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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 Rik Henson & Olaf Hauk
Websites	OSF UKRN BIDS
Suggested reading	Munafo et al, 2017, problems in science Button et al, 2013, power in neuroscience Poldrack et al, 2017, reproducible neuroimaging Marek et al, 2022, power in neuroimaging association studies
Suggested viewing	Open Cognitive Neuroscience (will give this talk live on day) Statistical power in neuroimaging PayWall: open access Comedian's Perspective on science and media
Tutorial slides and scripts	Open Science Talk Slides

Structural MRI - VBM and Surface-based Analysis Marta Correia
Software	FSL Freesurfer
Datasets	Freesurfer tutorial data Subset of the CamCAN dataset (~3GB) https://www.cam-can.org/, please sign data user agreement if using
Suggested reading	Introduction to GLM for structural MRI analysis Good et al, 2001, A VBM study of ageing Smith et al, 2004, Structural MRI analysis in FSL Dale et al, 1999, Cortical surface-based analysis I Fischl et al, 1999, Cortical surface-based analysis II
Suggested viewing	Using the command line Introduction to MRI Physics and image contrast Slides
Tutorial slides and scripts	FSLVBM slides FSLVBM tutorial FSLVBM bash script FreeSurfer Cortical Thickness slides Freesurfer tutorials FS check location script FS visualising the output script FS group analysis script FS ROI analysis script

Diffusion MRI I - DTI Model Fitting and Group Analysis Marta Correia
Software	FSL
Datasets	BTC_preop
Suggested reading	FSL Diffusion Toolbox Wiki Le Bihan et al, 2015, What water tells us about biological tissues Soares et al, 2013, A short guide to Diffusion Tensor Imaging Smith et al, 2006, Tract-based spatial statistics (TBSS)
Suggested viewing	Introduction to Diffusion MRI - Part I Slides
Tutorial slides and scripts	FSL DTI and TBSS slides DTI and group analysis in TBSS tutorial FSL DTI pipeline script FSL TBSS script FSL Group QC script

Diffusion MRI II - Tractography and Structural Connectivity Marta Correia
Software	MRtrix3
Datasets	BTC_preop
Suggested reading	MRtrix3 documentation MR Diffusion Tractography
Suggested viewing	Introduction to Diffusion MRI - Part II Slides
Tutorial slides and scripts	MRtrix Tractography slides MRtrix Tractography tutorials MRtrix preprocessing script MRtrix CSD Tractography script MRtrix connectome script

fMRI I - Data management, structure, manipulation Dace Apšvalka
Software	HeudiConv, PyBIDS, NiBabel, Nilearn
Datasets	Wakeman Multimodal
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) fMRI Data Structure & Terminology by Martin Lindquist and Tor Wager (6:47)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI II - Quality control & Pre-processing Dace Apšvalka
Software	MRIQC, fMRIprep, Nipype
Datasets	Wakeman Multimodal
Suggested reading	Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods Ashburner J & Friston KJ (2004), Rigid body registration Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review Sladky et al. (2011), Slice-timing effects and their correction in functional MRI Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI
Suggested viewing	fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57) Pre-processing I by Martin Lindquist and Tor Wager (10:17) Pre-processing II by Martin Lindquist and Tor Wager (7:42)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI III - Subject Level Analysis Dace Apšvalka
Software	Nipype, Nilearn, SPM12
Datasets	Wakeman Multimodal
Suggested reading	Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever? Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approach Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations Lindquist (2008), The Statistical Analysis of fMRI Data
Suggested viewing	The General Linear Model by Martin Lindquist and Tor Wager (12:24) GLM applied to fMRI by Martin Lindquist and Tor Wager (11:21) Model Building I – conditions and contrasts by Martin Lindquist and Tor Wager (11:48) Model Building II – temporal basis sets by Martin Lindquist and Tor Wager (11:08) Model Building III- nuisance variables by Martin Lindquist and Tor Wager (13:58) GLM Estimation by Martin Lindquist and Tor Wager (9:11) Noise Models- AR models by Martin Lindquist and Tor Wager (9:57) Inference- Contrasts and t-tests by Martin Lindquist and Tor Wager (11:05) Multiple Comparisons by Martin Lindquist and Tor Wager (9:03) FWER Correction by Martin Lindquist and Tor Wager (16:11) FDR Correction by Martin Lindquist and Tor Wager (5:25) More about multiple comparisons by Martin Lindquist and Tor Wager (14:39)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI IV - Group Level Analysis & Reporting Dace Apšvalka
Software	Nilearn, PySurfer, SPM12
Datasets	Wakeman Multimodal
Suggested reading	Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI Poldrack et al. (2008), Guidelines for reporting an fMRI study Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain Markiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data
Suggested viewing	Group-level Analysis I by Martin Lindquist and Tor Wager (7:05) Group-level Analysis II by Martin Lindquist and Tor Wager (10:09) Group-level Analysis III by Martin Lindquist and Tor Wager (14:01)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

Connectivity for fMRI Rik Henson
Software	SPM12
Datasets	Wakeman Multimodal
Suggested reading	Resting-state functional Connectivity Simple Intro to DCM fMRI preprocessing in SPM12 (for demo) SPM12 manual (Chapter 36)
Suggested viewing	fMRI Functional Connectivity, including DCM Bayesian Model Comparison (for DCM for fMRI/MEEG)
Tutorial slides and scripts	Tutorial for DCM for fMRI

Eye-tracking Edwin Dalmaijer
Software	Python NumPy, SciPy, Matplotlib
Datasets	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
Slides and scripts

EEG/MEG I – Pre-processing Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading	Digitial Filtering Filtering How To Maxwell Filtering General EEG/MEG Literature
Suggested viewing	Introduction to EEG/MEG Preprocessing What are we measuring with M/EEG?
Slides and scripts	Notebooks and Slides

EEG/MEG II – Source Estimation Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading	Linear source estimation and spatial resolution General EEG/MEG Literature
Suggested viewing	Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM
Slides and scripts	Notebooks and Slides

EEG/MEG III – Time-Frequency and Functional Connectivity Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading	Tutorial on Functional Connectivity Analyzing Neural Time Series Data General EEG/MEG Literature
Suggested viewing	Introduction to time-frequency and functional connectivity analysis Time-Frequency Analysis of EEG Time Series
Slides and scripts	Notebooks and Slides

Graph Theory Caroline Nettekoven Zoom link
Software	Brain Connectivity Toolbox in Matlab, BCT Documentation
Datasets	Coding exercises Exercise solutions
Suggested reading	Complex brain networks: graph theoretical analysis of structural and functional systems
Suggested viewing	Slides
Slides and scripts

MVPA/RSA I Daniel Mitchell
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 (See toolbox webpage for a lower resolution alternative)
Suggested reading	Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide 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: Introduction to MVPA Introduction to classification. (I've suggested these two, but the others are worth a look too.)
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 (Please see the 3rd slide for an overview of the file structure expected by the demo scripts)

MVPA/RSA II Daniel Mitchell
Software	The RSA toolbox in Matlab 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: Version 3.0)
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 Kriegeskorte & Kievit (2013) Representational geometry: integrating cognition, computation, and the brain Nili et al. (2014) A toolbox for representational similarity analysis EEG/MEG: Tutorial on EEG/MEG decoding Temporal Generalization
Suggested viewing	Martin Hebart's lecture on RSA
Slides and scripts	Slides for afternoon session - RSA These functions should be saved in rsa-master/Demos EEGMEG Notebooks and Slides

Statistics in R Peter Watson
Software	R Data&Code
Datasets	Data&Code Readme
Suggested reading	Statistical Methods for Psychology (Howell) Introduction to R Discovering statistics using R An introduction to statistical learning with applications in R
Suggested viewing	CBU Statistics Lectures
Slides and scripts

Brain Stimulation Ajay Halai
Software	SIMNIBS (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and k-wave
Datasets	tutorial_data
Suggested reading	Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2
Suggested viewing	slides
Slides and scripts	scripts

DCM for M/EEG Pranay Yadav & Rik Henson
Software	SPM12
Datasets	Wakeman Multimodal
Suggested reading	Preprocessing M/EEG in SPM12 Simple Intro to DCM
Suggested viewing	Talk on DCM for M/EEG MEEG connectivity other than DCM (not demo'ed, and related to Hauk talks above)
Tutorial slides and scripts	Tutorial for DCM for ERP

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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="734px" tableheight="248px"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="100%"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 I - Voxel-based morphometry'''~+''' '''+~<<BR>> Marta Correia ||
+||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI - VBM and Surface-based Analysis '''+~<<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 - The Diffusion Tensor Model'''+~ <<BR>> Marta Correia ||
+||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  I - DTI Model Fitting and Group Analysis'''+~ <<BR>> Marta Correia ||
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-||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and the Anatomical Connectome'''+~ <<BR>> Marta Correia ||
+||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and Structural Connectivity'''+~ <<BR>> Marta Correia ||
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-||||||<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]] ||
+||||||<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)]] ||
||__Tutorial slides and scripts__ ||[[attachment:Multimodal_DCM_cognestic_tutorial_fMRI.pdf|Tutorial for DCM for fMRI]] ||




<<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 ||
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-<<BR>> <<Anchor(eegmeg4)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG VI – Priors and Multimodal Imaging'''+~ <<BR>> Olaf Hauk ||
+<<BR>> <<Anchor(eegmeg3)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity'''+~ <<BR>> Olaf Hauk ||
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-||__Suggested reading__ || ||
||__Suggested viewing__ || ||
||Slides and scripts__ __ || ||
+||__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 [[https://us02web.zoom.us/j/81982692386?pwd=TUZsdmpHZDEySUJLSFJIcDN6TXNFdz09|Zoom link]] ||
||<10%>Software__ __ ||[[https://sites.google.com/site/bctnet/|Brain Connectivity Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]], [[https://sites.google.com/site/bctnet/list-of-measures?authuser=0|BCT Documentation]] ||
||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 || ||
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+<<BR>> <<Anchor(statistics)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Statistics in R'''+~ <<BR>> Peter Watson ||
||<10%>__Software__ ||[[https://www.r-project.org/|R]] Data&Code ||
||__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]] <<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]] ||
||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/statswiki/StatsCourse2021/recordings|CBU Statistics Lectures]] ||
||Slides and scripts__ __ || ||
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-||||||<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]] ||
||Slides and scripts || ||
+||||||<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:AH_slides.pptx|slides]] ||
||Slides and scripts ||[[attachment:AH_scripts.zip|scripts]] ||




<<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)]] ||
||__Tutorial slides and scripts__ ||[[attachment:Multimodal_DCM_cognestic_tutorial_MEEG.pdf|Tutorial for DCM for ERP]] ||

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Course Material for COGNESTIC 2022