features that warrant special considerations. The associated data are heterogeneous, difficult to compare, highly specific, and collected from small cohorts with treatment resistant conditions, posing additional complications when attempting to perform generalizable analyses across projects. We present the Data Archive for the BRAIN Initiative (DABI) and describe features of the platform that are designed to overcome these and other challenges. DABI is a data repository and portal for BRAIN Initiative projects that collect human and animal intracranial recordings, and it allows users to search, visualize, and analyze multimodal data from these projects. The data providers maintain full control of data sharing privileges and can organize and manage their data with a user-friendly and intuitive interface. We discuss data privacy and security concerns, example analyses from two DABI datasets, and future goals for DABI.

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“…All electrodes traversing the orbitofrontal and ventromedial cortex were assigned to the vmPFC ROI. For group level visualizations, electrode coordinates were linearly transformed into standard space (MNI305) 48 . From a total of 2472 channels, 423 channels were in an ROI.…”

Section: Methodsmentioning

confidence: 99%

Intracranial EEG spectral feature analysis and focal brain stimulation reveal affective specialization within dorsal anterior cingulate cortex

Metzger

Kalva

Mocchi

et al. 2022

Preprint

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Emotion is represented in several limbic and prefrontal cortical brain areas herein referred to as the Affective Salience Network (ASN). Within this network, less is known about how valence and intensity are processed in the dorsal anterior cingulate (dACC), and how affective processes in dACC compare to activity in other nodes within the ASN. Using a novel spectral feature approach to analyze intracranial electrophysiological data, we discover hemispheric specialization in the dACC such that the right hemisphere is sensitive to intensity while the left hemisphere is sensitive to valence and negative affective bias. We further applied 130 Hz continuous stimulation to the anterior cingulum bundle while patients viewed emotional faces. Faces were rated happier in all patients, an effect modulated by baseline affective bias, suggesting a causal role for the dACC during the processing of external affective stimuli.

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RAVE: comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data

Cited by 7 publications

References 28 publications

The Neurodata Without Borders ecosystem for neurophysiological data science

The Neurodata Without Borders ecosystem for neurophysiological data science

Data Archive for the BRAIN Initiative (DABI)

Intracranial EEG spectral feature analysis and focal brain stimulation reveal affective specialization within dorsal anterior cingulate cortex

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