= 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 [[https://www.mrc-cbu.cam.ac.uk/conferences/cognestic2022/|COGNESTIC webpage]].

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 ||
||<10%>__Websites__ ||[[https://osf.io/|OSF]] <<BR>> [[https://www.ukrn.org/primers/|UKRN]] <<BR>> [[https://bids.neuroimaging.io/|BIDS]] ||
||__Suggested reading__ ||[[https://doi.org/10.1038/s41562-016-0021|Munafo et al, 2017, problems in science]] <<BR>> [[https://doi.org/10.1038/nrn3475|Button et al, 2013, power in neuroscience]] <<BR>> [[https://doi.org/10.1038/nrn.2016.167|Poldrack et al, 2017, reproducible neuroimaging]] <<BR>> [[https://doi.org/10.1038/s41586-022-04492-9|Marek et al, 2022, power in neuroimaging association studies]] ||
||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=D0VKyjNGvrs|Statistical power in neuroimaging]] <<BR>> [[https://www.youtube.com/watch?v=zAzTR8eq20k|PayWall: open access]] <<BR>> [[https://www.facebook.com/LastWeekTonight/videos/896755337120143|Comedian's Perspective on science and media]] ||




<<BR>> <<Anchor(structuralmri)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI - VBM and Surface-based Analysis '''+~<<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]] ||
||__Suggested reading__ ||[[attachment:IntroductionToGLM.pdf|Introduction to GLM for structural MRI analysis]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/11525331/|Good et al, 2001, A VBM study of ageing]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/15501092/|Smith et al, 2004, Structural MRI analysis in FSL]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/9931268/|Dale et al, 1999, Cortical surface-based analysis I]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/9931269/|Fischl et al, 1999, Cortical surface-based analysis II]] ||
||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=6eJMxh7PlOY|Using the command line]] <<BR>> [[https://youtu.be/Psh-GovQLiI|Introduction to MRI Physics and image contrast]] ||




<<BR>> <<Anchor(diffusionmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  I - DTI Model Fitting and Group Analysis'''+~ <<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/|FSL]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds001226/versions/00001|BTC_preop]] ||
||__Suggested reading__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT|FSL Diffusion Toolbox Wiki]] <<BR>> [[https://doi.org/10.1371/journal.pbio.1002203|Le Bihan et al, 2015, What water tells us about biological tissues]] <<BR>> [[https://doi.org/10.3389/fnins.2013.00031|Soares et al, 2013, A short guide to Diffusion Tensor Imaging]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/16624579/|Smith et al, 2006, Tract-based spatial statistics (TBSS)]] ||
||__Suggested viewing__ ||[[https://youtu.be/stpmlzO7b6c|Introduction to Diffusion MRI - Part I]] ||




<<BR>> <<Anchor(diffusionmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and Structural Connectivity'''+~ <<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://www.mrtrix.org/|MRtrix3]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds001226/versions/00001|BTC_preop]] ||
||__Suggested reading__ ||[[https://mrtrix.readthedocs.io/en/latest/|MRtrix3 documentation]] <<BR>> [[https://www.sciencedirect.com/science/article/pii/B9780123964601000196|MR Diffusion Tractography]] ||
||__Suggested viewing__ ||[[https://youtu.be/QDJJ6G2ZouA|Introduction to Diffusion MRI - Part II]] ||




<<BR>> <<Anchor(fmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI I - Data management, structure, manipulation'''+~ <<BR>> Dace Apšvalka ||
||<10%>__Software__ ||[[https://heudiconv.readthedocs.io/en/latest/|HeudiConv]], [[https://bids-standard.github.io/pybids/|PyBIDS]], [[https://nipy.org/nibabel/|NiBabel]], [[https://nilearn.github.io/stable/index.html|Nilearn]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Suggested reading__ ||[[https://doi.org/10.1038/sdata.2016.44|Gorgolewski et al, 2016, The brain imaging data structure (BIDS)]] ||
||__Suggested viewing__ ||[[https://osf.io/fbj5u|BIDS for MRI: Structure and Conversion]] by Taylor Salo (13:39) <<BR>> [[https://youtu.be/OuRdQJMU5ro|fMRI Data Structure & Terminology]] by Martin Lindquist and Tor Wager (6:47) ||




<<BR>> <<Anchor(fmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI II - Quality control & Pre-processing'''+~ <<BR>> Dace Apšvalka ||
||<10%>__Software__ ||[[https://mriqc.readthedocs.io/en/latest/|MRIQC]], [[https://fmriprep.org/en/stable/|fMRIprep]], [[https://nipype.readthedocs.io/en/latest/|Nipype]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Suggested reading__ ||...coming soon... ||
||__Suggested viewing__ ||...coming soon... ||




<<BR>> <<Anchor(fmri3)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI III - Statistical Analysis'''+~ <<BR>> Dace Apšvalka ||
||<10%>__Software__ ||[[http://nipype.readthedocs.io/en/latest/|Nipype]], [[http://nilearn.github.io/stable/index.html|Nilearn]], [[http://www.fil.ion.ucl.ac.uk/spm/software/spm12/|SPM12]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Suggested reading__ ||...coming soon... ||
||__Suggested viewing__ ||...coming soon... ||




<<BR>> <<Anchor(fmri4)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI IV - Reporting'''+~ <<BR>> Dace Apšvalka ||
||<10%>__Software__ ||[[http://nilearn.github.io/stable/index.html|Nilearn]], [[https://pysurfer.github.io/|PySurfer]], [[http://www.fil.ion.ucl.ac.uk/spm/software/spm12/|SPM12]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Suggested reading__ ||...coming soon... ||
||__Suggested viewing__ ||...coming soon... ||




<<BR>> <<Anchor(connectivityfmri)>>
||||||<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)]] ||




<<BR>> <<Anchor(eyetracking)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Eye-tracking'''+~ <<BR>> Edwin Dalmijer ||
||<10%>__Software__ ||Python NumPy, [[https://scipy.org/|SciPy]], [[https://matplotlib.org/|Matplotlib]] ||
||__Datasets__ ||EyeLink EDF examples (to be provided) ||
||__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 ||




<<BR>> <<Anchor(eegmeg1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG I – Pre-processing'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]neuro ||
||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/preprocessing/index.html|Tutorials]] ||
||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/25128257/|Digitial Filtering]] <<BR>>[[https://www.sciencedirect.com/science/article/pii/S0896627319301746|Filtering How To]] <<BR>> [[https://iopscience.iop.org/article/10.1088/0031-9155/51/7/008|Maxwell Filtering]] ||
||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG1.mp4|Preprocessing]] <<BR>>[[https://mediacentral.ucl.ac.uk/Player/2909|What are we measuring with M/EEG]]? ||




<<BR>> <<Anchor(eegmeg2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG II  – Source Estimation'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]] ||
||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Tutorials]] ||
||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/35390459/|Linear source estimation and spatial resolution]] ||
||__Suggested viewing__ ||[[https://mediacentral.ucl.ac.uk/Player/2917|M/EEG Source Analysis in SPM]] ||




<<BR>> <<Anchor(eegmeg3)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]] ||
||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/time-freq/index.html|Tutorials]] ||
||__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]] ||
||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG3.mp4|Time-frequency and functional connectivity analysis]] <<BR>> [[https://www.youtube.com/watch?v=wB417SAbdak|Time-Frequency Analysis of EEG Time Series]] ||




<<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]] ||




<<BR>> <<Anchor(rsa1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''MVPA/RSA I'''+~ <<BR>> Daniel Mitchell ||
||<10%>__Software__ ||[[https://sites.google.com/site/tdtdecodingtoolbox/|The Decoding Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]] ||
||__Datasets__ ||[[https://www.bccn-berlin.de/tdt/downloads/sub01_firstlevel.zip|The Decoding Toolbox example dataset]] <<BR>> (See toolbox webpage for a lower resolution alternative) ||
||__Suggested reading__ ||[[https://academic.oup.com/scan/article/4/1/101/1613450|Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide]]<<BR>>[[https://www.frontiersin.org/articles/10.3389/fninf.2014.00088/full|Hebart  et al. (2014) The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data]] ||
||__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]] ||




<<BR>> <<Anchor(rsa2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''MVPA/RSA II'''+~ <<BR>> Daniel Mitchell ||
||<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) ||
||__Datasets__ || ||
||__Suggested reading__ ||[[https://www.frontiersin.org/articles/10.3389/neuro.06.004.2008/full|Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience]]<<BR>>[[https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(13)00127-7|Kriegeskorte & Kievit (2013) Representational geometry: integrating cognition, computation, and the brain]] <<BR>>[[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003553|Nili et al. (2014) A toolbox for representational similarity analysis]] ||
||__Suggested viewing__ ||[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]] ||




<<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]] ||
||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/statswiki/StatsCourse2021/recordings|CBU Statistics Lectures]] ||




<<BR>> <<Anchor(brainstimulation)>>
||||||<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]] ||




<<BR>> <<Anchor(dcmemeg1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''DCM for M/EEG'''+~ <<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__ ||[[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__ ||Talk on DCM for M/EEG coming soon <<BR>> [[https://youtu.be/6b35VvQpPDU|MEEG connectivity other than DCM (not demo'ed, and related to Hauk talks above)]] ||




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