1112024-06-11 15:57:44OlafHauk1102024-06-10 13:40:42OlafHaukreverted to original 2023 version and created separate page for COGNESTIC 20241092024-06-10 13:36:37OlafHauk1082024-06-10 13:29:02OlafHauk1072024-06-10 13:27:01OlafHauk1062024-06-10 13:24:17OlafHauk1052023-10-02 11:20:05DannyMitchell21042023-10-02 11:19:35DannyMitchell21032023-09-29 15:18:05OlafHauk1022023-09-29 15:17:30OlafHauk1012023-09-29 06:25:36AjayHalai1002023-09-28 13:07:52OlafHauk992023-09-27 14:31:52DaceApsvalka982023-09-27 14:02:24OlafHauk972023-09-27 13:58:48OlafHauk962023-09-27 08:32:26DannyMitchell2952023-09-26 14:51:40MateAller942023-09-26 13:34:10MateAller932023-09-26 09:01:46DannyMitchell2922023-09-26 08:55:01DannyMitchell2912023-09-25 16:35:35OlafHauk902023-09-25 16:32:59OlafHauk892023-09-25 16:29:55OlafHauk882023-09-25 16:25:02OlafHauk872023-09-25 16:22:15OlafHauk862023-09-22 09:01:45OlafHauk852023-09-21 21:17:43MartaCorreia842023-09-21 21:12:37MartaCorreia832023-09-20 21:23:47MartaCorreia822023-09-20 21:22:03MartaCorreia812023-09-20 21:14:08MartaCorreia802023-09-20 20:20:21MartaCorreia792023-09-20 16:25:07OlafHauk782023-09-20 10:47:16MartaCorreia772023-09-20 09:32:04MartaCorreia762023-09-20 09:26:25MartaCorreia752023-09-20 08:47:26OlafHauk742023-09-19 16:16:12DannyMitchell2732023-09-19 08:40:01DannyMitchell2722023-09-19 08:36:31DannyMitchell2712023-09-19 08:35:15DannyMitchell2702023-09-15 08:50:35DaceApsvalka692023-09-14 08:02:04OlafHauk682023-09-14 08:01:21OlafHauk672023-09-14 06:45:05DaceApsvalka662023-09-14 06:42:46DaceApsvalka652023-09-14 06:39:41DaceApsvalka642023-09-14 06:36:53DaceApsvalka632023-09-13 16:51:10MateAller622023-09-13 16:47:11MateAller612023-09-13 14:38:27OlafHauk602023-09-13 14:37:19OlafHauk592023-09-13 14:23:50OlafHauk582023-09-13 14:11:46OlafHauk572023-09-13 14:08:03OlafHauk562023-09-13 14:07:11OlafHauk552023-09-06 14:35:00OlafHauk542023-09-06 14:34:14OlafHauk532023-09-06 14:33:19OlafHauk522023-09-06 14:31:59OlafHauk512023-09-06 11:24:01OlafHauk502023-09-06 11:12:44OlafHauk492023-09-05 13:55:24IsaacSebenius482023-09-04 12:16:25AjayHalai472023-09-04 12:13:34AjayHalai462023-09-04 09:31:22PetarRaykov452023-09-03 15:15:17PetarRaykov442023-08-30 13:49:18DannyMitchell2432023-08-29 11:47:58DannyMitchell2422023-08-29 11:44:38DannyMitchell2412023-08-29 11:40:57DannyMitchell2402023-08-29 09:22:52OlafHauk392023-08-17 18:03:38OlafHauk382023-08-17 18:00:34OlafHauk372023-06-29 08:27:47DannyMitchell2362023-06-29 08:27:22DannyMitchell2352023-06-29 08:26:08DannyMitchell2342023-04-03 14:06:36DaceApsvalka332023-04-03 13:29:24OlafHauk322023-04-03 13:26:28OlafHauk312023-03-31 13:00:58MartaCorreia302023-03-31 12:59:21MartaCorreia292023-03-31 12:58:17MartaCorreia282023-03-31 12:40:28OlafHauk272023-03-31 12:36:56OlafHauk262023-03-31 12:34:53OlafHauk252023-03-31 12:33:38OlafHauk242023-03-31 08:25:50AjayHalai232023-03-31 08:19:48AjayHalai222023-03-30 15:50:03OlafHauk212023-03-30 15:41:59OlafHauk202023-03-30 15:41:03OlafHauk192023-03-29 14:23:08OlafHauk182023-03-29 14:18:20OlafHauk172023-03-29 14:17:57OlafHauk162023-03-24 13:02:55DannyMitchell2152023-03-24 13:02:12DannyMitchell2142023-03-24 13:00:50DannyMitchell2132023-03-24 08:58:37DannyMitchell2122023-03-23 17:19:43DannyMitchell2112023-03-13 16:27:50OlafHauk102023-03-13 15:33:12OlafHauk92023-03-13 15:29:32OlafHauk82023-03-13 15:29:13OlafHauk72023-03-13 15:27:57OlafHauk62023-03-13 15:23:23OlafHauk52023-03-13 15:22:26OlafHauk42023-03-13 15:21:49OlafHauk32023-03-13 15:21:23OlafHauk22023-03-13 15:20:24OlafHauk12023-03-13 15:16:43OlafHauk

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.

