CogNeuro: Difference between revisions

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)
- Link
- Preprint; Scientific Data paper in press
Narratives fMRI corpus (English)
- Link
- Data paper
NBD fMRI corpus (Dutch)
- Link
- Data paper
Alice fMRI (English)
Alice EEG (English)
- Link
- Data paper
Appleseed MEG (English)
- Link
- Paper
MASC-MEG (English)
- Link
- Preprint
10 hour within-participant MEG narrative (English)
- Link
- Data paper
Mother of unification studies (MOUS) MEG/fMRI (Dutch)
- Link
- Data paper
LPP EEG (26 languages)
- Data collection underway
- Data paper

Toolkits

Eelbrain for EEG/MEG analysis (Python)
- Link
- Paper
- Tutorial TBD
SPM for fMRI analysis (Matlab)
- SPM Analysis; read Poldrack first!
- SPM Parametric Modulation
- Stats for fMRI
fMRI image viewer (for figures)
- FSLeyes
- Mango
Nilearn for fMRI (Python)
Neuroscount
- Link
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:
- Neural Bases of Language through NYU
- The Human Brain course through MIT

@@ Line 1: / Line 1: @@
 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
+;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)
-**Link
+**[https://openneuro.org/datasets/ds003643/versions/2.0.1 Link]
-**Data paper
+**[https://www.biorxiv.org/content/10.1101/2021.10.02.462875v1.abstract Preprint; Scientific Data paper in press]
-*Narratives fMRI corpus
+*Narratives fMRI corpus (English)
-**Link
+**[https://openneuro.org/datasets/ds002345/versions/1.1.4 Link]
-**Data paper
+**[https://www.nature.com/articles/s41597-021-01033-3? Data paper]
-*NBD fMRI corpus
+*NBD fMRI corpus (Dutch)
-**Link
+**[https://osf.io/utpdy/ Link]
-**Data paper
+**[http://lrec-conf.org/workshops/lrec2018/W9/pdf/book_of_proceedings.pdf#page=17 Data paper]
 *Alice fMRI (English)
-**Link to whole brain
+**[https://openneuro.org/datasets/ds002322/versions/1.0.4 Link to whole brain data]
-**Link to ROIs
+**[https://sites.lsa.umich.edu/cnllab/2016/06/11/data-sharing-fmri-timecourses-story-listening/ Link to ROIs]
-**Data paper
+**[https://aclanthology.org/2020.lrec-1.15/ Data paper]
 *Alice EEG (English)
-**Link
+**[https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f92t Link]
-**Data paper
+**[https://aclanthology.org/2020.lrec-1.15/ Data paper]
-*Appleseed MEG
+*Appleseed MEG (English)
-**Link
+**[https://datadryad.org/stash/dataset/doi:10.5061/dryad.nvx0k6dv0 Link]
-**Paper
+**[https://elifesciences.org/articles/72056 Paper]
-*MASC-MEG
+*MASC-MEG (English)
-**Link
+**[https://osf.io/ag3kj/ Link]
-**Paper
+**[https://arxiv.org/abs/2208.11488 Preprint]
-*MUC MEG/fMRI
+*10 hour within-participant MEG narrative (English)
-**Link
+**[https://data.donders.ru.nl/collections/di/dccn/DSC_3011085.05_995?1 Link]
-**Paper
+**[https://www.nature.com/articles/s41597-022-01382-7 Data paper]
-*LPP EEG
+*Mother of unification studies (MOUS) MEG/fMRI (Dutch)
-**Paper
+**[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)
-*Eelbrain for EEG/MEG
+**[https://eelbrain.readthedocs.io/en/stable/ Link]
-**Link
+**[https://www.biorxiv.org/content/10.1101/2021.08.01.454687v1 Paper]
-**Paper
+**Tutorial TBD
-*Nilearn for fMRI
+*SPM for fMRI analysis (Matlab)
-**Link to GLM tutorial
+**[https://andysbrainbook.readthedocs.io/en/latest/SPM/SPM_Overview.html SPM Analysis]; read Poldrack first!
-*SPM for fMRI
+**[https://andysbrainbook.readthedocs.io/en/latest/PM/PM_Overview.html SPM Parametric Modulation]
-**Link to tutorial
+**[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 Background'''
+==Relevant Background==
 *Papers:
-**Naturalistic sentence comprehension
+**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]
-**The revolution will not be controlled
+**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]
-**Neurocomputational Models of Language Processing
+**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
-**Brain Inspired podcast with Brennan
+**[https://braininspired.co/podcast/47/ Brain Inspired 047: David Poeppel - Wrong in interesting ways]
-**Brain Inspired podcast with Ev Federenko & Emily Bender
+**[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's Kanwisher's TED talk: A Neural Portrait of the Human Mind]
+**[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]

CogNeuro: Difference between revisions

Computational Linguistics and Information Processing

Latest revision as of 17:35, 25 August 2022

Datasets

Toolkits

Relevant Background