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Below you will find documents, videos and web links that will be used for the course or can be used for preparation. <<BR>><<BR>> <<Anchor(openscience)>> ||||||<tablewidth="100%"style="text-align:center">~+'''Introduction and Open Science'''+~ <<BR>> Rik Henson & Olaf Hauk || |
Below you will find documents, videos and web links that will be used for the course or can be used for preparation. <<BR>><<BR>> <<Anchor(openscience)>> ||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Introduction and Open Science'''+~ <<BR>> Rik Henson & Olaf Hauk || |
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<<BR>> <<Anchor(pythonprimer)>> ||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Primer on Python'''+~ <<BR>> Edwin Dalmijer || ||<10%>__Websites__ ||[[https://www.python.org/|Python]], [[https://numpy.org/|NumPy]], [[https://scipy.org/|SciPy]], [[https://matplotlib.org/|Matplotlib]], [[https://psychopy.org/|PsychoPy]] || ||__Suggested reading__ ||None || ||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=H8Du3llCa6w|saliency-mapping of Taylor Swift music videos]] || ||__Tutorial slides and scripts__ ||None || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI - VBM and Surface-based Analysis '''+~<<BR>> Marta Correia || | ||||||<tablewidth="100%"style="text-align:center">'''Structural MRI I - Voxel-based morphometry'''~+''' '''+~<<BR>> Marta Correia || |
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<<BR>> <<Anchor(structuralmri2)>> ||||||<tablewidth="100%"style="text-align:center">'''Structural MRI II - Surface-based analyses'''~+''' '''+~<<BR>> Marta Correia || ||<10%>__Software__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/|FSL]] [[https://surfer.nmr.mgh.harvard.edu/|Freesurfe]]r || ||__Datasets__ ||[[https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Data|Freesurfer tutorial data]] <<BR>> Subset of the CamCAN dataset (~3GB) https://www.cam-can.org/, please sign [[attachment:CamCAN Data User Agreement_COGNESTIC.docx|data user agreement]] if using || ||__Suggested reading__ || || ||__Suggested viewing__ || || ||__Tutorial slides and scripts__ || || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI I - DTI Model Fitting and Group Analysis'''+~ <<BR>> Marta Correia || | ||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI I - The Diffusion Tensor Model'''+~ <<BR>> Marta Correia || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI II - Tractography and Structural Connectivity'''+~ <<BR>> Marta Correia || | ||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI II - Tractography and the Anatomical Connectome'''+~ <<BR>> Marta Correia || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Connectivity for fMRI'''+~ <<BR>> Rik Henson || ||<10%>__Software__ ||[[https://www.fil.ion.ucl.ac.uk/spm/software/spm12/|SPM12]] || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ ||[[http://dx.doi.org/10.1016/j.tics.2013.09.016|Resting-state functional Connectivity]] <<BR>> [[https://doi.org/10.1016/j.neuroimage.2013.07.008|Simple Intro to DCM]] <<BR>> [[https://www.frontiersin.org/articles/10.3389/fnins.2019.00300/full#supplementary-material|fMRI preprocessing in SPM12 (for demo)]] <<BR>> [[https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf|SPM12 manual (Chapter 36)]] || ||__Suggested viewing__ ||[[https://youtu.be/1VOKsWWLgjk|fMRI Functional Connectivity, including DCM]] <<BR>> [[https://youtu.be/1cbEmn_Qgkc|Bayesian Model Comparison (for DCM for fMRI/MEEG)]] || ||Slides and scripts__ __ || || <<BR>> <<Anchor(eyetracking)>> ||||||<tablewidth="100%"style="text-align:center">~+'''Eye-tracking'''+~ <<BR>> Edwin Dalmaijer || ||<10%>Software__ __ ||Python NumPy, [[https://scipy.org/|SciPy]], [[https://matplotlib.org/|Matplotlib]] || ||Datasets__ __ ||[[https://www.pygaze.org/resources/downloads/PEP/ED_pupil.asc|Example Data]] EyeLink || ||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 || |
