Size: 25703
Comment:
|
Size: 26613
Comment:
|
Deletions are marked like this. | Additions are marked like this. |
Line 119: | Line 119: |
||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks and Slides]]|| | ||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks and Slides]] || |
Line 130: | Line 130: |
||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks and Slides]]|| | ||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks and Slides]] || |
Line 141: | Line 141: |
||Slides and scripts__ __ ||[[attachment:EEGMEG3-timefrequency.zip|Notebooks and Slides]]|| | ||Slides and scripts__ __ ||[[attachment:EEGMEG3-timefrequency.zip|Notebooks and Slides]] || |
Line 159: | Line 159: |
||<10%>__Software__ ||[[https://sites.google.com/site/tdtdecodingtoolbox/|The Decoding Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]] || | ||<10%>__Software__ ||[[https://sites.google.com/site/tdtdecodingtoolbox/|The Decoding Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]]. (This might not be accessible from the CBU internet connection, so please download it in advance or use a difffernt wifi connection) || |
Line 162: | Line 162: |
||__Suggested viewing__ ||Excellent presentations from Martin Hebart's MVPA course, on:<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/02_lecture1|Introduction to MVPA]]<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/03_lecture2|Introduction to classification]] || ||Slides and scripts__ __ || || |
||__Suggested viewing__ ||Excellent presentations from Martin Hebart's MVPA course, on:<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/02_lecture1|Introduction to MVPA]]<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/03_lecture2|Introduction to classification]]. (I've suggested these two, but the others are worth a look too.) || ||Slides and scripts__ __ ||[[attachment:COGNESTIC22_MVPA_djm_Part1.pptx|Slides for morning session - MVPA]] [[attachment:TDTdemoFunctions.zip|These functions should be saved in a subfolder of the Decoding Toolbox demos folder]] [[attachment:FunctionsToGoInTopFolder.zip|These functions should be saved in the top-level folder]] || |
Line 170: | Line 170: |
||<10%>Software__ __ ||[[http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/|The RSA toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]]<<BR>>(Alternatively, https://git.fmrib.ox.ac.uk/hnili/rsa) || | ||<10%>Software__ __ ||[[http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/|The RSA toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]]<<BR>>For the demos, please download this alternative version: https://git.fmrib.ox.ac.uk/hnili/rsa) (Note that the toolbox development has recently switched to Python. We will not be demoing this version, but you can find it here: [[https://github.com/rsagroup/rsatoolbox|Version 3.0]]) || |
Line 174: | Line 174: |
||Slides and scripts ||[[attachment:EEGMEG4-decoding.zip|EEGMEG Notebooks and Slides]]|| | ||Slides and scripts ||[[attachment:COGNESTIC22_MVPA_djm_Part1.pptx|Slides for afternoon session - RSA]] [[attachment:FunctionsToGoInRsa-MasterDemos.zip|These functions should be saved in rsa-master/Demos]] [[attachment:EEGMEG4-decoding.zip|EEGMEG Notebooks and Slides]] || |
Line 183: | Line 183: |
||__Suggested reading__ || [[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjZrKTMs635AhWQUMAKHZfTA6gQFnoECAYQAQ&url=https://labs.la.utexas.edu/gilden/files/2016/05/Statistics-Text.pdf|Statistical Methods for Psychology (Howell)]] <<BR>> [[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjB1Pnxs635AhXOQkEAHfHvBqgQFnoECBQQAQ&url=https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf|Introduction to R]] <<BR>> [[https://www.amazon.co.uk/Discovering-Statistics-Using-Andy-Field/dp/1446200469|Discovering statistics using R]] <<BR>> [[https://www.amazon.co.uk/Introduction-Statistical-Learning-Applications-Statistics/dp/1071614177/ref=sr_1_1?crid=ZCK7U9XROUWC&keywords=an+introduction+to+statistical+learning+with+applications+in+r&qid=1664181428&s=books&sprefix=an+introduction+to+statistical+learning%2Cstripbooks%2C56&sr=1-1| An introduction to statistical learning with applications in R]] || | ||__Suggested reading__ ||[[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjZrKTMs635AhWQUMAKHZfTA6gQFnoECAYQAQ&url=https://labs.la.utexas.edu/gilden/files/2016/05/Statistics-Text.pdf|Statistical Methods for Psychology (Howell)]] <<BR>> [[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjB1Pnxs635AhXOQkEAHfHvBqgQFnoECBQQAQ&url=https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf|Introduction to R]] <<BR>> [[https://www.amazon.co.uk/Discovering-Statistics-Using-Andy-Field/dp/1446200469|Discovering statistics using R]] <<BR>> [[https://www.amazon.co.uk/Introduction-Statistical-Learning-Applications-Statistics/dp/1071614177/ref=sr_1_1?crid=ZCK7U9XROUWC&keywords=an+introduction+to+statistical+learning+with+applications+in+r&qid=1664181428&s=books&sprefix=an+introduction+to+statistical+learning,stripbooks,56&sr=1-1|An introduction to statistical learning with applications in R]] || |
Course Material for COGNESTIC 2022
The Cognitive Neuroscience Skills Training In Cambridge (COGNESTIC) is a 2-week course that provides researchers with training in state-of-the-art methods for neuroimaging and neurostimulation. You can find more information on the COGNESTIC webpage.
