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||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=D0VKyjNGvrs|Statistical power in neuroimaging]] <<BR>> [[https://www.youtube.com/watch?v=zAzTR8eq20k|PayWall: open access]] <<BR>> [[https://www.facebook.com/LastWeekTonight/videos/896755337120143|Comedian's Perspective on science and media]] || | ||__Suggested viewing__ ||[[https://youtu.be/kTVtc7kjVQg|Open Cognitive Neuroscience (will give this talk live on day)]] <<BR>> [[https://www.youtube.com/watch?v=D0VKyjNGvrs|Statistical power in neuroimaging]] <<BR>> [[https://www.youtube.com/watch?v=zAzTR8eq20k|PayWall: open access]] <<BR>> [[https://www.facebook.com/LastWeekTonight/videos/896755337120143|Comedian's Perspective on science and media]] || ||__Tutorial slides and scripts__ ||[[attachment:COGNESTIC_OpenCogNeuro.pdf|Open Science Talk Slides]] || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI '''+~<<BR>> Marta Correia || | ||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI - VBM and Surface-based Analysis '''+~<<BR>> Marta Correia || |
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||__Datasets__ ||[[https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Data|Freesurfer tutorial data]] || ||__Suggested reading__ ||[[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>> Introduction to MRI Physics talk coming soon. || |
||__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]] || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI I'''+~ <<BR>> Marta Correia || | ||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI I - DTI Model Fitting and Group Analysis'''+~ <<BR>> Marta Correia || |
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||__Suggested reading__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT|FSL Diffusion Toolbox Wiki]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/16624579/|Smith et al, 2006, Tract-based spatial statistics (TBSS)]] || ||__Suggested viewing__ ||Introduction to Diffusion MRI talk coming soon. || |
||__Suggested reading__ ||[[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT|FSL Diffusion Toolbox Wiki]] <<BR>> [[https://doi.org/10.1371/journal.pbio.1002203|Le Bihan et al, 2015, What water tells us about biological tissues]] <<BR>> [[https://doi.org/10.3389/fnins.2013.00031|Soares et al, 2013, A short guide to Diffusion Tensor Imaging]] <<BR>> [[https://pubmed.ncbi.nlm.nih.gov/16624579/|Smith et al, 2006, Tract-based spatial statistics (TBSS)]] || ||__Suggested viewing__ ||[[https://youtu.be/stpmlzO7b6c|Introduction to Diffusion MRI - Part I]] <<BR>> [[attachment:IntroductionToDiffusionMRI_I.pdf|Slides]] || ||__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]] || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI II'''+~ <<BR>> Marta Correia || | ||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI II - Tractography and Structural Connectivity'''+~ <<BR>> Marta Correia || |
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||__Suggested reading__ || || ||__Suggested viewing__ ||Introduction to Diffusion MRI talk coming soon. || |
||__Suggested reading__ ||[[https://mrtrix.readthedocs.io/en/latest/|MRtrix3 documentation]] <<BR>> [[https://www.sciencedirect.com/science/article/pii/B9780123964601000196|MR Diffusion Tractography]] || ||__Suggested viewing__ ||[[https://youtu.be/QDJJ6G2ZouA|Introduction to Diffusion MRI - Part II]] <<BR>> [[attachment:IntroductionToDiffusionMRI_II.pdf|Slides]] || ||__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]] || |
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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, The brain imaging data structure (BIDS)]] || |
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||Slides and scripts__ __ ||https://github.com/dcdace/COGNESTIC-fMRI || | |
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||<10%>__Software__ || || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ || || ||__Suggested viewing__ || || |
||<10%>Software__ __ ||[[https://mriqc.readthedocs.io/en/latest/|MRIQC]], [[https://fmriprep.org/en/stable/|fMRIprep]], [[https://nipype.readthedocs.io/en/latest/|Nipype]] || ||Datasets__ __ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||Suggested reading__ __ ||[[https://link.springer.com/article/10.1007/s11065-015-9294-9|Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods]]<<BR>> [[https://mriquestions.com/uploads/3/4/5/7/34572113/ch2.pdf|Ashburner J & Friston KJ (2004), Rigid body registration]]<<BR>> [[https://doi.org/10.1002/mrm.24314|Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review]]<<BR>> [[https://doi.org/10.1016/j.neuroimage.2011.06.078|Sladky et al. (2011), Slice-timing effects and their correction in functional MRI]]<<BR>> [[https://doi.org/10.1006/nimg.2000.0609|Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis]]<<BR>> [[https://www.nature.com/articles/s41592-018-0235-4|Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI]] || ||Suggested viewing__ __ ||[[https://youtu.be/7Kk_RsGycHs|fMRI Artifacts and Noise]] by Martin Lindquist and Tor Wager (11:57)<<BR>>[[https://youtu.be/Qc3rRaJWOc4|Pre-processing I]] by Martin Lindquist and Tor Wager (10:17)<<BR>>[[https://youtu.be/qamRGWSC-6g|Pre-processing II]] by Martin Lindquist and Tor Wager (7:42) || ||Slides and scripts ||https://github.com/dcdace/COGNESTIC-fMRI || |
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||||||<tablewidth="100%"style="text-align:center">~+'''fMRI III - Statistical Analysis'''+~ <<BR>> Dace Apšvalka || ||<10%>__Software__ || || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ || || ||__Suggested viewing__ || || |