||||||<tablewidth="734px" tableheight="239px"style="text-align:center">~+'''fMRI Connectivity'''+~ <<BR>> Petar Raykov || ||<10%>__Software__ ||[[https://carpentries-incubator.github.io/SDC-BIDS-fMRI/aio/index.html|Nilearn Python]] || ||__Datasets__ ||[[https://nilearn.github.io/dev/modules/generated/nilearn.datasets.fetch_development_fmri.html|movie dataset]] || ||__Suggested reading__ ||[[http://dx.doi.org/10.1016/j.tics.2013.09.016|Resting-state functional Connectivity]] || ||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=1VOKsWWLgjk|fMRI Functional and Effective Connectivity in fMRI]] || ||__Tutorial slides and scripts__ ||[[attachment:Multimodal_DCM_cognestic_tutorial_fMRI.pdf|DCM tutorial in SPM (not covered in-person)]] || <<BR>> <<Anchor(networks)>> ||||||<tablewidth="100%"style="text-align:center">~+'''Brain Network Analysis'''+~ <<BR>> Lena Dorfschmidt || ||<10%>Software__ __ ||[[https://pypi.org/project/bctpy/|Brain connectivity toolbox in python]] || ||Datasets__ __ || || ||Suggested reading__ __ ||Bullmore, E., Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. ''Nat Rev Neurosci'' '''10''', 186–198 (2009). [[https://doi.org/10.1038/nrn2575|https://doi.org/10.1038/nrn2575]] <<BR>> Fornito, Alex, Andrew Zalesky, and Edward Bullmore. ''Fundamentals of brain network analysis''. Academic press, 2016. || ||Suggested viewing__ __ ||[[https://www.youtube.com/watch?v=HjSGqwAFRcc|Understanding your brain as a network and as art]] || |
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||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks and Slides]]|| | ||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks and Slides]] || |
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||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks and Slides]]|| <<BR>> <<Anchor(eegmeg3)>> ||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity'''+~ <<BR>> Olaf Hauk || |
||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks and Slides]] || <<BR>> <<Anchor(eegmeg4)>> ||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG VI – Priors and Multimodal Imaging'''+~ <<BR>> Olaf Hauk || |
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||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/26778976/|Tutorial on Functional Connectivity]]<<BR>> [[https://mitpress.mit.edu/books/analyzing-neural-time-series-data|Analyzing Neural Time Series Data]]<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] || ||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG3.mp4|Introduction to time-frequency and functional connectivity analysis]] <<BR>> [[https://www.youtube.com/watch?v=wB417SAbdak|Time-Frequency Analysis of EEG Time Series]] || ||Slides and scripts__ __ ||[[attachment:EEGMEG3-timefrequency.zip|Notebooks and Slides]]|| <<BR>> <<Anchor(graphtheory)>> ||||||<tablewidth="100%"style="text-align:center">~+'''Graph Theory'''+~ <<BR>> Caroline Nettekoven || ||<10%>Software__ __ ||[[https://sites.google.com/site/bctnet/|Brain Connectivity Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]] || ||Datasets__ __ ||[[https://www.caroline-nettekoven.com/slides/graph-theory-exercises/|Coding exercises]]<<BR>> [[https://www.caroline-nettekoven.com/slides/graph-theory-exercises-solutions/|Exercise solutions]] || ||Suggested reading__ __ ||[[https://www.nature.com/articles/nrn2575|Complex brain networks: graph theoretical analysis of structural and functional systems]] || ||Suggested viewing__ __ ||[[https://www.caroline-nettekoven.com/slides/graph-theory-lecture/|Slides]] || ||Slides and scripts || || |
||__Suggested reading__ || || ||__Suggested viewing__ || || ||Slides and scripts__ __ || || |
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||<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) || |
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||__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]]<<BR>> [[attachment:TDTdemoFunctions.zip|These functions should be saved in a subfolder of the Decoding Toolbox demos folder]]<<BR>> [[attachment:FunctionsToGoInTopFolder.zip|These functions should be saved in the top-level folder]] <<BR>>(Please see the 3rd slide for an overview of the file structure expected by the demo scripts) || |
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||<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 <<BR>>(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]]) || |
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||Slides and scripts ||[[attachment:EEGMEG4-decoding.zip|EEGMEG Notebooks and Slides]]|| <<BR>> <<Anchor(statistics)>> ||||||<tablewidth="100%"style="text-align:center">~+'''Statistics in R'''+~ <<BR>> Peter Watson || ||<10%>__Software__ ||[[https://www.r-project.org/|R]] || ||__Datasets__ ||[[attachment:PW SEPT 2022 R COURSE.zip|Data&Code]] [[attachment:README R COURSE.txt|Readme]] || ||__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]] || ||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/statswiki/StatsCourse2021/recordings|CBU Statistics Lectures]] || ||Slides and scripts__ __ || || |
