1942023-03-31 12:36:10OlafHaukBack to 2022 page1932023-03-31 12:31:33OlafHaukonly temporary1922023-03-31 10:42:51OlafHaukRevision contained material for COGNESTIC 2023, now on separate Wiki page1912023-03-31 10:40:43OlafHauk1902023-03-30 16:35:29OlafHauk1892023-03-29 14:41:12OlafHauk1882023-03-29 14:39:07OlafHauk1872023-03-29 14:30:44OlafHauk1862023-03-25 10:33:46RikHenson1852023-03-25 10:31:47RikHenson1842023-03-23 21:15:55RikHenson1832023-03-23 21:10:37RikHenson1822023-03-23 15:29:13OlafHauk1812023-03-23 13:56:20OlafHauk1802023-03-23 13:55:12OlafHauk1792023-03-23 13:54:35OlafHauk1782022-11-15 17:05:19OlafHauk1772022-11-15 17:03:59OlafHauk1762022-11-11 13:51:44RikHenson1752022-10-18 11:14:00RikHenson1742022-09-29 12:07:21AjayHalai1732022-09-29 12:00:14AjayHalai1722022-09-29 11:56:05AjayHalai1712022-09-27 14:25:05DannyMitchell21702022-09-27 14:24:37DannyMitchell21692022-09-27 14:23:55DannyMitchell21682022-09-27 14:20:15OlafHauk1672022-09-27 14:17:49OlafHauk1662022-09-27 14:17:04DannyMitchell21652022-09-27 14:15:36DannyMitchell21642022-09-27 14:14:48DannyMitchell21632022-09-27 08:37:00DannyMitchell21622022-09-27 08:35:10DannyMitchell21612022-09-26 08:39:45PeterWatson1602022-09-26 08:36:19PeterWatson1592022-09-23 14:35:25OlafHauk1582022-09-23 14:29:15OlafHauk1572022-09-23 08:34:40OlafHauk1562022-09-22 18:15:38OlafHauk1552022-09-22 16:08:23MartaCorreia1542022-09-22 16:05:42MartaCorreia1532022-09-22 10:18:58OlafHauk1522022-09-21 10:37:45MartaCorreia1512022-09-21 10:30:03MartaCorreia1502022-09-21 06:10:41DaceApsvalka1492022-09-20 13:05:53RikHenson1482022-09-20 13:05:31RikHenson1472022-09-20 12:56:17OlafHauk1462022-09-20 08:23:15RikHenson1452022-09-20 08:17:20RikHenson1442022-09-19 21:06:24MartaCorreia1432022-09-19 20:29:41MartaCorreia1422022-09-19 20:27:10MartaCorreia1412022-09-19 20:14:09MartaCorreia1402022-09-19 20:12:20MartaCorreia1392022-09-19 20:11:18MartaCorreia1382022-09-19 19:44:57MartaCorreia1372022-09-19 19:13:09MartaCorreia1362022-09-19 14:16:27OlafHauk1352022-09-19 14:12:41OlafHauk1342022-09-19 14:00:06OlafHauk1332022-09-19 13:59:26OlafHauk1322022-09-16 16:09:51DannyMitchell21312022-09-16 16:04:49DannyMitchell21302022-09-15 11:00:01MartaCorreia1292022-09-14 08:13:37RikHenson1282022-09-13 09:42:28RikHenson1272022-09-13 09:00:57OlafHauk1262022-09-12 14:29:04OlafHauk1252022-09-12 14:27:48OlafHauk1242022-09-12 13:52:14OlafHauk1232022-09-12 13:51:47OlafHauk1222022-09-12 11:18:19PranayYadavAdded DCM for M/EEG talk1212022-09-06 19:28:25DaceApsvalka1202022-09-06 19:04:45DaceApsvalka1192022-09-06 18:46:12DaceApsvalka1182022-09-06 18:44:51DaceApsvalka1172022-09-06 18:39:30DaceApsvalka1162022-09-06 18:25:48DaceApsvalka1152022-09-06 16:33:40DaceApsvalka1142022-09-06 16:20:35DaceApsvalka1132022-09-06 16:15:10DaceApsvalka1122022-09-06 16:14:31DaceApsvalka1112022-09-06 15:42:13DaceApsvalka1102022-09-05 13:41:16MartaCorreia1092022-09-05 11:17:57MartaCorreia1082022-09-05 11:04:57MartaCorreia1072022-09-05 11:01:48MartaCorreia1062022-09-05 10:39:29MartaCorreia1052022-09-05 10:38:17MartaCorreia1042022-08-22 15:18:22MartaCorreia1032022-08-22 15:17:15MartaCorreia1022022-08-22 15:15:58MartaCorreia1012022-08-22 14:49:28RikHenson1002022-08-19 17:02:37MartaCorreia992022-08-19 17:01:50MartaCorreia982022-08-19 16:59:10MartaCorreia972022-08-19 16:58:27MartaCorreia962022-08-19 16:58:03MartaCorreia952022-08-19 16:56:21MartaCorreia942022-08-19 16:55:41MartaCorreia932022-08-19 16:55:17MartaCorreia922022-08-19 16:54:11MartaCorreia912022-08-19 16:51:06MartaCorreia902022-08-19 16:43:17MartaCorreia892022-08-19 16:41:38MartaCorreia882022-08-19 16:40:37MartaCorreia872022-08-19 16:40:08MartaCorreia862022-08-19 16:37:08MartaCorreia852022-08-19 14:56:45AjayHalai842022-08-19 14:39:17AjayHalai832022-08-19 14:37:58AjayHalai822022-08-19 14:23:21AjayHalai812022-08-19 14:22:12OlafHauk802022-08-19 14:19:28OlafHauk792022-08-19 14:12:41OlafHauk782022-08-19 00:30:11DaceApsvalka772022-08-19 00:23:34DaceApsvalka762022-08-19 00:17:59DaceApsvalka752022-08-18 23:55:16DaceApsvalka742022-08-18 