Diff for "COGNESTIC2023" - Methods
location: Diff for "COGNESTIC2023"
Differences between revisions 9 and 111 (spanning 102 versions)
⇤ ← Revision 9 as of 2023-03-13 15:29:32 →
Size: 24154
Editor: OlafHauk
Comment:
← Revision 111 as of 2024-06-11 15:57:44 → ⇥
Size: 30454
Editor: OlafHauk
Comment:
Deletions are marked like this. Additions are marked like this.
Line 2: Line 2:
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 ||		 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.

<<BR>><<BR>> <<Anchor(openscience)>>
||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Introduction and Open Science'''+~ <<BR>> Rik Henson & Olaf Hauk ||
Line 14: Line 21:
<<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 ||
Line 15: Line 32:
||||||<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]] <<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__ ||[[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>> [[attachment:IntroductionToMRIPhysics.pdf|Slides]] ||
||__Tutorial slides and scripts__ ||[[attachment:COGNESTIC_FSLVBM.pdf|FSLVBM slides]] <<BR>> [[attachment:FSLVBM_tutorials.docx|FSLVBM tutorial]] <<BR>> [[attachment:FSLVBM_cognestic_all.sh|FSLVBM bash script]] <<BR>> <<BR>> [[attachment:COGNESTIC_FS_CorticalThickness.pdf|FreeSurfer Cortical Thickness slides]] <<BR>> [[attachment:FreeSurfer_tutorials.docx|Freesurfer tutorials]] <<BR>> [[attachment:FS_check_location.sh|FS check location script]] <<BR>> [[attachment:FS_visualising_output.sh|FS visualising the output script]]<<BR>> [[attachment:FS_group_analysis.sh|FS group analysis script]] <<BR>> [[attachment:FS_ROI_analysis.sh|FS ROI analysis script]] ||		 ||||||<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]] ||
||__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]] <<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]] ||
||__Suggested viewing__ ||[[https://youtu.be/Psh-GovQLiI|Introduction to MRI Physics and image contrast]] <<BR>> [[attachment:IntroductionToMRIPhysics.pdf|Slides]] ||
||__Tutorial slides and scripts__ ||[[attachment:Intro_Commmand_Line_2023.docx|Intro to command line]] <<BR>> [[attachment:FSL_VBM.pdf|VBM slides]]<<BR>> [[attachment:FSLVBM_tutorials_2023.docx|FSL VBM tutorials]] <<BR>> [[attachment:FSLVBM_cognestic_all.sh|FSL VBM script]]<<BR>> [[attachment:COGNESTIC_exercises_2023.docx|Hands on exercises for structural and diffusion MRI]] ||




