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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: Datasets (EEG/MEG/fMRI); Toolkits (for neuroimaging data analysis); Relevant Papers & Podcasts (selected for background knowledge);
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:


==Datasets==
*LPP-fMRI corpus (English, Chinese, French)
**[https://openneuro.org/datasets/ds003643/versions/2.0.1 Link]
**[https://www.biorxiv.org/content/10.1101/2021.10.02.462875v1.abstract Preprint; Scientific Data paper in press]
*Narratives fMRI corpus (English)
**[https://openneuro.org/datasets/ds002345/versions/1.1.4 Link]
**[https://www.nature.com/articles/s41597-021-01033-3? Data paper]
*NBD fMRI corpus (Dutch)
**[https://osf.io/utpdy/ Link]
**[http://lrec-conf.org/workshops/lrec2018/W9/pdf/book_of_proceedings.pdf#page=17 Data paper]
*Alice fMRI (English)
**[https://openneuro.org/datasets/ds002322/versions/1.0.4 Link to whole brain data]
**[https://sites.lsa.umich.edu/cnllab/2016/06/11/data-sharing-fmri-timecourses-story-listening/ Link to ROIs]
**[https://aclanthology.org/2020.lrec-1.15/ Data paper]
*Alice EEG (English)
**[https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f92t Link]
**[https://aclanthology.org/2020.lrec-1.15/ Data paper]
*Appleseed MEG (English)
**[https://datadryad.org/stash/dataset/doi:10.5061/dryad.nvx0k6dv0 Link]
**[https://elifesciences.org/articles/72056 Paper]
*MASC-MEG (English)
**[https://osf.io/ag3kj/ Link]
**[https://arxiv.org/abs/2208.11488 Preprint]
*10 hour within-participant MEG narrative (English)
**[https://data.donders.ru.nl/collections/di/dccn/DSC_3011085.05_995?1 Link]
**[https://www.nature.com/articles/s41597-022-01382-7 Data paper]
*Mother of unification studies (MOUS) MEG/fMRI (Dutch)
**[https://data.donders.ru.nl/collections/di/dccn/DSC_3011020.09_236?0 Link]
**[https://www.nature.com/articles/s41597-019-0020-y Data paper]
*LPP EEG (26 languages)
**Data collection underway
**[https://aclanthology.org/2020.lincr-1.6/ Data paper]


Toolkits:
==Toolkits==
*Eelbrain for EEG/MEG analysis (Python)
**[https://eelbrain.readthedocs.io/en/stable/ Link]
**[https://www.biorxiv.org/content/10.1101/2021.08.01.454687v1 Paper]
**Tutorial TBD
*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/auto_examples/00_tutorials/plot_decoding_tutorial.html Decoding]
**[https://nilearn.github.io/stable/plotting/index.html#plotting Plotting Brain Images]
*Neuroscount
**[https://neuroscout.org/ Link]
*More: [https://www.nitrc.org/top/toplist.php?type=downloads NITRC]




Relevant Papers:
==Relevant Background==
*Papers:
**Brennan, J. (2016). Naturalistic sentence comprehension in the brain. Language and Linguistics Compass, 10(7), 299-313. [https://compass.onlinelibrary.wiley.com/doi/abs/10.1111/lnc3.12198?casa_token=I7XqafbB33gAAAAA%3AnjBW3gi-S8SrssJjV3DL4eakxrvrclLYk7nnPxWdZgxrd6JVOhFjFIkNWKXXig-T3-EpZgFDJWOlz_o 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. [https://www.tandfonline.com/doi/pdf/10.1080/23273798.2018.1499946 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. [https://www.annualreviews.org/doi/abs/10.1146/annurev-linguistics-051421-020803?casa_token=JXxXJu6VZ-gAAAAA%3A3r_TXUVNJuMp0rEX9TuEBK-wV4CAwbwdQxFG-EKCm26MZXSw4VXEOinDH0-1m-WdqnqSZFJEnniD&journalCode=linguistics Link]
*Podcasts
**[https://braininspired.co/podcast/47/ Brain Inspired 047: David Poeppel - Wrong in interesting ways]
**[https://braininspired.co/podcast/53/ Brain Inspired 053: Jonathan Brennan - Linguistics in Minds and Machines]
**[https://braininspired.co/podcast/144/ Brain Inspired 144: Emily Bender & Ev Federenko - Large Language Models]
*Talks:
**[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.youtube.com/watch?v=YxAlcQKsgJc Laura Gwilliams: Towards a mechanistic account of speech comprehension]
*Books:
**[https://sites.google.com/site/fmridataanalysis/home Russel Poldrack: Handbook of Functional MRI Analysis]
**[https://mitpress.mit.edu/9780262122771/ Steven Luck: An Introduction to the Event-Related Potential Technique]
**[https://www.routledge.com/Cognitive-Neuroscience-of-Language/Kemmerer/p/book/9781848726215 David Kemmerer: Cognitive Neuroscience of Language]
**[https://www.indiebound.org/book/9780198814764 Jonathan Brennan: Language and the Brain - A Slim Guide to Neurolinguistics]
**[https://mitpress.mit.edu/9780262543262/neurolinguistics/ Giosuè Baggio: Neurolinguistics]
*Course videos:
**[https://www.youtube.com/playlist?list=PLwW-nea-Z6h-TiG0rBIviCQ5XaTyGq5WQ Neural Bases of Language through NYU]
**[https://ocw.mit.edu/courses/9-13-the-human-brain-spring-2019/ The Human Brain course through MIT]

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

Toolkits


Relevant Background

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