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| 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 || |
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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 - 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]] || |
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| ||||||<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 || |
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| ||__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]] || |
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| ||||||<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 || |
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| ||__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]] || |
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| ||__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)]] || |
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| ||Slides and scripts__ __ ||https://github.com/dcdace/COGNESTIC-fMRI || | ||Slides and scripts__ __ ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || |
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| ||<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]] || |
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| ||Slides and scripts ||https://github.com/dcdace/COGNESTIC-fMRI || | ||Slides and scripts ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || |
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| ||<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]] || |
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| ||Slides and scripts__ __ ||https://github.com/dcdace/COGNESTIC-fMRI || | ||Slides and scripts__ __ ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || |
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| ||<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]] || |
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| ||Slides and scripts ||https://github.com/dcdace/COGNESTIC-fMRI || | ||Slides and scripts ||https://github.com/dcdace/fMRI-COGNESTIC-23/ || |
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| ||||||<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 || ||Slides and scripts || || |
||||||<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__ __ ||[[https://wetransfer.com/downloads/63962eae4f0d86771c6a23ed6737c0b920230912095002/1aa609|Sample HCP data]] || ||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/]] || |
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| ||__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]] || |
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| ||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]] || |
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| ||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]] || | ||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]] || |
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| ||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]] || |
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| ||__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]] || |
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| ||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 || || |
||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]] || ---- |
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| ||<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: as of 25/9/23 the above links appear to have stopped working. This may be a temporary issue with the host website. As an alternative, try to download from [[attachment:02_lecture1_MVPA_intro.mp4|here]] and [[attachment:03_lecture2_Classification.mp4|here]]. || ||Slides and scripts__ __ ||We will be using the demo scripts from within the "examples" folder of CoSMoMVPA. || |
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| ||<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(statistics)>> ||||||<tablewidth="100%"style="text-align:center">~+'''Statistics in R'''+~ <<BR>> Peter Watson || ||<10%>__Software__ ||[[https://www.r-project.org/|R]] [[attachment:PW SEPT 2022 R COURSE.zip|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__ __ || || <<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]] || <<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]] || |
||<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://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]] <<BR>> (If the link fails, try to download from [[attachment:08_lecture6_RSA.mp4|here)]] || ||Slides and scripts ||We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox. <<BR>> [[attachment:EEGMEG5-decoding.zip|EEGMEG Notebooks]] [[attachment:EMEG2_4_Decoding.pdf|EEG/MEG Slides]]<<BR>> || <<BR>> <<Anchor(psychophysiol)>> ||||||<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:BrainStim_slides.pptx|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 |
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Websites |
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Suggested reading |
Munafo et al, 2017, problems in science |
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Suggested viewing |
Open Cognitive Neuroscience (will give this talk live on day) |
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Tutorial slides and scripts |
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Primer on Python |
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Websites |
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Suggested reading |
None |
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Suggested viewing |
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Tutorial slides and scripts |
None |
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Structural MRI I - Voxel-based morphometry |
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Software |
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Datasets |
Subset of the CamCAN dataset (~3GB) https://www.cam-can.org/, please sign data user agreement if using |
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Suggested reading |
Introduction to GLM for structural MRI analysis |
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Suggested viewing |
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Tutorial slides and scripts |
Intro to command line |
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Structural MRI II - Surface-based analyses |
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Software |
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Datasets |
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Suggested reading |
Dale et al, 1999, Cortical surface-based analysis I |
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Suggested viewing |
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Tutorial slides and scripts |
Freesurfer slides |
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Diffusion MRI I - The Diffusion Tensor Model |
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Software |
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Datasets |
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Suggested reading |
FSL Diffusion Toolbox Wiki |
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Suggested viewing |
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Tutorial slides and scripts |
FSL DTI and TBSS slides |
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Diffusion MRI II - Tractography and the Anatomical Connectome |
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Software |
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Datasets |
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Suggested reading |
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Suggested viewing |
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Tutorial slides and scripts |
MRTrix tractography slides |
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fMRI I - Data management, structure, manipulation |
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Software |
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Datasets |
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Suggested reading |
Gorgolewski et al., 2016 |
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Suggested viewing |
BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) |
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Slides and scripts |
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fMRI II - Quality control & Pre-processing |
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Software |
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Datasets |
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Suggested reading |
Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods |
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Suggested viewing |
fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57) |
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Slides and scripts |
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fMRI IV - Group Level Analysis & Reporting |
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Software |
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Datasets |
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Suggested reading |
Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data |
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Suggested viewing |
Group-level Analysis I by Martin Lindquist and Tor Wager (7:05) |
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Slides and scripts |
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fMRI Connectivity |
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Software |
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Datasets |
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Suggested reading |
Resting-state functional Connectivity |
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Suggested viewing |
fMRI Functional Connectivity in fMRI |
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Tutorial slides and scripts |
Functional Connectivity Nilearn Practical |
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Brain Network Analysis |
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Software |
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Datasets |
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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 |
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Suggested viewing |
- Introduction to Network Neuroscience, minutes 0:00-48:30. A wonderful introduction to brain networks by Prof. Bratislav Misic. |
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Slides and scripts |
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EEG/MEG I – Pre-processing |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
Digitial Filtering |
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Suggested viewing |
Introduction to EEG/MEG Preprocessing |
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Slides and scripts |
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EEG/MEG II – Source Estimation |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
Linear source estimation and spatial resolution |
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Suggested viewing |
Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM |
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Slides and scripts |
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EEG/MEG III – Time-Frequency and Functional Connectivity |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
Tutorial on Functional Connectivity |
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Suggested viewing |
Introduction to time-frequency and functional connectivity analysis |
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Slides and scripts |
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EEG/MEG IV – Advanced Topics |
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Software |
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Datasets |
Sample dataset in MNE-Python. Tutorials |
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Suggested reading |
Estimating subcortical sources from EEG/MEG |
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Suggested viewing |
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Slides and scripts |
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MVPA/RSA I |
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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.) |
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Datasets |
Tutorial data from CoSMoMVPA toolbox |
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Suggested reading |
Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide |
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Suggested viewing |
Excellent presentations from Martin Hebart's MVPA course, on: |
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Slides and scripts |
We will be using the demo scripts from within the "examples" folder of CoSMoMVPA. |
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MVPA/RSA II |
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Software |
Python implementation of the RSA Toolbox: Version 3.0 |
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Datasets |
Example data included with RSA toolbox |
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Suggested reading |
Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience |
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Suggested viewing |
Martin Hebart's lecture on RSA |
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Slides and scripts |
We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox. |
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Brain Stimulation, Pethysmography, Electromyography |
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Software |
Brain Stimulation: |
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Datasets |
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Suggested reading |
Brain Stimulation: |
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Suggested viewing |
Brain Stimulation: SimNIBs tutorial and SimNIBS youtube videos |
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Slides and scripts |
Brain Stimulation: slides |
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