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI III - Subject Level Analysis'''+~ <<BR>> Dace Apšvalka || ||<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]] || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ ||[[https://doi.org/10.1002/hbm.460020402|Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2012.01.133|Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever?]]<<BR>>[[https://doi.org/10.3389/fnhum.2011.00028|Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approach]]<<BR>>[[https://doi.org/10.1191/0962280203sm341ra|Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2008.05.021|Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2013.12.058|Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations]]<<BR>>[[https://doi.org/10.1214/09-STS282|Lindquist (2008), The Statistical Analysis of fMRI Data]] || ||__Suggested viewing__ ||[[https://youtu.be/GDkLQuV4he4|The General Linear Model]] by Martin Lindquist and Tor Wager (12:24)<<BR>>[[https://www.youtube.com/watch?v=OyLKMb9FNhg|GLM applied to fMRI]] by Martin Lindquist and Tor Wager (11:21)<<BR>>[[https://www.youtube.com/watch?v=7MibM1ATai4|Model Building I – conditions and contrasts]] by Martin Lindquist and Tor Wager (11:48)<<BR>>[[https://www.youtube.com/watch?v=YfeMIcDWwko|Model Building II – temporal basis sets]] by Martin Lindquist and Tor Wager (11:08)<<BR>>[[https://www.youtube.com/watch?v=DEtwsFdFwYc|Model Building III- nuisance variables]] by Martin Lindquist and Tor Wager (13:58)<<BR>>[[https://www.youtube.com/watch?v=Ab-5AbJ8gAs|GLM Estimation]] by Martin Lindquist and Tor Wager (9:11)<<BR>>[[https://youtu.be/Mb9LDzvhecY|Noise Models- AR models]] by Martin Lindquist and Tor Wager (9:57)<<BR>>[[https://youtu.be/NRunOo7EKD8|Inference- Contrasts and t-tests]] by Martin Lindquist and Tor Wager (11:05)<<BR>>[[https://youtu.be/AalIM9-5-Pk|Multiple Comparisons]] by Martin Lindquist and Tor Wager (9:03)<<BR>>[[https://youtu.be/MxQeEdVNihg|FWER Correction]] by Martin Lindquist and Tor Wager (16:11)<<BR>>[[https://youtu.be/W9ogBO4GEzA|FDR Correction]] by Martin Lindquist and Tor Wager (5:25)<<BR>>[[https://youtu.be/N7Iittt8HrU|More about multiple comparisons]] by Martin Lindquist and Tor Wager (14:39) || ||Slides and scripts__ __ ||https://github.com/dcdace/COGNESTIC-fMRI || |
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||||||<tablewidth="100%"style="text-align:center">~+'''fMRI IV - Reporting'''+~ <<BR>> Dace Apšvalka || ||<10%>__Software__ || || ||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||__Suggested reading__ || || ||__Suggested viewing__ || || |
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI IV - Group Level Analysis & Reporting'''+~ <<BR>> Dace Apšvalka || ||<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]] || ||Datasets__ __ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] || ||Suggested reading__ __ ||[[https://doi.org/10.1109/MEMB.2006.1607668|Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data]]<<BR>>[[https://www.nature.com/articles/nn.4500|Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2007.11.048|Poldrack et al. (2008), Guidelines for reporting an fMRI study]]<<BR>>[[https://doi.org/10.1016/j.neuroimage.2015.04.016|Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain]]<<BR>>[[https://doi.org/10.7554/eLife.71774|Markiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data]] || ||Suggested viewing__ __ ||[[https://youtu.be/__cOYPifDWk|Group-level Analysis I]] by Martin Lindquist and Tor Wager (7:05)<<BR>>[[https://youtu.be/-abMLQSjMSI|Group-level Analysis II]] by Martin Lindquist and Tor Wager (10:09)<<BR>>[[https://youtu.be/-yaHTygR9b8|Group-level Analysis III]] by Martin Lindquist and Tor Wager (14:01) || ||Slides and scripts ||https://github.com/dcdace/COGNESTIC-fMRI || |
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||__Suggested viewing__ ||[[https://youtu.be/1VOKsWWLgjk|fMRI Functional Connectivity, including DCM]] <<BR>> Talk on Bayesian Model Comparison coming soon || | ||__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)]] || ||Slides and scripts__ __ || || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Eye-tracking'''+~ <<BR>> Edwin Dalmijer || ||<10%>__Software__ || || ||__Datasets__ || || ||__Suggested reading__ || || ||__Suggested viewing__ || || |
||||||<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 || || |
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||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]neuro || ||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/preprocessing/index.html|Tutorials]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/25128257/|Digitial Filtering]] <<BR>>[[https://www.sciencedirect.com/science/article/pii/S0896627319301746|Filtering How To]] <<BR>> [[https://iopscience.iop.org/article/10.1088/0031-9155/51/7/008|Maxwell Filtering]] || ||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG1.mp4|Preprocessing]] <<BR>>[[https://mediacentral.ucl.ac.uk/Player/2909|What are we measuring with M/EEG]]? || |
||<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/preprocessing/index.html|Tutorials]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/25128257/|Digitial Filtering]] <<BR>>[[https://www.sciencedirect.com/science/article/pii/S0896627319301746|Filtering How To]] <<BR>> [[https://iopscience.iop.org/article/10.1088/0031-9155/51/7/008|Maxwell Filtering]] <<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] || ||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG1.mp4|Introduction to EEG/MEG Preprocessing]] <<BR>>[[https://mediacentral.ucl.ac.uk/Player/2909|What are we measuring with M/EEG]]? || ||Slides and scripts__ __ ||[[attachment:EEGMEG1-preprocessing.zip|Notebooks and Slides]] || |