<<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 ||
||__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]] <<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]] ||
||__Tutorial slides and scripts__ ||[[attachment:FS_CorticalThickness.pdf|Freesurfer slides]] <<BR>> [[attachment:FreeSurfer_tutorials_2023.docx|Freesurfer tutorials]] <<BR>> [[attachment:FS_check_location.sh|FS check location script]]<<BR>> [[attachment:FS_visualising_output.sh|FS visualising the output script]] <<BR>> [[attachment:FS_group_analysis.sh|FS group analysis script]] <<BR>> [[attachment:FS_ROI_analysis.sh|FS ROI analysis script]] ||
Line 26: Line 54:
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI I - DTI Model Fitting and Group Analysis'''+~ <<BR>> Marta Correia || ||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI I - The Diffusion Tensor Model'''+~ <<BR>> Marta Correia ||
Line 31: Line 59:
||__Tutorial slides and scripts__ ||[[attachment:COGNESTIC_FSL_DTI&TBSS.pdf|FSL DTI and TBSS slides]] <<BR>> [[attachment:FSL_FDT_DTI_tutorials.docx|DTI and group analysis in TBSS tutorial]]<<BR>> [[attachment:FDT_DTI_pipeline.sh|FSL DTI pipeline script]] <<BR>> [[attachment:FDT_DTI_TBSS.sh|FSL TBSS script]]<<BR>> [[attachment:FDT_DTI_group_QC.sh|FSL Group QC script]] || ||__Tutorial slides and scripts__ ||[[attachment:FSL_DTI&TBSS.pdf|FSL DTI and TBSS slides]] <<BR>> [[attachment:FSL_FDT_DTI_tutorials_2023.docx|DTI and group analysis in TBSS tutorials]] <<BR>> [[attachment:FDT_DTI_pipeline_LiveDemo.sh|FSL DTI pipeline script]] <<BR>> [[attachment:FDT_DTI_TBSS.sh|FSL TBSS script]] <<BR>> [[attachment:FDT_DTI_group_QC.sh|FSL group QC script]] ||
Line 37: Line 65:
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI II - Tractography and Structural Connectivity'''+~ <<BR>> Marta Correia || ||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI II - Tractography and the Anatomical Connectome'''+~ <<BR>> Marta Correia ||
Line 42: Line 70:
||__Tutorial slides and scripts__ ||[[attachment:COGNESTIC_MRtrix_tractography.pdf|MRtrix Tractography slides]] <<BR>> [[attachment:MRtrix_dMRI_tutorials.docx|MRtrix Tractography tutorials]] <<BR>> [[attachment:MRTrix_dMRI_preprocessing.sh|MRtrix preprocessing script]] <<BR>> [[attachment:MRTrix_dMRI_CSD_tractography.sh|MRtrix CSD Tractography script]] <<BR>> [[attachment:MRTrix_dMRI_connectome.sh|MRtrix connectome script]] || ||__Tutorial slides and scripts__ ||[[attachment:MRTrix_tractography.pdf|MRTrix tractography slides]] <<BR>> [[attachment:MRtrix_dMRI_tutorials_2023.docx|MRTrix tractography tutorials]] <<BR>> [[attachment:MRtrix_dMRI_preprocessing_LiveDemo.sh|MRTrix preprocessing script]] <<BR>> [[attachment:MRtrix_dMRI_CSD_tractography_LiveDemo.sh|MRTrix tractography script]] <<BR>> [[attachment:MRtrix_dMRI_connectome_LiveDemo.sh|MRTrix connectome script]] ||
Line 51: Line 79:
||__Suggested reading__ ||[[https://doi.org/10.1038/sdata.2016.44|Gorgolewski et al., 2016, The brain imaging data structure (BIDS)]] || ||__Suggested reading__ ||[[https://doi.org/10.1038/sdata.2016.44|Gorgolewski et al., 2016]] <<BR>>[[https://bids.neuroimaging.io/|The brain imaging data structure (BIDS)]] <<BR>>[[https://arxiv.org/abs/2309.05768|The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)]] ||
Line 53: Line 81:
||Slides and scripts__ __ ||https://github.com/dcdace/COGNESTIC-fMRI || ||Slides and scripts__ __ ||https://github.com/dcdace/fMRI-COGNESTIC-23/ ||
Line 60: Line 88:
||<10%>Software__ __ ||[[https://mriqc.readthedocs.io/en/latest/|MRIQC]], [[https://fmriprep.org/en/stable/|fMRIprep]], [[https://nipype.readthedocs.io/en/latest/|Nipype]] || ||<10%>Software__ __ ||[[https://mriqc.readthedocs.io/en/latest/|MRIQC]], [[https://fmriprep.org/en/stable/|fMRIprep]] ||
Line 64: Line 92:
||Slides and scripts ||https://github.com/dcdace/COGNESTIC-fMRI || ||Slides and scripts ||https://github.com/dcdace/fMRI-COGNESTIC-23/ ||
Line 71: Line 99:
||<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]] || ||<10%>__Software__ ||[[http://nilearn.github.io/stable/index.html|Nilearn]] ||
Line 75: Line 103:
||Slides and scripts__ __ ||https://github.com/dcdace/COGNESTIC-fMRI || ||Slides and scripts__ __ ||https://github.com/dcdace/fMRI-COGNESTIC-23/ ||
Line 82: Line 110:
||<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]] || ||<10%>Software__ __ ||[[http://nilearn.github.io/stable/index.html|Nilearn]] ||
Line 86: Line 114:
||Slides and scripts ||https://github.com/dcdace/COGNESTIC-fMRI || ||Slides and scripts ||https://github.com/dcdace/fMRI-COGNESTIC-23/ ||
Line 92: Line 120:
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI Connectivity'''+~ <<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]] ||		 ||||||<tablewidth="734px" tableheight="239px"style="text-align:center">~+'''fMRI Connectivity'''+~ <<BR>> 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]]<<BR>> [[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]]<<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>> 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 <<BR>> - (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. <<BR>>- [[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]] ||
Line 105: Line 144:
||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/preprocessing/index.html|Tutorials]]<<BR>> [[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]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] ||
Line 108: Line 147:
||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks and Slides]] || ||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]] ||
Line 116: Line 155:
||Datasets__ __ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Tutorials]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] ||
||Suggested reading__ __ ||[[https://pubmed.ncbi.nlm.nih.gov/35390459/|Linear source estimation and spatial resolution]]<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] ||		 ||Datasets__ __ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Tutorials]]<<BR>> [[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)<<BR>> [[https://pubmed.ncbi.nlm.nih.gov/35390459/|Linear source estimation and spatial resolution]]<<BR>> [[https://pubmed.ncbi.nlm.nih.gov/24971512/|Guidelines for head modelling]] (incl. FEM)<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] ||
Line 119: Line 158:
||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks and Slides]] || ||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]] ||
Line 127: Line 166:
||__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]] || ||__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]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] ||
Line 130: Line 169:
||Slides and scripts__ __ ||[[attachment:EEGMEG3-timefrequency.zip|Notebooks and Slides]] ||