Below you will find documents, videos and web links that will be used for the course or can be used for preparation.
Introduction and Open Science |
||
Websites |
||
Suggested reading |
Munafo et al, 2017, problems in science |
|
Suggested viewing |
Open Cognitive Neuroscience (will give this talk live on day) |
|
Tutorial slides and scripts |
Structural MRI - VBM and Surface-based Analysis |
||
Software |
||
Datasets |
Freesurfer tutorial data |
|
Suggested reading |
Introduction to GLM for structural MRI analysis |
|
Suggested viewing |
Using the command line |
|
Tutorial slides and scripts |
FSLVBM slides |
Diffusion MRI I - DTI Model Fitting and Group Analysis |
||
Software |
||
Datasets |
||
Suggested reading |
FSL Diffusion Toolbox Wiki |
|
Suggested viewing |
||
Tutorial slides and scripts |
FSL DTI and TBSS slides |
Diffusion MRI II - Tractography and Structural Connectivity |
||
Software |
||
Datasets |
||
Suggested reading |
||
Suggested viewing |
||
Tutorial slides and scripts |
MRtrix Tractography slides |
fMRI I - Data management, structure, manipulation |
||
Software |
||
Datasets |
||
Suggested reading |
Gorgolewski et al., 2016, The brain imaging data structure (BIDS) |
|
Suggested viewing |
BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) |
|
Slides and scripts |
fMRI II - Quality control & Pre-processing |
||
Software |
||
Datasets |
||
Suggested reading |
Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods |
|
Suggested viewing |
fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57) |
|
Slides and scripts |
fMRI IV - Group Level Analysis & Reporting |
||
Software |
||
Datasets |
||
Suggested reading |
Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data |
|
Suggested viewing |
Group-level Analysis I by Martin Lindquist and Tor Wager (7:05) |
|
Slides and scripts |
Connectivity for fMRI |
||
Software |
||
Datasets |
||
Suggested reading |
Resting-state functional Connectivity |
|
Suggested viewing |
fMRI Functional Connectivity, including DCM |
|
Slides and scripts |
|
Eye-tracking |
||
Software |
Python NumPy, SciPy, Matplotlib |
|
Datasets |
||
Suggested reading |
https://doi.org/10.3758/s13428-021-01762-8 Paper on eye-tracking reporting standards (great for beginners and experts alike) |
|
Suggested viewing |
https://www.youtube.com/watch?v=F5eBln42VyM Talk at the MRC CBU on how to hack pupillometry studies |
|
Slides and scripts |
|
EEG/MEG I – Pre-processing |
||
Software |
||
Datasets |
Sample dataset in MNE-Python. Tutorials |
|
Suggested reading |
Digitial Filtering |
|
Suggested viewing |
Introduction to EEG/MEG Preprocessing |
|
Slides and scripts |
EEG/MEG II – Source Estimation |
||
Software |
||
Datasets |
Sample dataset in MNE-Python. Tutorials |
|
Suggested reading |
Linear source estimation and spatial resolution |
|
Suggested viewing |
Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM |
|
Slides and scripts |
EEG/MEG III – Time-Frequency and Functional Connectivity |
||
Software |
||
Datasets |
Sample dataset in MNE-Python. Tutorials |
|
Suggested reading |
Tutorial on Functional Connectivity |
|
Suggested viewing |
Introduction to time-frequency and functional connectivity analysis |
|
Slides and scripts |
Graph Theory |
||
Software |
||
Datasets |
||
Suggested reading |
Complex brain networks: graph theoretical analysis of structural and functional systems |
|
Suggested viewing |
||
Slides and scripts |
|
MVPA/RSA I |
||
Software |
The Decoding Toolbox in Matlab. (This might not be accessible from the CBU internet connection, so please download it in advance or use a difffernt wifi connection) |
|
Datasets |
The Decoding Toolbox example dataset |
|
Suggested reading |
Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide |
|
Suggested viewing |
Excellent presentations from Martin Hebart's MVPA course, on: |
|
Slides and scripts |
Slides for morning session - MVPA These functions should be saved in a subfolder of the Decoding Toolbox demos folder These functions should be saved in the top-level folder |
MVPA/RSA II |
||
Software |
The RSA toolbox in Matlab |
|
Datasets |
Group-averged example data from Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex |
|
Suggested reading |
Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience |
|
Suggested viewing |
||
Slides and scripts |
Slides for afternoon session - RSA These functions should be saved in rsa-master/Demos EEGMEG Notebooks and Slides |
Statistics in R |
||
Software |
||
Datasets |
||
Suggested reading |
Statistical Methods for Psychology (Howell) |
|
Suggested viewing |
||
Slides and scripts |
|
Brain Stimulation |
||
Software |
SIMNIBS (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and k-wave |
|
Datasets |
||
Suggested reading |
Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2 |
|
Suggested viewing |
||
Slides and scripts |
|
DCM for M/EEG |
||
Software |
||
Datasets |
||
Suggested reading |
||
Suggested viewing |
Talk on DCM for M/EEG |
|
Slides and scripts |
|