||Slides and scripts ||[[attachment:COGNESTIC22_MVPA_djm_Part2.pptx|Slides for afternoon session - RSA]] <<BR>>[[attachment:FunctionsToGoInRsa-MasterDemos.zip|These functions should be saved in rsa-master/Demos]]<<BR>> [[attachment:EEGMEG4-decoding.zip|EEGMEG Notebooks and Slides]] || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Brain Stimulation'''+~ <<BR>> Ajay Halai || ||<10%>Software__ __ ||[[https://simnibs.github.io/simnibs/build/html/index.html|SIMNIBS]] (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and [[http://www.k-wave.org/|k-wave]] || ||Datasets__ __ ||[[https://simnibs.github.io/simnibs/build/html/dataset.html|tutorial_data]] || ||Suggested reading__ __ ||[[https://www.sciencedirect.com/science/article/pii/S1053811916001191?via=ihub|Approaches to brain stimulation]] ; [[https://direct.mit.edu/jocn/article/33/2/195/95534/Inferring-Causality-from-Noninvasive-Brain|what can we infer from brain stimulation]]; [[https://www.nature.com/articles/nrneurol.2010.30.pdf|using NIBS clinically]] ; focused ultrasound [[https://www.nature.com/articles/srep34026.pdf|1]] and [[https://www.nature.com/articles/s41598-018-28320-1.pdf|2]] || ||Suggested viewing__ __ ||[[attachment:COGNESTIC_slides|slides]] || |
||||||<tablewidth="100%"style="text-align:center">~+'''Brain Stimulation, Plethysmography, Electromyography'''+~ <<BR>> Ajay Halai, Alexis Deighton MacIntyre, Hristo Dimitrov || ||<10%>Software ||Plethysmography: https://github.com/alexisdmacintyre/SpeechBreathingToolbox || ||Datasets || || ||Suggested reading__ __ ||Brain Stimulation: <<BR>> [[https://www.sciencedirect.com/science/article/pii/S1053811916001191?via=ihub|Approaches to brain stimulation]] ; [[https://direct.mit.edu/jocn/article/33/2/195/95534/Inferring-Causality-from-Noninvasive-Brain|what can we infer from brain stimulation]]; [[https://www.nature.com/articles/nrneurol.2010.30.pdf|using NIBS clinically]] ; focused ultrasound [[https://www.nature.com/articles/srep34026.pdf|1]] and [[https://www.nature.com/articles/s41598-018-28320-1.pdf|2]] <<BR>><<BR>> Plethysmography: <<BR>>Heck, D. H., McAfee, S. S., Liu, Y., Babajani-Feremi, A., Rezaie, R., Freeman, W. J., ... & Kozma, R. (2017). Breathing as a fundamental rhythm of brain function. Frontiers in neural circuits, 10, 115. https://doi.org/10.3389/fncir.2016.00115 <<BR>> Varga, S., & Heck, D. H. (2017). Rhythms of the body, rhythms of the brain: Respiration, neural oscillations, and embodied cognition. Consciousness and Cognition, 56, 77-90. https://doi.org/10.1016/j.concog.2017.09.008 <<BR>>Allen, M., Varga, S., & Heck, D. H. (2022). Respiratory rhythms of the predictive mind. Psychological Review. https://doi.org/10.1037/rev0000391 || ||Suggested viewing__ __ ||Brain Stimulation: [[attachment:AH_slides.pptx|slides]] || |
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<<BR>> <<Anchor(dcmemeg1)>> ||||||<tablewidth="100%"style="text-align:center">~+'''DCM for M/EEG'''+~ <<BR>> Pranay Yadav & Rik Henson || ||<10%>__Software__ ||[[https://www.fil.ion.ucl.ac.uk/spm/software/spm12/|SPM12]] || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ ||[[https://doi.org/10.3389/fnins.2019.00300|Preprocessing M/EEG in SPM12]] <<BR>> [[https://doi.org/10.1016/j.neuroimage.2013.07.008|Simple Intro to DCM]] || ||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=HNaAvKmVCYo|Talk on DCM for M/EEG]] <<BR>> [[https://youtu.be/6b35VvQpPDU|MEEG connectivity other than DCM (not demo'ed, and related to Hauk talks above)]] || ||Slides and scripts__ __ || || |
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 |
Open Cognitive Neuroscience (will give this talk live on day) |
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Tutorial slides and scripts |
Primer on Python |
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Websites |
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Suggested reading |
None |
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Suggested viewing |
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Tutorial slides and scripts |
None |
Structural MRI I - Voxel-based morphometry |
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Software |
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Datasets |
Freesurfer tutorial data |
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Suggested reading |
Introduction to GLM for structural MRI analysis |
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Suggested viewing |
Using the command line |
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Tutorial slides and scripts |
FSLVBM slides |
Structural MRI II - Surface-based analyses |
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Software |
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Datasets |
Freesurfer tutorial data |