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||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]] || ||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Tutorials]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/35390459/|Linear source estimation and spatial resolution]] || ||__Suggested viewing__ ||[[https://mediacentral.ucl.ac.uk/Player/2917|M/EEG Source Analysis in SPM]] || |
||<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/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]] || ||Suggested viewing__ __ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG2_SourceEstimation.mp4|Introduction to EEG/MEG Source Estimation]] [[https://mediacentral.ucl.ac.uk/Player/2917|M/EEG Source Analysis in SPM]] || ||Slides and scripts ||[[attachment:EEGMEG2-sourceestimation.zip|Notebooks and Slides]] || |
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||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]] || ||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/time-freq/index.html|Tutorials]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/26778976/|Tutorial on Functional Connectivity]]<<BR>> [[https://mitpress.mit.edu/books/analyzing-neural-time-series-data|Analyzing Neural Time Series Data]] || ||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG3.mp4|Time-frequency and functional connectivity analysis]] <<BR>> [[https://www.youtube.com/watch?v=wB417SAbdak|Time-Frequency Analysis of EEG Time Series]] || |
||<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]] || ||__Suggested reading__ ||[[https://pubmed.ncbi.nlm.nih.gov/26778976/|Tutorial on Functional Connectivity]]<<BR>> [[https://mitpress.mit.edu/books/analyzing-neural-time-series-data|Analyzing Neural Time Series Data]]<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] || ||__Suggested viewing__ ||[[https://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures?action=AttachFile&do=view&target=EEGMEG3.mp4|Introduction to time-frequency and functional connectivity analysis]] <<BR>> [[https://www.youtube.com/watch?v=wB417SAbdak|Time-Frequency Analysis of EEG Time Series]] || ||Slides and scripts__ __ ||[[attachment:EEGMEG3-timefrequency.zip|Notebooks and Slides]] || |
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||||||<tablewidth="100%"style="text-align:center">~+'''Graph Theory'''+~ <<BR>> Caroline Nettekoven || ||<10%>__Software__ ||[[https://sites.google.com/site/bctnet/|Brain Connectivity Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]] || ||__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]] || |
||||||<tablewidth="100%"style="text-align:center">~+'''Graph Theory'''+~ <<BR>> Caroline Nettekoven [[https://us02web.zoom.us/j/81982692386?pwd=TUZsdmpHZDEySUJLSFJIcDN6TXNFdz09|Zoom link]] || ||<10%>Software__ __ ||[[https://sites.google.com/site/bctnet/|Brain Connectivity Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]], [[https://sites.google.com/site/bctnet/list-of-measures?authuser=0|BCT Documentation]] || ||Datasets__ __ ||[[https://www.caroline-nettekoven.com/slides/graph-theory-exercises/|Coding exercises]]<<BR>> [[https://www.caroline-nettekoven.com/slides/graph-theory-exercises-solutions/|Exercise solutions]] || ||Suggested reading__ __ ||[[https://www.nature.com/articles/nrn2575|Complex brain networks: graph theoretical analysis of structural and functional systems]] || ||Suggested viewing__ __ ||[[https://www.caroline-nettekoven.com/slides/graph-theory-lecture/|Slides]] || ||Slides and scripts || || |
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||<10%>__Software__ ||[[https://sites.google.com/site/tdtdecodingtoolbox/|The Decoding Toolbox]] in [[https://uk.mathworks.com/products/matlab.html|Matlab]] || | ||<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) || |
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||__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]] || | ||__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]] || |
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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>>(Alternatively, https://git.fmrib.ox.ac.uk/hnili/rsa) || ||__Datasets__ || || ||__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]] || ||__Suggested viewing__ ||[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]] || |
||<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) (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]] || |
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||__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]] || | ||__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]] || |
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||Slides and scripts__ __ || || | |
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||<10%>__Software__ || || ||__Datasets__ || || ||__Suggested reading__ || || ||__Suggested viewing__ || || |
||<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:COGNESTIC_slides|slides]] || ||Slides and scripts || || |
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||||||<tablewidth="100%"style="text-align:center">~+'''DCM for M/EEG'''+~ <<BR>> Rik Henson || | ||||||<tablewidth="100%"style="text-align:center">~+'''DCM for M/EEG'''+~ <<BR>> Pranay Yadav & Rik Henson || |
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||__Suggested viewing__ ||Talk on DCM for M/EEG coming soon <<BR>> [[https://youtu.be/6b35VvQpPDU|MEEG connectivity other than DCM (not demo'ed, and related to Hauk talks above)]] || |
||__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)]] || ||Slides and scripts__ __ || || ---- ---- |
Course Material for COGNESTIC 2022
The Cognitive Neuroscience Skills Training In Cambridge (COGNESTIC) is a 2-week course that provides researchers with training in state-of-the-art methods for neuroimaging and neurostimulation. You can find more information on the COGNESTIC webpage.
Below you will find documents, videos and web links that will be used for the course or can be used for preparation.
Introduction and Open Science |
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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 |
Structural MRI - VBM and Surface-based Analysis |
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Software |
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Datasets |
Freesurfer tutorial data |
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Suggested reading |
Introduction to GLM for structural MRI analysis |
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Suggested viewing |
Using the command line |
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Tutorial slides and scripts |
FSLVBM slides |
Diffusion MRI I - DTI Model Fitting and Group Analysis |
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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 |
Diffusion MRI II - Tractography and Structural Connectivity |
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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 |
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, The brain imaging data structure (BIDS) |
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Suggested viewing |
BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) |
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Slides and scripts |
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 |
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 |
Connectivity for fMRI |
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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, including DCM |
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Slides and scripts |
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Eye-tracking |
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Software |
Python NumPy, SciPy, Matplotlib |
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Datasets |
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Suggested reading |
https://doi.org/10.3758/s13428-021-01762-8 Paper on eye-tracking reporting standards (great for beginners and experts alike) |
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Suggested viewing |
https://www.youtube.com/watch?v=F5eBln42VyM Talk at the MRC CBU on how to hack pupillometry studies |
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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 |
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 |
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 |
Graph Theory |
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Software |
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Datasets |
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Suggested reading |
Complex brain networks: graph theoretical analysis of structural and functional systems |
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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 |
The Decoding Toolbox in Matlab. (This might not be accessible from the CBU internet connection, so please download it in advance or use a difffernt wifi connection) |
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Datasets |
The Decoding Toolbox example dataset |
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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 |
Slides for morning session - MVPA |
MVPA/RSA II |
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Software |
The RSA toolbox in Matlab |
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Datasets |
Group-averged example data from Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex |
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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 |
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Slides and scripts |
Slides for afternoon session - RSA |
Statistics in R |
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Software |
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Datasets |
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Suggested reading |
Statistical Methods for Psychology (Howell) |
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Suggested viewing |
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Slides and scripts |
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Brain Stimulation |
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Software |
SIMNIBS (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and k-wave |
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Datasets |
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Suggested reading |
Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2 |
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Suggested viewing |
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Slides and scripts |
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DCM for M/EEG |
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Software |
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Datasets |
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Suggested reading |
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Suggested viewing |
Talk on DCM for M/EEG |
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Slides and scripts |
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