Access to much of the COGNESTIC-23 materials is available via Virtual Machine (VM). You will need at least 70GB of free space on your local hard drive, and at least 4GB of RAM. For instructions on how to install and set up the Cognestic23 VM see section ‘1 COGNESTIC Virtual Machine (full hands-on)’ for instructions. Data for the Structural MRI and Diffusion MRI are located inside the VM. Full installation instructions can be found here. The installation can take some time (potentially more than an hour, depending on your download speed), so please reserve some time for this ahead of the event. Introduction and Open Science Rik Henson & Olaf Hauk Websites OSF UKRN BIDS Suggested reading Munafo et al, 2017, problems in science Button et al, 2013, power in neuroscience Poldrack et al, 2017, reproducible neuroimaging Marek et al, 2022, power in neuroimaging association studies Suggested viewing Open Cognitive Neuroscience (will give this talk live on day) Statistical power in neuroimaging PayWall: open access Comedian's Perspective on science and media Tutorial slides and scripts Open Science Talk Slides Primer on Python Edwin Dalmijer Websites Python, NumPy, SciPy, Matplotlib, PsychoPy Suggested reading None Suggested viewing saliency-mapping of Taylor Swift music videos Tutorial slides and scripts None Structural MRI I - Voxel-based morphometry Marta Correia Software FSL Datasets Subset of the CamCAN dataset (~3GB) , please sign data user agreement if using Suggested reading Introduction to GLM for structural MRI analysis Good et al, 2001, A VBM study of ageing Smith et al, 2004, Structural MRI analysis in FSL Suggested viewing Introduction to MRI Physics and image contrast Slides Tutorial slides and scripts Intro to command line VBM slides FSL VBM tutorials FSL VBM script Hands on exercises for structural and diffusion MRI Structural MRI II - Surface-based analyses Marta Correia Software Freesurfer Datasets Freesurfer tutorial data Suggested reading Dale et al, 1999, Cortical surface-based analysis I Fischl et al, 1999, Cortical surface-based analysis II Suggested viewing Using the command line Tutorial slides and scripts Freesurfer slides Freesurfer tutorials FS check location script FS visualising the output script FS group analysis script FS ROI analysis script Diffusion MRI I - The Diffusion Tensor Model Marta Correia Software FSL Datasets BTC_preop Suggested reading FSL Diffusion Toolbox Wiki Le Bihan et al, 2015, What water tells us about biological tissues Soares et al, 2013, A short guide to Diffusion Tensor Imaging Smith et al, 2006, Tract-based spatial statistics (TBSS) Suggested viewing Introduction to Diffusion MRI - Part I Slides Tutorial slides and scripts FSL DTI and TBSS slides DTI and group analysis in TBSS tutorials FSL DTI pipeline script FSL TBSS script FSL group QC script Diffusion MRI II - Tractography and the Anatomical Connectome Marta Correia Software MRtrix3 Datasets BTC_preop Suggested reading MRtrix3 documentation MR Diffusion Tractography Suggested viewing Introduction to Diffusion MRI - Part II Slides Tutorial slides and scripts MRTrix tractography slides MRTrix tractography tutorials MRTrix preprocessing script MRTrix tractography script MRTrix connectome script fMRI I - Data management, structure, manipulation Dace Apšvalka Software HeudiConv, PyBIDS, NiBabel, Nilearn Datasets Wakeman Multimodal Suggested reading Gorgolewski et al., 2016 The brain imaging data structure (BIDS) The Past, Present, and Future of the Brain Imaging Data Structure (BIDS) Suggested viewing BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) fMRI Data Structure & Terminology by Martin Lindquist and Tor Wager (6:47) Slides and scripts fMRI II - Quality control & Pre-processing Dace Apšvalka Software MRIQC, fMRIprep Datasets Wakeman Multimodal Suggested reading Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods Ashburner J & Friston KJ (2004), Rigid body registration Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review Sladky et al. (2011), Slice-timing effects and their correction in functional MRI Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI Suggested viewing fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57)Pre-processing I by Martin Lindquist and Tor Wager (10:17)Pre-processing II by Martin Lindquist and Tor Wager (7:42) Slides and scripts fMRI III - Subject Level Analysis Dace Apšvalka Software Nilearn Datasets Wakeman Multimodal Suggested reading Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear 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 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 fMRI Connectivity Petar Raykov Software Nilearn Python Datasets movie dataset Suggested reading Resting-state functional Connectivity Learning and comparing functional connectomes across subjects Suggested viewing fMRI Functional Connectivity in 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 Isaac Sebenius Software Python 3.7+, nxviz, python-louvain Datasets Suggested reading - (Review article) Bullmore, E., Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10, 186–198 (2009). - (Textbook reference for more