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||__Suggested reading__ ||[[https://doi.org/10.1002/hbm.460020402|Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2012.01.133|Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever?]]<<BR>>[[https://doi.org/10.1191/0962280203sm341ra|Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2008.05.021|Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2013.12.058|Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations]]<<BR>>[[https://doi.org/10.1214/09-STS282|Lindquist (2008), The Statistical Analysis of fMRI Data]] || | ||__Suggested reading__ ||[[https://doi.org/10.1002/hbm.460020402|Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2012.01.133|Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever?]]<<BR>>[[https://doi.org/10.3389/fnhum.2011.00028|Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approach]]<<BR>>[[https://doi.org/10.1191/0962280203sm341ra|Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2008.05.021|Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2013.12.058|Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations]]<<BR>>[[https://doi.org/10.1214/09-STS282|Lindquist (2008), The Statistical Analysis of fMRI Data]] || |
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||__Suggested reading__ ||...coming soon... || ||__Suggested viewing__ ||[[https://youtu.be/__cOYPifDWk|Group-level Analysis I]] by Martin Lindquist and Tor Wager (7:05)<<BR>>[[https://youtu.be/-abMLQSjMSI|Group-level Analysis II]] by Martin Lindquist and Tor Wager (10:09)<<BR>>[[https://youtu.be/-yaHTygR9b8|Group-level Analysis III]] by Martin Lindquist and Tor Wager (14:01)|| |
||__Suggested reading__ ||[[https://doi.org/10.1109/MEMB.2006.1607668|Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data]]<<BR>>[[https://www.nature.com/articles/nn.4500|Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2007.11.048|Poldrack et al. (2008), Guidelines for reporting an fMRI study]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2015.04.016|Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain]]<<BR>>[[https://doi.org/10.7554/eLife.71774|Markiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data]]|| ||__Suggested viewing__ ||[[https://youtu.be/__cOYPifDWk|Group-level Analysis I]] by Martin Lindquist and Tor Wager (7:05)<<BR>>[[https://youtu.be/-abMLQSjMSI|Group-level Analysis II]] by Martin Lindquist and Tor Wager (10:09)<<BR>>[[https://youtu.be/-yaHTygR9b8|Group-level Analysis III]] by Martin Lindquist and Tor Wager (14:01) || |
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 |
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Websites |
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Suggested reading |
Munafo et al, 2017, problems in science |
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Suggested viewing |
Statistical power in neuroimaging |
Structural MRI - VBM and Surface-based Analysis |
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Software |
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Datasets |
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Suggested reading |
Introduction to GLM for structural MRI analysis |
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Suggested viewing |
Using the command line |
Diffusion MRI I - DTI Model Fitting and Group Analysis |
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Software |
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Datasets |
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Suggested reading |
FSL Diffusion Toolbox Wiki |
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Suggested viewing |
Diffusion MRI II - Tractography and Structural Connectivity |
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Software |
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Datasets |
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Suggested reading |
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Suggested viewing |
fMRI I - Data management, structure, manipulation |
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Software |
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Datasets |
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Suggested reading |
Gorgolewski et al., 2016, The brain imaging data structure (BIDS) |
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Suggested viewing |
BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) |
fMRI II - Quality control & Pre-processing |
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Software |
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Datasets |
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Suggested reading |
Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods |
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Suggested viewing |
fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57) |
fMRI IV - Group Level Analysis & Reporting |
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Software |
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Datasets |
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Suggested reading |
Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data |
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Suggested viewing |
Group-level Analysis I by Martin Lindquist and Tor Wager (7:05) |
Connectivity for fMRI |
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Software |
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Datasets |
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Suggested reading |
Resting-state functional Connectivity |
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Suggested viewing |
fMRI Functional Connectivity, including DCM |
Eye-tracking |
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Software |
Python NumPy, SciPy, Matplotlib |
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Datasets |
EyeLink EDF examples (to be provided) |
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Suggested reading |
https://doi.org/10.3758/s13428-021-01762-8 Paper on eye-tracking reporting standards (great for beginners and experts alike) |
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Suggested viewing |
https://www.youtube.com/watch?v=F5eBln42VyM Talk at the MRC CBU on how to hack pupillometry studies |
EEG/MEG I – Pre-processing |
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Software |
MNE-Pythonneuro |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
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Suggested viewing |
EEG/MEG II – Source Estimation |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
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Suggested viewing |
EEG/MEG III – Time-Frequency and Functional Connectivity |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
Tutorial on Functional Connectivity |
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Suggested viewing |
Time-frequency and functional connectivity analysis |
Graph Theory |
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Software |
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Datasets |
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Suggested reading |
Complex brain networks: graph theoretical analysis of structural and functional systems |
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Suggested viewing |
MVPA/RSA I |
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Software |
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Datasets |
The Decoding Toolbox example dataset |
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Suggested reading |
Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide |
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Suggested viewing |
Excellent presentations from Martin Hebart's MVPA course, on: |
MVPA/RSA II |
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Software |
The RSA toolbox in Matlab |
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Datasets |
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Suggested reading |
Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience |
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Suggested viewing |
Statistics in R |
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Software |
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Datasets |
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Suggested reading |
Statistical Methods for Psychology (Howell) |
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Suggested viewing |
Brain Stimulation |
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Software |
SIMNIBS (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and k-wave |
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Datasets |
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Suggested reading |
Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2 |
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Suggested viewing |
DCM for M/EEG |
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Software |
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Datasets |
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Suggested reading |
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Suggested viewing |
Talk on DCM for M/EEG coming soon |