1342024-09-27 12:06:10OlafHauk1332024-09-25 16:34:21DannyMitchell21322024-09-25 16:33:29DannyMitchell21312024-09-23 12:46:27OlafHauk1302024-09-17 15:09:07DannyMitchell21292024-09-17 10:52:08KshipraGurunandan1282024-09-17 10:41:21KshipraGurunandan1272024-09-17 09:51:12OlafHauk1262024-09-17 09:35:19OlafHauk1252024-09-17 09:28:32DaceApsvalka1242024-09-17 09:25:40DaceApsvalka1232024-09-09 20:04:08DaceApsvalka1222024-09-09 15:39:15OlafHauk1212024-09-09 15:30:37OlafHauk1202024-09-09 15:28:57OlafHauk1192024-08-01 15:08:14OlafHauk1182024-07-30 16:07:19RikHenson1172024-07-24 16:45:03DannyMitchell21162024-07-24 16:15:55DannyMitchell21152024-07-19 10:21:25OlafHauk1142024-07-19 10:20:44OlafHauk1132024-07-19 10:18:28OlafHauk1122024-07-18 15:53:25KshipraGurunandan1112024-07-18 10:04:07OlafHauk1102024-07-18 10:03:25OlafHauk1092024-07-17 16:11:19OlafHauk1082024-07-17 16:10:42OlafHauk1072024-07-17 15:41:56OlafHauk1062024-07-17 15:41:48OlafHauk1052024-07-17 15:38:16OlafHauk1042024-07-17 15:35:33OlafHauk1032024-07-17 15:31:59OlafHauk1022024-07-17 15:29:18OlafHauk1012024-07-17 15:23:36OlafHauk1002024-07-16 16:27:55DannyMitchell2992024-07-16 16:27:07DannyMitchell2982024-07-16 16:26:44DannyMitchell2972024-07-16 16:25:38DannyMitchell2962024-07-16 16:21:07DannyMitchell2952024-07-16 16:18:42DannyMitchell2942024-07-16 15:31:47PetarRaykov932024-07-15 16:38:34DaceApsvalka922024-07-15 16:32:39DaceApsvalka912024-07-15 16:29:09DaceApsvalka902024-07-15 15:28:21DaceApsvalka892024-07-15 15:26:31DaceApsvalka882024-07-15 15:18:29DaceApsvalka872024-07-15 14:45:52DaceApsvalka862024-07-15 13:06:35DannyMitchell2852024-07-15 13:05:45DannyMitchell2842024-07-14 09:54:35KshipraGurunandan832024-07-12 17:11:19KshipraGurunandan822024-07-12 08:45:16KshipraGurunandan812024-07-12 08:40:45KshipraGurunandan802024-07-10 11:43:21OlafHauk792024-07-10 11:40:13OlafHauk782024-07-10 11:36:26OlafHauk772024-07-10 11:35:04OlafHauk762024-07-10 11:33:55OlafHauk752024-07-10 11:32:19OlafHauk742024-07-10 11:28:32OlafHauk732024-07-10 11:26:50OlafHauk722024-07-10 11:22:15OlafHauk712024-07-04 10:57:17MartaCorreia702024-07-04 10:56:26MartaCorreia692024-07-04 10:54:32MartaCorreia682024-07-04 10:51:51MartaCorreia672024-07-04 09:15:20MartaCorreia662024-07-04 09:14:39MartaCorreia652024-07-04 09:07:48MartaCorreia642024-07-04 09:05:36MartaCorreia632024-07-04 09:04:39MartaCorreia622024-07-04 08:54:21DannyMitchell2612024-07-04 08:52:43DannyMitchell2602024-07-04 08:51:41DannyMitchell2592024-07-03 09:45:53DannyMitchell2582024-07-03 09:34:59DannyMitchell2572024-07-03 09:33:39DannyMitchell2562024-07-03 09:32:13DannyMitchell2552024-07-03 09:30:12DannyMitchell2542024-07-03 09:28:44DannyMitchell2532024-07-03 09:27:55DannyMitchell2522024-07-03 09:11:03DannyMitchell2512024-07-03 09:07:19DannyMitchell2502024-07-01 09:21:25OlafHauk492024-07-01 09:10:46OlafHauk482024-06-12 15:24:05RikHenson472024-06-12 15:20:43RikHenson462024-06-12 15:10:40RikHenson452024-06-12 13:58:01OlafHauk442024-06-12 13:57:07OlafHauk432024-06-12 13:55:32OlafHauk422024-06-12 13:54:46OlafHauk412024-06-12 13:52:51OlafHauk402024-06-12 13:43:15OlafHauk392024-06-12 13:42:17OlafHauk382024-06-12 13:38:56OlafHauk372024-06-12 13:35:38OlafHauk362024-06-12 13:29:41OlafHauk352024-06-12 12:30:29OlafHauk342024-06-12 12:28:49OlafHauk332024-06-12 12:27:49OlafHauk322024-06-12 12:26:16OlafHauk312024-06-12 12:24:44OlafHauk302024-06-12 12:22:08OlafHauk292024-06-12 12:19:53OlafHauk282024-06-12 11:27:04OlafHauk272024-06-12 11:23:41OlafHauk262024-06-11 16:06:43OlafHauk252024-06-11 16:04:33OlafHauk242024-06-11 16:01:40OlafHauk232024-06-11 15:59:35OlafHauk222024-06-10 16:00:32OlafHauk212024-06-10 15:58:27OlafHauk202024-06-10 15:56:49OlafHauk192024-06-10 15:55:37OlafHauk182024-06-10 15:52:37OlafHauk172024-06-10 15:51:02OlafHauk162024-06-10 15:34:20OlafHauk152024-06-10 15:29:27OlafHauk142024-06-10 15:25:02OlafHauk132024-06-10 15:21:05OlafHauk122024-06-10 15:20:15OlafHauk112024-06-10 14:19:21OlafHauk102024-06-10 14:18:49OlafHauk92024-06-10 14:18:05OlafHauk82024-06-10 13:58:30OlafHauk72024-06-10 13:55:30OlafHauk62024-06-10 13:51:46OlafHauk52024-06-10 13:51:00OlafHauk42024-06-10 13:50:15OlafHauk32024-06-10 13:47:02OlafHauk22024-06-10 13:44:04OlafHauk12024-06-10 13:38:02OlafHauk

