= Course Material for COGNESTIC 2024 =
The Cognitive Neuroimaging Skills Training In Cambridge (COGNESTIC) is a 2-week  course that provides researchers with training in state-of-the-art methods for reproducible and open neuroimaging analysis and related methods. You can find more information on the [[https://www.mrc-cbu.cam.ac.uk/events/cognestic-2024/|COGNESTIC webpage]].

Below you  will find documents, videos and web links that will be used for the course or can be used for preparation.

== Software Installation Instructions ==
TBA

== Essential Preparation Materials ==
You will find the course easier if you can study as much of the material below in advance (e.g, many of the videos below give the theory to the examples we will work through in the course).

<<BR>><<BR>> <<Anchor(openscience)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Background to Open Science'''+~ <<BR>> Rik Henson ||
||__Viewing__ ||[[https://youtu.be/kTVtc7kjVQg|Open Cognitive Neuroscience]] ||




<<BR>> <<Anchor(pythonprimer )>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);    "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''Primer on Python'''+~ <<BR>> Kshipra Gurunandan ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Viewing__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">[[https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb|Introduction to Python and notebooks]] ||




<<BR>> <<Anchor(structuralmri)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI I and II - VBM and surface-based analysis'''+~<<BR>> Marta Correia ||
||__Viewing__ ||[[https://youtu.be/Psh-GovQLiI|Introduction to MRI Physics and image contrast]] <<BR>> [[attachment:IntroductionToMRIPhysics.pdf|Slides]] ||




<<BR>> <<Anchor(diffusionmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  I - Preprocessing, model fitting and group analysis'''+~ <<BR>> Marta Correia ||
||__Viewing__ ||[[https://youtu.be/stpmlzO7b6c|Introduction to Diffusion MRI - Part I]] <<BR>> [[attachment:IntroductionToDiffusionMRI_I.pdf|Slides]] ||




<<BR>> <<Anchor(diffusionmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and the anatomical connectome'''+~ <<BR>> Marta Correia ||
||__Viewing__ ||[[https://youtu.be/QDJJ6G2ZouA|Introduction to Diffusion MRI - Part II]] <<BR>> [[attachment:IntroductionToDiffusionMRI_II.pdf|Slides]] ||




<<BR>> <<Anchor(fmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI I - Data Management'''+~ <<BR>> Dace Apšvalka ||
||Viewing ||[[https://youtu.be/OuRdQJMU5ro|fMRI Data Structure & Terminology]] (6:47)<<BR>>[[https://youtu.be/5H6XaJLp2V8?si=39BLjouIy8aUaEo7|Brain imaging data structure]] (11:07) ||




<<BR>> <<Anchor(fmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI II - Preprocessing'''+~ <<BR>> Dace Apšvalka ||
||Viewing ||[[https://youtu.be/7Kk_RsGycHs|fMRI Artifacts and Noise]] (11:57) <<BR>> [[https://youtu.be/Qc3rRaJWOc4|Pre-processing I]] (10:17) <<BR>> [[https://youtu.be/qamRGWSC-6g|Pre-processing II]] (7:42) ||




<<BR>> <<Anchor(fmri3)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI III - Analysis'''+~ <<BR>> Dace Apšvalka ||
||Viewing ||[[https://www.youtube.com/watch?v=OyLKMb9FNhg|GLM applied to fMR]]I (11:21) <<BR>> [[https://www.youtube.com/watch?v=7MibM1ATai4|Model Building – conditions and contrasts]] (11:48) <<BR>> [[https://www.youtube.com/watch?v=DEtwsFdFwYc%20|Model Building - nuisance variables]] (13:58) <<BR>> [[https://youtu.be/AalIM9-5-Pk|Multiple Comparisons]] (9:03) <<BR>> [[https://youtu.be/__cOYPifDWk|Group-level Analysis I]] (7:05) ||




<<BR>> <<Anchor(connectivityfmri)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI Connectivity'''+~ <<BR>> Petar Raykov ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=SqyNPbsgHNQ&ab_channel=PetarRaykov|Functional Connectivity in fMRI]] ||




<<BR>> <<Anchor(networks)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Network Analysis'''+~ <<BR>> Rik Henson ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=H2q3fPxiuvw|Introduction to Network Neuroscience]] ||