<<BR>> <<Anchor(networks)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Network Modelling'''+~ <<BR>> Lena Dorschmidt ||
||<10%>Software__ __ || ||
||Datasets__ __ || ||
||Suggested reading__ __ || ||
||Suggested viewing__ __ || ||
||Slides and scripts || ||



 ||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]] ||




<<BR>> <<Anchor(eegmeg4)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG IV – Advanced Topics'''+~ <<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]] ||




----
Line 148: Line 188:
||<10%>__Software__ ||[[https://sites.google.com/site/tdtdecodingtoolbox/|The Decoding Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]]. (This might not be accessible from the CBU internet connection, so please download it in advance or use a difffernt wifi connection) ||
||__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]]. (I've suggested these two, but the others are worth a look too.) ||
||Slides and scripts__ __ ||[[attachment:COGNESTIC22_MVPA_djm_Part1.pptx|Slides for morning session - MVPA]]<<BR>> [[attachment:TDTdemoFunctions.zip|These functions should be saved in a subfolder of the Decoding Toolbox demos folder]]<<BR>> [[attachment:FunctionsToGoInTopFolder.zip|These functions should be saved in the top-level folder]] <<BR>>(Please see the 3rd slide for an overview of the file structure expected by the demo scripts) ||		 ||<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.) <<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]]. ||
||__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]]<<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]]<<BR>>[[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:<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/02_lecture1|Introduction to MVPA]]<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/03_lecture2|Introduction to classification]]. I've suggested these two, but the others are worth a look too. <<BR>> (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.<<BR>> 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.<<BR>>[[attachment:example_djm_partitions.m|extra example 1]]<<BR>>[[attachment:example_djm_unbalanced.m|extra example 2]]<<BR>>[[attachment:COGNESTIC23_MVPA_djm_part1.pptx|slides]] ||
Line 159: Line 199:
||<10%>Software__ __ ||[[http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/|The RSA toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]]<<BR>>For the demos, please download this alternative version: https://git.fmrib.ox.ac.uk/hnili/rsa <<BR>>(Note that the toolbox development has recently switched to Python. We will not be demoing this version, but you can find it here: [[https://github.com/rsagroup/rsatoolbox|Version 3.0]]) ||
||Datasets__ __ ||[[attachment:imageryexp.zip|Group-averged example data]] from [[https://www.nature.com/articles/srep20232|Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex]] ||
||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]]<<BR>> <<BR>>EEG/MEG: <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/27779910/%20|Tutorial on EEG/MEG decoding]]<<BR>> [[https://pubmed.ncbi.nlm.nih.gov/27779910|Temporal Generalization]] ||
||Suggested viewing__ __ ||[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]] ||
||Slides and scripts ||[[attachment:COGNESTIC22_MVPA_djm_Part2.pptx|Slides for afternoon session - RSA]] <<BR>>[[attachment:FunctionsToGoInRsa-MasterDemos.zip|These functions should be saved in rsa-master/Demos]]<<BR>> [[attachment:EEGMEG4-decoding.zip|EEGMEG Notebooks and Slides]] ||