23:38:25DaceApsvalka732022-08-18 23:27:47DaceApsvalka722022-08-18 23:20:40DaceApsvalka712022-08-18 16:10:38MartaCorreia702022-08-18 15:43:03MartaCorreia692022-08-17 17:46:40DaceApsvalka682022-08-16 10:53:26RikHenson672022-08-16 10:52:38RikHenson662022-08-16 10:52:24RikHenson652022-08-16 10:51:56RikHenson642022-08-15 13:47:31MartaCorreia632022-08-15 13:46:49MartaCorreia622022-08-15 13:46:12MartaCorreia612022-08-15 13:44:34MartaCorreia602022-08-15 13:42:51MartaCorreia592022-08-15 13:42:02MartaCorreia582022-08-15 13:41:29MartaCorreia572022-08-15 13:38:57MartaCorreia562022-08-15 13:38:17MartaCorreia552022-08-15 13:37:47MartaCorreia542022-08-15 13:36:19MartaCorreia532022-08-15 13:35:02MartaCorreia522022-08-15 13:32:04MartaCorreia512022-08-15 13:23:39MartaCorreia502022-08-15 13:23:16MartaCorreia492022-08-15 12:35:43MartaCorreia482022-08-15 12:12:16RikHenson472022-08-15 12:11:53RikHenson462022-08-14 17:08:34RikHenson452022-08-14 17:07:58RikHenson442022-08-14 16:54:31RikHenson432022-08-14 16:53:36RikHenson422022-08-14 16:52:57RikHenson412022-08-14 16:52:39RikHenson402022-08-14 16:19:51DannyMitchell2392022-08-14 10:17:18DannyMitchell2382022-08-14 09:18:30DannyMitchell2372022-08-14 09:12:20DannyMitchell2362022-08-12 15:51:30MartaCorreia352022-08-12 09:34:48RikHenson342022-08-12 09:24:30RikHenson332022-08-12 09:23:40RikHenson322022-08-10 16:26:14RikHenson312022-08-10 16:25:15RikHenson302022-08-10 16:21:45RikHenson292022-08-10 15:00:44RikHenson282022-08-08 17:19:58RikHenson272022-08-08 17:17:42RikHenson262022-08-08 16:32:10RikHenson252022-08-07 13:08:15RikHenson242022-08-07 13:04:44RikHenson232022-08-07 13:00:58RikHenson222022-08-04 15:20:26OlafHauk212022-08-04 15:11:57OlafHauk202022-08-04 15:09:32OlafHauk192022-08-04 15:05:11OlafHauk182022-08-04 15:02:51OlafHauk172022-08-04 14:39:53OlafHauk162022-08-04 14:36:34OlafHauk152022-08-04 14:32:26OlafHauk142022-08-04 14:31:25OlafHauk132022-08-04 14:25:50OlafHauk122022-08-04 14:24:50OlafHauk112022-08-04 14:22:20OlafHauk102022-08-04 14:19:14OlafHauk92022-08-04 12:18:33OlafHauk82022-08-04 12:14:39OlafHauk72022-08-04 11:54:32OlafHauk62022-08-04 11:52:23OlafHauk52022-08-04 11:51:45OlafHauk42022-08-04 11:46:04OlafHauk32022-08-04 11:44:49OlafHauk22022-08-04 11:20:57OlafHauk12022-08-04 11:15:29OlafHauk

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 Rik Henson & Olaf Hauk Websites OSF UKRN BIDS Suggested reading Munafo et al, 2017, problems in science Button et al, 2013, power in neuroscience Poldrack et al, 2017, reproducible neuroimaging Marek et al, 2022, power in neuroimaging association studies Suggested viewing Open Cognitive Neuroscience (will give this talk live on day) Statistical power in neuroimaging PayWall: open access Comedian's Perspective on science and media Tutorial slides and scripts Open Science Talk Slides Structural MRI - VBM and Surface-based Analysis Marta Correia Software FSL Freesurfer Datasets Freesurfer tutorial data Subset of the CamCAN dataset (~3GB) , please sign data user agreement if using Suggested reading Introduction to GLM for structural MRI analysis Good et al, 2001, A VBM study of ageing Smith et al, 2004, Structural MRI analysis in FSL Dale et al, 1999, Cortical surface-based analysis I Fischl et al, 1999, Cortical surface-based analysis II Suggested viewing Using the command line Introduction to MRI Physics and image contrast Slides Tutorial slides and scripts FSLVBM slides FSLVBM tutorial FSLVBM bash script FreeSurfer Cortical Thickness slides Freesurfer tutorials FS check location script FS visualising the output script FS group analysis script FS ROI analysis script Diffusion MRI I - DTI Model Fitting and Group Analysis Marta Correia Software FSL Datasets BTC_preop Suggested reading FSL Diffusion Toolbox Wiki Le Bihan et al, 2015, What water tells us about biological tissues Soares et al, 2013, A short guide to Diffusion Tensor Imaging Smith et al, 2006, Tract-based spatial statistics (TBSS) Suggested viewing Introduction to Diffusion MRI - Part I