<<BR>> <<Anchor(eegmeg1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG I – Measurement and Pre-processing'''+~ <<BR>> Olaf Hauk ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=S24QG_n6KXk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|Overview of EEG/MEG data processing from raw data to source estimates]] <<BR>>Event-related  paradigm, sample dataset, power spectrum, pre-processing, artefact  correction, epoching and averaging, visualization, source estimation.<<BR>>  2. [[https://www.youtube.com/watch?v=GGDc6qZoDZ4&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=2&pp=iAQB|The generation of EEG/MEG signals]] <<BR>>Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.<<BR>>3.[[https://www.youtube.com/watch?v=fLAoRsB2MF8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=5&pp=iAQB|Frequency and temporal filtering of EEG/MEG data]]<<BR>>Frequency  spectrum, temporal smoothing, relationship between frequency and time  domain, filters (low-/high-/band-pass, Notch), aliasing, decibels. <<BR>> 4. [[https://www.youtube.com/watch?v=OZFiYeIR2Xk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=7&pp=iAQB|Differential sensitivity of EEG and MEG]] <<BR>>Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources. <<BR>> 5.[[https://www.youtube.com/watch?v=DYOnFu2Cuyw&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=16|Event-related potentials and fields]]  <<BR>>Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression. <<BR>> Fore more on this topic see [[#eegmeg1b|here.]] ||




<<BR>> <<Anchor(eegmeg2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG II  – Head Modelling and Source Estimation'''+~ <<BR>> Olaf Hauk ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=duhU5nOsAoc&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=8&pp=iAQB|The EEG/MEG forward model]]<<BR>>Basic  formulation of the EEG/MEG forward problem, linear equation, basics of  head modelling, examples of sensory evoked responses.<<BR>>  2. [[https://www.youtube.com/watch?v=KlRJ5kpT3eA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=11&pp=iAQB|The EEG/MEG inverse problem]]<<BR>>Non-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity. <<BR>> 3. [[https://www.youtube.com/watch?v=X4EZCGPvI1k&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=12&pp=iAQB|The spatial resolution of linear EEG/MEG source estimation]]<<BR>>Leakage  and blurring, resolution matrix, point-spread functions (PSFs),  cross-talk functions (CTFs), examples of PSFs and CTFs,  regions-of-interest for source estimation.<<BR>> 4. [[http://www.youtube.com/watch?v=XgYev3N1rR0&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=14&pp=iAQB|Noise and regularisation in EEG/MEG source estimates]] <<BR>>Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix. <<BR>> Fore more on this topic see [[#eegmeg2b|here.]] ||




<<BR>> <<Anchor(eegmeg3)>>
||||||<tablewidth="751px"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity '''+~~+'''Analysis '''+~ <<BR>> Olaf Hauk ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=N4Pm1_C8hlA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=18&pp=iAQB|Frequency spectra and the Fourier analysis]] <<BR>> Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response. <<BR>> 2. [[https://www.youtube.com/watch?v=ac0LbTm1Eb8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=19&pp=iAQB|Time-frequency analysis and wavelets]] <<BR>>Fourier  analysis, wavelets, trade-off between time and frequency resolution,  wavelets, number of cycles, evoked and induced activity, beta bursts. <<BR>> 3.[[https://www.youtube.com/watch?v=omWqJ8xD2gs&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=20&pp=iAQB|The basics of functional connectivity methods]] <<BR>>Types  of connectivity, amplitude envelope correlation, resting state  analysis, Hilbert envelope, phase-locking, coherence, SNR bias,  time-resolved connectivity. <<BR>> Fore more on this topic see [[#eegmeg3b|here.]] ||




<<BR>> <<Anchor(eegmeg4)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG IV – Further Topics and BIDS'''+~ <<BR>> Olaf Hauk & Máté Aller ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=sW2i5sZC0zA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=22&pp=iAQB|Primer on group statistics for EEG/MEG data]]<<BR>>Regions-of-interest (ROI) analysis, multiple comparison problem, cluster-based permutation tests, problems estimating cluster extent, MNE-Python tutorial.<<BR>> 2. [[https://www.youtube.com/watch?v=08_VgAlVjIg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=23&pp=iAQB|Primer on decoding and RSA with EEG/MEG data]]<<BR>>Basics of linear decoding, temporal generalisation, interpreting decoding weights, back-projection, representational similarity analysis (RSA).<<BR>> 3. [[https://www.youtube.com/watch?v=95WZzPGXJes&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=24&pp=iAQB|Primer on multimodal integration]] <<BR>> Types of neural “activity”, differential sensitivity of EEG/MEG vs fMRI, source weighting and priors, estimating deep sources with EEG/MEG. <<BR>> Fore more on this topic see [[#eegmeg4b|here.]] ||