<<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:AH_slides.pptx|slides]] ||
||Slides and scripts ||[[attachment:AH_scripts.zip|scripts]] ||		 ||<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]]<<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]] ||
||Suggested viewing__ __ ||[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]] <<BR>>(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. <<BR>> [[attachment:COGNESTIC23_MVPA_djm_part2.pptx|slides]]<<BR>>[[attachment:EEGMEG5-decoding.zip|EEGMEG Notebooks]] [[attachment:EMEG5_Decoding.pdf|EEG/MEG Slides]]<<BR>> ||
Line 180: Line 209:
||||||<tablewidth="100%"style="text-align:center">~+'''Behavioural and Psychophysiological Methods for Neuroimaging'''+~ <<BR>> Edwin TBA ||
||<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 ||
||Slides and scripts || ||		 ||||||<tablewidth="100%"style="text-align:center">~+'''Brain Stimulation, Pethysmography, Electromyography'''+~ <<BR>> Ajay Halai, Alexis Deighton McIntyre, Hristo Dimitrov ||
||<10%>Software__ __ ||Brain Stimulation: <<BR>> [[https://simnibs.github.io/simnibs/build/html/index.html|E-field modelling for non-invasive brain stimulation using SimNIBS]] <<BR>> Plethysmography: <<BR>> [[https://github.com/alexisdmacintyre/SpeechBreathingToolbox|Speech breathing-oriented toolbox for breath-belt data (MATLAB)]] ||
||Datasets__ __ || ||
||Suggested reading__ __ ||Brain Stimulation: <<BR>> [[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]] <<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 <<BR>><<BR>> EMG: <<BR>> [[https://www.sciencedirect.com/science/article/pii/S1050641120300419|Consensus for experimental design in electromyography]]<<BR>> [[https://www.sciencedirect.com/science/article/pii/S1050641122000293|Tutorial high-density EMG]]<<BR>> [[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]]<<BR>> Plethysmography: [[attachment:MacIntyre_COGNESTIC.pdf|slides]] ||

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 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 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

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


Primer on Python
Edwin Dalmijer

Websites

Python, NumPy, SciPy, Matplotlib, PsychoPy

Suggested reading

None

Suggested viewing

saliency-mapping of Taylor Swift music videos

Tutorial slides and scripts

None


Structural MRI I - Voxel-based morphometry
Marta Correia

Software

FSL

Datasets

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

Suggested viewing

Introduction to MRI Physics and image contrast
Slides

Tutorial slides and scripts

Intro to command line
VBM slides
FSL VBM tutorials
FSL VBM script
Hands on exercises for structural and diffusion MRI


Structural MRI II - Surface-based analyses
Marta Correia

Software

Freesurfer

Datasets

Freesurfer tutorial data

Suggested reading

Dale et al, 1999, Cortical surface-based analysis I
Fischl et al, 1999, Cortical surface-based analysis II

Suggested viewing

Using the command line

Tutorial slides and scripts

Freesurfer slides
Freesurfer tutorials
FS check location script
FS visualising the output script
FS group analysis script
FS ROI analysis script


Diffusion MRI I - The Diffusion Tensor Model
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 tutorials
FSL DTI pipeline script
FSL TBSS script
FSL group QC script