Slides Tutorial slides and scripts FSL DTI and TBSS slides DTI and group analysis in TBSS tutorial FSL DTI pipeline script FSL TBSS script FSL Group QC script Diffusion MRI II - Tractography and Structural Connectivity Marta Correia Software MRtrix3 Datasets BTC_preop Suggested reading MRtrix3 documentation MR Diffusion Tractography Suggested viewing Introduction to Diffusion MRI - Part II Slides Tutorial slides and scripts MRtrix Tractography slides MRtrix Tractography tutorials MRtrix preprocessing script MRtrix CSD Tractography script MRtrix connectome script fMRI I - Data management, structure, manipulation Dace Apšvalka Software HeudiConv, PyBIDS, NiBabel, Nilearn Datasets Wakeman Multimodal Suggested reading Gorgolewski et al., 2016, The brain imaging data structure (BIDS) Suggested viewing BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) fMRI Data Structure & Terminology by Martin Lindquist and Tor Wager (6:47) Slides and scripts fMRI II - Quality control & Pre-processing Dace Apšvalka Software MRIQC, fMRIprep, Nipype Datasets Wakeman Multimodal Suggested reading Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods Ashburner J & Friston KJ (2004), Rigid body registration Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review Sladky et al. (2011), Slice-timing effects and their correction in functional MRI Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI Suggested viewing fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57)Pre-processing I by Martin Lindquist and Tor Wager (10:17)Pre-processing II by Martin Lindquist and Tor Wager (7:42) Slides and scripts fMRI III - Subject Level Analysis Dace Apšvalka Software Nipype, Nilearn, SPM12 Datasets Wakeman Multimodal Suggested reading Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approachPoline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever?Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approachNichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative reviewChumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fieldsWoo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendationsLindquist (2008), The Statistical Analysis of fMRI Data Suggested viewing The General Linear Model by Martin Lindquist and Tor Wager (12:24)GLM applied to fMRI by Martin Lindquist and Tor Wager (11:21)Model Building I – conditions and contrasts by Martin Lindquist and Tor Wager (11:48)Model Building II – temporal basis sets by Martin Lindquist and Tor Wager (11:08)Model Building III- nuisance variables by Martin Lindquist and Tor Wager (13:58)GLM Estimation by Martin Lindquist and Tor Wager (9:11)Noise Models- AR models by Martin Lindquist and Tor Wager (9:57)Inference- Contrasts and t-tests by Martin Lindquist and Tor Wager (11:05)Multiple Comparisons by Martin Lindquist and Tor Wager (9:03)FWER Correction by Martin Lindquist and Tor Wager (16:11)FDR Correction by Martin Lindquist and Tor Wager (5:25)More about multiple comparisons by Martin Lindquist and Tor Wager (14:39) Slides and scripts fMRI IV - Group Level Analysis & Reporting Dace Apšvalka Software Nilearn, PySurfer, SPM12 Datasets Wakeman Multimodal Suggested reading Mumford & Nichols (2006), Modeling and inference of multisubject fMRI dataNichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRIPoldrack et al. (2008), Guidelines for reporting an fMRI studyGorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brainMarkiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data Suggested viewing Group-level Analysis I by Martin Lindquist and Tor Wager (7:05)Group-level Analysis II by Martin Lindquist and Tor Wager (10:09)Group-level Analysis III by Martin Lindquist and Tor Wager (14:01) Slides and scripts Connectivity for fMRI Rik Henson Software SPM12 Datasets Wakeman Multimodal Suggested reading Resting-state functional Connectivity Simple Intro to DCM fMRI preprocessing in SPM12 (for demo) SPM12 manual (Chapter 36) Suggested viewing fMRI Functional Connectivity, including DCM Bayesian Model