Course Material for COGNESTIC 2024

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.

Below you will find documents, videos and web links that will be used for the course or can be used for preparation.

Software Installation Instructions

Access to much of the COGNESTIC-23 materials is available via Virtual Machine (VM). You will need at least 70GB of free space on your local hard drive, and at least 4GB of RAM. For instructions on how to install and set up the Cognestic23 VM see section ‘1 COGNESTIC Virtual Machine (full hands-on)’ for instructions. Data for the Structural MRI and Diffusion MRI are located inside the VM.

Full installation instructions can be found here. The installation can take some time (potentially more than an hour, depending on your download speed), so please reserve some time for this ahead of the event.

Essential

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).

(sessions below should be ordered as they will be in course, but just did my ones for an example)

Background to Open Science Rik Henson
Viewing	Open Cognitive Neuroscience

Network Analysis Rik Henson
Viewing	Introduction to Network Neuroscience

Additional Extra

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

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). https://doi.org/10.1038/nrn2575 - (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

***Below not updated yet

Primer on Python Kshipra Gurunandan
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) https://www.cam-can.org/, 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	https://github.com/dcdace/fMRI-COGNESTIC-23/

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	https://github.com/dcdace/fMRI-COGNESTIC-23/

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 approach Poline & 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 approach Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations Lindquist (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	https://github.com/dcdace/fMRI-COGNESTIC-23/

fMRI IV - Group Level Analysis & Reporting Dace Apšvalka
Software	Nilearn
Datasets	Wakeman Multimodal
Suggested reading	Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI Poldrack et al. (2008), Guidelines for reporting an fMRI study Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain Markiewicz 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	https://github.com/dcdace/fMRI-COGNESTIC-23/

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 fMRI Overview of Effective Connectivity (not covered in person)
Tutorial slides and scripts	Functional Connectivity Nilearn Practical DCM tutorial in SPM (not covered in-person)

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	TBA

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 model Basic 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 problem Non-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	TBA

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 connectivity Connectivity 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	TBA

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	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 guide Hebart et al. (2014) The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data Oosterhof et al. (2016) CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave
Suggested viewing	Excellent presentations from Martin Hebart's MVPA course, on: Introduction to MVPA Introduction 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 1 extra example 2 slides

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 neuroscience Kriegeskorte & 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 geometries EEG/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. slides EEGMEG Notebooks EEG/MEG Slides

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Course Material for COGNESTIC 2024

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