information) Alex Fornito, Andrew Zalesky, and Edward Bullmore. Fundamentals of brain network analysis. Academic press, 2016. Suggested viewing - Introduction to Network Neuroscience, minutes 0:00-48:30. A wonderful introduction to brain networks by Prof. Bratislav Misic. - Understanding your brain as a network and as art by Prof. Dani Bassett. Slides and scripts https://github.com/isebenius/COGNESTIC_network_analysis/ Slides EEG/MEG I – Pre-processing Olaf Hauk Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic Download Datasets Suggested reading Digitial Filtering Filtering How To Maxwell Filtering General EEG/MEG Literature Suggested viewing Introduction to EEG/MEG Preprocessing What are we measuring with M/EEG? Slides and scripts Notebooks Exercises Slides1 Slides2 Slides3 EEG/MEG II – Source Estimation Olaf Hauk Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic Download Datasets Suggested reading Comparison of common head models (e.g. BEM) Linear source estimation and spatial resolution Guidelines for head modelling (incl. FEM) General EEG/MEG Literature Suggested viewing Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM Slides and scripts Notebooks Exercises Slides1 Slides2 Slides3 EEG/MEG III – Time-Frequency and Functional Connectivity Olaf Hauk Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic Download Datasets Suggested reading Tutorial on Functional Connectivity Analyzing Neural Time Series Data General EEG/MEG Literature Suggested viewing Introduction to time-frequency and functional connectivity analysis Time-Frequency Analysis of EEG Time Series Slides and scripts Notebooks Exercises Slides1 Slides2Slides3 EEG/MEG IV – Advanced Topics Olaf Hauk & Máté Aller Software MNE-Python MNE Installation for Cognestic Datasets Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic M/EEG combined dataset Download Datasets Suggested reading Estimating subcortical sources from EEG/MEG Tutorial on converting MEG data to BIDS format Example using MNE-BIDS-Pipeline for processing combined M/EEG data Suggested viewing Talk on Multimodal Integration Slides and scripts Notebooks Exercises Slides1 Slides2 Notebooks mne-bids-pipeline Slides mne-bids-pipeline MVPA/RSA I Daniel Mitchell Software CoSMoMVPA using Octave. (These are not included in the virtual machine; you will need to install them yourself. If you have Matlab, you are welcome to use it instead of Octave, but the demos will be in Octave because it is open source.) To visualise MRI data, you can use your software of choice, although for nifti format data you might like to consider MRIcroN. Datasets Tutorial data from CoSMoMVPA toolbox Suggested reading Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guideHebart et al. (2014) The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging dataOosterhof et al. (2016) CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave Suggested viewing Excellent presentations from Martin Hebart's MVPA course, on:Introduction to MVPAIntroduction to classification. I've suggested these two, but the others are worth a look too. (Note: on 25/9/23-26/9/23 the above links stopped working due to a temporary issue with the host website. If this happens again, let me know.) Slides and scripts We will be using the demos from the "examples" folder of the CoSMoMVPA toolbox. Exercises can be copied from the files here, pasted into an empty Octave file, and you can try to fill in the missing snippets.extra example 1extra example 2slides MVPA/RSA II Daniel Mitchell Software Python implementation of the RSA Toolbox: Version 3.0 Datasets Example data included with RSA toolbox Suggested reading Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems 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 Suggested viewing Martin Hebart's lecture on RSA (Note: on 25/9/23-26/9/23 this link stopped working due to a temporary issue with the host website. If this happens again, let me know.) Slides and scripts We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox. slidesEEGMEG Notebooks EEG/MEG Slides Brain Stimulation, Pethysmography, Electromyography Ajay Halai, Alexis Deighton McIntyre, Hristo Dimitrov Software Brain Stimulation: E-field modelling for non-invasive brain stimulation using SimNIBS Plethysmography: Speech breathing-oriented toolbox for breath-belt data (MATLAB) Datasets Suggested reading Brain Stimulation: Approaches to brain stimulation; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2 Plethysmography: Heck, D. H., McAfee, S. S., Liu, Y., Babajani-Feremi, A., Rezaie, R., Freeman, W. J., ... & Kozma, R. (2017). Breathing as a fundamental rhythm of brain function. Frontiers in neural circuits, 10, 115. 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. Allen, M., Varga, S., & Heck, D. H. (2022). Respiratory rhythms of the predictive mind. Psychological Review. EMG: Consensus for experimental design in electromyography Tutorial high-density EMG Noninvasive Neural Interfacing With Wearable Muscle Sensors Suggested viewing Brain Stimulation: SimNIBs tutorial and SimNIBS youtube videos Slides and scripts Brain Stimulation: slides Plethysmography: slides