Diffusion MRI II - Tractography and the Anatomical Connectome
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 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)
The Past, Present, and Future of 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/fMRI-COGNESTIC-23/


fMRI II - Quality control & Pre-processing
Dace Apšvalka

Software

MRIQC, fMRIprep

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/fMRI-COGNESTIC-23/


fMRI III - Subject Level Analysis
Dace Apšvalka

Software

Nilearn

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/fMRI-COGNESTIC-23/


fMRI IV - Group Level Analysis & Reporting
Dace Apšvalka

Software

Nilearn

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/fMRI-COGNESTIC-23/


fMRI Connectivity
Petar Raykov

Software

Nilearn Python

Datasets

movie dataset

Suggested reading

Resting-state functional Connectivity
Learning and comparing functional connectomes across subjects

Suggested viewing

fMRI Functional Connectivity in fMRI
Overview of Effective Connectivity (not covered in person)

Tutorial slides and scripts

Functional Connectivity Nilearn Practical
DCM tutorial in SPM (not covered in-person)


Brain Network Analysis
Isaac Sebenius

Software

Python 3.7+, nxviz, 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

- Introduction to Network Neuroscience, minutes 0:00-48:30. A wonderful introduction to brain networks by Prof. Bratislav Misic.
- Understanding your brain as a network and as art by Prof. Dani Bassett.

Slides and scripts

https://github.com/isebenius/COGNESTIC_network_analysis/ Slides


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 Download Datasets

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 Exercises Slides1 Slides2 Slides3


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 Download Datasets

Suggested reading

Comparison of common head models (e.g. BEM)
Linear source estimation and spatial resolution
Guidelines for head modelling (incl. FEM)
General EEG/MEG Literature

Suggested viewing

Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM

Slides and scripts

Notebooks Exercises Slides1 Slides2 Slides3


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 Download Datasets

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 Exercises Slides1 Slides2Slides3


EEG/MEG IV – Advanced Topics
Olaf Hauk & Máté Aller

Software

MNE-Python
MNE Installation for Cognestic

Datasets

Sample dataset in MNE-Python. Tutorials
MNE Installation for Cognestic
M/EEG combined dataset Download Datasets

Suggested reading

Estimating subcortical sources from EEG/MEG
Tutorial on converting MEG data to BIDS format
Example using MNE-BIDS-Pipeline for processing combined M/EEG data

Suggested viewing

Talk on Multimodal Integration

Slides and scripts

Notebooks Exercises Slides1 Slides2
Notebooks mne-bids-pipeline Slides mne-bids-pipeline


MVPA/RSA I
Daniel Mitchell

Software

CoSMoMVPA using 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 MRIcroN.

Datasets

Tutorial data from CoSMoMVPA toolbox

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
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:
Introduction to MVPA
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 here, pasted into an empty Octave file, and you can try to fill in the missing snippets.
extra example 1
extra example 2
slides


MVPA/RSA II
Daniel Mitchell

Software

Python implementation of the RSA Toolbox: Version 3.0

Datasets

Example data included with RSA toolbox

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
Schutt et al. (2023) Statistical inference on representational geometries
EEG/MEG:
Tutorial on EEG/MEG decoding
Temporal Generalization Interpretation of Weight Vectors

Suggested viewing

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.
slides
EEGMEG Notebooks EEG/MEG Slides


Brain Stimulation, Pethysmography, Electromyography
Ajay Halai, Alexis Deighton McIntyre, Hristo Dimitrov

Software

Brain Stimulation:
E-field modelling for non-invasive brain stimulation using SimNIBS
Plethysmography:
Speech breathing-oriented toolbox for breath-belt data (MATLAB)

Datasets

Suggested reading

Brain Stimulation:
Approaches to brain stimulation; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 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:
Consensus for experimental design in electromyography
Tutorial high-density EMG
Noninvasive Neural Interfacing With Wearable Muscle Sensors

Suggested viewing

Brain Stimulation: SimNIBs tutorial and SimNIBS youtube videos

Slides and scripts

Brain Stimulation: slides
Plethysmography: slides

None: COGNESTIC2023 (last edited 2024-06-11 15:57:44 by OlafHauk)