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 reproducible and open neuroimaging analysis and related methods. You can find more information on the COGNESTIC webpage.

Attendees must read and follow these pre-course preparations.

You will find the course easier if you can study as much of the material below in advance (e.g, many of the videos below give the theory to the examples we will work through in the course). This section contains essential viewing; a second section contains less critical background, but which you might nonetheless find useful. Background to Open Science Rik Henson Viewing Open Cognitive Neuroscience Primer on Python Kshipra Gurunandan Viewing Introduction to Python and notebooks Structural MRI I and II - VBM and surface-based analysis Marta Correia Viewing Introduction to MRI Physics and image contrast Slides Diffusion MRI I - Preprocessing, model fitting and group analysis Marta Correia Viewing Introduction to Diffusion MRI - Part I Slides Diffusion MRI II - Tractography and the anatomical connectome Marta Correia Viewing Introduction to Diffusion MRI - Part II Slides fMRI I - Data Organisation Dace Apšvalka Viewing fMRI Data Structure & Terminology (6:47)Brain imaging data structure (11:07) fMRI II - Preprocessing Dace Apšvalka Viewing fMRI Artifacts and Noise (11:57) Pre-processing I (10:17) Pre-processing II (7:42) fMRI III - Analysis Dace Apšvalka Viewing GLM applied to fMRI (11:21) Model Building – conditions and contrasts (11:48) Model Building - nuisance variables (13:58) Multiple Comparisons (9:03) Group-level Analysis I (7:05) fMRI Connectivity Petar Raykov Viewing Functional Connectivity in fMRI Network Analysis Rik Henson Viewing Introduction to Network Neuroscience EEG/MEG I – Measurement and Pre-processing Olaf Hauk Viewing 1. Overview of EEG/MEG data processing from raw data to source estimates Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation. 2. The generation of EEG/MEG signals Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.3.Frequency and temporal filtering of EEG/MEG dataFrequency spectrum, temporal smoothing, relationship between frequency and time domain, filters (low-/high-/band-pass, Notch), aliasing, decibels. 4. Differential sensitivity of EEG and MEG Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources. 5.Event-related potentials and fields Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression. Fore more on this topic see here. EEG/MEG II – Head Modelling and Source Estimation Olaf Hauk Viewing 1. The EEG/MEG forward modelBasic formulation of the EEG/MEG forward problem, linear equation, basics of head modelling, examples of sensory evoked responses. 2. The EEG/MEG inverse problemNon-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity. 3. The spatial resolution of linear EEG/MEG source estimationLeakage and blurring, resolution matrix, point-spread functions (PSFs), cross-talk functions (CTFs), examples of PSFs and CTFs, regions-of-interest for source estimation. 4. Noise and regularisation in EEG/MEG source estimates Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix. Fore more on this topic see here. EEG/MEG III – Time-Frequency and Functional Connectivity Analysis Olaf Hauk Viewing 1. Frequency spectra and the Fourier analysis Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response. 2. Time-frequency analysis and wavelets Fourier analysis, wavelets, trade-off between time and frequency resolution, wavelets, number of cycles, evoked and induced activity, beta bursts. 3.The basics of functional connectivity methods Types of connectivity, amplitude envelope correlation, resting state analysis, Hilbert envelope, phase-locking, coherence, SNR bias, time-resolved connectivity. Fore more on this topic see here. EEG/MEG IV – Further Topics and BIDS Olaf Hauk & Máté Aller Viewing 1. Primer on group statistics for EEG/MEG dataRegions-of-interest (ROI) analysis, multiple comparison problem, cluster-based permutation tests, problems estimating cluster extent, MNE-Python tutorial. 2. Primer on decoding and RSA with EEG/MEG dataBasics of linear decoding, temporal generalisation, interpreting decoding weights, back-projection, representational similarity analysis (RSA). 3. Primer on multimodal integration Types of neural “activity”, differential sensitivity of EEG/MEG vs fMRI, source weighting and priors, estimating deep sources with EEG/MEG. Fore more on this topic see here. MVPA/RSA I and II Daniel Mitchell & Máté Aller Viewing Excellent presentations from Martin Hebart's MVPA course, on:Introduction to MVPAIntroduction to classification. If the links don't work, download from here and here. Martin Hebart's lecture on RSA. If the link fails, download from here. Primer on decoding and RSA with EEG/MEG data

If you want additional background, consider some of the below: Background to Open Science Rik Henson Websites OSF UKRN BIDS 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 Viewing Statistical power in neuroimaging PayWall: open access Comedian's Perspective on science and media Slides Open Science Talk Slides Primer on Python Kshipra Gurunandan Software Python, Pandas, NumPy, Matplotlib, Seaborn Datasets Wakeman Multimodal Useful references Python concepts with examples, Quick reference, Cheatsheets Slides and scripts Slides Notebooks and HTMLs Structural MRI I - Voxel-based morphometry Marta Correia Software FSL 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 Structural MRI II - Surface-based analyses Marta Correia Software Freesurfer 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 Diffusion MRI I - Preprocessing, Model Fitting and Group Analysis Marta Correia Software dipy, FSL 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) Diffusion MRI II - Tractography and the Anatomical Connectome Marta Correia Software dipy Suggested reading MR Diffusion Tractography fMRI I - Data Organisation Dace Apšvalka Software HeudiConv, PyBIDS, NiBabel, Nilearn Websites Brain Imaging Data Structure BIDS Starter Kit BIDS Specification v1.9.0 Suggested reading The brain imaging data structure (BIDS), Gorgolewski et al., 2016The past, present, and future of the brain imaging data structure (BIDS), Poldrack et al., 2024 fMRI II - Pre-processing Dace Apšvalka Software MRIQC, fMRIprep, NiPype Suggested reading Functional Magnetic Resonance Imaging Methods, Chen & Glover, 2015 Quality control in functional MRI studies with MRIQC and fMRIPrep, Provins et al., 2023 fMRIPrep: a robust preprocessing pipeline for functional MRI, Esteban et al., 2018 Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python, Gorgolewski et al., 2011 fMRI III - Analysis Dace Apšvalka Software Nilearn Suggested reading The Statistical Analysis of fMRI Data, Lindquist, 2008 Controlling the familywise error rate in functional neuroimaging: a comparative review, Nichols & Hayasaka, 2003 Analysis of task-based functional MRI data preprocessed with fMRIPrep, Esteban et al., 2020 Guidelines for reporting an fMRI study, Poldrack et al., 2008 Suggested viewing Model Building - temporal basis sets (11:08)GLM Estimation (9:11)Noise Models- AR models (9:57)Inference- Contrasts and t-tests (11:05)Multiple Comparisons by Martin Lindquist and Tor Wager (9:03)FWER Correction (16:11)FDR Correction (5:25)More about multiple comparisons (14:39) fMRI Connectivity Petar Raykov Software Nilearn Datasets movie dataset Reading Resting-state functional Connectivity Learning and comparing functional connectomes across subjects Viewing fMRI Functional Connectivity in fMRIOverview of Effective Connectivity (not covered in person) Tutorial slides and scripts Functional Connectivity Nilearn PracticalDCM tutorial in SPM (not covered in-person) Brain Network Analysis Rik Henson Software Python 3.7+, nxviz, python-louvain Datasets 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). - (Textbook reference for more information) Alex Fornito, Andrew Zalesky, and Edward Bullmore. Fundamentals of brain network analysis. Academic press, 2016. Viewing Understanding your brain as a network and as art by Prof. Dani Bassett. Slides https://github.com/isebenius/COGNESTIC_network_analysis/ Slides EEG/MEG I – Measurement and Pre-processing Olaf Hauk Software and datasets This will be part of a download that will become available later. MNE-Python software homepage MNE stand-alone installation instructions for COGNESTIC Jupyter script to download sample datasets in MNE-Python Essential and suggested viewing 0. Overview of EEG/MEG data processing from raw data to source estimates Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation. 1. A brief history of timing A brief overview of the history of bioelectromagnetism, EEG and MEG. 2. The generation of EEG/MEG signals Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields. 3. Basics of EEG/MEG artefact correction Physiological and non-physiological artefacts, data decompositions, frequency/temporal/spatial filtering. 4. Frequency and temporal filtering of EEG/MEG data Frequency spectrum, temporal smoothing, relationship between frequency and time domain, filters (low-/high-/band-pass, Notch), aliasing, decibels. 5. Topographical artefact correction of EEG/MEG data Independent Component Analysis (ICA), Signal Space Projection (SSP), eye movement and heart beat artefacts.6. Maxfiltering of MEG data Signal Space Separation, options of Maxfilter software (e.g. movement compensation). 7. Differential sensitivity of EEG and MEG Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources. 8. Event-related potentials and fields Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression. + Origin, significance, and interpretation of EEG (Michael X Cohen) + Analysing MEG data with MNE-Python and its ecosystem (Alex Gramfort) + List of EEG/MEG lectures MNE-Python tutorials:Overview of MNE-Python processing pipeline from preprocessing to source estimation Preprocessing Suggested reading Digitial Filtering Filtering How To Maxwell Filtering General EEG/MEG Literature Slides and scripts Slides: 1 2 3 Scripts EEG/MEG II – Head Modelling and Source Estimation Olaf Hauk Software and datasets This will be part of a download that will become available later. MNE-Python software homepage MNE stand-alone installation instructions for COGNESTIC Jupyter script to download sample datasets in MNE-Python Essential and suggested viewing 0. Overview of EEG/MEG data processing from raw data to source estimates Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation. 1. The EEG/MEG forward modelBasic formulation of the EEG/MEG forward problem, linear equation, basics of head modelling, examples of sensory evoked responses. 2. Source spaces for EEG/MEG source estimation Cortical surface, volumetric source space, spatial sampling, spatial normalisation, subcortical areas, source orientation. 3. Head models for EEG/MEG source estimation <<BR>>Volume conduction, Boundary Element Method (BEM), Finite Element Method (FEM), head model accuracy. 4. The EEG/MEG inverse problemNon-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity. 5. The spatial resolution of linear EEG/MEG source estimation Leakage and blurring, resolution matrix, point-spread functions (PSFs), cross-talk functions (CTFs), examples of PSFs and CTFs, regions-of-interest for source estimation. 6. Comparison of spatial resolution for linear EEG/MEG source estimation methods Point-spread functions (PSFs), cross-talk functions (CTFs), resolution metrics (localisation error, spatial deviation), combination of EEG and MEG, PSFs and CTFs for minimum-norm type methods and beamformers, comparison of resolution metrics for minimum-norm type methods and beamformers. 7. Noise and regularisation in EEG/MEG source estimates Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix. + List of EEG/MEG lectures MNE-Python Tutorials: Forward Models and Source Spaces Source Estimation Suggested reading Linear source estimation and spatial resolution Comparison of common head models (e.g. BEM) Guidelines for head modelling (incl. FEM) General EEG/MEG Literature Slides and scripts Slides: 1 2 3 Scripts EEG/MEG III – Time-Frequency and Functional Connectivity Analysis Olaf Hauk Software and datasets This will be part of a download that will become available later. MNE-Python software homepage MNE stand-alone installation instructions for COGNESTIC Jupyter script to download sample datasets in MNE-Python Essential and suggested viewing 1. The basics of signals in the frequency domain Oscillations, periodic signals, sine and cosine, polar representation, complex numbers. 2. Frequency spectra and the Fourier analysis Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response. 3. Time-frequency analysis and wavelets Fourier analysis, wavelets, trade-off between time and frequency resolution, wavelets, number of cycles, evoked and induced activity, beta bursts. 4. The basics of functional connectivity methods Types of connectivity, amplitude envelope correlation, resting state analysis, Hilbert envelope, phase-locking, coherence, SNR bias, time-resolved connectivity. 5. Spatial resolution (leakage) and connectivityConnectivity in sensor and source space, point-spread and cross-talk, (non-)zero-lag signals, orthogonalisation, imaginary part of coherency, source space parcellations. Suggested reading Tutorial on Functional Connectivity Analyzing Neural Time Series Data General EEG/MEG Literature Slides and scripts Slides: 1 2 3 Scripts EEG/MEG IV – Statistics and BIDS 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 Python 3.7+, including numpy, matplotlib, nilearn & scikit-learn. (To visualise MRI data, you can use your software of choice, although for nifti format data you might like to consider MRIcroN or MRIcroGL.) Datasets "NI-edu-data-minimal" faces dataset Reading Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guideSlides and scripts Notebooks and slides are on the COGNESTIC github MVPA/RSA II Daniel Mitchell & Máté Aller Software Python implementation of the RSA Toolbox: Version 3.0 Datasets Example data included with RSA toolbox Reading Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscienceKriegeskorte & 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 geometriesEEG/MEG: Tutorial on EEG/MEG decoding Temporal Generalization Interpretation of Weight Vectors Slides and scripts We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox, also available, along with the slides, on the COGNESTICgithub. EEGMEG Notebooks EEG/MEG Slides Brain Stimulation Elizabeth Michael & Ajay Halai Reading TMS-EEG: TMS-fMRI: SlidesGeneralTMS+EEG TMS+fMRI TMS+fMRI_Artefacts