Comparison (for DCM for fMRI/MEEG) Tutorial slides and scripts Tutorial for DCM for fMRI Eye-tracking Edwin Dalmaijer Software Python NumPy, SciPy, Matplotlib Datasets Example Data EyeLink Suggested reading Paper on eye-tracking reporting standards (great for beginners and experts alike) Suggested viewing Talk at the MRC CBU on how to hack pupillometry studies Slides and scripts EEG/MEG I – Pre-processing Olaf Hauk Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic Suggested reading Digitial Filtering Filtering How To Maxwell Filtering General EEG/MEG Literature Suggested viewing Introduction to EEG/MEG Preprocessing What are we measuring with M/EEG? Slides and scripts Notebooks and Slides EEG/MEG II – Source Estimation Olaf Hauk Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic Suggested reading Linear source estimation and spatial resolution General EEG/MEG Literature Suggested viewing Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM Slides and scripts Notebooks and Slides EEG/MEG III – Time-Frequency and Functional Connectivity Olaf Hauk Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic Suggested reading Tutorial on Functional Connectivity Analyzing Neural Time Series Data General EEG/MEG Literature Suggested viewing Introduction to time-frequency and functional connectivity analysis Time-Frequency Analysis of EEG Time Series Slides and scripts Notebooks and Slides Graph Theory Caroline Nettekoven Zoom link Software Brain Connectivity Toolbox in Matlab, BCT Documentation Datasets Coding exercises Exercise solutions Suggested reading Complex brain networks: graph theoretical analysis of structural and functional systems Suggested viewing Slides Slides and scripts MVPA/RSA I Daniel Mitchell 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) Datasets The Decoding Toolbox example dataset (See toolbox webpage for a lower resolution alternative) Suggested reading Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guideHebart 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:Introduction to MVPAIntroduction to classification. (I've suggested these two, but the others are worth a look too.) Slides and scripts Slides for morning session - MVPA These functions should be saved in a subfolder of the Decoding Toolbox demos folder These functions should be saved in the top-level folder (Please see the 3rd slide for an overview of the file structure expected by the demo scripts) MVPA/RSA II Daniel Mitchell Software The RSA toolbox in MatlabFor the demos, please download this alternative version: (Note that the toolbox development has recently switched to Python. We will not be demoing this version, but you can find it here: Version 3.0) Datasets Group-averged example data from Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex Suggested 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 analysisEEG/MEG: Tutorial on EEG/MEG decoding Temporal Generalization Suggested viewing Martin Hebart's lecture on RSA Slides and scripts Slides for afternoon session - RSA These functions should be saved in rsa-master/Demos EEGMEG Notebooks and Slides Statistics in R Peter Watson Software R Data&Code Datasets Data&Code Readme Suggested reading Statistical Methods for Psychology (Howell) Introduction to R Discovering statistics using R An introduction to statistical learning with applications in R Suggested viewing CBU Statistics Lectures Slides and scripts Brain Stimulation Ajay Halai Software SIMNIBS (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and k-wave Datasets tutorial_data Suggested reading Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2 Suggested viewing slides Slides and scripts scripts DCM for M/EEG Pranay Yadav & Rik Henson Software SPM12 Datasets Wakeman Multimodal Suggested reading Preprocessing M/EEG in SPM12 Simple Intro to DCM Suggested viewing Talk on DCM for M/EEG MEEG connectivity other than DCM (not demo'ed, and related to Hauk talks above) Tutorial slides and scripts Tutorial for DCM for ERP