<<BR>> <<Anchor(rsa1)>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);                      "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''MVPA/RSA I and II'''+~ <<BR>> Daniel Mitchell & Máté Aller ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Viewing__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">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>> If the links don't work, download from [[https://imaging.mrc-cbu.cam.ac.uk/methods/COGNESTIC2023?action=AttachFile&do=view&target=02_lecture1_MVPA_intro.mp4|here]] and [[https://imaging.mrc-cbu.cam.ac.uk/methods/COGNESTIC2023?action=AttachFile&do=view&target=03_lecture2_Classification.mp4|here]]. <<BR>> [[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]]. If the link fails, download from [[https://imaging.mrc-cbu.cam.ac.uk/methods/COGNESTIC2023?action=AttachFile&do=view&target=08_lecture6_RSA.mp4|here]]. <<BR>> [[https://www.youtube.com/watch?v=08_VgAlVjIg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=23|Primer on decoding and RSA with EEG/MEG data]] ||




<<BR>>

== Additional Extra ==
If you want additional background, consider some of the below:

<<BR>>
||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Background to Open Science'''+~ <<BR>> Rik Henson ||
||__Websites__ ||[[https://osf.io/|OSF]] <<BR>> [[https://www.ukrn.org/primers/|UKRN]] <<BR>> [[https://bids.neuroimaging.io/|BIDS]] ||
||__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]] ||
||__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]] ||
||__Slides__ ||[[attachment:COGNESTIC_OpenCogNeuro.pdf|Open Science Talk Slides]] ||




<<BR>> <<Anchor(pythonprimer)>>
||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Primer on Python'''+~ <<BR>> Kshipra Gurunandan ||
||<10%>__Software__ ||[[https://www.python.org/|Python]], [[https://pandas.pydata.org/|Pandas]], [[https://numpy.org/|NumPy]], [[https://matplotlib.org/|Matplotlib]], [[https://seaborn.pydata.org/|Seaborn]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Useful references__ ||[[https://www.w3schools.com/python/default.asp|Python concepts with examples]], [[https://quickref.me/python.html|Quick reference]], [[https://blog.finxter.com/python-cheat-sheets/|Cheatsheets]] ||
||__Slides and scripts__ ||To be added ||




<<BR>> <<Anchor(structuralmri)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI I - Voxel-based morphometry'''+~''' '''<<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/|FSL]] ||
||__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>> <<Anchor(structuralmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI II - Surface-based analyses'''+~''' '''<<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://surfer.nmr.mgh.harvard.edu/|Freesurfe]]r ||
||__Suggested reading__ ||[[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>> <<Anchor(diffusionmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  I - Preprocessing, Model Fitting and Group Analysis '''+~<<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://dipy.org/|dipy]], [[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/|FSL]] ||
||__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)]] ||




<<BR>> <<Anchor(diffusionmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and the Anatomical Connectome'''+~ <<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://dipy.org/|dipy]] ||
||__Suggested reading__ ||[[https://www.sciencedirect.com/science/article/pii/B9780123964601000196|MR Diffusion Tractography]] ||




<<BR>> <<Anchor(fmri1extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI I - Data Management'''+~ <<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]] ||
||<10%>Websites ||[[https://bids.neuroimaging.io/|Brain Imaging Data Structure]] <<BR>> [[https://bids-standard.github.io/bids-starter-kit/|BIDS Starter Kit]] <<BR>> [[https://bids-specification.readthedocs.io/en/stable/|BIDS Specification v1.9.0]] ||
||Suggested reading ||[[https://www.nature.com/articles/sdata201644|The brain imaging data structure (BIDS)]], Gorgolewski et al., 2016<<BR>>[[https://doi.org/10.1162/imag_a_00103|The past, present, and future of the brain imaging data structure (BIDS)]], Poldrack et al., 2024<<BR>> ||




