CogNeuro: Difference between revisions
Computational Linguistics and Information Processing
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This is a repository that is updated periodically with resources to analyze continuous, naturalistic neuroimaging data with computational tools. It is split into three sections: | This is a repository that is updated periodically with resources to analyze continuous, naturalistic neuroimaging data with computational tools. It is split into three sections: | ||
;Datasets: EEG/MEG | ;Datasets: Naturalistic fMRI/EEG/MEG | ||
;Toolkits & Tutorials :For neuroimaging data analysis | ;Toolkits & Tutorials :For neuroimaging data analysis | ||
;Relevant Background:Selected papers, podcasts, talks, course videos, books | ;Relevant Background:Selected papers, podcasts, talks, course videos, books | ||
| Line 27: | Line 27: | ||
*MASC-MEG (English) | *MASC-MEG (English) | ||
**[https://osf.io/ag3kj/ Link] | **[https://osf.io/ag3kj/ Link] | ||
**[https://arxiv.org/abs/2208.11488 | **[https://arxiv.org/abs/2208.11488 Preprint] | ||
*10 hour within-participant MEG narrative (English) | *10 hour within-participant MEG narrative (English) | ||
**[https://data.donders.ru.nl/collections/di/dccn/DSC_3011085.05_995?1 Link] | **[https://data.donders.ru.nl/collections/di/dccn/DSC_3011085.05_995?1 Link] | ||
| Line 39: | Line 39: | ||
==Toolkits== | ==Toolkits== | ||
*Eelbrain for EEG/MEG | *Eelbrain for EEG/MEG analysis (Python) | ||
**[https://eelbrain.readthedocs.io/en/stable/ Link] | **[https://eelbrain.readthedocs.io/en/stable/ Link] | ||
**[https://www.biorxiv.org/content/10.1101/2021.08.01.454687v1 Paper] | **[https://www.biorxiv.org/content/10.1101/2021.08.01.454687v1 Paper] | ||
**Tutorial TBD | **Tutorial TBD | ||
*Nilearn for fMRI | *SPM for fMRI analysis (Matlab) | ||
**[https://andysbrainbook.readthedocs.io/en/latest/SPM/SPM_Overview.html SPM Analysis]; read Poldrack first! | |||
**[https://andysbrainbook.readthedocs.io/en/latest/PM/PM_Overview.html SPM Parametric Modulation] | |||
**[https://andysbrainbook.readthedocs.io/en/latest/Stats/Stats_Overview.html Stats for fMRI] | |||
*fMRI image viewer (for figures) | |||
**[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes FSLeyes] | |||
**[https://ric.uthscsa.edu/mango/ Mango] | |||
*Nilearn for fMRI (Python) | |||
**[https://nilearn.github.io/stable/glm/index.html#glm GLM analysis] | **[https://nilearn.github.io/stable/glm/index.html#glm GLM analysis] | ||
**[https://nilearn.github.io/stable/auto_examples/00_tutorials/plot_decoding_tutorial.html Decoding] | **[https://nilearn.github.io/stable/auto_examples/00_tutorials/plot_decoding_tutorial.html Decoding] | ||
* | **[https://nilearn.github.io/stable/plotting/index.html#plotting Plotting Brain Images] | ||
**Link | *Neuroscount | ||
**[https://neuroscout.org/ Link] | |||
*More: [https://www.nitrc.org/top/toplist.php?type=downloads NITRC] | *More: [https://www.nitrc.org/top/toplist.php?type=downloads NITRC] | ||
| Line 61: | Line 69: | ||
**[https://braininspired.co/podcast/144/ Brain Inspired 144: Emily Bender & Ev Federenko - Large Language Models] | **[https://braininspired.co/podcast/144/ Brain Inspired 144: Emily Bender & Ev Federenko - Large Language Models] | ||
*Talks: | *Talks: | ||
**[http://nancysbraintalks.mit.edu/video/nancys-ted-talk-neural-portrait-human-mind Nancy | **[http://nancysbraintalks.mit.edu/video/nancys-ted-talk-neural-portrait-human-mind Nancy Kanwisher's TED talk: A Neural Portrait of the Human Mind] | ||
**[https://www.mpi.nl/events/neurobiology-language-key-issues-and-ways-forward/videos Jonathan Brennan: Building bridges between computation and implementation for natural language understanding] | **[https://www.mpi.nl/events/neurobiology-language-key-issues-and-ways-forward/videos Jonathan Brennan: Building bridges between computation and implementation for natural language understanding] | ||
**[https://www.youtube.com/watch?v=YxAlcQKsgJc Laura Gwilliams: Towards a mechanistic account of speech comprehension] | **[https://www.youtube.com/watch?v=YxAlcQKsgJc Laura Gwilliams: Towards a mechanistic account of speech comprehension] | ||
Latest revision as of 17:35, 25 August 2022
This is a repository that is updated periodically with resources to analyze continuous, naturalistic neuroimaging data with computational tools. It is split into three sections:
- Datasets
- Naturalistic fMRI/EEG/MEG
- Toolkits & Tutorials
- For neuroimaging data analysis
- Relevant Background
- Selected papers, podcasts, talks, course videos, books
Datasets
- LPP-fMRI corpus (English, Chinese, French)
- Narratives fMRI corpus (English)
- NBD fMRI corpus (Dutch)
- Alice fMRI (English)
- Alice EEG (English)
- Appleseed MEG (English)
- MASC-MEG (English)
- 10 hour within-participant MEG narrative (English)
- Mother of unification studies (MOUS) MEG/fMRI (Dutch)
- LPP EEG (26 languages)
- Data collection underway
- Data paper
Toolkits
- Eelbrain for EEG/MEG analysis (Python)
- SPM for fMRI analysis (Matlab)
- SPM Analysis; read Poldrack first!
- SPM Parametric Modulation
- Stats for fMRI
- fMRI image viewer (for figures)
- Nilearn for fMRI (Python)
- Neuroscount
- More: NITRC
Relevant Background
- Papers:
- Brennan, J. (2016). Naturalistic sentence comprehension in the brain. Language and Linguistics Compass, 10(7), 299-313. Link
- Hamilton, L. S., & Huth, A. G. (2020). The revolution will not be controlled: natural stimuli in speech neuroscience. Language, cognition and neuroscience, 35(5), 573-582. Link
- Hale, J. T., Campanelli, L., Li, J., Bhattasali, S., Pallier, C., & Brennan, J. R. (2022). Neurocomputational models of language processing. Annual Review of Linguistics, 8, 427-446. Link
- Podcasts
- Talks:
- Books:
- Course videos: