= Course Material for COGNESTIC 2023 = 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 neuroimaging analysis and related methods. You can find more information on the [[https://www.mrc-cbu.cam.ac.uk/cognestic-2023/|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 == Access to much of the COGNESTIC-23 materials is available via Virtual Machine (VM). You will need at least 70GB of free space on your local hard drive, and at least 4GB of RAM. For instructions on how to install and set up the Cognestic23 VM see section ‘1 COGNESTIC Virtual Machine (full hands-on)’ for instructions. Data for the Structural MRI and Diffusion MRI are located inside the VM. Full '''installation instructions''' can be found [[attachment:COGNESTIC-23_hands-on_materials.pdf|here]]. The installation can take some time (potentially more than an hour, depending on your download speed), so please reserve some time for this ahead of the event. <
><
> <> ||||||~+'''Introduction and Open Science'''+~ <
> Rik Henson & Olaf Hauk || ||<10%>__Websites__ ||[[https://osf.io/|OSF]] <
> [[https://www.ukrn.org/primers/|UKRN]] <
> [[https://bids.neuroimaging.io/|BIDS]] || ||__Suggested reading__ ||[[https://doi.org/10.1038/s41562-016-0021|Munafo et al, 2017, problems in science]] <
> [[https://doi.org/10.1038/nrn3475|Button et al, 2013, power in neuroscience]] <
> [[https://doi.org/10.1038/nrn.2016.167|Poldrack et al, 2017, reproducible neuroimaging]] <
> [[https://doi.org/10.1038/s41586-022-04492-9|Marek et al, 2022, power in neuroimaging association studies]] || ||__Suggested viewing__ ||[[https://youtu.be/kTVtc7kjVQg|Open Cognitive Neuroscience (will give this talk live on day)]] <
> [[https://www.youtube.com/watch?v=D0VKyjNGvrs|Statistical power in neuroimaging]] <
> [[https://www.youtube.com/watch?v=zAzTR8eq20k|PayWall: open access]] <
> [[https://www.facebook.com/LastWeekTonight/videos/896755337120143|Comedian's Perspective on science and media]] || ||__Tutorial slides and scripts__ ||[[attachment:COGNESTIC_OpenCogNeuro.pdf|Open Science Talk Slides]] || <
> <> ||||||~+'''Primer on Python'''+~ <
> 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 || <
> <> ||||||~+'''Structural MRI I - Voxel-based morphometry'''+~''' '''<
> Marta Correia || ||<10%>__Software__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/|FSL]] || ||__Datasets__ ||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__ ||[[attachment:IntroductionToGLM.pdf|Introduction to GLM for structural MRI analysis]] <
> [[https://pubmed.ncbi.nlm.nih.gov/11525331/|Good et al, 2001, A VBM study of ageing]] <
> [[https://pubmed.ncbi.nlm.nih.gov/15501092/|Smith et al, 2004, Structural MRI analysis in FSL]] || ||__Suggested viewing__ ||[[https://youtu.be/Psh-GovQLiI|Introduction to MRI Physics and image contrast]] <
> [[attachment:IntroductionToMRIPhysics.pdf|Slides]] || ||__Tutorial slides and scripts__ ||[[attachment:Intro_Commmand_Line_2023.docx|Intro to command line]] <
> [[attachment:FSL_VBM.pdf|VBM slides]]<
> [[attachment:FSLVBM_tutorials_2023.docx|FSL VBM tutorials]] <
> [[attachment:FSLVBM_cognestic_all.sh|FSL VBM script]]<
> [[attachment:COGNESTIC_exercises_2023.docx|Hands on exercises for structural and diffusion MRI]] || <
> <> ||||||~+'''Structural MRI II - Surface-based analyses'''+~''' '''<
> Marta Correia || ||<10%>__Software__ ||[[https://surfer.nmr.mgh.harvard.edu/|Freesurfe]]r || ||__Datasets__ ||[[https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Data|Freesurfer tutorial data]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/9931268/|Dale et al, 1999, Cortical surface-based analysis I]] <
> [[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]] || ||__Tutorial slides and scripts__ ||[[attachment:FS_CorticalThickness.pdf|Freesurfer slides]] <
> [[attachment:FreeSurfer_tutorials_2023.docx|Freesurfer tutorials]] <
> [[attachment:FS_check_location.sh|FS check location script]]<
> [[attachment:FS_visualising_output.sh|FS visualising the output script]] <
> [[attachment:FS_group_analysis.sh|FS group analysis script]] <
> [[attachment:FS_ROI_analysis.sh|FS ROI analysis script]] || <
> <> ||||||~+'''Diffusion MRI I - The Diffusion Tensor Model'''+~ <
> 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]] <
> [[https://doi.org/10.1371/journal.pbio.1002203|Le Bihan et al, 2015, What water tells us about biological tissues]] <
> [[https://doi.org/10.3389/fnins.2013.00031|Soares et al, 2013, A short guide to Diffusion Tensor Imaging]] <
> [[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]] <
> [[attachment:IntroductionToDiffusionMRI_I.pdf|Slides]] || ||__Tutorial slides and scripts__ ||[[attachment:FSL_DTI&TBSS.pdf|FSL DTI and TBSS slides]] <
> [[attachment:FSL_FDT_DTI_tutorials_2023.docx|DTI and group analysis in TBSS tutorials]] <
> [[attachment:FDT_DTI_pipeline_LiveDemo.sh|FSL DTI pipeline script]] <
> [[attachment:FDT_DTI_TBSS.sh|FSL TBSS script]] <
> [[attachment:FDT_DTI_group_QC.sh|FSL group QC script]] || <
> <> ||||||~+'''Diffusion MRI II - Tractography and the Anatomical Connectome'''+~ <
> 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]] <
> [[https://www.sciencedirect.com/science/article/pii/B9780123964601000196|MR Diffusion Tractography]] || ||__Suggested viewing__ ||[[https://youtu.be/QDJJ6G2ZouA|Introduction to Diffusion MRI - Part II]] <
> [[attachment:IntroductionToDiffusionMRI_II.pdf|Slides]] || ||__Tutorial slides and scripts__ ||[[attachment:MRTrix_tractography.pdf|MRTrix tractography slides]] <
> [[attachment:MRtrix_dMRI_tutorials_2023.docx|MRTrix tractography tutorials]] <
> [[attachment:MRtrix_dMRI_preprocessing_LiveDemo.sh|MRTrix preprocessing script]] <
> [[attachment:MRtrix_dMRI_CSD_tractography_LiveDemo.sh|MRTrix tractography script]] <
> [[attachment:MRtrix_dMRI_connectome_LiveDemo.sh|MRTrix connectome script]] || <
> <> ||||||~+'''fMRI I - Data management, structure, manipulation'''+~ <
> 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]] <
>[[https://bids.neuroimaging.io/|The brain imaging data structure (BIDS)]] <
>[[https://arxiv.org/abs/2309.05768|The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)]] || ||__Suggested viewing__ ||[[https://osf.io/fbj5u|BIDS for MRI: Structure and Conversion]] by Taylor Salo (13:39) <
> [[https://youtu.be/OuRdQJMU5ro|fMRI Data Structure & Terminology]] by Martin Lindquist and Tor Wager (6:47) || ||Slides and scripts__ __ ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || <
> <> ||||||~+'''fMRI II - Quality control & Pre-processing'''+~ <
> Dace Apšvalka || ||<10%>Software__ __ ||[[https://mriqc.readthedocs.io/en/latest/|MRIQC]], [[https://fmriprep.org/en/stable/|fMRIprep]] || ||Datasets__ __ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||Suggested reading__ __ ||[[https://link.springer.com/article/10.1007/s11065-015-9294-9|Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods]]<
> [[https://mriquestions.com/uploads/3/4/5/7/34572113/ch2.pdf|Ashburner J & Friston KJ (2004), Rigid body registration]]<
> [[https://doi.org/10.1002/mrm.24314|Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review]]<
> [[https://doi.org/10.1016/j.neuroimage.2011.06.078|Sladky et al. (2011), Slice-timing effects and their correction in functional MRI]]<
> [[https://doi.org/10.1006/nimg.2000.0609|Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis]]<
> [[https://www.nature.com/articles/s41592-018-0235-4|Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI]] || ||Suggested viewing__ __ ||[[https://youtu.be/7Kk_RsGycHs|fMRI Artifacts and Noise]] by Martin Lindquist and Tor Wager (11:57)<
>[[https://youtu.be/Qc3rRaJWOc4|Pre-processing I]] by Martin Lindquist and Tor Wager (10:17)<
>[[https://youtu.be/qamRGWSC-6g|Pre-processing II]] by Martin Lindquist and Tor Wager (7:42) || ||Slides and scripts ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || <
> <> ||||||~+'''fMRI III - Subject Level Analysis'''+~ <
> Dace Apšvalka || ||<10%>__Software__ ||[[http://nilearn.github.io/stable/index.html|Nilearn]] || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ ||[[https://doi.org/10.1002/hbm.460020402|Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach]]<
>[[https://doi.org/10.1016/j.neuroimage.2012.01.133|Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever?]]<
>[[https://doi.org/10.3389/fnhum.2011.00028|Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approach]]<
>[[https://doi.org/10.1191/0962280203sm341ra|Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review]]<
>[[https://doi.org/10.1016/j.neuroimage.2008.05.021|Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields]]<
>[[https://doi.org/10.1016/j.neuroimage.2013.12.058|Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations]]<
>[[https://doi.org/10.1214/09-STS282|Lindquist (2008), The Statistical Analysis of fMRI Data]] || ||__Suggested viewing__ ||[[https://youtu.be/GDkLQuV4he4|The General Linear Model]] by Martin Lindquist and Tor Wager (12:24)<
>[[https://www.youtube.com/watch?v=OyLKMb9FNhg|GLM applied to fMRI]] by Martin Lindquist and Tor Wager (11:21)<
>[[https://www.youtube.com/watch?v=7MibM1ATai4|Model Building I – conditions and contrasts]] by Martin Lindquist and Tor Wager (11:48)<
>[[https://www.youtube.com/watch?v=YfeMIcDWwko|Model Building II – temporal basis sets]] by Martin Lindquist and Tor Wager (11:08)<
>[[https://www.youtube.com/watch?v=DEtwsFdFwYc|Model Building III- nuisance variables]] by Martin Lindquist and Tor Wager (13:58)<
>[[https://www.youtube.com/watch?v=Ab-5AbJ8gAs|GLM Estimation]] by Martin Lindquist and Tor Wager (9:11)<
>[[https://youtu.be/Mb9LDzvhecY|Noise Models- AR models]] by Martin Lindquist and Tor Wager (9:57)<
>[[https://youtu.be/NRunOo7EKD8|Inference- Contrasts and t-tests]] by Martin Lindquist and Tor Wager (11:05)<
>[[https://youtu.be/AalIM9-5-Pk|Multiple Comparisons]] by Martin Lindquist and Tor Wager (9:03)<
>[[https://youtu.be/MxQeEdVNihg|FWER Correction]] by Martin Lindquist and Tor Wager (16:11)<
>[[https://youtu.be/W9ogBO4GEzA|FDR Correction]] by Martin Lindquist and Tor Wager (5:25)<
>[[https://youtu.be/N7Iittt8HrU|More about multiple comparisons]] by Martin Lindquist and Tor Wager (14:39) || ||Slides and scripts__ __ ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || <
> <> ||||||~+'''fMRI IV - Group Level Analysis & Reporting'''+~ <
> Dace Apšvalka || ||<10%>Software__ __ ||[[http://nilearn.github.io/stable/index.html|Nilearn]] || ||Datasets__ __ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||Suggested reading__ __ ||[[https://doi.org/10.1109/MEMB.2006.1607668|Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data]]<
>[[https://www.nature.com/articles/nn.4500|Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI]]<
>[[https://doi.org/10.1016/j.neuroimage.2007.11.048|Poldrack et al. (2008), Guidelines for reporting an fMRI study]]<
>[[https://doi.org/10.1016/j.neuroimage.2015.04.016|Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain]]<
>[[https://doi.org/10.7554/eLife.71774|Markiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data]] || ||Suggested viewing__ __ ||[[https://youtu.be/__cOYPifDWk|Group-level Analysis I]] by Martin Lindquist and Tor Wager (7:05)<
>[[https://youtu.be/-abMLQSjMSI|Group-level Analysis II]] by Martin Lindquist and Tor Wager (10:09)<
>[[https://youtu.be/-yaHTygR9b8|Group-level Analysis III]] by Martin Lindquist and Tor Wager (14:01) || ||Slides and scripts ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || <
> <> ||||||~+'''fMRI Connectivity'''+~ <
> Petar Raykov || ||<10%>__Software__ ||[[https://nilearn.github.io/stable/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]]<
> [[https://doi.org/10.1016/j.neuroimage.2013.04.007|Learning and comparing functional connectomes across subjects]] || ||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=SqyNPbsgHNQ&ab_channel=PetarRaykov|fMRI Functional Connectivity in fMRI]]<
>[[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]]<
>[[attachment:Multimodal_DCM_cognestic_tutorial_fMRI.pdf|DCM tutorial in SPM (not covered in-person)]] || <
> <> ||||||~+'''Brain Network Analysis'''+~ <
> Isaac Sebenius || ||<10%>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__ __ || || ||Suggested 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 <
> - (Textbook reference for more information) Alex Fornito, Andrew Zalesky, and Edward Bullmore. ''Fundamentals of brain network analysis''. Academic press, 2016. || ||Suggested viewing__ __ ||- [[https://www.youtube.com/watch?v=H2q3fPxiuvw|Introduction to Network Neuroscience]], minutes 0:00-48:30. A wonderful introduction to brain networks by Prof. Bratislav Misic. <
>- [[https://www.youtube.com/watch?v=HjSGqwAFRcc|Understanding your brain as a network and as art]] by Prof. Dani Bassett. || ||Slides and scripts ||[[https://github.com/isebenius/COGNESTIC_network_analysis/tree/main|https://github.com/isebenius/COGNESTIC_network_analysis/]] [[attachment:COGNESTIC23-presentation_Sebenius.pdf|Slides]] || <
> <> ||||||~+'''EEG/MEG I – Pre-processing'''+~ <
> Olaf Hauk || ||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]<
> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] || ||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/preprocessing/index.html|Tutorials]]<
> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/25128257/|Digitial Filtering]] <
>[[https://www.sciencedirect.com/science/article/pii/S0896627319301746|Filtering How To]] <
> [[https://iopscience.iop.org/article/10.1088/0031-9155/51/7/008|Maxwell Filtering]] <
> [[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=EEGMEG1.mp4|Introduction to EEG/MEG Preprocessing]] <
>[[https://mediacentral.ucl.ac.uk/Player/2909|What are we measuring with M/EEG]]? || ||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks]] [[attachment:Exercises_EEGMEG.pdf|Exercises]] [[attachment:EMEG1_1_Measurement.pdf|Slides1]] [[attachment:EMEG1_2_Preprocessing.pdf|Slides2]] [[attachment:EMEG1_3_Averaging.pdf|Slides3]] || <
> <> ||||||~+'''EEG/MEG II – Source Estimation'''+~ <
> Olaf Hauk || ||<10%>Software__ __ ||[[https://mne.tools/stable/index.html|MNE-Python]]<
> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] || ||Datasets__ __ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Tutorials]]<
> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] || ||Suggested reading__ __ ||[[https://pubmed.ncbi.nlm.nih.gov/24434678/|Comparison of common head models]] (e.g. BEM)<
> [[https://pubmed.ncbi.nlm.nih.gov/35390459/|Linear source estimation and spatial resolution]]<
> [[https://pubmed.ncbi.nlm.nih.gov/24971512/|Guidelines for head modelling]] (incl. FEM)<
> [[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=EEGMEG2_SourceEstimation.mp4|Introduction to EEG/MEG Source Estimation]] [[https://mediacentral.ucl.ac.uk/Player/2917|M/EEG Source Analysis in SPM]] || ||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks]] [[attachment:Exercises_EEGMEG.pdf|Exercises]] [[attachment:EMEG2_1_ForwardModelling.pdf|Slides1]] [[attachment:EMEG2_2_MNE.pdf|Slides2]] [[attachment:EMEG2_3_SpatialResolution.pdf|Slides3]] || <
> <> ||||||~+'''EEG/MEG III – Time-Frequency and Functional Connectivity'''+~ <
> Olaf Hauk || ||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]<
> [[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]]<
> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/26778976/|Tutorial on Functional Connectivity]]<
> [[https://mitpress.mit.edu/books/analyzing-neural-time-series-data|Analyzing Neural Time Series Data]]<
> [[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]] <
> [[https://www.youtube.com/watch?v=wB417SAbdak|Time-Frequency Analysis of EEG Time Series]] || ||Slides and scripts__ __ ||[[attachment:EEGMEG3-timefrequency.zip|Notebooks]] [[attachment:Exercises_EEGMEG.pdf|Exercises]] [[attachment:EMEG3_1_TimeFrequency.pdf|Slides1]] [[attachment:EMEG3_2_FunctionalConnectivity.pdf|Slides2]][[attachment:EMEG3_3_AdvancedFunctionalConnectivity.pdf|Slides3]] || <
> <> ||||||~+'''EEG/MEG IV – Advanced Topics'''+~ <
> Olaf Hauk & Máté Aller || ||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]<
> [[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]]<
> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]]<
> [[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]]<
> [[https://mne.tools/mne-bids/stable/auto_examples/convert_mne_sample.html|Tutorial on converting MEG data to BIDS format]]<
> [[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]]<
> [[attachment:Notebooks_mne_bids_pipeline.zip|Notebooks mne-bids-pipeline]] [[attachment:mne-bids-pipeline_cognestic.pdf|Slides mne-bids-pipeline]] || ---- <
> <> ||||||~+'''MVPA/RSA I'''+~ <
> Daniel Mitchell || ||<10%>__Software__ ||[[http://cosmomvpa.org/|CoSMoMVPA]] using [[https://octave.org/|Octave]]. (These are not included in the virtual machine; you will need to install them yourself. If you have Matlab, you are welcome to use it instead of Octave, but the demos will be in Octave because it is open source.) <
> 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]]. || ||__Datasets__ ||[[https://cosmomvpa.org/datadb-v0.3.zip|Tutorial data]] from CoSMoMVPA toolbox || ||__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]]<
>[[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]]<
>[[https://www.frontiersin.org/articles/10.3389/fninf.2016.00027/full|Oosterhof et al. (2016) CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave]] || ||__Suggested viewing__ ||Excellent presentations from Martin Hebart's MVPA course, on:<
>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/02_lecture1|Introduction to MVPA]]<
>[[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. <
> (Note: on 25/9/23-26/9/23 the above links stopped working due to a temporary issue with the host website. If this happens again, let me know.) || ||Slides and scripts__ __ ||We will be using the demos from the "examples" folder of the CoSMoMVPA toolbox.<
> Exercises can be copied from the files [[https://www.cosmomvpa.org/matindex_skl.html|here]], pasted into an empty Octave file, and you can try to fill in the missing snippets.<
>[[attachment:example_djm_partitions.m|extra example 1]]<
>[[attachment:example_djm_unbalanced.m|extra example 2]]<
>[[attachment:COGNESTIC23_MVPA_djm_part1.pptx|slides]] || <
> <> ||||||~+'''MVPA/RSA II'''+~ <
> Daniel Mitchell || ||<10%>Software ||Python implementation of the RSA Toolbox: [[https://github.com/rsagroup/rsatoolbox|Version 3.0]] || ||Datasets__ __ ||Example data included with RSA toolbox || ||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]]<
>[[https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(13)00127-7|Kriegeskorte & Kievit (2013) Representational geometry: integrating cognition, computation, and the brain]] <
>[[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003553|Nili et al. (2014) A toolbox for representational similarity analysis]]<
> [[https://elifesciences.org/articles/82566|Schutt et al. (2023) Statistical inference on representational geometries]]<
>EEG/MEG: <
> [[https://pubmed.ncbi.nlm.nih.gov/27779910/%20|Tutorial on EEG/MEG decoding]]<
> [[https://www.sciencedirect.com/science/article/pii/S1364661314000199|Temporal Generalization]] [[https://www.sciencedirect.com/science/article/pii/S1053811913010914|Interpretation of Weight Vectors]] || ||Suggested viewing__ __ ||[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]] <
>(Note: on 25/9/23-26/9/23 this link stopped working due to a temporary issue with the host website. If this happens again, let me know.) || ||Slides and scripts ||We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox. <
> [[attachment:COGNESTIC23_MVPA_djm_part2.pptx|slides]]<
>[[attachment:EEGMEG5-decoding.zip|EEGMEG Notebooks]] [[attachment:EMEG5_Decoding.pdf|EEG/MEG Slides]]<
> || <
> <> ||||||~+'''Brain Stimulation, Pethysmography, Electromyography'''+~ <
> Ajay Halai, Alexis Deighton McIntyre, Hristo Dimitrov || ||<10%>Software__ __ ||Brain Stimulation: <
> [[https://simnibs.github.io/simnibs/build/html/index.html|E-field modelling for non-invasive brain stimulation using SimNIBS]] <
> Plethysmography: <
> [[https://github.com/alexisdmacintyre/SpeechBreathingToolbox|Speech breathing-oriented toolbox for breath-belt data (MATLAB)]] || ||Datasets__ __ || || ||Suggested reading__ __ ||Brain Stimulation: <
> [[https://www.sciencedirect.com/science/article/pii/S1053811916001191|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]] <
><
> Plethysmography: <
> 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 <
> 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 <
> Allen, M., Varga, S., & Heck, D. H. (2022). Respiratory rhythms of the predictive mind. ''Psychological Review''. https://doi.org/10.1037/rev0000391 <
><
> EMG: <
> [[https://www.sciencedirect.com/science/article/pii/S1050641120300419|Consensus for experimental design in electromyography]]<
> [[https://www.sciencedirect.com/science/article/pii/S1050641122000293|Tutorial high-density EMG]]<
> [[https://ieeexplore.ieee.org/document/9467400|Noninvasive Neural Interfacing With Wearable Muscle Sensors]] || ||Suggested viewing__ __ ||Brain Stimulation: [[https://simnibs.github.io/simnibs/build/html/tutorial/tutorial.html|SimNIBs tutorial]] and [[https://www.youtube.com/playlist?list=PLDCjI20ZMvu2G4dGH9CIEztYHTEHg2oam|SimNIBS youtube videos]] || ||Slides and scripts ||Brain Stimulation: [[attachment:AH_slides.pptx|slides]]<
> Plethysmography: [[attachment:MacIntyre_COGNESTIC.pdf|slides]] || ---- ----