<<BR>> <<Anchor(fmri2extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI II - 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]] ||
||Suggested reading__ __ ||[[https://link.springer.com/article/10.1007/s11065-015-9294-9|Functional Magnetic Resonance Imaging Methods]], Chen & Glover, 2015 <<BR>> [[https://doi.org/10.3389/fnimg.2022.1073734|Quality control in functional MRI studies with MRIQC and fMRIPrep]], Provins et al., 2023 <<BR>> [[https://www.nature.com/articles/s41592-018-0235-4|fMRIPrep: a robust preprocessing pipeline for functional MRI]], Esteban et al., 2018 <<BR>> [[https://doi.org/10.3389/fninf.2011.00013|Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python]], Gorgolewski et al., 2011 ||




<<BR>> <<Anchor(fmri3extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI III - Analysis'''+~ <<BR>> Dace Apšvalka ||
||<10%>Software ||[[http://nilearn.github.io/stable/index.html|Nilearn]] ||
||Suggested reading ||[[https://doi.org/10.1214/09-STS282|The Statistical Analysis of fMRI Data]], Lindquist, 2008 <<BR>> [[https://doi.org/10.1191/0962280203sm341ra|Controlling the familywise error rate in functional neuroimaging: a comparative review]], Nichols & Hayasaka, 2003 <<BR>> [[https://www.nature.com/articles/s41596-020-0327-3|Analysis of task-based functional MRI data preprocessed with fMRIPrep]], Esteban et al., 2020 <<BR>> [[https://doi.org/10.1016/j.neuroimage.2007.11.048|Guidelines for reporting an fMRI study]], Poldrack et al., 2008 ||
||Suggested viewing ||[[https://www.youtube.com/watch?v=YfeMIcDWwko|Model Building - temporal basis sets]] (11:08)<<BR>>[[https://www.youtube.com/watch?v=Ab-5AbJ8gAs|GLM Estimation]] (9:11)<<BR>>[[https://youtu.be/Mb9LDzvhecY|Noise Models- AR models]] (9:57)<<BR>>[[https://youtu.be/NRunOo7EKD8|Inference- Contrasts and t-tests]] (11:05)<<BR>>[[https://youtu.be/AalIM9-5-Pk|Multiple Comparisons]] by Martin Lindquist and Tor Wager (9:03)<<BR>>[[https://youtu.be/MxQeEdVNihg|FWER Correction]] (16:11)<<BR>>[[https://youtu.be/W9ogBO4GEzA|FDR Correction]] (5:25)<<BR>>[[https://youtu.be/N7Iittt8HrU|More about multiple comparisons]] (14:39) <<BR>> ||




<<BR>> <<Anchor(connectivityfmri)>>
||||||<tablewidth="734px" tableheight="239px"style="text-align:center">~+'''fMRI Connectivity'''+~  <<BR>> Petar Raykov ||
||<10%>__Software__ ||[[https://nilearn.github.io/stable/index.html|Nilearn]] ||
||__Datasets__ ||[[https://nilearn.github.io/dev/modules/generated/nilearn.datasets.fetch_development_fmri.html|movie dataset]] ||
||__Reading__ ||[[http://dx.doi.org/10.1016/j.tics.2013.09.016|Resting-state functional Connectivity]]<<BR>> [[https://doi.org/10.1016/j.neuroimage.2013.04.007|Learning and comparing functional connectomes across subjects]] ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=SqyNPbsgHNQ&ab_channel=PetarRaykov|fMRI Functional Connectivity in fMRI]]<<BR>>[[https://www.youtube.com/watch?v=1VOKsWWLgjk&ab_channel=RikHenson&t=15m10s|Overview of Effective Connectivity (not covered in person)]] ||
||__Tutorial slides and scripts__ ||[[https://github.com/ppraykov/FCCognestic2023|Functional Connectivity Nilearn Practical]]<<BR>>[[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>> Rik Henson ||
||__Software__ ||[[https://pypi.org/project/bctpy/|Python 3.7+,]] [[https://nxviz.readthedocs.io/en/latest/|nxviz]], [[https://python-louvain.readthedocs.io/en/latest/|python-louvain]] ||
||__Datasets__ || ||
||__Reading__ ||- (Review article) 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 <<BR>> - (Textbook reference for more information) Alex Fornito, Andrew Zalesky, and Edward Bullmore. ''Fundamentals of brain network analysis''. Academic press, 2016. ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=HjSGqwAFRcc|Understanding your brain as a network and as art]] by Prof. Dani Bassett. ||
||__Slides__ ||[[https://github.com/isebenius/COGNESTIC_network_analysis/tree/main|https://github.com/isebenius/COGNESTIC_network_analysis/]] [[attachment:COGNESTIC23-presentation_Sebenius.pdf|Slides]] ||




<<BR>> <<Anchor(eegmeg1b)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG I – Measurement and Pre-processing'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software and datasets__ ||This will be part of a download that will become available later.<<BR>> [[https://mne.tools/stable/index.html|MNE-Python]] software homepage <<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE stand-alone installation instructions for COGNESTIC]]<<BR>> [[attachment:MNE-Python_datasets.ipynb|Jupyter script to download sample datasets in MNE-Pytho]]n ||
||'''Essential''' and suggested viewing ||'''0. [[https://www.youtube.com/watch?v=S24QG_n6KXk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|Overview of EEG/MEG data processing from raw data to source estimates]]''' <<BR>>Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation.<<BR>><<BR>> '''1. '''[[https://www.youtube.com/watch?v=KQoR9uXLxTg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|A brief history of timing]]<<BR>> A brief overview of the history of bioelectromagnetism, EEG and MEG'''.''' <<BR>> '''2. [[https://www.youtube.com/watch?v=GGDc6qZoDZ4&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=2&pp=iAQB|The generation of EEG/MEG signals]]''' <<BR>>Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.<<BR>>''' 3. '''[[http://www.youtube.com/watch?v=tHzBtNQaoSI&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=3&pp=iAQB|Basics of EEG/MEG artefact correction]] <<BR>> Physiological and non-physiological artefacts, data decompositions, frequency/temporal/spatial filtering. <<BR>>'''4.''' '''[[https://www.youtube.com/watch?v=fLAoRsB2MF8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=5&pp=iAQB|Frequency and temporal filtering of EEG/MEG data]]'''<<BR>>''' '''Frequency spectrum, temporal smoothing, relationship between frequency and time domain, filters (low-/high-/band-pass, Notch), aliasing, decibels. <<BR>>'''5.''' [[https://www.youtube.com/watch?v=mCvPlPlY9Og&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=7&pp=iAQB|Topographical artefact correction of EEG/MEG data]] <<BR>>Independent Component Analysis (ICA), Signal Space Projection (SSP), eye movement and heart beat artefacts.<<BR>>'''6.''' [[https://www.youtube.com/watch?v=liMV6hm_uEs&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=5&pp=iAQB|Maxfiltering of MEG data]]<<BR>>  Signal Space Separation, options of Maxfilter software (e.g. movement compensation).<<BR>> '''7. [[https://www.youtube.com/watch?v=OZFiYeIR2Xk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=7&pp=iAQB|Differential sensitivity of EEG and MEG]]''' <<BR>>Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources. <<BR>> '''8.''' '''[[https://www.youtube.com/watch?v=DYOnFu2Cuyw&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=16|Event-related potentials and fields]]'''  <<BR>>Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression.<<BR>> [[https://www.youtube.com/watch?v=Bmt89hHyxuM|+ Origin, significance, and interpretation of EEG]] (Michael X Cohen) <<BR>>[[https://www.youtube.com/watch?v=z0JlHS9kulA|+ Analysing MEG data with MNE-Python and its ecosystem]] (Alex Gramfort)<<BR>> [[https://www.youtube.com/playlist?list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5|+ List of EEG/MEG lectures]]<<BR>> <<BR>> MNE-Python tutorials:<<BR>>[[http://mne.tools/stable/auto_tutorials/intro/10_overview.html#sphx-glr-auto-tutorials-intro-10-overview-py|Overview of MNE-Python processing pipeline from preprocessing to source estimation]]<<BR>> [[https://mne.tools/stable/auto_tutorials/preprocessing/index.html|Preprocessing]] ||
||__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]] <<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] ||
||Slides and scripts__ __ ||TBA ||