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Suggested reading |
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Suggested viewing |
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Tutorial slides and scripts |
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Diffusion MRI I - The Diffusion Tensor Model |
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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 |
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Tutorial slides and scripts |
FSL DTI and TBSS slides |
Diffusion MRI II - Tractography and the Anatomical Connectome |
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Software |
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Datasets |
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Suggested reading |
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Suggested viewing |
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Tutorial slides and scripts |
MRtrix Tractography slides |
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) |
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Slides and scripts |
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) |
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Slides and scripts |
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) |
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Slides and scripts |
fMRI Connectivity |
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Software |
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Datasets |
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Suggested reading |
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Suggested viewing |
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Tutorial slides and scripts |
Brain Network Analysis |
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Software |
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Datasets |
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Suggested reading |
Bullmore, E., Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10, 186–198 (2009). https://doi.org/10.1038/nrn2575 |
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Suggested viewing |
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Slides and scripts |
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EEG/MEG I – Pre-processing |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
Digitial Filtering |
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Suggested viewing |
Introduction to EEG/MEG Preprocessing |
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Slides and scripts |
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 |
Linear source estimation and spatial resolution |
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Suggested viewing |
Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM |
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Slides and scripts |
EEG/MEG VI – Priors and Multimodal Imaging |
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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 |
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Slides and scripts |
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MVPA/RSA I |
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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) |
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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: |
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Slides and scripts |
Slides for morning session - MVPA |
MVPA/RSA II |
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Software |
The RSA toolbox in Matlab |
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Datasets |
Group-averged example data from Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex |
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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 |
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Slides and scripts |
Slides for afternoon session - RSA |
Brain Stimulation, Plethysmography, Electromyography |
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Software |
Plethysmography: https://github.com/alexisdmacintyre/SpeechBreathingToolbox |
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Datasets |
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Suggested reading |
Brain Stimulation: |
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Suggested viewing |
Brain Stimulation: slides |
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Slides and scripts |
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