<<BR>> <<Anchor(eegmeg2b)>>
||||||<tablewidth="751px" tableheight="626px"style="text-align:center">~+'''EEG/MEG II  – Head Modelling and Source Estimation'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software and datasets__ ||This will be part of a download that will become available later.<<BR>> [[https://mne.tools/stable/index.html|MNE-Python]] software homepage <<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE stand-alone installation instructions for COGNESTIC]]<<BR>> [[attachment:MNE-Python_datasets.ipynb|Jupyter script to download sample datasets in MNE-Pytho]]n ||
||'''Essential''' and suggested viewing ||'''0. [[https://www.youtube.com/watch?v=S24QG_n6KXk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|Overview of EEG/MEG data processing from raw data to source estimates]]''' <<BR>>Event-related  paradigm, sample dataset, power spectrum, pre-processing, artefact  correction, epoching and averaging, visualization, source estimation.<<BR>><<BR>> '''1. [[https://www.youtube.com/watch?v=duhU5nOsAoc&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=8&pp=iAQB|The EEG/MEG forward model]]'''<<BR>>Basic formulation of the EEG/MEG forward problem, linear equation, basics of head modelling, examples of sensory evoked responses.''' '''<<BR>> '''2.''' [[https://www.youtube.com/watch?v=BsvKPknaSNo&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=9&pp=iAQB|Source spaces for EEG/MEG source estimation]]<<BR>> Cortical surface, volumetric source space, spatial sampling, spatial normalisation, subcortical areas, source orientation. <<BR>> '''3.''' [[https://www.youtube.com/watch?v=259MhTSCVMg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=10&pp=iAQB|Head models for EEG/MEG source estimation <<BR>>]]Volume conduction, Boundary Element Method (BEM), Finite Element Method (FEM), head model accuracy.  <<BR>> '''4. [[https://www.youtube.com/watch?v=KlRJ5kpT3eA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=11&pp=iAQB|The EEG/MEG inverse problem]]'''<<BR>>Non-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity'''. '''<<BR>> '''5. [[https://www.youtube.com/watch?v=X4EZCGPvI1k&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=12&pp=iAQB|The spatial resolution of linear EEG/MEG source estimation]]'''<<BR>>''' '''Leakage and blurring, resolution matrix, point-spread functions (PSFs), cross-talk functions (CTFs), examples of PSFs and CTFs, regions-of-interest for source estimation.''' '''<<BR>> '''6. '''[[https://www.youtube.com/watch?v=OyXzuo6gKcg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=13&pp=iAQB|Comparison of spatial resolution for linear EEG/MEG source estimation methods]] <<BR>>Point-spread functions (PSFs), cross-talk functions (CTFs), resolution metrics (localisation error, spatial deviation), combination of EEG and MEG, PSFs and CTFs for minimum-norm type methods and beamformers, comparison of resolution metrics for minimum-norm type methods and beamformers. <<BR>> '''7.''' '''[[http://www.youtube.com/watch?v=XgYev3N1rR0&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=14&pp=iAQB|Noise and regularisation in EEG/MEG source estimates]] '''<<BR>>Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix.''' '''<<BR>> + [[https://www.youtube.com/playlist?list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5|List of EEG/MEG lectures]]<<BR>> <<BR>> MNE-Python Tutorials: <<BR>> [[https://mne.tools/stable/auto_tutorials/forward/index.html|Forward Models and Source Spaces]]<<BR>> [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Source Estimation]] ||
||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/35390459/|Linear source estimation and spatial resolution]]<<BR>> [[https://pubmed.ncbi.nlm.nih.gov/24434678/|Comparison of common head models]] (e.g. BEM)<<BR>> [[https://pubmed.ncbi.nlm.nih.gov/24971512/|Guidelines for head modelling]] (incl. FEM)<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] ||
||Slides and scripts__ __ ||TBA ||




<<BR>> <<Anchor(eegmeg3b)>>
||||||<tablewidth="751px"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity '''+~~+'''Analysis '''+~ <<BR>> Olaf Hauk ||
||<10%>__Software and datasets__ ||This will be part of a download that will become available later.<<BR>> [[https://mne.tools/stable/index.html|MNE-Python]] software homepage <<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE stand-alone installation instructions for COGNESTIC]]<<BR>> [[attachment:MNE-Python_datasets.ipynb|Jupyter script to download sample datasets in MNE-Pytho]]n ||
||'''Essential''' and suggested viewing ||'''1.''' [[https://www.youtube.com/watch?v=zl3tyPLuUm8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=17&pp=iAQB|The basics of signals in the frequency domain]] <<BR>>Oscillations, periodic signals, sine and cosine, polar representation, complex numbers. <<BR>> '''2. ''' '''[[https://www.youtube.com/watch?v=N4Pm1_C8hlA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=18&pp=iAQB|Frequency spectra and the Fourier analysis]]''' <<BR>> Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response. <<BR>> '''3. ''' '''[[https://www.youtube.com/watch?v=ac0LbTm1Eb8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=19&pp=iAQB|Time-frequency analysis and wavelets]]''' <<BR>>Fourier analysis, wavelets, trade-off between time and frequency resolution, wavelets, number of cycles, evoked and induced activity, beta bursts. <<BR>> '''4.''' '''[[https://www.youtube.com/watch?v=omWqJ8xD2gs&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=20&pp=iAQB|The basics of functional connectivity methods]]''' <<BR>>Types of connectivity, amplitude envelope correlation, resting state analysis, Hilbert envelope, phase-locking, coherence, SNR bias, time-resolved connectivity.  <<BR>>'''5. '''[[https://www.youtube.com/watch?v=gqm2RAz9I8A&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=21&pp=iAQB|Spatial resolution (leakage) and connectivity]]<<BR>>Connectivity in sensor and source space, point-spread and cross-talk, (non-)zero-lag signals, orthogonalisation, imaginary part of coherency, source space parcellations. ||
||__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]]__ __ ||
||Slides and scripts ||TBA ||




<<BR>> <<Anchor(eegmeg4b)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG IV – Statistics and BIDS'''+~ <<BR>> Olaf Hauk & Máté Aller ||
||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] ||
||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/time-freq/index.html|Tutorials]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]]<<BR>> [[https://openneuro.org/datasets/ds000248/versions/1.2.4|M/EEG combined dataset]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] ||
||__Suggested reading__ ||[[https://www.pnas.org/doi/10.1073/pnas.1705414114|Estimating subcortical sources from EEG/MEG]]<<BR>> [[https://mne.tools/mne-bids/stable/auto_examples/convert_mne_sample.html|Tutorial on converting MEG data to BIDS format]]<<BR>> [[https://mne.tools/mne-bids-pipeline/1.4/examples/ds000248_base.html|Example using MNE-BIDS-Pipeline for processing combined M/EEG data]] ||
||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=F0Ex9s-GZyg|Talk on Multimodal Integration]] ||
||__Slides and scripts__ ||[[attachment:EEGMEG4-advanced.zip|Notebooks]] [[attachment:Exercises_EEGMEG.pdf|Exercises]] [[attachment:EMEG4_1_Stats.pdf|Slides1]] [[attachment:EMEG4_2_Multimodal.pdf|Slides2]]<<BR>> [[attachment:Notebooks_mne_bids_pipeline.zip|Notebooks mne-bids-pipeline]] [[attachment:mne-bids-pipeline_cognestic.pdf|Slides mne-bids-pipeline]] ||




<<BR>> <<Anchor(rsa1)>>
||||||<tablewidth="100%"style="text-align:center;">~+'''MVPA/RSA I'''+~''' '''<<BR>> Daniel Mitchell ||
||<12%>__Software__ ||[[https://www.python.org/|Python 3.7+]], including numpy, matplotlib, & [[https://scikit-learn.org/stable/|scikit-learn]]. <<BR>> (To visualise MRI data, you can use your software of choice, although for nifti format data you might like to consider [[https://www.nitrc.org/projects/mricron|MRIcroN]] or [[https://www.nitrc.org/projects/mricrogl|MRIcroGL]].) ||
||__Datasets__ || ||
||__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>> ||
||__Slides and scripts __ ||To be added nearer the time. ||




<<BR>> <<Anchor(rsa2)>>
||||||<tablewidth="100%"style="text-align:center;">~+'''MVPA/RSA II'''+~''' '''<<BR>> Daniel Mitchell & Máté Aller ||
||<12%>__Software__ ||Python implementation of the RSA Toolbox: [[https://github.com/rsagroup/rsatoolbox|Version 3.0]] ||
||__Datasets__ ||Example data included with RSA toolbox ||
||__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]]<<BR>> [[https://elifesciences.org/articles/82566|Schutt et al. (2023) Statistical inference on representational geometries]]<<BR>>EEG/MEG: <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/27779910/%20|Tutorial on EEG/MEG decoding]]<<BR>> [[https://www.sciencedirect.com/science/article/pii/S1364661314000199|Temporal Generalization]] [[https://www.sciencedirect.com/science/article/pii/S1053811913010914|Interpretation of Weight Vectors]] ||
||__Slides and scripts__ ||We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox. <<BR>>[[attachment:EEGMEG5-decoding.zip|EEGMEG Notebooks]] [[attachment:EMEG5_Decoding.pdf|EEG/MEG Slides]]<<BR>> ||




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